A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
- Cameron Park1,2,11,
- Shouvik Mani2,3,11,
- Nicolas Beltran-Velez3,
- Katie Maurer4,5,6,
- Teddy Huang4,7,
- Shuqiang Li4,7,
- Satyen Gohil4,
- Kenneth J. Livak4,
- David A. Knowles3,8,9,10,
- Catherine J. Wu4,5,6 and
- Elham Azizi1,2,3,8
- 1Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA;
- 2Irving Institute for Cancer Dynamics, Columbia University, New York, New York 10027, USA;
- 3Department of Computer Science, Columbia University, New York, New York 10027, USA;
- 4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
- 5Harvard Medical School, Boston, Massachusetts 02115, USA;
- 6Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
- 7Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
- 8Data Science Institute, Columbia University, New York, New York 10027, USA;
- 9New York Genome Center, New York, New York 10013, USA;
- 10Department of Systems Biology, Columbia University, New York, New York 10032, USA
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↵11 These authors contributed equally to this work.
Abstract
Characterizing cell–cell communication and tracking its variability over time are crucial for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies. Existing tools fail to capture time-dependent intercellular interactions and primarily rely on databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method utilizes structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their coevolution and incorporates prior knowledge of receptor–ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from T cells cocultured with lymphoma cells, demonstrating its potential to uncover dynamic cell–cell cross talk.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279126.124.
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Freely available online through the Genome Research Open Access option.
- Received February 15, 2024.
- Accepted September 3, 2024.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
