measuring transgenic barley lc–ms proﬁ ling of the...

Measuring Transgenic Barley Protein Expression with LC–MS

LC–MS Profi ling of the Yeast Metabolome

Characterizing Crude Oils with Direct Insertion Probe–MS

Quantitative Imaging Mass Spectrometry

S U P P L E M E N T T O

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6 Current Trends in Mass Spectrometry July 2012 www.spec t roscopyonl ine .com

Articles

Identification of Organic Additives in Nitrile Rubber Materials by Pyrolysis-GCÐMS 8Peter Kusch

Nitrile rubber materials were studied using flash analytical pyrolysis-GC–MS to demonstrate that this technique is a good tool to identify the additives in nitrile rubber.

A Novel Approach to Measure Crop Plant Protein Expression 16Wayne Skinner, Zina Dahmani, Yingzhi Lu, Jean C. Kridl, and Gia C. Fazio

Liquid chromatography–mass spectrometry (LC–MS) successfully differentiated transgenic from native protein in a case where the proteins were highly homologous and could not be differentiated by traditional methods. This methodology may be useful for other studies of transgenic crops.

Nontargeted, Discovery-Based Profiling of the Yeast Metabolome Using 22QTOF LCÐMS and LCÐMS-MS Stefan Jenkins, Steven M. Fischer, and Theodore R. Sana

A discovery-based, untargeted metabolomics analysis of hundreds of yeast metabolites under robust, controlled extraction conditions followed by identification is described.

Direct Insertion ProbeÐMass Spectrometry in the Characterization of Opportunity Crudes 28Cristina Flego and Carla Zannoni

DIP–MS is a fast and easy tool that can identify classes of compounds in opportunity crudes (heavy and ultraheavy crude oils, asphaltenes, and tar sands) in the field, without prior separation or treatment. It may enable fast screening of real samples to make a rough evaluation of the potential of reservoirs and oil fields.

Toward Quantitative Imaging Mass Spectrometry 36Gregory Hamm, David Bonnel, Christine Michel, Raphael Legouffe,

Fabien Pamelard, Guillaume Hochart, and Jonathan Stauber

The main limitations of quantification using MALDI imaging are discussed and the different approaches used for quantitative measurement in MSI are evaluated.

Polar VaporÐEnhanced Separations with Planar 42 Differential Mobility SpectrometryÐMass SpectrometryThomas R. Covey, Bradley B. Schneider, J.C. Yves Le Blanc, and Erkinjon G. Nazarov

A brief historical overview of DMS, followed by a synopsis of the instrumentation, physics, and chemistry behind the separation principles

Cover image courtesy of Frank Krahmer/Getty Images.

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www.spec t roscopyonl ine .com8 Current Trends in Mass Spectrometry July 2012

Peter Kusch

The criteria for assessing the quality of rubber materials are the polymer or copolymer composition and the additives. These additives include plasticizers, extender oils, carbon black, inorganic fillers, antioxidants, heat and light stabilizers, processing aids, cross-linking agents, accelerators, retarders, adhesives, pigments, smoke and flame retardants, and oth-ers. Determination of additives in polymers or copolymers generally requires the extraction of these substances from the matrix as a first step, which can be challenging, and the subse-quent analysis of the extracted additives by gas chromatography (GC), GC–mass spectrom-etry (MS), high performance liquid chromatography (HPLC), HPLC–MS, capillary electropho-resis, thin-layer chromatography, and other analytical techniques. In the present work, nitrile rubber materials were studied using direct analytical flash pyrolysis hyphenated to GC and electrospray ionization MS in both scan and selected ion monitoring modes to demonstrate that this technique is a good tool to identify the organic additives in nitrile rubber.

Identification of Organic Additives in Nitrile Rubber Materials by Pyrolysis-GC–MS

Commercial plastics and rubbers always contain low-molecular-weight additives. These compounds are essential in polymer or copolymer processing and

in ensuring the end-use properties of a polymer or copo-lymer. Additives can improve or modify the mechanical properties (fillers and reinforcements), modify the color and appearance (pigments and dyestuffs), give resistance to heat degradation (antioxidants and stabilizers), provide resistance to light degradation (UV stabilizers), improve the f lame resistance (f lame retardants), improve the per-formance (antistatic or conductive additives, plasticizers, blowing agents, lubricants, mold release agents, surfac-tants, and preservatives), and improve the processing characteristics (recycling additives) of polymers or co-polymers (1,2). Some of these additives accumulate in the environment and affect our health and the environment (3). Knowledge of additives is important for evaluating the environmental impact and interaction of polymeric materials, investigating long-term properties and degra-

dation mechanisms, verifying ingredients, investigating manufacturing problems, qualifying control polymeric materials, identifying odorants, avoiding workplace ex-posure, and insuring safety of food packaging and medi-cal products (3). Identification of polymer or copolymer additives is also desired if competitor products are inves-tigated. The quantification of additives is important for quality control and troubleshooting of the manufacturing processes. Both identification and quantification are dif-ficult tasks because there are a wide variety of different additives, usually mixtures of additives are used, and the added amount is often low and can be further decreased because of degradation (4,5).

Most analytical methods reported for the determina-tion of polymer or copolymer additives require previous extraction of the additives from the polymeric material. For this purpose, widely used sample preparation tech-niques such as liquid extraction with various solvents, Soxhlet extraction, ultrasonic-assisted extraction, and

www.spec t roscopyonl ine .com July 2012 Current Trends in Mass Spectrometry 9

pressurized liquid extraction like microwave assisted extraction, accel-erated solvent extraction, supercriti-cal f luid extraction, and extraction processes using autoclaves are used (3–6,9). Al l of these methods are very laborious and time-consuming. Subsequent analysis of the extracted additives has been performed using thin-layer chromatography, super-critical f luid chromatography, gas chromatography (GC), high per-formance l iquid chromatography (HPLC), and capillary electropho-resis (CE), or their conjunction with mass spectrometry (GC–MS, HPLC–MS, and CE–MS) (5–9). Infrared or UV spectroscopic investigations of polymers or copolymers, particularly in the form of a thin film are only ap-plicable for a sample containing just a single additive or if determination of a sum-parameter is sufficient (6).

Several reports on the use of ma-trix-assisted laser desorption–ion-ization mass spectrometry (MALDI-MS) for the detection of additives in plastic samples exist (5). Never-theless, these methods require fine grinding of the polymer or extrac-tion and dissolution of the sample and subsequent analysis of the ex-tract. Besides investigations on thin polymer films using time-of-f light secondary ion mass spectrometry (TOF-SIMS), poly mer add it ives have been detected directly in poly-mer samples by laser-MS (5). The direct MS methods are the focus of current research for identif ication of solid polymeric materials (5).

The thermo-analytical techniques, such as thermogravimetric analysis or temperature-programmed ana-lytical pyrolysis, specif ical ly take advantage of relatively slow heating, particularly in combination with ap-propriate detection modes like ther-mogravimetric-MS, thermogravi-metric-Fourier transform infrared spectroscopy, and temperature-pro-grammed pyrolysis-GC–MS (8,9). Also headspace solid-phase micro-extraction (3,16) and thermal extrac-tion (2) in conjunction with GC–MS have been applied for the extraction and identification of several common

polymer or copolymer additives. In such volati le removal techniques, the additives are usually detected at temperatures below the decomposi-tion temperature of the polymer or copolymer. It is also possible to gain information about additives from f lash analytical pyrolysis (8–15). The f lash analytical pyrolysis technique hyphenated to GC–MS has extended the range of possible tools for char-acterization of synthetic polymers or copolymers. Reproducible decom-position products characteristic for the original polymer or copolymer sample are formed under controlled conditions at elevated temperatures (500–1400 °C) in the presence of an inert gas. The pyrolysis products are separated chromatographically using a fused-silica capillary column and are subsequently identif ied by in-terpretation of the obtained mass spectra or by using mass spectral li-braries (for example, NIST or Wiley). Pyrolysis methods el iminate the need for pretreatment by performing analyses directly on the solid poly-

mer or copolymer sample. The py-rolysis unit is directly connected to the injector port of a gas chromato-graph. A f low of an inert carrier gas, such as helium, f lushes the pyroly-zates into the fused-silica capillary column. The detection technique of the chromatographically separated compounds is typically MS.

The study of rubbers is the oldest application of the analytical f lash pyrolysis-GC–MS technique. In the literature there is a lack of methods for identification of additives in ni-trile rubber by this analytical tech-nique. In the present work, nitrile rubber materials have been studied using f lash ana ly t ica l pyrolysis-GC–MS to demonstrate that this technique is a good tool to identify the additives in nitrile rubber.

ExperimentalSamples

Membrane of a hydraulic cylinder from the automotive industry and an O-ring seal were used in the in-vestigation.

N N N

XX

Figure 1: Chemical structure of poly(acrylonitrile-co-butadiene) (nitrile rubber).

Ab

un

da

nce

(X

10

6)

06.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00

Time (min)

2.0

4.0

6.0

8.0

2

3

48

7

65

19

10

A

B

11 C

D

Figure 2: Pyrolysis-GC–MS chromatogram (TIC) of the membrane of a hydraulic cylinder from

the automotive industry at 700 °C.

www.spec t roscopyonl ine .com

Apparatus and

Analytical Conditions

Approximately 100–200 µg of solid rubber sample was cut out with a sca lpel and inserted without any further preparation into the bore of the pyrolysis solids injector and then placed with the plunger on the quartz wool of the quartz tube in the furnace pyrolyzer (Pyrojector II, SGE). The pyrolyzer was oper-ated at a constant temperature of 700 °C. The pressure of the helium carrier gas at the inlet to the fur-nace was 95 kPa. The pyrolyzer was connected to a 7890A gas chromato-graph with a series 5975C quadru-pole mass spectrometer (Agi lent Technologies Inc.) operated in the electron ionization (EI) mode and in the selected ion monitoring (SIM) mode. A 60 m × 0.25 mm, 0.25-µm df DB-5ms fused-silica capillary col-umn was used. Helium (grade 5.0, Westfa len AG) was used as a car-rier gas. The GC conditions were as fol lows: programmed tempera-ture of the capil lary column from 75 °C (1 min hold) at 7 °C/min to 280 °C (hold to the end of analysis) and programmed pressure of he-lium from 122.2 kPa (1 min hold) at 7 kPa/min to 212.9 kPa (hold to the end of analysis). The tempera-ture of the split–split less injector was 250 °C and the split ratio was 20:1. The transfer line temperature was 280 °C. The EI ion source tem-perature was kept at 230 °C. The

ionization occurred with a kinetic energy of the impacting electrons of 70 eV. The quadrupole tempera-ture was 150 °C. Mass spectra and reconstructed chromatograms (total ion current [TIC]) were obtained by automatic scanning in the mass range m/z 35–750 u. GC–MS data were processed with ChemStation software (Agilent Technologies) and the NIST 05 mass spectral library (Agilent Technologies).

Results and Discussion

The commonly used rubbers are natural rubber (polyisoprene), syn-thetic polyisoprene, polybutadiene, styrene–butadiene copolymers, and nitrile rubber.

Nitrile rubber (poly[acrylonitrile-co-butadiene]) (Figure 1) is a copo-lymer containing 15–50% acrylo-nitrile, manufactured by emulsion poly mer i z at ion of acr y lon it r i le and 1,3-butadiene. It was invented around the same time as styrene–butadiene copolymers in the Ger-man program (at t he end of t he 1920s) to find substitutes for natural rubber (17). The major applications for this material are in areas requir-ing oil and solvent resistance. The largest market for nitrile rubber is in the automotive industry because of its solvent and oi l resistance. Major end uses are for hoses, fuel l ines, O-rings, gaskets, and seals. In blends with poly(vinyl chloride) and poly(acrylonitri le-co-butadi-

2.0

4.0

6.0

8.0

0

E

F

G

H

I

1

2

3

459 10

6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00

Time (min)

Ab

un

da

nce

(X

10

6)

Figure 3: Pyrolysis-GC–MS chromatogram (TIC) of an O-ring seal at 700 °C.

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ene-co-styrene), nitrile rubber acts as an impact modifier. Some nitrile rubber is sold in latex form for the production of grease-resistant tapes,

gasketing material, and abrasive pa-pers. Latex also is used to produce solvent resistant gloves (17).

The analysis of rubber by f lash

pyrolysis-GC–MS is complex and possible interferences are numer-ous. Some rubbers contain more than 15 ingredients, some at a low

Figure 4: EI full-scan mass spectra at 70 eV and the chemical formulas of additives or substances formed by the thermal decomposition of

additives, identified in nitrile rubber materials: (a) diethylene glycol mono-n-butylether, (b) benzothiazole, (c) N-phenyl-1,4-benzenediamine, d)

N-(1-methylethyl)-N´-phenyl-1,4-benzodiamine, (e) 2-ethyl-1-hexene, (f) 2-ethyl-1-hexanol, (g) phthalic anhydride, (h) bis(2-ethylhexyl) phthalate,

and (i) 2-ethylhexyl benzoate.

1.0

2.0

3.0

4.0

5.0

020

29

41

45 57

75

89101

115130

144

Diethylene glycol monobutylether

OO

N

S

OH

30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210m/z

Ab

un

dan

ce (

X10

5)

Ab

un

dan

ce (

X10

5)

Benzothiazole

1.0

2.0

3.0

4.0

5.0

6.0

30

3245 58

69

82 91

108

118

135

Molecular ion

40 50 60 70 80 90 100 110 120 130 1400

m/z

30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

m/z

Ab

un

dan

ce (

X10

5)

0

0.5

1.0

1.5

2.0

2.5

39 51 6580 91

107

118 130 143 154166

184

NH

N-Phenyl-1,4-benzenediamineMolecular ion

H2N

(a)

(b)

(c)

Ab

un

dan

ce (

X10

5)

Ab

un

dan

ce (

X10

6)

02.0

5177

91105

119139

167

183

NH

NH

211

226

Molecular ion

1.0

2.0

3.0

4.0

N-(1-Methylethyl)-N-phenyl-1,4-benzodiamine

40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230

m/z

(d)

2-Ethyl-1-hexene

(e)

0

4.0

8.0

12.0

16.0

20.070

55

41

29

112

9183 Molecular ion

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 05 110 15 120 25

m/z

OH

(f)

Ab

un

dan

ce (

X10

5)

0

2.0

4.0

6.0

8.0

20 30 40 50 60 70 80 90 100 110 120 130

57

41

29

70 83

11298

2-Ethyl-1-hexanol

Ab

un

dan

ce (

X10

5)

76

104

148

50

149

105

70

77

845541

29

112

123

135 167

167

184

279104

81

71

57

43

32

6138

O

O

O

OOO

O

OO

20

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

30 40 50 60 70 80 90 110 120 130 140100 150

m/z

m/z

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

m/z

Phthalic anhydride

Bis(2-Ethylhexyl) phthalate

2-Ethylhexyl benzoate

Molecular ion

0

0

0

2.0

1.0

1.0

2.0

3.0

4.0

2.0

3.0

4.0

5.0

4.0

6.0

8.0(g)

(h)

Ab

un

dan

ce (

X10

4)

(i)

Ab

un

dan

ce (

X10

5)


(<1%) level (9). Because of the low concentration of additives and the possible pyrolyzate interferences from the original polymer or co-poly mer mat r i x, t he eva luat ion and interpretat ion of pyrograms requires a lot of t ime and experi-ence. The peaks obtained for addi-tives are often recognizable as small irregularit ies within the charac-teristic pyrogram of the pyrolyzed material. Figures 2 and 3 show the TIC (pyrograms) of both investi-gated rubber samples obtained after pyrolysis at 700 °C. The degrada-tion products identified by EI mass spectrometry are summarized in Table I. Both investigated samples were ident i f ied as nitr i le rubber based on the decomposition prod-ucts l ike propene, 1,3-butadiene, acr ylonit r i le , met hacr ylonit r i le , 1,3-cyclopentadiene, 1,4-cyclohexa-diene, benzene, toluene, styrene, and benzonitrile. The main feature of the pyrolysis of nitr i le rubber is the formation of the monomers

Table I: Pyrolysis products of the nitrile rubber materials at 700 °C. Peak numbers are as shown in Figures 2 and 3.

PeakRetention Time

(min)Pyrolysis Products of the Nitrile Rubber

Materials at 700 ¡C

1 6.67 Propylene

2 6.76 1,3-Butadiene

3 6.98 Acrylonitrile

4 7.05 1,3-Cyclopentadiene

5 7.24 Methacrylonitrile

6 7.50 1,4-Cyclohexadiene

7 7.66 Benzene

8 8.57 Toluene

E 8.73 2-Ethyl-1-hexene

9 10.25 Styrene

10 11.83 Benzonitrile

F 12.38 2-Ethyl-1-hexanol

A 15.36 Diethylene glycol mono-n-butylether

B 16.61 Benzothiazole

G 18.24 Phthalic anhydride

11 20.83 2,4-Dimethylquinoline

H 23.56 Bis(2-ethylhexyl) phthalate

I 25.52 2-Ethylhexyl benzoate

C 29.96 N-Phenyl-1,4-benzenediamine

D 32.35 N-(1-Methylethyl)-N´-phenyl-1,4-benzodiamine


1,3-butadiene (t R = 6.76 min) and acrylonitri le (t R = 6.98 min). The presence of benzonitrile (tR = 11.83 min) in pyrograms is also charac-teristic for the pyrolysis of nitri le rubber (18). Other substances that appear in pyrograms (Figures 2 and 3) and in Table I, indicated with the letters A to I, have been identif ied by EI MS as nitrile rubber additives or their thermal degradation prod-ucts. The mass spectra obtained in ful l-scan mode and the appropri-ate chemical structures of the sub-stances are shown in Figure 4. The substance diethylene glycol mono-n-butylether (2-[2-butoxyethoxy]-ethanol, CAS No. 112-34-5) (Figure 2, peak A), identif ied in the mem-bra ne of t he hydrau l ic c y l inder from the automotive industry, is a residual solvent from the rubber sample. The substance is known as an excel lent coa lescing and cou-pling agent. The identif ied benzo-thiazole (Figure 2, peak B) has been formed by the thermal degradation of 2-mercaptobenzothiazole (CAS number 149-30-4). 2-Mercaptoben-zothiazole is used as an accelerator for the vulcanization of rubber and as an antioxidant. The identif ied

N-(1-methylethyl)-N´-phenyl-1,4-benzod ia mine (N-isopropyl-N´-phenyl-p-phenylendiamine, CAS number 101-72-4) (Figure 2, peak D) is a very ef fect ive antioxidant and antiozonant that provides me-dium to long term protection for a l l synthetic and natura l rubber. Fur thermore, N-phenyl-1,4-ben-zenediamine (Figure 2, peak C) was generated from N-(1-methylethyl)-N´-phenyl-1,4-benzodiamine dur-ing the pyrolysis of the nitrile rub-ber sample.

