Real-Time Monitoring of CAR T Cell Dynamics in Tumor Patient-Derived Organoids using the OrganoIDNet algorithm

Real-Time Monitoring of CAR T Cell Dynamics in Tumor Patient-Derived Organoids using the OrganoIDNet algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Time Monitoring of CAR T Cell Dynamics in Tumor Patient-Derived Organoids using the OrganoIDNet algorithm Nathalia Ferreira, Camille Dourlens, Philipp Stroebel, Daniel Schäfer, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8532399/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Patient-derived organoids (PDOs) provide physiologically relevant 3D tumor models for preclinical drug testing, yet robust, scalable methods to quantify dynamic responses to immunotherapies remain limited. OrganoIDNet is a deep learning–based image analysis framework that enables automated, label-free segmentation and longitudinal quantification of organoid morphology. Here, we extend the application of OrganoIDNet to evaluate chimeric antigen receptor (CAR) T cell activity against pancreatic ductal adenocarcinoma (PDAC) PDOs targeting the tumor-associated antigen CD318. Results CD318-directed CAR T cells were co-cultured with PDAC PDOs using a Matrigel-based sandwich system and monitored by time-lapse bright-field imaging. OrganoIDNet enabled accurate single-organoid segmentation and continuous quantification of organoid number and area across multiple effector-to-target ratios. CAR-318 T cells induced robust, antigen-dependent cytotoxicity, characterized by progressive reductions in organoid number and size that correlated with increased T cell activation and reduced exhaustion markers. Dynamic imaging further revealed rapid T cell–organoid interactions and early tumor cell elimination, capturing killing kinetics and spatial patterns not accessible by conventional endpoint assays. Conclusions By integrating organoid–immune co-cultures with OrganoIDNet-driven live-cell imaging, we established a scalable, automated, and high-content platform for real-time assessment of CAR T cell efficacy in solid tumors. This approach surpasses traditional 2D and bulk cytotoxicity assays by providing continuous, spatially resolved, and patient-specific readouts of therapeutic response, supporting its utility as a preclinical framework for CAR T cell development and personalized immunotherapy evaluation. Organoids CAR T cells PDAC Artificial Intelligence Co-cultures Figures Figure 1 Figure 2 Background Patient-derived organoids (PDOs) represent an attractive model for cancer research, offering accurate in vitro recapitulation of the genetic and physiological features of patient tumors ( 1 ). This 3D in vitro system has shown robust utility across various tumor types, including pancreatic ductal adenocarcinoma (PDAC) ( 2 ), a particularly aggressive malignancy with limited therapeutic options ( 3 ). While chimeric antigen receptor (CAR) T cell therapy has been highly effective in hematological cancers, its application to solid tumors like PDAC is hindered by the immunosuppressive microenvironment and tumor antigen heterogeneity ( 4 ). Recently, a comprehensive screen of tumor-specific antigens suitable for CAR T cell therapy in PDAC identified CD318 as a promising candidate. CAR-318 T cells demonstrated potent in vivo tumor-killing effects and exhibited a favorable safety profile, supporting their potential for clinical translation ( 5 ). Given the use of PDOs as rapid, physiologically relevant preclinical models, we assessed CAR T efficacy by employing our established mixed cell culture system, which involves human PDAC PDOs co-cultured with or without immune cells. In combination with OrganoIDNet, our previously developed image-analysis algorithm, we assessed morphological changes in PDAC organoids over time using the Incucyte live-cell imaging system. Materials and Methods PDAC organoid establishment and culture Human PDAC patient-derived organoids (PDOs) were generated from resected pancreatic cancer tissue as previously described11. Organoids were cultured in PDAC organoid medium (Stem Cell Technologies, #100–0781) embedded in Matrigel domes. Isolation of T cells and generation of CAR T cells Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation from the whole blood of healthy anonymous donors. T cells were purified from PBMCs using the Pan T Cell Isolation Kit, human (Miltenyi Biotec, #130-096-535) and activated in TexMACS™ Medium (Miltenyi Biotec, #170-076-306) containing T Cell Trans-Act™, human (Miltenyi Biotec, #130-111-160) and 12.5 ng/ml of both recombinant human interleukins IL-7 and IL-15 (Miltenyi Biotec, respectively #130-095-367 and #130-095-760). T cells were transduced 24 hours after activation using vesicular stomatitis virus glycoprotein G (VSV-G) pseudotyped lentiviral supernatants derived from transfected HEK293T cells. Supernatants were concentrated and stored at − 70°C until transduction. Three days post activation, T Cell TransAct™, human, was washed out of the medium, and T cells were cultured with IL-7 and IL-15 containing TexMACS™ Medium. Approximately 10 days after purification from PBMCs, T cells were frozen with TexMACS™ Medium supplemented with 10% of Fetal Calf Serum (FCS, Eximus®, #BS-2020-500) and 10% of Dimethylsulfoxid (DMSO, Sigma-Aldrich®, #D8418) until further use for in vitro testing. Frozen T cells were thawed 24 hours before use in TexMACS™ Medium with IL-7 and IL-15. On the day of use, the number of viable CAR T cells was determined by flow cytometry following staining with 7-AAD and anti-human low-affinity nerve growth factor receptor (LNGFR) (Miltenyi Biotec, #130-111-568 and #130-112-602, respectively). PDAC organoid-T cell co-culture assays Co-cultures of human PDAC PDOs and CAR T cells were made following the protocol described before7. Briefly, organoids from six confluent wells were enzymatically dissociated using TrypLE-EDTA (Sigma-Aldrich, #T3924), counted, and replated with 10,000 cells in 20 µL Matrigel (Corning, #356231) domes. After three days of organoid maturation, and on the day of co-culture, PDAC PDOs were gently removed from the Matrigel by pipetting the organoid suspension up and down using a 10 mL pipette fitted with a yellow end-cut tip. Co-cultures were established at 2:1, 5:1, and 8:1 E:T ratios. For the co-culture setup, 30 µL of 25% Matrigel (in DMEM (Gibco, #41966-029)) was added to 96-well plates and solidified. T cell–organoid mixtures were added (20 µL), overlaid with an additional Matrigel layer, and topped with 200 µL of 1:1 PDAC organoid: TexMACS™ medium (without cytokines). Live-imaging Organoid growth and T cell-mediated cytotoxicity were monitored using the Incucyte® S3 Live-Cell Analysis System (Sartorius). Brightfield images were captured every 4 to 63 hours using the organoid imaging mode, as described before6. OrganoIDNet analysis Images from the Incucyte® system were analyzed with OrganoIDNet, the previously established deep-learning algorithm for organoid