Showing papers by "Cole Trapnell published in 2019"
TL;DR: A cell atlas of mouse organogenesis provides a global view of developmental processes occurring during this critical period, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
Abstract: Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. Data from single-cell combinatorial-indexing RNA-sequencing analysis of 2 million cells from mouse embryos between embryonic days 9.5 and 13.5 are compiled in a cell atlas of mouse organogenesis, which provides a global view of developmental processes occurring during this critical period.
1,865 citations
TL;DR: A multiplex, expression quantitative trait locus (eQTL)-inspired framework for mapping enhancer-gene pairs by introducing random combinations of CRISPR/Cas9-mediated perturbations to each of many cells, followed by single-cell RNA sequencing (RNA-seq).
389 citations
TL;DR: The dataset defines the succession of gene expression changes associated with almost every cell division in an animal's embryonic cell lineage, and provides an extensive resource that will guide future investigations of gene regulation and cell fate decisions in C. elegans.
Abstract: Caenorhabditis elegans is an animal with few cells but a wide diversity of cell types. In this study, we characterize the molecular basis for their specification by profiling the transcriptomes of 86,024 single embryonic cells. We identify 502 terminal and preterminal cell types, mapping most single-cell transcriptomes to their exact position in C. elegans' invariant lineage. Using these annotations, we find that (i) the correlation between a cell's lineage and its transcriptome increases from middle to late gastrulation, then falls substantially as cells in the nervous system and pharynx adopt their terminal fates; (ii) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and (iii) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.
315 citations
TL;DR: The NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping.
Abstract: Author(s): Snyder, Michael P; Lin, Shin; Posgai, Amanda; Atkinson, Mark; Regev, Aviv; Rood, Jennifer; Rozenblatt-Rosen, Orit; Gaffney, Leslie; Hupalowska, Anna; Satija, Rahul; Gehlenborg, Nils; Shendure, Jay; Laskin, Julia; Harbury, Pehr; Nystrom, Nicholas A; Silverstein, Jonathan C; Bar-Joseph, Ziv; Zhang, Kun; Borner, Katy; Lin, Yiing; Conroy, Richard; Procaccini, Dena; Roy, Ananda L; Pillai, Ajay; Brown, Marishka; Galis, Zorina S; Cai, Long; Shendure, Jay; Trapnell, Cole; Lin, Shin; Jackson, Dana; Snyder, Michael P; Nolan, Garry; Greenleaf, William James; Lin, Yiing; Plevritis, Sylvia; Ahadi, Sara; Nevins, Stephanie A; Lee, Hayan; Schuerch, Christian Martijn; Black, Sarah; Venkataraaman, Vishal Gautham; Esplin, Ed; Horning, Aaron; Bahmani, Amir; Zhang, Kun; Sun, Xin; Jain, Sanjay; Hagood, James; Pryhuber, Gloria; Kharchenko, Peter; Atkinson, Mark; Bodenmiller, Bernd; Brusko, Todd; Clare-Salzler, Michael; Nick, Harry; Otto, Kevin; Posgai, Amanda; Wasserfall, Clive; Jorgensen, Marda; Brusko, Maigan; Maffioletti, Sergio; Caprioli, Richard M; Spraggins, Jeffrey M; Gutierrez, Danielle; Patterson, Nathan Heath; Neumann, Elizabeth K; Harris, Raymond; deCaestecker, Mark; Fogo, Agnes B; van de Plas, Raf; Lau, Ken; Cai, Long; Yuan, Guo-Cheng; Zhu, Qian; Dries, Ruben; Yin, Peng; Saka, Sinem K; Kishi, Jocelyn Y; Wang, Yu; Goldaracena, Isabel; Laskin, Julia; Ye, DongHye; Burnum-Johnson, Kristin E; Piehowski, Paul D | Abstract: Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.
298 citations
TL;DR: Garnett is described, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single- cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes.
Abstract: Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting. We describe Garnett, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single-cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.
297 citations
TL;DR: The results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution and address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress.
Abstract: Single cell RNA sequencing can yield high-resolution cell-type–specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to Arabidopsis (Arabidopsis thaliana) root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type–specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat-shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.
222 citations
TL;DR: Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory ‘checkpoints’ in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.
Abstract: Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-β. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete 'partial' stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory 'checkpoints' in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.
137 citations
TL;DR: It is shown that TH promotes maturation of both cell types but in distinct ways, and how a single endocrine factor integrates very different cellular activities during the generation of adult form is shown.
