1. Single Cell Informatics MI MII entry end

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

3. 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

4. 4 Decision making in cells: switching from one state to another sporulation apoptosis differentiation filamentation Similar cells respond differently to the same signal What can lead to variable responses? 1.The cells differ in some aspects (type, size, …) 2.Molecular “noise” signal cell state change

5. How can we study this? MI MII entry end 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?

6. The GFP revolution Allows tagging and monitoring a specific protein in vivo Different variants/colors allow multiple tagging in the same cell.

7. 7 Example: Yeast entry into meiosis meiosis Difference between cells: time of decision starvation meiosis & sporulation

8. 8 meiosis MIMII replication end Yeast have a decision point cell cycle starvation new nutrients commitment When do cells commit? What controls this timing and variability?

9. 9 Regulation of entry into meiosis Ime1 early genes middle genes late genes acetate nitrogen glucose signals master regulator transcriptional program We can fluorescently tag different levels along this pathway!

10. 10 poor medium Controlled temperature, flow Approach: live cell imaging 30-50 positions, every 5-10 min (1000-4000 cells/experiment) DIC images YFP images t Custom image analysis early geneYFP rich medium Annotation of events+more

11. Image analysis steps Cell segmentation Cell tracking Fluorescent signal measurement These have to be tailored to cell type, motility, signal location, etc.

12. 12 Example: Image analysis for yeast nuclear signals 1) Identify Cells 3) Identify *FP “blobs” 2) Map cells between time points t # cells mapped identified 4) Map blobs to cells t cell

13. 13 Large number of single cells over time Automated experiment + post-process In silico synchronization,elutriation 5) Event timing detection Time YFP level MIMII Results of image analysis uIntensities uNum of signals uDistance uCell Size

14. 14 Data extraction: timing distributions t MI t MII early genes↑ t early Time t early = onset time of early meiosis genes “ wait ” progress

15. 15 Two-color use for event annotation t nutrient shift 11.1±2.2hr Conclusion: Countdown to meiosis occurs in parallel to the cell cycle Htb2-mCherry ▄▄ Dmc1-YFP ▄▄ t t early last mitosis 6.3±2.3hr Adding another fluorescent marker allows annotating more events. Hypothesis: meiosis entry is determined by last mitosis

16. Two colors: level vs. timing promoter activity t early 16 Regulator promoter activity affects entry time Molecular “noise” → spread in decision times early genes Regulator promoter activity t early regulator

17. 17 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.

18. Comparing two promoter activities The time tracks verify the circuit model: The red and green genes are anti-correlated

19. 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