A water droplet-in-oil digestion (WinO) method was compared to an in-solution digestion (ISD) method to evaluate the improvement in the number and quantification of proteins in a single cell or trace samples. By using the WinO method, 400.3 ± 32.5 proteins were quantified from a single cell, whereas 140.8 ± 51.8 proteins were quantified by the ISD method. The recovery of proteins on the WinO method was 10.21-fold greater than the ISD method. The data described in this paper have been deposited to jPOST with the identifier JPST001390.

We established a deep proteome analysis method of cell-derived proteins in fetal bovine serum-containing culture supernatants and successfully detected more than 3500 cell-derived proteins.

Nanoflow liquid chromatography/tandem mass spectrometry (nanoLC/MS/MS) data was obtained with bioinertized systems for the highly sensitive analysis of phosphopeptides by depleting metal ions from the mobile phase. We found that not only direct contact of phosphopeptides with metal components, but also indirect contact with nanoLC pumps through the mobile phase causes significant losses during the recovery of phosphopeptides. Therefore, metal ions were depleted by inserting an on-line metal ion removal device containing metal-chelating membranes between the gradient mixer and the autosampler.

We developed a proteomic tool to enrich and analyze nascent polypeptidome by combining O-propargyl-puromycin (OPP) and stable isotope-labelled amino acids (SILAC) labelling.

Molecular chaperones help the folding of many client proteins in most organisms. We acquired the proteomic changes by the depletion of GroEL/ES chaperone, the deletion of DnaK/DnaJ chaperones, and the additional effect of the deletion of cytosolic proteases under both conditions to investigate the clearance process of known chaperone client proteins in the absence of the chaperones in Escherichia coli.

APEX2-based spatiotemporal proteomics identifies proximal proteins to STING ER exit foci. Label-free quantification of streptavidin pulldown from hTBJ1 cells expressing APEX2-STING with or without cGAMP was performed.

To apply MS-based proteomics for medical and medicinal applications, it is necessary to improve the analytical throughput based on the requirement of analyzing large number of samples. We precisely evaluated DIA parameters for 5-min gradient LC and reached a depth of >5,000 proteins from 1,000 ng HEK293T cell digest in a single-shot run, with an analytical throughput of 80 samples/day¹.

To analyse deep protein profiling of dried blood spot (DBS). We developed a simple method using sodium carbonate precipitation (SCP). SCP enriches hydrophobic proteins from DBS, allowing substantial removal of soluble proteins. In combination with SCP, we used quantitative LC-MS/MS proteome analysis in a data-independent acquisition mode (DIA) to enhance the sensitivity and quantification limits of proteome analysis.

Proteomic study was performed to identify protein expressed on the Arabidopsis leaf epidermis inoculated with or without plant pathogenic Fusarium gramineaum. The data set of this paper has been deposited to jPOST with the identifiers JPST002005.

The proteomic landscape caused by differentiation on CD4+ helper T (Th) cell subsets, such as Th1, Th2, Th17, and regulatory T cells (Treg) is still unclear. We performed DIA-MS for deep-proteomics analysis¹ from TRIzol lysis Th cell subset samples after RNAs isolation and successfully detected 8,312 protein groups².

To examine whether patient-derived sarcoma models recapitulate the heterogeneity seen in patients, proteomic analysis of both sarcoma models with different subtypes and passage numbers and their primary tumors was performed using mass spectrometry. The levels of stromal proteins derived from tumors were lower in patient-derived xenografts (PDXs) and cell lines, and some human stromal proteins were replaced by the corresponding mouse proteins in PDXs.

Xeno-free culture system is useful for research of regenerative medicine, but variability in passaging steps sometimes affects reproducibility. We compared the protein expression levels of iPSCs that were passaged using the revised methods with successive generations. We showed no significant protein expression difference with successive generations, and increased viability of human iPSCs by revised method. The data accompanying this paper have been deposited to jPOST with identifier JPST001327 / PXD028731.

To compare the difference between the public database search and sample-specific database search generated by home-made software “OncoProGx”, we conducted two LC-MS/MS runs for two patient-derived undifferentiated pleomorphic sarcoma cell lines. The numbers of identified peptides and proteins were almost same between two database search methods. The novel sample-specific peptides such as mutated peptides and new transcript isoforms were identified using only “OncoProGx.”

The identification of accurate differentially expressed proteins (DEPs) is of paramount for proteomics analysis and biological interpretation. Other than the match between runs approach, various imputation and differential expression (DE) strategies have been developed to address the issue of missing values generated by the stochastic nature of data-dependent acquisition in MS-based proteomics¹. However, a comprehensive evaluation of the analysis pipelines’ performance in accurately identifying protein candidates is still lacking. To address this gap and benchmark the effectiveness of these methods, a proteomics dataset was generated, which encompasses proteins derived from human, yeast, and drosophila, each present in defined ratios². The data described here have been deposited to jPOST^3,4 with the identifier JPST001395.

Proteomic data for systematic identification of Anaplastic lymphoma kinase (ALK) substrates by integrated phosphoproteome and interactome analysis¹.

Colorectal cancer (CRC) remains one of the leading gastrointestinal malignancies worldwide. Development of effective therapeutics for patients diagnosed with CRC remains challenging, mainly due to innate or acquired chemoresistance arising upon treatment administration. Different mechanisms employed by CRC cells have been identified to be responsible for chemoresistance development, including mutations, cellular signalling pathways, transport-based signalling pathways and drug metabolism, among others. Additionally, biomarkers have been identified that assist in predicting treatment response, as well as providing an indication for chemoresistance development. However, the strategies and models currently used to understand this phenomenon are not enough, demanding a shift into other fields of high potential which have so far, due to technical complexity, been given less importance. This limitation is most distinct for studies related to post-translational modifications (PTMs), particularly protein methylation, and their use as predictive biomarkers for CRC chemoresistance. The development of comprehensive proteomic strategies for analysing PTMs can achieve a better understanding of potential protein methylation marks suitable as biomarker candidates for predicting treatment response in CRC. Thus, this review aims to provide a brief introduction on methodologies utilised for PTM analysis, followed by an in-depth discussion on the current strategies and models developed for analysing methyl-proteomics, together with their strengths and limitations. Lastly, this synopsis will be used to propose the future perspectives of methyl-proteomics and its involvement in predicting CRC treatment response. The identification of such biomarkers will also improve the biochemical understanding of CRC chemoresistance.