Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Abstract

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

Exploiting Charge State Distribution To Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses

“…Using retention time and mass bins dramatically improves partitioning of the peptide search spaceover 95% of peptides can be separated using 2 ppm mass bins with 0.5 min retention time bin. Such a level of orthogonality between retention time and mass is well-known and, thus, the retention time dimension is currently actively employed in the state-of-the-art proteomics data analysis pipelines. − Although direct infusion was among the early techniques used in proteomics, it has gained renewed attention in recent research. , Our analysis shows that utilizing CSD information substantially aids in the discrimination of peptide candidates, although not to the same degree as the retention time dimension. While retention time and mass information allows superior partitioning of the peptide space, including the CSD dimension further adds a small but consistent improvement in discrimination across all investigated conditions (red bars in Figure ).…”

“…31−33 Although direct infusion was among the early techniques used in proteomics, 34 it has gained renewed attention in recent research. 35,36 Our analysis shows that utilizing CSD information substantially aids in the discrimination of peptide candidates, although not to the same degree as the retention time dimension. While retention time and mass information allows superior partitioning of the peptide space, including the CSD dimension further adds a small but consistent improvement in discrimination across all investigated conditions (red bars in Figure 4).…”

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

“…The first two iterations of DISPA relied on introduction of stable isotope labeled standard peptides for quantification, which is an expensive requirement that also reduces our data collection bandwidth and depth by wasting time on the heavy ion signal. We wanted to determine to what extent we could use DISPA for label free quantification (DISPA-LFQ) . We found that using DISPA with csoDIAq excluding stable isotopes, we could quadruple our identifications to over 2,000 proteins from cultured human cells.…”

Repeat-Enhancing Featured Ion-Guided Stoichiometry for Identification and Quantification of Direct Infusion Proteome

“…9 CsoDIAq could identify peptides with improved accuracy, but the quantification could be only available for heavily labeled 10 samples and restricted peptides. 11 Although DISPA-LFQ (12) proposed a new label-free quantification (LFQ) strategy based on fragment ion intensity recently, the LFQ tools for DI-SPA data are still seriously inadequate. Therefore, it becomes an urgent demand for an efficient explanation for DI-SPA data, especially for the prevalent label-free DIA experiments.…”

“…Therefore, in this study, we introduce the RE-FIGS, a method to simultaneously identify peptides from (repeated) DI-SPA data with high confidence, deep proteome coverage, and quantify label-free experiments by the adaption of our original FIGS method. Different from extracting representative y-ion fragment intensity or the sum of all detected fragment ion intensities of common peptides for peptide quantification (DISPA-LFQ), 12 our RE-FIGS is a universal method for peptide quantification of DIA data based on dynamic deconvolution and featured ions rather than all fragment ions. Furthermore, using bootstrap aggregating linear discriminant analysis, the RE-FIGS method automatically scores the spectrum−spectrum matching (SSM) and dynamically deconvolutes the mixed experimental spectra to obtain peptide quantification.…”