Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Abstract

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

“…A protein matrix including 337 serum proteins, with 21.5% missing values, was generated ( Table S3B ) using OpenSWATH (version 2.4), with an in-house serum spectral library containing 3474 peptide precursors and 536 protein groups using the SubLib strategy. 22 …”

“… 21 We next built a subset library containing 3474 peptides precursors and 536 protein groups for SWATH-MS data analysis, as previously described. 22 …”

To the proteome and beyond: advances in single-cell omics profiling for plant systems

“…Acquiring MS data in DIA mode can reduce stochasticity in peptide identifications and can be more sensitive than typical data-dependent acquisition (DDA) for single-cell analysis ( Saha-Shah et al, 2019 ). Although approaches for generating in silico spectrum libraries for DIA analyses exist ( Demichev et al, 2020 ; Pino et al, 2020 ; Yang et al, 2020b ; Zhang et al, 2020 ; Mehta et al, 2021 ), the development of cell type-specific DDA spectral libraries for DIA may reduce false positives and increase the certainty that particular proteins are expressed in specific cells ( Rosenberger et al, 2017 ; Ge et al, 2020 ). These studies represent substantial advancements in single-cell analysis and further technological improvements to instrumentation and data analysis pipelines will increase the detection of low-abundance proteins from a single cell.…”

Narrow Precursor Mass Range for DIA–MS Enhances Protein Identification and Quantification in Arabidopsis

“…As such, they were able to identify 29% more proteins [ 23 ]. Furthermore, using a small sample specific library was shown to reduce the false discovery rate (FDR) in the spectral match and improve in protein identification and quantification [ 24 , 25 ]. Certainly, with advances in MS technology, either with faster scan rates and incorporation of additional separation dimensions such as ion mobility, we would expect to be able to quantify high numbers of proteins in a single shot.…”