The evolution of mass spectrometry (MS) and analytical techniques has led to the demand for proteome analysis with high proteome coverage in single-shot measurements. Focus has been placed on data-independent acquisition (DIA)-MS and ion mobility spectrometry as techniques for deep proteome analysis. We aimed to expand the proteome coverage by single-shot measurements using optimizing high-field asymmetric waveform ion mobility spectrometry parameters in DIA-MS. With our established proteome analysis system, more than 10,000 protein groups were identified from HEK293 cell digests within 120 min of MS measurement time. Additionally, we applied our approach to the analysis of host proteins in mouse feces and detected as many as 892 host protein groups (771 upregulated/121 downregulated proteins) in a mouse model of repeated social defeat stress (R-SDS) used in studying depression. Interestingly, 285 proteins elevated by R-SDS were related to mental disorders. The fecal host protein profiling by deep proteome analysis may help us understand mental illness pathologies noninvasively. Thus, our approach will be helpful for an in-depth comparison of protein expression levels for biological and medical research because it enables the analysis of highly proteome coverage in a single-shot measurement.

Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.

Ex vivo lung perfusion (EVLP) enables assessment of marginal donor lungs for transplantation. We aimed to discover biomarkers in EVLP perfusate that could predict development of primary graft dysfunction (PGD). From September 2008 to August 2013, 100 clinical EVLPs were performed. Eleven patients developed PGD grade 3 within 72 h after transplant. The non‐PGD group consisted of 34 patients without PGD grade 3. Nonbilateral lung transplants or transplant after extracorporeal life support were excluded from analyses. Soluble intercellular adhesion molecule 1 (sICAM‐1), soluble VCAM‐1 (sVCAM‐1), and soluble E selectin (sE‐selectin) levels, as markers of endothelial activation, were measured in the perfusate of EVLP by enzyme‐linked immunosorbent assay and were correlated with clinical outcome. Levels of sICAM‐1 at 1 h and sVCAM‐1 at 1 and 4 h were significantly higher in the PGD group compared with the non‐PGD group. The sE selectin levels were not statistically different between the study groups. Higher levels of sVCAM‐1 at 1 and 4 h were statistically significantly associated with PGD either alone or after adjustment for other PGD risk factors. These adhesion molecules may help identify donor lungs at higher risk of PGD during EVLP.

In recent years, there has been a growing demand for low-input proteomics, particularly in the context of single-cell proteomics (SCP). In this study, we have developed a lauryl maltose neopentyl glycol (LMNG)-assisted sample preparation (LASP) method. This method effectively reduces protein and peptide loss in samples by incorporating LMNG, a surfactant, into the digestion solution and subsequently removing the LMNG simply via reversed phase solid-phase extraction. The advantage of removing LMNG during sample preparation for general proteomic analysis is the prevention of mass spectrometry (MS) contamination. When we applied the LASP method to the low-input SP3 method and on-bead digestion in co-immunoprecipitation-MS, we observed a significant improvement in the recovery of the digested peptides. Furthermore, we have established a simple and easy sample preparation method for SCP based on the LASP method (scpLASP) and identified a median of 1,175 proteins from a single HEK239F cell using liquid chromatography (LC)-MS/MS with a throughput of 80 samples per day.

Proteomics has become an increasingly important tool in medical and medicinal applications. It is necessary to improve the analytical throughput for these applications, particularly in large-scale drug screening to enable measurement of a large number of samples. In this study, we aimed to establish an ultrafast proteomic method based on 5-min gradient LC and quadrupole-Orbitrap mass spectrometer (Q-Orbitrap MS). We precisely optimized data-independent acquisition (DIA) parameters for 5-min gradient LC and reached a depth of >5000 and 4200 proteins from 1000 and 31.25 ng of HEK293T cell digest in a single-shot run, respectively. The throughput of our method enabled the measurement of approximately 80 samples/day, including sample loading, column equilibration, and wash running time. We demonstrated that our method is applicable for the screening of chemical responsivity via a cell stimulation assay. These data show that our method enables the capture of biological alterations in proteomic profiles with high sensitivity, suggesting the possibility of large-scale screening of chemical responsivity.

A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study onJatropha curcasis presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.