segmentation and quantification from organoid-immune cell co-cultures8. Organoid number and area were normalized to the initial time points. Code is available upon request from the corresponding author. Confocal microscopy Live cell imaging was performed using a Zeiss LSM880 confocal microscope. T cells were pre-labeled 4 hours before the co-culture with BioTracker Cystine-FITC (5 µM, SCT047, Sigma). The fluorescent-labeled antibody against EpCAM-AF594 (1:100; 324228, Biolegend) was added in a post-co-culture setup for tumor organoid staining. Imaging was conducted at 37°C and 5% CO₂ using 488 and 594 nm laser lines for FITC and AF594 excitation, respectively. Data analysis was performed using Imaris v10. Flow cytometry Samples from the PDAC PDOs alone or PDAC PDOs/CAR T cells were frozen with a 90% FBS/10% DMSO solution and stored at -80°C until the flow cytometry characterization. Single-cell suspensions were stained with fluorophore-conjugated antibodies in PEB buffer (PBS/0.5% BSA/2 mM EDTA), blocked with human FcR reagent (Miltenyi Biotec, #130-059-901), and analyzed using a BD LSR Fortessa-20 cytometer (Becton Dickinson) for CD318 expression in the PDOs and MACSQuant® Analyzer 16 Flow Cytometer for PDAC PDOs/CAR T cells samples phenotyping. Antibody details are listed in Table 1 . Data analysis was performed using FlowJo v10.10.0 and MACSquantify v2.13.0, respectively. Gating strategies for CD318 expression in PDAC PDOs, as well as organoid and CAR T cell phenotyping after co-cultures, are shown in Supplementary Fig. S3 and S4, respectively. Table 1 List of antibodies used for flow cytometric analysis. Target Conjugate Catalog number FcR blocking reagent - 130-059-901 CD326 (EpCAM) FITC 130-110-998 CD271 (LNFGR) APC 130-110-998 CD366 (TIM-3) PE-Vio770 130-121-334 CD69 APC-Vio770 130-112-616 7-AAD - 130-111-568 CD318 PE 130-101-215 Statistical analysis Statistical comparisons were conducted using GraphPad Prism 10. Unpaired t-tests were used for two-group comparisons; one-way ANOVA for multiple comparisons. Data represent mean ± S.E.M. Experiments included triplicates and three independent T cell donors, and organoids from two different PDAC patients. Results Human PDOs derived from two PDAC patients, 41T and 66T, showed approximately 40% and 90% CD318 expression in tumor cells, respectively (Fig. 1 A and S1A). These CD318-positive PDOs were co-cultured either with CAR-318 T cells (presenting a transduction efficiency from 40 to 60%), or with non-transduced T cells (NTCs) from three healthy donors as controls (Fig. 1 B), using the Matrigel-based “sandwich method”7. After 3 days of co-culture, we quantified tumor cells (EpCAM + cells) and CAR T cells, distinguishing early (LNGFR+/CD69 + cells) and late activation states (LNGFR+/TIM-3 + cells). From Incucyte images, OrganoIDNet was applied to determine the organoid number and area across multiple effector-to-target (E:T) ratios (Fig. 1 C). CAR-318 T cell treatment reduced the number of live tumor cells (EpCAM + 7-AAD- cells) in both PDAC PDO lines in an E:T-dependent manner. At an 8:1 E:T ratio, CAR-318 T cells mediated ~ 45% killing in 41T organoids (Fig. 1 D) and ~ 40% killing in 66T organoids (Fig. S1 B). In contrast, NTCs did not significantly alter tumor cell viability in either PDO. We next evaluated T cell dynamics by assessing CAR T cell frequency (via the expression of low-affinity nerve growth factor, LNGFR) and by evaluating their activation states. In 41T co-cultures, CAR T cells exhibited a 14% increase in CD69 + cells (Fig. 1 E) and a trend toward an ~ 8% reduction in TIM-3 + cells (Fig. 1 F), irrespective of the E:T ratio. This phenotype indicates robust CAR T cell activation with reduced signs of exhaustion or inhibitory checkpoint signaling. Similar trends were observed in 66T co-cultures, with increased CD69 + cells (Fig. S1 C) and decreased TIM-3 + CAR T cell numbers (Fig. S1 D). Using live-cell imaging in “organoid mode” on the Incucyte system, OrganoIDNet accurately segmented individual organoids, as illustrated by the mask overlays in which distinct colors represent different PDAC organoids (Fig. 2 A). Consistent with our flow cytometry findings, both the number and area of 41T organoids were significantly reduced following exposure to CAR-318 T cells and, to a lesser extent, upon co-culture with NTCs. The most pronounced reductions occurred after 36 hours, particularly in the 8:1 E:T condition, which showed ~ 40% fewer organoids (Fig. 2 B) and ~ 50% reduced organoid areas (Fig. S2 ) compared with organoid cultures without T cells. A similar response was observed in 66T organoids, with substantial area reduction occurring after 48 hours of co-culture, and was again most prominent in the 8:1 E:T condition, which showed ~ 50% reduction in organoid area (Fig. S2 ). To directly visualize T cell-organoid interactions, we performed confocal live imaging on 41T organoids co-cultured with either CAR-318 T cells or NTCs (5:1 E:T ratio) using the sandwich method. As anticipated, CAR-318 T cells exhibited increased dynamic interactions and proximity to PDAC organoids. Interestingly, EpCAM+-stained tumor cells were eliminated within 6 hours in CAR-318 T cell co-cultures, while EpCAM expression persisted in NTC conditions (Fig. 2 C). Discussion To overcome limitations in the in vitro evaluation of CAR T cell function, we established a stable Z-stack co-culture system combining CAR T cells with PDAC organoids. The additional application of OrganoIDNet ( 6 ), a previously established deep learning–based algorithm, to bright-field images of cancer organoids acquired using Incucyte live-cell imaging enables accurate, time-resolved quantification of cancer organoid responses to CD318-directed CAR T cell therapy. CD318-CAR T cells elicited robust, antigen-dependent cytotoxicity across patient-derived PDAC organoids derived from multiple patients, despite heterogeneity in CD318 surface expression. Notably, dynamic cytotoxicity results correlated with increased T cell activation markers and reduced exhaustion-associated phenotypes, indicating sustained functional capacity of CD318-CAR T cells. This is in line with previous in vivo studies, which demonstrated the high potency of CD318-CAR T cells for PDAC treatment ( 5 ). By integrating organoid–immune co-cultures with automated single-organoid segmentation and accurate morphological tracking, we show that OrganoIDNet transforms bright-field imaging into a multidimensional, high-content assay. Unlike conventional readouts that offer a single or indirect measure of tumor growth, OrganoIDNet provides continuous quantification of organoid area and clearance while simultaneously capturing the spatial context of T cell–tumor interactions. This temporal and spatial resolution enables precise mapping of killing kinetics, the onset and durability of CAR T cell activity, and emergent resistance phenotypes. By contrast, conventional assays for CAR T cell cytotoxicity, including flow