{"doi":"10.1101/2025.05.29.656930","title":"Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic Regression","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Purpose</jats:title>\n                  <jats:p>To characterize cell type specific transcriptional changes during human retinal aging and develop machine learning model for cellular age discrimination in a Chinese cohort.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Design</jats:title>\n                  <jats:p>Cross-sectional, laboratory-based observational study.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Participants</jats:title>\n                  <jats:p>Eighteen unfrozen retinas from 12 Chinese donors (9 young, 34-55y; 9 old, 68-92 y).</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>Single-cell RNA sequencing (10x, v3.1) generated 223612 cells, batch-corrected with scVI; age-related signatures were defined by intersecting single-cell and pseudo-bulk differentially expressed genes, then cell-type-specific panels were rank-ordered with L1-regularised logistic regression plus recursive feature elimination and interpreted through hallmark-pathway enrichment and transcription-factor regulon mapping.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Main Outcome Measures</jats:title>\n                  <jats:p>Age-related cellular composition shifts; cell-type-specific differentially expressed genes; machine-learning classifier accuracy and feature rankings; transcription factor regulon activity changes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Eleven major retinal cell populations were identified. Aging showed declining rod-to-cone ratios, reduced bipolar cell proportions among interneurons, and increased astrocyte abundance. Müller glial cells exhibited the most pronounced transcriptional changes, followed by bipolar cells and rods. Machine-learning classifiers achieved 80-96% accuracy across cell types (microglia 96%, horizontal cells 93%, bipolar cells 91%, cones 90%, rods 89%). Shared aging signatures included mitochondrial dysfunction and inflammatory activation. Cell specific vulnerabilities emerged: mitochondria-centric stress in rods/bipolar cells, proteostasis-retinoid metabolism in cones, and structural-RNA maintenance in horizontal cells.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions</jats:title>\n                  <jats:p>This study provides the first machine learning derived, cell-type specific aging signatures for human retina in a Chinese cohort, revealing both conserved molecular hallmarks and distinctive cellular vulnerabilities that inform targeted therapeutic strategies for retinal aging.</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":632807,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1427394,"name":"Sen Lin","orcid":"0000-0001-8187-8680","position":1,"is_corresponding":false},{"id":1640423,"name":"Yiwen Tao","orcid":null,"position":2,"is_corresponding":false},{"id":1009742,"name":"Qi Pan","orcid":"0000-0003-0135-9122","position":3,"is_corresponding":false},{"id":1640424,"name":"Tengda Cai","orcid":null,"position":4,"is_corresponding":false},{"id":1640425,"name":"Yunyan Ye","orcid":null,"position":5,"is_corresponding":false},{"id":876363,"name":"Jianhui Liu","orcid":"0000-0003-1055-7964","position":6,"is_corresponding":false},{"id":1388916,"name":"Yang Zhou","orcid":"0000-0003-0377-4338","position":7,"is_corresponding":false},{"id":1640426,"name":"Yongqing Shao","orcid":null,"position":8,"is_corresponding":false},{"id":1388053,"name":"Quanyong Yi","orcid":"0000-0002-9369-3998","position":9,"is_corresponding":false},{"id":1640427,"name":"Zen Huat Lu","orcid":"0000-0002-9408-0579","position":10,"is_corresponding":false},{"id":1356363,"name":"Lie Chen","orcid":"0000-0001-9764-4299","position":11,"is_corresponding":false},{"id":1640428,"name":"Gareth McKay","orcid":null,"position":12,"is_corresponding":false},{"id":1640429,"name":"Richard Rankin","orcid":null,"position":13,"is_corresponding":false},{"id":1369226,"name":"Fan Li","orcid":"0000-0001-5757-1689","position":14,"is_corresponding":false},{"id":466988,"name":"Weihua Meng","orcid":"0000-0001-5388-8494","position":15,"is_corresponding":false},{"id":1640422,"name":"Luning Yang","orcid":"0000-0001-9056-5950","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic Regression","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Purpose</jats:title>\n                  <jats:p>To characterize cell type specific transcriptional changes during human retinal aging and develop machine learning model for cellular age discrimination in a Chinese cohort.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Design</jats:title>\n                  <jats:p>Cross-sectional, laboratory-based observational study.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Participants</jats:title>\n                  <jats:p>Eighteen unfrozen retinas from 12 Chinese donors (9 young, 34-55y; 9 old, 68-92 y).</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>Single-cell RNA sequencing (10x, v3.1) generated 223612 cells, batch-corrected with scVI; age-related signatures were defined by intersecting single-cell and pseudo-bulk differentially expressed genes, then cell-type-specific panels were rank-ordered with L1-regularised logistic regression plus recursive feature elimination and interpreted through hallmark-pathway enrichment and transcription-factor regulon mapping.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Main Outcome Measures</jats:title>\n                  <jats:p>Age-related cellular composition shifts; cell-type-specific differentially expressed genes; machine-learning classifier accuracy and feature rankings; transcription factor regulon activity changes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Eleven major retinal cell populations were identified. Aging showed declining rod-to-cone ratios, reduced bipolar cell proportions among interneurons, and increased astrocyte abundance. Müller glial cells exhibited the most pronounced transcriptional changes, followed by bipolar cells and rods. Machine-learning classifiers achieved 80-96% accuracy across cell types (microglia 96%, horizontal cells 93%, bipolar cells 91%, cones 90%, rods 89%). Shared aging signatures included mitochondrial dysfunction and inflammatory activation. Cell specific vulnerabilities emerged: mitochondria-centric stress in rods/bipolar cells, proteostasis-retinoid metabolism in cones, and structural-RNA maintenance in horizontal cells.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions</jats:title>\n                  <jats:p>This study provides the first machine learning derived, cell-type specific aging signatures for human retina in a Chinese cohort, revealing both conserved molecular hallmarks and distinctive cellular vulnerabilities that inform targeted therapeutic strategies for retinal aging.</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4410933797","authors":[],"funders":[{"funder_name":"the Pioneer and Leading Goose R&D Program of Zhejiang Province 2023","grant_id":"2023C04049","title":null},{"funder_name":"Ningbo International Collaboration Program 2023","grant_id":"2023H025","title":null},{"funder_name":"the National Natural Science Foundation of China","grant_id":"82201227","title":null},{"funder_name":"the Natural Science Foundation of Guangdong Province, China","grant_id":"2023A1515011225","title":null}],"total_grants":4,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"https://www.biorxiv.org/about/FAQ#license","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/06/01/2025.05.29.656930.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/06/01/2025.05.29.656930.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.05.29.656930","host_type":"publisher"},{"url":"https://doi.org/10.1101/2025.05.29.656930","host_type":"repository"}],"fields_of_study":["Single-cell and spatial transcriptomics","Cell Image Analysis Techniques","Advanced Fluorescence Microscopy Techniques"],"mesh_terms":[],"keywords":["Transcriptome","Retina","Cell","Cell type","Cell biology","Computer science","Computational biology","Biology","Type (biology)","Artificial intelligence","Neuroscience","Gene","Gene expression","Genetics","Ecology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T10:22:32.448224Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}