1 / 14

Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR

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

addison
Download Presentation

Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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. Expression Profiling for CMP

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

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

  11. 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.

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

  13. 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

  14. Thank you • Questions?

More Related