Multimodal Charting of Molecular and Functional Cell States

Abstract

Understanding the complex relationships between molecular profiles and functional electrophysiological states at the single-cell level is crucial for revealing the mechanisms underlying tissue function, development, and dysfunction of electrogenic tissues, such as cardiac and neural tissues. However, traditional approaches have been limited in their ability to simultaneously capture both functional and molecular information from the same cells within intact tissues. To address this critical gap, this dissertation introduces the development of technologies that seamlessly integrate electrophysiological mapping with molecular profiling at the single-cell level, revealing the dynamic relationships between gene expression and cellular function in electrogenic tissues. Chapter 2 introduces tissue-embedded mesh nanoelectronics for non-invasive chronic monitoring of tissue-wide electrical activity to study tissue maturation. Building upon this approach, Chapter 3 reveals the role of endothelial cells in promoting the electrical maturation of cardiomyocytes within 3D cardiac microtissues. By incorporating single-cell RNA sequencing (scRNA-seq), Chapter 3 also charts the molecular interactions between endothelial cells and cardiomyocytes that could contribute to the electrical maturation of cardiomyocytes. To directly correlate electrophysiology with gene expression at the single-cell level, Chapter 4 introduces in situ electro-sequencing (electro-seq), which combines electrical recording with in situ RNA sequencing, enabling the simultaneous spatial charting of electrical activity and single-cell gene expression. In situ electro-seq enables multimodal joint cell clustering, cross-modal inference of gene expression profiles from continuous electrical measurements, and identification of gene programs directly relevant to electrophysiology. Finally, to apply in situ electro-seq to in vivo applications, Chapter 5 charts the spatially resolved cell-type-specific transcriptional landscape at the brain-electrode interface to develop electrodes that minimize cellular responses, which could otherwise compromise electrical recordings and hinder the correlation between electrical activity and gene expression. This analysis differentiates the impacts of tissue-like versus conventional probes and correlates electrode-induced transcriptional changes with neurodegenerative disease mechanisms. In summary, this dissertation introduces innovative technologies that progressively bridge the gap between molecular profiling and electrophysiological mapping at the single-cell level, providing insights into the complex relationships between gene expression and cellular function in electrogenic tissues.