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

Expression Profiling for CMP

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

Thank you

• Questions?