Cell Systems Science Group NIST

1. Cell Systems Science GroupNIST Summary of activities for McKay Lab Feb 2010

2. Cell responses measured on the cell-by-cell level provide more information Single cell techniques Average expression • Gene expression arrays • Proteomics • Western blots • Etc • Provides average response over a population of cells • Flow cytometry • Cell by cell imaging • Allows assessment of probability of rare events. • Provides correlations between markers that are directly related within a single cellular entity. • Provides measure of biologically relevant variability within a population, which can provide mechanistic information.

3. Variability is inherent to cell populations • This is a stable single cell clone from NIH-3T3 transfected with a destabilized EGFP reporter driven by the tenascin-C gene promoter. • Spread area, other morphological characteristics, GFP expression…these are some of the dynamic parameters of interest.

4. Measuring Population Distributions • This Approach Allows: • Quantitation of fluorescence in every cell, even the cells which aren’t bright. • Validation that each fluorescent object counted is a cell. • Simultaneous quantitation of multiple cellular characteristics (shape, biomarker(s) expression, cell-cell proximity). • Automated microscopy, which allows collection of data from hundreds of cells in an unbiased manner. Texas red maleimide used to label cytoplasmic proteins to provide and easy to generate mask of the cell outline Nuclear stain (usually DAPI) to validate bright object as a cell Texas red mask used to quantify biomarker expression (eg. GFP or antibody marker) Elliott, et al. Cytometry 2002

5. Nonfibrillar collagen Fibrillar collagen Relative number of cells Relative number of cells 5 Replicate Films 5 Replicate Films 1 2 3 Relative number of cells 100 frames, ~1000 cells 4 5 6 Cell Area (mm2) Cell Area (mm2) 7 8 9 Cell Area (mm2) Measuring Population Distributions:Measurement Uncertainty vs. Biological Uncertainty • Multiple measurements of populations and random sampling define measurement uncertainty. • The distribution of responses is biological uncertainty, ie, genetically identical cells can have a range of responses • Quantifying biological uncertainty requires knowledge of measurement uncertainty.

6. 10x 5x • Systems level information can be obtained by examining the distributions in responses. • In this case we use the cell volume distribution to estimate the rates of cell growth (and the noise in cell growth rates) and rates of cell division (and the noise in cell division rates) in the population. • Cell volume measurements are easy to make and routine in many labs. Interpreting population distributions: Cell volume distributions Halter et al., (2009) J. Theor. Biol. 257, 124

7. Interpreting population distributions : Cell volume distributions • MODEL ASSUMPTIONS: • Volume changes at a constant rate for individual cells during the cell cycle (Conlon and Raff, J. of Biol., 2003) • Individual cells can have different growth rates. The population of cells exhibits a normal distribution of growth rates • At division, each cell divides exactly in half • Cell cycle times are normally distributed Provides a mechanistic understanding of the observed distribution and increases confidence in the measurement. Halter et al., (2009) J. Theor. Biol. 257, 124

8. Interpreting population distributions : Cell volume distributions Use of a drug to increase cell cycle time Mean Generation Time Aphidicolin 0 nm 50 nm 100 nm 29 h 36 h 50 h Model fits data well and provides physical parameters that define a biological state.

9. Interpreting population distributions : Cell volume distributions Proliferating cells Senescent cells? Proliferating cells? Example: Distributions of volumes of Mesenchymal Stem Cells with passage collaboration with FDA (Steve Bauer, CBER) passage 3 HMSC p. 4-6 Relative cell number passage 8 Cells are deviating from initial model Cell Volume (mm3) • Change in distribution of cell volumes indicates changes in fundamental metabolic state of cells (ie, change in growth rates and/or division times). • Is this kind of measurement potentially useful for QCing cell lines and comparing their state at different times and in different labs?

10. Interpreting population distributions : Cell volume distributions Another example: Lung (Endothelial?) Cells isolated from neonatal animal 2 Cell Volume Distributions 2 1 1 fraction of cells volume um3 Morphology 1 round shape 2 spindly shape • Cell culture changes phenotype changes with passage number AL Mancia, et al, in preparation, collaboration with Hollings Marine Laboratory, SC

11. Relative Cell Number mean Relative GFP Fluorescence More about distributions: we are studying how to interpret distributions to provide knowledge about underlying mechanisms. Gaussian-like Response Distribution of Cell Areas Cell Morphology Measurement Average mean area (n=4) =647±44 mm2 (CV=0.07) Relative Cell Number mean Cell Area (mm2) Highly Non-Gaussian Response Distribution of GFP GFP Fluorescence Measurement dsEGFP is ligated to the promoter for the ECM protein tenascin-C Average mean intensity (n=4) =73297±8300 (CV=0.11) Cell spread area and TN-C promoter activity have very different distributions - what is the relationship between the mechanisms that results in these distributions?

