We have already demonstrated a proof-of-principle through the development of plexDIA. That endeavor increased the sensitivity of low-input samples using commercially available reagents. We now aim to scale the throughput by 100-1000 fold by synergistically optimizing mass spectrometry methods, creation of novel barcodes, and implementing deep learning analysis. This multiplicative increase in throughput arises from the simultaneous parallelization of both samples (single cells per unit time) and the depth of proteome coverage (peptides quantified per sample). This inspires the name of this FRO, Parallel Squared.