The examined O-ring was iden-tif ied as nitri le rubber with high content of the plast icizers bis(2-ethylhexyl) phthalate (CAS number 117-81-7) (Figure 3, peak H, t R = 23.56 min) and 2-ethylhexyl benzo-ate (CAS number 5444-75-7) (Figure 3, peak I, tR = 25.52 min). The ther-mal decomposition of the plasticizer bis(2-ethylhexyl) phthalate leads to the formation of 2-ethyl-1-hexene (Figure 3, peak E, t R = 8.73 min), 2-ethyl-1-hexanol (Figure 3, peak F, tR = 12.38 min), and phthalic anhy-dride (Figure 3, peak G, tR = 18.24 min) during the pyrolysis. The ob-tained full-scan EI mass spectra of the compounds and the appropri-

ate chemical structures are shown in Figure 4 , and t he react ion of the thermal degradation of bis(2-ethylhexyl) phthalate is presented in Figure 5. The chemical reaction scheme is consistent with the previ-ously published work (19).

SIM mode in MS can be used for detection of substances with known m/z of the molecular or fragment ions and for the quantif ication. In our invest igat ion, SIM was used to fol low t he appeara nce of t he ion current curves of the molecu-lar ions m/z 135 of benzothiazole (peak B), m/z 184 of N-phenyl-1,4-benzenediamine (peak C), m/z 226 of N-(1-methylethyl)-N´-phenyl-1,4-benzodiamine (peak D), and of the base fragment ions m/z 70 of 2-ethyl-1-hexene (peak E), m/z 57 of 2-ethyl-1-hexanol (peak F), m/z 104 of phthalic anhydride (peak G), and m/z 105 of 2-ethylhexyl benzoate (peak I). The obtained SIM traces are shown in Figure 6.

ConclusionThe analytical f lash pyrolysis hy-phenated to gas chromatography and EI mass spectrometry in both scan and SIM modes has proven to be a valuable technique for the iden-t i f icat ion of organic addit ives in nitrile rubber materials. This tech-nique allows for the direct analysis of very small solid rubber sample amounts without the need for time-consuming sample preparation. The presented method can be used in R&D, manufacturing processes in the rubber industry, failure analy-sis, and environmental protection.

Acknowledgments I would like to thank my daughter Maria Kusch, M.A., for her critical reading of the manuscript.

References (1) R. Gächter and H. Müller in Plastics

Additives Handbook, 4th Edition

(Hansa Publishers, Munich, Ger-

many, 1993).

(2) M. Herrera, G. Matuschek, and A.

Kettrup, J. Anal. Appl. Pyrolysis 70,

35–42 (2003).

O

O

O

O

O

O

O

H3C

H3C

HO

600 oC

(a) (b) (c)

H3

C

H2C

CH3

CH3

CH3CH3

CH3

Figure 5: Chemical reaction of the thermal decomposition of bis(2-ethylhexyl) phthalate at 700

°C leading to obtain (a) phthalic anhydride, (b) 2-ethyl-1-hexanol, and (c) 2-ethyl-1-hexene.


(3) M. Hakkarainen in Advances in

Polymer Science, Vol. 211, Chroma-

tography for Sustainable Polymeric

Materials, A.-C. Albertsson and M.

Hakkarainen, Eds. (Springer-Verlag,

Berlin, Heidelberg, Germany,

2008), pp. 23–50.

(4) M. Himmelsbach, W. Buchberger,

and E. Reingruber, Polym. Degrad.

Stab. 94, 1213–1219 (2009).

(5) W. Buchberger and M. Stiftinger in

Advances in Polymer Science, Vol.

248, Mass Spectrometry of Polymers

– New Techniques, M. Hakkarainen,

Ed. (Springer-Verlag, Berlin, Heidel-

berg, Germany, 2012), pp. 39–68.

(6) S. M. Reiter, W. Buchberger, and C.

W. Klampfl, Anal. Bioanal. Chem.

400, 2317–2322 (2011) and refer-

ences cited therein.

(7) M. Himmelsbach and W. Buch-

berger, GIT Labor-Fachzeitschrift

52(3), 214–216 (2008).

(8) J.C.J. Bart, J. Anal. Appl. Pyrolysis

58–59, 3–28 (2001) and references

cited therein.

(9) J.C.J. Bart in Additive in

Polymers, Industrial Analysis and

Application (John Wiley & Sons,

Chichester, England, 2005),

pp. 29–48.

(10) F. Cheng-Yu Wang, J. Chromatogr. A

883, 199–210 (2000).


886, 225–235 (2000).

(12) F. Cheng-Yu Wang and W.C. Bu-

zanowski, J. Chromatogr. A 891,

313–324 (2000).


891, 325–336 (2000).

(14) M. Blazsó, Zs. Czégény, and Cs.

Csoma, J. Anal. Appl. Pyrolysis 64,

249–261 (2002).

(15) K.D. Jansson, C.P. Zawodny, and

T.P. Wampler, J. Anal. Appl. Pyroly-

sis 79, 353–361 (2007).

(16) R. Rogalewicz, A. Voelkel, and I.

Kownacki, J. Environ. Monit. 8(3),

377–383 (2006).

(17) D.F Graves in Rubber in Kent and

Riegels Handbook of Industrial

Chemistry and Biotechnology, Part

1, J.A. Kent, Ed. (Springer Science

+ Business Media, New York, New

York, 2007), pp. 689–718.

(18) S.-S. Choi and D.-H. Han, J.

Anal. Appl. Pyrolysis 80, 53–60

(2007).

(19) J.L. Bove and P. Dalven, The Sci-

ence of the Total Environment

36(1),

313–318 (1984).

Dr. Peter Kusch is a scientist

at the Bonn-Rhine-Sieg University of

Applied Sciences in the Department of

Applied Natural Sciences in Rheinbach,

Germany. Please direct correspondence

to: [email protected]. ◾

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16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00

16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00

16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00

6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Benzothiazole

2-Ethyl-1-hexene

2-Ethyl-1-hexanol

Phthalic anhydride

Time (min)

2-Ethylhexyl benzoate

N-Phenyl-1,4-benzenediamine

N-(1-Methylethyl)-N-phenyl-1,4-benzenediamine

16.611Ion 135.00 (134.70 to 135.70)

Ion 184.00 (183.70 to 184.70)

Ion 57.00 (56.70 to 57.70)

Ion 226.00 (225.70 to 226.70)

Ion 104.00 (103.70 to 104.70)

Ion 105.00 (104.70 to 105.70)

Ion 70.00 (69.70 to 70.70)

32.347

29.963

8.731

12.381

18.245

25.521

Figure 6: SIM traces of additives or their thermal decomposition products, identified in nitrile rubber materials: (a) benzothiazole, (b) N-phenyl-

1,4-benzenediamine, (c) N-(1-methylethyl)-N´-phenyl-1,4-benzodiamine, (d) 2-ethyl-1-hexene, (e) 2-ethyl-1-hexanol, (f) phthalic anhydride, and

(g) 2-ethylhexyl benzoate.

For more information on this topic, please visit our homepage at: www.spectroscopyonline.com


Wayne Skinner, Zina Dahmani, Yingzhi Lu, Jean C. Kridl, and Gia C. Fazio

Crop development to improve yield or disease resistance has been explored for centuries and the technologies to measure these improvements have subsequently become complex. The use of transgenes in crop plants is a more technically advanced approach than traditional breeding and the success of this approach is best assessed using modern techniques that accurately quantify the desired traits. Here, we applied targeted liquid chromatography–mass spectrometry (LC–MS) using synthetic stable isotope–labeled peptides to identify and quantify the relative levels of transgenic to native protein. The methodology was developed using rice plants in which mRNA expression and phenotypic effect of the transgene had been validated. Relative quantification of transgenic barley alanine aminotransferase (AlaAT) used targeted LC–MS of tryptic protein fragments. We chose the LC–MS method as a superior technique to directly measure protein levels because other methods such as western blot analysis and RNA were unable to distinguish the minor amino acid differences between the transgenic and native proteins. Establishment of this methodology is a first step toward using LC–MS as a predictive tool to quantify the value of genetically engineered plants before the high investment of a full field trial.

A Novel Approach to Measure Crop Plant Protein Expression

The improvement of crop plants for yield, insect resistance, and abiotic stress tolerance is a continuous process in ag-riculture. In addition to traditional breeding, genetic engi-

neering offers an approach to introduce changes with a targeted adjustment in a plant’s ability to grow. While the ultimate goal in crop improvement is often focused on increasing yield, the tools to measure the biological changes in the plant vary. Selecting the right tool to quantify the improvement hinges on many factors but all other things being equal, the most important consideration is the balance of time and cost.

Here we describe the use of liquid chromatography–mass spec-trometry (LC–MS) to quantify the expression levels of a transgenic protein, barley alanine aminotransferase (AlaAT) across multiple rice lines. LC–MS is an increasingly popular tool in proteomic analyses because it bypasses the difficulties and time often needed for generating antibodies to detect specific proteins (1–5). LC–MS would be particularly useful when specific antibodies are not avail-able, as is the case for barley AlaAT. The AlaAT protein superfamily is highly conserved, and currently available antibodies detect both the native and transgenic protein, thus confounding our ability to quantify the presence of the specific transgene. Therefore, LC–MS technology was selected based on its ability to accurately differenti-ate transgenic from native proteins.

We detected and quantified transgenic proteotypic peptides using stable isotope–labeled peptides as internal standards and

spiked them into rice leaf samples to accurately quantify the endog-enous levels of transgenic protein. This workflow is similar to other targeted proteomic workflows for the identification of biomarkers and low-level endogenous proteins in complex matrices (6,7).

Our goal was to measure the amount of transgenic barley AlaAT protein against the amount of native AlaAT across multiple rice lines. If the technique proves to be a robust and accurate means to measure protein levels, in the future we could apply LC–MS to evaluate the potential performance of a rice line before the invest-ment in space, time, and resources for a field trial. By correlating yield improvements to transgenic protein levels in the greenhouse or growth chamber, we could significantly reduce the number of transformation lines that require field testing. The first step in this process is to establish the methodology with plant lines that have been evaluated in the field and determine whether LC–MS is a suit-able tool for measuring protein levels. The work we describe here established the necessary resources to differentiate transgenic from native protein using LC–MS as a primary tool.

Experimental MethodsTissue Collection Flag leaves were collected at the booting stage from field-grown rice plants in which mRNA expression level and phenotypic ef-fect of the transgene had been validated. The plant leaves were immediately frozen in liquid nitrogen and stored until manually

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ground to homogenization using a chilled mortar and pestle.

Total Protein

Quantification and Normalization Protein samples were prepared using the NucleoSpin RNA/Protein kit and quan-tified using the Protein Quantification Assay (both from Macherey-Nagel) ac-cording to manufacturer instruction. All protein samples were analyzed by 4–12% Bis-Tris sodium dodecyl sulfate–polyacryl-amide gel electrophoresis (SDS-PAGE) in MES buffer (Invitrogen).

Protein Gel Electrophoresis A 5-µL volume of Kaleidoscope Prestained Standard (Bio-Rad) and 70 µg of each total protein sample were loaded onto a NuPAGE Novex 4–12% Bis-Tris Gel 1.5 mm, 10-well precast polyacrylamide gel (Invitrogen). The protein bands were visualized using the Colloidal Blue Staining Kit (Invitrogen).

Excision of Barley AlaAT Protein Bands The gel was cut into approximately 1-mm

pieces for each sample between the BSA band (<78 kDa) and the carbonic anhy-drase band (>45.7 kDa), a section that contains the target barley AlaAT protein. Blank lanes were used as controls. Gel pieces were stored separately at -20 °C in 1.5-mL siliconized Eppendorf tubes.

Peptides and Digest The bands corresponding to AlaAT were excised, destained, reduced with tris (2-carboxyethyl)phosphine (TCEP), and alkylated with iodoacetamide before di-gestion using an in-gel tryptic digestion kit (Thermo Fisher Scientific). Quantifi-cation of AlaAT in transgenic rice used targeted LC–MS of four peptides. We pre-viously discovered which peptides could be readily detected in the transgenic rice lines. Peptides were selected based on re-sults from transgenic rice samples and are described in Table I. Liquid chromatogra-phy–multiple reaction monitoring-mass spectrometry (LC–MRM-MS) was used to determine which of the specific pro-teotypic peptides generated clear MRM

signals in the endogenous matrix of back-ground peptides. The peptides were also selected based on unique sequence iden-tity for barley over the rice native AlaAT. Four synthetic heavy peptides (Table I, Thermo Fisher Scientific) were mixed with sequencing-grade porcine tryp-sin (Promega) just before its addition to excised gel pieces. Protein was digested using a protein-to-enzyme ratio of ap-proximately 50:1 and incubated at 37 °C for 16 h. The amount of each stable iso-tope (13C/15N)–labeled peptide added to each gel sample was 1500 fmol. For each analysis, one-half of the recovered tryptic digest was analyzed.

Chromatography

and Mass Spectrometry Chromatography of peptides used a Para-digm MDLC MS4 LC pump and a 150 mm × 0.2 mm, 3-µm dp, 200-Å C18AQ column (Michrom Bioresources). Peptides were eluted using a 2-µL/min flow rate and a gradient of acetonitrile (solvent B) in 0.1% formic acid (solvent A) as follows: 5–40% B over 50 min, 40–80% B over 1 min, hold at 80% B for 1 min, 80–5% B over 1 min, and hold at 5% B for 14 min.

An LCQ Deca XP-plus ion-trap mass spectrometer (Thermo Scientific) equipped with a Michrom Advance Spray Source was used for MS-MS analysis in positive-ion electrospray mode. The source spray voltage was 1200 kV and the capillary temperature was 200 °C. The MS-MS fil-ters, instrument conditions, and voltages were optimized for each targeted peptide by direct infusion and LC–MS analyses of the heavy synthetic peptides. LC–MS data were integrated and processed using Xcali-bur software (Thermo Scientific).

Barley AlaATRice AlaAT






MAA-TVAVDNLNPKVLKCEYAVRGEIVIHAQRLQEQLKTQPGSLPFDEILYCNIGNPQSLGQQPVTFFREVLALCDHPDLLQREEIKTLFSADSIS

MAAPSVAVDNLNPKVLNCEYAVRGEIVIHAQRLQQQLQTQPGSLPFDEILYCNIGNPQSLGQKPVTFFREVIALCDHPCLLEKEETKSLFSADAIS

RAKQILAMIPGRATGAYSHSQGIKGLRDAIASGIASRDGFPANADDIFLTDGASPGVHLMMQLLIRNEKDGILVPIPQYPLYSASIALHGGALVPY

RATTILASIPGRATGAYSHSQGIKGLRDAIAAGIASRDGYPANADDIFLTDGASPGVHMMMQLLIRNEKDGILCPIPQYPLYSASIALHGGALVPY

YLNESTGWGLETSDVKKQLEDARSRGINVRALVVINPGNPTGQVLAEENQYDIVKFCKNEGLVLLADEVYQENIYVDNKKFHSFKKIVRSLGYGEE

YLNESTGWGLEISDLKKQLEDSRLKGIDVRALVVINPGNPTGQVLAEENQRDIVKFCKNEGLVLLADEVYQENIYVDNKKFNSFKKIARSMGYNED

DLPLVSYQSVSKGYYGECGKRGGYFEITGFSAPVREQIYKIASVNLCSNITGQILASLVMNPPKASDESYASYKAEKDGILASLARRAKALEHAFN

DLPLVSFQSVSKGYYGECGKRGGYMEITGFSAPVREQIYKVASVNLCSNITGQILASLVMNPPKAGDASYASYKAEKDGILQSLARRAKALENAFN

KLEGITCNEAEGAMYVFPQICLPQKAIEAAKAANKAPDAFYALRLLESTGIVVVPGSGFGQVPGTWHFRCTILPQEDKIPAVISRFTVFHEAFMSE

SLEGITCNKTEGAMYLFPQLSLPQKAIDAAKAANKAPDAFYALRLLEATGIVVVPGSGFGQVPGTWHIRCTILPQEEKIPAIISRFKAFHEGFMAA

YRD

YRD

Peptide 4 Peptide 3

Peptide 2

Peptide 1

Figure 1: Protein sequence alignment of the barley and rice AlaATs. The underlined regions of

the barley sequence indicate the peptide sequences used to measure AlaAT and the names are

listed above the indicated peptide. The underlined residues in the rice AlaAT sequence denote

the amino acids that are different from the barley AlaAT. Note that for the first occurring peptide

sequence (labeled peptide 4) is identical in barley and rice.

Table I: Targeted peptides of barley alanine aminotransferase. The peptide mass filters and quantitative ions used for the detec-tion of AlaAT in the prepared plant tissue samples for all four peptide sequences. ÒLightÓ denotes the native peptides; ÒheavyÓ denotes the peptides containing a stable isotope label at the terminal residue. Note the retention times do not shift between the labeled and unlabeled peptides, the only observed change is the quantitative ions.

Peptide MS-MS filter (collision energy was 35%) tR (min) Quant Ions

1 (Light) 915.8 m/z, IW = 2.0, Scan 250–2000 m/z 32 800.6

1 (Heavy) 925.8 m/z, IW = 2.0, Scan 250–2000 m/z 32 810.6

2 (Light) 1036.2 m/z, IW = 2.5, Scan 285–2000 m/z 37 798.5, 1107.5

2 (Heavy) 1040.2 m/z, IW = 2.5, Scan 285–2000 m/z 37 806.5, 1115.5

3 (Light) 548.8 m/z, IW = 2.0, Scan 150–2000 m/z 26 735.3, 882.3


4 (Light) 517.0 m/z , IW = 2.0, Scan 140–2000 m/z 15 624.2, 723.3



Results

For the first iteration of barley AlaAT detec-tion, we needed to establish a cost-effective protein separation technique that could later be applied to multiple plant lines in a moderate high-throughput manner. Pro-teins from all samples were extracted and purified by SDS-PAGE. While there are other ways to separate proteins in a crude extract, separation by SDS-PAGE allows for a rapid, relatively inexpensive way to process a high number of samples. The ex-pression of transgenic barley AlaAT was ex-pected to be low, therefore, minimizing the influence of other, more highly abundant proteins through SDS-PAGE provided a potentially reproducible approach for mul-tiple plant lines and species. The protein bands were digested, in gel, with trypsin in the presence of synthetic heavy peptides to minimize the variables that could interfere with relative quantification.

The next step was to select AlaAT pep-tides that could differentiate between na-tive rice and transgenic barley protein. We chose four peptides that were frequently detected in LC–MS analysis for AlaAT and used stable isotope–labeled pep-tides to identify and measure the levels of transgenic protein in rice. Peptides 1-3 are specific to barley AlaAT and peptide 4 is present in both barley and rice AlaATs based on their known protein sequences (Figure 1). These synthetic peptides were used for quantification of barley AlaAT in the transformed rice lines using the isotopic dilution technique (8) and full-scan MS-MS detection at the expected retention times to maximize sensitivity. The MS-MS filters, instrument condi-tions, and voltages were optimized for each targeted peptide by direct infusion and LC–MS-MS analysis of the individual heavy synthetic peptides (Table II).

