{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:52:57Z","timestamp":1787169177640,"version":"build-2736575974"},"reference-count":175,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circulation Research"],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>Cardiovascular function is tightly linked to tissue architectures, where the spatial organization of cells, extracellular matrix (ECM), vascular networks, and remodeling processes governs physiological performance and disease progression. Spatial proteomics has, therefore, emerged as a powerful framework for understanding cardiovascular biology and cardiovascular disease mechanisms by revealing spatially organized protein regulation across various physiological and pathological states. In this review, we focus on spatial proteomics strategies most relevant to cardiovascular research and discuss their applications through representative examples. These approaches can be broadly categorized into region-of-interest\u2013based methods, which enable precise characterization of localized cellular heterogeneity, and tissue mapping strategies, which capture spatial organization and biologically relevant region-to-region variability across larger tissue domains. In addition, spatial proteomics platforms differ in their capacity for targeted or untargeted protein analysis, influencing both proteome coverage and their suitability for hypothesis-driven versus discovery-based studies. We evaluate the strengths and limitations of state-of-the-art technologies across 3 key parameters, molecular depth, spatial coverage, and spatial resolution, and discuss how these parameters shape study design and biological understanding. Building on these considerations, we argue that a comprehensive understanding of cardiovascular tissue biology requires spatial proteomics strategies that capture both localized molecular details and spatial organization across large tissue areas, as neither alone is sufficient to explain complex tissue behavior. We propose an integrated workflow in which untargeted, whole-tissue mapping of thousands of proteins is first used to unbiasedly discover spatial patterns and generate hypotheses by identifying candidate regions and proteins of interest, followed by hypothesis testing and validation using high-precision region-of-interest\u2013based proteomics and targeted protein imaging. This sequential framework leverages the complementary strengths of tissue-wide mapping and region-of-interest\u2013based approaches to provide multiscale, mechanistic insights into spatially organized disease processes. Finally, we discuss emerging directions that are poised to expand the scope of spatial proteomics in cardiovascular research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circresaha.126.327475","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:00:30Z","timestamp":1785434430000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Decoding Cardiovascular Disease Through Spatial Proteomics"],"prefix":"10.1161","volume":"139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4203-5215","authenticated-orcid":true,"given":"Min","family":"Ma","sequence":"first","affiliation":[{"name":"Proteomics &amp; Bioanalysis Lab, New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo (M.M., C.X., M.Z., J.Q.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9497-5145","authenticated-orcid":true,"given":"Yingyi","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3795-0889","authenticated-orcid":true,"given":"Erfei","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1934-907X","authenticated-orcid":true,"given":"Chao","family":"Xue","sequence":"additional","affiliation":[{"name":"Proteomics &amp; 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