Insulin restores retinal ganglion cell functional connectivity and promotes visual recovery in glaucoma.

Dendrite pathology and synaptic loss result in neural circuit dysfunction, a common feature of neurodegenerative diseases. There is a lack of strategies that target dendritic and synaptic regeneration to promote neurorecovery. We show that daily human recombinant insulin eye drops stimulate retinal ganglion cell (RGC) dendrite and synapse regeneration during ocular hypertension, a risk factor to develop glaucoma. We demonstrate that the ribosomal protein p70S6 kinase (S6K) is essential for insulin-dependent dendritic regrowth. Furthermore, S6K phosphorylation of the stress-activated protein kinase-interacting protein 1 (SIN1), a link between the mammalian target of rapamycin complexes 1 and 2 (mTORC1/2), is required for insulin-induced dendritic regeneration. Using two-photon microscopy live retinal imaging, we show that insulin rescues single-RGC light-evoked calcium (Ca2+) dynamics. We further demonstrate that insulin enhances neuronal survival and retina-brain connectivity leading to improved optomotor reflex-elicited behaviors. Our data support that insulin is a compelling pro-regenerative strategy with potential clinical implications for the treatment and management of glaucoma.

Maximising Large Language Model Utility in Cardiovascular Care: A Practical Guide.

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, demonstrating remarkable capabilities in natural language processing and generation. In this article, we explore the potential applications of LLMs in enhancing cardiovascular care and research. We discuss how LLMs can be used to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarising medical articles and extracting key information. In addition, we highlight the role of LLMs in categorising and analysing unstructured data, such as medical notes and test results, which could revolutionise data handling and interpretation in cardiovascular research. However, we also emphasise the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigourous validation in medical contexts. This review provides a practical guide for cardiovascular professionals to understand and harness the power of LLMs while navigating their limitations. We conclude by discussing the future directions and implications of LLMs in transforming cardiovascular care and research.

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform.

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby improving diagnostic precision and accuracy and thus enhancing current tests. However, the integration of AI within health care is fraught with difficulties. Heterogeneity among health care system applications, reliance on proprietary closed-source software, and rising cybersecurity threats pose significant challenges. Moreover, before their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow, which is difficult to achieve without dedicated software. Finally, the use of AI techniques in health care raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in health care and provides guidelines on how to move forward. We describe an open-source solution that we developed that integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offer a pathway toward responsible, fair, and effective deployment of AI models in health care. In addition, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model to enhance standardisation and reproducibility.

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Evaluation of stenoses using AI video models applied to coronary angiography.

The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.

Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology.

In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and effects of these biases, which challenge their reliability and widespread applicability in health care. Using a case study, we highlight the complexities involved in addressing these biases from a clinical viewpoint. The goal of this review is to equip researchers and clinicians with the practical knowledge needed to identify, understand, and mitigate these biases, advocating for the creation of AI solutions that are not just technologically sound, but also fair and effective for all patients.

Enhanced detection of antigen-specific T cells by a multiplexed AIM assay.

Broadly applicable methods to identify and characterize antigen-specific CD4+ and CD8+ T cells are key to immunology research, including studies of vaccine responses and immunity to infectious diseases. We developed a multiplexed activation-induced marker (AIM) assay that presents several advantages compared to single pairs of AIMs. The simultaneous measurement of four AIMs (CD69, 4-1BB, OX40, and CD40L) creates six AIM pairs that define CD4+ T cell populations with partial and variable overlap. When combined in an AND/OR Boolean gating strategy for analysis, this approach enhances CD4+ T cell detection compared to any single AIM pair, while CD8+ T cells are dominated by CD69/4-1BB co-expression. Supervised and unsupervised clustering analyses show differential expression of the AIMs in defined T helper lineages and that multiplexing mitigates phenotypic biases. Paired and unpaired comparisons of responses to infections (HIV and cytomegalovirus [CMV]) and vaccination (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) validate the robustness and versatility of the method.

ALCAM on human oligodendrocytes mediates CD4 T cell adhesion.