To establish the differentiation of barley AlaAT over native, a wild-type rice sample was extracted and spiked with synthetic peptides 1–4. The only native peptide de-rived from endogenous protein that was detected in this sample corresponded to peptide 4. The three remaining peptides were not detected in the wild-type rice sam-ples. An example of the chromatogram and spectra from the wild-type Nipponbare rice sample is shown in Figure 2. The detection of peptide 4 is clear and the lack of any ions representing peptides 1–3, the three barley-

specific AlaAT peptides, indicates barley AlaAT is not present.

Next, we evaluated the ability of LC–MS to detect and relatively quantify the levels of barley AlaAT protein in six transgenic rice lines using the selected synthetic peptides specific to barley AlaAT. Two rice cultivars, Nipponbare and Taipei, were used to exam-ine the potential differences in response to nitrogen stress. Four transgenic lines, two from the Nipponbare cultivar (NAAT1 and NAAT2) and two from the Taipei cultivar (TAAT3 and TAAT4) were compared to their parental wild-type (Nipponbare and Taipei) varieties grown at the same time to ensure any variation detected was be-cause of natural differences among differ-ent transformation events. Each transgenic

cultivar contained a single copy of AlaAT isolated from barley (Hordeum vulgare) as previously described (9). Though mRNA expression level and phenotypic effect of the transgene had been validated in the plants, the protein level of barley AlaAT in each of these lines is unknown.

A representative LC–MS chromato-gram and spectra of all the peptides in the transgenic NAAT2 rice sample is shown in Figure 3a. The base peaks correspond-ing to each of the heavy and light barley AlaAT peptides have been selected. Fig-ure 3b shows the same three peptides in the transgenic rice sample TAAT3. Each of the peptides is eluted separately and is distinguishable from other potentially interfering peptide ions as indicated by

Table II: Peptide qualities used for MS-MS detection of barley AlaAT and the sequences of the four labeled and unlabeled peptides and their molecular weights. The molecular weight (MW) of the peptides when compared to the data from Table I, demonstrates that the doubly charged ions are measured for peptides 2–4, whereas the singly charged form of peptide 1 is monitored.

Peptide Sequence MW

1 (Light) DGILASLAR 915.1

1 (Heavy) DGILASLAR (13C/15N-Arg) 925.1

2 (Light) SLGYEEDLPLVSYQSVSK 2071.3

2 (Heavy) SLGYEEDLPLVSYQSVSK (13C/15N-Lys) 2079.3

3 (Light) TLFSADSISR 1096.2

3 (Heavy) TLFSADSISR (13C/15N-Arg) 1106.2

4 (Light) GEIVIHAQR 1022.2

4 (Heavy) GEIVIHAQR (13C/15N-Arg) 1032.2

Nipponbare

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Heavy 4

Heavy 3

Heavy 1

Heavy 2

Light 2

Light 1

Light 3

Light 4

10018.41 634.11

733.30

624.20

723.23

892.26

745.22

685.53

833.17

920.50

810.37

907.32

806.23

1115.34

702.22

1049.29

741.91

618.13820.85

926.22

1081.191004.09

18.39

29.75

30.04

33.83

32.16

34.39

38.64

37.13

0 10 20 30 40 50

NL: 1.59E6

m/z = 798.50+1107.50

ms2 [email protected]

NL: 4.45E7

m/z = 810.50


NL: 3.64E6

m/z = 634.20+733.30


RT: 18.41


RT: 18.39


RT: 29.75


RT: 29.55-29.95


RT: 33.83


RT: 33.63-34.03


RT: 38.64


RT: 38.44-38.84


NL: 1.85E5

m/z = 624.20+723.30


NL: 5.79E7

m/z = 745.30+892.30


NL: 3.31E5

m/z = 735.30+882.30


NL: 1.63E6

m/z = 800.50 ms2

[email protected]

NL: 2.56E8

m/z = 806.50+1115.50


Time (min) m/z

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Figure 2: Chromatograms and spectra of all four heavy peptides and one light peptide in the

Nipponbare wild-type rice sample. Unlabeled ions for peptides 3, 1, and 2 were expected at

882.25, 800.23, and 798.16, respectively, but were not found at the expected retention times.


the retention times. We determined that targeting the scan methods to the specific ions from each peptide greatly increased the sensitivity over the use of data-de-pendent MS-MS analysis and provided a

limit of detection of approximately 5 fmol per gel-purified sample. This is equivalent to about 4 ppm, based on total protein levels in the extract before SDS-PAGE purification.

The results for all six rice lines are sum-marized in Table III. The values represent the femtomolar amount of each peptide measured in all samples. The area under each peak of the relevant peptide was mea-sured and calculated as a ratio of heavy to light. Based on the results of the ratio, the total amount (in femtomoles) of each peptide was reported. To calculate the ap-proximate amounts of barley AlaAT pro-tein in the samples, the values for peptides 1–3 were averaged and are reported as a function of the total protein loaded on the gel. While the values for peptide 4 are pre-sented, they were not used to calculate the amount of barley AlaAT in the transgenic rice samples.

The measured amounts for each of the peptides in all of the samples were affected relatively similarly. That is, if a low amount of peptide 3 was found in NAAT1, it was also found to be within a similarly low range in NAAT2, TAAT3, and TAAT4. Therefore, it appears that the peptides were affected equally in each sample. The only exception was peptide 4, which was ex-pected because this peptide detects both en-dogenous AlaAT and barley AlaAT. There are currently no publicly available data that measure native AlaAT in rice flag leaves.

The data indicate that barley AlaAT pro-tein levels are relatively equal across these selected transgenic lines: NAAT1 and NAAT2 were calculated to contain 134 and 160 fmol of barley AlaAT per gram of plant leaf tissue, respectively. The low amounts of peptides 1–3 in NAAT1 clearly contributed to a reduced calculated protein value and the lowest overall. Interestingly, the peptide 4 value in NAAT1 was measured nearly as high as the Taipei wild-type sample. The Taipei transgenic lines TAAT3 and TAAT4 contained 170 and 204 fmol of bar-ley AlaAT protein per gram of leaf tissue,

Re

lati

ve

ab

un

da

nce

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ab

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nce

Heavy 3

(a)

(b)

Heavy 1

Heavy 2

Heavy 2

Heavy 1

Heavy 3

Light 1

Light 2

Light 2

Light1

Light 3

Light 3

10027.01

28.60

26.95

31.92

31.96

35.99

35.97

28.31

28.40

32.99

33.03

36.37

36.35

46.27

798.16

892.22

745.31

735.31

882.25

810.36

800.36

1115.38

806.21

1107.41

798.15

806.20

1107.43

1115.40

800.23

810.36

734.99

882.25

892.22

745.38

NL: 1.17E7

m/z = 745.30+892.30


NL: 8.07E5

m/z = 735.30+882.30


NL: 4.92E7

m/z = 810.50


NL: 7.77E6

m/z = 800.50


NL: 1.18E8

m/z = 806.50+1115.50:

[email protected]

NL: 4.40E7

m/z = 798.50+1107.50:


NL: 5.50E6

m/z = 745.30+892.30:


NL: 5.58E5

m/z = 735.30+882.30:


NL: 2.42E7

m/z = 810.50


NL: 2.92E6

m/z = 800.50


NL: 1.21E8

m/z = 806.50+1115.50:


NL: 4.41E7

m/z = 798.50+1107.50:


50

0

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RT: 35.97

Full ms2 [email protected]

RT: 28.31


RT: 28.40


RT: 32.99


RT: 33.03


RT: 36.37


RT: 36.35


RT: 35.99


RT: 31.96


RT: 31.92


RT: 26.95


RT: 27.01


Time (min) m/z

700 800 900 1000 11000 10 20 30 40 50 60

Time (min) m/z

700 800 900 1000 11000 10 20 30 40 50 60

Figure 3: Representative chromatograms and spectra of the three unique barley AlaAT heavy

peptides in the transgenic (a) Nipponbare (NAAT2) and (b) Taipei (TAAT3) rice samples. Unlabeled

ions for peptides 1–3 were found at comparable levels in all the samples.

Table III: Presence of AlaAT protein in transgenic and wild-type rice leaf samples. Individual amounts (fmol) of peptides from each plant sample. Transgenic NAAT1 and NAAT2 share the Oryza sativa L. Japonica Nipponbare parent. Transgenic TAAT3 and TAAT4 originate from Oryza sativa cv. Taipei. Peptide 4 is known to share sequence identity in rice and barley and the measured values are indicated, but are not included in the overall average for the transgenic lines.

Sample Peptide 1 Peptide 2 Peptide 3 Peptide 4 Average Total

NAAT1 199.0 531.7 32.3 592.5 254 134

NAAT2 248.6 572.8 86.3 445.5 303 160

Nipponbare ND ND ND 119.9 — —

TAAT3 253.7 592.5 120.3 122.2 322 170

TAAT4 345.4 662.3 150.0 904.8 386 204

Taipei ND ND ND 601.5 — —


respectively. Overall, the Taipei transgenic lines contained slightly more protein than the Nipponbare transgenic lines; however the difference is not significant. The slight difference could be because of the timing of the harvest. Although all the plants were harvested during the booting phase, Taipei takes slightly longer to mature and therefore the developmental stages could have been slightly different. The values for each pep-tide in all samples agreed with the average calculated protein levels of barley AlaAT. For example, if peptide 1 was higher in TAAT3 than NAAT1 that was also true for peptides 2 and 3. The values of peptide 4 varied the most among all the peptides, ranging from 120 to 602 fmol; however, re-call that this peptide is shared between rice and barley and therefore its levels are likely skewed because of interference from the na-tive levels of AlaAT, which could also vary among the lines and rice varieties.

DiscussionHere we provide evidence that LC–MS is useful for the quantification of a transgenic protein that has high identity (89%) with its native homolog. The sequences are merely punctuated by single amino acid differ-ences that we believe are best observed using LC–MS. Our previous attempts to quantify AlaAT included several iterations of antibody generation and assay optimi-zation that only resulted in a simple scale of transgene protein levels that was not quantitative because of interference from endogenous AlaAT. The amounts of barley AlaAT could only be measured relative to background (wild-type) and other trans-genic lines. Moreover, barley AlaAT has the same mass as the most abundant protein in plant leaves, Rubisco, and migrates on its gel front further interfering with reliable detection. We found that using LC–MS to measure the relative levels of barley AlaAT was a reliable alternative to traditional pro-tein detection techniques. Once peptides were identified that would work for LC–MS detection of barley AlaAT, optimization of the MS parameters for consistent and accurate detection was relatively simple and provided a way to overcome interfer-ence from other proteins such as Rubisco. Within a month, the limit of detection was established, all four peptides were produc-ing reliable spectra, and several plant lines had been screened. The methodology has

become so facile that we intend to apply the techniques to other rice lines that have not yet been evaluated in the field.

LC–MS can be subject to unpredict-able matrix affects, creating difficulty in measuring specific target proteins. Here we found that even though the relative amounts of each peptide varied within a sample, the averaged values provided a way to more accurately quantify the levels of transgenic protein in rice leaf tissue.

Before the development of LC–MS, researchers relied on using antibodies to determine whether the targeted transgenic protein was present in transformed mate-rial. However, antibodies are limiting in specificity, expensive to develop, and re-quire several months to fully develop a reli-able assay. Targeted protein detection using MS reduces the total investment of time and money, while applying methodology that accurately detects the target separate from other proteins that may share similar or identical sequences that may not be discern-ible by older techniques.

Conducting field trials requires an ex-tremely demanding investment of time and money and is the core of the agricultural business. Demonstrating field efficacy is paramount to the potential commercializa-tion of a product; without clear field data, the product is not considered. Therefore, several field trials are conducted over sev-eral growing seasons, often yielding only one or two lead events that demonstrate a potential commercial product. The process can take at least 4–5 repeated trials with the single event before the data can be com-pounded to show the trait is working. The process of plant transformation lends itself to a range of lines with extremely low or no gene expression through high expression. In the event that protein levels can be tied to trait efficacy, LC–MS provides the ability to prescreen the transformation events in the greenhouse before investment in full field evaluation.

ConclusionWe applied targeted LC–MS using syn-thetic stable isotope–labeled peptides to measure the levels of transgenic barley AlaAT protein in various plant samples. LC–MS offered a unique approach to mea-sure the transgenic protein against native protein levels. We were able to establish a methodology that can be applied to other

rice transgenic events that may have dif-ferent protein expression levels. The next step will be to discover the relationship be-tween protein levels and trait efficacy and then continue to expand the methodology to other plant species. Until recently, detec-tion of transgenic proteins mostly relied on older methodology that could not discern subtle differences in protein composition. As we continue to develop MS techniques, even more enhancements can be made to detect minor differences between molecu-lar species and a broader application can be discovered.

AcknowledgmentThe authors would like to thank Dr. Brett Phinney and Rudy Alvarado for initially establishing the detected peptides and providing helpful advice and guidance regarding proteomic analyses. We would also like to thank Dr. Margaret Miller and Dr. Ann Slade for careful reading of this manuscript.

References (1) C.C. Wu and J.R. Yates III, Nat. Biotech-

nol. 21(3), 262–267 (2003).

(2) S.E. Ong and M. Mann, Nat. Chem.

Biol. 1(5), 252–262 (2005).

(3) U. Lehmann et al., Plant J. 55(6),

1039–4106 (2008).

(4) M. Mann, J. Proteome Res. 7(8),

3065–3065 (2008).

(5) T. Fortin et al., Mol. Cell Proteomics

8(5), 1006–1015 (2009).

(6) A.K. Yocum and A.M. Chinnaiyan,

Brief. Funct. Genomic. Proteomic. 8(2),

145–157 (2009).

(7) A. Prakash et al., J. Proteome Res.

8(6), 2733–2739 (2009).

(8) H.W. Lahm and H. Langen, Electropho-

resis 21(11), 2105–2114 (2000).

(9) A.K. Shrawat et al., Plant Biotechnol. J.

6(7), 722–732 (2008).

Wayne Skinner, Zina Dahmani,

Yingzhi Lu, Jean C. Kridl, and

Gia C. Fazio are with Arcadia Biosciences,

Inc., in Davis, California. Direct correspon-

dence to: [email protected]. ◾



Stefan Jenkins, Steven M. Fischer, and Theodore R. Sana

This article describes a nontargeted, discovery-based approach for analyzing the yeast metabolome in response to calcium and immunosuppressant drug treatment, using multiple liquid chromatography (LC) conditions and quadrupole time-of-flight mass spectrometry (QTOF-MS) analysis. Yeast cultures were exposed to calcium only or calcium and one of two immunosuppressant drugs with the goal of perturbing the calcineurin- and any other calcium- or immunosuppressant-responsive pathways. Compounds were extracted from the raw data using an unbiased extraction algorithm. These results were subsequently analyzed using multidimensional statistical analysis and data visualization software and reduced to a list of compounds that were differentially expressed according to culture condition. Accurate-mass–based identifications were done using an endogenous metabolite database. MS-MS spectra were acquired for a subset of compounds, allowing for matching at multiple fragmentation energies against an endogenous metabolite library to provide higher confidence identifications.

Nontargeted, Discovery-Based Profiling of the Yeast Metabolome Using QTOF LC–MS and LC–MS-MS

Baker’s yeast, Saccharomyces cerevisiae, is used extensively as a model organism for all eukaryotic cells, facilitating research into the biochemistry and biological pathways of more com-

plex organisms. It is especially attractive because it can grow for ex-tended periods under highly controlled conditions (1). Moreover, its genome has been fully sequenced (2–4). Metabolomics entails the study of an organism’s complete set of small molecule metabolites, which includes a wide variety of compounds. An understanding of the yeast metabolome can shed light on the metabolic pathways of higher organisms and their response to drugs and environmental changes. Methods are needed to provide as much coverage as pos-sible of the yeast metabolome.

This article describes a discovery-based, untargeted metabo-lomics analysis of hundreds of yeast metabolites under robust, controlled extraction conditions followed by identification, which is a prerequisite for studying biological pathways, especially when comparing response to environmental stress (5,6).

A metabolite extraction protocol was optimized to maximize metabolome coverage. The liquid chromatography quadrupole time-of-flight (LC–QTOF) system was run in multiple chromato-graphic and detection modes to separate and detect large numbers

of metabolites. Data analysis was performed using MassHunter Qualitative Analysis (Agilent Technologies) and Mass Profiler Professional (MPP) software, as well as the Personal Compound Database and Library (PCDL) containing content derived from the Agilent METLIN database. Yeast cultures were exposed to calcium only or calcium and one of two immunosuppressant drugs (cyclo-sporin A and FK 506), with the goal of perturbing the calcineu-rin- and any other calcium- or immunosuppressant-responsive pathways (7,8). Initial liquid chromatography–mass spectrometry (LC–MS) accurate mass matches to METLIN content resulted in many compounds with differential abundances. Subsequent con-firmation by rerunning samples on the LC–QTOF system for LC–MS-MS analysis provided the highest-confidence identifications.

ExperimentalInstrumentation

This study was performed using a 1200 SL Series LC system (Agi-lent Technologies) with a binary pump and degasser, a well-plate autosampler with thermostat, and a thermostated column com-partment. Both reversed-phase and aqueous normal-phase chro-matography were performed to maximize coverage of the yeast

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metabolome. The LC method parameters are listed in Table I.

The LC system was coupled to an Agi-lent 6530 Accurate-Mass Q-TOF mass spectrometer with an atmospheric-pressure chemical ionization (APCI) source oper-ated in positive ion mode, as well as an elec-trospray ionization (ESI) source operated in positive and negative ion modes. Dynamic mass axis calibration was achieved by con-tinuous infusion of a reference mass solu-tion using an isocratic pump. The MS and MS-MS method parameters are given in Table II.

Sample preparation

S.cerevisiae strain BJ5459 was cultured in parallel and at an OD600 of 0.8 exposed to vehicle control (4 mL of 90:10 ethanol–Tween 20) for wild type-WT and calcium control-CA, or 4 μg/mL FK506 (FK), or 4 μg/mL cyclosporinA (CY), suspended in 90:10 ethanol–Tween 20. After 1 h, an equal fraction of media containing CaCl2 was added to FK and CY cultures as well as to the calcium-only exposed culture

(CA), to a final concentration of 100 mM. After 15 min of exposure to either vehicle control or calcium, cultures were centri-fuged and washed with phosphate-buff-ered saline (PBS) to remove any residual media. Quenching was done with 1 mL of methanol added to the final pellet at -40 °C. After quenching, the final sample was lyophilized.

Wet milling was performed with 5 mg of dry sample in 2-mL Eppendorf tubes in 1.1 mL of an extraction solvent of 5:3:3 chloroform–methanol–water. A Retsch MM301 ball mixer mill was used with a single 5-mm ball bearing as a tube in-sert to facilitate mechanical rupture of the yeast. External standards were added before milling. Nine samples for each culture condition were processed for 3 × 1 min cycles at 30 Hz. The milling process resulted in polar and nonpolar phases. The polar-phase supernatants were filtered first through 0.2-µm and then 10 kDa ultrafiltration membranes to ensure removal of any residual protein or cellular debris.

