<p>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 10<sup>7</sup> protein copies from a single mammalian cell with the sensitivity to detect low-abundance proteins down to 10<sup>5</sup> 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.</p>

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Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging

  • Latiefa Kamarulzaman,
  • Sooyeon Kim,
  • Takuya Hidaka,
  • Misaki Tsuchida,
  • Yuichi Taniguchi

摘要

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.