Gene-level alignment of single-cell trajectories

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

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Publication Details

Journal
Nature Methods
Published
2024-09-19
DOI
https://doi.org/10.1038/s41592-024-02378-4
Citations
15
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
3.51

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Gene-level alignment of single-cell trajectories

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Nature Methods
Single-cell and spatial transcriptomics
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Gene-level alignment of single-cell trajectories

Ana-Maria Cujba, Woochan Lee, Dinithi Sumanaweera, Sarah Amalia Teichmann, Chenqu Suo, Amanda J. Oliver, Bianca Dumitrascu, Alexander S. Steemers, Daniele Muraro, Emma Dann, Krzysztof Polański, Jong-Eun Park, Kerstin B. Meyer
article en
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Abstract

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Nature MethodsVol. 22(1)
Seoul National University (KR), Korea Advanced Institute of Science and Technology (KR), Wellcome/MRC Cambridge Stem Cell Institute (GB), University of Cambridge (GB), Macmillan Cancer Support (GB), Columbia University Irving Medical Center (US), Cambridge University Hospitals NHS Foundation Trust (GB), Wellcome Sanger Institute (GB), Princess Máxima Center (NL), Columbia University (US)
Medical Research Council, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 6%
Single-cell and spatial transcriptomics
3.51
DOIOpen Access PDFOpenAlex
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