[2/10] We started this project with an interest in how cells give rise to other cells. We asked whether there were features in sequencing data, particularly single-cell RNA sequencing (scRNA-seq) data, that reliably could predict the order in which cells appear in a given tissue.
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[3/10] Surprisingly, we observed that a simple feature of scRNA-seq data, the number of genes expressed in a cell, decreases during differentiation. We saw this to be the case across multiple datasets, tissues, species, and scRNA-seq platforms.
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[4/10] In fact, we found that the number of genes expressed in a cell decreased across ontogeny (from zygote to the most mature cells in the body, e.g. neutrophils).pic.twitter.com/hWQxMg4Vli
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[5/10] We next asked whether gene counts could be a surrogate for chromatin accessibility, or the openness of a cell’s DNA. We analyzed ATAC-seq data and found that, like gene counts, chromatin accessibility decreased with differentiation.pic.twitter.com/rCY1fOUqXx
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[6/10] However, gene counts alone is a noisy measure of differentiation. We, therefore, developed CytoTRACE, a computational framework that leverages gene counts and similarity between single cells to improve the prediction of differentiation status in scRNA-seq data.pic.twitter.com/q9eSRmt7Z6
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[7/10] CytoTRACE can be applied across multiple datasets with cells at different stages of differentiation and to complex, multi-branched datasets. When combined with total RNA content, CytoTRACE can distinguish quiescent stem cells from active stem cells and progenitors.pic.twitter.com/tt6NWgO7ys
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[8/10] One of the main motivations behind developing CytoTRACE was to create a tool for rapid and accurate identification of both normal and cancer stem cells in humans. Cancer stem cells are cells in the tumor that drive tumor growth and are resistant to treatment.
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[9/10] We applied CytoTRACE to scRNA-seq data from human breast cancer samples kindly donated to us from patients. We found GULP1, a gene, predicted by CytoTRACE to be in cancer stem cells. When we removed GULP1 in patient samples, tumor growth was significantly reduced.pic.twitter.com/qhaQN1vV8S
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[10/10] We hope that CytoTRACE will expedite the discovery of therapeutically relevant stem cell populations in normal and diseased tissues. Annotated code, datasets used in this study, and a user-friendly interface to run CytoTRACE are available at https://cytotrace.stanford.edu .pic.twitter.com/zxrx4bEBdv
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This would not have been possible without support and guidance from my PhD mentors, Dr. Aaron Newman, Dr. Michael Clarke, and Dr. Irving Weissman. Also, thankful to an amazing interdisciplinary team of biologists, computer scientists, statisticians, and clinicians!
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