cytometry-based killing assays, bioluminescence imaging, and 2D co-cultures, offer only fragmented insight into the dynamics of CAR T cell towards cancer cells. Flow cytometry remains largely restricted to end-point analysis, requires fluorescent labeling to distinguish effector and target compartments, and provides minimal information on the spatial pattern of tumor clearance. Bioluminescence-based systems permit continuous readouts but requires genetic manipulation, and are influenced by metabolic state and substrate availability ( 12 ). These constraints are particularly limiting in solid tumors, where therapeutic efficacy depends on the temporal coordination of infiltration, synapse formation, serial killing, and resistance mechanisms, features that are not readily captured with conventional 2D or bulk assays. As we show here, 3D organoids and organoid-immune co-cultures address many of these shortcomings by more faithfully recapitulating tumor architecture, microenvironmental barriers, and patient-specific heterogeneity. So far, organoid-based assays remain technically demanding. Substantial variability in organoid size, morphology, and structural complexity across and within patient samples complicates quantitative imaging and limits assay scalability. These hurdles are met by the application of OrganoIDNet-driven image analysis which allows single-organoid segmentation, quantitative morphological metrics, and direct visualization of T cell-tumor interactions. While our work advances the field of CAR T cell live-imaging analysis, some limitations must be acknowledged. Like all other organoid-based assays, our protocol is complex and technically demanding. The Matrigel-based sandwich co-culture format further introduces operator-dependent variability and relies on a murine-derived extracellular matrix with batch-to-batch compositional differences that do not fully reflect the dense, desmoplastic microenvironment of pancreatic tumors and may influence immune cell motility and killing kinetics. These challenges are mitigated in part by OrganoIDNet-driven single-organoid segmentation and objective morphological quantification, but continued methodological refinement, including simplified workflows and defined matrix alternatives, will be essential to improve reproducibility and translational applicability. In summary, our system collectively enables multidimensional profiling of therapeutic efficacy of CAR T cells at temporal resolution unattainable with traditional assays. Conclusion By combining organoid–immune co-cultures with OrganoIDNet live-imaging analyses, we established a scalable, automated, and high-content platform for real-time assessment of CAR T cell efficacy in tumor patient-derived organoids. This approach enables continuous, spatially resolved, and patient-specific quantification of immune-mediated tumor killing, overcoming key limitations of traditional endpoint and bulk assays. Our framework provides a robust preclinical tool for evaluating CAR T cell specificity, potency, and durability and supports the broader application of organoid-based platforms in personalized immunotherapy development for clinical translation. Abbreviations CAR chimeric antigen receptor CD318 cluster of differentiation 318 EpCAM epithelial cell adhesion molecule E:T effector-to-target ratio LNGFR low-affinity nerve growth factor receptor NTC non-transduced T cell PDAC pancreatic ductal adenocarcinoma PDO patient-derived organoid TIM-3 T cell immunoglobulin and mucin domain–containing protein 3 7-AAD 7-aminoactinomycin D. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request Competing interests CD, DS and OH are employees of Miltenyi Biotec B.V. & Co. KG. All other authors declare no competing interests Funding This work was supported by the Scientific Service Facility Cell Sorting at the University Medical Center Göttingen (Germany) through the DFG project number (DFG project number 442249343, BD LSRFortessa X-20, Becton Dickinson) Authors Contributions N.F. conceived the study, developed the experimental strategies, and wrote the manuscript. C.D. isolated and generated the CAR T cells and performed the flow cytometry analyses. P.S. provided the patient-derived PDAC organoids and protocols for organoid expansion. D.S., O.H., and F.A. contributed manuscript revision. F.A. supervised the project and provided financial support. Acknowledgments The authors thank Bärbel Heidrich and Regine Kruse for their excellent technical support. We further thank the Microscopy Facility at the Max Planck Institute for Multidisciplinary Sciences for providing imaging support and to Jennifer Appelhans (University Medical Center Göttingen, UMG) for her assistance in generating PDAC PDOs. We thank Katharina Ströle for her assistance in the statistics analysis. We also acknowledge Sartorius for providing Incucyte equipment. Finally, we thank the Scientific Core Facility Cell Sorting at the UMG for their support. References Drost J, Clevers H. Organoids in cancer research. Nat Rev Cancer. 2018;18:407–18. Krieger TG, et al. Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy. Nat Commun. 2021;12:5826. Halbrook CJ, Lyssiotis CA, Di Pasca M, Maitra A. Pancreatic cancer: Advances and challenges. Cell. 2023;186:1729–54. Sterner RC, Sterner RM. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 2021;11:69. Schäfer D, et al. Identification of CD318, TSPAN8 and CD66c as target candidates for CAR T cell based immunotherapy of pancreatic adenocarcinoma. Nat Commun. 2021;12:1453. Ferreira N, et al. OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data. Cell Oncol. 2025;48:101–22. Ferreira N, Alves F, Markus A. Organoid-Immune Cell Co-culture for Stable Live Imaging. New York, NY: in (Springer US; 2025. Kulkarni A, Ferreira N, Scodellaro R, Alves F. A Curated Cell Life Imaging Dataset of Immune-enriched Pancreatic Cancer Organoids with Pre-trained AI Models. (2024). Schnalzger TE, et al. 3D model for CAR -mediated cytotoxicity using patient‐derived colorectal cancer organoids. EMBO J. 2019;38:e100928. Logun M, et al. Patient-derived glioblastoma organoids as real-time avatars for assessing responses to clinical CAR-T cell therapy. Cell Stem Cell. 2025;32:181–e1904. Duman ET et al. A single-cell strategy for the identification of intronic variants related to mis-splicing in pancreatic cancer (2023). Kiesgen S, Messinger JC, Chintala NK, Tano Z, Adusumilli PS. Comparative analysis of assays to measure CAR T-cell-mediated cytotoxicity. Nat Protoc. (2021). Supplementary Files FerreiraetalSupplementarydataJTM.