{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wagner P"],"funding":["Berlin Institute for the Foundations of Learning and Data (BIFOLD)","Bundesministerium für Bildung und Forschung","Johann Wolfgang Goethe-Universität, Frankfurt am Main","German Research Foundation (DFG) as Math+: Berlin Mathematics Research Center","Mature T-cell Lymphomas - mechanisms of perturbed clonal T-cell homeostasis","Institute of Information & Communications Technology Planning & Evaluation (IITP) by the Korea Government","Berlin Institute for the Foundations of Learning and Data"],"pagination":["18991"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9643435"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-cl"],"journal":["Scientific reports"],"pubmed_title":["New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning."],"pmcid":["PMC9643435"],"funding_grant_id":["01IS18025A","FOR 1961, HA 1284/7, project-ID:225165194","EXC 2046/1, project-ID: 390685689","2019-0-00079","01IS18037A","031LO207"],"pubmed_authors":["Marban A","Strodthoff N","Samek W","Seegerer P","Klauschen F","Wagner P","Schafer H","Hansmann ML","Wurzel P","Loth A","Hartmann S","Muller KR","Scharf S"],"additional_accession":[]},"is_claimable":false,"name":"New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning.","description":"Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-cl","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-04T13:46:34.138Z","creation":"2025-04-04T13:46:34.138Z"},"accession":"S-EPMC9643435","cross_references":{"pubmed":["36347879"],"doi":["10.1038/s41598-022-18097-9"]}}