Biology subjects

O'Connor, E. C.

Publications and source records attributed to O'Connor, E. C..

4 recordsLinked to original sources

bioRxiv2026

MUC4-targeted CAR-T Cells Restrain Chemotherapy-resistant Colorectal Cancer

Colorectal cancer (CRC) is the leading cause of cancer mortality in young adults, with chemoresistance and metastatic progression severely limiting treatment options. MUC4, a membrane-bound mucin normally confined to the apical surface of epithelial cells, becomes aberrantly overexpressed across the tumor cell membrane in CRC and other cancer types, promoting cancer survival, immune evasion, and metastasis. Therefore, MUC4 is a promising candidate for the development of targeted therapies. We detected MUC4 cell surface expression in a panel of human CRC cell lines. We engineered MUC4-specific chimeric antigen receptor (CAR) T cells and evaluated their activity against methotrexate-resistant MUC4-expressing human CRC cell lines HT29-MTX and T84. MUC4 CAR-T cells efficiently eliminated HT29-MTX and T84 cells in vitro and delayed HT29-MTX subcutaneous tumor growth in vivo. Importantly, MUC4 CAR-T cell treatment significantly reduced tumor burden and improved survival in a highly aggressive and lethal CRC model, intraperitoneal HT29-MTX CRC metastatic dissemination in NSG mice. Mouse necropsies revealed no off-target in vivo toxicities associated with MUC4 CAR-T cell therapy. Altogether, our findings establish MUC4 as an important and clinically relevant CAR-T cell target and demonstrate therapeutic efficacy of MUC4 CAR-T cell therapy for invasive CRC, a setting where chemotherapy typically fails. MUC4-targeted CAR-T cell therapy has the potential to fill a critical treatment gap for patients with chemoresistant and invasive CRC and may extend to other MUC4-expressing solid tumors. Statement of PriorityColorectal cancer is increasingly diagnosed in younger individuals, a trend accompanied by a higher prevalence of aggressive and chemotherapy-resistant disease. Despite advances in systemic therapy, outcomes for patients with refractory or metastatic colorectal cancer remain poor, underscoring a critical unmet clinical need. CAR-T cell therapy offers a mechanism to selectively target tumor cells that evade conventional treatments and may thus be particularly valuable in mucinous, chemotherapy-resistant subsets. Developing effective CAR-T strategies for new targets in colorectal carcinoma, such as MUC4, is an urgent priority in this era of rising early-onset disease.

immunology↗

bioRxiv2024

Forestwalk: A machine learning workflow brings new insights into posture and balance in rodent beam walking

AbstractThe beam walk is widely used to study coordination and balance in rodents. While the task has ethological validity, the main endpoints of foot slip counts and time to cross are prone to human-rater variability and offer limited sensitivity and specificity. We asked if machine learning-based methods could reveal previously hidden, but biologically relevant, insights from the task. Marker-less pose estimation, using DeepLabCut, was deployed to label 13 anatomical points on mice traversing the beam. Next, we automated classical endpoint detection, including foot slips, with high recall (>90%) and precision (>80%). A total of 395 features were engineered and a random-forest classifier deployed that, together with skeletal visualizations, could test for group differences and identify determinant features. This workflow, named Forestwalk, uncovered pharmacological treatment effects in C57BL/6J mice, revealed phenotypes in transgenic mice used to study Angelman syndrome and SLC6A1-related neurodevelopmental disorder, and will facilitate a deeper understanding of how the brain controls balance in health and disease.

animal behavior and cognition↗

bioRxiv2023

Analysis of behavioral flow resolves latent phenotypes

The nuanced detection of rodent behavior in preclinical biomedical research is essential for understanding disease conditions, genetic phenotypes, and internal states. Recent advances in machine vision and artificial intelligence have popularized data-driven methods that segment complex animal behavior into clusters of behavioral motifs. However, despite the rapid progress, several challenges remain: Statistical power typically decreases due to multiple testing correction, poor transferability of clustering approaches across experiments limits practical applications, and individual differences in behavior are not considered. Here, we introduce "behavioral flow analysis" (BFA), which creates a single metric for all observed transitions between behavioral motifs. Then, we establish a "classifier-in-the-middle" approach to stabilize clusters and enable transferability of our analyses across datasets. Finally, we combine these approaches with dimensionality reduction techniques, enabling "behavioral flow fingerprinting" (BFF) for individual animal assessment. We validate our approaches across large behavioral datasets with a total of 443 open field recordings that we make publicly available, comparing various stress protocols with pharmacologic and brain-circuit interventions. Our analysis pipeline is compatible with a range of established clustering approaches, it increases statistical power compared to conventional techniques, and has strong reproducibility across experiments within and across laboratories. The efficient individual phenotyping allows us to classify stress-responsiveness and predict future behavior. This approach aligns with animal welfare regulations by reducing animal numbers, and enhancing information extracted from experimental animals

neuroscience↗

bioRxiv2021

Striatum-projecting prefrontal cortex neurons support working memory maintenance

The medial prefrontal cortex (mPFC) and the dorsomedial striatum (dmStr) are linked to working memory (WM) but how striatum-projecting mPFC neurons contribute to WM encoding, maintenance, or retrieval remains unclear. Here, we probed mPFC[->]dmStr pathway function in freely-moving mice during a T-maze alternation test of spatial WM. Fiber photometry of GCaMP6m-labeled mPFC[->]dmStr projection neurons revealed strongest activity during the delay period that requires WM maintenance. Demonstrating causality, optogenetic inhibition of mPFC[->]dmStr neurons only during the delay period impaired performance. Conversely, enhancing mPFC[->]dmStr pathway activity--via pharmacological suppression of HCN1 or by optogenetic activation during the delay-- alleviated WM impairment induced by NMDA receptor blockade. Consistently, cellular-resolution miniscope imaging resolved preferred activation of >50% mPFC[->]dmStr neurons during WM maintenance. This subpopulation was distinct from neurons showing preference for encoding and retrieval. In all periods, including the delay, neuronal sequences were evident. Striatum-projecting mPFC neurons thus critically contribute to spatial WM maintenance.

neuroscience↗