Biology subjects

Okuda, Y.

Publications and source records attributed to Okuda, Y..

8 recordsLinked to original sources

bioRxiv2026

A High-Sensitivity and High-Throughput LC-MS Workflow for Low-Input and Single-Cell Proteomics

Single-cell proteomics (SCP) requires sample preparation that minimizes sample loss and highly sensitive LC-MS analysis because the amount of protein available from a single cell is extremely limited. Here, we developed an integrated workflow for low-input and single-cell proteomics based on single-cell proteomics by LMNG-assisted sample preparation (scpLASP), which enables microliter-scale sample preparation using standard laboratory consumables. Systematic optimization of peptide reconstitution conditions identified 0.01% n-dodecyl-{beta}-D-maltoside (DDM) combined with 0.01% lauryl maltose neopentyl glycol (LMNG) as the condition yielding the highest number of identified peptides among those tested for low-input samples. Optimization of the LC mobile-phase composition identified 0.5% acetic acid with 1% dimethyl sulfoxide (DMSO) as the optimal condition, increasing the mean number of identified protein groups from 6,767 with 0.1% formic acid to 7,409 for 250 pg HeLa digests analyzed using an Orbitrap Astral Zoom. Shortening the LC gradient from 11 to 5.5 min enabled higher analytical throughput while maintaining mean identification counts of 6,558 and 7,088 protein groups from 100 and 250 pg HeLa digests, respectively, with median protein-level coefficients of variation of 9.20% and 6.99%. When the optimized workflow was applied to HEK293-derived Expi293F single cells using the same 5.5-min LC gradient, an effective analytical throughput of 135 samples per day was achieved with an injection volume of 4.5 {micro}L, accounting for sample-loading time. This workflow identified a median of 5,655 protein groups per cell using an empirical spectral library generated from 20-cell samples. Overall, this study establishes a sensitive and high-throughput workflow for low-input and single-cell proteomics that combines low-loss microliter-scale sample preparation with optimized LC-MS analysis, achieving high proteome depth and analytical throughput without requiring specialized nanoliter-scale sample-preparation devices.

biochemistry↗

bioRxiv2026

Serum preprocessing workflows differentially shape biological readout in data-independent acquisition proteomics of systemic juvenile idiopathic arthritis

Serum proteomics is increasingly used for minimally invasive biomarker discovery and disease phenotyping, and the choice of serum preprocessing workflow can shape proteome depth, quantitative characteristics, and downstream biological readouts. However, disease-oriented comparisons within a single cohort remain limited. Here, we compared four serum preprocessing workflows--Top14 depletion (TOP14D), tomato lectin affinity purification (TomAP), and two nanoparticle-based enrichment workflows (NPA and NPB)--using serum from six patients with systemic juvenile idiopathic arthritis (sJIA) and six age- and sex-matched healthy controls, and analyzed them using unified data-independent acquisition mass spectrometry (DIA-MS) and a statistical pipeline. We evaluated proteome depth, missingness, quantitative characteristics, group separation, differential abundance signatures, pathway enrichment, curated sJIA-related gene set coverage, pre-ranked gene set enrichment analysis (GSEA) results, and detection of inflammasome/interferon-related proteins. TomAP yielded the greatest proteome depth (7612 proteins), followed by NPB (6735 proteins) and NPA (6602 proteins), whereas TOP14D yielded the smallest protein set (3303 proteins). Principal component analysis (PCA) showed a separation between the sJIA and control groups for all workflows. Differentially expressed proteins (DEPs) showed limited overlap, with only 75 DEPs common to all four workflows. Functional enrichment patterns were workflow-dependent; TOP14D and TomAP mainly captured neutrophil/myeloid and inflammatory processes, whereas NPA and NPB captured RNA processing- and translation-related signals. TomAP showed relatively broad coverage and positive enrichment of curated sJIA-related gene sets associated with inflammation, innate immunity, and macrophage activation syndrome (MAS). Inflammasome/interferon-related proteins, including NLRC4, PYCARD, GSDMD, MEFV, IL-18, OAS3, and MYD88, showed workflow-dependent detectability and differential abundance. These findings support a disease-oriented benchmark for fit-for-purpose workflow selection according to the disease axis and analytical objective rather than proteome depth alone.

