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Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

Kiya W. Govek, Emma C. Troisi, Steven Woodhouse, Pablo G. Camara
doi: https://doi.org/10.1101/672501
Kiya W. Govek
Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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Emma C. Troisi
Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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Steven Woodhouse
Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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Pablo G. Camara
Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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  • For correspondence: pcamara@pennmedicine.upenn.edu
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Abstract

Histology provides a unique window into the cellular and molecular architecture of tissues and is a critical component of biomedical research and clinical practice. Highly-multiplexed immunohistochemistry1-6 (mIHC) enables the routine staining and quantification of dozens of antigens in the same tissue section with single-cell resolution. However, the amount of cell types and states that can be simultaneously identified by mIHC is limited. In contrast, cells are finely disaggregated into distinct types in single-cell transcriptomic analyses but spatial information is lost. To bridge this gap, we developed an approach for enriching mIHC histology slides with single-cell RNA-seq data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements7, 8. Our approach, Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA), increases the level of detail in histological analyses by enabling detection of subtle cell populations, spatial patterns of transcription, and cell-to-cell interactions. It provides an improvement in throughput, resolution, and simplicity with respect to existing spatially-resolved methods for simultaneous proteomics and transcriptomics. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell populations in the murine spleen using published mIHC images.

Footnotes

  • https://github.com/CamaraLab/STvEA

  • https://camara-lab.shinyapps.io/stvea/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 15, 2019.
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Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping
Kiya W. Govek, Emma C. Troisi, Steven Woodhouse, Pablo G. Camara
bioRxiv 672501; doi: https://doi.org/10.1101/672501
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Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping
Kiya W. Govek, Emma C. Troisi, Steven Woodhouse, Pablo G. Camara
bioRxiv 672501; doi: https://doi.org/10.1101/672501

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