transcription factor profiling in individual hematopoietic progenitors by digital rt-pcr luigi...
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![Page 1: Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R](https://reader035.vdocument.in/reader035/viewer/2022062309/56649edb5503460f94beb461/html5/thumbnails/1.jpg)
Transcription factor profiling in individual hematopoietic
progenitors by digital RT-PCR
Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R. Quake
![Page 2: Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R](https://reader035.vdocument.in/reader035/viewer/2022062309/56649edb5503460f94beb461/html5/thumbnails/2.jpg)
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
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Early Progenitors in the Hematopoietic Lineage Tree
![Page 4: Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR Luigi Warren, David Bryder, Irving L. Weissman, and Stephen R](https://reader035.vdocument.in/reader035/viewer/2022062309/56649edb5503460f94beb461/html5/thumbnails/4.jpg)
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
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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
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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
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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
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Expression Profiling for CMP
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FACS
• Fractionation of CMP cells into flk2+ and flk2- subsets
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Digital Array Chip
• cDNA from individual HSCs
• Green: GAPDH
• Red: PU.1• Each well
captures ~0 or 1 template molecules
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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.
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Further Optimizations
• Threshold values• Reference endogenous controls– Weighted normalization of data
• mRNA vs protein turnover rate• Measurement noise
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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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Thank you
• Questions?