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Real-Time Multivariate Detection from Single Cells Monitoring the Metabolism of Methylobacterium extorquens AM1

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Real-Time Multivariate Detection from Single Cells. Monitoring the Metabolism of Methylobacterium extorquens AM1. Overview. Microscale Life Science Center Methylobacterium extorquens AM1 Green Fluorescent Protein (GFP) as a transcriptional reporter Detection of respiration rates - PowerPoint PPT Presentation

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Page 1: Real-Time Multivariate Detection from Single Cells

Real-Time Multivariate Detection from Single Cells

Monitoring the Metabolism of Methylobacterium extorquens

AM1

Page 2: Real-Time Multivariate Detection from Single Cells

Overview

Microscale Life Science Center Methylobacterium extorquens AM1 Green Fluorescent Protein (GFP) as a

transcriptional reporter Detection of respiration rates Multi-variate detection of single cells

Page 3: Real-Time Multivariate Detection from Single Cells

MLSC

Funded by NIH CEGS To develop technologies for single cell

research Lab-on-a-chip modality

Page 4: Real-Time Multivariate Detection from Single Cells

Why Single Cells?

Variable of interest Bulk data represents

averages Averages may not

represent behavior of subpopulations

1 2 3 4 5 6 7

Singular Resonse50% response

Range of Response0

1

2

3

4

5

6

7

8

9

10

Intensity of Response

Potential Resonse Profiles for a Population

Page 5: Real-Time Multivariate Detection from Single Cells

Methylobacterium extorquens AM1

Gram- bacterium (like E. coli) Capable of growing on

methanol and multicarbon substrates (succinate)

Industrial interest for production of value added products

Page 6: Real-Time Multivariate Detection from Single Cells

periplasm

cytoplasm

MeOH

Methylotrophic Metabolism

Formaldehyde

Central Metabolism

(Methanol Dehydrogenase)

(Formaldehyde Activating Enzyme)

(Carbon Assimilation)

Page 7: Real-Time Multivariate Detection from Single Cells

Goals

Hypothesis: Behavior of single cells differ from that of

averaged populations Approach:

Develop and utilize technology to study single cells

Characterize single cells in contrast to populations

Page 8: Real-Time Multivariate Detection from Single Cells

Populations to Single Cells

Use GFP as a reporter of transcriptional activity Will reflect promoter activity

Observed GFP fluorescence during growth on methanol and succinate Observe in bulk and at the single cell level

Page 9: Real-Time Multivariate Detection from Single Cells

Green Fluorescent Protein

HO

O

N

N

N

OH

O

BA

HO

O

N

N

N

OH

O

HO

O

N

N

N

OH

HO

O

N

N

HOHO

O

N

N

N

OH

O

BA

First isolated from Aequorea victoria Emits fluorescence at 509nm

Coral is another source for many color variants

Page 10: Real-Time Multivariate Detection from Single Cells

Genetic ManipulationSuicide Vector

Chromosome

Chromosome

KanR GFPuv

Double Crossover Event

Red regions = homologous sequence

Page 11: Real-Time Multivariate Detection from Single Cells

Genetic Fusions

PMDH GFPuv

Transcriptional Fusion

•Methanol Growth Higher GFP expression•Succinate Growth Lower GFP expression

Page 12: Real-Time Multivariate Detection from Single Cells

FluorimetryGFPuv Accumulation During Growth

0

50

100

150

200

250

300

350

0.2 0.3 0.4 0.5 0.6 0.7 0.8

OD600nm

RFU

(509

nm)

MethanolSuccinate

Strovas et al. In preparation.

Page 13: Real-Time Multivariate Detection from Single Cells

Data can be used for the calculation of promoter activities

Is a gauge of gene transcription in bulk culture

Promoter activity dictated by multiple variables

Calculating Promoter Activities

Page 14: Real-Time Multivariate Detection from Single Cells

Equations for Modeling Promoter Activity

Leveau and Lindow, 2001

Non-fluorescent FP (n)

Fluorescent FP (f)

Dilution from Cell Division

Degradation

MaturationSynthesis

P m n

n f

Vmax nn + f + KM

Vmax fn + f + KM

Page 15: Real-Time Multivariate Detection from Single Cells

Establish RFU/O.D. 600nm plot P = fss*(1 + /m)

fss = RFU/OD600nm

= generation time m = maturation rate of GFP

Units are RLU/OD600nm*hr

Equations for Modeling Promoter Activity

Page 16: Real-Time Multivariate Detection from Single Cells

FluorimetryGFPuv Accumulation During Growth

0

50

100

150

200

250

300

350

0.2 0.3 0.4 0.5 0.6 0.7 0.8

OD600nm

RFU

(509

nm)

MethanolSuccinate

Strovas et al. In preparation.

349.1 +/- 82.59

264.3 +/- 10.27

Page 17: Real-Time Multivariate Detection from Single Cells

Single Cell Growth Assays

Observed growth of single cells

Determined divisions rates

Measured fluorescence content

Page 18: Real-Time Multivariate Detection from Single Cells

Single Cell Growth Assays

Video using LSM software

Page 19: Real-Time Multivariate Detection from Single Cells

LSM Experiments

Single Cell Growth Profile

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 5 10 15 20

Time (hrs)

Cel

l Len

gth

( m

)

Strovas et al. In preparation.

Page 20: Real-Time Multivariate Detection from Single Cells

LSM Experiments

Single Cell Growth Profile

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 5 10 15 20

Time (hrs)

Cel

l Len

gth

( m

)

Strovas et al. In preparation.

