analytical chemistry: from data (pre)-treatment

EA4041, Groupe de Chimie Analytique de Paris-Sud ‘GCAPS’

Analytical chemistry: from data (pre)-treatment optimization to data mining, data fusion and

big data

April 1st, 2014

Cell membrane lipidomics

Lipids in skin barrier

Lipids: from natural

substances to heritage objects

Lipid analogues for

diagnostic and

therapeutic aims

Four principle themes

Data (pre)-processing Multivariate analysis

Chemometric techniques

Different analytical tools

Data acquisition

Separation techniques,

mainly chromatographic

Coupled mass spectrometry

techniques (LC, GC, GCxGC/MS)

Vibrational spectroscopy

(infrared, near infrared and

Raman)

Schedule

4

10h00-10h30: «Analytical chemistry: cell membrane lipidomics and data analysis», Sana Tfaili 10h30-11h00: «Analytical chemistry and chemometrics: a tool for skin physiological and physiopathological charecterization», Ali Tfayli 11h00-11h10: «Analytical chemistry: from data (pre)-treatment optimization to data mining, data fusion and big data», S&A Tfai(y)li

Analytical chemistry: cell membrane lipidomics and data analysis

Sana Tfaili

Lipidomics

• Recent comparing to other omics

• Borrows heavily from metabolomics

• Specific analysis of lipids

6


Infectious diseases

•Leishmaniasis

•Impact of treatment on the lipid composition of membranes Leishmania

donovani

Collaboration UMR BioCIS / Ph. Loiseau


Hereditary diseases and RBCs

•Lipid composition and sickle cell disease

Olivier Blanc-Brude Paris Centre de Recherche Cardiovasculaire (PARCC)

•Lipid composition and Gaucher disease (LETIAM)

Institut National de Transfusion Sanguine (Pr. Le Van Kim et Dr. M Franco)

Service de Neuro-Pédiatrie de l’Hôpital Trousseau (Pr. T. Billette de Villemeur et Dr

C. Mignot)


Lipidomics macrophages & Atherosclerosis

•Impact of membrane incorporation of w3 PUFA on cholesterol efflux from macrophages

•Intracellular trafficking of cholesterol from

macrophages:study of the inhibition of Rab7 and the role of oxysterols

Collaboration EA4529 (cross-cutting theme in the

future unit Lip (Sys) ²)

10

Chromatography

Lipids and polarity

Solubility parameter d d’Hildebrand

5 10 15 20 25 0

O

O

O

O

HO

O

O O

O

O

H

H

H

O

H

H

O

H

H

H

OH

H

H

HN

OH

O

OHH

HN

O

O

OHH

O

OO

O

O

O

O

HO

O

R1

R2

OOH

O

OHOH

OOH

OH

OHOH

O

O

O

HO

R1

O ONH

2

P

O

O

O

R2

O

O

HO

O ON

+P

O

O

O

R2

R1

O

O

HOH

R O ON

+P

O

O

11

Normal Phase / Reversed Phase Liquid chromatography LC

5

10

1

5

20

2

5

0

d

Solv

ent

stre

ngt

h

Solven

t strength

Lipids are separated according to their polar moeties.

Lipids are separated according to their chain length & number and position of double bonds

LIP

ID C

LASS

ES A

NA

LYSI

S

LIP

ID M

OLE

CU

LAR

SP

ECIE

S A

NA

LYSI

S

12

13

Mass spectrometry MS

e-

ABC ABC

e-

e-

+ Ionisation IE

E interne AB+ + C

fragmentation (BC+)* + A OU

B+ + C Mass spectrometry by electronic impact

(GC-MS)

LC-MS acquisitions: Different ionization modes

+ - OOO

OOO

OOO

SA

SA

SA

Laurent Imbert, PhD thesis, GCAPS,EA4041, 2012, Univ. Paris Sud

Lipid molecular species

Lipidomics: coming to grips with lipid diversity Andrej Shevchenko & Kai Simons Nature Reviews Molecular Cell Biology 11, 593-598 (August 2010)

Lipid Classes LC MS

Sub-classes LC MS or in HRMS LC/MS

Isobars LC MS LC/MS

Isomers LC MS LC/MS

15

http://www.nature.com/nrm/journal/v11/n8/full/nrm2934.html

LC-MS data matrix

Data = 3D matrix need to concatenate and "unfold" files

In the data matrix: Objects (lines) = sample

Variables (columns) = couple (Tr, m/z)

LC-MS data matrix processing

Univariate statistical analysis using XCMS online: Paired Student t-test between the two

groups of signals

Evident significant difference between the intensities of the ions.

