{"id":"CU25424","slug":"tissue-resident-memory-t-cells--CU25424","source":{"id":"CU25424","dataset":"techtransfer","title":"Tissue-resident memory T cells for immune modulation","description_":"<p>This technology is a single-cell-resolution spatiotemporal database of graft-versus-host disease (GVHD) patients from allogeneic hematopoietic stem cell transplantation recipients, along with a multi-omics computational toolkit for its analysis.</p>\r\r<h2>Unmet Need: Longitudinal tracking of alloreactive T cells in GVHD</h2>\r\r<p>Acute graft-versus-host disease (GVHD) remains a leading cause of morbidity and mortality after allogeneic hematopoietic cell transplantation despite current prophylactic strategies. Existing clinical and laboratory approaches do not enable prospective identification or longitudinal tracking of the specific donor T cell clones that drive tissue damage, nor do they provide sufficient resolution to define their functional states or tissue interactions in patients. As a result, clinicians lack reliable biomarkers to predict disease onset, monitor progression, or tailor interventions, which contributes to high mortality and treatment-related toxicity. Addressing these limitations is essential to improve patient outcomes and to advance a broader understanding of human T cell-mediated pathology across immune-driven diseases.</p>\r\r<h2>The Technology: High-resolution computational toolkit for tracking pathogenic T cells in GVHD</h2>\r\r<p>This technology is a multimodal toolkit that enables high-resolution spatiotemporal analysis of human T cell responses in patients with clinical annotations. It combines pre-transplant identification of alloreactive T cells with longitudinal tracking and single-cell profiling, integrating T cell receptor (TCR) sequencing, transcriptional phenotyping, and spatial mapping within affected tissues. Computational modeling enables the identification of pathogenic clones, the characterization of their biophysical and transcriptional features, and the analysis of clonal dynamics over time. By preserving tissue architecture and incorporating spatial transcriptomics, this technology captures localized immune-tissue interactions, revealing how specific T cell populations expand, differentiate, and contribute to tissue injury.</p>\r\r<h2>Applications:</h2>\r\r<ul>\r<li>Biomarker discovery for graft-versus-host-disease (GVHD)</li>\r<li>Therapeutic targeting for GVHD</li>\r<li>Computational toolbox for temporal and spatial dynamics of diverse diseases, including autoimmune and cancer</li>\r<li>Research tool for understanding T cell-mediated pathology over time</li>\r</ul>\r\r<h2>Advantages:</h2>\r\r<ul>\r<li>Improves understanding of T cell-mediated pathology in longitudinal human studies</li>\r<li>Multi-modal dissection of both temporal and spatial dynamics in one computational toolkit</li>\r<li>Generalizable computational toolkit and framework to other complex immune-related diseases</li>\r</ul>\r\r<h2>Lead Inventor:</h2>\r\r<p><a href=\"https://www.bme.columbia.edu/faculty/elham-azizi\">Elham Azizi, Ph.D.</a> </p>\r\r<h2>Patent Information:</h2>\r\r<p>Patent Pending</p>\r\r<h2>Tech Ventures Reference:</h2>\r\r<ul>\r<li><p>IR CU25424</p></li>\r<li><p>Licensing Contact: <a href=\"mailto:techtransfer@columbia.edu\">Joan Martinez</a> </p></li>\r</ul>","tags":["Allotransplantation","Autoimmune disease","Biomarker","Blood cell","Cancer","Computer simulation","Disease","Graft-versus-host disease","Hematopoietic stem cell","Mortality rate","Preventive healthcare","T cell","T-cell receptor"],"file_number":"CU25424","collections":[],"meta_description":"A multimodal, single-cell spatiotemporal toolkit for tracking alloreactive T cells in GVHD, enabling biomarker discovery and targeted therapies.","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":3.0,\"timeliness\":4.0},\"weighted_score\":3.9,\"risks\":[\"Early-stage, computational toolkit/database with patent-pending status; needs clinical validation and regulatory alignment\",\"Potentially narrow immediate clinical utility without translational data\",\"IP landscape andData privacy considerations in multi-omics clinical datasets\"],\"one_sentence_take\":\"High novelty and timeliness with solid potential impact, but readiness and scalability require clinical validation and regulatory alignment.\"}","inventors":["Ajna Uzuni","David Harle","Elham Azizi","Lingting Shi","Ran Reshef","Ximi K. Wang"],"manager":"Joan Martinez","depts":["Biomedical Engineering","Medicine","Oncology"],"divs":["Columbia University Medical Center (CUMC)","Fu Foundation School of Engineering and Applied Science (SEAS)"],"date_released":"2026-04-01"},"highlight":{},"matched_queries":null,"score":0.0}