0.55m/hr

0.73 m/hr

Page 21: Real-Time Multivariate Detection from Single Cells

LSM Experiments

Division Times for Growth on Succinate

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

Tim

e (h

rs)

Distribution of Division Times During Growth on Succinate

0

2

4

6

8

10

12

14

16

18

20

Time (hrs)

Fre

qu

ency

Strovas et al. In preparation.

3.12 +/- 0.55 hrs (N = 115)

Page 22: Real-Time Multivariate Detection from Single Cells

LSM Experiments

Divisions Times During Methanol Growth

0

1

2

3

4

5

6

7

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191

Tim

e (h

rs)

Distribution of Division Times During Growth on Methanol

0

5

10

15

20

25

Time (hrs)

Fre

qu

ency

Strovas et al. In preparation.

3.73 +/- 0.63 hrs (N = 195)

Page 23: Real-Time Multivariate Detection from Single Cells

LSM ExperimentsSingle Cell Growth on Succinate

Strovas et al. In preparation.

1400

1500

1600

1700

1800

1900

2000

2100

2200

0 0.2 0.4 0.6 0.8 1 1.2

Growth Rate (m/hr)

RF

U

1400

1500

1600

1700

1800

1900

2000

2100

2200

1.5 2 2.5 3 3.5 4 4.5 5

Area (m^2)

RFU

Page 24: Real-Time Multivariate Detection from Single Cells

LSM ExperimentsSingle Cell Growth on Methanol

Strovas et al. In preparation.

1800

1900

2000

2100

2200

2300

2400

2500

0 0.2 0.4 0.6 0.8 1 1.2

m^2/hr

RF

U

1800

1900

2000

2100

2200

2300

2400

2500

1.5 2 2.5 3 3.5 4 4.5

Area (m^2)

RF

U

Page 25: Real-Time Multivariate Detection from Single Cells

LSM Experiments

Succinate -> MeOH

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

0 5 10 15 20 25 30 35

Time (hrs)

Sin

gle

Cel

l RF

U/

m^2

Methanol -> Succinate

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

0 5 10 15 20 25 30 35 40

Time (hrs)

Sin

gle

Cel

l RF

U/

m^2

Single Cell Carbon Shifts

Succinate: 1993.15 +/- 468.14 RFU/m^2 (N = ~1000)Methanol: 3075.30 +/- 243.35 RFU/m^2 (N = ~1000)

Strovas et al. In preparation.

Page 26: Real-Time Multivariate Detection from Single Cells

GFPuv is a viable reporter in M. extorquens AM1

Data averages obscure subpopulation dynamics

Populations to Single Cells

Page 27: Real-Time Multivariate Detection from Single Cells

Measuring Respiration Rates

Measured respiration rates from bulk cultures of M. extorquens AM1

Utilized Pt-porphyrin doped beads that are an inverse sensor of [O2]

Signals acquired are phosphorescent lifetimes

Samples and beads were sealed in 4ml cuvette and monitored over time

Page 28: Real-Time Multivariate Detection from Single Cells

Fluorescence Phosphorescence

Intersystemcrossing

Absorption

Quenching

O2

Ene

rgy

Singlet Excited State

TripletExcited State

Bulk Respiration rates

Page 29: Real-Time Multivariate Detection from Single Cells

Light Dark

Io(1 – e-Kt) Ioe-Kt

a

b

Log(b/a) = Lifetime of decay

Bulk Respiration rates

Page 30: Real-Time Multivariate Detection from Single Cells

y = 5160.2x + 1.0671

R2 = 0.9978

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.0001 0.0002 0.0003 0.0004 0.0005

Mol O/L

To /

T

0

10

20

30

40

50

60

70

Life

times

(s)

Bulk Respiration rates

Strovas and Dragavon et al. J. Environ Microbiol. (accepted)

Page 31: Real-Time Multivariate Detection from Single Cells

Bulk Respiration rates

Strovas and Dragavon et al. J. Environ Microbiol. (accepted)

B

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0 10 20 30 40 50 60Time (min)

Mol

O/L

A

0

10

20

30

40

50

60

0 10 20 30 40 50 60Time (min)

Lif

etim

es (

sec)

Respiration rate (Mol O/min*cell e-17) Methanol = 5.4 +/- 0.74

Succinate = 3.8 +/- 0.89

Page 32: Real-Time Multivariate Detection from Single Cells

Multi-variate detection from single cells

Utilize multiple fluorescent proteins as transcriptional probes

Measure respiration rates as a gauge of metabolic activity and cell health

Page 33: Real-Time Multivariate Detection from Single Cells

Methylotrophic Metabolism

GFP

YFP

RFP

Methanol Oxidation

Formaldehyde Oxidation

Carbon Assimilation

Central Metabolism

Page 34: Real-Time Multivariate Detection from Single Cells

Current Approach

Aqueous phaseHydrophobic Phase

Hydrophobic Phase

Oil water separation for spatial isolationUtilize 50-100m square capillariesUse free floating porphyrin beads

Page 35: Real-Time Multivariate Detection from Single Cells

Oil and Water

250m capillary4nL aqueous volumes

Page 36: Real-Time Multivariate Detection from Single Cells

End Goals

Achieve single respiration rate detection Measure gene expression in single cells with

three fluorescent proteins Use all four measurements as a

comprehensive analysis of M. extorquens AM1 response to growth on methanol and succinate

Page 37: Real-Time Multivariate Detection from Single Cells

Acknowledgements

Dr. Mary Lidstrom MLSC The Lidstrom Lab

Dr. Joseph Chao Dr. Mark Holl Joe Dragavon Tim Molter Cody Young Linda Sauter Tylor Hankins Angela Burnside