Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning

Abstract Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each individual protein, inherently limiting their throughput and scalability. Here, we present CombPlex (COMBinatorial multiPLEXing), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of proteins that can be measured from C up to 2 C − 1, and is applicable to any mass spectrometry based or fluorescence-based microscopy platform. In CombPlex, every protein can be imaged in several channels, and every channel contains agglomerated images of several proteins. These combinatorically-compressed images are then decompressed to individual protein-images using deep learning and optimization. We perform feasibility experiments in silico and achieve accurate (F1=0.98, R=0.99) reconstruction for compressing the stains of twenty-two proteins to five imaging channels. We test our approach experimentally and obtain accurate (F1=0.97, R=0.93) images of seven proteins using three channels, both in fluorescence microscopy and in mass-based imaging. We demonstrate that combinatorial staining coupled with deep-learning decompression can serve to escalate the number of proteins measured using any imaging modality, without the need for specialized instrumentation. Coupling CombPlex with instruments for high dimensional imaging could pave the way to image hundreds of proteins at single-cell resolution in intact tissue sections.