<HashMap><database>JPOST Repository</database><file_versions><headers><Content-Type>application/xml</Content-Type></headers><body><files><Tabular>https://storage.jpostdb.org/JPST003835/files/DIA_plasma.pr_matrix.tsv</Tabular><Tabular>https://storage.jpostdb.org/JPST003835/files/DIA_Infin.pr_matrix.tsv</Tabular><Tabular>https://storage.jpostdb.org/JPST003835/files/DIA_organs_plasma.pr_matrix.tsv</Tabular><Tabular>https://storage.jpostdb.org/JPST003835/files/DDA_plasma_combined_modified_peptide.tsv</Tabular><Tabular>https://storage.jpostdb.org/JPST003835/files/DDA_organs_combined_modified_peptide.tsv</Tabular><Other>https://storage.jpostdb.org/JPST003835/files/Hippocampus_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Plasma_DDA_6uLeq_2.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Cerebellum_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Muscle_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Plasma_DIA_6uLeq_1.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Spleen_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Heart_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Plasma_DIA_6uLeq_2.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Lung_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Adrenalgland_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Liver_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Plasma_DIA_6uLeq_3.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Pancreas_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Cerebralcortex_400ugeq.d.zip</Other><Other>https://storage.jpostdb.org/JPST003835/files/Kidney_400ugeq.zip</Other></files><type>primary</type></body><statusCode>OK</statusCode><statusCodeValue>200</statusCodeValue></file_versions><scores/><additional><omics_type>Proteomics</omics_type><submitter>Yoshio Kodera</submitter><species>Mus Musculus (mouse)</species><full_dataset_link>https://repository.jpostdb.org/entry/JPST003835</full_dataset_link><submitter_affiliation>Kitasato University</submitter_affiliation><sample_protocol></sample_protocol><repository>jPOST</repository><data_protocol></data_protocol><pubmed_abstract>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 an 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 5500 peptides per run, with over 2000 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.</pubmed_abstract><pubmed_title>Organ-Derived Spectral Libraries Improve Sensitivity in DIA Plasma Peptidomics.</pubmed_title><pubmed_authors>Okuda Yusei Y, Konno Ryo R, Taguchi Tomomi T, Itakura Makoto M, Tamari Ami A, Matsui Takashi T, Miyatsuka Takeshi T, Ohara Osamu O, Kawashima Yusuke Y, Kodera Yoshio Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>DIA-MS based plasma peptidomics workflow for profiling organ-derived peptides</name><description>In this study, we acquired DDA-MS data and DIA-MS data from peptide extracts of plasma and eleven organs (lung, spleen, adrenal gland, kidney, heart, liver, muscle, pancreas, cerebellum, hippocampus, and cerebral cortex). These data were analyzed using FragPipe v23 to obtain peptide identification results and to construct an empirical spectral library. We also acquired DIA-MS data from plasma peptide extracts and analyzed them with DIA-NN v2.3.1 using three empirical spectral libraries that we constructed, thereby obtaining peptide identification results.</description><dates><publication>Mon Jul 13 00:00:00 BST 2026</publication></dates><accession>PXD073758</accession><cross_references><TAXONOMY>10090</TAXONOMY><pubmed>42434928</pubmed></cross_references></HashMap>