Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.
L, K., S, K., T, H., M, T., & Y, T. (2026). Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.. Nature communications. https://doi.org/10.1038/s41467-026-74840-0
L K, S K, T H, M T, Y T. Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.. Nature communications. 2026; doi: 10.1038/s41467-026-74840-0
L K, S K, T H, et al. Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.[J]. Nature communications. 2026. DOI: 10.1038/s41467-026-74840-0.
@article{l2026,
author = {Kamarulzaman L and Kim S and Hidaka T and Tsuchida M and Taniguchi Y},
title = {Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.},
journal = {Nature communications},
year = {2026},
doi = {10.1038/s41467-026-74840-0},
note = {PMID: 42420286},
}
TY - JOUR AU - Kamarulzaman L AU - Kim S AU - Hidaka T AU - Tsuchida M AU - Taniguchi Y TI - Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging. T2 - Nature communications PY - 2026 DO - 10.1038/s41467-026-74840-0 AN - PMID:42420286 ER -
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 105 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.