Data Analysis

Compounds were extracted from the raw data files using an unbiased, molecular fea-ture extraction (MFE) algorithm in Agilent MassHunter Qualitative Analysis B.04.00 software. The processed data files were subsequently analyzed using MPP multidi-mensional statistical analysis and data visu-alization software. Provisional compound identification was performed by matching accurate mass results to content from the Agilent METLIN PCDL. Orthogonal reten-tion time matching was used to increase the specificity of the database match by requir-ing a match to the retention time informa-tion contained in the PCDL. In addition, MS-MS spectra, acquired for ions present in samples that were previously identified by database matching, were queried against a library of LC–MS-MS spectra acquired from more than 2200 individual standards.


Feature Extraction

The MFE algorithm in MassHunter finds all ions that are related, including isotopes

Table I: LC conditions for both reversed-phase and aqueous normal-phase chromatography

LC Conditions

Reversed Phase

Column Guard column: 30 mm × 2.1 mm, 3.5-µm Zorbax C-8 Analytical column: 50 mm × 2.1 mm, 1.8-µm Zorbax C18 SB-Aq

Column temperature 60 °CInjection volume 10 µLAutosampler temperature 4 °CNeedle wash 3 s in wash port

Mobile phase A = 0.2% acetic acid in water B = 0.2% acetic acid in methanol

Flow rate 0.6 mL/min

Linear gradient

• 2% B to 98% B in 13 min• 6 min hold at 98% B• Stop time: 19 min• Post time: 5 min

Aqueous Normal Phase

Column 150 mm × 2.1 mm, 4-µm, 100-Å Cogent Diamond Hydride HPLC column,standard endfittings. Wash new columns with 10% water–90% isopropyl alcohol overnight.

Column temperature 60 °C

Injection volume 10 µL

Autosampler temperature 4 °C

Needle wash 3 s in wash port

Mobile phase

Positive Ion: Negative Ion:A = 50% water–50% isopropyl A = 50% water–50% isopropyl alcohol– alcohol–0.1% formic acid 0.025% formic acid and 5 µM EDTAB = 3% water–97% acetonitrile– B = 10% water/90% acetonitrile with 5 mM 0.1% formic acid ammonium formate with 5 µM EDTA adjusted to pH 7.0 using NH3

Flow rate 0.6 mL/min

Linear gradient

Positive Ion: Negative Ion:• 97% B to 20% B in 15 min • 99% B to 20% B in 15 min• Stop time: 15 min • Stop time: 15 min• Post time: 5 min • Post time: 5 min


and any adducts, such as Na+ or K+ and di-mers and sums all ion signals into one value (feature), in a fully automated mode. Table III shows the results for the four culture types: wild type (WT), calcium-exposed (CA), FK 506-treated (FK), and cyclo-sporin A–treated (CY). Each culture was analyzed using both reversed phase and aqueous normal-phase chromatography, with QTOF-MS analysis done using an ESI source operated in positive and nega-tive modes, as well as an APCI source op-erated in positive ion mode. For the most part, the results for all four culture types were similar, with aqueous normal-phase ESI+ generating the most features. By in-corporating two chromatographic separa-tion techniques, two MS sources (ESI and APCI), as well as both positive and negative ionization modes, broader detection cover-age of the yeast metabolome was possible.

Data Analysis in

Mass Profiler Professional

Data filtering for the biological replicates resulted in a condensed list of compounds of interest, which was then assessed for statistical significance to determine differ-ential abundance of metabolites according to the culture conditions. Chemical enti-ties (features) that were present in at least six of the nine data files, and in at least one of the four culture conditions were retained (column 1, Table IV). The filtered list of compounds was then queried against the Agilent METLIN content of >25,000 com-pounds in PCDL (column 2). The filtered features were then evaluated for statistical significance between culture conditions at a cutoff of p <0.05 (column 3). Features that passed this cutoff were again queried in terms of accurate mass against the MET-LIN PCDL (column 4).

Principal Component Analysis

Principal component analysis (PCA) re-sulted in spatial separation for all four of the culture treatments (Figure 1). Analysis of the differential abundances for several compounds revealed the effect of the vari-ous treatments on several metabolic path-ways. One example was the metabolites 5′-methylthioadenosine and S-adenosyl-homocysteine which are both substrates of the enzyme 5′-methylthioadenosine nucleosidase (EC 3.2.2.9) in the cysteine and methionine metabolism pathway.

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x-axis

x-axis

x-axis

RP Pos

ANP Pos ANP Neg

Culture condition

Wild type

Calcium control

FK506

Cyclosporin A

RP Neg

APCI

Figure 1: Principal component analysis (PCA) plots generated using MPP for each analysis type.

Spatial separation for each culture condition (n = 9) was observed, indicating the unique effect

of each culture treatment.

5’ - Methylthioadenosine S-Adenosylhomocysteine

2,000,000

Culture condition

Ab

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ce

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0WT CA FK CY

WT CA FK CY

0.14 0.22 0.08 0.09 0.14 0.12 0.06 0.06

2,500,000

3,000,000

3,500,000

4,000,000

Figure 2: Differential abundances calculated in MPP at p<0.05 for compounds identified by

accurate mass in PCDL as 5’-methylthioadenosine and S-adenosylhomocysteine. Evaluation of

extracted ion chromatograms (EICs) confirmed differential abundance. The charts represent the

average abundance according to culture condition (n = 9), with the relative standard deviation

(RSD) in red.


The abundances of these two metabolites increased in the calcium treated data set (CA), indicating possible inhibition of 5′-methylthioadenosine nucleosidase in a calcium dependent manner (Figure 2). This was previously reported in an orthologous pathway in Arabidopsis thaliana (9). Good reproducibility for the biological replicates

was reflected by the low relative standard deviations (RSDs).

MS-MS Spectral Matching

MS-MS spectral matching can provide the most confidence in discovery-based MS identification, and spectral matching in PCDL provides a level of confidence that

cannot be obtained using other publicly available libraries. The addition of MS-MS library searching capability against 2200 standards with MS-MS spectra in PCDL provided a chromatography-independent means for compound identification.

As shown in Figure 3, several com-pounds in this study that showed differen-

Table II: QTOF-MS and MS-MS conditions

MS Conditions

Ion mode ESI, positive and negative; APCI positive ionization

Drying gas temperature 325 °C

Vaporizer temperature 350 °C

Drying gas flow 10 L/min ESI, 5 L/min APCINebulizer pressure 45 psi

Capillary voltage4000 V positive ion mode4000 V negative ion mode3500V APCI

Spectra acquisition rate 1.4 spectra/s

MS-MS Conditions

Column 150 mm × 2.1 mm, 4-µm, 100-Å Cogent Diamond Hydride HPLC column,standard endfittings. Wash new columns with 10% water–90% isopropyl alcohol overnight.

Column temperature 60 °CInjection volume 10 µLAutosampler temperature 4 °CNeedle wash 3 s in wash portFlow rate 0.6 mL/minQuad resolution High resolutionIon mode Both positive and negative Drying gas temperature 325 °C Drying gas flow 9 L/minNebulizer pressure 45 psigCapillary voltage 4000 V (positive mode)/3500 V (negative mode)Fragmentor 140 VSkimmer 65 VOCT1RFVpp 750 VIsolation width ~1.3 m/z

Reference delivery Agilent 1100 isocratic pump with 100:1 splitter

Reference pump flow 1 mL/min for 10 μL/min to nebulizer

Reference ions Positive mode: 121.050873 and 922.009798 Negative mode: 119.036320 and 966.000725

Instrument mass range 1700 DaAcquisition rate 3.35 spectra/sTOF spectra mass range 25–1000 m/z

Collision energy (eV) 10 and 20 Data storage Centroid Threshold 100 (MS) and 5 (MS-MS)Instrument mode Extended dynamic range

Table III: MFE results by treatment and analysis type

Wild Type Calcium Treated FK506 Treated Cyclosporin A Treated

RP-ESI+ 418 417 411 416RP-ESI- 439 488 442 462RP-APCI+ 260 277 381 569ANP-ESI+ 992 966 961 924ANP-ESI- 267 211 191 183RP: reversed-phase chromatography; ANP: aqueous normal-phase chromatography; ESI+: electrospray ionization, positive polarity; ESI-: electrospray ionization, negative polarity ; APCI+: atmospheric pressure, positive polarity. Average number of features found for samples in each culture condition (n = 9) and analysis type.


tial abundance with statistical significance (p <0.05) were identi-fied using MS-MS spectral matching, represented by mirroring acquired spectra (top) with library spectra (bottom). Both forward and reverse searches were performed for each compound, and the high scores are shown in black for each collision energy. At the increased collision energies of 20 and 40 eV, a decreased abundance of parent ion is observed in conjunction with higher abundance of lower mass fragments.

Conclusions

The utility of a nontargeted, discovery based accurate mass ap-proach for analyzing the yeast metabolome in response to calcium and immunosuppressant drug treatment was demonstrated. This approach provided significant coverage of the yeast metabolome, as the results yielded several hundred compounds, many of which were differential between controls and treated groups.

The Agilent METLIN PCDL was used for provisional identifica-tion of compounds using accurate mass analysis, with subsequent confirmation of metabolite identity done by matching acquired sample MS-MS spectra to the library. This discovery-based work-flow can be an efficient, cost-effective tool for finding and identi-fying differential compounds in yeast in response to an environ-mental change such as calcium and immunosuppressant treatment. Furthermore, the results can be used to design the next series of hypotheses and experiments for additional insight into the possible biological relevance of the identified compounds. Such studies may provide insights into the metabolism of higher organisms.

References

(1) M.J. Herrgård, N. Swainston, P. Dobson, W.B. Dunn, K. Yalçin

Arga, M. Arvas, N. Blüthgen, S. Borger, R. Costenoble, M. Heine-

mann, M. Hucka, N. Le Novère, P. Li, W. Liebermeister, M.L.

Mo, A.P. Oliveira, D. Petranovic, S. Pettifer, E. Simeonidis, K.

Smallbone, I. Spasic, D. Weichart, R. Brent, D.S. Broomhead,

H.V. Westerhoff, B. Kirdar, M. Penttilä, E. Klipp, B.Ø. Palsson, U.

Sauer, S.G. Oliver, P. Mendes, J. Nielsen, and D.B. Kell, Nat. Bio-

technol. 26(10), 1155–1160 (2008).

(2) D. Botstein, S.A. Chervitz, and M. Cherry, Science 277(5330),

1259–1260 (1997).

(3) Saccharomyces Genome Database- http://www.yeastge-

nome.org.

(4) European Saccharomyces Cerevisiae Archive for Functional

analysis- http://web.uni-frankfurt.de/fb15/mikro/euroscarf.

(5) S.G. Villas-Bôas, J. Højer-Pedersen, M. Akesson, J. Smeds-

gaard, and J. Nielsen, Yeast 22(14), 1155–1169 (2005).

(6) A. Margarida, A.M. Martins, W. Sha, C. Evans, S. Martino-Catt, P.

Mendes, and V. Shulaev, Yeast 24(3), 181–188 (2007).

(7) J. Aramburu, A. Rao, and C.B. Klee, Curr. Top. Cell Regul.

36, 237–295 (2000).

(8) M.S. Cyert, Biochem. Biophys. Res. Commun. 311(4), 1143–1150

(2003).

(9) S.-I. Oh, J. Park, S. Yoon, Y. Kim, S. Park, M. Ryu, M.J. Nam,

S.H. Ok, J.-K. Kim, J.-S. Shin, and K.-N. Kim, Plant Physiol.

148(4), 1883–1896 (2008).

Stefan Jenkins is a Field Service Engineer, Steven M.

Fischer is a Senior Applications Scientist, and Theodore

R. Sana is a Senior Scientist with Agilent Technologies.

Please direct correspondence to: [email protected]. ◾


Table IV: MPP results by analysis type

Compounds Passing Frequency Filter

METLIN PCDL Identified Compounds

Compounds Passing Filter at p <0.05

METLIN PCDL Identified Compounds

at p <0.05

RP-ESI + 300 112 158 79

RP-ESI- 523 50 418 32

RP-APCI+ 364 48 333 37

ANP-ESI+ 398 81 129 25

ANP-ESI- 276 88 213 63

RP: reversed-phase chromatography; ANP: aqueous normal-phase chromatography; ESI+: electrospray ionization, positive polarity ; ESI-: electrospray ionization, negative polarity ; APCI+: atmospheric pressure, positive polarity.

10 eV

20 eV

40 eV

41/99

37/96

55/89

10 eV

20 eV

40 eV

83/99

56/99

63/99

10 eV

20 eV

40 eV

98/99

97/99

79/100

(a) (b)

(b)

(f)

(c)

(e)

10 eV

20 eV

40 eV

87/99

69/99

6/100

10 eV

20 eV

40 eV

89/89

95/97

96/98

10 eV

20 eV

40 eV

99/99

96/96

96/96

Adenosine monophosphatem/z 348.0704 (+)

Anthranilic acidm/z 122.0444 (+)

Citric acidm/z 191.0197 (-)

Hypoxanthinem/z 137.0458 (+)

Malic acidm/z 133.0143 (-)

Inosinem/z 267.0740 (-)

Figure 3: MS-MS spectra were collected for each compound in the samples

at 10, 20, and 40 eV (a–f). The spectra were matched to the METLIN PCDL

and scored using a “Forward and Reverse” library match coring result (in

black) for each collision energy and a maximum possible score of 100.


Cristina Flego and Carla Zannoni

The evaluation of opportunity crudes (heavy and ultraheavy crude oils, asphaltenes, and tar sands) has economical and technical importance because of the shortage of conventional oil reserves and the exploitation of new potential reservoirs. Direct insertion probe–mass spectrometry (DIP-MS) is a new application that can characterize fuels, from crude oils to tar sands, without previous separa-tion or treatment. It is based on the introduction of samples directly into the ionization chamber, followed by vaporization, and eventually ionization by electronic impact. The single components present in the mixture are detected up to masses of 950 m/z, as a function of their boiling point and volatility, by programming the probe temperature ramp. The temperature ramp behaves as a fractional distillation without the limitations of chromatographic methods. DIP-MS offers a fast and direct evaluation of opportunity crudes through characteristic fingerprint maps, without the need to draw an exhaustive and comprehensive map of all the components of the organic matter. It is pro-posed for an on-field application for fast screening of real samples to acquire information about the most relevant organic species and characteristics for comparative purposes and for a rough evalua-tion of the potential of reservoirs and oil fields.

Direct Insertion Probe–Mass Spectrometry in the Characterization of Opportunity Crudes

The growth in the economy of any country is connected to a strong increase in the energy demand that now combines with the decline of conventional oil reserves.

The shortage of oil or known petroleum reserves makes less attended energy resources more attractive, especially consid-ering that their availability ratio to conventional crude oils is 5:1. Processing unconventional or opportunity crudes (that is, heavy and extra-heavy oil, tar sands, bitumen, high total acid number [TAN] crudes, and oil shale) is considered a huge potential resource to fulfill energy requirements and is the most viable option during a time of rising oil prices and dis-proportionate demand for light sweet crudes (1).

Comprehension of the dependence of the macro physi-cal properties of crudes from their major chemical char-

acteristics is fundamental to both upstream (for example, reservoir evaluation, migration and maturity, and degrada-tion processes) and downstream operations (processing, tightening of refinery specifications, and environmental impact). Concerning the upstream approach, the physical properties of these crudes (American Petroleum Institute gravity [°API] and viscosity) represent the fingerprint of the mixture of thousands of different compounds and de-termine reservoir potentiality. As far as the downstream operations are concerned, the need for a more efficient ex-ploitation of heavier or poorly known feedstocks has led to an increased interest in elucidating the molecular nature of oil components, to understand their behavior in thermal and catalytic processes.


In recent years, the study of oppor-tunity crudes has rapidly become more important, requiring the combination of several techniques (for example, vi-brational spectroscopies, nuclear mag-netic resonance [NMR] spectroscopy, chromatographic methods, and high resolution mass spectrometry [MS]) or very complex and time-consuming pretreatment or separation procedures (2,3). The accurate map of the oil

components is the subject of updated petroleomic research (4–7).

The direct insertion probe–mass spectrometry (DIP-MS) technique has been applied for the analysis of samples for more than 30 years (8) and has only recently been used to analyze insoluble or solid organic species (9). The tech-nique is based on the introduction of the samples directly into the ionization chamber, followed by their vaporization

and eventual ionization by electronic impact. Because it is often mentioned as a fast alternative for polar or ther-mally labile samples (for example, aro-mas) that do not require gas chromato-graphic separation, this technique has been considered a poor alternative for analyzing solid and insoluble samples characterized by high boiling points or hindrances that prohibit or limit their analysis by chromatographic methods. DIP-MS is shown here as a fast and easy tool that is able to identify classes of compounds in opportunity crudes and relate them with some physical proper-ties of interest in complex organic mix-tures.

Principle and Potential Applications of DIP-MSIn DIP-MS, the solid sample is intro-duced into a quartz cup located on the tip of the probe (Figure 1), which enters the vacuum chamber through an inlet. The tip of the probe is directly intro-duced into the ionization chamber, close to the ionization source (the distance is less than 5 mm). The inner diameter of the cup is 1 mm, the length is 7 mm, and the sample amount that can be in-troduced is about 0.2 mg. In the presence of light samples, the heat of the filament under vacuum conditions is enough to vaporize the components and the detec-tion of the signal begins immediately.

The high-boiling components of opportunity crudes need more heat to vaporize. Therefore, the tip of the probe must be heated in a tempera-ture-programmed mode to detect the different components of the sample with a procedure similar to fractional distillation, but with the separation enhanced by the combined effect of vacuum and heat.

The heating rate is chosen to avoid a too-rapid vaporization of the sample and the saturation of the signal. Eventually, these species are ionized by electronic impact and monitored. A turbomolec-ular pump system coupled to the mass spectrometer creates a dynamic vacuum in the ionization chamber to maintain high vacuum conditions (7 × 10-5 mbar). The very short distance between the ionization source and the vaporized molecules coming from the tip of the

Samplecup

Samplecup

SampleHeater

Thermalinsulation

MS spectrometer

T controller

Probe

Ionizationchamber

Tip of theprobe

Figure 1: Schematic of a DIP-MS apparatus.

Temperature program

Tem

pera

ture

(oC

)Sample run 1

100

100

100

150

250

350

200

300

110 120

Rela

tive

ab

un

dan

ceR

ela

tive

ab

un

dan

ce

Rela

tive

ab

un

dan

ce

90

90

80

80

70

70

60

60

50

50

40

40

30

30

20

20

10

40100

0

10

20

30

40

50

60

70

80

9036

32

28

24

20

16

12

8

4

0

56.96

290.07

276.04

350.07350.07

350.07

225.05

181.51

175.01

350.07

350.07

311.11

121.21

138.21

118.25191.30

503.30520.33

535.35

111.23315.15

291.15

252.06

210.06

350.07168.11

113.11136.99

131.52

112.95

51.9390.81

364.11

374.11341.10

328.08

315.08

300.04202.01

193.53

291.08

186.97

181.44

175.01

168.43

163.03

156.50150.01

143.52124.99

378.14

406.17420.18

392.15

256.06

211.04

119.99

112.97162.99 231.04

217.02

175.01

300.06

304.08

316.10

350.12

364.15374.17

100 200 300 400 500

m/z

600 700 100 200 300 400 500

m/z

600 700 100 200 300 400 500

m/z

600 700

10

301.15

Figure 2: DIP-MS analysis of a bitumen sample. Total ion current (TIC) profile (top) and the

corresponding mass profiles (bottom) at three selected ranges of vaporization temperature

(120–220 °C, 240–260 °C, and 340 °C, from left to right, respectively).


probe, the small amount of molecules undergoing the collision with the elec-tron flux, and the dynamic vacuum pre-vent the permanence of the molecules near the source zone with consequent molecule collision and recombination, and guarantee rapid ionization before thermolytic degradation.

