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TimsTOF HT improves protein identification and quantitative reproducibility for deep unbiased plasma protein biomarker discovery (Loading curve dataset)

ABSTRACT: Mass spectrometry (MS) has emerged as a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide range of plasma protein concentrations along with technical and biological variabilities, continue to present significant challenges for deep and reproducible protein quantitation across large patient cohorts. Here we demonstrate the qualitative and quantitative performance gain of the timsTOF HT over the timsTOF Pro 2 mass spectrometer in the analysis of neat (unfractionated) and Proteograph™ (PG)-processed plasma across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV<20%) with timsTOF HT compared to timsTOF Pro 2. In an exploratory study of 20 late-stage cancer and 20 control sampleswe observed a ~50% increase in total and statistically significant plasma peptide precursors (q<0.05) with timsTOF HT compared to Pro 2. Our data demonstrated the superior performance of timsTOF HT in identifying and quantifying differences between biologically diverse samples, which can improve disease biomarker discovery in large cohort studies. Moreover, researchers can leverage datasets from this study to optimize their LCMS workflows for plasma protein profiling and biomarker discovery. See the details in a paper, entitled "timsTOF HT improves protein identification and quantitative reproducibility for deep unbiased plasma protein biomarker discovery".

INSTRUMENT(S): timsTOF, timsTOF Pro 2

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Joon-Yong Lee

LAB HEAD: Bruce Wilcox

PROVIDER: PXD047854 | Pride | 2024-01-16

REPOSITORIES: Pride

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			Action	DRS
	Loading_Test_100SPD_BOB2_NP1_DIANN_report.tsv	Tabular
	Loading_Test_100SPD_BOB2_NP2_DIANN_report.tsv	Tabular
	Loading_Test_100SPD_BOB2_NP3_DIANN_report.tsv	Tabular
	Loading_Test_100SPD_BOB2_NP4_DIANN_report.tsv	Tabular
	Loading_Test_100SPD_BOB2_NP5_DIANN_report.tsv	Tabular

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Project description:Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.

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TimsTOF HT improves protein identification and quantitative reproducibility for deep unbiased plasma protein biomarker discovery (Loading curve dataset)

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