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An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics

Chia‐Feng Tsai, Rui Zhao, Sarah Williams, Ronald Moore, Kendall Schultz, William Chrisler, Ljiljana Paša‐Tolić, Karin Rodland, Richard Smith, Tujin Shi, Ying Zhu, Tao Liu

2020Molecular & Cellular Proteomics176 citationsDOIOpen Access PDF

Abstract

5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

Topics & Concepts

Boosting (machine learning)Isobaric labelingProteomicsIsobaric processQuantitative proteomicsComputational biologyChemistryComputer scienceArtificial intelligenceBiologyPhysicsBiochemistryThermodynamicsGeneSingle-cell and spatial transcriptomicsAdvanced Proteomics Techniques and ApplicationsCell Image Analysis Techniques