Conserved cell types with divergent features in human versus mouse cortex

Nature, Год журнала: 2019, Номер 573(7772), С. 61 - 68

Опубликована: Авг. 21, 2019

Язык: Английский

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The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas

Cell, Год журнала: 2020, Номер 181(4), С. 936 - 953.e20

Опубликована: Май 1, 2020

Язык: Английский

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Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

European Journal of Immunology, Год журнала: 2019, Номер 49(10), С. 1457 - 1973

Опубликована: Окт. 1, 2019

These guidelines are a consensus work of considerable number members the immunology and flow cytometry community. They provide theory key practical aspects enabling immunologists to avoid common errors that often undermine immunological data. Notably, there comprehensive sections all major immune cell types with helpful Tables detailing phenotypes in murine human cells. The latest techniques applications also described, featuring examples data can be generated and, importantly, how analysed. Furthermore, tips, tricks pitfalls avoid, written peer-reviewed by leading experts field, making this an essential research companion.

Язык: Английский

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An atlas of the protein-coding genes in the human, pig, and mouse brain

Science, Год журнала: 2020, Номер 367(6482)

Опубликована: Март 6, 2020

Mapping the mammalian brain The diverse physiology of is reflected in its complex organization at regional, cellular, and subcellular levels. Sjöstedt et al. combined data—both newly acquired from other large-scale mapping projects—from transcriptomics, single-cell genomics, situ hybridization, antibody-based protein profiling to map molecular profiles human, pig, mouse brain. analysis consistent with a conserved basic architecture during evolution, but it does show differences regional gene expression profiles. Science , this issue p. eaay5947

Язык: Английский

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Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

PLoS ONE, Год журнала: 2018, Номер 13(12), С. e0209648 - e0209648

Опубликована: Дек. 26, 2018

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied archived frozen specimens. We used well-matched scRNA-seq datasets from mouse visual cortex compare type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal types similarly discriminated with both methods if intronic sequences included analysis. estimate the nuclear proportion total mRNA varies 20% 50% for large small pyramidal neurons, respectively. Together, these results illustrate high information content RNA characterization diversity brain tissues.

Язык: Английский

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An introduction to spatial transcriptomics for biomedical research

Genome Medicine, Год журнала: 2022, Номер 14(1)

Опубликована: Июнь 27, 2022

Abstract Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact viable from tissue. This precluded many cell types study largely destroys spatial context that could otherwise inform analyses of identity function. An increasing number platforms now facilitate spatially resolved, high-dimensional assessment gene transcription, known as ‘spatial transcriptomics’. Here, we introduce different classes method, which either record locations hybridized mRNA molecules tissue, image positions themselves prior assessment, or employ arrays probes pre-determined location. We review sizes tissue area can assessed, their resolution, genes profiled. discuss if preservation influences choice platform, provide guidance on whether specific may better suited discovery screens hypothesis testing. Finally, bioinformatic methods analysing transcriptomic data, including pre-processing, integration with existing inference cell-cell interactions. Spatial -omics are already improving our understanding human tissues research, diagnostic, therapeutic settings. To build upon these recent advancements, entry-level those seeking own research.

Язык: Английский

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A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation

Neuron, Год журнала: 2019, Номер 103(5), С. 785 - 801.e8

Опубликована: Июль 11, 2019

Язык: Английский

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A multimodal cell census and atlas of the mammalian primary motor cortex

Nature, Год журнала: 2021, Номер 598(7879), С. 86 - 102

Опубликована: Окт. 6, 2021

Here we report the generation of a multimodal cell census and atlas mammalian primary motor cortex as initial product BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved morphological electrophysiological properties cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance collective knowledge understanding brain cell-type organization

Язык: Английский

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The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Cell, Год журнала: 2020, Номер 181(2), С. 236 - 249

Опубликована: Апрель 1, 2020

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic ecosystem. Transformative single-cell genomics technologies spatial multiplex situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of National Cancer Institute (NCI) Moonshot Initiative, will establish a clinical, experimental, computational, organizational framework generate informative accessible three-dimensional atlases cancer for diverse set types. This effort complements both ongoing efforts map healthy organs previous large-scale approaches focused on bulk sequencing single point time. Generating single-cell, multiparametric, longitudinal integrating them with clinical outcomes should help identify novel predictive biomarkers features as well therapeutically relevant cell types, states, cellular across transitions. resulting have profound impact our understanding biology potential improve detection, prevention, discovery better precision-medicine treatments patients those risk cancer.

Язык: Английский

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A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

Nature, Год журнала: 2023, Номер 624(7991), С. 317 - 332

Опубликована: Дек. 13, 2023

The mammalian brain consists of millions to billions cells that are organized into many cell types with specific spatial distribution patterns and structural functional properties1-3. Here we report a comprehensive high-resolution transcriptomic cell-type atlas for the whole adult mouse brain. was created by combining single-cell RNA-sequencing (scRNA-seq) dataset around 7 million profiled (approximately 4.0 passing quality control), approximately 4.3 using multiplexed error-robust fluorescence in situ hybridization (MERFISH). is hierarchically 4 nested levels classification: 34 classes, 338 subclasses, 1,201 supertypes 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, visualize whole-brain along MERFISH datasets. systematically analysed neuronal non-neuronal across identified high degree correspondence between identity specificity each type. results reveal unique features organization different regions-in particular, dichotomy dorsal ventral parts part contains relatively fewer yet highly divergent types, whereas more numerous closely related other. Our study also uncovered extraordinary diversity heterogeneity neurotransmitter neuropeptide expression co-expression types. Finally, found transcription factors major determinants classification combinatorial factor code defines all establishes benchmark reference foundational resource integrative investigations cellular circuit function, development evolution

Язык: Английский

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