It is important to note that vacuum dramatically decreases the boiling temperature of the compounds, allow-ing the vaporization of high-boiling-point species even when not very high temperatures are applied. By thermo-dynamic calculations we were able to determine the change of boiling point of some representative polyaromatic hydrocarbons from the atmospheric to the pressure value of 7 × 10-5 mbar (that is, the maximum vacuum level present in the mass spectrometer). For example, the boiling point of benzocor-onene is 566 °C at atmospheric pressure and decreases to 303 °C at 10 mbar and 48 °C at 7 × 10-5 mbar.

The versatility of DIP-MS analysis with a temperature-programmed probe extends itself to data analysis because both traditional mass spectra and con-tour maps can be obtained, graphically displayed selecting different vaporiza-tion temperatures, and analyzed with different approaches.

The total ion current (TIC) profile plots the sum of the intensity of all masses vs. the time of analysis. It does not identify the single compounds present in the sample, although it provides some information about their vaporization process. For example, in the case of a bitumen sample (Figure 2, top), low and intermediate tempera-ture ranges and the final isotherm at 340 °C can be defined by plotting the temperature profile against the time of the analysis. The relative intensity of the different areas can be used as a sort of fingerprint of the sample and to separate them into light, medium, or heavy species.

At each point of the TIC profile (that is, at a chosen temperature of the probe) a conventional mass spectrum is regis-tered that describes the species able to vaporize at that temperature. The melted mixture creates a “vapor” phase at every single instant (that is, temperature) and

the presence of a dynamic vacuum pre-vents secondary reactions among the different components.

This mass spectrum does not corre-spond to the fragmentation pattern of a single compound and consequently cannot be used to accurately identify the compound, but it represents the sum of the fragmentation pattern of all the spe-cies vaporized at that moment (Figure 2, bottom). In this way, general infor-mation can be obtained on the nature (aliphatic or aromatic) of the species, the presence of homologous sequences, and the molecular ions, when stable polyaromatic compounds are vaporized. Because this approach is not focused on identifying single compounds, it does not require the application of highly sophisticated and expensive high-reso-lution mass spectrometers.

A more performance-based represen-tation of DIP-MS data is obtained with the contour map. In the mass spectrom-eter, the magnitude I (dependent variable) of the ion flux is recorded as a function of the mass-to-charge ratio, m/z (indepen-dent variable). In DIP-MS analysis with a temperature-programmed probe, I can therefore be plotted as a function of m/z and T (temperature of the probe), t (time of the temperature program), or n (scan number linearly related to t) simultane-

ously. This plot can be displayed as a con-tour-line map (Figure 3), where m/z and n are assigned to the x- and y-axes, respec-tively, and I values (from 2 × 103 up to 2 × 107 MS counts) are shown by contour lines and different colors from the black of the background to the top of the most intense peaks in the pale blue, green, yel-low, brown, and red color ranges.

InstrumentationDIP-MS analyses were performed with a Trace DSQ Thermo system equipped with a quadrupole mass spectrometer detector with electron-impact ioniza-tion and a direct insertion probe mod-ule. The probe temperature program follows: 120 °C for 10 s; 60 °C/min up to 200 °C; 50 °C/min up to 340 °C; 340 °C for 15 min, which was maintained up to 240 min, only in some selected experi-ments. The analyses were performed with an ionization voltage of 70 eV, a source temperature of 270 °C, a mass range between 33–1000 m/z, a scan rate of 2.14 scan/s, and a vacuum of less than 7 × 10-5 mbar.

Application of DIP-MS to Opportunity CrudesCrude Oil Fingerprinting

The three contour plots obtained from DIP-MS experiments (Figure 3) repre-

Light oil

1

1

1

2

2

3

2

3 3

4

4

4

Medium oil

Heavy oil

1000

800

600

400

200

1000

800

600

400

200

1000

800

600

400

200

340

Scan

Scan

Scan

Tem

pera

ture

(oC

)Te

mp

era

ture

(oC

)

Tem

pera

ture

(oC

)

340 340

340

120

340

340

120

120

100 200 300 400 500

m/z

600 700 800 900

100 200 300 400 500

m/z

600 700 800 900

100

60

Deg

ree o

f A

PI

50

40

30

20

10

00.0020.000.00 40.00 60.00 80.00 100.00

High boiling fraction (%)

Heavy oils / bitumen

Crude oils

200 300 400 500

m/z

600 700 800 900

Figure 3: Representative contour maps of different types of crude oils and a plot of the high-boiling fraction

(% mol) as obtained from DIP-MS intensity vs. American Petroleum Institute gravity (°API).


sent a schematic and simple approach to analyze crude oils. Because each crude oil gives rise to a specific mass evolution during the sample heating, the contour plot can be applied as a fingerprint to distinguish the samples. Their similari-ties and differences because of different chemical compositions find a parallel-ism with the physical properties of the

oils (2,3). For example, the presence in the plot of large and intense splashes of color is peculiar of a large amount of species (population) with high mass (x-axis) and temperature values (y-axis), characteristic of dense and vis-cous heavy oils (Figure 3, bottom left). Every plot can be roughly divided into four areas, in which the population

grows on the basis of the oil type. In the first type of contour plot (Figure 3, top left), which is considered a fingerprint of light oils (for example, with a degree of API around 50 and kinematic viscos-ity of 0.7 mm2/s), only areas 1 and 2 are populated. Area 1 includes signals of light aliphatic fragments, while area 2 is related to species up to 400 m/z, which includes both heavier mass fragments and molecular ions. The oils represented by this contour plot are characterized by light (mass range: 30Ð600 m/z) and low-boiling components (vaporization temperature: 120Ð240 ¡C).

In the second type of contour plot (that is, the fingerprint of medium-gravity oils), areas 1 and 2 are more in-tensely populated than in the previous case (Figure 3, top right), and an extra partial cloud, constituted by medium boiling-point species in the 300Ð700 m/z mass range, begins to appear in areas 3 and 4. This is in agreement with ¡API of approximately 30 and kinematic viscos-ity of 9 mm2/s.

In the third contour plot, character-istic of heavy oils (that is, oils with ap-proximately 20 wt% of asphaltenes and API gravity lower than 20), the distin-guishing mark is an evident and some-times very intense cloud in areas 3 and 4, in addition to those in areas 1 and 2 (Figure 3, bottom left). This means that these kinds of oils are constituted by a heterogeneous series of chemical components, among which also high-boiling species with molecular weight beyond 900 m/z are present, which is in agreement with their high density and viscosity values.

The amount of signals in areas 3 and 4 approximately corresponds to the fraction of the crude oil vaporized at higher temperatures and can be used to create correlations with properties of great importance in crude oil character-ization, such as API gravity and kine-matic viscosity. The data are distributed in two different series (Figure 3, bottom right) as a function of the nature of the crude and independently from where they came from: Light and medium-gravity crude oils are placed according to a relationship in which the higher the high-boiling fraction, the lower is the API gravity, and heavy oils and bitu-

0

2.0

4.0

6.0

57.05

104.94275.14

57.05

71.09149.08

105.12

239.13

262.17

376.15

290.22

298.24

304.24

316.28

330.31

346.38386.34 436.60 469.94

240.14

211.11

165.1171.09

57.08

254.15

298.28

320.33371.30

268.26

474.52 473.38

110.08

71.10 170.15

176.15

161.17190.16

137.71

290.20

149.06

83.04

57.05

165.16

129.14

276.17

250.17

332.16

346.19

360.27

370.32422.52

478.53

83.04

57.03

111.03

145.02

179.13

225.11 313.20

332.15346.21

398.35472.38

480.51

276.16

300.18

314.20

332.19

149.04

57.05 111.08281.15

239.13

225.14

346.25

356.32400.47

456.00 508.70

284.31 371.32 440.28 471.60

500

m/z

400300200100 500

m/z

400300200100500

m/z

400300200100

500

m/z

400300200100 500

m/z

400300200100 500 600

m/z

400300200100

8 scans

129 scans 172 scans

91.5 oC 58 scans 151 oC 85 scans 170 oC

200 oC 225 oC 260 scans 275 oC

Ab

un

dan

ce (

X10

7)

0

2.0

4.0

6.0

Ab

un

dan

ce (

X10

7)

0

2.0

4.0

6.0

Ab

un

dan

ce (

X10

7)

0

2.0

4.0

6.0

Ab

un

dan

ce (

X10

7)

0

2.0

4.0

6.0

Ab

un

dan

ce (

X10

7)

0

2.0

4.0

6.0

Ab

un

dan

ce (

X10

7)

Figure 4: Sequence of mass spectra registered at different times (expressed in s) and temperatures

during the DIP-MS analysis.

100

Fragmentsarea

Molecularionsarea

Fragmentsarea

Fragmentsarea

Fragmentsarea

Molecularionsarea

Molecularions area

Molecularions area

Fragmentsarea

Molecularions area

365.13

295.04190.55

114.87

160.83

144.92

122.96

91.90

267.06168.92

114.91

160.50

81.57

365.18

385.15

438.21

484.29

554.31 622.41 680.13 118.45 818.39 686.58

442.20

410.17

458.25

512.25

525.25

540.21

111.81

160.85

96.59

225.01255.08 365.12

56.56

11.01

160.51

114.90211.02

239.03325.10 431.21

419.25

122.45

636.36

594.40

164.50

192.54

818.55

848.60

814.50

510.51

160.65

96.94

111.51211.02

239.01361.15 409.18

513.29

536.30

618.31

566.36158.15

150.50

118.53

806.55

818.54

636.54

56.34

66.58

114.58211.01

295.01 409.19 451.24453.69 632.31

130.45192.56 834.51

876.51

914.63

501.25

512.22

554.25

595.33624.31

638.38652.39

666.41

660.42

128.16

136.50

154.51

181.52812.45 914.45

240–360 s335–340 oC

360–480 s340 oC

0–120 s120–225 oC

120–240 s225–335 oC

600–900 s340 oC

480–600 s340 oC

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

Ab

un

dan

ce (

X10

7)

Ab

un

dan

ce (

X10

7)

Ab

un

dan

ce (

X10

7)

Ab

un

dan

ce (

X10

7)

Ab

un

dan

ce (

X10

7)

0

0.2

0.4

0.6

0.8

1.0

568.33

562.34

596.36

610.31

638.36

120.45616.39

184.51 860.42

200 300 400 500 600 700 800 900

100 200 300 400 500 600 700 800 900

m/z

m/z

100 200 300 400 500 600 700 800 900

m/z

100 200 300 400 500 600 700 800 900

m/z

100 200 300 400 500 600 700 800 900

m/z

Figure 5: Sequence of mass spectra of a heavy oil collected in a time range of 120 s during

DIP-MS analysis.


mens are located in an underlying area defined by lower values of API gravity.

Vaporization Sequence of Heavy Oils

In a conventional mass spectrum (for example, Figure 2 bottom), all the frag-ments and molecular ions ionized from the crude components are summed and, consequently, a detailed investigation is constricted by overlapping the species vaporized in different events during the whole experiment. When the mass pro-file is monitored at a defined time (that is, at a defined vaporization tempera-ture), a sequence of pictures is obtained that more clearly describes the evolution of homologous series and compound classes at decreasing volatility. For ex-ample, from a medium-gravity crude oil at least two homologous series (denoted by crosses and a triangle in Figure 4) of stable molecular ions (even m/z values) can be separated at a vaporization tem-perature of 91.5 °C starting from 170 m/z (mass value and boiling point compat-ible with trimethylnaphthalene isomers) and 176 m/z (compatible with three trimethylbenzo[b]thiophene isomers), respectively. At 151 °C only one series (cross-marked) survives and lasts up to 170 °C. Meanwhile other species, start-ing with the ion of 262 m/z (diamond-marked, isomers of dimethylbenzo[b]naphtho[2,3-d]thiophene), grow up to 200 °C and then decrease, as another series (hexagon-marked, starting mo-lecular ion at 286 m/z; for example, dinaphthothiophene or styrylnaphtho-thiophene derivatives) appears, disap-pearing at 225 °C. The cross-marked species are isobaric with paraffins, but their mass spectra with intense and sta-ble molecular ions and no fragmentation do not match with the typical paraffin mass spectra and appear more consistent with those of stable polyaromatics. Ad-ditional high-temperature gas chroma-tography–mass spectrometry (GC–MS) experiments confirmed the presence of these proposed polyaromatic structures.

Similarly, the vaporization sequence is applied to heavy oil fractions to acquire information about the nature of their as-phaltenes (Figures 5 and 6). Each spec-trum is constituted by the species vola-tized during a time range of 120 s and confirms the different behavior between

two heavy crude oils. In the first example (Figure 5), the

mass spectra can be divided into two parts: the area preferentially populated

by fragments (odd mass values) and the area preferentially covered by molecular ions (even mass values). The molecular ions, characterized by very intense signals,

Fragmentsarea

Molecularions area

Fragmentsarea

Molecularions area

Fragmentsarea

Molecularions area

Fragmentsarea

Molecularions area

Fragmentsarea

Molecularions area

51.00

11.00

54.33

94.96

91.01

143.02

69.01

81.03

111.04

115.05

211.11351.14

331.19 409.25 483.33 630.50 150.59 192.13 683.81

191.10

211.01295.13

369.30 431.21 568.29 610.43 648.41 122.58 192.64

69.94

60.58

95.00

125.01

51.01

11.03

125.01

169.01253.13 323.18 395.24 453.29 521.36 521.18 130.60 166.65 625.16 690.59

115.04

211.29

245.10313.15 363.23 153.30 636.48 666.54 108.60 118.11 848.45654.51

81.98

111.01

94.31

51.00 151.01

231.06

351.09

295.11 365.20 438.25 512.33 568.41 630.46 105.56 118.51 851.82 914.1

114.91 261.09

381.12344.16 414.29 482.25 554.30 622.40 695.12 161.69 681.11 530.81

0–120 s

120–225 oC

240–360 s

335–340 oC

120–240 s

225–335 oC

360–480 s

340 oC

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

480–600 s

340 oC

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

600–900 s

340 oC

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

100 200 300 400 500 600 700 800 900

m/z

0

0.2

0.4

0.6

0.8

1.0

Ab

un

dan

ce (

X10

7)

Figure 6: Sequence of mass spectra of a heavy oil collected in a time range of 120 s during

DIP-MS analysis.

245 min

0 min

100 200 300 400 500 600 700

100

95

9085

8075

70

65

6055

50

45

4035

3025

2015

105

0260

220

180 200 220 240 260 280 300 320 340 360 380 400

240 260 280 300 320 340 360 380 400 420 440 460 480 500

m/z

m/z

260 300 320 340 360 380

350.08

374.10

364.17

376.11

378.12

390.15388.15348.08

340.09326.06300.03

402.11406.15426.18

398.15400.15

474.17452.24454.22

456.23

350.10

276.03

290.07

280.04

294.09

300.06

304.08

316.10318.14

332.12

346.22

266.05252.04

239.62226.00

210.97188.95

354.11

340.11

326.07300.04

276.05

290.07316.09

364.16

366.17

368.17380.19

382.20

OH

OH

442.22

44-0.24

488.28

400 420 440 460 480 500 520 540 560 580 600

350 + n14

376 + n14

374 + n14

326 + n14

350 + n14

366 + n14

252 + n14

276 + n14

300 + n -OH

398 + n14

400 + n14

424 + n14

m/z

m/z

Rela

tive a

bu

nd

an

ce

100

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

5

0

Rela

tive a

bu

nd

an

ce

100

959085

8075

70

65

60

55

5045

40

35

302520

15

10

50

Rela

tive a

bu

nd

an

ce

Figure 7: Contour plot (left side) and average DIP-MS spectrum (right side) of one asphaltene;

polyaromatics are labeled by indicative structures and alkyl-aromatics by colored boxes.


move to higher mass values during the experiment and achieve the top of inten-sity in the time range 120–240 s (that is, 225–335 °C). The fragment area is popu-lated by intense peaks differing by 14 m/z, related to “heavy fragments” (200–400 m/z in the second spectrum and 200–600 m/z in the last one), whereas light aliphatic fragments are minor components. This suggests the presence of a heavy frac-tion made of “aromatic cores” in the oil linked by –(CH2)n– bridges (with n < 4) and with few and short aliphatic branches. These molecules do not fragment eas-ily, and this explains the unusually high intensity of the molecular ions. In fact, the breaking of a C-C bond in a bridge between aromatic cores produces high-molecular-weight fragments and not light aliphatic ones. These findings agree with those obtained by conventional GC–MS experiments, characterized by only a few weak peaks because of light aliphatic (n- and iso-) compounds up to C18 and aromatics (C0–C4 benzenes, C0–C2 naphthalenes, and C1–C2 benzo-thiophenes). This means that the heavier species present in the crude (and deeply contributing to its physical properties) cannot be detected after conventional GC separation.

The mass spectra of the second crude oil (Figure 6) show hardly any vis-

ible peaks in the molecular ions area, agreeing with a molecular model with branched molecules with long aliphatic bridges among small aromatic cores that are more easily fragmented. Furthermore, according to this hypothetic molecular model, the fragments area shows intense peaks in the range of the light aliphatic fragments. In the conventional GC–MS analysis it gives rise to a very complex total ion current (TIC) profile, crowded with n-, iso- and cycloaliphatic species up to C30 and aromatics (from C0–C16 ben-zenes up to C1–C2 phenanthrenes), as well as biomarkers such as pristane, phytane, chlolestane, and baccharane.

Asphaltene and Bitumen Study

Another field of application for the DIP-MS technique is the mapping of polyaromatics in asphaltenes. The com-prehension of the asphaltenes compo-sition was the subject of several stud-ies, developed with the contribution of several techniques (10–21). Because of the very low volatility of these materi-als, the temperature program must be prolonged up to 240 min isotherm at 340 °C in a high vacuum environment. This treatment allows the heaviest poly-cyclic aromatic hydrocarbon (PAH) to be vaporized and made clearly visible by the mass spectrometer.

