RNA velocity of single cells

By G. L. Manno, Ruslan A. Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, M. Kastriti, P. Lönnerberg, Alessandro Furlan and 13 more. Nature, 2018

3,593 citations recorded by the source corpus.

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans. RNA velocity, estimated in single cells by comparison of spliced and unspliced mRNA, is a good indicator of transcriptome dynamics and will provide a useful tool for analysis of developmental lineage.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6116e1d2c16e435b1b236c2e6ff0735551f91de

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Citations in the corpus

Evolutionary velocity with protein language models (2021, background): “Because visualizing this flow in two dimensions can be prone to information loss or distortion through dimensionality reduction (LaManno et al., 2018), we sought to further quantify the relation-”
Best practices for single-cell analysis across modalities (2023, background): “These databases were found to be biased towards specific pathways, functional categories and tissue-enriched proteins 126 .”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “In order to alleviate dropout effects and measurement noises as well as to improve the robustness of the estimation, the original RNA velocity method (LaManno et al., 2018) utilizes themean expression (first moment) of each gene across cells, calculated based on the k-nearest neighbor graph of cells”
CellRank for directed single-cell fate mapping (2020, positive): “To further take advantage of the information captured by the KNN graph and to increase robustness of the algorithm with respect to noisy velocity vectors, we combine the velocity based transition matrix Pv with a similarity based transition matrix Ps via P = λPv + (1− λ)Ps for λ ∈ [0, 1] .”