Multiple sclerosis is a chronic neuroinflammatory disorder characterized by demyelination, oligodendrocyte damage/loss and neuroaxonal injury in the context of immune cell infiltration in the CNS. No neuroprotective therapy is available to promote the survival of oligodendrocytes and protect their myelin processes in immune-mediated demyelinating diseases. Pro-inflammatory CD4 Th17 cells can interact with oligodendrocytes in multiple sclerosis and its animal model, causing injury to myelinating processes and cell death through direct contact. However, the molecular mechanisms underlying the close contact and subsequent detrimental interaction of Th17 cells with oligodendrocytes remain unclear. In this study we used single cell RNA sequencing, flow cytometry and immunofluorescence studies on CNS tissue from multiple sclerosis subjects, its animal model and controls to characterize the expression of cell adhesion molecules by mature oligodendrocytes. We found that a significant proportion of human and murine mature oligodendrocytes express melanoma cell adhesion molecule (MCAM) and activated leukocyte cell adhesion molecule (ALCAM) in multiple sclerosis, in experimental autoimmune encephalomyelitis and in controls, although their regulation differs between human and mouse. We observed that exposure to pro-inflammatory cytokines or to human activated T cells are associated with a marked downregulation of the expression of MCAM but not of ALCAM at the surface of human primary oligodendrocytes. Furthermore, we used in vitro live imaging, immunofluorescence and flow cytometry to determine the contribution of these molecules to Th17-polarized cell adhesion and cytotoxicity towards human oligodendrocytes. Silencing and blocking ALCAM but not MCAM limited prolonged interactions between human primary oligodendrocytes and Th17-polarized cells, resulting in decreased adhesion of Th17-polarized cells to oligodendrocytes and conferring significant protection of oligodendrocytic processes. In conclusion, we showed that human oligodendrocytes express MCAM and ALCAM, which are differently modulated by inflammation and T cell contact. We found that ALCAM is a ligand for Th17-polarized cells, contributing to their capacity to adhere and induce damage to human oligodendrocytes, and therefore could represent a relevant target for neuroprotection in multiple sclerosis.

TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. METHODS: This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. RESULTS: High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90-0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. CONCLUSIONS: TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures.

Classification of T lymphocyte motility behaviors using a machine learning approach.

T lymphocytes migrate into organs and interact with local cells to perform their functions. How human T lymphocytes communicate with organ-specific cells and participate in pathobiological processes remains unresolved. Brain infiltration of T lymphocytes is associated with multiple neurological disorders. Thus, to characterize the behavior of human T lymphocytes reaching the human brain, we performed time-lapse microscopy on human CD8+ T lymphocytes co-cultured with either primary human astrocytes or neurons. Using traditional manual and visual assessment of microscopy data, we identified distinct CD8+ T lymphocyte motility behaviors. However, such characterization is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8+ T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell behaviors (synapse vs. kinapse) as well as visually identified behaviors (scanning, dancing, and poking). Feature selection was performed during 3-fold cross-validation using the minimum redundancy maximum relevance algorithm. Results show promising performances when tested on a held-out dataset of CD8+ T lymphocytes interacting with astrocytes with a new experimenter and a held-out independent dataset of CD8+ T lymphocytes interacting with neurons. When tested on the independent CD8+ T cell-neuron dataset, the final model achieved a binary classification accuracy of 0.82 and a 3-class accuracy of 0.79. This novel automated classification approach could significantly reduce the time required to label cell motility behaviors while facilitating the identification of interactions of T lymphocytes with multiple cell types.

Spontaneous HIV expression during suppressive ART is associated with the magnitude and function of HIV-specific CD4+ and CD8+ T cells.

