4.8 Article

Fully Automated Sample Processing and Analysis Workflow for Low-Input Proteome Profiling

Journal

ANALYTICAL CHEMISTRY
Volume 93, Issue 3, Pages 1658-1666

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.0c04240

Keywords

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Funding

  1. National Cancer Institute of the National Institutes of Health [R33CA225248, R01GM138931, R01CA235575, U24CA210972]
  2. BYU Department of Chemistry and Biochemistry through the Roland K. Robins Fellowship
  3. BYU Simmons Center for Cancer Research

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Recent advancements in sample preparation and analysis have led to the development of a fully automated platform, autoPOTS, which uses commercially available instrumentation for processing and analyzing low-input and single-cell proteomics samples, making it a convenient option for many laboratories.
Recent advances in sample preparation and analysis have enabled direct profiling of protein expression in single mammalian cells and other trace samples. Several techniques to prepare and analyze low-input samples employ custom fluidics for nanoliter sample processing and manual sample injection onto a specialized separation column. While being effective, these highly specialized systems require significant expertise to fabricate and operate, which has greatly limited implementation in most proteomic laboratories. Here, we report a fully automated platform termed autoPOTS (automated preparation in one pot for trace samples) that uses only commercially available instrumentation for sample processing and analysis. An unmodified, low-cost commercial robotic pipetting platform was utilized for one-pot sample preparation. We used low-volume 384-well plates and periodically added water or buffer to the microwells to compensate for limited evaporation during sample incubation. Prepared samples were analyzed directly from the well plate with a commercial autosampler that was modified with a 10-port valve for compatibility with 30 mu m i.d. nanoLC columns. We used autoPOTS to analyze 1-500 HeLa cells and observed only a moderate reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to our previously developed nanoPOTS platform. To evaluate clinical feasibility, we identified an average of 1095 protein groups from similar to 130 sorted B or T lymphocytes. We anticipate that the straightforward implementation of autoPOTS will make it an attractive option for low-input and single-cell proteomics in many laboratories.

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