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

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

Early Progenitors in the Hematopoietic Lineage Tree

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

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

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

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

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

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

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.

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

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

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