Back

Comprehensive molecular profiling of single-cell proteome via gel electrophoresis and 3D single-molecule imaging

Kamarulzaman, L.; Kim, S.; Hidaka, T.; Tsuchida, M.; Taniguchi, Y.

2026-01-15 biophysics
10.64898/2026.01.14.699423 bioRxiv
Show abstract

Recent advances in shotgun proteomics and immunoassays have yielded powerful single-cell proteomics technologies. However, current methods lack the sensitivity required to comprehensively quantify protein abundances in individual cells. Here, we present single-cell PAGE-PISA, an ultra-sensitive proteome profiling strategy that combines gel electrophoresis with 3D single-molecule fluorescence imaging. Our approach labels all proteins in single cells with fluorescent dyes, separates them by electrophoresis, and counts with single-molecule resolution. This technique quantified over 107 protein copies from a single mammalian cell with the sensitivity to detect low-abundance proteins down to 104 copies per species. Single-cell PAGE-PISA successfully classified cells into distinct cell types based on their proteomic profiles. Furthermore, our single-cell proteome data strongly correlated with predicted developmental states during cardiomyocyte differentiation, providing complementary information to single-cell transcriptome data. Together, single-cell PAGE-PISA enables highly sensitive and quantitative proteome profiling at the single-cell level, capturing subtle proteomic differences that distinguish diverse cellular states.

Published in Nature Communications (predicted rank #1) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.