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- W4386692890 abstract "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." @default.
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- W4386692890 date "2023-09-12" @default.
- W4386692890 modified "2023-09-26" @default.
- W4386692890 title "Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning" @default.
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- W4386692890 doi "https://doi.org/10.1101/2023.09.09.556962" @default.
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