T cell migration potentiates HIV infection by enhancing viral fusion and integration

López, Paúl; Ajibola, Oluwaseun; Pagliuzza, Amélie; Zayats, Romaniya; Koh, Wan Hon; Herschhorn, Alon; Chomont, Nicolas; Murooka, Thomas T.

doi:10.1016/j.celrep.2022.110406

Cited by 10 publications

(8 citation statements)

References 46 publications

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“…Our results confirm and extend the results of Shan and colleagues ( Shan et al., 2017 ) showing that activated-to-memory transitioning CD4 + T cells are more prone to latent HIV-1 infection. Using a dual-reporter virus that distinguishes between productively and latently infected cells ( Battivelli et al., 2018 ; Lopez et al., 2022 ; Ratnapriya et al., 2021 ), we show that the frequency of latent cells increases in activated-to-memory transitioning CD4 + T cells, which, indeed, display a transient increase in CCR5 expression. The mechanism involved in the gradual increase in CCR5 expression following the activation of the TCR with anti-CD3/CD28 antibodies appears partially linked to a decrease in the levels of p53 mRNA.…”

Section: Discussionmentioning

confidence: 96%

MiRNA-103 downmodulates CCR5 expression reducing human immunodeficiency virus type-1 entry and impacting latency establishment in CD4+ T cells

Bellini¹,

Lodge²,

Pham³

et al. 2022

iScience

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Section: Discussionmentioning

confidence: 96%

MiRNA-103 downmodulates CCR5 expression reducing human immunodeficiency virus type-1 entry and impacting latency establishment in CD4+ T cells

Bellini¹,

Lodge²,

Pham³

et al. 2022

iScience

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“…T cells were labeled at days 6–8 using CMAC (10 µM) cell tracker dye (Invitrogen), and glass slide chambers were constructed as previously described ( Lopez et al, 2019 ; Lopez et al, 2022 ). Briefly, 2 × 10 6 cells were mixed in 270 µl of bovine collagen (Advanced Biomatrix, Cat# 5005-100ML) at a final concentration of 1.7 mg/ml.…”

Section: Methodsmentioning

confidence: 99%

Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

Pulfer,

Pizzagalli,

Gagliardi

et al. 2024

eLife

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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 label-free 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.

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“…T cells were labeled at day 6-8 using CMAC (10μM) cell tracker dye (Invitrogen) and glass slide chambers were constructed as previously described 63,64 . Briefly, 2×10 6 cells were mixed in 270μL of bovine collagen (Advanced Biomatrix cat #5005-100ML) at a final concentration of 1.7 mg/mL.…”

Section: Methodsmentioning

confidence: 99%

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging

Pulfer

Pizzagalli²,

Gagliardi

et al. 2022

Preprint

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Live-cell imaging allows the study of apoptosis at cellular level, highlighting morphological hallmarks such as nuclear shrinkage, membrane blebbing, and cell disruption. Identifying the exact location and timing of this process is essential to foster the understanding of its spatial-temporal regulation. However, the analysis of live-cell imaging datasets is complex, whereas computational tools tailored to this task are yet scarce. Therefore, we developed ADeS, an Apoptosis Detection System based on deep learning and activity recognition. ADeS uses morpho-dynamic hallmarks to detect the exact location and timing of apoptotic events in different cell types, reaching an accuracy above 97% in the classification of our validation datasets acquired in vitro and in vivo. Moreover, ADeS is the first successful implementation of a deep learning network for the automatic detection of apoptotic cells in full microscopy movies in an end-to-end fashion, outperforming human in the same task. As a case study, we employed ADeS for the analysis of cell survival in vitro, and for tissue damage assessment in vivo, showing its potential application for toxicity assays, treatments evaluation and measuring of tissue dynamics.

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T cell migration potentiates HIV infection by enhancing viral fusion and integration

Cited by 10 publications

References 46 publications

MiRNA-103 downmodulates CCR5 expression reducing human immunodeficiency virus type-1 entry and impacting latency establishment in CD4+ T cells

MiRNA-103 downmodulates CCR5 expression reducing human immunodeficiency virus type-1 entry and impacting latency establishment in CD4+ T cells

Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging

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