12. More about distributions A D r= 0.59 r= 0.22 800000 800000 600000 600000 Fibrillar collagen 400000 400000 200000 200000 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 B E r= 0.64 Relative fluorescence intensity r= 0.18 Low concentration collagen 800000 800000 600000 600000 400000 400000 200000 200000 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 fibronectin F C r = 0.65 r not significant 800000 800000 Cell area 600000 600000 400000 400000 200000 200000 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 m 2 Cell area ( m ) Cell by cell analysis allows examination of correlations between multiple cell features such as tenascin promoter activity (i.e.GFP expression) and cell spreading. Data averaged over entire populations suggest a correlation. (has been suggested often in the literature). Cell by cell data indicate weak to no correlation Such data allow us to begin understanding how to interpret distributions and dissect pathway networks. Langenbach et al. (2006) BMC Biotechnol

13. Live Cell Microscopy While steady state data are good, and distribution data are important, they don’t tell us anything about the fate of any individual cell Movie: >62 hours, phase contrast on left, GFP fluorescence on right • Cells are expressing GFP linked to the promoter for the ECM protein tenascin-C. • Cells express different amounts of GFP. • Individuals express different amounts over time. 36 fields @ 15 min intervals

14. Live Cell Microscopy To make analysis easier, fibronectin is placed on surface in an array of spots to keep cells from moving around a lot. To quantitatively track promoter activity: 1) Measure GFP Degradation Rates in Individual Cells Halter et al. Cytometry A. 2007 Oct;71(10):827-34.

15. Live Cell Microscopy • GFP intensities of identified single cells are quantified from the timelapse image sets • To measure GFP degradation, cycloheximide (100µg/ml) is added 2 hrs after image collection begins (blocks 60s ribosome) Samples from Analysis of 500+ single cells

16. Live Cell Microscopy Analysis of time dependence of loss of GFP intensities Degradation rate constants calculated by fitting each trajectory to a single exponential Control expt: Extent of photobleaching determined from multiple exposure sequences (phase and GFP)

17. Live Cell Microscopy Relative Cell Number mean Relative GFP Fluorescence Variability in GFP degradation rates is much smaller than variability in GFP intensities over the population GFP Intensity= promoter activity + GFP degradation GFP degradation rate constants GFP-TN-C promoter activity CV ~0.3 CV ~1.2 (distribution is stable over multiple passages) • Variability of GFP intensities is much higher than variability in degradation rates. • Does the shape of this distribution tell us something about how the TN-C promoter is regulated? • The variation in cellular GFP is determined by more than the variability in degradation rates.

18. Live Cell Microscopy Can we understand the source of cell-to-cell variability, and get more insight into how to predict cell fate, by examining changes in individual cells in time? This automated segmentation routine was validated with manual segmentation. • Track individual cells in each frame of phase contrast images over time. For each cell, note time of division, and follow each new cell until it begins to divide again (rounding indicates entrance into mitosis). • Quantify how GFP intensities change with time as a function of position in the cell cycle. • Examine correlation between phenotype (migration rate, division time) and gene expression. • Develop analysis to probe lineage effects and epigenetic gene regulation

19. Live Cell Microscopy GFP fluorescence intensity Time from division (hrs) Averaging over all cells Relative GFP fluorescence intensity fraction of cell cycle GFP-TN Promoter expression in single cells over the cell cycle • Each line represents a single cell and its GFP intensity change as it progresses through the cell cycle. • In some cells GFP production increases during cycle, in some it decreases, and in some it remain relatively unchanged. • These data show 1)TN-C promoter is upregulated, on average, toward the end of the cell cycle…2)suggesting that TN expression is associated with cell division… 3) but since this is not observed in every cell, upregulation of TN-C is not required for cell division. • What stochastic processes in gene regulation give rise to this variability? What environmental parameters might change the probability or characteristics of TN-C upregulation? • How does the sum of individual cell behaviors give rise to the stable distribution of GFP intensities? GFP fluorescence intensity fraction of progression through cell cycle

20. Another issue about quantitative cell-by cell analysis on fixed cells: Validate assays. • rigorously determine appropriate fixation conditions • validate with an orthogonal measurements. Y27632 H D Fn MLC-P MHC 3h after plating In situ cell-by-cell analysis Fixing time- 1-16h MBS Relative GFP Intensity DSSP PFA Time (min) GTP-RhoA ROCK MLC - P Contractility, morphology, migration, proliferation Bhadriraju et al BMC-Cell Biology 2007

21. Investigator name Antibody Protein target fluorophore Cell_line_identifier Experiment type Databasing and searching image dataHow do we effectively use these data to develop and test hypotheses? 2. Relationships between search terms can be explored. 1. Data must be accompanied by sufficient information on conditions and protocols 3. Semantic approaches organize data in a hierarchical fashion, and allow selection of search terms from a list to make searching unambiguous.

22. Nanoparticle Protein nanosphere Protein nanosphere NP DNA Reporter genes In vitro/in vivo Imaging ProbesFabrication of protein nanospheres that carry multiple modality probes for tracking stem cells in culture and in vivo. • Multiple protein units • fluorescently labeled • partitioned during cell division • Initially targeted to parent cell • reports on proximity of parent to daughter cells • calibration of expressed fluorescent proteins • multimodal imaging 1st, 2nd, 3rd, etc. generation labeling • Encodes: • several distinguishable fluorescent proteins • bioluminescent gene products • stem cell differentiation markers • small peptides: metal/semiconductor binding • intracellular trapping Multimodal imaging nanoplatform