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通过原位电测序对分子和功能细胞状态进行多模式绘图。

Multimodal charting of molecular and functional cell states via in situ electro-sequencing.

机构信息

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA.

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.

出版信息

Cell. 2023 Apr 27;186(9):2002-2017.e21. doi: 10.1016/j.cell.2023.03.023. Epub 2023 Apr 19.

DOI:10.1016/j.cell.2023.03.023
PMID:37080201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11259179/
Abstract

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 neural 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.

摘要

单细胞基因表达和电生理学的配对图谱对于理解发电组织中的基因与功能关系至关重要。在这里,我们开发了原位电测序(electro-seq),它将柔性生物电子学与原位 RNA 测序相结合,能够稳定地从完整的生物网络中的同一细胞中映射毫秒级电活动,并对单细胞基因表达进行分析,包括心脏和神经贴片。当应用于人类诱导多能干细胞衍生的心肌细胞贴片中时,原位电测序能够在细胞水平上进行多模态的心肌细胞电生理学和基因表达的原位分析,共同定义细胞状态和发育轨迹。使用基于机器学习的跨模态分析,原位电测序确定了整个心肌细胞发育过程中的基因与电生理学关系,并根据长期稳定的电测量准确重建基因表达谱的演变。原位电测序可应用于创建发电组织中的时空多模态图谱,促进发现负责电生理功能和功能障碍的细胞类型和基因程序。