For example, the contour plot of one asphaltene obtained after thermal treat-ment of crude residue (Figure 7, left) pres-ents three main regions along the y-axis (m/z) separated by different temperature ranges of vaporization and two main regions along the x-axis corresponding to medium-light and heavy molecular weights. The species in the low m/z re-gion (50–250 m/z) are developed at all temperatures, their origin changing from fragments and light species vaporized at low temperatures to double-charge spe-cies formed by vaporization of very stable polyaromatic compounds at the highest temperatures. The species at high molecu-lar weight (250–550 m/z) show an intense development at the beginning of the iso-therm at 340 °C and continue their vapor-ization along the whole isotherm, form-ing separated blobs, whose nature can be easily evaluated with point-by-point mass analysis. In fact, the mass spectrum (Fig-ure 7, right) of the high-molecular-weight fraction shows a different homologous series when analyzed at increasing times. Homologous series with parent structures of 350, 374, 398, 424, and 448 m/z (corre-sponding to 8–9 polyaromatic condensed rings with ordered addition of 14 m/z in the alkyl chains) are visible in the latest part of the experiment (Figure 7, left top), while homologous series with parent structures of 252, 276, and 300 m/z (corre-sponding to 5–6 polyaromatic condensed rings) are identified at the beginning of the experiment (Figure 7, left bottom). Obviously, the chemical structures related to the mass peaks are representative of a class of molecules including the isomers and cannot be ascribed with confidence to one specific structure.

Tar Sand Analysis

In the case of the analysis of bitumen from tar sands, it is important to point out that its characterization by conventional ana-lytical methods is possible only after a time- and solvent-consuming extraction procedure (22,23), which is not necessary in the case of the direct analysis of tar sand. DIP-MS analysis performed on tar sand, directly from the inorganic matrix essentially composed of quartz and clay minerals, evaluates the tar without previ-ous extraction procedures. Similar mass profiles appear for tar sand and extracted

1400

Mn

Reference A B C D

m/z (265–340 oC)

m/z (340 oC)

1200

1000

800

600

400

200

0

Figure 8: Comparison between the mass values (m/z) of the first even molecular ions present

at the high vaporization temperature ranges and the number-average molecular weight (Mn,

expressed as Da) from gel permeation chromatography for a series of tar sands.


bitumen and confirm the applicability of the approach to this kind of material. A complex organic composition is present: beside aliphatic fragments in the 41–123 m/z range, intense fragments (at 161, 175, 191, and 203 m/z) grow in the 150–300 m/z range, although fragments or mo-lecular ions are present in the whole range of masses (up to 900 m/z). The presence of aliphatic and light (2–4 condensed rings) aromatic compounds is suggested by the fragmentation pattern at low vaporization temperatures. The general behavior of the molecules that constitute the more aro-matic fraction may be evaluated from the evolution of the first even molecular ion at high vaporization temperature ranges. In Figure 8, the trend of the molecular weight of the first even molecular ions agrees with the molecular weight obtained from gel permeation chromatography (GPC) with a series of tar sands. Although the bitumens contain a large fraction of heavy molecules or nanoaggregates that clearly cannot be vaporized under the experimental con-ditions applied in DIP-MS analysis, the “light” portion (<1000 m/z) of bitumen may be considered as a continuum in the nature with the heavy one (24) and there-fore may be taken as a probe of the whole composition.

As a general matter, the information collected by the DIP-MS analysis alone directly from tar sands agrees well with the physicochemical properties of the bi-tumens obtained by several techniques.

Furthermore, the modes of data evalua-tion previously shown can also be applied in the case of tar sands without any limita-tion caused by the inorganic matrix.

Conclusions

The DIP-MS technique is based on the introduction of samples directly into the ionization chamber, followed by their vaporization, and eventual ionization by electronic impact. A new application of this technique is used for the analysis of opportunity crudes, such as heavy crude oils, asphaltenes, or tar sands. By pro-gramming a suitable probe temperature ramp that behaves as a fractional distil-lation, analysts can detect opportunity crudes as a function of their boiling point and volatility without previous separation or limitations of the most-used chromato-graphic methods. The data processing of-

fers a large variety of options and allows a deeper evaluation of the mass spectra according to the vaporization propensity of the single compounds. Differences are evidenced between the samples on the basis of the evolution of their mass spec-tra, and some physical properties can be anticipated by their comparison.

The DIP-MS analytical approach al-lows users to simply and rapidly predict the nature of opportunity crudes and is therefore proposed for an on-field appli-cation for fast screening of real samples to acquire information on the most relevant organic species for comparative purposes and rough evaluation of the potential of oil fields.

References

(1) Opportunity Crudes Report II: Technol-

ogies & Strategiesfor Meeting Evolving

Market & Environmental Challenges

(Hydrocarbon Publishing Co. Ed.,

Southereastern, Pennsylvania, 2011).

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Research, C.S. Hsu, Ed. (Kluwer Aca-

demic/Plenum Publ., New York, New

York, 2003).

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Speight, Ed. (Wiley Interscience, To-

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Asphaltenes, Heavy Oils and Petro-

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Hammami, and A.G. Marshall, Eds.

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J. Ruzicka, and M. Sanders, Energy

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Herod, and R. Kandiyoti, Energy Fuels

21, 2121–2128 (2007).

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Monte, A. Middea, and A.L. de Sousa,

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(2006).

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1230–1236 (2009).

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557–564 (2005).

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Jain, and A.K. Bhatnagar, Fuel 75,

999–1008 (1996).

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chi, Fuel 86, 2528–2534 (2007).

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Haya, and O.C. Mullins, Energy Fuel 21,

2863–2868 (2007).

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Guglielmetti, R. Seraglia, and P. Traldi,

Rapid Commun. Mass Spectrom. 23,

725–728 (2009).

(17) A. Rizzi, P. Cosmina, C. Flego, L. Mon-

tanari, R. Seraglia, and P. Traldi, J. Mass

Spectrom. 41, 1232–1241 (2006).

(18) C.A. Islas-Flores, E. Buenrostro-Gon-

zalez, and C. Lira-Galeana, Fuel 85,

1842–1850 (2006).

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Boduszynski, and F. Su, Anal. Chem.

59, 1393–1401 (1987).

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H.J. Räder, and K. Müllen, Anal. Chem.

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diyoti, Energy Fuels 21, 2176–2203

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ers, and A.G. Marshall, Energy Fuels

24, 2929–2938 (2010).

Cristina Flego and Carla Zannoni

are with eni r&m, Research Center, CHIF, in

S. Donato, Milanese, Italy. They work in the

Physical Chemistry Department. They are spe-

cialists in the application of chromatographic

separation (gas, liquid, and gel permeation)

and mass spectrometry to the comprehen-

sion of complex mixtures, developing special-

ized expertise in the application of a multidis-

ciplinary approach to the study of heavy crude

oils and bitumens. Direct correspondence

to: [email protected]. ◾



Gregory Hamm, David Bonnel, Christine Michel, Raphael Legouffe, Fabien Pamelard, Guillaume Hochart, and Jonathan Stauber

Mass spectrometry imaging (MSI) is a common technique to detect the localization of molecules (drugs and their metabolites, peptides, proteins, and lipids) directly on the surface of biological tissues without labeling. Today, several ionization modes are used to perform MSI experiments, such as desorption electrospray ionization (DESI), secondary ion mass spectrometry (SIMS), laser ablation electrospray ionization (LAESI), and the most commonly used matrix-assisted laser desorption–ionization (MALDI). In this report, we describe the main limitations of quantification using MALDI imaging and evaluate the different approaches applied for quantitative measurement in MSI.

Toward Quantitative Imaging Mass Spectrometry

The combination of quantitative and distribution data in the same experiment provides valuable and precise in-formation about the behavior of a compound in the or-

ganism (for example, pharmacokinetics, toxicity, or efficiency). Traditionally, this type of experiment is performed using quan-titative whole-body autoradiography (QWBA), magnetic reso-nance imaging (MRI), positron emission tomography (PET), or liquid chromatography coupled with mass spectrometry (LC–MS) after organ dissection. However these techniques require some extensive and time-consuming preparation. Mass spec-trometry imaging (MSI) has therefore been introduced to ad-dress these issues, enabling the label-free study of several com-pounds of interest simultaneously on the same tissue section.

First introduced by Caprioli in 1997 (1), MSI has undergone an evolution during the past decade to reach the same level as “traditional” imaging techniques. Advances in technology and methods have led to increased robustness, sensitivity, lateral resolution, and class of compounds detected through this tech-nique. Today, several ionization modes are used to perform MSI experiments, such as desorption electrospray ionization (DESI), secondary ion mass spectrometry (SIMS), laser ablation elec-trospray ionization (LAESI), and matrix-assisted laser desorp-tion–ionization (MALDI), which is the most commonly used mode. MSI offers several advantages (such as specificity and lateral resolution) compared to other techniques, but it does not provide robust quantitative information within molecular dis-tribution studies yet. Recently, numerous studies have dealt with the growing interest in combining quantitative and distribution analyses using MSI, particularly with applications for evaluat-ing small molecule local concentration in early pharmaceutical

discovery. Despite this, the difficulty in obtaining an absolute quantification of experimental data remains one of MSIs major limitations. As mentioned by Stoeckli and colleagues in 2007 (2), quantification by MSI using the MALDI technique requires consideration of several fundamental aspects. First, the tissue-specific ion suppression is dependent on the surrounding envi-ronment and interactions taking place in the biological sample. Secondly, a molecule has a specific ionization yield in MALDI. Finally, the detected signal is highly dependent on the matrix deposition, its properties, and extraction capacity. Several meth-ods addressing these issues are described in literature (3–9), but none have universal applications. The three types of approaches in quantitative MSI are depicted in Figure 1.

These include the use of a dilution range deposit on a con-trol tissue section, a dilution range mixed with targeted tissue and then reconstituted to obtain a matrix matched material, and additional information about ion suppression in tissue and an in-solution dilution range. This short review will focus on the new development of quantitative MSI, specifically for the MALDI imaging technique.

Sample PreparationCarrying out quality sample preparation that is both reproducible and robust represents a key challenge in MSI analyses (10). First, the tissue of interest, which can be a single organ, a tissue biopsy, or an entire animal, is collected and conditioned. In quantita-tive experiments, biological sample replicates must be used to compare MSI results to other techniques (QWBA or LC–MS). Second, the tissue is sectioned and mounted onto a conductive slide. The tissue is prepared using a cryomicrotome operating


at a temperature between -20 °C and -25 °C, depending on the sample. The thickness of the section is adapted to the sample type or size: For example, a thickness of 20 µm is suitable for whole-body studies, whereas 10 µm is the optimal single-organ section thickness. Next, the tissue section is mounted on an indium tin oxide (ITO)–coated glass slide or steel target plate, while verify-ing the flatness of the section. A pretreatment of the tissue sample can be performed to improve chemical information, to provide an increase in signal intensity or decrease in ion suppression ef-fect, especially for peptides and proteins (11). Salt or lipids can be removed by washing with an optimized solvent composition. The last preparation step is one of the most critical in MALDI imaging experiments: the matrix deposition on sample.

The quality of MS images is highly dependent on the matrix application. Aspects such as homogeneity and reproducibility of applied matrix layer, optimal molecule extraction through tissue section, wetting, and speed, are important factors to take into account for quantitative analysis. In recent years, this approach has evolved from the manual sprayer device to laboratory-built (12) or commercial automatic depositions systems. In our molecular imaging department, we compared the reproducibility of an automatic (SunCollect) and a man-ual thin layer chromatography (TLC) matrix sprayer. A drug standard (propranolol) at known concentration (10 pmol/µL) mixed with matrix solution (2,5-dihydroxybenzoic acid [DHB] at 40 mg/mL) was used to cover tissue sections (10-µm-thick rat liver tissue) mounted onto an ITO glass slide. Optical im-ages of each matrix layer obtained by microscope are shown in Figure 2a and 2b. The matrix crystal size appears significantly smaller (less than 40 µm) when an automatic sprayer is used. Then, an MSI experiment was performed and the distribution of the standard molecule was visualized for the two matrix deposition methods (Figure 2c and 2d). The distribution of the standard ion appears more homogeneous with the automatic sprayer, which is confirmed by a relative intertissue standard deviation of signal intensity calculated at 3% compared to the manual device at 30%. The signal intensity of this ion is also stronger on tissue using the automatic system. The automatic sprayer device enables a high degree of matrix deposition re-producibly and homogeneity in accordance with quantitative experiment requirements.

Matrix Matched Standard

MSI quantitation requires a suitable dilution series. The dilu-tion series must accurately represent the signal obtained from the sample while taking into account the tissue suppression effect that is specific to MSI. The quantitative possibility of whole-body autoradiography was justified by Schweitzer and colleagues in 1987 (13) by generating a calibration curve from a dilution series of labeled target molecule in blood for QWBA. Since then, numerous quantitative MSI experiments have ap-plied this strategy of “matrix matched standard.”

The first example of this approach was established by Nilsson and colleagues (7) in 2010 and corresponds to the method in Figure 1a. The aim of this study was to assess the concentration of an inhaled reference compound, tiotropium, within the lungs of dosed rats. A dilution series of the analyte was deposited on top of the tissue section and a calibration curve was then ob-tained (Figure 3). Dosed and spotted control tissues were simul-taneously imaged in MS and MS-MS modes. Three regions of interest corresponding to high, medium, and low intensity areas are outlined. The average values of each region of interest (ROI) are matched with the calibration curve. The amount of drug evaluated by MS and MS-MS analyses correlated very well with LC–MS quantification data, which demonstrated the robustness of the method. Another approach was reported by Koeniger and colleagues in 2011 (6). In this method, a dilution series was created from a range of rat liver dosed with increasing amounts of a drug (olanzapine from 0–100 mg/kg). Serial sections of dosed tissue divided into two groups were analyzed in parallel

On-tissue dilution range

In-tissue dilution range

In-solution dilution range

(a)

(b)

(c)

Dosed tissue

Control tissue

Spiked control tissue

Dilution range

Figure 1: An overview of the three different approaches used to provide

quantitative information using mass spectrometry imaging (MSI).

(a)

(c)

(d)

(b)

100 µm

400 µm 0%

100%

0%

100%

400 µm

100 µm

Figure 2: Optical image of 2,5-dihydroxybenzoic acid matrix layer on

glass slide deposited with (a) a manual TLC sprayer and (b) an automatic

sprayer. MS image (200 µm of spatial resolution) of the distribution of

propranolol (m/z 260.16) mixed with matrix on tissue deposited with (c)

a manual TLC sprayer and (d) an automatic sprayer. Dashed yellow lines

indicate outline of tissues on the slide.


using MSI and LC–MS. A proportional response was observed between LC–MS and MSI over two orders of magnitude, which proves the complementarity of the two techniques for quantification.

The second method used for quantifi-cation using MSI involves the generation of a dilution series directly inside a tis-sue (Figure 1b). Tissue homogenates are created to mimic the behavior of a com-pound in a complex environment. Con-sequently, it takes into account the ion suppression effect specific to the type of tissue. The workflow of the method is re-ported in Figure 4. Becker and colleagues (14,15) extensively described this method-ology to analyze atomic species by laser ablation inductively coupled plasma ion-ization–mass spectrometry (LA-ICP-MS) imaging. A dilution series of the analyte was mixed with tissue homogenate from different origins (slugs [14] and rat brain [15]), reconstituted by fast freezing, sec-tioned, mounted next to the correspond-ing dosed tissue, and finally, analyzed by MSI. Signal intensities from the dilution series were then correlated to dosed signal data to obtain a relative concentration of the atomic species. This matrix matched standard strategy demonstrated promis-ing results in quantification by MSI.

To further develop this approach, we used MALDI imaging for analysis. In this method, drug mixed into tissue ho-mogenate must be spread homogeneously throughout the entire mixture, especially after the fast frozen phase, to be repro-ducible. MALDI imaging of transversal and longitudinal cross sections were carried out to follow the distribution of spiked drug in the tissue. An adequate homogeneity was observed with an in-tertissue mean signal variation of 15%. This strategy is well-adapted to a ho-mogenous single organ drug distribution study as it is adapted to a specific tissue type. However, generating a matrix stan-dard for each organ or tissue in a whole body tissue section containing more than 20 heterogeneous histological areas (for example, the eye or brain), remains time consuming and requires a large number of tissues.

Ion Suppression Effect The main limitation in MALDI imag-ing is the high dependence of the MS

signal on the chemical and biological environment, also known as the ion suppression effect. Unlike an isolated drug in a solution deposit on a steel target, a molecule trapped in a tissue undergoes many noncovalent interac-tions, both strong and weak (such as,

hydrogen or van der Waals bindings). Molecules also experience the ioniza-tion competition phenomenon linked to other compounds present in its im-mediate environment that have higher ionization yield (2). An MSI compari-son of analyte response on a whole body

(a)

LOW

LOW

20%

20%

20%

20%

MEDIUM

MEDIUM

HIGH

HIGH

100%

0% 0%

100%

0%

100%

(b)

(e) Standard curveTissue measurement

R2 = 0.995

Standard curveTissue measurement

R2 = 0.995

No

rm. in

t. o

f m/z

392

Inte

nsi

ty o

f m/z

152

3000

2000

1000

0

2000

1000

00 1 2 3

0 0.2 0.4 0.6

0.5

0.8

Amount (pmol)

Amount (pmol)

0 1.2

1.5 2.5

1.4

(f)

(c)

(d)

Figure 3: On-tissue dilution range approach: assessment of drug concentration in lung tissue

from rats dosed with tiotropium (a, b) by comparison to drug standard samples (from 20 fmol/µL

to 10 pmol/µL) spotted on control tissue (c, d) in MS (top) and MS-MS mode (bottom) and related

calibration curves (e, f) with a linearity over two orders of magnitude. Adapted from reference 7.

Control

(a)

(b)

(d)

(e)

(c)

Dosed

Figure 4: Quantification process by mass spectrometry imaging using in-tissue dilution range

approach: (a) Kidney is removed from biological sample and then ground; (b) dilution series of

standard are spiked in tissue homogenates; (c) matrix matched tissues are reconstituted by fast

frozen and sectioned; (d) dosed kidney is sectioned and mounted on a slide; (e) matrix solution

is deposited on dosed and matrix matched standard sections before MS imaging experiment.


section might be significantly different if tissue and analyte suppression effects are not evaluated. Several papers report the use of an internal standard to ad-dress this issue (9–16). In these studies, a labeled version of the analyte (9) or an analog compound (16) is deposited on a dosed tissue section. The normalization of the analyte signal also can be per-formed, therefore reducing the specific ion suppression caused by the heteroge-neity of the target tissue (for example, lung biopsy or brain section).

We developed a new method for quan-titation by MSI based on the specificity of ion suppression effect that does not require a labeled version or an analog of the target compound. Figure 5 depicts the global workflow of our method and corresponds to the third approach in Fig-ure 1c. The first step (Figure 5a) involves the determination of the tissue extinc-tion coefficient (TEC). A control section is covered homogenously by a mixture of target molecule (in blue) at a known concentration and the matrix solution (in yellow). MSI is then performed on

the tissue section and support. ROIs are selected on each type of organ or tissue (T1, T2, and T3 in Figure 5) and on sup-port corresponding to the reference area. This allows calculation of TEC values for each type of tissue (more than 20 in a whole-body) and mass filter (signal cor-responding to standard mixed with ma-trix) by dividing the signal of standard on tissue region by the same signal on the reference area. Secondly, a dilution series of target compound is spotted near the dosed tissue section (Figure 5b) and matrix solution is deposited on the entire sample (Figure 5c). From this dilution se-ries of target molecule analyzed by MSI, mean intensity values can be extracted and correlated to the amount of drug per surface unit. The best calibration curve model, a mathematical equation and a correlation coefficient are also defined. Finally, a molecular image of dosed tis-sue is used to estimate the amount of drug in gram per gram of tissue. The spectral values of the drug related to a specific organ are normalized by the corresponding TEC value then inserted

into the calibration equation for quan-tification taking into account the mass of the tissue and molecular weight of the compounds.

