{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T15:24:28Z","timestamp":1764343468818,"version":"3.46.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM158094","R01GM134020","P41GM103712"],"award-info":[{"award-number":["R35GM158094","R01GM134020","P41GM103712"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI-2238093","DBI-2422619","DBI-1949629","IIS-2007595","IIS-2211597","MCB-2205148"],"award-info":[{"award-number":["DBI-2238093","DBI-2422619","DBI-1949629","IIS-2007595","IIS-2211597","MCB-2205148"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Localizing macromolecules in crowded cellular cryo-electron tomography (cryo-ET) images or tomograms is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. In this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-ET datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared with supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios.<\/jats:p>","DOI":"10.1093\/bib\/bbaf630","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:01:05Z","timestamp":1762606865000},"source":"Crossref","is-referenced-by-count":0,"title":["Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels"],"prefix":"10.1093","volume":"26","author":[{"given":"Mostofa Rafid","family":"Uddin","sequence":"first","affiliation":[{"name":"Ray and Stephanie Lane Computational Biology Department , Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajmain Yasar","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , The Pennsylvania State University, 201 Old Main University Park, PA 16802,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H M Shadman","family":"Tabib","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , Bangladesh University of Engineering and Technology, ECE Building, Azimpur Road, Dhaka 1205,","place":["Bangladesh"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Toki","family":"Tahmid","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Princeton University, 1 Nassau Hall, Princeton, NJ 08540,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Zarif Ul","family":"Alam","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences , University of Massachusetts Amherst, 300 Massachusetts Ave, Amherst, MA 01003,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zachary","family":"Freyberg","sequence":"additional","affiliation":[{"name":"Department of Psychiatry , University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260,","place":["United States"]},{"name":"Department of Cell Biology , University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-5891","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"Ray and Stephanie Lane Computational Biology Department , Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"2025112810210760500_ref1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/nmeth.4115","article-title":"Cryo-electron tomography","volume":"14","author":"Doerr","year":"2017","journal-title":"Nat Methods"},{"key":"2025112810210760500_ref2","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1002\/1873-3468.13948","article-title":"The promise and the challenges of cryo-electron tomography","volume":"594","author":"Turk","year":"2020","journal-title":"FEBS Lett"},{"key":"2025112810210760500_ref3","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1038\/nature06523","article-title":"The molecular sociology of the cell","volume":"450","author":"Robinson","year":"2007","journal-title":"Nature"},{"key":"2025112810210760500_ref4","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1038\/s41592-019-0591-8","article-title":"A complete data processing workflow for cryo-et and subtomogram averaging","volume":"16","author":"Chen","year":"2019","journal-title":"Nat Methods"},{"key":"2025112810210760500_ref5","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.jsb.2006.05.009","article-title":"EMAN2: an extensible image processing suite for electron microscopy","volume":"157","author":"Tang","year":"2007","journal-title":"J Struct Biol"},{"key":"2025112810210760500_ref6","doi-asserted-by":"publisher","first-page":"168068","DOI":"10.1016\/j.jmb.2023.168068","article-title":"Computational methods toward unbiased pattern mining and structure determination in cryo-electron tomography data","volume":"435","author":"Kim","year":"2023","journal-title":"J Mol Biol"},{"key":"2025112810210760500_ref7","doi-asserted-by":"publisher","first-page":"e2213149120","DOI":"10.1073\/pnas.2213149120","article-title":"High-throughput cryo-et structural pattern mining by unsupervised deep iterative subtomogram clustering","volume":"120","author":"Zeng","year":"2023","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025112810210760500_ref8","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1038\/s41592-023-02045-0","article-title":"NextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography","volume":"20","author":"Liu","year":"2023","journal-title":"Nat Methods"},{"key":"2025112810210760500_ref9","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s42003-019-0437-z","article-title":"SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM","volume":"2","author":"Wagner","year":"2019","journal-title":"Commun Biol"},{"key":"2025112810210760500_ref10","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1038\/s41592-021-01275-4","article-title":"Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms","volume":"18","author":"Moebel","year":"2021","journal-title":"Nat Methods"},{"key":"2025112810210760500_ref11","doi-asserted-by":"publisher","first-page":"14245","DOI":"10.1073\/pnas.230282097","article-title":"Toward detecting and identifying macromolecules in a cellular context: template matching applied to electron tomograms","volume":"97","author":"B\u00f6hm","year":"2000","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025112810210760500_ref12","doi-asserted-by":"crossref","first-page":"2090","DOI":"10.1038\/s41467-024-46041-0","article-title":"DeepETPicker: fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning","volume":"15","author":"Liu","year":"2024","journal-title":"Nat Commun"},{"key":"2025112810210760500_ref13","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41592-022-01746-2","article-title":"Convolutional networks for supervised mining of molecular patterns within cellular context","volume":"20","author":"de Teresa-Trueba","year":"2023","journal-title":"Nat Methods"},{"key":"2025112810210760500_ref14","first-page":"644","article-title":"Accurate detection of proteins in cryo-electron tomograms from sparse labels","volume-title":"European Conference on Computer Vision","author":"Huang","year":"2022"},{"key":"2025112810210760500_ref15","first-page":"124","article-title":"CryoSAM: training-free cryoet tomogram segmentation with foundation models","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Zhao","year":"2024"},{"key":"2025112810210760500_ref16","article-title":"Positive-unlabeled learning with non-negative risk estimator","author":"Kiryo","year":"2017","journal-title":"Advances in neural information processing systems, Neural Information Processing Systems Foundation, Long Beach, California, USA"},{"key":"2025112810210760500_ref17","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw"},{"key":"2025112810210760500_ref18","volume-title":"Empiar-10988: Negative Stain Electron Microscopy of Nucleosome Bound by Engineered Minimalist Reader mbtd1 Binding Module","author":"European Bioinformatics Institute","year":"2023"},{"key":"2025112810210760500_ref19","volume-title":"EMPIAR-10499: Tilt Series of Native M. Pneumoniae Cells Treated with Chloramphenicol","author":"Tegunov","year":"2020"},{"key":"2025112810210760500_ref20","volume-title":"CCP-EM Denoiser","author":"Ong"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf630\/65613675\/bbaf630.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf630\/65613675\/bbaf630.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T15:21:16Z","timestamp":1764343276000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf630\/8351049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":20,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf630","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,11,1]]},"article-number":"bbaf630"}}