In Situ Graphene-Seq: Spatial Transcriptomics and Chronic Electrophysiological Characterization of Tissue Microenvironments

Abstract

Biological systems are composed of diverse, interconnected cell types, yet capturing both their functional dynamics and molecular identities at high spatiotemporal resolution remains challenging. While electrophysiological measurements provide real-time insights into cellular activities, they cannot fully describe the molecular architecture and states of the measured cells. Conversely, transcriptomics reveals cell gene expression patterns but does not capture functional states. Bridging these modalities is essential for a holistic understanding of the molecular mechanisms driving functional changes. In this study, we introduce in situ graphene-sequencing (graphene-seq), a unique platform that seamlessly integrates chronic electrophysiology with imaging-based, spatially resolved 3D transcriptomics, overcoming longstanding limitations of current multimodal approaches. This system leverages stretchable mesh nanoelectronics for long-term, single-cell-level interfacing and incorporates transparent graphene/PEDOT:PSS electrodes, enabling seamless integration of electrical recordings and optical imaging. By combining electrophysiology with high-throughput, imaging-based in situ sequencing, this platform allows comprehensive multimodal, spatially resolved analysis of cell microenvironment within spatially heterogeneous tissues. We validate in situ graphene-seq by charting multimodal profiles of human-induced pluripotent stem cell-derived cardiomyocyte and endothelial cell co-cultures, examining how spatial heterogeneity in cell composition influences both electrophysiological activity and gene expression. This scalable, integrated approach offers a powerful tool for studying the complex interplay between cellular function and molecular identity. It also provides insights into how tissue microenvironments shape cell behavior and molecular states, advancing applications in regenerative medicine, stem cell therapy, and disease modeling.