Direct, high-throughput linking of single cell imaging and gene expression
Catherine Xu,
Georg Meisl,
Nikita Moshkov,
Karolis Goda,
Alexey Shkarin,
Maximilian Schlögel,
Tuomas PJ Knowles,
Linas Mazutis,
Jochen Guck
bioRxiv 10.1101/2025.09.01.672798
(2025)
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Single-cell technologies have transformed our ability to dissect cellular heterogeneity by enabling measurements of individual molecular modalities, from genome and transcriptome to proteome and metabolome. However, at the single-cell level, the physical properties of cells, such as size, morphology, and mechanical state, remain largely disconnected from molecular profiling, limiting our understanding of the relationship between these aspects of cellular phenotype and gene expression. We introduce im-seq, a high-throughput, flow-based platform that integrates live-cell imaging with droplet-based mRNA sequencing at the single-cell level. By optically barcoding individual cells, im-seq enables the joint capture of physical and transcriptional profiles from single cells. We demonstrate that this multimodal approach can resolve physical and molecular features across cell lines, to reveal genes associated with phenotypic properties at unprecedented resolution. Our results establish im-seq as a versatile high-throughput framework for linking genetic information to physical properties, providing the large scale, information-dense datasets needed to power the next generation of data-driven discoveries in cell biology.
Max-Planck-Zentren und -Schulen
Datenerfassung
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