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MII. end. entry. MI. Single Cell Informatics. Motivation. Some phenomena can only be seen when filmed at single cell level! (Here: excitability). Outline. Motivation: Similar cells respond differently most methods don’t see that: uarrays, gels, blots Possible reasons:

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Presentation Transcript
motivation
Motivation

Some phenomena can only be seen when filmed at single cell level! (Here: excitability)

outline
Outline
  • Motivation: Similar cells respond differently
    • most methods don’t see that: uarrays, gels, blots
  • Possible reasons:
    • The cells are actually not similar
    • molecular “noise”
  • How can we tell? Look at single cells!
    • Imaging
    • Image analysis
    • Statistical analysis/model fitting
  • Examples
    • Yeast meiosis
    • Apoptosis
    • Competence in bacteria
decision making in cells switching from one state to another

filamentation

apoptosis

differentiation

sporulation

Decision making in cells:switching from one state to another

signal

cell state change

Similar cells respond differently to the same signal

What can lead to variable responses?

The cells differ in some aspects (type, size, …)

Molecular “noise”

how can we study this

Most methods average over cells

end

MII

entry

westerns

MI

microarrays

meiosis marker

How can we study this?

Need to follow manysingle cellsover time along the process

But how do we track molecular levels in living cells?

the gfp revolution
The GFP revolution
  • Allows tagging and monitoring a specific protein in vivo
  • Different variants/colors allow multiple tagging in the same cell.
example yeast entry into meiosis
Example: Yeast entry into meiosis

Difference between cells: time of decision

starvation

meiosis

meiosis & sporulation

yeast have a decision point

MI

MII

end

replication

meiosis

commitment

new nutrients

Yeast have a decision point

cell cycle

starvation

  • When do cells commit?
  • What controls this timing and variability?
regulation of entry into meiosis
Regulation of entry into meiosis

nitrogen

signals

acetate

glucose

Ime1

master regulator

early genes

transcriptional program

middle genes

We can fluorescently tag different levels along this pathway!

late genes

approach live cell imaging

early gene

YFP

30-50 positions, every 5-10 min (1000-4000 cells/experiment)

t

DIC images

YFP images

rich medium

Custom image analysis

poor medium

Annotation of events+more

Approach: live cell imaging
  • Controlled temperature, flow
image analysis steps
Image analysis steps
  • Cell segmentation
  • Cell tracking
  • Fluorescent signal measurement

These have to be tailored to cell type, motility, signal location, etc.

example image analysis for yeast nuclear signals

1) Identify Cells

3) Identify *FP “blobs”

2) Map cells between time points

4) Map blobs to cells

identified

# cells

cell

mapped

t

t

Example: Image analysis for yeast nuclear signals
results of image analysis

MII

MI

Results of image analysis
  • Intensities
  • Num of signals
  • Distance
  • Cell Size
  • Large number of single cells over time
  • Automated experiment + post-process
  • In silico synchronization,elutriation

YFP level

Time

5) Event timing detection

data extraction timing distributions

early genes↑

tearly

Data extraction: timing distributions

tMI

tMII

Time

“wait”

progress

tearly = onset time of early meiosis genes

two color use for event annotation

last mitosis

tearly

6.3±2.3hr

11.1±2.2hr

Two-color use for event annotation

Adding another fluorescent marker allows annotating more events.

Hypothesis: meiosis entry is determined by last mitosis

Htb2-mCherry ▄▄Dmc1-YFP ▄▄

t

nutrient shift

Conclusion: Countdown to meiosis occurs in parallel to the cell cycle

t

two colors level vs timing

tearly

Two colors: level vs. timing

regulator

Regulator promoter activity

early genes

t early

promoter activity

Regulator promoter activity affects entry time

Molecular “noise” → spread in decision times

model of causative effects
Model of causative effects

cell size

cell cycle phase

40%

nutrient signals

pIME1 activity

35%

onset time of early genes

80%

decision time

Large number of single cell measurements let us build a model of causative links between molecular levels, phenotypes, event timings.

comparing two promoter activities
Comparing two promoter activities

The time tracks verify the circuit model:

The red and green genes are anti-correlated

summary
Summary
  • Similar cells behave differently
    • molecular noise, non-molecular factors
  • Quantitative fluorescent time lapse microscopy
    • Follow single cells over time
    • Track protein levels/promoter activities in them
  • Test dynamics of circuits (network motifs)
  • Test dependencies between molecular levels, event times, morphological properties