1. Single Cell Variability The contribution of noise to biological systems

2. Outline Background Why single cells? Noise in biological systems Cool studies Conclusions

3. Background – Microscale Life Sciences Center Funded by NIH CEGS To develop technologies for single cell research Lab-on-a-chip modality Collaborative approach

4. Why Single Cells? Variable of interest Bulk data represents averages Averages may not represent behavior of subpopulations

5. Why Single Cells? – One Example =?

6. Why Single Cells? – One Example Gaussian Bimodal

7. Why Single Cells? – One Example Gaussian Bimodal =

8. Variability in populations – What we know so far Population response is governed by: Variability at the single cell level Subpopulations Noise inherent to any complex system

9. Noise in biological systems “Chemical analysis are affected by two types of noise: chemical noise and instrumental noise”* What is chemical noise? What is instrument noise? In general: Noise = σ/mean *Principals of Instrumental Analysis. 1998. Skoog, Holler, and Nieman.

10. Noise in biological systems “Chemical analysis are affected by two types of noise: chemical noise and instrumental noise”* What is chemical noise? Fluctuations in Temp, concentration, vibrations, light, gradients, etc What is instrument noise? Composite of noise from individual components of a system *Principals of Instrumental Analysis. 1998. Skoog, Holler, and Nieman.

11. Noise in biological systems Noise in a nutshell Chemical noise = intrinsic (inherent) variability Instrument noise = extrinsic (global) variability Will show examples from literature and my research

12. Noise in biological systems Intrinsic noise: Inherent Order of events Entropy Binding of substrate to enzyme

13. Noise in biological systems Extrinsic noise: Concentrations of system components Regulatory proteins, polymerase Chemical flux through components Enzyme activities Substrate to product conversion Global effects of all components

14. Extrinsic Noise – cell growth Global variability that is a composite of intrinsic noise from each component of a system. First observed by Kelly and Rahn in 1932* Measured 2-3 fold variation in the division times of single E. coli cells No correlation between division time of mother cell and division time of either of the two daughter cells *Kelly & Rahn, J. Bacteriol., 1932

15. Extrinsic Noise – cell growth *Kelly & Rahn, J. Bacteriol., 1932 Cells imbedded in soft agar

16. Extrinsic Noise – cell growth Reservoir Lung (50ft tubing) Environmental Chamber Waste Air tank Pump Light Source hv Objective vent

17. Extrinsic Noise LSM Data

18. Strovas et al. In preparation. Single Cell Growth over Time Extrinsic Noise

19. Single Cell Growth over Time Strovas et al. In preparation. 0.55  m/hr 0.73  m/hr Extrinsic Noise

20. Strovas et al. In preparation. 3.12 +/- 0.55 hrs (N = 115) 3.73 +/- 0.63 hrs (N = 195) Succinate Methanol Over 2 fold range in division rates Extrinsic noise differs based on carbon source Extrinsic Noise

21. Intrinsic Noise - Transcription The noise inherent to a system component What are components of a biological system? Focus on noise in transcription How does one measure transcription rates?

22. Intrinsic Noise - Transcription Promoter Activities via Transcriptional Fusions light Plac

23. Intrinsic Noise - Transcription http://meds.queensu.ca/~mbio318/EXTRA_MATERIAL.html

24. Intrinsic Noise - Transcription http://meds.queensu.ca/~mbio318/EXTRA_MATERIAL.html

25. Intrinsic Noise Elowitz et al, 2002 Elegant experiment to show intrinsic noise Made two transcriptional fusions in E. coli: Plac-YFP Plac-CFP Observed YFP and CFP fluorescence w/ and w/out IPTG present

26. Intrinsic Noise Elowitz et al, Science, 297, 1183-1186, 2002

27. Intrinsic Noise Fluorescence vs. Growth rate Strovas et al. In preparation. Succinate Methanol R 2 = 0.0257 R 2 = 0.0049

28. Succinate -> Methanol Carbon Shift 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. Intrinsic Noise

29. Noise in biological systems - Summary Variability in biological systems at the population and single cell level is governed by intrinsic and extrinsic noise. Extrinsic noise dominates variability as a whole Intrinsic noise dominates the variability observed from individual components of a system Intrinsic noise can be independent of extrinsic noise

30. Now what? Since noise in biological systems can govern biological variability, can’t we cure cancer and move on? No! Like all complex systems we must characterize them! What we know is just the tip of the iceberg!

31. Nifty stuff – Balaban et al. Bacterial persistence as a phenotypic switch Balaban et al. 2004. Science. 305: 1622-1625 Demonstrated the ability of single cells from an E. coli clonal population to survive treatment with antibiotics.

32. Nifty stuff – Balaban et al.

34. Persister cells were susceptible to subsequent antibiotic treatment Heterogeneity (variance) within the population attributed to presence of persisters Why can persisters survive and how is it useful? What type of noise governs this response?

35. Nifty stuff – Raser and Shea Control of stochasticity in eukaryotic gene expression Raser and Shea. 2004. Science. 304: 1811-1814 Used similar methods to Elowitz et al. only using yeast. Suggests that noise is an evolvable trait that can help balance fidelity and diversity

36. Nifty stuff – Raser and Shea Time course during phosphate starvation

37. Showed extrinsic noise dominates total noise in yeast Intrinsic noise only contributed 2-20% Transcription in eukaryotes has been described as pulsative Results in variable mRNA levels from cell to cell Causes phenotypic diversity in clonal yeast populations Nifty stuff – Raser and Shea

38. Conclusions Population averages skew the underlying contributions of subpopulations Subpopulations are the result of variable cellular response within a clonal population Cellular variability arises from intrinsic noise, but governed by extrinsic noise Cellular variability allows for adaptation to environmental perturbations