{"doi":"10.1101/2022.05.13.491828","title":"Single particle tracking with compressive sensing using progressive refinement method on sparse recovery (spt-PRIS)","abstract":"Abstract Single particle tracking (SPT) is an indispensable tool for scientific studies. However, SPT for datasets with a high density of particles is still challenging, especially for the study of particle interactions where the point spread functions (PSFs) are overlapping. In this study, we present spt-PRIS, a new SPT solution where we apply compressive sensing to SPT by integrating the progressive refinement method on sparse recovery (PRIS) into the framework of the state-of-the-art SPT algorithm (uTrack). We systematically characterized and validated spt-PRIS performance using simulations, applied it to the experimental data of membrane-bound KRAS4b proteins in either 2-lipid or 8-lipid membrane supported lipid bilayers (SLB), and compared the results to the conventional method (uTrack). Our results show that spt-PRIS is effective for SPT when the data contains overlapping PSFs and provides unprecedented information about KRAS4b subpopulations. spt-PRIS is helpful for a broad range of scientific studies where precise and fast high-density localization is beneficial. spt-PRIS is also flexible for extensions for multi-species, multi-multi-channel, and multi-dimensional SPT methods with the generalization of PRIS reconstruction schemes.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":308570,"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":0.9532,"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":761949,"name":"Rebika Shrestha","orcid":"0000-0001-7899-9668","position":1,"is_corresponding":false},{"id":1003089,"name":"Torin McDonald","orcid":"0000-0002-1177-8470","position":2,"is_corresponding":false},{"id":490380,"name":"De Chen","orcid":"0000-0003-2160-9037","position":3,"is_corresponding":false},{"id":308946,"name":"Harsh Bhatia","orcid":"0000-0001-8712-7773","position":4,"is_corresponding":false},{"id":392981,"name":"Valerio Pascucci","orcid":"0000-0002-8877-2042","position":5,"is_corresponding":false},{"id":441288,"name":"Thomas J. Turbyville","orcid":"0000-0003-2638-9520","position":6,"is_corresponding":false},{"id":308951,"name":"Peer‐Timo Bremer","orcid":"0000-0003-4107-3831","position":7,"is_corresponding":false},{"id":1003088,"name":"Xiyu Yi","orcid":"0000-0001-7456-4675","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:33:03.486542Z","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":[]}