biochemistry↗

bioRxiv2026

Development of an Ethylenediaminetetraacetic Acid-Enhanced Deep Proteomic Profiling Method for Dried Blood Spots and Its Application in Mouse Disease Models

Dried blood spots (DBS) are well-established microsamples used in clinical testing and newborn screening. However, their use in deep proteomics is hindered by highly abundant blood proteins and inefficient protein recovery from filter paper matrices. The non-targeted analysis of non-specifically DBS-absorbed proteins (NANDA) workflow partially overcomes the impact of abundant blood proteins and has enabled the identification of over 5,000 proteins from DBS samples. Nonetheless, residual abundant proteins, including hemoglobin and fibrinogen, constrain deep proteomic analysis. Therefore, this study aimed to evaluate the effects of the metal chelator ethylenediaminetetraacetic acid (EDTA) on the depth of DBS proteomic analysis. An optimized EDTA-enhanced NANDA protocol that incorporated a 100 mM EDTA wash step was compatible with standard DBS collection procedures and required no modification of current clinical workflows, markedly enhancing the depletion of abundant proteins and facilitating its potential use in clinical and translational settings. When combined with Orbitrap Astral data-independent acquisition mass spectrometry, this approach enabled the single-shot identification of more than 7,000 proteins from DBS samples; to the best of our knowledge, this represents the deepest proteome coverage reported to date, and the workflow further supported high-throughput and highly reproducible analyses. Additionally, its application to mouse disease models revealed disease-specific systemic immune signatures from minimal blood volumes. Collectively, these results establish EDTA-enhanced NANDA as a practical and scalable workflow that overcomes longstanding limitations of DBS proteomics, thereby enabling deep, high-throughput, minimally invasive proteomic profiling across diverse biological and experimental contexts.

molecular biology↗

bioRxiv2026

TomAP-MS: an improved tomato lectin affinity purification-based mass spectrometry workflow enabling ultra-deep plasma proteomics

Plasma proteomics is increasingly important for biomarker discovery and disease stratification; however, comprehensive and high-throughput analysis remains challenging because of the extreme dynamic range of plasma proteins. We previously established tomato lectin affinity purification-based mass spectrometry (TomAP-MS), a workflow that enhances plasma proteome coverage via tomato lectin-mediated enrichment. The initial workflow depended on a 4% sodium dodecyl sulfate (SDS) elution, followed by SP3-based purification and digestion, which raised complexity and restricted throughput. In this study, we developed an improved TomAP-MS workflow incorporating lauryl maltose neopentyl glycol (LMNG)-assisted acid elution (LAcE), in which proteins are eluted under acidic conditions in the presence of LMNG. This process is followed by pH adjustment and direct tryptic digestion without SP3 cleanup. Compared with conventional acid elution and the original SDS/SP3 workflow, LAcE increased protein identifications while simplifying sample preparation and improving throughput. Using the optimized workflow, we identified more than 7,500 proteins from human plasma and demonstrated broader applicability in extracellular vesicle enrichment and protein interaction analysis workflows. We demonstrated that ethylenediaminetetraacetic acid plasma was the preferred specimen type, enabling the identification of over 5,000 proteins from just 1 {micro}L of plasma, with minimal impact on proteomic profiles after up to three freeze-thaw cycles. Additionally, the analysis of plasma from 200 healthy individuals reproducibly detected 4,117 proteins across all samples, including many proteins associated with inherited disorders. These findings establish TomAP-MS with LAcE as a practical platform for deep plasma proteomics, supporting its future application in proteomics-based screening and diagnostics.

