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Ipilimumab, a monoclonal antibody that recognizes cytotoxic T lymphocyte antigen (CTLA)-4, was the first approved “checkpoint”-blocking anticancer therapy. In mouse tumor models, the response to antibodies against CTLA-4 depends entirely on expression of the Fcγ receptor (FcγR), which may facilitate antibody-dependent cellular phagocytosis, but the contribution of simple CTLA-4 blockade remains unknown. To understand the role of CTLA-4 blockade in the complete absence of Fc-dependent functions, we developed H11, a high-affinity alpaca heavy chain-only antibody fragment (VHH) against CTLA-4. The VHH H11 lacks an Fc portion, binds monovalently to CTLA-4, and inhibits interactions between CTLA-4 and its ligand by occluding the ligand-binding motif on CTLA-4 as shown crystallographically. We used H11 to visualize CTLA-4 expression in vivo using whole-animal immuno-PET, finding that surface-accessible CTLA-4 is largely confined to the tumor microenvironment. Despite this, H11-mediated CTLA-4 blockade has minimal effects on antitumor responses. Installation of the murine IgG2a constant region on H11 dramatically enhances its antitumor response. Coadministration of the monovalent H11 VHH blocks the efficacy of a full-sized therapeutic antibody. We were thus able to demonstrate that CTLA-4–binding antibodies require an Fc domain for antitumor effect.

CD47 is an antiphagocytic ligand broadly expressed on normal and malignant tissues that delivers an inhibitory signal through the receptor signal regulatory protein alpha (SIRPα). Inhibitors of the CD47–SIRPα interaction improve antitumor antibody responses by enhancing antibody-dependent cellular phagocytosis (ADCP) in xenograft models. Endogenous expression of CD47 on a variety of cell types, including erythrocytes, creates a formidable antigen sink that may limit the efficacy of CD47-targeting therapies. We generated a nanobody, A4, that blocks the CD47–SIRPα interaction. A4 synergizes with anti–PD-L1, but not anti-CTLA4, therapy in the syngeneic B16F10 melanoma model. Neither increased dosing nor half-life extension by fusion of A4 to IgG2a Fc (A4Fc) overcame the issue of an antigen sink or, in the case of A4Fc, systemic toxicity. Generation of a B16F10 cell line that secretes the A4 nanobody showed that an enhanced response to several immune therapies requires near-complete blockade of CD47 in the tumor microenvironment. Thus, strategies to localize CD47 blockade to tumors may be particularly valuable for immune therapy.

Programmed death ligand 1 (PD-L1) is expressed on a number of immune and cancer cells, where it can downregulate antitumor immune responses. Its expression has been linked to metabolic changes in these cells. Here we develop a radiolabeled camelid single-domain antibody (anti-PD-L1 VHH) to track PD-L1 expression by immuno-positron emission tomography (PET). PET-CT imaging shows a robust and specific PD-L1 signal in brown adipose tissue (BAT). We confirm expression of PD-L1 on brown adipocytes and demonstrate that signal intensity does not change in response to cold exposure or β-adrenergic activation. This is the first robust method of visualizing murine brown fat independent of its activation state. Current approaches to visualise brown adipose tissue (BAT) rely primarily on markers that reflect its metabolic activity. Here, the authors show that PD-L1 is expressed on brown adipocytes, does not change upon BAT activation, and that BAT volume in mice can be measured by PET-CT with a radiolabeled anti-PD-L1 antibody.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer’s disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer’s disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data. The authors present a deep learning method that deconvolutes ATAC-seq samples into cell type-specific chromatin accessibility profiles. Applied on 191 samples, the method unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience to Alzheimer’s disease.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Background Preclinical evidence suggests that young plasma has beneficial effects on multiple organ systems in aged mice. Whether young plasma exerts beneficial effects in an aging human population remains highly controversial. Despite lacking data, young donor plasma infusions have been promoted for age-related conditions. Given the preclinical evidence that young plasma exerts beneficial effects by attenuating inflammation, this study examined whether administering a young plasma protein fraction to an elderly population would exert anti-inflammatory and immune modulating effects in humans, using surgery as a tissue injury model. Methods This double-blind, placebo-controlled study enrolled and randomized 38 patients undergoing major joint replacement surgery. Patients received four separate infusions of a plasma protein fraction derived from young donors, or placebo one day before surgery, before and after surgery on the day of surgery, and one day after surgery. Blood specimens for proteomic and immunological analyses were collected before each infusion. Based on the high-content assessment of circulating plasma proteins with single-cell analyses of peripheral immune cells, proteomic signatures and cell-type-specific signaling responses that separated the treatment groups were derived with regression models. Results Elastic net regression models revealed that administration a young plasma protein fraction significantly altered the proteomic (AUC = 0.796, p  = 0.002) and the cellular immune response (AUC 0.904, p  < 0.001) to surgical trauma resulting in signaling pathway- and cell type-specific anti-inflammatory immune modulation. Affected proteomic pathways regulating inflammation included JAK-STAT, NF-kappa B, and MAPK ( p  < 0.001). These findings were confirmed at the cellular level as the MAPK and JAK/STAT signaling responses were diminished and IkB, the negative regulator of NFkB, was elevated in adaptive immune cells. Conclusion Reported findings provide a first proof of principle in humans that a young plasma protein fraction actively regulates inflammatory and immune responses in an elderly population. They provide a solid rationale for elucidating active principles in young plasma that may be of therapeutic benefits for a range of age-related pathologies. Trial registration ClinicalTrials.gov, NCT 03981419.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Background Multiple lines of evidence support peripheral organs in the initiation or progression of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinson’s Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). However, the potential contribution of the peripheral immune response to LBD remains unclear. This study aims to characterize peripheral immune responses unique to participants with LBD at single-cell resolution to highlight potential biomarkers and increase mechanistic understanding of LBD pathogenesis in humans. Methods In a case–control study, peripheral mononuclear cell (PBMC) samples from research participants were randomly sampled from multiple sites across the United States. The diagnosis groups comprise healthy controls (HC, n = 159), LBD (n = 110), Alzheimer’s disease dementia (ADD, n = 97), other neurodegenerative disease controls (NDC, n = 19), and immune disease controls (IDC, n = 14). PBMCs were activated with three stimulants (LPS, IL-6, and IFNa) or remained at basal state, stained by 13 surface markers and 7 intracellular signal markers, and analyzed by flow cytometry, which generated 1,184 immune features after gating. Results The model classified LBD from HC with an AUROC of 0.87 ± 0.06 and AUPRC of 0.80 ± 0.06. Without retraining, the same model was able to distinguish LBD from ADD, NDC, and IDC. Model predictions were driven by pPLCγ2, p38, and pSTAT5 signals from specific cell populations under specific activation. The immune responses characteristic for LBD were not associated with other common medical conditions related to the risk of LBD or dementia, such as sleep disorders, hypertension, or diabetes. Conclusions and Relevance Quantification of PBMC immune response from multisite research participants yielded a unique pattern for LBD compared to HC, multiple related neurodegenerative diseases, and autoimmune diseases thereby highlighting potential biomarkers and mechanisms of disease.

Background We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer’s disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. Methods This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR’s relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer’s Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. Results CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele ( P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. Conclusions Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N  = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a ‘clock’ of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal ‘clock’ of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected ( P  = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times ( P  = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability ( P  = 0.043, Spearman’s), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA ( P  = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.