Compressed sensing for imaging transcriptomics


引用 10|浏览63
Gaining a systematic molecular understanding of tissue physiology in health and disease will require the ability to rapidly profile the abundances of many genes at high resolution over large tissue volumes. Many current methods of imaging transcriptomics are based on single-molecule fluorescent hybridization, with barcodes to allow multiplexing across genes. These approaches have serious limitations with respect to (i) the number of genes that can be studied and (ii) imaging time, due to the need for high-resolution to resolve individual signals. Here, we show that both challenges can be overcome by introducing an approach that leverages the biological fact that gene expression is often structured across both cells and tissue organization. We develop Composite Imaging (CISI), that combines this biological insight with algorithmic advances in compressed sensing to achieve greater efficiency. We demonstrate that CISI accurately recovers the spatial abundance of each of 37 individual genes in the mouse primary motor cortex (MOp) from 10 composite measurements and without the need for spot-level resolution. CISI achieves the current scale of multiplexing with an order of magnitude greater efficiency, and can be leveraged in combination with existing methods to multiplex far beyond current scales.
AI 理解论文