Step 1: file by file, detection of ions (> threshold) scan by scan

Step 2: ion chromatogram generation, file by file

Step 3: file by file, peak detection table (ion; rt, intensity) (data file)

Step 4: Alignment: Setting a tolerance window (m/z, rt) based in general on the first chromatogram. (data set)

18


Alignment tools using MzMine

Principal component analysis Unsupervised method Data mining PLS Discriminant analysis Supervised method

http://fiehnlab.ucdavis.edu/staff/kind/Statistics/Concepts/OPLS-PLSDA

http://www.nlpca.org/pca_principal_component_analysis.html

19


Orthogonal PLS-DA (OPLS-DA)

Maximizes the discrimination between two classes in its first component

S-Plot expresses the relationship between the original variables (rt, m/z)

and scores on the selected axis.

Published in: Susanne Wiklund; Erik Johansson; Lina Sjöström; Ewa J. Mellerowicz; Ulf Edlund; John P. Shockcor; Johan Gottfries; Thomas Moritz; Johan Trygg; Anal. Chem. 2008, 80, 115-122.

20


21

O-PLS and S-plot using SimcaP


22


S-plot / Selection of discriminate variables

23

Online databases

24

Online databases

25

Online databases

Chemometric tools for LC-MS lipidomics profiles analysis.

Samples

LC-MS profiles

Highlighting specific MS signals of the 2 groups

Stat

isti

cal

anal

ysis

Biomarkers

Analytical chemistry and chemometrics: a tool for skin physiological and

physiopathological characterization

Ali Tfayli

Largest organ in human body Barrier function

Sensation

Heat regulation

Secretion end excretion

Dermis Epidermis

- Stratum Basale - Struatum Spinosum - Stratum Granulosum

- Stratum Corneum

- Superficial - Deep

General structure of the skin

Introduction

28

Skin Barrier function

Introduction

Barrier against

External insult

Water loss

STRATUM

CORNEUM

Keratin

Intercellular lipids

SC structure

29


Introduction

CHOLESTEROL

CERAMIDES

Composition of SC lipids

OTHERS

FATTY ACIDS

Structure of Ceramides

Different polar heads

Different chain lengths

Presence of double bonds

30


Introduction

Conformational order

Lateral packing of SC lipids

trans

gauche

Orthorhombic Disordered Hexagonal

HIGHLY ORGANIZED

GOOD BARRIER PROPERTIES

31

Cosmetology

Toxicology

Dermatology

Characterization of skin barrier

Skin aging

-Physiological status

-Physiopathological status

Skin hydration and dry skin diseases

Mechanical stress

32

Analyses of skin barrier

Composition and profiling

Organization, lateral packing

Vibrational spectroscopies: Infrared and Raman

Separative techniques - mass

33

Vibrational spectroscopies

µ = BA

BA

mm

mm

.

k

CC 2

1_

ν = nombre d’onde, ν = fréquence, µ = masse réduite

INTERACTION

RAYONNEMENT - MATIERE

ABSORPTION EMISSION

DIFFUSION

SPECTROSCOPIES OPTIQUES

6N3

1nn0n00

n

0

0000

6N3

1nn0

n

p

0ptotttQ

dQ

dE

2

1tEtQ

dQ

d

coscos)cos(cos

)(34

Vibrational spectroscopies

Jablonski diagram

35

Vibrational signal collection

Individual spectral collection In depth spectral collection

0 10 20 30 40 50 60 70 80 90 0

0.01

profondeur (µm)

inte

nsité

(a

.u.)

x: profondeur y: intensité du pic à 1191 cm-1

36

X/Y table

Vibrational signal collection

2D spectral mapping 3D spectral mapping

37

Data pre-processing

-Dark current

-CCD response correction

-Optical components contribution

-Smoothing

-Baseline correction

-Normalization

38

Physiological / Physiopathological status

Raman descriptors of SC barrier

Vibrational spectroscopies VS barrier function

Structural information

Amide I band

dCH3 Rocking

Intensity (a.u.) 20 15 10 5 0

2700

2800

2900

3000

3100

Wav

enu

mb

er (

cm-1

)