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 07 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8532399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571077340,"identity":"fbcb63ba-1aa9-4e9a-85ca-7598f8ffa76a","order_by":0,"name":"Nathalia Ferreira","email":"","orcid":"","institution":"Max-Planck-Institut für Multidisziplinäre Naturwissenschaften - City-Campus: Max-Planck-Institut fur Multidisziplinare Naturwissenschaften - City-Campus","correspondingAuthor":false,"prefix":"","firstName":"Nathalia","middleName":"","lastName":"Ferreira","suffix":""},{"id":571077341,"identity":"ce2dbfd3-05cc-4e71-9f18-b70262375e13","order_by":1,"name":"Camille Dourlens","email":"","orcid":"","institution":"Miltenyi Biotec BV \u0026 Co KG","correspondingAuthor":false,"prefix":"","firstName":"Camille","middleName":"","lastName":"Dourlens","suffix":""},{"id":571077342,"identity":"cfc46d0b-0be5-4c99-beba-e4aca7359fae","order_by":2,"name":"Philipp Stroebel","email":"","orcid":"","institution":"University Medical Center Göttingen: Universitatsmedizin Gottingen","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Stroebel","suffix":""},{"id":571077343,"identity":"d18ac136-9dbf-485c-b7fb-1c5d9ae7c2d0","order_by":3,"name":"Daniel Schäfer","email":"","orcid":"","institution":"Miltenyi Biotec BV \u0026 Co KG","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Schäfer","suffix":""},{"id":571077344,"identity":"6040626b-a3f5-41a8-9894-c37a9fa20dfe","order_by":4,"name":"Olaf Hardt","email":"","orcid":"","institution":"Miltenyi Biotec BV \u0026 Co KG","correspondingAuthor":false,"prefix":"","firstName":"Olaf","middleName":"","lastName":"Hardt","suffix":""},{"id":571077345,"identity":"4b8fc1a0-c97c-453b-9f91-df1114ba17aa","order_by":5,"name":"Frauke Alves","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBAC9gbmBiBlA8QJDAw8xGjhOcAI0pJGupbDpGhhP9gm8eHPebsNx9MvMLypIEYLT2Kb5My228kbzrwpYJxzhggt9gyJzca8DbeTzW7kJDDzthFjC//DZuM/f85BtfwjRotEYuNjBrYDdmY30g8w8zYQpeVh48PetuQE+zNvGA7OOUaUw5IPHPjxx85esj394YM3NURogYHEBgYegwMkaACFHAP7A5J0jIJRMApGwcgBAKXvPYAgClI+AAAAAElFTkSuQmCC","orcid":"","institution":"Max Planck Institute for Multidisciplinary Sciences - City-Campus: Max-Planck-Institut fur Multidisziplinare Naturwissenschaften - City-Campus","correspondingAuthor":true,"prefix":"","firstName":"Frauke","middleName":"","lastName":"Alves","suffix":""}],"badges":[],"createdAt":"2026-01-06 14:25:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8532399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8532399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100014573,"identity":"bd9d8e9d-7d57-464a-a923-7ee0dfcf2f7a","added_by":"auto","created_at":"2026-01-12 06:24:49","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10203,"visible":true,"origin":"","legend":"","description":"","filename":"jtrmJTRMD2600321.xml","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/63d783f41b8a5e276f1cc158.xml"},{"id":100014596,"identity":"f1b8eeb7-e96e-453d-9362-9b9e6ca42a58","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1059,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD2600321166686.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/1dc365defe87d81b727a3460.xml"},{"id":100361949,"identity":"8dea065c-0581-4f91-849e-c12bb609d539","added_by":"auto","created_at":"2026-01-16 07:45:58","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":837,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD2600321Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/780f3b9123127e4fd43c2892.xml"},{"id":100014588,"identity":"197693b4-b80f-42ba-bc38-5f460765040b","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49976,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD26003210enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/3c4ef85892c7ddbdd74fb408.xml"},{"id":100014609,"identity":"1fb5b8c0-7a17-4874-91a9-804e1f97395e","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113278,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/d4f7d813b651bcc61199e0b7.png"},{"id":100014551,"identity":"dbb78667-a701-4848-b831-ee274ca4b7b8","added_by":"auto","created_at":"2026-01-12 06:24:48","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342298,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/4b08fdd2291662441bc32ac0.png"},{"id":100014585,"identity":"8122d735-591e-48ac-a1a2-e669f5040bce","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48860,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD26003210structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/cff13200f25260524d56842d.xml"},{"id":100014581,"identity":"74591305-65f6-4f4a-b8ed-f45a73b23274","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55742,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/fafdf2909c2eba866681ed9c.html"},{"id":100014571,"identity":"f64dfe26-ebec-4782-834c-77d76acf273b","added_by":"auto","created_at":"2026-01-12 06:24:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":527595,"visible":true,"origin":"","legend":"\u003cp\u003eFlow cytometric analysis of tumor and immune cell populations in human PDAC PDO organoid co-cultures with CAR-318 T cells after 3 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Quantification of live EpCAM⁺CD318⁺ double-positive cells as a percentage of total live CD318⁺ tumor cells in 41T human PDAC organoids. Data represent mean ± s.e.m. from two independent experiments.\u003cstrong\u003e (B)\u003c/strong\u003e Histogram showing CAR-318 transduction efficiency in T cells from three healthy donors (donors 1-3), assessed by LNGFR expression. Non-transduced T cells (NTCs) serve as controls. \u003cstrong\u003e(C)\u003c/strong\u003e \u0026nbsp;Schematic workflow for the in vitro evaluation of CAR T cell dynamics during co-culture with PDAC PDOs. OrganoIDNet enabled automated segmentation and quantification of organoid responses to CAR T cells. At the end of the co-culture period, flow cytometry was performed to characterize both PDO populations and CAR T cell activation states. \u003cstrong\u003e(D)\u003c/strong\u003eFrequency of EpCAM⁺7-AAD- tumor cells as a percentage of total live tumor cells in 41T PDAC PDOs after 3-day co-culture with CAR-318 T cells (41T+CAR-318), non-transduced control T cells (41T+NTC), or in the absence of T cells (41T) at varying effector-to-target (E:T) ratios. Data represent mean ± S.E.M. from three independent CAR/NTC donor co-cultures. Statistical significance was assessed using a One-Way ANOVA test followed by Dunnett’s comparison test; p \u0026lt; 0.01 (**). \u003cstrong\u003e(E)\u003c/strong\u003e Proportion of LNGFR⁺CD69⁺ double-positive cells indicating early T cell activation following 3-day co-culture of CAR-318 or NTC T cells with 41T PDAC organoids at different E:T ratios. \u003cstrong\u003e(F)\u003c/strong\u003e Proportion of LNGFR⁺TIM-3⁺ double-positive cells indicating late T cell activation under the same co-culture conditions. In d and f, data represent mean ± S.E.M. from three independent donors. Statistical significance was assessed using a two-tailed unpaired Student’s t-test; p \u0026lt; 0.001 (***).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/2718eacc83665b75ce75fa3c.png"},{"id":100014586,"identity":"6102b433-5708-49b8-9dcb-84c8f633d016","added_by":"auto","created_at":"2026-01-12 06:24:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2285260,"visible":true,"origin":"","legend":"\u003cp\u003eOrganoIDNet analysis of real-time human PDAC organoid responses to CAR T cell targeting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Representative Incucyte image of PDAC PDOs co-cultured with CAR-318 T cells, illustrating OrganoIDNet-generated segmentation masks in which each organoid is assigned a distinct color.