This approach had been applied in sev-eral studies and has produced promising results, particularly in whole body drug distribution studies. For example, pro-pranolol was quantified in whole body sections of dosed mice using our approach (18) and then was compared to QWBA results (17). A strong correlation (stan-dard deviation inferior to 10%) was ob-served between quantitative MSI (qMSI) and QWBA results for different organs as shown in Table I.

Conclusion

In this review, we discussed the recent advances in quantification by MSI and also all of the parameters to consider when carrying out robust experiments, especially in sample preparation. Many strategies have been, and continue to be, tested in order to the address limi-tations in qMSI. MSI experiments also generate large sets of data, which in turn involves significant data treatment and signal normalization. For this reason, suitable software must be developed to interpret complex imaging data pro-duced by quantification experiments. In the coming years, more applications will be developed for qMSI, especially in the discovery and development of new drugs. Continued qMSI instrument and method development will be integrated into preclinical and clinical research to accelerate distribution and quantifica-tion studies.

References

(1) R.M. Caprioli, T.B. Farmer, and J. Gile,

Anal. Chem. 23, 4751–4760 (1997).

(2) M. Stoeckli, D. Staab, and A. Schweitzer,

Int. J. Mass Spectrom. 2–3, 195–202

(2007).

Control

(a)

(b)

(c)

Dosed

Figure 5: Quantification process by MSI using the tissue extinction coefficient (TEC) approach.

Table I: Quantification of propranolol in mice whole body section (three target organs), 20 min postinjection (10 mg/kg). Experiments were performed in triplicate. Comparison of quantitative MSI (qMSI) and QWBA data (17).

Propranolol Quantification

TissueqMSI

Concentration (µg/g tissue) % RSD

QWBA Concentration (µg/g tissue)

Method comparison % RSD

Kidney 5.6 15.9% 5.5 2.1%

Lung 17.7 13.2% 19.2 7.8%

Brain 10.8 18.9% 10.3 5.0%


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(16) B. Prideaux, V. Dartois, D. Staab, D.M.

Weiner, A. Goh, L.E. Via, C.E. Barry

III, and M. Stoeckli, Anal. Chem. 83,

2112–2118 (2011).

(17) V. Kertesz, G.J. Van Berkel, M. Vavrek,

K.A. Koeplinger, B.B. Schneider, and

T.R. Covey, Anal. Chem. 13, 5168–

5177 (2008).

(18) G. Hamm, D. Bonnel, R. Legouffe,

F. Pamelard, J.-M. Delbos, F. Bouzom,

C. Piveteau, N. Willand, B. Déprez, I.

Fournier, M. Salzet, and J. Stauber,

“Could Mass Spectrometry Imaging

be a Drug Quantification Technique?”

presented at the 59th ASMS Denver,

Colorado (2011).

Gregory Hamm, PhD, is a research

engineer, David Bonnel, PhD, is a

project manager, Christine Michel is an

account executive, Raphael Legouffe

is a study engineer, Fabien Pamelard

is an IT manager, Guillaume Hochart

is a study engineer, and Jonathan

Stauber, PhD, is CEO and CSO. They are

all with the MALDI imaging service depart-

ment at ImaBiotech, in Parc Eurasanté, Loos,

France. Please direct correspondence to:

[email protected]. ◾



Thomas R. Covey, Bradley B. Schneider, J.C. Yves Le Blanc, and Erkinjon G. Nazarov

This article provides a brief historical overview of differential mobility spectrometry (DMS) and its coupling to mass spectrometry (MS). This overview is followed by a qualitative description of the instrumentation, physics, and chemistry behind the separation principles with particular reference to the use of polar molecules to enhance gas-phase separations. Practical analytical examples are also shown, demonstrating that this technique, when used as the entrance to a mass spec-trometer, can improve the analytical power of MS measurements because the principles of DMS operation are orthogonal to MS.

Polar Vapor–Enhanced Separations with Planar Differential Mobility Spectrometry–Mass Spectrometry

High-field asymmetric waveform ion mobility spectrom-etry (FAIMS) and differential ion mobility spectrometry (DMS) are both a form of radio frequency, field-driven

ion mobility that share the same physical separation principle, fundamentally based on the difference in the high- and low-field ion mobilities of a particular ionic chemical species. FAIMS and DMS are distinguished on the basis of their analyzer geometries, which impart important different analytical properties. One of the unique characteristics of curved cell geometry FAIMS is the ability to focus ions at atmospheric pressure (1). One of the unique characteristics of the planar DMS geometry is that it pro-vides optimal conditions to enhance separations by using polar chemical vapors as clustering agents (2), and several examples of its utility have been demonstrated (3–6). Analyte ions interact with the chemical vapors in ways that reflect their specific gas-phase clustering chemistry. Although the mechanisms are dif-ferent, chemically enhanced mobility separations of this type are reminiscent of liquid-phase high performance liquid chromatog-raphy (HPLC) separations. One practical difference is that with the gas-phase technique, separations occur in the millisecond time frame whereas with the liquid-phase techniques separa-tions are measured in a time frame of seconds to tens of seconds.

In this article we provide a brief historical overview of DMS, followed by a synopsis of the instrumentation, physics, and chemistry behind the separation principles. Practical analytical examples are shown that demonstrate why this technique, when used as the entrance to a mass spectrometer, can be expected to improve mass spectral selectivity because of its strongly or-thogonal nature to the mass spectrometer. In this respect, it bears some resemblance to HPLC coupled to mass spectrometry (MS).

DMS prefiltering can reduce the chemical noise for MS, improve the limits of detection and quantification (LOD and LOQ) for various analytes, and increase sample throughput by reducing or eliminating the necessary LC run time.

HistoryWith the advent of chemical warfare agents during the First World War, the development of handheld chemical sensors has been of great interest for the military, as well as industrial and security applications. One of the many technologies emerging from this century of research and development was a form of ion mobility that operates at atmospheric pressure and utilizes radio frequency (rf) fields to separate ions of chemical species. It was invented in the 1980s in the Soviet Union (7), with the vision for it to ultimately become a compact device to detect landmines on the battlefield. Over the years this form of ion mobility has as-sumed a variety of different names; the earliest ones were nonde-scriptive, such as “gas analyzer of ions,” “field ion spectrometer,” and “drift spectrometer.”

By the late 1980s, the geometry of the separation cell began to diverge down two different pathways. A pair of flat planar electrodes defined one approach (8) and a pair of curved or co-axially cylindrical electrodes defined the other (9) (see Figure 1). These geometries each represent important, different analyti-cal properties to the devices that are particularly relevant to the content of this paper.

The technology transferred to the West in the mid-1990s and at that point the nomenclature became both more descriptive and associated with specific cell geometries. The cylindrical ge-ometry moved from Russia to a company in Pittsburgh, Penn-


sylvania called Mine Safety Appliances (MSA) (10). The MSA device was adapted as a front end to a mass spectrometer at the National Research Council in Ot-tawa, Canada headed by Roger Guevre-mont. At this point the devise took on the descriptive title high-field asymmetric waveform ion mobility spectroscopy (1). FAIMS was commercialized by Ionalytics, later bought by Thermo Electron Corp., and continues to be associated with the curved electrode geometry of this type of ion mobility.

Differential mobility sensors with pla-nar electrodes were developed at research sites in Uzbekistan and Siberia in the late 1980s and early 1990s (11). After the mi-gration of Drs. Erkin Nazarov (Tashkent, Uzbekistan) and Evgeny Krylov (Siberia) to Gary Eiceman’s laboratory at New Mexico State University (NMSU), the planar geometry evolved further and was referred to at that time in the western lit-erature as differential mobility spectrom-etry (3). The first prototype of a microma-chined DMS sensor was built in Charles Stark Draper Laboratory in Cambridge, Massachusetts, and was characterized at NMSU. Later on, the base of this tech-nology, a handheld gas chromatography (GC)–DMS analyzer, was developed in a new spin-off company (Sionex Corp.). AB Sciex worked with the Sionex team to de-velop a planar DMS-MS system that was commercialized under the trade name Selexion (details in http://www.absciex.com/products/ion-mobility-spectrom-etry/ab-sciex-selexion-technology). The term DMS continues to be associated with the planar electrode geometry today.

Several academic laboratories, govern-ment institutions, and instrumentation companies further refined and com-mercialized both planar and cylindrical geometries during the last decade. For a recent overview of commercial mobility and MS instrumentation see reference 12.

Because the focus of this paper is on using transport gases modified with vaporized solvents, attention is drawn to DMS because the planar geometry provides the best conditions to take ad-vantage of the chemical shift with polar transport gases.

Separation Principles The DMS cell operates at atmospheric

pressure. The transport gas that moves the ions through the cell is typically an inert nitrogen curtain gas that can be modified by adding vaporized volatile liquids.

As shown in Figure 2, the separation cell is sealed to the mass spectrometer inlet aperture and the electrospray ion source is near the entrance of the mobil-ity cell. Approximate dimensions are in-dicated in the figure for the DMS system described throughout this paper. The vacuum pulls the transport gas, and the

ions being carried with it, through the de-vice into the mass spectrometer analyzer with high transfer efficiency. Flight times through the cell are approximately 5 ms. For a more in-depth description of this approach see reference 13.

Figure 3 is a computer simulation of the trajectory of four different ions with different mobility characteristics in the presence of the applied rf and dc volt-ages. The high-field portion of the rf pe-riod displaces the ions from the central

1980

Moscow

Siberia

Uzbekistan

New Mexico State University

Boston

North Eastern U.

Toronto

UK -Owlstone

Mine Safety Appliances

Ionalytics-Ottawa San Jose

Pacific Northwest

DMSplanaranalyzer

FAIMScurvedanalyzer

SCIEX

Sionex

NMSU

20091980

81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 09080706050403020100

TIE

DTI

PNNL

IA ThermoMSA

IAP

COAXIAL

PLANAR

OWL

Figure 1: Historical development and migration of DMS (planar cell geometry) and FAIMS (curved cell geometry) technology from the Soviet Union to the West. IAP = Applied Physics Institute (SU); DTI = Design-Technological Institute of Geophysical and Ecological Engineering (SU); TIE = Tashkent Institute of Electronics (SU); MSA = Mine Safety Appliance (USA); PNNL = Pacific North National Laboratory (USA); IA = Ionalytics (Canada); NMSU = New Mexico State University (USA); Sionex Corp. (USA); OWL = Owlstone (UK); Thermo Electron (USA); GAI = gas analyzer of ions; DS = drift spectrometer; DMS = differential mobility spectrometry; IMIS = ion mobility increment spectrometer; FIS = field ion spectrometer; and FAIMS = high field asymmetric waveform ion mobility spectrometry.

2.3 Torr

2.8 L/min

3 cm X 10 mm wide X 1 mm gap

760 Torr

DMS cell Mass spectrometer

inlet apertureAtmospheric pressure

ion source

(ESI and APCI)

Transport gas and ions

Figure 2: Schematic drawing of the planar DMS-MS system described in this article. Approximate dimensions are cited (not drawn to scale). The cell is sealed to the mass spectrometer inlet aperture, which allows the transport gas flow through the cell to be driven by the MS vacuum system. This also provides high ion transfer efficiency from the DMS cell into the mass spectrometer.


axis of the electrodes and the low-field portion drives them back in the other direction, creating a sawtooth ion trajec-tory. If the mobility of a particular ion differs between the high- and low-field portions of the rf waveform, it will drift toward and eventually strike one of the electrodes as seen with three of the four ions in Figure 3. A compensation dc voltage is used to steer an ion back on axis, as is shown occurring in Figure 3 with one of the four ions. This ion will traverse the cell and be detected by the mass spectrometer when this particular compensation voltage is applied. The compensation voltage can be swept sequentially, allowing all ions to pass at their respective compensation voltage in a way similar to the scanning of a quadrupole mass spectrometer. Alternatively, the dc voltage can be fixed allowing only the targeted species to pass. The rf is approximately 3 MHz. Given an ion flight time through the cell of approximately 5 ms, determined by the gas flow rate, this process results in 15,000 high-to-low field oscillations during transit through the entire length of the cell. During this time, the ions are colliding with the neu-tral background transport gas (nitrogen), as well as clustering

and declustering with the background chemical vapors if they have been added to the transport gas.

The addition of clustering agents to the transport gas en-hances the separation power of DMS by amplifying the dif-ference in the high- and low-field mobilities of a particular ion and it does this in a chemically specific way. As shown in Figure 4, ions cluster in the low field then decluster in the high field of each period of the waveform as a result of rf heating. This clustering and declustering occurs approximately 15,000 times during the flight through the cell. The separation amplification is a consequence of the phenomena of ions clustering with neu-tral molecules in accordance with their specific ion–molecule chemistry. The clustered ion is large, which dramatically reduces its mobility, and it has a unique shape established by the interac-tion of the molecular bonds of the ion with the polar transport gas. The declustered ion is small, which increases its mobility, and its shape is established by the naked ion. The improvements to the selectivity of the separation are fundamentally based on the unique gas-phase ion chemistries of individual ions. A rigor-ous derivation of the clustering model was developed in Gary Eiceman’s laboratory (14,15).

Clustering agents have been used in classical constant low-field ion mobility instruments for many years. However, the same separation power amplification is not achieved, because with the constant low field mobility analyzers there is no alter-nating between clustered and declustered states. Successful ap-plication of this approach to improved peak capacity has been limited to planar DMS devices (2). The reasons for this limited applicability are not entirely clear, but the primary difference between the two geometries is the electric field densities within the cells (16). Planar geometries produce homogeneous fields. The fields within cylindrical devices are inhomogeneous; this results in operational modes in which ion focusing can occur to improve transmission (1), but resolution is lost in the presence of polar transport gases. It is speculated that the inhomogeneity of the fields within a curved device reduces resolution in the pres-ence of vapor modified transport gases because the clustering and declustering effect is highly dependent on the radial position of the ion within the separation cell. Because the fields are vari-able at different positions within a curved cell and the clustering and declustering process occurs at different positions relative to the electrodes throughout the length of the analyzer, a loss of resolution can be expected. Planar devices, comprising two flat plates, do not suffer from distorted field effects. Because the fields are homogeneous, the cluster–decluster process is not affected by position within the analyzer, resulting in good resolution.

Data and Examples The descriptive term ionogram was coined by Roger Guevre-mont to describe the experiment in which a sample containing a chemical mixture is infused and the compensation voltage is scanned, and the mass spectrometer is used as the detector (17). All ionograms presented in this work were obtained with SelexIon Technology on the AB Sciex Triple Quad 5500. Figure 5 shows three ionograms of a three-compound mixture taken using different transport gas compositions. Figures 5a and 5b are ionograms generated with different compositions of inert gases,

+

+

+

+

High field declustering;mobility increases

dramatically

High voltage≈3 kV

Low field clustering-mobility decreases dramatically

Low voltage~ 0.6 kV

Figure 4: Conceptual drawing of the clustering and declustering that

occurs between an ion and the polar background gas during the high

and low field portions of each period of the rf cycle.

Figure 3: Computer simulation of the trajectories of four different ions

through a planar DMS system with the rf separation voltage on and the

dc compensation voltage set to a specific value (not scanning).


showing marginal improvements in selec-tivity. Figure 5c shows the effect of a trans-port gas containing 1.5% isopropanol and 98.5% nitrogen; dramatic improvement is evident in the separation as a result of the chemical manipulation of the transport gas, which is somewhat analogous to the mobile phase in HPLC.

The term ionogram seems even more appropriate now than ever before because of its linguistic similarity to chromato-gram. Because chemistry is being lever-aged to improve separations, the results are reminiscent of HPLC. It is clear that the fundamental mechanisms are differ-ent in the gas and liquid phases, but the observation that the two leverage chemi-cal interactions to produce separations and gain specificity is clear from this example. This is quite distinct from MS where differences in nuclear properties of molecules are the basis of separations.

Another example of this concept is illustrated in Figure 6, in which differ-ent polar modifiers are added to the transport gas to effect the separation of a seven-compound mixture. As would be expected, the separation resolution is af-fected differently with the different gases. Closer inspection shows that the relative location of the individual drug molecules within the ionogram is reversed. This can be seen to be the case with ephedrine and acyclovir in Figures 6a and 6b (isopropa-nol vs. acetone modifier). This is reminis-cent of reversing the order of elution or relative retention times of compounds in HPLC by changing the mobile or station-ary phases.

Although we are indicating a rela-tionship between HPLC and chemically driven DMS, the two have distinctly dif-ferent mechanisms and therefore should show some degree of orthogonality and demonstrate different types of selectivity. Figures 7a and 7b show two LC–MS-MS chromatograms of the same sample from a targeted quantitative assay for testoster-one. In Figure 7a the DMS filter is turned off, and in Figure 7b the filter is turned on and set to the compensation voltage to pass testosterone. A polar modifier has been added to the transport gas to en-hance separation. Under these particular assay conditions chemical interferants are apparent without the mobility separation activated (Figure 7a). Figure 7b shows an

improvement in the signal-to-noise ratio resulting from the additional selectivity imparted by the chemically based mobil-ity separation.

Because of the speed of separations, it is an attractive proposition to use DMS to substitute for HPLC in situations where this approach could offer practical advan-tages. One good example is the chemical profiling of tissue slices for the location of dosed drugs and metabolites using elec-trospray ionization (ESI) (18,19). Local-ized areas of the tissue are extracted and infused into a nanoESI ion source without HPLC separation with this technique. It is based on the surface sampling probe developed at Oak Ridge National Labo-ratories by Gary Van Berkel. Figure 8a is a magnified view of the liquid junction that is used to extract chemical species from

surfaces. The spatial resolution is on the order of hundreds of micrometers. Figure 8b shows the device extracting a region of a whole mouse body tissue slice. The junction is maintained for a few seconds to maximize extraction efficiency and then is withdrawn into the pipette, trans-ferred to the ion source, and ionized by electrospray into a DMS cell for mobility separation followed by MS detection.

