{"doi":"10.1101/2022.06.03.494697","title":"Laser particle barcoding for multi-pass high-dimensional flow cytometry","abstract":"ABSTRACT Flow cytometry is a standard technology in life science and clinical laboratories used to characterize the phenotypes and functional status of cells, especially immune cells. Recent advances in immunology and immuno-oncology as well as drug and vaccine discovery have increased the demand to measure more parameters. However, the overlap of fluorophore emission spectra and one-time measurement nature of flow cytometry are major barriers to meeting the need. Here, we present multi-pass flow cytometry, in which cells are tracked and measured repeatedly through barcoding with infrared laser-emitting microparticles. We demonstrate the benefits of this approach on several pertinent assays with human peripheral blood mononuclear cells (PBMCs). First, we demonstrate unprecedented time-resolved flow characterization of T cells before and after stimulation. Second, we show 33-marker deep immunophenotyping of PBMCs, analyzing the same cells in 3 back-to-back cycles. This workflow allowed us to use only 10-13 fluorophores in each cycle, significantly reducing spectral spillover and simplifying panel design. Our results open a new avenue in multi-dimensional single-cell analysis based on optical barcoding of individual cells.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":299246,"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":0.9559,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":908592,"name":"Sarah Forward","orcid":null,"position":1,"is_corresponding":false},{"id":259609,"name":"Marissa Fahlberg","orcid":"0000-0002-2597-7147","position":2,"is_corresponding":false},{"id":989668,"name":"Sean Cosgriff","orcid":null,"position":3,"is_corresponding":false},{"id":989669,"name":"Seung Hyung Lee","orcid":null,"position":4,"is_corresponding":false},{"id":350953,"name":"Geoffrey W. Abbott","orcid":"0000-0003-4552-496X","position":5,"is_corresponding":false},{"id":590416,"name":"Zhu Han","orcid":"0000-0002-6606-5822","position":6,"is_corresponding":false},{"id":989670,"name":"Nicolas H. Minasian","orcid":null,"position":7,"is_corresponding":false},{"id":989671,"name":"A. Sean Vote","orcid":null,"position":8,"is_corresponding":false},{"id":651117,"name":"Nicola Martino","orcid":"0000-0002-4639-2930","position":9,"is_corresponding":false},{"id":908071,"name":"Seok‐Hyun Yun","orcid":"0000-0002-8176-9916","position":10,"is_corresponding":false},{"id":720522,"name":"Sheldon J. J. Kwok","orcid":"0000-0001-7880-0401","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T00:31:44.904250Z","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":[]}