\u003cstrong\u003e (B) \u003c/strong\u003eReal-time quantification of organoid numbers across multiple time points in 41T PDAC organoids cultured alone (41T), co-cultured with CAR-318 T cells at varying effector-to-target (E:T) ratios (41T+CAR-318), or co-cultured with non-transduced control T cells (41T+NTC). Data represent mean ± S.E.M. from three independent CAR/NTC donors, performed in triplicate. Statistical significance was assessed using One-Way ANOVA test followed by Dunnett’s comparison test; p \u0026lt; 0.05 (*), p \u0026lt; 0.01 (**).\u003cstrong\u003e (C) \u003c/strong\u003eRepresentative confocal live-cell images from one of the three regions of interest (ROIs) per condition, showing PDAC organoids stained with an antibody against EpCAM (magenta) and co-cultured with either CAR-318 or NTC T cells labeled with BioTracker Cystine-FITC (green). The nuclei of the cells were stained with Hoechst 3342 (blue). Insets highlight close T cell–organoid interactions. Scale bars in c) represent 50 µm.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/267dbf674ee6b77fa1e8b981.png"},{"id":100381265,"identity":"780c87e5-20ce-46ed-b62e-65b54318151a","added_by":"auto","created_at":"2026-01-16 10:37:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3312665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/e3f650e6-fb15-404c-83fa-46ec148bfc11.pdf"},{"id":100014558,"identity":"88a1e7cf-6964-4a57-ad02-dfc8d5bacd5d","added_by":"auto","created_at":"2026-01-12 06:24:48","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":629495,"visible":true,"origin":"","legend":"","description":"","filename":"FerreiraetalSupplementarydataJTM.docx","url":"https://assets-eu.researchsquare.com/files/rs-8532399/v1/87f6b0178cd2855341e89cfe.docx"}],"financialInterests":"","formattedTitle":"Real-Time Monitoring of CAR T Cell Dynamics in Tumor Patient-Derived Organoids using the OrganoIDNet algorithm","fulltext":[{"header":"Background","content":"\u003cp\u003ePatient-derived organoids (PDOs) represent an attractive model for cancer research, offering accurate in vitro recapitulation of the genetic and physiological features of patient tumors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This 3D in vitro system has shown robust utility across various tumor types, including pancreatic ductal adenocarcinoma (PDAC) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), a particularly aggressive malignancy with limited therapeutic options (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While chimeric antigen receptor (CAR) T cell therapy has been highly effective in hematological cancers, its application to solid tumors like PDAC is hindered by the immunosuppressive microenvironment and tumor antigen heterogeneity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Recently, a comprehensive screen of tumor-specific antigens suitable for CAR T cell therapy in PDAC identified CD318 as a promising candidate. CAR-318 T cells demonstrated potent \u003cem\u003ein vivo\u003c/em\u003e tumor-killing effects and exhibited a favorable safety profile, supporting their potential for clinical translation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the use of PDOs as rapid, physiologically relevant preclinical models, we assessed CAR T efficacy by employing our established mixed cell culture system, which involves human PDAC PDOs co-cultured with or without immune cells. In combination with OrganoIDNet, our previously developed image-analysis algorithm, we assessed morphological changes in PDAC organoids over time using the Incucyte live-cell imaging system.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePDAC organoid establishment and culture\u003c/h2\u003e \u003cp\u003eHuman PDAC patient-derived organoids (PDOs) were generated from resected pancreatic cancer tissue as previously described11. Organoids were cultured in PDAC organoid medium (Stem Cell Technologies, #100\u0026ndash;0781) embedded in Matrigel domes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIsolation of T cells and generation of CAR T cells\u003c/h3\u003e\n\u003cp\u003ePeripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation from the whole blood of healthy anonymous donors. T cells were purified from PBMCs using the Pan T Cell Isolation Kit, human (Miltenyi Biotec, #130-096-535) and activated in TexMACS\u0026trade; Medium (Miltenyi Biotec, #170-076-306) containing T Cell Trans-Act\u0026trade;, human (Miltenyi Biotec, #130-111-160) and 12.5 ng/ml of both recombinant human interleukins IL-7 and IL-15 (Miltenyi Biotec, respectively #130-095-367 and #130-095-760). T cells were transduced 24 hours after activation using vesicular stomatitis virus glycoprotein G (VSV-G) pseudotyped lentiviral supernatants derived from transfected HEK293T cells. Supernatants were concentrated and stored at \u0026minus;\u0026thinsp;70\u0026deg;C until transduction. Three days post activation, T Cell TransAct\u0026trade;, human, was washed out of the medium, and T cells were cultured with IL-7 and IL-15 containing TexMACS\u0026trade; Medium. Approximately 10 days after purification from PBMCs, T cells were frozen with TexMACS\u0026trade; Medium supplemented with 10% of Fetal Calf Serum (FCS, Eximus\u0026reg;, #BS-2020-500) and 10% of Dimethylsulfoxid (DMSO, Sigma-Aldrich\u0026reg;, #D8418) until further use for in vitro testing. Frozen T cells were thawed 24 hours before use in TexMACS\u0026trade; Medium with IL-7 and IL-15. On the day of use, the number of viable CAR T cells was determined by flow cytometry following staining with 7-AAD and anti-human low-affinity nerve growth factor receptor (LNGFR) (Miltenyi Biotec, #130-111-568 and #130-112-602, respectively).\u003c/p\u003e\n\u003ch3\u003ePDAC organoid-T cell co-culture assays\u003c/h3\u003e\n\u003cp\u003eCo-cultures of human PDAC PDOs and CAR T cells were made following the protocol described before7. Briefly, organoids from six confluent wells were enzymatically dissociated using TrypLE-EDTA (Sigma-Aldrich, #T3924), counted, and replated with 10,000 cells in 20 \u0026micro;L Matrigel (Corning, #356231) domes. After three days of organoid maturation, and on the day of co-culture, PDAC PDOs were gently removed from the Matrigel by pipetting the organoid suspension up and down using a 10 mL pipette fitted with a yellow end-cut tip. Co-cultures were established at 2:1, 5:1, and 8:1 E:T ratios. For the co-culture setup, 30 \u0026micro;L of 25% Matrigel (in DMEM (Gibco, #41966-029)) was added to 96-well plates and solidified. T cell\u0026ndash;organoid mixtures were added (20 \u0026micro;L), overlaid with an additional Matrigel layer, and topped with 200 \u0026micro;L of 1:1 PDAC organoid: TexMACS\u0026trade; medium (without cytokines).