Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan

Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan Nathan Basisty, Amit Dey, Bradley Olinger, Mozhgan Boroumand, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7179817/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Assessing and validating circulating biomarkers is essential for the development of pre-clinical biomarkers that predict biological aging and aging-phenotypes in mice. However, comprehensive proteomics of serum, especially in longitudinal mouse studies, are limited by low volumes of samples. In this study, we develop a workflow for comprehensive and quantitative proteomic analysis of low volume mouse serum and demonstrate its utility and performance in identifying and evaluating key associations with aging phenotypes and cellular senescence. Notably, a nanoparticle (NP)-based serum processing workflow coupled to mass spectrometry (MS) increases proteomic coverage by 3 to 6-fold across a range of volumes and provides a quantitative and reproducible (CV < 10%) pipeline for NP-based studies. In a study of 30 mice (aged 12, 24, and 30 months), we uncovered 3992 protein groups across all samples (2235 on average) in 20 µL of serum and highlight novel insights into aging-associated changes in serum and associations with glucose and body composition. With 1 µL additional serum, a 48-cytokine assay quantified 39 additional proteins not identified by MS. This study establishes a powerful workflow that enables deep quantitative proteomics of biologically relevant proteins, including hundreds of senescence-associated proteins, in volumes feasibly obtained from mice (21 µL of serum) and presents fundamental insights into the aging serum proteome. Biological sciences/Biochemistry/Proteomics Biological sciences/Biological techniques/Proteomic analysis Biological sciences/Biological techniques/Mass spectrometry Biological sciences/Biological techniques/Biological models/Animal disease models Biological sciences/Computational biology and bioinformatics/Data processing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFiguresV2.pdf Supplemental Figures Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7179817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493305256,"identity":"37c67562-07be-46c8-8021-d850be72ef3b","order_by":0,"name":"Nathan Basisty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNhCRwMDA2ACkbRjYJAjqYEbScoAhjSQtQHCA4TADAyEt5uznjz34uYMhj7//cPPnj23n8/ikG5hffGzDrcWyJ5ndsPcMQ7HEjcQ2iYNtt4vZZA6wWc7Eo8XgQDKbBG8bQ2LDDcY2BqAWoMYENmOeM3i0nH/MJvkXqGX++YPNHw62nSNCy41kNmmQLRsOJDYAHXYApIX5MU8FPi2PzaRl2ySKDUF+OXMuGUgmtjHOwKflfOIzybdtNnly548//lBRZpc4f0by4Q8fDHBrgQJoXDCC44ixjXBsIsAfMMn8gQQto2AUjIJRMPwBAFBVVneN+9GDAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6173-1139","institution":"National Institute on Aging, NIH","correspondingAuthor":true,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Basisty","suffix":""},{"id":493305257,"identity":"134db4a5-9793-4595-ad86-a697de300e19","order_by":1,"name":"Amit Dey","email":"","orcid":"https://orcid.org/0000-0003-3106-7091","institution":"National Institute on Aging","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Dey","suffix":""},{"id":493305258,"identity":"590568ca-d4f8-49fa-b8ec-010c3b1b2904","order_by":2,"name":"Bradley Olinger","email":"","orcid":"https://orcid.org/0009-0007-6196-4443","institution":"Translational Gerontology Branch, National Institute on Aging, NIH","correspondingAuthor":false,"prefix":"","firstName":"Bradley","middleName":"","lastName":"Olinger","suffix":""},{"id":493305259,"identity":"08b02e82-5c89-42dd-9ea8-556724ca3fe7","order_by":3,"name":"Mozhgan Boroumand","email":"","orcid":"","institution":"National Institute on Aging, NIH","correspondingAuthor":false,"prefix":"","firstName":"Mozhgan","middleName":"","lastName":"Boroumand","suffix":""},{"id":493305260,"identity":"089fb404-2633-4596-b0d4-6bf8f0491132","order_by":4,"name":"Maria Fernandez","email":"","orcid":"https://orcid.org/0000-0001-7808-1478","institution":"NIA, NIH","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Fernandez","suffix":""},{"id":493305261,"identity":"3ecf19c6-a397-4763-94bc-0d4f536e2791","order_by":5,"name":"SLAM Investigators","email":"","orcid":"","institution":"National Institute on Aging, NIH","correspondingAuthor":false,"prefix":"","firstName":"SLAM","middleName":"","lastName":"Investigators","suffix":""},{"id":493305262,"identity":"8f8a5352-f7aa-46df-8ab3-a4f6c03f4dee","order_by":6,"name":"Simonetta Camandola","email":"","orcid":"","institution":"National Institute on Aging, NIH","correspondingAuthor":false,"prefix":"","firstName":"Simonetta","middleName":"","lastName":"Camandola","suffix":""},{"id":493305263,"identity":"5fccd209-27d5-4f49-89d5-6dab7876918d","order_by":7,"name":"Nathan Price","email":"","orcid":"https://orcid.org/0000-0002-2821-9608","institution":"National Institute on Aging, National Instiutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Price","suffix":""},{"id":493305264,"identity":"dc9fee77-dd51-48c4-9124-8f3b1305fbc2","order_by":8,"name":"Rafael de Cabo","email":"","orcid":"https://orcid.org/0000-0002-3354-2442","institution":"National Institute on Aging, National Instiutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"de Cabo","suffix":""}],"badges":[],"createdAt":"2025-07-21 17:10:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7179817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7179817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87973628,"identity":"fa7957c4-7445-4202-b16f-5b24d00cc25a","added_by":"auto","created_at":"2025-07-31 03:42:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":793380,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of study design for a comprehensive and quantitative proteomic study in mouse serum. A) Schematic summary of study design with two phases: 1) a feasibility and quantitative benchmarking study and 2) an aging study. In phase 1, to initially assess the feasibility of this workflow at low volumes, optimized nanoparticle enrichment and LC-MS workflow with pooled mouse serum was conducted with four different volumes (10, 20, 50, and 100 μL), and compared to traditional neat serum digest (no enrichment) processed in parallel. To assess quantitative accuracy, a benchmarking study was conducted on defined ratios serum from two organisms (bovine and human). In phase 2, to demonstrate the power of the workflow in a biological study, an aging study of 30 mice was conducted at three ages: 12 (young), 24 (old), and 30 (geriatric) months. B) The automated serum-nanoparticle processing and digestion were performed using a Proteograph (Seer, inc) workflow. Peptide data was acquired by nLC-MS/MS and analyzed with DIA-NN in a library-free mode. NP-peptide enrichment was assessed by comparing two generic label-free quantification algorithms, MaxRep and MaxLFQ. C) A panel of 48 mouse cytokines were measured using proximity extension assay (O-link, Thermo Scientific). D) Biomarkers and pathways associated with aging and clinically relevant phenotypes were assessed.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/3ee0d97a30ee75a4b780c823.png"},{"id":87973631,"identity":"8718d60a-996c-42b2-a848-3723dd938e7b","added_by":"auto","created_at":"2025-07-31 03:42:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148349,"visible":true,"origin":"","legend":"\u003cp\u003eQualitative and quantitative evaluation of proteomic data generated NP-coupled MS workflow. A) A quantitative benchmarking study was performed using five fixed ratios of human and bovine serum (n=5 each). B) Peptide abundance ratios in each dilution versus the 50% bovine serum sample. Dotted lines depict the expected ratios. C) Peptides identified in at least one sample by NP. D) Proteins quantified in at least one sample are shown for each NP. E) Principal Component Analysis was used to show clustering patterns between nanoparticles on the peptide level. F) Spearman correlation values for peptide intensities between NP. G) Coefficient of variation (SD/Mean abundances) is shown by NP on the peptide level for 3 triplicate samples. For each NP, only those peptides detected in all 3 samples were used for calculation. For combined, intensities were added between all NP and proteins detected in all 3 samples (regardless of which NP) were kept for analysis. H) Protein identifications in mouse aging study. I) Correlation of protein intensities calculated by the MaxRep versus MaxLFQ approaches. J) Protein CVs in the MaxRep and MaxLFQ quantitation pipelines. K) Number of SenSig proteins detected in neat serum and serum processed by the NP workflow.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/c0f70b81c3734542e170dc3d.png"},{"id":87973626,"identity":"b2312e0f-444b-45c9-8ad3-126dffa651e4","added_by":"auto","created_at":"2025-07-31 03:42:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106612,"visible":true,"origin":"","legend":"\u003cp\u003eAge-related proteomic changes measured with proximity extension assays. A) Heatmap of scaled protein abundance for all proteins measured with PEA in the aging study (n=30 mice). B) Principal component analysis of protein abundance by age and sex. C) Proteins detected using both NP-MS and PEA methods. D) Correlation of IL16 levels measured by NP-MS and PEA. E) Pearson modeling revealing age-associated proteins measured by PEA using the formula: Protein ~ Age + Sex. F) Ifnl2 levels by age in males and females.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/fb5e8f8c1ada71d5d9a10955.png"},{"id":87973627,"identity":"553f016f-e46e-4797-8ca4-45098ce82698","added_by":"auto","created_at":"2025-07-31 03:42:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133126,"visible":true,"origin":"","legend":"\u003cp\u003eAge-associated changes in circulating proteins measured by NP-MS. A) Principal component analysis of quantified proteins by age and sex. B) Pearson linear modeling to identify age-associated proteins, using the model protein ~ age + sex. Only proteins present in at least one sample from each age and sex group and with at least 2 peptides were examined (N = 2662). C) Scaled abundance of the top 10 linearly increasing age-associated proteins. D) Meta-gene expression of protein groups with stage-specific abundance changes with age. E) Meta-gene expression of protein groups with opposing stage-specific abundance changes. F) Differentially expressed proteins between young, old, and geriatric samples. Counts of proteins showing differential expression (p-values \u0026lt; 0.05) are shown at each stage. G) Gene Set Enrichment Analysis of protein abundance changes from ages 12 to 24 months (Biological Process). H) Gene Set Enrichment Analysis of protein abundance changes from ages 24 to 30 months (Biological Process).