Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.
Συγγραφείς: Chen RQ; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA., Lee Y; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA., Joffe B; Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA., Serafini CE; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Casteleiro Costa P; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Wang B; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Kanwar B; Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA., Balakirsky S; Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA., Silva Trenkle AD; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Kippner LE; The Marcus Center of Excellence for Biomanufacturing, Georgia Institute of Technology, Atlanta, Georgia, USA; The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA., LeCompte I; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Li Y; The Marcus Center of Excellence for Biomanufacturing, Georgia Institute of Technology, Atlanta, Georgia, USA; The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA., Brown CE; Department of Hematology & Hematopoietic Cell Transplantation (T Cell Therapeutics Research Laboratories), City of Hope Beckman Research Institute and Medical Center, Duarte, California, USA., Kwong GA; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Robles FE; Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA., Roy K; School of Engineering, Vanderbilt University, Nashville, Tennessee, USA., Li J; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. Electronic address: jli3175@gatech.edu.
Πηγή: Cytotherapy [Cytotherapy] 2026 May; Vol. 28 (5), pp. 102078. Date of Electronic Publication: 2026 Feb 03.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Country of Publication: England NLM ID: 100895309 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1477-2566 (Electronic) Linking ISSN: 14653249 NLM ISO Abbreviation: Cytotherapy Subsets: MEDLINE
Imprint Name(s): Publication: 2013- : London : Elsevier
Original Publication: Oxford, England : ISIS Medical Media, c1999-
Ιατρικοί όροι (MeSH): Microscopy*/methods , Image Processing, Computer-Assisted*/methods , Cell- and Tissue-Based Therapy*/methods, Humans ; Large Language Models ; Cell Count
Περίληψη: Background Aims: The scalable manufacturing of cell therapies creates a significant need for robust process analytical technologies, where automated analysis of noninvasive microscopy images offers a powerful method for monitoring critical quality attributes. However, conventional machine-learning models are often bottlenecked by extensive data labeling and poor generalizability across different batch effects. To overcome these limitations, we introduce a foundational model cascade for the zero-shot analysis of microscopy images.
Methods: In the first stage, a multimodal large language model (LLM) detects anomalies, and anomalous images immediately trigger an alert. Otherwise, the segment anything model performs exhaustive instance segmentation, and the detected objects are classified by the LLM to estimate cell counts and viability.
Results: This unified, zero-shot approach delivers robust anomaly detection together with quantitative measures of cell count and health, without any task-specific fine-tuning.
Conclusions: By combining pre-trained foundation models in a complementary cascade, our method provides a generalizable solution for real-time process monitoring and feedback control, paving the way for more scalable and automated cell therapy manufacturing.
(Copyright © 2026 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of Competing Interests GAK reports equity or consulting roles for Sunbird Bio, Port Therapeutics, Send Biotherapeutics and Ridge Biotechnologies. CEB reported patent royalties and research support from Mustang Bio during the conduct of the study. The other authors have no commercial, proprietary, or financial interest in the products or companies described in this article.
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PeerJ. 2014 Jun 19;2:e453. (PMID: 25024921)
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Sci Rep. 2017 Dec 4;7(1):16878. (PMID: 29203879)
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Nat Med. 2023 Sep;29(9):2307-2316. (PMID: 37592105)
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Nat Rev Drug Discov. 2022 Sep;21(9):631-632. (PMID: 35650421)
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Grant Information: R01 AI171892 United States AI NIAID NIH HHS; R35 GM147437 United States GM NIGMS NIH HHS; T32 GM145735 United States GM NIGMS NIH HHS
Contributed Indexing: Keywords: anomaly detection; cell counting; cell therapy manufacturing; cell viability estimation; foundation models; large language models; microscopy image analysis
Entry Date(s): Date Created: 20260228 Date Completed: 20260710 Latest Revision: 20260813
Update Code: 20260813
PubMed Central ID: PMC13325518
DOI: 10.1016/j.jcyt.2026.102078
PMID: 41762954
Βάση Δεδομένων: MEDLINE
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– Name: Abstract
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  Data: Background Aims: The scalable manufacturing of cell therapies creates a significant need for robust process analytical technologies, where automated analysis of noninvasive microscopy images offers a powerful method for monitoring critical quality attributes. However, conventional machine-learning models are often bottlenecked by extensive data labeling and poor generalizability across different batch effects. To overcome these limitations, we introduce a foundational model cascade for the zero-shot analysis of microscopy images.<br />Methods: In the first stage, a multimodal large language model (LLM) detects anomalies, and anomalous images immediately trigger an alert. Otherwise, the segment anything model performs exhaustive instance segmentation, and the detected objects are classified by the LLM to estimate cell counts and viability.<br />Results: This unified, zero-shot approach delivers robust anomaly detection together with quantitative measures of cell count and health, without any task-specific fine-tuning.<br />Conclusions: By combining pre-trained foundation models in a complementary cascade, our method provides a generalizable solution for real-time process monitoring and feedback control, paving the way for more scalable and automated cell therapy manufacturing.<br /> (Copyright © 2026 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declaration of Competing Interests GAK reports equity or consulting roles for Sunbird Bio, Port Therapeutics, Send Biotherapeutics and Ridge Biotechnologies. CEB reported patent royalties and research support from Mustang Bio during the conduct of the study. The other authors have no commercial, proprietary, or financial interest in the products or companies described in this article.
– Name: Ref
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  Group: RefInfo
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(PMID: <searchLink fieldCode="PM" term="%2240184852%22">40184852)</searchLink><br />Crit Rev Biochem Mol Biol. 2016;51(2):102-9. (PMID: <searchLink fieldCode="PM" term="%2226806341%22">26806341)</searchLink><br />Optica. 2021 Jan 20;8(1):6-14. (PMID: <searchLink fieldCode="PM" term="%2234368406%22">34368406)</searchLink><br />Med Image Anal. 2024 May;94:103143. (PMID: <searchLink fieldCode="PM" term="%2238507894%22">38507894)</searchLink><br />Biomed Opt Express. 2019 Jun 26;10(7):3605-3621. (PMID: <searchLink fieldCode="PM" term="%2231360607%22">31360607)</searchLink><br />Nat Methods. 2019 Dec;16(12):1233-1246. (PMID: <searchLink fieldCode="PM" term="%2231133758%22">31133758)</searchLink><br />Nature. 2024 Oct;634(8035):970-978. (PMID: <searchLink fieldCode="PM" term="%2239232164%22">39232164)</searchLink><br />Cytotherapy. 2025 Jun;27(6):678-685. (PMID: <searchLink fieldCode="PM" term="%2240047735%22">40047735)</searchLink><br />Biotechnol Rep (Amst). 2022 Feb 07;33:e00708. 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            NameFull: Kippner LE
      – PersonEntity:
          Name:
            NameFull: LeCompte I
      – PersonEntity:
          Name:
            NameFull: Li Y
      – PersonEntity:
          Name:
            NameFull: Brown CE
      – PersonEntity:
          Name:
            NameFull: Kwong GA
      – PersonEntity:
          Name:
            NameFull: Robles FE
      – PersonEntity:
          Name:
            NameFull: Roy K
      – PersonEntity:
          Name:
            NameFull: Li J
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: 2026 May
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1477-2566
          Numbering:
            – Type: volume
              Value: 28
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Cytotherapy
              Type: main
ResultId 1