Details

Autor(en) / Beteiligte

• McCown, Michaela A.
• Allen, Carolyn
• Machado, Daniel D.
• Payne, Samuel H.

Titel

Abstract 274: Comparison of proteomics identification pipelines for lymphocyte characterization

Ist Teil von

• Cancer research (Chicago, Ill.), 2021-07, Vol.81 (13_Supplement), p.274-274

Erscheinungsjahr

2021

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Abstract Proteomics analyses can be adapted to samples as small as a single cell, however such sample reduction severely limits the number proteins identified and quantified. Multiple software pipelines exist for processing the raw data to quantitative protein tables. For very small sample amounts, the sensitivity of software algorithms can have a significant impact on the final results. We collected whole blood samples for two subjects by venous blood draws. Using antibody guided flow cytometry cell sorting, we separated B and T cell lymphocytes. For two subjects and two different blood collections, five replicates of 145 B cells or T cells were prepared for mass spectrometry for proteomics using the AutoPOTS workflow. Using the raw files from these forty runs, we perform protein quantification with a selection of common tools such as MaxQuant, ProteomeDiscoverer, and FragPipe and compare the number of identified and quantified proteins. In small sample proteomics, the primary objective is obtaining the most quantified proteins. Although each tool has advantages in terms of usability, speed, and sensitivity, our primary concern is sensitivity. At such a low sample input, we observe that the number of quantified proteins increases by 30% using the most sensitive algorithm. For identifying and quantifying proteins from small samples, the sensitivity of the software is the most important factor to consider. In addition to the identification sensitivity of each algorithm, we are exploring optimal parameters for increased protein quantification. Differences between samples appear regardless of the processing tool, showing that using a less sensitive tool will still characterize the trends but with a significant cost to the number of proteins. Using the most sensitive algorithm for small sample proteomics will greatly improve the number of proteins identified and quantified. Citation Format: Michaela A. McCown, Carolyn Allen, Daniel D. Machado, Samuel H. Payne. Comparison of proteomics identification pipelines for lymphocyte characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 274.