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In situ electro-sequencing in three-dimensional tissues

Qiang Li, Zuwan Lin, Ren Liu, Xin Tang, Jiahao Huang, View ORCID ProfileYichun He, Haowen Zhou, Hao Sheng, Hailing Shi, View ORCID ProfileXiao Wang, View ORCID ProfileJia Liu
doi: https://doi.org/10.1101/2021.04.22.440941
Qiang Li
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Zuwan Lin
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
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Ren Liu
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Xin Tang
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Jiahao Huang
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Yichun He
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Haowen Zhou
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Hao Sheng
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Hailing Shi
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Xiao Wang
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
4Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
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  • For correspondence: jia_liu@seas.harvard.edu xwangx@mit.edu
Jia Liu
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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  • ORCID record for Jia Liu
  • For correspondence: jia_liu@seas.harvard.edu xwangx@mit.edu
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Abstract

Pairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introduce in situ electro-sequencing (electro-seq), combining soft bioelectronics with in situ RNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We applied in situ electro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodal in situ analysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis, in situ electro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation, in situ electro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

Competing Interest Statement

A patent application has been filed by Broad Institute of MIT and Harvard related to this work; all methods and protocols are freely available.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 23, 2021.
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In situ electro-sequencing in three-dimensional tissues
Qiang Li, Zuwan Lin, Ren Liu, Xin Tang, Jiahao Huang, Yichun He, Haowen Zhou, Hao Sheng, Hailing Shi, Xiao Wang, Jia Liu
bioRxiv 2021.04.22.440941; doi: https://doi.org/10.1101/2021.04.22.440941
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In situ electro-sequencing in three-dimensional tissues
Qiang Li, Zuwan Lin, Ren Liu, Xin Tang, Jiahao Huang, Yichun He, Haowen Zhou, Hao Sheng, Hailing Shi, Xiao Wang, Jia Liu
bioRxiv 2021.04.22.440941; doi: https://doi.org/10.1101/2021.04.22.440941

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