AUTO-SP: Automated Sample Preparation for Analyzing Proteins and Protein Modifications
收藏NIAID Data Ecosystem2026-05-02 收录
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https://figshare.com/articles/dataset/AUTO-SP_Automated_Sample_Preparation_for_Analyzing_Proteins_and_Protein_Modifications/29656830
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资源简介:
Liquid chromatography
(LC) tandem mass spectrometry (MS/MS) is
one of the widely used proteomic techniques to study the alterations
occurring at the protein level as well as post-translational modifications
(PTMs) of proteins that are relevant to different physiological or
pathological statuses. The mass spectrometric analysis of peptides
digested from proteins (bottom-up proteomics) has emerged as one of
the major approaches for proteomics. In this approach, proteins are
first cleaved by one or more proteases into peptides for MS analysis,
and peptides with PTMs are further enriched, followed by the LC-MS/MS
analysis. To achieve a reproducible and quantitative proteomic characterization,
a well-established protease digestion and PTM peptide enrichment protocol
is critical. In this study, we developed AUTO-SP, a sample preparation
platform providing automated protocols for BCA analysis, protein digestion,
and PTM enrichment for protein and PTM analyses. We utilized patient-derived
xenograft (PDX) breast cancer tumor tissues (basal-like and luminal
subtypes) to demonstrate the efficacy of AUTO-SP. The protein amount
was quantified, and proteins were further digested by using AUTO-SP
for each PDX sample. Based on the data-independent acquisition (DIA)-MS
data, we observed that samples of the same breast cancer subtypes
were highly correlated (≥0.98). Additionally, >25,000 phosphopeptides
and >14,000 ubiquitinated peptides were identified in the PDX samples
when using AUTO-SP for PTM enrichment, while unique pathways were
enriched from the differentially expressed ubiquitinated peptides
of basal-like and luminal subtypes. AUTO-SP demonstrated its efficacy
to provide a reliable and reproducible sample preparation procedure
for MS-based proteomic and PTM analyses.
创建时间:
2025-08-12