\u003c/p\u003e\n\u003ch3\u003eLive-imaging\u003c/h3\u003e\n\u003cp\u003eOrganoid growth and T cell-mediated cytotoxicity were monitored using the Incucyte\u0026reg; S3 Live-Cell Analysis System (Sartorius). Brightfield images were captured every 4 to 63 hours using the organoid imaging mode, as described before6.\u003c/p\u003e\n\u003ch3\u003eOrganoIDNet analysis\u003c/h3\u003e\n\u003cp\u003eImages from the Incucyte\u0026reg; system were analyzed with OrganoIDNet, the previously established deep-learning algorithm for organoid segmentation and quantification from organoid-immune cell co-cultures8. Organoid number and area were normalized to the initial time points. Code is available upon request from the corresponding author.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConfocal microscopy\u003c/h2\u003e \u003cp\u003eLive cell imaging was performed using a Zeiss LSM880 confocal microscope. T cells were pre-labeled 4 hours before the co-culture with BioTracker Cystine-FITC (5 \u0026micro;M, SCT047, Sigma). The fluorescent-labeled antibody against EpCAM-AF594 (1:100; 324228, Biolegend) was added in a post-co-culture setup for tumor organoid staining. Imaging was conducted at 37\u0026deg;C and 5% CO₂ using 488 and 594 nm laser lines for FITC and AF594 excitation, respectively. Data analysis was performed using Imaris v10.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFlow cytometry\u003c/h3\u003e\n\u003cp\u003eSamples from the PDAC PDOs alone or PDAC PDOs/CAR T cells were frozen with a 90% FBS/10% DMSO solution and stored at -80\u0026deg;C until the flow cytometry characterization. Single-cell suspensions were stained with fluorophore-conjugated antibodies in PEB buffer (PBS/0.5% BSA/2 mM EDTA), blocked with human FcR reagent (Miltenyi Biotec, #130-059-901), and analyzed using a BD LSR Fortessa-20 cytometer (Becton Dickinson) for CD318 expression in the PDOs and MACSQuant\u0026reg; Analyzer 16 Flow Cytometer for PDAC PDOs/CAR T cells samples phenotyping. Antibody details are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Data analysis was performed using FlowJo v10.10.0 and MACSquantify v2.13.0, respectively. Gating strategies for CD318 expression in PDAC PDOs, as well as organoid and CAR T cell phenotyping after co-cultures, are shown in Supplementary Fig. S3 and S4, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of antibodies used for flow cytometric analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConjugate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCatalog number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFcR blocking reagent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-059-901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD326 (EpCAM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-110-998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD271 (LNFGR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-110-998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD366 (TIM-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE-Vio770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-121-334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPC-Vio770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-112-616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-AAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-111-568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e130-101-215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical comparisons were conducted using GraphPad Prism 10. Unpaired t-tests were used for two-group comparisons; one-way ANOVA for multiple comparisons. Data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.E.M. Experiments included triplicates and three independent T cell donors, and organoids from two different PDAC patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eHuman PDOs derived from two PDAC patients, 41T and 66T, showed approximately 40% and 90% CD318 expression in tumor cells, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and S1A). These CD318-positive PDOs were co-cultured either with CAR-318 T cells (presenting a transduction efficiency from 40 to 60%), or with non-transduced T cells (NTCs) from three healthy donors as controls (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), using the Matrigel-based \u0026ldquo;sandwich method\u0026rdquo;7. After 3 days of co-culture, we quantified tumor cells (EpCAM\u0026thinsp;+\u0026thinsp;cells) and CAR T cells, distinguishing early (LNGFR+/CD69\u0026thinsp;+\u0026thinsp;cells) and late activation states (LNGFR+/TIM-3\u0026thinsp;+\u0026thinsp;cells). From Incucyte images, OrganoIDNet was applied to determine the organoid number and area across multiple effector-to-target (E:T) ratios (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). CAR-318 T cell treatment reduced the number of live tumor cells (EpCAM\u0026thinsp;+\u0026thinsp;7-AAD- cells) in both PDAC PDO lines in an E:T-dependent manner. At an 8:1 E:T ratio, CAR-318 T cells mediated\u0026thinsp;~\u0026thinsp;45% killing in 41T organoids (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD) and ~\u0026thinsp;40% killing in 66T organoids (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eB). In contrast, NTCs did not significantly alter tumor cell viability in either PDO. We next evaluated T cell dynamics by assessing CAR T cell frequency (via the expression of low-affinity nerve growth factor, LNGFR) and by evaluating their activation states. In 41T co-cultures, CAR T cells exhibited a 14% increase in CD69\u0026thinsp;+\u0026thinsp;cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE) and a trend toward an ~\u0026thinsp;8% reduction in TIM-3\u0026thinsp;+\u0026thinsp;cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF), irrespective of the E:T ratio. This phenotype indicates robust CAR T cell activation with reduced signs of exhaustion or inhibitory checkpoint signaling. Similar trends were observed in 66T co-cultures, with increased CD69\u0026thinsp;+\u0026thinsp;cells (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eC) and decreased TIM-3\u0026thinsp;+\u0026thinsp;CAR T cell numbers (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003eUsing live-cell imaging in \u0026ldquo;organoid mode\u0026rdquo; on the Incucyte system, OrganoIDNet accurately segmented individual organoids, as illustrated by the mask overlays in which distinct colors represent different PDAC organoids (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Consistent with our flow cytometry findings, both the number and area of 41T organoids were significantly reduced following exposure to CAR-318 T cells and, to a lesser extent, upon co-culture with NTCs. The most pronounced reductions occurred after 36 hours, particularly in the 8:1 E:T condition, which showed\u0026thinsp;~\u0026thinsp;40% fewer organoids (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) and ~\u0026thinsp;50% reduced