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Macosko, Evan

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Macosko

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Evan

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Macosko, Evan
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    Robust decomposition of cell type mixtures in spatial transcriptomics
    (SpringerNature, 2020-05-08) Cable, Dylan; Murray, Evan; Zou, Luli; Goeva, Aleksandrina; Macosko, Evan; Chen, Fei; Irizarry, Rafael
    Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD’s recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.
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    A Molecular Census of Arcuate Hypothalamus and Median Eminence Cell Types
    (2017) Campbell, John; Macosko, Evan; Fenselau, Henning; Pers, Tune H.; Lyubetskaya, Anna; Tenen, Danielle; Goldman, Melissa; Verstegen, Anne; Resch, Jon; McCarroll, Steven; Rosen, Evan; Lowell, Bradford; Tsai, Linus
    The hypothalamic arcuate-median eminence complex (Arc-ME) controls energy balance, fertility, and growth through molecularly distinct cell types, many of which remain unknown. To catalog cell types in an unbiased way, we profiled gene expression in 20,921 individual cells in and around the adult mouse Arc-ME using Drop-seq. We identify 50 transcriptionally distinct Arc-ME cell populations, including a rare tanycyte population at the Arc-ME diffusion barrier, a novel leptin-sensing neuronal population, multiple AgRP and POMC subtypes, and an orexigenic somatostatin neuronal population. We extended Drop-seq to detect dynamic expression changes across relevant physiological perturbations, revealing cell type-specific responses to energy status, including distinctly responsive subtypes of AgRP and POMC neurons. Finally, integrating our data with human GWAS data implicates two previously unknown neuronal subtypes in the genetic control of obesity. This resource will accelerate biological discovery by providing insights into molecular and cell type diversity from which function can be inferred.
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    Publication
    Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
    (Springer Science and Business Media LLC, 2020-12-07) Stickels, Robert; Murray, Evan; Kumar, Pawan; Li, Jilong; Marshall, Jamie; Di Bella, Daniela; Arlotta, Paola; Macosko, Evan; Chen, Fei
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    Publication
    Molecular Logic of Cellular Diversification in the Mouse Cerebral Cortex
    (Springer Science and Business Media LLC, 2021-06-23) Di Bella, Daniela; Habibi, Ehsan; Stickels, Robert; Scalia, Gabriele; Brown, Juliana; Yadollahpour, Payman; Yang, Sung Min; Abbate, Catherine; Biancalani, Tommaso; Macosko, Evan; Chen, Fei; Regev, Aviv; Arlotta, Paola
    The mammalian cerebral cortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function. However, the molecular logic that governs the establishment and organization of cortical cell types remains elusive, largely due to the large number of cell classes undergoing dynamic cell-state transitions over extended developmental timelines. Here, we have generated a comprehensive single-cell RNA-seq and single-cell ATAC-seq atlas of the developing mouse neocortex, sampled every day throughout embryonic corticogenesis and at early postnatal ages, complemented with a spatial transcriptomics time-course. We computationally reconstruct developmental trajectories across the diversity of cortical cell classes, and infer their spatial organization and the gene regulatory programs that accompany their lineage bifurcation decisions and differentiation trajectories. Finally, we demonstrate how this developmental map pinpoints the origin of lineage-specific developmental abnormalities linked to aberrant corticogenesis in mutant animals. The data provides a global picture of the regulatory mechanisms governing cellular diversification in the neocortex.