Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Karren Dai Yang, Anastasiya Belyaeva, Saradha Venkatachalapathy, Karthik Damodaran, Abigail Katcoff, Adityanarayanan Radhakrishnan, G. V. Shivashankar and Caroline Uhler ()
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Karren Dai Yang: Massachusetts Institute of Technology
Anastasiya Belyaeva: Massachusetts Institute of Technology
Saradha Venkatachalapathy: National University of Singapore
Karthik Damodaran: National University of Singapore
Abigail Katcoff: Massachusetts Institute of Technology
Adityanarayanan Radhakrishnan: Massachusetts Institute of Technology
G. V. Shivashankar: National University of Singapore
Caroline Uhler: Massachusetts Institute of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

Date: 2021
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DOI: 10.1038/s41467-020-20249-2

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