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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Related Experiment Video

Updated: Jan 22, 2026

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier
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DART-ID increases single-cell proteome coverage.

Albert Tian Chen1,2, Alexander Franks3, Nikolai Slavov1,2,4

  • 1Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.

Plos Computational Biology
|July 2, 2019
PubMed
Summary
This summary is machine-generated.

Data-driven Alignment of Retention Times for IDentification (DART-ID) enhances proteomic analysis of small samples. This method increases protein identification by 30-50%, reducing missing data and boosting statistical power for cell-type specific discoveries.

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Area of Science:

  • Proteomics
  • Biochemistry
  • Computational Biology

Background:

  • Liquid chromatography and tandem mass spectrometry (LC-MS/MS) enables large-scale protein identification and quantification.
  • Analyzing proteomes from limited cell numbers (e.g., single cells) is challenging due to reduced peptide sequencing and increased missing data.

Purpose of the Study:

  • To develop a computational method to improve peptide identification and quantification in proteomic analyses, particularly for small samples.
  • To enhance confidence in peptide-spectrum matches by leveraging retention time information.

Main Methods:

  • Development of Data-driven Alignment of Retention Times for IDentification (DART-ID), a computational tool.
  • Implementation of Bayesian frameworks for global retention time alignment.
  • Integration of retention time estimates to improve peptide-spectrum match confidence scoring.

Main Results:

  • DART-ID increased the number of identified proteins by 30-50% across various sample types (bulk and single-cell) at 1% FDR.
  • The method significantly reduced missing proteomic data points.
  • Statistical power was boosted, doubling the number of differentially abundant proteins identified in specific cell types like monocytes and T-cells.

Conclusions:

  • DART-ID effectively enhances proteomic data analysis, especially for challenging small-sample scenarios.
  • The tool improves protein identification, quantification, and statistical power, facilitating cell-type specific discoveries.
  • DART-ID is a versatile method applicable to diverse experimental designs and is publicly available.