About
Carolyn Glass is a professor affiliated with Duke University who has contributed to research in the field of cellular senescence. She is an author on a publication titled "Single-cell and spatial detection of senescent cells using DeepScence," published in Cell Genomics in December 2025. This work involves the development of DeepScence, a method based on deep neural networks designed to accurately identify senescent cells in single-cell and spatial transcriptomics data. The method utilizes CoreScence, a curated senescence-associated gene set that integrates information from multiple published gene sets. The research demonstrates that DeepScence can effectively identify senescent cells in gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, outperforming existing methods. This contribution highlights Professor Glass's involvement in advancing computational approaches to studying cellular senescence and transcriptomic analysis.
Research topics
- Medicine
- Biology
- Genetics
- Internal medicine
- Immunology
- Cancer research
- Gerontology
- Oncology
- Surgery
Selected publications
Laboratory Investigation · 2026-03-01
articleOpen accessLaboratory Investigation · 2026-03-01
articleOpen accessLaboratory Investigation · 2026-03-01
articleA porcine model of acute rejection for cardiac transplantation
Frontiers in Cardiovascular Medicine · 2025-07-18 · 2 citations
articleOpen accessEx vivo machine perfusion has been growing in utility for preserving donor organs prior to transplantation. This modality has tremendous potential for bioengineering and conditioning organs prior to transplantation using small molecule or advanced therapeutics. To safely translate potential interventions, well characterized models of disease are crucial for testing the therapeutic and possible side effects that could manifest from the interventions. Acute cellular rejection remains a significant complication in organ transplantation that affects transplant recipients with significant morbidity and mortality. This disease could potentially be mitigated with therapeutic intervention during ex vivo machine perfusion. A porcine animal model of acute rejection could be characterized in order to translate human biological processes with high fidelity. The Yucatan pig breed has been increasingly used in both biomedical research and xenotransplantation applications given its similarity to the human heart. A challenge with utilizing this pig breed for designing a model of acute rejection is its highly conserved ancestral lineage, which could make it difficult to induce acute rejection in a timely and consistent manner. We present a detailed characterization of a porcine model of acute rejection based on swine leukocyte antigen mismatching paired with a limited period of clinically relevant immunosuppression. The result is a robust and consistent protocol that results in fulminant acute rejection of an intra-abdominally transplanted heart.
2025-01-01
book-chapterThe Journal of Heart and Lung Transplantation · 2025-04-01
articleOpen accessAdvancing biological understanding of cellular senescence with computational multiomics
Nature Genetics · 2025-09-15 · 22 citations
reviewOpen accessTransplant International · 2025-06-02 · 4 citations
articleOpen accessNormothermic ex-vivo organ perfusion (EVP) systems not only provide a physiological environment that preserves donor organ function outside the body but may also serve as platforms for ex-vivo organ modification via gene therapy. In this study, we demonstrated that a rationally designed muscle-tropic recombinant AAV, AAV-SLB101, delivered to the donor heart during brief normothermic EVP achieves durable cardiac transgene expression out to 90 and 120 days post-transplant in a porcine preclinical model. Moreover, transgene expression was detectable as early as 48 h post-transplant. Histological and MRI analyses of the donor myocardium showed no functional or structural impact on the allograft and no off-target gene expression in the recipient. This work will serve as a critical foundation to inform translational studies with therapeutic transgenes to improve allo-, xeno-, and auto-heart transplant outcomes.
Laboratory Investigation · 2025-03-01
articleOpen accessSenior authorSingle-cell and spatial detection of senescent cells using DeepScence
Cell Genomics · 2025-10-07 · 3 citations
articleOpen accessAccurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.
Frequent coauthors
- 123 shared
Benjamin R. Kipp
Mayo Clinic in Arizona
- 113 shared
Karen Fritchie
Cleveland Clinic
- 112 shared
Michael Bonert
McMaster University
- 111 shared
Gregory A. Fishbein
University of California, Los Angeles
- 107 shared
Ivy John
University of Pittsburgh Medical Center
- 106 shared
Lynette M. Sholl
Brigham and Women's Hospital
- 105 shared
William C. Faquin
Massachusetts General Hospital
- 105 shared
Yuri Fedoriw
University of North Carolina at Chapel Hill
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