Optimizing Linear Ion-Trap Data-Independent Acquisition toward Single-Cell Proteomics

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A linear ion trap (LIT) is an affordable, robust mass spectrometer that provides fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry (MS) measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on the column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.
OriginalsprogEngelsk
TidsskriftAnalytical Chemistry
Vol/bind95
Udgave nummer26
Sider (fra-til)9881-9891
Antal sider11
ISSN0003-2700
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported in part by the Pelotonia Institute for Immuno-Oncology and National Institutes of Health Grant R01-GM133981 to B.C.S. Mass spectrometry analysis at the DTU proteomics core was funded by a grant from the Novo Nordisk Foundation to E.M.S. with reference number NNF21OC0071016. Work at CPR was funded in part by a grant from the Novo Nordisk Foundation (NNF14CC0001).

Funding Information:
T.P. would like to thank J. A. Madsen at The Novo Nordisk Foundation Center for Protein Research, L. R. Woltereck at the Technical University of Munich, and R. Bruderer and O. Bernhardt from Biognosys AG, P. E. Geyer at the Exact Sciences, and K. B. Emdal at The Novo Nordisk Foundation Center for Protein Research for fruitful discussion. E.K. would like to thank M. Mann for financial support, M. Strauss for fruitful discussion, and S. Grégoire for his input on data visualization. This work was supported in part by the Pelotonia Institute for Immuno-Oncology and National Institutes of Health Grant R01-GM133981 to B.C.S. Mass spectrometry analysis at the DTU proteomics core was funded by a grant from the Novo Nordisk Foundation to E.M.S. with reference number NNF21OC0071016. Work at CPR was funded in part by a grant from the Novo Nordisk Foundation (NNF14CC0001).

Publisher Copyright:
© 2023 American Chemical Society.

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