800

1000

1200

1400

1600

1800

(CH2) (CH3)

(C-C)

dCH2 scissoring

Conformational order

Compacity of packing

Chain end conformers

Organisation

Polar heads interactions

Chain conformation

39

Ex vivo et in vivo

TFAYLI A. et al. EJD 2012

Volontaires

13 F & 7 M (22 à 64 ans)

Méthode

Surface nettoyée

2ème

acquisition in vivo

1ère

acquisition in vivo

Extraction des lipides

Ex vivo

Pas de séparation

Spectres in vivo avant l’extraction

–

Spectres in vivo après l’extraction

=

Signal in vivo des lipides

In vivo


Skin aging


40

in vivo


Observations directes

2800 2850 2900 2950 3000

0

0.02

0.04

0.06

0.08

0.1

0.12

Nombre d’ondes (cm-1)

Inte

nsi

té (u

.a.)

Peau jeune

Peau mature

kruskal Wallis

2845-3020 cm-1


Skin aging


41

in vivo

-1 0 1 2 3 4

-3

-2

-1

0

1

2

PC1

PC2

32M

32M

60M

60M

59F

59F

22M

33F

33F

42F

42F

30M

30M 27F

27F

26F

26F

58F

58F

27F

28M

28M

26F

26F

24F

24F 39F

42M

42M

31F 31F

64M

64M

Analyse en composantes principales sur la gamme:

2845-3020 cm-1

Trois groupes en fonction de l’âge: •22-30 ans •30-42 ans •50-64 ans



Skin aging


42

Water structure

RH : 2.5% » 75%

Ex vivo

VYUMVUHORE R. et al. Analyst, 2013

Nombre d'Ondes (cm-1)

Inte

nsi

té (u

.a.)

500 1 000 1 500 2 000 2 500 3 000 3 500

0.0

0.2

0.4

0.6

0.8

1.0

RH=4%

RH=75%

RH=98%

Eau


Skin hydration / dry skin diseases


43


3465.8

3343.3

3280.0

3212.0

1200

1000

800

600

400

200

0 3200 3300 3400 3500 3600

Inte

nsi

té (u

.a.)

Nombre d’Ondes (cm-1)

Eau fortement liée

Eau partiellement liée

Eau non-liée

Ex vivo

Water structure




44

0 10 20 30 40 50 60 70 80

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Rap

po

rt: T

ran

s/ga

uch

e

Humidité Relative (%)

ν C-C (1060+1130/1080 cm-1)

Ex vivo

Conformation des chaînes lipidiques


Structure secondaire de la kératine:

bande Amide I




45

Tension mécanique du SC Bande totale νOH

Ex vivo

VYUMVUHORE R. et al. J. Biophotonics, 2014

Courbure du substrat

due au séchage

sx

sy

Lamelle en verre

SC hydraté

séch

age


Hydration and mechanical stress


46

0 1 2 3 4 5 6 7 81.1

1.15

1.2

1.25

Rapport

: S

1060 /

S1080

Temps (h)

Ex vivo

01

23

45

67

81.1

1.1

5

1.2

1.2

5

Rapport : S1060 / S1080

Tem

ps (h

)

0 1 2 3 4 5 6 7 8 37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

Aire Sous Courbe 1652 (%)

Temps (h)

Hélices α

0 1 2 3 4 5 6 7 8 25

25.5

26

26.5

27

27.5

28

28.5

Aire Sous Courbe 1671 (%)

Temps (h)

Feuillets β

Conformation des lipides Structures secondaires des protéines

VYUMVUHORE R. et al. J. Biophotonics, 2014


Hydration and mechanical stress


47

Pics discriminants

Analyse statistique multivariée

2 3 4 5 6 7 8 9 10 11 12 13

2 3 4 5 6 7 8 9 10 11 12 13

Yva

r (T

ract

ion

(%

))

Ypred (Traction (%))

