Biology subjects

Murooka, T.

Publications and source records attributed to Murooka, T..

2 recordsLinked to original sources

bioRxiv2025

Persistent antigen is essential for sustaining Leishmania-specific memory CD4+ T cells and long-term immunity

Memory T cells are critical for secondary immunity against pathogens, yet their persistence in the absence of antigen remains unclear. While memory CD8+ T cells are known to persist independently of their cognate antigen, the durability of memory CD4+ T cells in the absence of antigen remains controversial. Recovery from cutaneous leishmaniasis confers lifelong immunity, largely mediated by CD4+ T cells, but the necessity of persistent parasites for sustaining this immunity has not been empirically confirmed. We investigated immunity in mice infected with a dihydrofolate reductase-thymidylate synthase (dhfr-ts)-deficient Leishmania major, which cannot persist due to their thymidine salvage deficiency. These mice lost protection against wild-type challenge, correlating with a decline in Leishmania (PEPCK)-specific CD4+ T cells. To further dissect this relationship, we generated PEPCK-specific CD4+ TCR transgenic (PEG) mice, enabling precise tracking of Leishmania-specific memory CD4+ T cells. Both in vitro and in vivo-generated memory PEG cells gradually disappeared over time in the absence of antigen, irrespective of the hosts MHC II status, and this loss paralleled the erosion of infection-induced immunity. PEG and endogenous PEPCK-specific memory cells were not maintained in mice infected with dhfr-ts- or PEPCK-deficient L. major, resulting in loss of recall responses and secondary immunity. These findings demonstrate that continuous antigen presence is crucial for maintaining Leishmania-specific memory CD4+ T cells and highlight the role of persistent antigen in sustaining long-term immunity against chronic infections.

immunology↗

bioRxiv2022

ADeS: a deep learning based Apoptosis Detection System for live cell imaging.

Intravital microscopy has revolutionized live cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy time-lapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy time-lapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in vivo, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

bioinformatics↗