Multi-domain translation between single-cell imaging and sequencing data using autoencoders.
IF: 17.694
Cited by: 46


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.


Gene Expression
Spatial Transcriptomics

MeSH terms

CD4-Positive T-Lymphocytes
Cell Nucleus
Gene Expression Profiling
Gene Expression Regulation
Principal Component Analysis
ROC Curve
Reproducibility of Results
Sequence Analysis, RNA
Single-Cell Analysis


Yang, Karren Dai
Belyaeva, Anastasiya
Venkatachalapathy, Saradha
Damodaran, Karthik
Katcoff, Abigail
Radhakrishnan, Adityanarayanan
Shivashankar, G V
Uhler, Caroline

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