Academic Journal
Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.
| Τίτλος: | Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing. |
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| 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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| 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 |
| ISSN: | 1477-2566 |
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| DOI: | 10.1016/j.jcyt.2026.102078 |