Spontaneous transcription and translation of HIV can persist during suppressive antiretroviral therapy (ART). The quantity, phenotype, and biological relevance of this spontaneously "active" reservoir remain unclear. Using multiplexed single-cell RNAflow-fluorescence in situ hybridization (FISH), we detect active HIV transcription in 14/18 people with HIV on suppressive ART, with a median of 28/million CD4+ T cells. While these cells predominantly exhibit abortive transcription, p24-expressing cells are evident in 39% of participants. Phenotypically diverse, active reservoirs are enriched in central memory T cells and CCR6- and activation-marker-expressing cells. The magnitude of the active reservoir positively correlates with total HIV-specific CD4+ and CD8+ T cell responses and with multiple HIV-specific T cell clusters identified by unsupervised analysis. These associations are particularly strong with p24-expressing active reservoir cells. Single-cell vDNA sequencing shows that active reservoirs are largely dominated by defective proviruses. Our data suggest that these reservoirs maintain HIV-specific CD4+ and CD8+ T responses during suppressive ART.

Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics.

BACKGROUND: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings. RESULTS: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus). CONCLUSIONS: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.

A third SARS-CoV-2 mRNA vaccine dose in people receiving hemodialysis overcomes B cell defects but elicits a skewed CD4+ T cell profile.

Cellular immune defects associated with suboptimal responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccination in people receiving hemodialysis (HD) are poorly understood. We longitudinally analyze antibody, B cell, CD4+, and CD8+ T cell vaccine responses in 27 HD patients and 26 low-risk control individuals (CIs). The first two doses elicit weaker B cell and CD8+ T cell responses in HD than in CI, while CD4+ T cell responses are quantitatively similar. In HD, a third dose robustly boosts B cell responses, leads to convergent CD8+ T cell responses, and enhances comparatively more T helper (TH) immunity. Unsupervised clustering of single-cell features reveals phenotypic and functional shifts over time and between cohorts. The third dose attenuates some features of TH cells in HD (tumor necrosis factor alpha [TNFα]/interleukin [IL]-2 skewing), while others (CCR6, CXCR6, programmed cell death protein 1 [PD-1], and HLA-DR overexpression) persist. Therefore, a third vaccine dose is critical to achieving robust multifaceted immunity in hemodialysis patients, although some distinct TH characteristics endure.

Single-Cell Transcriptomics Identifies Brain Endothelium Inflammatory Networks in Experimental Autoimmune Encephalomyelitis.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease characterized by infiltration of immune cells in multifocal areas of the CNS. The specific molecular processes allowing autoreactive immune cells to enter the CNS compartment through the blood-brain barrier remain elusive. METHODS: Using endothelial cell (EC) enrichment and single-cell RNA sequencing, we characterized the cells implicated in the neuroinflammatory processes in experimental autoimmune encephalomyelitis, an animal model of MS. Validations on human MS brain sections of the most differentially expressed genes in venous ECs were performed using immunohistochemistry and confocal microscopy. RESULTS: We found an upregulation of genes associated with antigen presentation and interferon in most populations of CNS-resident cells, including ECs. Interestingly, instead of transcriptionally distinct profiles, a continuous gradient of gene expression separated the arteriovenous zonation of the brain vasculature. However, differential gene expression analysis presented more transcriptomic alterations on the venous side of the axis, suggesting a prominent role of venous ECs in neuroinflammation. Furthermore, analysis of ligand-receptor interactions identified important potential molecular communications between venous ECs and infiltrated immune populations. To confirm the relevance of our observation in the context of human disease, we validated the protein expression of the most upregulated genes (Ackr1 and Lcn2) in MS lesions. DISCUSSION: In this study, we provide a landscape of the cellular heterogeneity associated with neuroinflammation. We also present important molecular insights for further exploration of specific cell processes that promote infiltration of immune cells inside the brain of experimental autoimmune encephalomyelitis mice.

MCAM+ brain endothelial cells contribute to neuroinflammation by recruiting pathogenic CD4+ T lymphocytes.

