Multimodal charting of molecular and functional cell states via in situ electro-seq
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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.
- python 3.7
- jupyter notebook
For all cardiac spatial transcriptomics data, please go to Single Cell Portal Other data have been attached in this GitHub
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Distributed under the GPL-3.0 license. See LICENSE.txt for more information.
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