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Furchtgott, Leon

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Furchtgott

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Leon

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Furchtgott, Leon
Author's Publications: search.filters.isAuthorOfPublication.f78d980d-f737-4d57-b16d-e2fc1b898409

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    Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states
    (eLife Sciences Publications, Ltd, 2017) Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad
    The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001
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    Discovering sparse transcription factor codes for cell states and state transitions during development
    (eLife Sciences Publications, Ltd, 2017) Furchtgott, Leon; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad
    Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001
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    Simultaneous Inference of Cell Types, Lineage Trees, and Regulatory Genes From Gene Expression Data
    (2016-05-13) Furchtgott, Leon; Amir, Ariel; Murray, Andrew W.; Scadden, David T.; Hogle, James M.
    Important goals of developmental biology include identifying cell types, understanding the sequence of lineage choices made by multipotent cells and unconvering the molecular networks controlling these decisions. Achieving these goals through computational analysis of gene expression data has been difficult. In this dissertation supervised by Sharad Ramanathan, I develop a probabilistic framework to identify cell types, infer lineage relationships and discover core gene networks controlling lineage decisions. Working with Sandeep Choubey and Sumin Jang, we infer the gene expression dynamics of early differentiation of mouse embryonic stem cells, revealing discrete state transitions across nine cell states. Using a probabilistic model of the gene regulatory networks, we predict that these states are further defined by distinct responses to perturbations and experimentally verify three such examples of state-dependent behavior. Working with Vilas Menon and Sam Melton, we infer a lineage tree for early neural development and putative regulatory transcription factors from single-cell transcriptomic profiles. The lineage tree shows a prominent bifurcation between cortical and mid/hindbrain cell types, and the inferred lineage relationships were confirmed by clonal analysis experiments. In summary, this study provides a framework to infer predictive models of the gene regulatory networks that drive cell fate decisions.
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    A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development
    (Elsevier BV, 2017-01) Yao, Zizhen; Mich, John; Ku, Sherman; Menon, Vilas; Krostag, Anne-Rachel; Martinez, Refugio A.; Furchtgott, Leon; Mulholland, Heather; Bort, Susan; Fuqua, Margaret; Gregor, Ben; Hodge, Rebecca; Jayabalu, Anu; May, Ryan; Melton, Samuel; Nelson, Angelique; Ngo, N. Kiet; Shapovalova, Nadiya; Shehata, Soraya; Smith, Michael; Tait, Leah; Thompson, Carol; Thomsen, Elliot; Ye, Chaoyang; Glass, Ian; Kaykas, Ajamete; Yao, Shuyuan; Phillips, John; Grimley, Joshua; Levi, Boaz; Wang, Yanling; Ramanathan, Sharad
    During human brain development, multiple signaling pathways generate diverse cell types with varied regional identities. Here, we integrate single-cell RNA sequencing and clonal analyses to reveal lineage trees and molecular signals underlying early forebrain and mid/hindbrain cell differentiation from human embryonic stem cells (hESCs). Clustering single-cell transcriptomic data identified 41 distinct populations of progenitor, neuronal, and non-neural cells across our differentiation time course. Comparisons with primary mouse and human gene expression data demonstrated rostral and caudal progenitor and neuronal identities from early brain development. Bayesian analyses inferred a unified cell-type lineage tree that bifurcates between cortical and mid/hindbrain cell types. Two methods of clonal analyses confirmed these findings and further revealed the importance of Wnt/β-catenin signaling in controlling this lineage decision. Together, these findings provide a rich transcriptome-based lineage map for studying human brain development and modeling developmental disorders.