Régression PLS , Modèle + Traction 4%, 7%, 10%

y=1*x+4.556e-007 R2=0.9903

Interprétation structurale

État moléculaire lié au stress mécanique

VYUMVUHORE R. et al. J. Raman spectroscopy, 2013

Ex vivo

SC mechanical strains


48

Deux domaines

zone élastique

zone plastique

Compacité des lipides

Structure secondaire des protéines

VYUMVUHORE R. et al. J. Raman spectroscopy, 2013

SC mechanical strains


49

Analyse multi-paramétrique du SC

1. Identification des relations entre les différents paramètres

du Stratum Corneum

2. Développement d’un outil multi-informationnel

Patients

pH : pH mètre

PIE : Tewl-mètre

Hydratation globale du SC : Cornéomètre

Composition lipidique : Chromatographies

Information moléculaire + profondeur : Raman

11 volontaires F sains

âgés de 57 à 62 ans

Analyse sur bras et

mollet

In vivo

VYUMVUHORE R. et al. JBO, 2014

Physiological status

“QR code” of the skin

50

∑

=

CER AG Chol PIE Hydr pH

Y1

Y2

Yn

Paramètres de la Peau

No

uve

aux

Ech

anti

on

s

BCER BAG BChol BPIE Bhydr BpH

Coefficients de Régression (B)

X1

X2

Xn

Nouveaux Spectres Raman

Xm : spectre moyen Xstd : écart-type des spectes Ym : moyenne de Y Ystd : écart-type de Y

Données centrées-réduites; besoin de:

Modèle de prédiction

VYUMVUHORE R. et al. JBO, 2014

Physiological status

“QR code” of the skin

51

Sources de peau

Peau animale

Peau humaine

Restriction Interdiction

Manque de reproductibilité

disponibilité

Restriction

Peau synthétique

Culture de keratinocytes

Raman et peau synthétique

HDR: 05 février 2014

52

Evaluation des peaux

Validité en tant que substits

Morphologie

marqueurs biomécaniques

Tests d’irritation

Tests de phototoxicité

Composition protéique

Composition lipidique

Perméabilité

Perméabilité PLUS ÉLEVÉE

Les classes lipidiques majoritaires

sont présentes

Comparaison de la composition et de

l’organisation des lipides



53

Composition des lipides

Composition globale

Cholestérol

esters de cholestérol

triglycérides

Acides gras

céramides

Peau humaine: bleu

Peau synthétique: noir



54

Distribution hétérogène des lipides

Imagerie Raman + NCLS

-Taille de l’image: 600*600 µm2

-Taille du pixel: 4 µm

-Pas: 20 µm

Kératine Cholestérol Cer. et acides gras

Stratum corneum

reconstruit



55

Coupes de SC: imagerie Raman + NCLS

Stratum corneum humain

Stratum corneum

synthétique

1. Lumière blanche

2. Kératine

3. Cholestérol

4. Cer. et acides gras



56

MERCI

GO RAIBH

MAITH AGAT URAKOZE

Analytical chemistry: from data (pre)-treatment optimization to data mining, data fusion and big data

Sana et Ali Tfai(y)li

Raman analyses: correlation with LC/GC/MS.., biometric data

pH, Hydration, TEWL,…

LC/

GC

/ M

S..

Physiopathological state: Diagnosis

… In vivo

Raman

59

LC-MS acquisitions: Different ionization modes (Data fusion?)

+ - OOO

OOO

OOO

SA

SA

SA

Laurent Imbert, PhD thesis, GCAPS,EA4041, 2012, Univ. Paris Sud

61

Data fusion and data mining

Perspectives: Data fusion between: • RPLC and NPLC in chromatography • Different ionization modes in mass spectrometry • Increase the separation dimensionality LCxLC MS… (new treatment approach) • Between Separation techniques, coupled mass spectrometry • Between several techniques: Raman, IR, separative techniques • Multi-block analysis (specific algorithms for data processing and fusion)?

Additional analysis will increase the time for data processing: other approaches

for data processing ?

Virtual data project (LAL): work on a cloud and save the image Buy cores (possible demand through a project in process)

62

Data storage

Perspectives: DATA ARE NOT CENTRALIZED. • centralize data • Data storage and management • Results storage and management •Generate our own databases

analytical chemistry: from data (pre)-treatment

Documents