\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/557afa339917f87a791847b2.png"},{"id":87974070,"identity":"2bfd451a-e210-48a4-8c5e-3117da0ea98b","added_by":"auto","created_at":"2025-07-31 03:50:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":215811,"visible":true,"origin":"","legend":"\u003cp\u003eCirculating protein associated with sex and clinical traits. A) Pearson linear modeling to identify sex-associated proteins measured by the NP-MS workflow, using the model protein ~ sex + age. B) Pearson linear modeling to identify sex-associated proteins measured by PEA workflow, using the model protein ~ sex + age. C) Overrepresentation analysis of proteins upregulated in males (Cellular Component). D) Overrepresentation analysis of proteins upregulated in females (Molecular Function). E) Glucose levels with age. F) Total body fat percentage with age. G) Pearson linear modeling to identify glucose-associated proteins measured by the NP-MS workflow, using the model protein ~ glucose + sex + age. H) Pearson linear modeling to identify fat percent associated proteins measured by the NP-MS workflow, using the model protein ~ fat percent + sex + age.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/13e0b47b2c9642e31c32e64b.png"},{"id":95314789,"identity":"b4d9e900-0288-43e9-a537-9a2b00f8e816","added_by":"auto","created_at":"2025-11-06 15:53:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1432462,"visible":true,"origin":"","legend":"Article File","description":"","filename":"manuscriptv15.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1_covered_d76dd31e-5084-450c-a30b-def0211b96d2.pdf"},{"id":87973629,"identity":"a447b3ab-75ec-4ef4-91eb-14436d0bd8e0","added_by":"auto","created_at":"2025-07-31 03:42:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":658904,"visible":true,"origin":"","legend":"Supplemental Figures","description":"","filename":"SupplementaryFiguresV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179817/v1/f06846db65f2a2de919d7cf6.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7179817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7179817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Assessing and validating circulating biomarkers is essential for the development of pre-clinical biomarkers that predict biological aging and aging-phenotypes in mice. However, comprehensive proteomics of serum, especially in longitudinal mouse studies, are limited by low volumes of samples. In this study, we develop a workflow for comprehensive and quantitative proteomic analysis of low volume mouse serum and demonstrate its utility and performance in identifying and evaluating key associations with aging phenotypes and cellular senescence. Notably, a nanoparticle (NP)-based serum processing workflow coupled to mass spectrometry (MS) increases proteomic coverage by 3 to 6-fold across a range of volumes and provides a quantitative and reproducible (CV \u003c 10%) pipeline for NP-based studies. In a study of 30 mice (aged 12, 24, and 30 months), we uncovered 3992 protein groups across all samples (2235 on average) in 20 µL of serum and highlight novel insights into aging-associated changes in serum and associations with glucose and body composition. With 1 µL additional serum, a 48-cytokine assay quantified 39 additional proteins not identified by MS. This study establishes a powerful workflow that enables deep quantitative proteomics of biologically relevant proteins, including hundreds of senescence-associated proteins, in volumes feasibly obtained from mice (21 µL of serum) and presents fundamental insights into the aging serum proteome.","manuscriptTitle":"Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 03:42:22","doi":"10.21203/rs.3.rs-7179817/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-aging","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nataging","sideBox":"Learn more about [Nature Aging](https://www.nature.com/nataging/)","snPcode":"","submissionUrl":"","title":"Nature Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cf56dbe0-0898-40a8-b1c7-1b0cac9eae80","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52387015,"name":"Biological sciences/Biochemistry/Proteomics"},{"id":52387016,"name":"Biological sciences/Biological techniques/Proteomic analysis"},{"id":52387017,"name":"Biological sciences/Biological techniques/Mass spectrometry"},{"id":52387018,"name":"Biological sciences/Biological techniques/Biological models/Animal disease models"},{"id":52387019,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"}],"tags":[],"updatedAt":"2026-05-08T15:41:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 03:42:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7179817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7179817","identity":"rs-7179817","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}