{"doi":"10.1007/978-1-0716-0266-9_1","title":"Exploration of T-Cell Diversity Using Mass Cytometry","abstract":null,"journal":"Methods in Molecular Biology","year":2020,"id":613597,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"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":626762,"name":"Takuya Ohtani","orcid":null,"position":1,"is_corresponding":false},{"id":105455,"name":"Sasikanth Manne","orcid":"0000-0001-9856-0871","position":2,"is_corresponding":false},{"id":640103,"name":"Bertram Bengsch","orcid":"0000-0003-2552-740X","position":3,"is_corresponding":false},{"id":250865,"name":"Sarah E. Henrickson","orcid":"0000-0001-5569-4132","position":4,"is_corresponding":false},{"id":36451,"name":"E. John Wherry","orcid":"0000-0003-0477-1956","position":5,"is_corresponding":false},{"id":1580848,"name":"Cecile Alanio","orcid":null,"position":6,"is_corresponding":false},{"id":821686,"name":"Kaitlin C. O’Boyle","orcid":"0000-0002-9239-9949","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Exploration of T-Cell Diversity Using Mass Cytometry","abstract":"T-cell diversity is multifactorial and includes variability in antigen specificity, differentiation, function, and cell-trafficking potential. Spectral overlap limits the ability of traditional flow cytometry to fully capture the diversity of T-cell subsets and function. The development of mass cytometry permits deep immunoprofiling of T-cell subsets, activation state, and function simultaneously from even small volumes of blood. This chapter describes our methods for mass cytometry and high-throughput data analysis of T cells in patient cohorts. We provide a pipeline that includes practical considerations when customizing a panel for mass cytometry. We also provide protocols for the conjugation and titration of metal-labeled antibodies (including two T-cell panels) and a staining procedure. Finally, with the aim to support translational science, we provide R scripts that contain a detailed workflow for initial evaluation of high-dimensional data generated from cohorts of patients.","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31933194","pmcid":null,"openalex_id":"https://openalex.org/W2999230806","authors":[],"funders":[],"total_grants":0,"fwci":2.0396,"citation_percentile":0.86720641,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2022,"count":3},{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"http://www.springer.com/tdm","oa_locations":[{"url":"http://link.springer.com/content/pdf/10.1007/978-1-0716-0266-9_1","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-1-0716-0266-9_1","host_type":"book series"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31933194","host_type":"repository"},{"url":"https://freidok.uni-freiburg.de/data/281427","host_type":"repository"}],"fields_of_study":["Single-cell and spatial transcriptomics","T-cell and B-cell Immunology","Gene Regulatory Network Analysis"],"mesh_terms":["Antibodies","Flow Cytometry","Humans","Lymphocyte Activation","Staining and Labeling","Cohort Studies","Immunophenotyping","T-Lymphocyte Subsets","Metals, Heavy","Systems Biology","High-Throughput Screening Assays","Workflow","Single-Cell Analysis"],"keywords":["Mass cytometry","Flow cytometry","Cytometry","Computational biology","Cell","Titration","Biology","Chemistry","Cell biology","Molecular biology","Biochemistry","Gene","Phenotype","Data processing","T cells","Clustering","systems biology","R","High-throughput Analysis","Cytof"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T08:39:45.217534Z","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":[]}