Abstract: Thyroid hormone (TH) regulates diverse developmental events and can drive disparate cellular outcomes. In zebrafish, TH has opposite effects on neural crest derived pigment cells of the adult stripe pattern, limiting melanophore population expansion, yet increasing yellow/orange xanthophore numbers. To learn how TH elicits seemingly opposite responses in cells having a common embryological origin, we analyzed individual transcriptomes from thousands of neural crest-derived cells, reconstructed developmental trajectories, identified pigment cell-lineage specific responses to TH, and assessed roles for TH receptors. We show that TH promotes maturation of both cell types but in distinct ways. In melanophores, TH drives terminal differentiation, limiting final cell numbers. In xanthophores, TH promotes accumulation of orange carotenoids, making the cells visible. TH receptors act primarily to repress these programs when TH is limiting. Our findings show how a single endocrine factor integrates very different cellular activities during the generation of adult form.
97 citations
TL;DR: WNT pathway inhibition in the endocrine domain of the differentiating clusters reveals a necessary role for the WNT inhibitor APC during islet formation in vivo, and causes an increase in the proportion of differentiated endocrine cells.
86 citations
TL;DR: It is found that islet morphology and endocrine differentiation to be directly related, and peninsula-like structures in differentiating human embryonic stem cells are induced, laying the ground for the generation of entire islets in vitro.
83 citations
TL;DR: The Uniform Manifold Approximation and Projection (UMAP) method is used on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae to identify clusters of genes that correspond to protein complexes and pathways, and finds novel protein interactions even within well-characterized complexes.
Abstract: Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies clusters of genes that correspond to protein complexes and pathways, and finds novel protein interactions even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.
TL;DR: A comprehensive map of the accessible chromatin landscape of the mouse hippocampus at single-cell resolution is presented and a new resource that can guide subsequent studies of the hippocampus is provided.
Abstract: Here we present a comprehensive map of the accessible chromatin landscape of the mouse hippocampus at single-cell resolution. Substantial advances of this work include the optimization of a single-cell combinatorial indexing assay for transposase accessible chromatin (sci-ATAC-seq); a software suite, scitools, for the rapid processing and visualization of single-cell combinatorial indexing data sets; and a valuable resource of hippocampal regulatory networks at single-cell resolution. We used sci-ATAC-seq to produce 2346 high-quality single-cell chromatin accessibility maps with a mean unique read count per cell of 29,201 from both fresh and frozen hippocampi, observing little difference in accessibility patterns between the preparations. By using this data set, we identified eight distinct major clusters of cells representing both neuronal and nonneuronal cell types and characterized the driving regulatory factors and differentially accessible loci that define each cluster. Within pyramidal neurons, we identified four major clusters, including CA1 and CA3 neurons, and three additional subclusters. We then applied a recently described coaccessibility framework, Cicero, which identified 146,818 links between promoters and putative distal regulatory DNA. Identified coaccessibility networks showed cell-type specificity, shedding light on key dynamic loci that reconfigure to specify hippocampal cell lineages. Lastly, we performed an additional sci-ATAC-seq preparation from cultured hippocampal neurons (899 high-quality cells, 43,532 mean unique reads) that revealed substantial alterations in their epigenetic landscape compared with nuclei from hippocampal tissue. This data set and accompanying analysis tools provide a new resource that can guide subsequent studies of the hippocampus.
TL;DR: Garnett is described, an algorithm and accompanying software for rapidly annotating cell types in scRNA-seq and scATAC-seq datasets, based on an interpretable, hierarchical markup language of cell type-specific genes.
Abstract: Single cell technologies for profiling tissues or even entire organisms are rapidly being adopted. However, the manual process by which cell types are typically annotated in the resulting data is labor-intensive and increasingly rate-limiting for the field. Here we describe Garnett, an algorithm and accompanying software for rapidly annotating cell types in scRNA-seq and scATAC-seq datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.
TL;DR: It is found that a strong lineage-transcriptome correlation in the early embryo breaks down in the final two cell divisions as cells adopt their terminal fates and that most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.
Abstract: C. elegans is an animal with few cells, but a striking diversity of cell types. Here, we characterize the molecular basis for their specification by profiling the transcriptomes of 84,625 single embryonic cells. We identify 284 terminal and pre-terminal cell types, mapping most single cell transcriptomes to their exact position in the invariant C. elegans lineage. We use these annotations to perform the first quantitative analysis of the relationship between lineage and the transcriptome for a whole organism. We find that a strong lineage-transcriptome correlation in the early embryo breaks down in the final two cell divisions as cells adopt their terminal fates and that most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state. Users can explore our data with a graphical application VisCello.
TL;DR: A cell cycle classifier is created, which, in addition to traditional cell cycle phases, also identifies a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas.
