1. Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R. Quake
Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR
Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R. Quake

2. Background Stem cell differentiation Chemical state machine
Sequencing logic implemented by cross-regulating transcription factors
State of the network realized in the abundance profile of these regulatory molecules
Transitions between states
Instability, stochastic fluctuation, external signals
Transcription factor PU.1
Cytokine receptor flk2
Housekeeping transcript GAPDH

3. Early Progenitors in the Hematopoietic Lineage Tree

4. Goals
Understand the behavior of transcriptional regulatory network for stem cell differentiation
Leads to understanding of development
Requires the ability to characterize network states quantitatively

5. Problems Network states cannot be characterized quantitatively
Current gene profiling methods not sensitive enough
Conventional gene expression assays
Stem cells not easily isolated in such quantities
Require thousands of cells’ worth of RNA as analyte
Population-average expression data provide an incomplete picture
Variations in network state determined by just a few phenotypic differences between cell types

6. Conventional PCR
Quantitation based on number of cycles required for dye fluorescence to reach given threshold
Exponential nature magnifies slight variations in amplification efficiency
Interassay comparisons only valid if gene-of-interest measurements are normalized to measurements on endogenous controls or synthetic standards

7. Solution Digital RT-PCR
Partition individual cDNA molecules into discrete reaction chambers before PCR amplification
Quantitation uses binary, positive/negative calls for each subreaction within partitioned analyte
Flow Cytometry
Reveals diversity in patterns of surface protein expression within populations of superficially similar cells

8. FACS
Fractionation of CMP cells into flk2+ and flk2- subsets

9. Digital Array Chip cDNA from individual HSCs Green: GAPDH Red: PU.1
Each well captures ~0 or 1 template molecules

10. Results Number of individual cells expressing PU.1
PU.1 expression up-regulated in CMP/flk2+
Down-regulated in CMP/flk2- cells and MEPs
Higher GAPDH expression in CMPflk2+cells.

11. Further Optimizations
Threshold values
Reference endogenous controls
Weighted normalization of data
mRNA vs protein turnover rate
Measurement noise

12. Future Application
Gene expression measurements can be made on an absolute, copy-number-per-cell basis
Sophisticated regulatory network analysis
Spread of public databases cataloguing cell-type-specific expression data
Refinement of taxonomies through single-cell survey approach

13. Thank you
Questions?