Exploring tissue architecture using spatial transcriptomics

Nature, Journal Year: 2021, Volume and Issue: 596(7871), P. 211 - 220

Published: Aug. 11, 2021

Language: Английский

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Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Cell, Journal Year: 2022, Volume and Issue: 185(10), P. 1777 - 1792.e21

Published: May 1, 2022

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view current methodologies precludes their systematic application analyze relatively large three-dimensional mid- late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays in situ RNA capture create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq generate mouse organogenesis spatiotemporal atlas (MOSTA), which maps with single-cell high sensitivity kinetics directionality transcriptional variation during organogenesis. used this information gain insight into molecular basis cell heterogeneity fate specification developing tissues dorsal midbrain. Our panoramic will facilitate in-depth investigation longstanding questions concerning normal abnormal development.

Language: Английский

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Museum of spatial transcriptomics

Nature Methods, Journal Year: 2022, Volume and Issue: 19(5), P. 534 - 546

Published: March 10, 2022

Language: Английский

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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Nature Reviews Genetics, Journal Year: 2021, Volume and Issue: 22(10), P. 627 - 644

Published: June 18, 2021

Language: Английский

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Spatial transcriptomics at subspot resolution with BayesSpace

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 39(11), P. 1375 - 1384

Published: June 3, 2021

Language: Английский

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Squidpy: a scalable framework for spatial omics analysis

Nature Methods, Journal Year: 2022, Volume and Issue: 19(2), P. 171 - 178

Published: Jan. 31, 2022

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools required to store, integrate visualize large diversity spatial data. Here, we present Squidpy, a Python framework that brings together from image analysis enable scalable description molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure numerous methods allow efficiently manipulate interactively is extensible can be interfaced with variety already existing libraries for

Language: Английский

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Best practices for single-cell analysis across modalities

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 550 - 572

Published: March 31, 2023

Language: Английский

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An introduction to spatial transcriptomics for biomedical research

Genome Medicine, Journal Year: 2022, Volume and Issue: 14(1)

Published: June 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.

Language: Английский

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Spatial omics and multiplexed imaging to explore cancer biology

Nature Methods, Journal Year: 2021, Volume and Issue: 18(9), P. 997 - 1012

Published: Aug. 2, 2021

Language: Английский

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An atlas of healthy and injured cell states and niches in the human kidney

Nature, Journal Year: 2023, Volume and Issue: 619(7970), P. 585 - 594

Published: July 19, 2023

Abstract Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles interactions within tissue neighbourhoods 1 . Here we applied multiple single-cell single-nucleus assays (>400,000 nuclei or cells) spatial imaging technologies to a broad spectrum healthy reference kidneys (45 donors) diseased (48 patients). This has provided high-resolution cellular atlas 51 main types, which include rare previously undescribed populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors localizations spanning entire kidney. We also define 28 states across nephron segments interstitium that were altered in injury, encompassing cycling, adaptive (successful maladaptive repair), transitioning degenerative states. Molecular signatures permitted localization these injury using transcriptomics, while large-scale 3D analysis (around 1.2 million neighbourhoods) corresponding linkages active immune responses. These analyses defined biological pathways are relevant time-course niches, including underlying epithelial repair predicted with decline function. integrated multimodal human represents comprehensive benchmark neighbourhoods, outcome-associated publicly available interactive visualizations.

Language: Английский

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