The trafficking of autoreactive leucocytes across the blood-brain barrier endothelium is a hallmark of multiple sclerosis pathogenesis. Although the blood-brain barrier endothelium represents one of the main CNS borders to interact with the infiltrating leucocytes, its exact contribution to neuroinflammation remains understudied. Here, we show that Mcam identifies inflammatory brain endothelial cells with pro-migratory transcriptomic signature during experimental autoimmune encephalomyelitis. In addition, MCAM was preferentially upregulated on blood-brain barrier endothelial cells in multiple sclerosis lesions in situ and at experimental autoimmune encephalomyelitis disease onset by molecular MRI. In vitro and in vivo, we demonstrate that MCAM on blood-brain barrier endothelial cells contributes to experimental autoimmune encephalomyelitis development by promoting the cellular trafficking of TH1 and TH17 lymphocytes across the blood-brain barrier. Last, we showcase ST14 as an immune ligand to brain endothelial MCAM, enriched on CD4+ T lymphocytes that cross the blood-brain barrier in vitro, in vivo and in multiple sclerosis lesions as detected by flow cytometry on rapid autopsy derived brain tissue from multiple sclerosis patients. Collectively, our findings reveal that MCAM is at the centre of a pathological pathway used by brain endothelial cells to recruit pathogenic CD4+ T lymphocyte from circulation early during neuroinflammation. The therapeutic targeting of this mechanism is a promising avenue to treat multiple sclerosis.

CLMP Promotes Leukocyte Migration Across Brain Barriers in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), peripheral immune cells use various cell trafficking molecules to infiltrate the CNS where they cause damage.The objective of this study was to investigate the involvement of coxsackie and adenovirus receptor-like membrane protein (CLMP) in the migration of immune cells into the CNS of patients with MS. METHODS: Expression of CLMP was measured in primary cultures of human brain endothelial cells (HBECs) and human meningeal endothelial cells (HMECs), postmortem brain samples, and peripheral blood mononuclear cells (PBMCs) from patients with MS and controls by RNA sequencing, quantitative PCR, immunohistochemistry, and flow cytometry. In vitro migration assays using HBECs and HMECs were performed to evaluate the function of CLMP. RESULTS: Using bulk RNA sequencing of primary cultures of human brain and meningeal endothelial cells (ECs), we have identified CLMP as a new potential cell trafficking molecule upregulated in inflammatory conditions. We first confirmed the upregulation of CLMP at the protein level on TNFα-activated and IFNγ-activated primary cultures of human brain and meningeal ECs. In autopsy brain specimens from patients with MS, we demonstrated an overexpression of endothelial CLMP in active MS lesions when compared with normal control brain tissue. Flow cytometry of human PBMCs demonstrated an increased frequency of CLMP+ B lymphocytes and monocytes in patients with MS, when compared with that in healthy controls. The use of a blocking antibody against CLMP reduced the migration of immune cells across the human brain and meningeal ECs in vitro. Finally, we found CLMP+ immune cell infiltrates in the perivascular area of parenchymal lesions and in the meninges of patients with MS. DISCUSSION: Collectively, our data demonstrate that CLMP is an adhesion molecule used by immune cells to access the CNS during neuroinflammatory disorders such as MS. CLMP could represent a target for a new treatment of neuroinflammatory conditions.

IL-27 shapes the immune properties of human astrocytes and their impact on encountered human T lymphocytes.