biochemistry↗

bioRxiv2026

DIA-MS based plasma peptidomic workflow for profiling organ-derived peptides

Plasma contains diverse bioactive peptides that play crucial roles in maintaining homeostasis and regulating disease responses. However, the presence of peptides derived from high-abundance proteins such as albumin makes comprehensive analysis of native peptides secreted by organs challenging. This study aimed to establish a highly sensitive plasma peptidomic approach by combining data-independent acquisition (DIA) with spectral libraries of plasma and organs. First, peptides were extracted from plasma and eleven organ types using a high-yield peptide extraction method, the differential solubilization method. These peptides were then measured via data-dependent acquisition (DDA) analysis using a timsTOF HT for constructing empirical spectral library. Subsequently, DIA-MS data from plasma samples were measured and analyzed using this spectral library. This strategy achieved identification of, on average, over 5,500 peptides per run, with over 2,000 organ-derived peptides including 19 known bioactive peptides. The novel strategy proposed here enables highly sensitive quantitative analysis of organ-derived peptides in plasma, linking them to their secreting organs. It is expected to substantially contribute not only to the discovery of biomarkers and novel bioactive peptides but also to elucidating the pathophysiology of systemic diseases.

biochemistry↗

bioRxiv2021

Biosynthesis system of Synechan, a sulfated exopolysaccharide, in the model cyanobacterium Synechocystis sp. PCC 6803

Extracellular polysaccharides of bacteria contribute to biofilm formation, stress tolerance, and infectivity. Cyanobacteria, the oxygenic photoautotrophic bacteria, uniquely and widely have sulfated extracellular polysaccharides and they may utilize the polysaccharides for survival in nature. In addition, sulfated polysaccharides of cyanobacteria and other organisms have been focused as beneficial biomaterial. However, very little is known about their biosynthesis machinery and function in cyanobacteria. Here we found that the model cyanobacterium, Synechocystis sp. PCC 6803, formed bloom-like cell aggregates using sulfated extracellular polysaccharides (designated as synechan) and identified whole set of genes responsible for synechan biosynthesis and its transcriptional regulation, thereby suggesting a model for the synechan biosynthesis apparatus. Because similar genes are found in many cyanobacterial genomes with wide variation, our findings may lead elucidation of various sulfated polysaccharides, their functions, and their potential application in biotechnology.

microbiology↗

bioRxiv2019

Identification of kit-ligand a as the Gene Responsible for the Medaka Pigment Cell Mutant few melanophore

The body coloration of animals is due to pigment cells derived from neural crest cells, which are multipotent and differentiate into diverse cell types. Medaka (Oryzias latipes) possesses four distinct types of pigment cells known as melanophores, xanthophores, iridophores, and leucophores. The few melanophore (fm) mutant of medaka is characterized by reduced numbers of melanophores and leucophores. We here identify kit-ligand a (kitlga) as the gene whose mutation gives rise to the fm phenotype. This identification was confirmed by generation of kitlga knockout medaka and the findings that these fish also manifest reduced numbers of melanophores and leucophores and fail to rescue the fm mutant phenotype. We also found that expression of sox5, pax7a, pax3a, and mitfa genes is down-regulated in both fm and kitlga knockout medaka, implicating c-Kit signaling in regulation of the expression of these genes as well as the encoded transcription factors in pigment cell specification. Our results may provide insight into the pathogenesis of c-Kit-related pigmentation disorders such as piebaldism in humans, and our kitlga knockout medaka may prove useful as a tool for drug screening.

molecular biology↗

bioRxiv2018

Integrative analysis of transcription factor occupancy at enhancers and disease risk loci in noncoding genomic regions

Noncoding regions of the human genome possess enhancer activity and harbor risk loci for heritable diseases. Whereas the binding profiles of multiple transcription factors (TFs) have been investigated, integrative analysis with the large body of public data available so as to provide an overview of the function of such noncoding regions has remained a challenge. Here we have fully integrated public ChIP-seq and DNase-seq data (n ~ 70,000), including those for 743 human transcription factors (TFs) with 97 million binding sites, and have devised a data- mining platform --designated ChIP-Atlas--to identify significant TF-genome, TF-gene, and TF-TF interactions. Using this platform, we found that TFs enriched at macrophage or T-cell enhancers also accumulated around risk loci for autoimmune diseases, whereas those enriched at hepatocyte or macrophage enhancers were preferentially detected at loci associated with HDL-cholesterol levels. Of note, we identified \"hotspots\" around such risk loci that accumulated multiple TFs and are therefore candidates for causal variants. Integrative analysis of public chromatin-profiling data is thus able to identify TFs and tissues associated with heritable disorders.

genomics↗