Abstract: In depth knowledge of the cellular states associated with normal and disease tissue homeostasis is critical for understanding disease etiology and uncovering therapeutic opportunities Here, we used single cell RNA-seq to survey the cellular states of neuroepithelial-derived cells in cortical and neurogenic regions of developing and adult mammalian brain to compare with 38,474 cells obtained from 59 human gliomas, as well as pluripotent ESCs, endothelial cells, CD45+ immune cells, and non-CNS cancers This analysis suggests that a significant portion of neuroepithelial-derived stem and progenitor cells and glioma cells that are not in G2/M or S phase exist in two states: G1 or Neural G0, defined by expression of certain neuro-developmental genes In gliomas, higher overall Neural G0 gene expression is significantly associated with less aggressive gliomas, IDH1 mutation, and extended patient survival, while also anti-correlated with cell cycle gene expression Knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down regulation of quiescence-associated markers, and loss of Neural G0 gene expression Thus, Neural G0 is a dynamic cellular state required for indolent cell cycles in neural-specified stem and progenitors poised for cell division As a result, Neural G0 occupancy may be an important determinant of glioma tumor progression
TL;DR: It is found that thyroid hormone promotes the maturation of both melanophores and xanthophores in distinct ways, promoting cellular senescence and carotenoid accumulation, respectively.
Abstract: Circulating endocrine factors are critical for orchestrating complex developmental processes during the generation of adult form. One such factor, thyroid hormone, regulates diverse cellular processes during post-embryonic development and can drive disparate morphological outcomes through mechanisms that remain essentially unknown. We sought to define how thyroid hormone elicits opposite responses in the abundance of two pigment cell classes during development of the zebrafish adult pigment pattern. By profiling individual transcriptomes from thousands of neural crest derived cells, including pigment cells, we reconstructed developmental trajectories and identified lineage-specific changes in response to thyroid hormone. Our findings show that thyroid hormone and its receptors regulate distinct events of cellular maturation across lineages, and illustrate how a single, global factor integrates seemingly divergent morphogenetic outcomes across developmental time.
TL;DR: In this article, the authors apply this approach to A. thaliana root cells to capture gene expression in 3,121 root cells and use machine learning to reconstruct single-cell developmental trajectories along pseudotime.
Abstract: Single-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to A. thaliana root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.
TL;DR: The developed sci-fate technique, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), is applied to a model system of cortisol response and characterized expression dynamics in over 6,000 single cells, to quantify the dynamics of the cell cycle and glucocorticoid receptor activation.
Abstract: Gene expression programs are dynamic, e.g. the cell cycle, response to stimuli, normal differentiation and development, etc. However, nearly all techniques for profiling gene expression in single cells fail to directly capture the dynamics of transcriptional programs, which limits the scope of biology that can be effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a proof-of-concept, we applied sci-fate to a model system of cortisol response and characterized expression dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for inferring the distribution of cell state transitions. We anticipate sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.
TL;DR: This work presents Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype, and uses it to recover cells that retain normal nuclear morphologies after paclitaxel treatment, then derive their single cell transcriptomes to identify multiple pathways associated with pac litaxel resistance in human cancers.
Abstract: Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Visual Cell Sorting uses automated imaging and phenotypic analysis to direct selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess the effect of hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we use Visual Cell Sorting to recover cells that retain normal nuclear morphologies after paclitaxel treatment, then derive their single cell transcriptomes to identify multiple pathways associated with paclitaxel resistance in human cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
TL;DR: These studies enhance the understanding of the core signal pathways necessary for the embryonic development of functional HSC, with the potential to advance in vitro engineering of therapeutically relevant pluripotent stem cell-derived HSC in stromal cell-free culture.
TL;DR: Adding interleukin-2 (IL2) to rapamycin in vivo supported a logarithmic increase in the half-life of adoptively transferred CFSE-labeled, autologous NHP Tregs, effectively doubling the number of cells in the peripheral blood Treg compartment compared to Treg infusion withRapamycin alone.
Abstract: As regulatory T cell (Treg) adoptive therapy continues to develop clinically, there is a need to determine which immunomodulatory agents pair most compatibly with Tregs to enable persistence and stabilize suppressor function. Prior work has shown that mechanistic target of rapamycin (mTOR) inhibition can increase stability of thymic Tregs. In this study we investigated the transcriptomic signatures of ex-vivo expanded Tregs after adoptive transfer in the setting of clinically relevant immunosuppression using a non-human primate (NHP) model as a prelude to future transplant studies. Here, we found that adding interleukin-2 (IL2) to rapamycin in vivo supported a logarithmic increase in the half-life of adoptively transferred CFSE-labeled, autologous NHP Tregs, effectively doubling the number of cells in the peripheral blood Treg compartment compared to Treg infusion with rapamycin alone. Using single cell transcriptomics, we found that transferred ex-vivo expanded Tregs initially exhibit a gene expression signature consistent with an activated state. Moreover, those cells with the highest levels of activation also expressed genes associated with p53-mediated apoptosis. In contrast, transferred Tregs interrogated at Day +20 post-transfer demonstrated a gene signature more similar to published profiles of resting Tregs. Together, these preclinical data further support combining IL2 and rapamycin in vivo as adjunctive therapy for ex-vivo expanded adoptively transferred Tregs and suggest that the activation status of ex-vivo expanded Tregs is critical to their persistence.