organoid areas (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e) compared with organoid cultures without T cells. A similar response was observed in 66T organoids, with substantial area reduction occurring after 48 hours of co-culture, and was again most prominent in the 8:1 E:T condition, which showed\u0026thinsp;~\u0026thinsp;50% reduction in organoid area (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo directly visualize T cell-organoid interactions, we performed confocal live imaging on 41T organoids co-cultured with either CAR-318 T cells or NTCs (5:1 E:T ratio) using the sandwich method. As anticipated, CAR-318 T cells exhibited increased dynamic interactions and proximity to PDAC organoids. Interestingly, EpCAM+-stained tumor cells were eliminated within 6 hours in CAR-318 T cell co-cultures, while EpCAM expression persisted in NTC conditions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo overcome limitations in the \u003cem\u003ein vitro\u003c/em\u003e evaluation of CAR T cell function, we established a stable Z-stack co-culture system combining CAR T cells with PDAC organoids. The additional application of OrganoIDNet (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), a previously established deep learning\u0026ndash;based algorithm, to bright-field images of cancer organoids acquired using Incucyte live-cell imaging enables accurate, time-resolved quantification of cancer organoid responses to CD318-directed CAR T cell therapy. CD318-CAR T cells elicited robust, antigen-dependent cytotoxicity across patient-derived PDAC organoids derived from multiple patients, despite heterogeneity in CD318 surface expression. Notably, dynamic cytotoxicity results correlated with increased T cell activation markers and reduced exhaustion-associated phenotypes, indicating sustained functional capacity of CD318-CAR T cells. This is in line with previous \u003cem\u003ein vivo\u003c/em\u003e studies, which demonstrated the high potency of CD318-CAR T cells for PDAC treatment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy integrating organoid\u0026ndash;immune co-cultures with automated single-organoid segmentation and accurate morphological tracking, we show that OrganoIDNet transforms bright-field imaging into a multidimensional, high-content assay. Unlike conventional readouts that offer a single or indirect measure of tumor growth, OrganoIDNet provides continuous quantification of organoid area and clearance while simultaneously capturing the spatial context of T cell\u0026ndash;tumor interactions. This temporal and spatial resolution enables precise mapping of killing kinetics, the onset and durability of CAR T cell activity, and emergent resistance phenotypes. By contrast, conventional assays for CAR T cell cytotoxicity, including flow cytometry-based killing assays, bioluminescence imaging, and 2D co-cultures, offer only fragmented insight into the dynamics of CAR T cell towards cancer cells. Flow cytometry remains largely restricted to end-point analysis, requires fluorescent labeling to distinguish effector and target compartments, and provides minimal information on the spatial pattern of tumor clearance. Bioluminescence-based systems permit continuous readouts but requires genetic manipulation, and are influenced by metabolic state and substrate availability (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These constraints are particularly limiting in solid tumors, where therapeutic efficacy depends on the temporal coordination of infiltration, synapse formation, serial killing, and resistance mechanisms, features that are not readily captured with conventional 2D or bulk assays. As we show here, 3D organoids and organoid-immune co-cultures address many of these shortcomings by more faithfully recapitulating tumor architecture, microenvironmental barriers, and patient-specific heterogeneity. So far, organoid-based assays remain technically demanding. Substantial variability in organoid size, morphology, and structural complexity across and within patient samples complicates quantitative imaging and limits assay scalability. These hurdles are met by the application of OrganoIDNet-driven image analysis which allows single-organoid segmentation, quantitative morphological metrics, and direct visualization of T cell-tumor interactions.\u003c/p\u003e \u003cp\u003eWhile our work advances the field of CAR T cell live-imaging analysis, some limitations must be acknowledged. Like all other organoid-based assays, our protocol is complex and technically demanding. The Matrigel-based sandwich co-culture format further introduces operator-dependent variability and relies on a murine-derived extracellular matrix with batch-to-batch compositional differences that do not fully reflect the dense, desmoplastic microenvironment of pancreatic tumors and may influence immune cell motility and killing kinetics. These challenges are mitigated in part by OrganoIDNet-driven single-organoid segmentation and objective morphological quantification, but continued methodological refinement, including simplified workflows and defined matrix alternatives, will be essential to improve reproducibility and translational applicability.\u003c/p\u003e \u003cp\u003eIn summary, our system collectively enables multidimensional profiling of therapeutic efficacy of CAR T cells at temporal resolution unattainable with traditional assays.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy combining organoid\u0026ndash;immune co-cultures with OrganoIDNet live-imaging analyses, we established a scalable, automated, and high-content platform for real-time assessment of CAR T cell efficacy in tumor patient-derived organoids. This approach enables continuous, spatially resolved, and patient-specific quantification of immune-mediated tumor killing, overcoming key limitations of traditional endpoint and bulk assays. Our framework provides a robust preclinical tool for evaluating CAR T cell specificity, potency, and durability and supports the broader application of organoid-based platforms in personalized immunotherapy development for clinical translation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echimeric antigen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD318\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecluster of differentiation 318\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEpCAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepithelial cell adhesion molecule\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE:T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeffector-to-target ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLNGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-affinity nerve growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-transduced T cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epancreatic ductal adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epatient-derived organoid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIM-3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT cell immunoglobulin and mucin domain\u0026ndash;containing protein 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e7-AAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e7-aminoactinomycin D.