Figures 9a and 9b show an example of using this surface-sampling system with DMS-MS to profile the tissues and organs of a whole body mouse cross sec-tion for the dosed drug propranolol and its glucuronide metabolites. The opti-cal image in Figure 9a shows four of the regions interrogated. Figure 9b is the ionogram from region 2 (an 800-µm-wide section of the liver) showing the

(a)

100%nitrogen

44%helium

1.5%isopropanol

CH3

CH3

CH3 CH

3CH

3

CH3

CH3

H3C

H2N

H2N

HN

CH3

H

H H

OH

O

O

O

OH

Cl

Cl

O

N

(b)

(c)

Dianabol(red)

Benoxinate(green)

Clenbuterol(blue)

Figure 5: Three ionograms of the same three drug mixtures under different transport gas conditions. All ionograms in this paper were acquired by infusing the sample into an electrospray ion source and scanning the compensation voltage at a fixed rf separation voltage (separation field = 116 Td). All data in this paper were acquired using AB Sciex SelexION DMS device coupled to an AB Sciex QTrap 5500 mass spectrometer operating in the multiple reaction monitoring (MRM) mode. In this case, the sample flow rate was 10 µL/min and the compensation voltage was scanned from -50 to +40 V. (a) 100% nitrogen as the transport gas. No clustering reaction occurring. (b) 44% helium in the nitrogen transport gas. No clustering reactions occurring. (c) 1.5% vaporized isopropanol in the nitrogen transport gas. Cluster reactions occurring and the separation mechanism shifts to a chemically dominated model.


mobility-based separation of the parent drug and two isomeric and isobaric gluc-uronide metabolites. Using acetonitrile as the transport gas modifier in the DMS cell, the two metabolites having identi-cal molecular weights are readily sepa-rated. The mass spectrometer cannot distinguish them based on the molecular weight of these species.

Conclusions

The geometries and nomenclature for asymmetric rf mobility analyzers has evolved during the past 30 years. Planar DMS cells have been shown to provide optimal conditions for polar transport gas–enhanced separations and demonstrate a capacity to add a useful new dimension of selectivity

for MS. In the present paper, we dem-onstrated that DMS devices can reduce the chemical background and improve the signal-to-noise ratio. In addition, the removal of interfering peaks from an analysis offers the possibility to improve analysis throughput by either shortening the total chromatographic analysis time, or, for some assays, elim-inating it completely.

Every technique has some caveats and both FAIMS and DMS are no exception. There are thermodynamic limitations with gas-phase ion-cluster chemistry. When the proton affinity of the trans-port gas modifier is higher than the analyte, the charge will be transferred to the modifier and a loss of analyte ions will result.

At LC flow rates in the hundreds of microliters per minute range, as opposed to nano- or microflow rates, ions are cre-ated as highly heterogeneous clusters in electrospray ion sources with varying numbers of different solvent molecules at-tached to the analyte. Without a mobility cell in place this poses no problem because the clusters are removed in the entrance optics of the mass spectrometer vacuum system. However, with a mobility cell in place it will do what it is supposed to do and separate components of different size and shape. Consequently, the ion current from the analyte will spread out over com-pensation voltage space, depending on how heterogeneous the cluster ion popu-lations are. Sensitivity will be reduced in a compound dependant way.

Finally, one practical difference be-tween HPLC and DMS is that HPLC separates components of mixtures preionization and DMS separates post- ionization. The implications of this center on the issue of ionization sup-pression. HPLC offers a means to re-duce or eliminate suppression. DMS does not. There are situations where ionization suppression is not an issue. When it is, a combination of some form of chromatography before DMS-MS is warranted. The added selectivity of DMS reduces the need for high-reso-lution HPLC. HPLC methods can be run faster and method development can be simpler.

References

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of USSR, No. 966583,G01N27/62.

Red: EphedrineCV (V)

CV

CV

(V)

-50 -40 -30 -20 -10 0

-50 -40 -30 -20 -10 0

(V)-50 -40 -30 -20 -10 0

1.0

0.8

0.6

0.4

0.2

0.0

No

rmalize

d s

ign

al

1.0

0.8

0.6

0.4

0.2

0.0

No

rmalize

d s

ign

al

1.0

0.8

0.6

0.4

0.2

0.0

No

rmalize

d s

ign

al

Black: Acyclovir

Green: Norfentanyl

Blue: Clenbuterol

Purple: Imipramine

Brown: Diazepam

Pink: Quinoxifen

Figure 6: Ionograms of a seven compound mixture using different transport gas modifiers at

1.5% of the total nitrogen gas flow. In each case the nitrogen transport gas has 1.5% of the

modifier. Compensation voltage scanned -60 to +10 V and sample infusion flow rate = 10 µL/min.

The mixture was composed of ephedrine (red), acyclovir (black), norfentanyl (green), clenbuterol

(blue), imipramine (purple), diazepam (brown), and quinoxifen (pink). (a) Isopropanol-modified

transport gas. (b) Acetone-modified transport gas. (c) Acetonitrile-modified transport gas.


(8) I.A Buryakov, E.V. Krylov, and V.P.

Soldatov, USSR Invention #1486808

G01N 27/62, 1989.

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and A. Tarasov, “Development

and Applications of a Transverse

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Mobility Spectrometry, Cambridge,

UK, 1995.

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Makas, E.G.Nazarov, V.V. Pervukhin,

and U.Kh. Rasulev, Pis’ma Zh.

Tech. Fiz 17(12),61–65 (1991)

(Russian).

(12) C. Lapthorn, B.Z. Chowdhry, and F.

Pullen, Mass Spectrom. Rev. (2012,

in press).

(a)

DMS off DMS on

3.3723.301

3.410

3.156

3.487

3.857

S/N = 155.9

Peak Int. (Subt.) = 1.8e4

3xStd.Dev. (Noise) = 1.2e2

2.5e5

18000

17000

16000

15000

14000

13000

12000

11000

10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2.4e5

2.3e5

2.2e5

2.1e5

2.0e5

1.9e5

1.8e5

1.7e5

1.6e5

1.5e5

1.4e5

1.3e5

1.2e5

1.1e5

1.0e5

9.0e4

8.0e4

7.0e4

6.0e4

5.0e4

4.0e4

3.0e4

2.0e4

1.0e4

0.0e02.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1

Time (min)

(b)

Time (min)

Peak of interest

(RT = 3.37 min)

Figure 7: (a) LC–MS-MS chromatogram of a precipitated human female plasma sample monitoring an MRM transition for low level testosterone, m/z 289 → m/z 97. The DMS rf and dc voltages are off enabling transmission of all ions through the mobility cell without separation. A Shimadzu HPLC system was used, with a flow rate of 200 µL/min. (b) A repeat injection of the same sample as in (a) with the DMS filter on and set to the compensation voltage that transmits testosterone.

(a)

Figure 8: (a) A magnified view of the liquid junction formed between a surface and the delivery pipette. The delivery pipette transports the sample postextraction to an Advion chip-based nanoelectrospray nozzle for electrospray ionization followed by DMS separation. (b) A view of a microscope slide–mounted whole body mouse section being surface sampled by a liquid extraction surface analysis (LESA) instrument commercialized by Advion.

(b)


(13) B.B. Schneider, T.R. Covey, S.L. Coy, E.V. Krylov, and E.G. Naz-

arov, Int. J. Mass Spectrom. 298, 45–54 (2010).

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(2009).

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R.A. Miller, Anal. Chem. 76(17), 4937–4944 (2004).

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(17) R.J. Guevremont, Chromatogr. A 1058, 3–19 (2004).

(18) W.B. Parsons, B.B. Schneider, V. Kertez, J.J. Corr, T.R. Covey,

and G.J Van Berkel, Rap. Commun. Mass Spectrom. 25,

3382–3386 (2011).

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Metab. Dispos. 40(3), 419–425 (2012).

Thomas R. Covey, Bradley B. Schneider, and

J.C. Yves Le Blanc are with AB Sciex in Concord, Ontario,

Canada.

Erkinjon G. Nazarov is with the Charles Stark Draper

Laboratory in Cambridge, Massachusetts. Please direct corre-

spondence to: [email protected]. ◾

(a)

(b) XIC of +MRM (6 pairs): 260.100/183.100 Da from Sample 1 (TuneSampleID) of MT20100621155825.wiff (Turbo Sp... Max. 3233.1 cps.

Propanolol

CV = -17.4 V

CV = -10.4 V

m/z 116.1 (2a)

O

O

O

R1

R2

R2

R1

NH

m/z 276.1 (2b)

m/z 276.1 (2a)

6424

6000

5500

5000

4500

4000

3500

-25.99

-28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4

COV (V)

Inte

nsi

ty (

cps)

3000

2500

2000

1500

1000

500

0

Figure 9: (a) The optical image of a whole body mouse cross section approximately 40 µm thick. Four regions were initially interrogated at a spatial resolution of 800–1000 µm. (b) The ionogram obtained while infusing sample from region 2 at 500 nL/min. The DMS compensation voltage was scanned from –30 to 0 volts and the triple quadrupole mass spectrometer was used to detect the drug and metabolites using multiple reaction monitoring of the molecular ions and two characteristic fragment ions. A concentration of 1.5% acetonitrile in nitrogen gas was used as the transport gas to effect separations. The structures of propranolol and its two glucuronide metabolites (R1 and R2) are shown.


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IC–ICP–MS Speciation Analysis of As in Apple Juice

Daniel Kutscher, Shona McSheehy, Julian Wills, and Detlef Jensen, Thermo Fisher Scientific

In this study, apple juice samples are analyzed by IC–

ICP–MS to determine the concentration of six arsenic

species: the two inorganic, and highly toxic, species

(As[V]) and As[III]) and four organic species (arseno-

betaine [AsB], arsenocholine [AsC], monomethylar-

sonic acid [MMA], and dimethylarsinic acid [DMA]).

General Analytical Conditions

An iCAP Qc ICP-MS (Termo Fisher Scientific, Bremen, Germany)

was equipped with a Peltier cooled cyclonic PFA spray chamber and

a PFA-LC nebuliser (Elemental Scientific, Omaha, Nebraska). A de-

mountable torch was equipped with a 2 mm i.d. quartz injector.

Chromatographic separations were carried out using a Dionex

ICS-5000 ion chromatogram (Termo Fisher Scientific, Sunny-

vale, California).

Te column outlet from the ICS-5000 was directly connected

to the PFA-LC nebulizer. Full bidirectional communication be-

tween the instruments was achieved using a trigger cable. All data

evaluation was performed using the tQuant features of the ICP-

MS Qtegra software platform.

Sample and Calibration Solution Preparation

Four different apple juices were bought in a local supermarket

and the total arsenic concentration was first determined after di-

lution of 1 mL juice in 7 mL of ultrapure water/2 mL of 2% nitric

acid. Samples that showed the detectable presence of total arsenic

were passed for subsequent speciation analysis.


Results from two of the four apple juices where arsenic was detected

are summarized in Table I. Method detection limits (MDLs) for

the arsenic species were calculated from three times the standard

deviation of four repeat blank injections. Total arsenic concentra-

tions for the two samples also are shown.

Te trace amounts of arsenic found in this study fit well with

a typical range of arsenic concentrations in apple juices (between

2 and 6 ng/g) as determined by the United States Food and Drug

Agency (US FDA) in a previous study (1).

Conclusion

A new IC-ICP-MS method for the separation and quantification

of six arsenic species in apple juice has been developed. Te com-

bination of ion chromatography with ICP-MS has been shown

to be a powerful tool for the detection of low concentrations of

arsenic species in fruit juices.

Although there is no legislative limit for arsenic species in fruit

juices currently, the detection limits obtained by this method (~5

pg/g) far exceed those in similar sample types, for example bottled

water (FDA limit set at 10 ng/g).

References

(1) FDA arsenic in apple juice results: http://www.fda.gov/Food/Food-

Safety/FoodContaminantsAdulteration/Metals/ucm272705.htm.

Table I: Arsenic species concentrations, method detection limits (MDLs), and total concentrations in two of the apple juice samples analyzed. All concentrations have units of ng/g.

AsB DMA As(III) AsC MMA As(V)Sum of Species

Total As

MDL 0.002 0.004 0.005 0.004 0.011 0.001 — 0.005

Juice 3 ND* ND* 0.5 ± 0.01 ND* ND* 0.7 ± 0.01 1.2 1.7 ± 0.05

Juice 4 ND* 0.4 ± 0.05 0.3 ± 0.01 ND* 0.1 ± 0.05 0.7 ± 0.01 1.5 1.8 ± 0.05

*ND indicates not detected.

Figure 1: Model separation of six arsenic species, each at 0.45 ng/g in dilute HNO3.

50 Mass Spectrometry ADVERTISEMENT

The emerging field of metabolomics is defined as the nontargeted detection and quantification of small molecule metabolites that

are found in biological materials. With the advantage of noninva-sive techniques, metabolomics is finding a growing number of ap-plications in: chronic disease, environmental exposure, functional genomics, neonatal screening, nutrition, sports science, toxicology, and transplantation to monitor the health of the transplant organ.

To identify and quantify the vast range of chemically diverse pri-mary and secondary metabolites, any analytical approach must en-compass reliable sampling, precise detection methods, and advanced data handling and interpretation capabilities. With two recognized approaches to metabolomics, the first is referred to as “footprinting” (1) and is used when profiling metabolites within the environmental medium into which they were secreted by cells. Alternatively, a more untargeted method to metabolomics would be to analyze all metabo-lites present in a more blanketed approach much simpler and required less frequently, resulting in a more robust system for long operation.

Transitioning Single Quadrupole (SQ) to Triple Quadrupole (TQ) GC–MSWhile metabolomics studies can be carried out using single quadru-pole (SQ) GC–MS instruments, these rely heavily on mass spectra deconvolution and some derivatization for polar compounds such as amino acids, organic acids, and simple sugars (2). Te application of single quadrupole GC–MS requires chromatographic temperature ramps and spectral scan rate to be matched in order to maximize the number of compounds that can be routinely identified for metabolite

profiling. Te scan rate becomes a limiting factor in metabolite profil-ing and the ability to effectively deconvolute data sets is critical.

To lessen the load of quantitative metabolomics, triple quad-rupole (TQ) GC–MS has been utilized to excellent effect. Te SCION™ TQ hardware (Figure 1) provides higher throughput and faster scan speeds for more efficient sampling runs, greater sensitivity, and zero cross talk and improved signal-to-noise ra-tio (S/N). Final outcomes include the ability to process more samples, resolve more compounds, and improve identification of compounds.

ExperimentalTe transition from single quad to triple quad can be illustrated with the use of GC–MS-MS with a standard 43 component mixture used for a QA/QC mix with derivatization (2).

Figure 2 illustrates a typical full scan with a significant peak (3). Following close examination of the peak, total ion chromatogram coelution of proline and leucine was identified. Te need for decon-volution to resolve these compounds was necessary (see Figure 3).

Comparing Single Quadrupole with Triple Quadrupole GC–MS-Based Metabolomics

Rob Trengove, Separation Science & Metabolomics Laboratory, Metabolomics Australia, Murdoch University, Murdoch, Western Australia

Figure 1: Bruker’s SCION GC–MS TQ platform with PC illustrating the compound-based scanning (CBS) software.

Figure 2: Full scan mass spectrometry of 43 components QA/QC mix (TMS).

QC mix split - 6-22-20A.1 11-39-55 AM 11 June 22 - full scan Metab split 1 .xms TIC Filtered

Co

un

ts

60045.0:600.0>

500

400

300

200

15.5 16.0 16.5 17.517.0 18.0Minutes

Figure 3: Upper: Full scan mass spectrometry of 43 components QC mix split (TIC filtered) with coelution at one peak.

Mass Spectrometry 51ADVERTISEMENT

Comparative Results: SQ vs TQWhen investigating metabolomic samples there are several hundreds of compounds and several may coelute. Tese compounds can be very similar in structure, such as sugars and deconvolution may not resolve them. Expanding the capabilities of the GC–MS-MS system is ideal with an instrument that undertakes data dependent MS and combines it with a series of scheduled multiple reaction monitoring (MRM) transitions. Tis extended system can be set-up to reference known coelution and accurate retention times of compounds.

In Figure 4 the mass spectra of the left and right markers are quite similar and dependent on the speed at which data can be collected. Deconvolution may not resolve the two compounds in the peak, but if analysis by MS-MS is undertaken (Figure 5), the baseline is cleaned up significantly improving S/N, and the limits of detection (LOD) and quantification are greatly improved. It then becomes obvious that two peaks exist at this retention time (RT), whereas in full scan it was only a single peak with coelution. Using the triple quad system, it only takes one transition to resolve the compounds in this instance.

ConclusionA triple quadrupole operating in MRM mode with compound-based scanning (CBS) software provides enhanced duty-cycle cov-erage so that resolution of co-eluted compounds is possible in the SCION GC–MS TQ. It also provides a faster scan rate with lower LOD and this will lead to improved metabolome coverage with routine metabolite profiling using built-in libraries.

Te TQ can also deliver precursor ion scans, product ion scans, neutral loss scans, and MRM to assist in the identification of un-knowns as well as quantitatively detect metabolites in really com-plex mixtures at very low levels.

Chemical ionization (CI) in combination with MRM mode provides high throughput metabolomics capability, where as full scan struggles to resolve compounds. In addition, when using CI, an improved transmission is possible. With the right combination of reagent gases, compound identification is further improved, more compounds are resolved, and S/N is substantially better. CI

also provides higher mass to charge (m/z) species of precursor ions for MS-MS with far less interference from the matrix. TQ MS that combines CI and MRM offers the potential to increase me-tabolome coverage and identification and provides both the LODs and improved precision required to do so.

AcknowledgmentsBruce Pebbles, Katherine Rousetty, Catherine Rawlinson, Garth Maker, Joel Gummer, Christian Krill, Hayley Abbis, and Bong Sze, Murdoch University; Australian Research Council, West Australian Government, Grain Research and Development Corporation, Grape and Wine Research and Development Corporation, Bioplatforms Australia and Murdoch University for funding; and Bruker Chemical and Applied Markets division.

References

(1) W.B. Dunn and D.I. Ellis, Trends in Analytical Chemistry 24(4), 285–294

(2005).

(2) S.L. Taylor et al., American Journal Renal Physiol. 298, F909–F922

(2010).

(3) “Transitioning from Single Quadrupole to Triple Quadrupole GC/MS-

based Metabolomics” live webcast, LCGC N. America. October, 2011.

100%

73.32.869e+8

58.21.501e+8

142.25.251e+8

73.33.379e+8

158.25.041e+8

Left

Mark

er

Rig

ht

Mark

er

75%

50%

25%

0%

10050 150 200 250

10050 150 200 250

100%

75%

50%

25%

0%

Figure 4: Deconvolution of proline and leucine by compound-based scanning (CBS) software.

400

QC Mix old v3.1 - 6-24-2011 3-19-26 PM 11 June 22 - MSMS Metab v3.1 .xms TIC Filtered

300

200

100

0

16.0 16.5

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er

Rig

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ma

rke

r

100%

75%

50%

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157 158 159

141 142 143

Acquired range m/z

Acquired rangem/z

17.0 17.5 18.0

Minutes

Co

un

ts

168.01.528e+9

142.04.333e+8

Figure 5: GC–MS-MS TQ of 43 components QA/QC mix illustrat-ing two peaks for coeluted compounds proline and leucine.

Bruker Daltonics, Inc.

Chemical and Applied Markets division

Fremont, CA

tel. +1 (510) 683-4300, Fax. +1 (510) 490-6586

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