In situ electro-seq

Multimodal charting of molecular and functional cell states via in situ electro-seq
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Table of Contents

1. About The Project
2. Data
3. Citation
4. License
5. Acknowledgments

About The Project

Paired mapping of single-cell gene expression and electrophysiology is essential to understand gene-to-function relationships in electrogenic tissues. Here, we developed in situ electro-sequencing (electro-seq) that combines flexible bioelectronics with in situ RNA sequencing to stably map millisecond-timescale electrical activity and profile single-cell gene expression from the same cells across intact biological networks including cardiac and neuron patches. When applied to human-induced pluripotent stem cell-derived cardiomyocyte patches, in situ electro-seq enabled multimodal in situ analysis of cardiomyocyte electrophysiology and gene expression at the cellular level, jointly defining cell states and developmental trajectories. Using machine learning-based cross-modal analysis, in situ electro-seq identified gene-to-electrophysiology relationships throughout cardiomyocyte development and accurately reconstructed the evolution of gene expression profiles based on long-term stable electrical measurements. In situ electro-seq could be applicable to create spatiotemporal multimodal maps in electrogenic tissues, potentiating the discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

Built With

• python 3.7
• jupyter notebook

Data

For all cardiac spatial transcriptomics data, please go to Single Cell Portal Other data have been attached in this GitHub

Citation

Please consider cite us if you find the data/code is useful to you.

License

Distributed under the GPL-3.0 license. See LICENSE.txt for more information.

Acknowledgments

We note that the data analyses for in situ electro-seq relies on many computational methods. We listed the most critical tools used in our paper here:

• For more details about spatial transcriptomics cell segmentation, please refer to ClusterMap
• For more details about single-cell clustering analysis and cell typing, please refer to Scanpy
• For more details about the ephys-gene joint integration analysis, please refer to Weighted Nearest Neighbor
• For more details about the ephys-gene relationship analysis, please refer to Sparse Reduced Rank Regression
• For more details about the cross-modal inference, please refer to coupled AutoEncoder