Single Cell Informatics

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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:

– 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

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Decision making in cells:switching from one state to another

sporulation

apoptosis

differentiation

filamentation

Similar cells respond differently to the same signalWhat can lead to variable responses?1.The cells differ in some aspects (type, size, …)2.Molecular “noise”

signal

cell state change

How can we study this?

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meiosis marker

Need to follow many single cells over time along the process

Most methods average over cells

microarrays

westerns

But how do we track molecular levels in living cells?

The GFP revolution

• Allows tagging and monitoring a specific protein in vivo

• Different variants/colors allow multiple tagging in the same cell.

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Example: Yeast entry into meiosis

meiosis

Difference between cells: time of decision

starvation

meiosis & sporulation

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meiosis

MI MIIreplication end

Yeast have a decision point

cell cycle

starvation

new nutrients

commitment

When do cells commit?What controls this timing and variability?

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Regulation of entry into meiosis

Ime1

early genes

middle genes

late genes

acetate nitrogen glucosesignals

master regulator

transcriptional program

We can fluorescently tag different levels along this pathway!

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poor medium

•Controlled temperature, flow

Approach: live cell imaging30-50 positions, every 5-10 min (1000-4000 cells/experiment)

DIC images YFP imagest

Custom image analysis

early gene YFP

rich medium

Annotation of events+more

Image analysis steps

• Cell segmentation• Cell tracking• Fluorescent signal measurement

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

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Example: Image analysis for yeast nuclear signals

1) Identify Cells 3) Identify *FP “blobs”

2) Map cells between time points

t

# ce

lls

mapped

identified

4) Map blobs to cells

t

cell

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• Large number of single cells over time• Automated experiment + post-process• In silico synchronization,elutriation

Time

YFP

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Results of image analysis Intensities

Num of signals

Distance

Cell Size

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Data extraction: timing distributions

tMI tMII

early genes↑

tearly Timetearly = onset time of early meiosis genes

“wait” progress

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Two-color use for event annotation

tnutrient shift

11.1±2.2hr

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

Htb2-mCherry ▄▄Dmc1-YFP ▄▄

tearlylast

mitosis

6.3±2.3hr

Adding another fluorescent marker allows annotating more events.

Hypothesis: meiosis entry is determined by last mitosis

Two colors: level vs. timing

promoter activity

t early

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Regulator promoter activity affects entry time

Molecular “noise” → spread in decision times

early genes

Regulator promoter activity

tearly

regulator

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Model of causative effects

decision time

cell size

onset time of early genes

pIME1 activity

40%

35%

80%

nutrient signals

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

Comparing two promoter activities

The time tracks verify the circuit model:The red and green genes are anti-correlated

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