Compressed sensing expands the multiplexity of imaging mass cytometry.

The multiplexity of current antibody-based imaging is limited by the number of reporters that can be detected simultaneously. Compressed sensing can be used to reconstruct high-dimensional information from low-dimensional measurements. Previously, compressed sensing using composite in situ imaging (CISI) of transcriptomic data leveraged gene co-regulation structure to recover spatial expression of 37 RNA species from images of 11 composite channels. Here, we extend the CISI framework to protein expression data measured by imaging mass cytometry (IMC). CISI-IMC accurately recovers spatial expression of 16 immune and stromal marker proteins from images of 8 composite channels with an average Pearson's correlation of 0.8 across protein. Training the CISI-IMC framework using data collected on multiple human tissues enables universal decompression of composite data from a wide range of tumor and healthy tissue types. The expression dictionary and barcoding matrix described here are immediately implementable for general immune and stromal cell type classification, but CISI-IMC can in principle be applied to other markers or other antibody-based imaging methods. Our work lays the foundation for much higher plex protein imaging.