An automated workflow for label-free and multiplexed single cell proteomics sample preparation at unprecedented sensitivity.

The analysis of single-cell proteomes has recently become a viable complement to transcriptomics and genomics studies. Proteins are the primary driver of cellular functionality and mRNA levels are often an unreliable proxy of such. Therefore, the global analysis of the proteome is essential to study cellular identities. Multiplexed and label-free mass spectrometry-based approaches with single-cell resolution have lately attributed surprising heterogeneity to presumed homogenous cell populations. Even though specialized experimental designs and instrumentation have demonstrated remarkable advances, the efficient sample preparation of single cells still lag. Here, we introduce the proteoCHIP, a universal option for single-cell proteomics sample preparation at surprising sensitivity and throughput. The automated processing using a commercial system combining single-cell isolation and picoliter dispensing, the cellenONE(R), reduces final sample volumes to low nanoliters submerged in a hexadecane layer simultaneously eliminating error-prone manual sample handling and overcoming evaporation. The specialized proteoCHIP design allows direct injection of single cells via a standard autosampler resulting in around 1,500 protein groups per analytical run at remarkable reporter ion signal to noise while reducing or eliminating the carrier proteome. We identified close to 2,600 proteins across 170 multiplexed single cells from two highly similar human cell types. This dedicated loss-less workflow allows distinguishing in vitro co-differentiated cell types of self-organizing cardiac organoids based on indicative markers across 150 single cells. In-depth characterization revealed enhanced cellular motility of cardiac endothelial cells and sarcomere organization in cardiomyocytes. Our versatile and automated sample preparation has not only proven to be easily adaptable but is also sufficiently sensitive to drive biological applications of single-cell proteomics.