CellRank 2: unified fate mapping in multiview single-cell data

Nature Methods, Год журнала: 2024, Номер 21(7), С. 1196 - 1205

Опубликована: Июнь 13, 2024

Abstract Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or velocity reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information utilize additional modalities, whereas methods that address these different data views cannot be combined do scale. Here we present CellRank 2, a versatile scalable framework study multiview single-cell of up millions cells in unified fashion. 2 consistently recovers terminal states probabilities across modalities human hematopoiesis endodermal development. Our also combining transitions within experimental points, feature use recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm Moreover, enable estimating cell-specific transcription degradation rates from metabolic-labeling data, which apply an intestinal organoid system delineate differentiation trajectories pinpoint regulatory strategies.

Язык: Английский

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Spatiotemporal omics for biology and medicine

Cell, Год журнала: 2024, Номер 187(17), С. 4488 - 4519

Опубликована: Авг. 1, 2024

Язык: Английский

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Mapping cells through time and space with moscot

Nature, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Abstract Single-cell genomic technologies enable the multimodal profiling of millions cells across temporal and spatial dimensions. However, experimental limitations hinder comprehensive measurement under native dynamics in their tissue niche. Optimal transport has emerged as a powerful tool to address these constraints facilitated recovery original cellular context 1–4 . Yet, most optimal applications are unable incorporate information or scale single-cell atlases. Here we introduce multi-omics (moscot), scalable framework for genomics that supports multimodality all applications. We demonstrate capability moscot efficiently reconstruct developmental trajectories 1.7 million from mouse embryos 20 time points. To illustrate space, enrich transcriptomic datasets by mapping profiles liver sample align multiple coronal sections brain. present moscot.spatiotemporal, an approach leverages gene-expression data both dimensions uncover spatiotemporal embryogenesis. also resolve endocrine-lineage relationships delta epsilon previously unpublished mouse, time-resolved pancreas development dataset using paired measurements gene expression chromatin accessibility. Our findings confirmed through validation NEUROD2 regulator progenitor model human induced pluripotent stem cell islet differentiation. Moscot is available open-source software, accompanied extensive documentation.

Язык: Английский

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Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plant Communications, Год журнала: 2022, Номер 4(3), С. 100508 - 100508

Опубликована: Дек. 20, 2022

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial metabolomics techniques been combined to analyze development. These used study transcriptomes metabolomes tissues level, enabling systematic investigation gene expression metabolism specific during defined developmental stages. In this review, we present an overview significant breakthroughs multi-omics plants, discuss how these approaches may soon play essential roles research.

Язык: Английский

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A guidebook of spatial transcriptomic technologies, data resources and analysis approaches

Computational and Structural Biotechnology Journal, Год журнала: 2023, Номер 21, С. 940 - 955

Опубликована: Янв. 1, 2023

Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs multicellular organisms and provided a theoretical basis for disease diagnosis therapy. However, both bulk single-cell RNA sequencing approaches lose spatial context cells within tissue microenvironment, development transcriptomics has made overall bias-free access to transcriptional information possible. Here, we elaborate help researchers select best-suited technology their goals integrate vast amounts data facilitate accessibility availability. Then, marshal various computational analyze purposes describe multimodal omics its potential application tumor tissue. Finally, provide detailed discussion outlook technologies, resources analysis guide current future research on transcriptomics.

Язык: Английский

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Spatial Transcriptomic Technologies

Cells, Год журнала: 2023, Номер 12(16), С. 2042 - 2042

Опубликована: Авг. 10, 2023

Spatial transcriptomic technologies enable measurement of expression levels genes systematically throughout tissue space, deepening our understanding cellular organizations and interactions within tissues as well illuminating biological insights in neuroscience, developmental biology a range diseases, including cancer. A variety spatial have been developed and/or commercialized, differing resolution, sensitivity, multiplexing capability, throughput coverage. In this paper, we review key enabling their applications the perspective techniques new emerging that are to address current limitations methodologies. addition, describe how transcriptomics data can be integrated with other omics modalities, complementing methods deciphering cellar phenotypes providing novel insight into organization.

Язык: Английский

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Advances and Challenges in Spatial Transcriptomics for Developmental Biology

Biomolecules, Год журнала: 2023, Номер 13(1), С. 156 - 156

Опубликована: Янв. 12, 2023

Development from single cells to multicellular tissues and organs involves more than just the exact replication of cells, which is known as differentiation. The primary focus research into mechanism differentiation has been differences in gene expression profiles between individual cells. However, it predominantly conducted at low throughput bulk levels, challenging efforts understand molecular mechanisms during developmental process animals humans. During last decades, rapid methodological advancements genomics facilitated ability study processes a genome-wide level finer resolution. Particularly, sequencing transcriptomes single-cell resolution, enabled by RNA-sequencing (scRNA-seq), was breath-taking innovation, allowing scientists gain better understanding cell lineage process. isolation scRNA-seq results loss spatial information consequently limits our specific functions performed different regions or organs. This greatly encourages emergence transcriptomic discipline tools. Here, we summarize recent application tools for biology. We also discuss limitations current approaches, well possible solutions future prospects.

Язык: Английский

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Spatial transcriptomics in development and disease

Molecular Biomedicine, Год журнала: 2023, Номер 4(1)

Опубликована: Окт. 9, 2023

Abstract The proper functioning of diverse biological systems depends on the spatial organization their cells, a critical factor for processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable simultaneously capturing both gene expression profiles locations cells. Hence, multitude spatially resolved technologies have emerged, offering novel dimension investigating regional expression, domains, interactions between Spatial transcriptomics (ST) is method that maps in while preserving information. It can reveal cellular heterogeneity, functional complex systems. ST also complement integrate with other omics to provide more comprehensive holistic view at multiple levels resolution. Since advent ST, new higher throughput resolution become available, holding significant potential expedite fresh insights into comprehending complexity. Consequently, rapid increase associated research has occurred, using these unravel complexity during developmental disease conditions. In this review, we summarize recent advancement historical, technical, application contexts. We compare different types based principles workflows, present bioinformatics tools analyzing integrating data modalities. highlight applications various domains biomedical research, especially development diseases. Finally, discuss current limitations challenges field, propose future directions ST.

Язык: Английский

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Integrated Spatial Transcriptomic and Proteomic Analysis of Fresh Frozen Tissue Based on Stereo-seq

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Апрель 28, 2023

To simultaneously detect whole transcriptomes and protein markers on the same tissue section, we combined Cellular Indexing of Transcriptomes Epitopes by Sequencing (CITE-seq) Stereo-seq to develop Stereo-CITE-seq workflow. Here, demonstrated that can co-detect mRNAs proteins in immune organs with high spatial resolution, reproducibility accuracy.

Язык: Английский

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Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Glomerular Diseases, Год журнала: 2024, Номер 4(1), С. 49 - 63

Опубликована: Март 13, 2024

Background: The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 recognized as the method of year by Nature Methods 2020. subsequent refinement expansion these other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, analyze protein addition gene expression have only added their significance impact on biomedical sciences. In this perspective, we focus two platforms for spatial transcriptomics, Visium, how been used provide novel insight into kidney disease. choice platform will depend largely experimental questions design. application clinically sourced biopsies presents opportunity identify specific tissue biomarkers that help define disease etiology more precisely target therapeutic interventions future. Summary: review, a description existing emerging can be capture data from tissue. These provided new heterogeneity diseases, reactions are distributed within which cells affected, molecular pathways predict response therapy. Key Message: upcoming years see intense use better pathophysiology diseases develop diagnostic tests guide personalized treatments patients.

Язык: Английский

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