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCD, DS and OH are employees of Miltenyi Biotec B.V. \u0026amp; Co. KG. All other authors declare no competing interests\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Scientific Service Facility Cell Sorting at the University Medical Center G\u0026ouml;ttingen (Germany) through the DFG project number (DFG project number 442249343, BD LSRFortessa X-20, Becton Dickinson)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN.F. conceived the study, developed the experimental strategies, and wrote the manuscript. C.D. isolated and generated the CAR T cells and performed the flow cytometry analyses. P.S. provided the patient-derived PDAC organoids and protocols for organoid expansion. D.S., O.H., and F.A. contributed manuscript revision. F.A. supervised the project and provided financial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors thank B\u0026auml;rbel Heidrich and Regine Kruse for their excellent technical support. We further thank the Microscopy Facility at the Max Planck Institute for Multidisciplinary Sciences for providing imaging support and to Jennifer Appelhans (University Medical Center G\u0026ouml;ttingen, UMG) for her assistance in generating PDAC PDOs. We thank Katharina Str\u0026ouml;le for her assistance in the statistics analysis. We also acknowledge Sartorius for providing Incucyte equipment. Finally, we thank the Scientific Core Facility Cell Sorting at the UMG for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDrost J, Clevers H. Organoids in cancer research. Nat Rev Cancer. 2018;18:407\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrieger TG, et al. Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy. Nat Commun. 2021;12:5826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalbrook CJ, Lyssiotis CA, Di Pasca M, Maitra A. Pancreatic cancer: Advances and challenges. Cell. 2023;186:1729\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterner RC, Sterner RM. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 2021;11:69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSch\u0026auml;fer D, et al. Identification of CD318, TSPAN8 and CD66c as target candidates for CAR T cell based immunotherapy of pancreatic adenocarcinoma. Nat Commun. 2021;12:1453.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira N, et al. OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data. Cell Oncol. 2025;48:101\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira N, Alves F, Markus A. Organoid-Immune Cell Co-culture for Stable Live Imaging. New York, NY: in (Springer US; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulkarni A, Ferreira N, Scodellaro R, Alves F. A Curated Cell Life Imaging Dataset of Immune-enriched Pancreatic Cancer Organoids with Pre-trained AI Models. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnalzger TE, et al. 3D model for CAR -mediated cytotoxicity using patient‐derived colorectal cancer organoids. EMBO J. 2019;38:e100928.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogun M, et al. Patient-derived glioblastoma organoids as real-time avatars for assessing responses to clinical CAR-T cell therapy. Cell Stem Cell. 2025;32:181\u0026ndash;e1904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuman ET et al. A single-cell strategy for the identification of intronic variants related to mis-splicing in pancreatic cancer (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiesgen S, Messinger JC, Chintala NK, Tano Z, Adusumilli PS. Comparative analysis of assays to measure CAR T-cell-mediated cytotoxicity. Nat Protoc. (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Organoids, CAR T cells, PDAC, Artificial Intelligence, Co-cultures","lastPublishedDoi":"10.21203/rs.3.rs-8532399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8532399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatient-derived organoids (PDOs) provide physiologically relevant 3D tumor models for preclinical drug testing, yet robust, scalable methods to quantify dynamic responses to immunotherapies remain limited. OrganoIDNet is a deep learning\u0026ndash;based image analysis framework that enables automated, label-free segmentation and longitudinal quantification of organoid morphology. Here, we extend the application of OrganoIDNet to evaluate chimeric antigen receptor (CAR) T cell activity against pancreatic ductal adenocarcinoma (PDAC) PDOs targeting the tumor-associated antigen CD318.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCD318-directed CAR T cells were co-cultured with PDAC PDOs using a Matrigel-based sandwich system and monitored by time-lapse bright-field imaging. OrganoIDNet enabled accurate single-organoid segmentation and continuous quantification of organoid number and area across multiple effector-to-target ratios. CAR-318 T cells induced robust, antigen-dependent cytotoxicity, characterized by progressive reductions in organoid number and size that correlated with increased T cell activation and reduced exhaustion markers. Dynamic imaging further revealed rapid T cell\u0026ndash;organoid interactions and early tumor cell elimination, capturing killing kinetics and spatial patterns not accessible by conventional endpoint assays.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBy integrating organoid\u0026ndash;immune co-cultures with OrganoIDNet-driven live-cell imaging, we established a scalable, automated, and high-content platform for real-time assessment of CAR T cell efficacy in solid tumors. This approach surpasses traditional 2D and bulk cytotoxicity assays by providing continuous, spatially resolved, and patient-specific readouts of therapeutic response, supporting its utility as a preclinical framework for CAR T cell development and personalized immunotherapy evaluation.\u003c/p\u003e","manuscriptTitle":"Real-Time Monitoring of CAR T Cell Dynamics in Tumor Patient-Derived Organoids using the OrganoIDNet algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:23:55","doi":"10.21203/rs.3.rs-8532399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-19T06:49:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T20:40:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T12:08:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-01-06T09:24:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90dd71b1-57ff-460a-814f-db05add72dfd","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T15:49:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:23:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8532399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8532399","identity":"rs-8532399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}