{"doi":"10.1002/sstr.202300204","title":"Data‐Driven and Cell‐Specific Determination of Nuclei‐Associated Actin Structure","abstract":"Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.","journal":"Small Structures","year":2024,"id":449954,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9562,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1269745,"name":"Nurbanu Bursa","orcid":"0000-0003-3747-5870","position":1,"is_corresponding":false},{"id":576020,"name":"Matthew Goelzer","orcid":"0000-0003-2102-5330","position":2,"is_corresponding":false},{"id":1270209,"name":"Madison Goldfeldt","orcid":null,"position":3,"is_corresponding":false},{"id":1270210,"name":"Chase Crandall","orcid":null,"position":4,"is_corresponding":false},{"id":898586,"name":"Sean Howard","orcid":"0000-0001-7556-1951","position":5,"is_corresponding":false},{"id":348014,"name":"Janet Rubin","orcid":"0000-0003-3534-8667","position":6,"is_corresponding":false},{"id":1195992,"name":"Anamaria G. Zavala","orcid":null,"position":7,"is_corresponding":false},{"id":1195640,"name":"Aykut C. Satici","orcid":"0000-0001-7405-7163","position":8,"is_corresponding":false},{"id":348011,"name":"Gunes Uzer","orcid":"0000-0002-1178-4942","position":9,"is_corresponding":false},{"id":1270208,"name":"N.N. Nikitina","orcid":null,"position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:02:20.585759Z","pmid":"39220563","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":[]}