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

2. Cell responses measured on the cell-by-cell level provide more information 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. Single cell techniques

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 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) 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. Elliott, et al. Cytometry 2002

5. Measuring Population Distributions : Measurement Uncertainty vs. Biological Uncertainty Cell Area (  m 2 ) Relative number of cells 1 2 3 6 9 87 45 100 frames, ~1000 cells 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. Nonfibrillar collagen Relative number of cells Cell Area (  m 2 ) 5 Replicate Films Relative number of cells Cell Area (  m 2 ) 5 Replicate Films Fibrillar collagen

6. Interpreting population distributions: Cell volume distributions 10x 5x Halter et al., (2009) J. Theor. Biol. 257, 124 1.Systems level information can be obtained by examining the distributions in responses. 2.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. 3.Cell volume measurements are easy to make and routine in many labs.

7. 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 Halter et al., (2009) J. Theor. Biol. 257, 124 Provides a mechanistic understanding of the observed distribution and increases confidence in the measurement. Interpreting population distributions : Cell volume distributions

8. Cell type a t (h)  t (h) r (μm 3 /h)  r (μm 3 /h) (r/r)e(r/r)e NIH 3T3 19.5  0.7 5.9 87  429  60.34  0.07 A10 29  3 8.7 140  20 52  70.37  0.07 A10 (50 nM) 36  3 10.8 139  1557  60.41  0.06 A10 (100 nM) 50  8 15 115  2351  100.43  0.12 Use of a drug to increase cell cycle time 0 nm 50 nm 100 nm 29 h 36 h 50 h Aphidicolin Mean Generation Time Interpreting population distributions : Cell volume distributions Model fits data well and provides physical parameters that define a biological state.

9. Example: Distributions of volumes of Mesenchymal Stem Cells with passage 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? Cell Volume (  m 3 ) Cells are deviating from initial model HMSC p. 4-6 Relative cell number Proliferating cells Proliferating cells?Senescent cells? passage 3 passage 8 collaboration with FDA (Steve Bauer, CBER) Interpreting population distributions : Cell volume distributions

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

11. More about distributions: we are studying how to interpret distributions to provide knowledge about underlying mechanisms. Cell Area (  m 2 ) Relative Cell Number Gaussian-like Response Distribution of Cell Areas Cell Morphology Measurement GFP Fluorescence Measurement =647±44  m 2 =73297±8300 Average mean intensity (n=4) Average mean area (n=4) (CV=0.07) (CV=0.11) Highly Non-Gaussian Response Distribution of GFP Relative GFP Fluorescence Relative Cell Number mean dsEGFP is ligated to the promoter for the ECM protein tenascin-C 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. Cell by cell analysis allows examination of correlations between multiple cell features such as tenascin promoter activity (i.e.GFP expression) and cell spreading. Langenbach et al. (2006) BMC Biotechnol 0 200000 400000 600000 800000 01000200030004000 0 200000 400000 600000 800000 01000200030004000 0 200000 400000 600000 800000 01000200030004000 Cell area (  m 2 ) r= 0.22 r= 0.18 r not significant 0 200000 400000 600000 800000 01000200030004000 0 200000 400000 600000 800000 01000200030004000 0 200000 400000 600000 800000 01000200030004000 r= 0.59 r= 0.64 r = 0.65 A B C D E F Relative fluorescence intensity Cell by cell data indicate weak to no correlation Data averaged over entire populations suggest a correlation. (has been suggested often in the literature). Fibrillar collagen Low concentration collagen fibronectin Cell area More about distributions Such data allow us to begin understanding how to interpret distributions and dissect pathway networks.

13. 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 are expressing GFP linked to the promoter for the ECM protein tenascin-C. Cells express different amounts of GFP.Cells express different amounts of GFP. Individuals express different amounts over time.Individuals express different amounts over time. 36 fields @ 15 min intervals Live Cell Microscopy

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

15. 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 Live Cell Microscopy

16. 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) Live Cell Microscopy

17. Variability in GFP degradation rates is much smaller than variability in GFP intensities over the population GFP Intensity= promoter activity + GFP degradation 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. Relative GFP Fluorescence Relative Cell Number mean CV ~1.2 CV ~0.3 GFP-TN-C promoter activity GFP degradation rate constants (distribution is stable over multiple passages) Live Cell Microscopy

18. 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? 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 This automated segmentation routine was validated with manual segmentation. Live Cell Microscopy

19. 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? Time from division (hrs) GFP fluorescence intensity fraction of progression through cell cycle GFP fluorescence intensity Averaging over all cells fraction of cell cycle Relative GFP fluorescence intensity Live Cell Microscopy

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

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

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