Studying stochastic systems biology of the cell with single-cell genomics data

Cell Systems, Год журнала: 2023, Номер 14(10), С. 822 - 843.e22

Опубликована: Сен. 25, 2023

Язык: Английский

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Unified fate mapping in multiview single-cell data

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

Опубликована: Июль 20, 2023

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, while 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 mouse 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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kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

Nature Protocols, Год журнала: 2024, Номер unknown

Опубликована: Окт. 10, 2024

Язык: Английский

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Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 22, 2024

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting transcriptome dynamics that unfold within low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots expression. However, information on the underlying can be extracted using RNA velocity, which models unspliced and spliced abundances to estimate rate change Available velocity algorithms fragile rely heuristics lack statistical control. Moreover, estimated vector field is not dynamically consistent with traversed expression Here, we develop generative model Bayesian inference approach solves these problems. Our couples manifold estimation reformulated, unified framework, so as coherently identify parameters an autonomous dynamical system. Focusing cell cycle, implemented VeloCycle study regulation one-dimensional periodic manifolds validated live-imaging its ability infer actual cycle periods. We benchmarked sensitivity analyses demonstrated one- multiple-sample testing. also conducted Markov chain Monte Carlo model, uncovering key relationships between gene-specific kinetics our gene-independent estimate. Finally, applied vivo samples vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes neural progenitors effect knockdowns speed. Ultimately, expands scRNA-seq analysis toolkit modular statistically rigorous framework.

Язык: Английский

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Dissecting the spatiotemporal diversity of adult neural stem cells

Molecular Systems Biology, Год журнала: 2024, Номер 20(4), С. 321 - 337

Опубликована: Фев. 16, 2024

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how assume different functional states support spatially patterned architecture. Here, we dissected the diversity of neural zebrafish brain, an organ that is characterized by pronounced zonation high regenerative capacity. We combined single-cell transcriptomics brain regions with massively parallel lineage tracing vivo RNA metabolic labeling to analyze regulation space time. detected a large cells, some subtypes being restricted single region, while others were found globally across brain. Global cell linked neurogenic differentiation, involved proliferative non-proliferative differentiation. Our work reveals principles organization establishes resource manipulation subtypes.

Язык: Английский

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