BACKGROUND: Interleukin-27 (IL-27) can trigger both pro- and anti-inflammatory responses. This cytokine is elevated in the central nervous system (CNS) of multiple sclerosis (MS) patients, but how it influences neuroinflammatory processes remains unclear. As astrocytes express the receptor for IL-27, we sought to determine how these glial cells respond to this cytokine and whether such exposure alters their interactions with infiltrating activated T lymphocytes. To determine whether inflammation shapes the impact of IL-27, we compared the effects of this cytokine in non-inflamed and inflamed conditions induced by an IL-1ß exposure. MAIN BODY: Transcriptomic analysis of IL-27-exposed human astrocytes showed an upregulation of multiple immune genes. Human astrocytes increased the secretion of chemokines (CXCL9, CXCL10, and CXCL11) and the surface expression of proteins (PD-L1, HLA-E, and ICAM-1) following IL-27 exposure. To assess whether exposure of astrocytes to IL-27 influences the profile of activated T lymphocytes infiltrating the CNS, we used an astrocyte/T lymphocyte co-culture model. Activated human CD4+ or CD8+ T lymphocytes were co-cultured with astrocytes that have been either untreated or pre-exposed to IL­27 or IL-1ß. After 24 h, we analyzed T lymphocytes by flow cytometry for transcription factors and immune molecules. The contact with IL-27-exposed astrocytes increased the percentages of T-bet, Eomes, CD95, IL-18Rα, ICAM-1, and PD-L1 expressing CD4+ and CD8+ T lymphocytes and reduced the proportion of CXCR3-positive CD8+ T lymphocytes. Human CD8+ T lymphocytes co-cultured with human IL-27-treated astrocytes exhibited higher motility than when in contact with untreated astrocytes. These results suggested a preponderance of kinapse-like over synapse-like interactions between CD8+ T lymphocytes and IL-27-treated astrocytes. Finally, CD8+ T lymphocytes from MS patients showed higher motility in contact with IL-27-exposed astrocytes compared to healthy donors' cells. CONCLUSION: Our results establish that IL-27 alters the immune functions of human astrocytes and shapes the profile and motility of encountered T lymphocytes, especially CD8+ T lymphocytes from MS patients.

Immune checkpoint expression on HIV-specific CD4+ T cells and response to their blockade are dependent on lineage and function.

BACKGROUND: Immune checkpoint blockade (ICB) partially reverses the dysfunctional state of antigen-specific T cell in chronic infections. However, its impact on the diverse subsets of CD4+ T cells in humans is largely unknown. METHODS: We examined immune checkpoint (IC) expression and function in HIV-specific CD4+ T cells of viremic individuals (≥5000 vRNA cp/ml, n = 17) prior to ART and persons with spontaneous (n = 11) or therapy-induced (n = 16) viral suppression (<40 cp/ml). We investigated IC patterns associated with exhaustion-related transcription factors and chemokine receptors using activation-induced marker assays. We determined effector functions representative of TFH, TH1, and TH17/TH22 using RNA flow cytometric fluorescence in situ hybridization (FISH). We compared increase in cytokine expression upon ICB across functions and patient status. FINDINGS: Expression of dysfunction-related molecules, such as transcription factors and ICs PD-1, TIGIT, and CD200, followed a hierarchy associated with infection status and effector profile. In vitro responsiveness to PD-L1 blockade varied with defined functions rather than IC levels: frequencies of cells with TH1- and TH17/TH22-, but not TFH-related functions, increased. Cells co-expressing TH1 and TFH functions showed response to ICB, suggesting that the cell's state rather than function dictates responsiveness to PD-L1 blockade. Response to PD-L1 blockade was strongest in viremic participants and reduced after ART initiation. INTERPRETATION: Our data highlight a polarization-specific regulation of IC expression and differing sensitivities of antigen-specific T helper subsets to PD-1-mediated inhibition. This heterogeneity may direct and constrain ICB efficacy in restoring CD4+ T cell function in HIV infection and other diseases. FUNDING: NIH, CIHR, CFI, FRQS.

Temporal associations of B and T cell immunity with robust vaccine responsiveness in a 16-week interval BNT162b2 regimen.

Spacing of BNT162b2 mRNA doses beyond 3 weeks raises concerns about vaccine efficacy. We longitudinally analyze B cell, T cell, and humoral responses to two BNT162b2 mRNA doses administered 16 weeks apart in 53 SARS-CoV-2 naive and previously infected donors. This regimen elicits robust RBD-specific B cell responses whose kinetics differs between cohorts, the second dose leading to increased magnitude in naive participants only. While boosting does not increase magnitude of CD4+ T cell responses further compared with the first dose, unsupervised clustering of single-cell features reveals phenotypic and functional shifts over time and between cohorts. Integrated analysis shows longitudinal immune component-specific associations, with early T helper responses post first dose correlating with B cell responses after the second dose, and memory T helper generated between doses correlating with CD8 T cell responses after boosting. Therefore, boosting elicits a robust cellular recall response after the 16-week interval, indicating functional immune memory.