Age and sex influence antibody profiles associated with tuberculosis progression.

Antibody features vary with tuberculosis (TB) disease state. Whether clinical variables, such as age or sex, influence associations between Mycobacterium tuberculosis-specific antibody responses and disease state is not well explored. Here we profiled Mycobacterium tuberculosis-specific antibody responses in 140 TB-exposed South African individuals from the Adolescent Cohort Study. We identified distinct response features in individuals progressing to active TB from non-progressing, matched controls. A multivariate antibody score differentially associated with progression (SeroScore) identified progressors up to 2 years before TB diagnosis, earlier than that achieved with the RISK6 transcriptional signature of progression. We validated these antibody response features in the Grand Challenges 6-74 cohort. Both the SeroScore and RISK6 correlated better with risk of TB progression in adolescents compared with adults, and in males compared with females. This suggests that age and sex are important, underappreciated modifiers of antibody responses associated with TB progression.

Humoral correlates of protection against Mycobacterium tuberculosis following intravenous Bacille Calmette-Guérin vaccination in rhesus macaques.

Altering the route of Bacille Calmette-Guérin (BCG) immunization from low-dose intradermal vaccination to high-dose intravenous (IV) vaccination resulted in a high level of protection against Mycobacterium tuberculosis ( Mtb ) infection, providing an opportunity to uncover immune correlates and mechanisms of protection. In addition to strong T cell immunity, IV BCG vaccination was associated with a robust expansion of humoral immune responses that tracked with bacterial control. However, given the near complete protection afforded by high-dose IV BCG immunization, a precise correlate of immune protection was difficult to define. Here we leveraged plasma and bronchoalveolar lavage fluid (BAL) from a cohort of rhesus macaques that received decreasing doses of IV BCG and aimed to define the correlates of immunity across macaques that experienced immune protection or breakthrough infection following Mtb challenge. We show an IV BCG dose-dependent induction of mycobacterial-specific humoral immune responses, both in the plasma and in the airways. Moreover, antibody responses at peak immunogenicity significantly predicted bacterial control following challenge. Multivariate analyses revealed antibody-mediated complement and NK cell activating humoral networks as key functional signatures associated with protective immunity. Collectively, this work extends our understanding of humoral biomarkers and potential mechanisms of IV BCG mediated protection against Mtb .

Airway T cells are a correlate of i.v. Bacille Calmette-Guerin-mediated protection against tuberculosis in rhesus macaques.

Bacille Calmette-Guerin (BCG), the only approved Mycobacterium tuberculosis (Mtb) vaccine, provides limited durable protection when administered intradermally. However, recent work revealed that intravenous (i.v.) BCG administration yielded greater protection in macaques. Here, we perform a dose-ranging study of i.v. BCG vaccination in macaques to generate a range of immune responses and define correlates of protection. Seventeen of 34 macaques had no detectable infection after Mtb challenge. Multivariate analysis incorporating longitudinal cellular and humoral immune parameters uncovered an extensive and highly coordinated immune response from the bronchoalveolar lavage (BAL). A minimal signature predicting protection contained four BAL immune features, of which three remained significant after dose correction: frequency of CD4 T cells producing TNF with interferon γ (IFNγ), frequency of those producing TNF with IL-17, and the number of NK cells. Blood immune features were less predictive of protection. We conclude that CD4 T cell immunity and NK cells in the airway correlate with protection following i.v. BCG.

Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.

BACKGROUND: Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction. METHODS: We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information. RESULTS: The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification. CONCLUSION: We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations.

Nucleocapsid-specific antibody function is associated with therapeutic benefits from COVID-19 convalescent plasma therapy.

Coronavirus disease 2019 (COVID-19) convalescent plasma (CCP), a passive polyclonal antibody therapeutic agent, has had mixed clinical results. Although antibody neutralization is the predominant approach to benchmarking CCP efficacy, CCP may also influence the evolution of the endogenous antibody response. Using systems serology to comprehensively profile severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) functional antibodies of hospitalized people with COVID-19 enrolled in a randomized controlled trial of CCP (ClinicalTrials.gov: NCT04397757), we find that the clinical benefits of CCP are associated with a shift toward reduced inflammatory Spike (S) responses and enhanced nucleocapsid (N) humoral responses. We find that CCP has the greatest clinical benefit in participants with low pre-existing anti-SARS-CoV-2 antibody function and that CCP-induced immunomodulatory Fc glycan profiles and N immunodominant profiles persist for at least 2 months. We highlight a potential mechanism of action of CCP associated with durable immunomodulation, outline optimal patient characteristics for CCP treatment, and provide guidance for development of a different class of COVID-19 hyperinflammation-targeting antibody therapeutic agents.

The Kinetics of SARS-CoV-2 Antibody Development Is Associated with Clearance of RNAemia.

Persistent SARS-CoV-2 replication and systemic dissemination are linked to increased COVID-19 disease severity and mortality. However, the precise immune profiles that track with enhanced viral clearance, particularly from systemic RNAemia, remain incompletely defined. To define whether antibody characteristics, specificities, or functions that emerge during natural infection are linked to accelerated containment of viral replication, we examined the relationship of SARS-CoV-2-specific humoral immune evolution in the setting of SARS-CoV-2 plasma RNAemia, which is tightly associated with disease severity and death. On presentation to the emergency department, S-specific IgG3, IgA1, and Fc-γ-receptor (Fcγ R) binding antibodies were all inversely associated with higher baseline plasma RNAemia. Importantly, the rapid development of spike (S) and its subunit (S1/S2/receptor binding domain)-specific IgG, especially FcγR binding activity, were associated with clearance of RNAemia. These results point to a potentially critical and direct role for SARS-CoV-2-specific humoral immune clearance on viral dissemination, persistence, and disease outcome, providing novel insights for the development of more effective therapeutics to resolve COVID-19. IMPORTANCE We showed that persistent SARS-CoV-2 RNAemia is an independent predictor of severe COVID-19. We observed that SARS-CoV-2-targeted antibody maturation, specifically Fc-effector functions rather than neutralization, was strongly linked with the ability to rapidly clear viremia. This highlights the critical role of key humoral features in preventing viral dissemination or accelerating viremia clearance and provides insights for the design of next-generation monoclonal therapeutics. The main key points will be that (i) persistent SARS-CoV-2 plasma RNAemia independently predicts severe COVID-19 and (ii) specific humoral immune functions play a critical role in halting viral dissemination and controlling COVID-19 disease progression.

Landscape of Human Immunodeficiency Virus Neutralization Susceptibilities Across Tissue Reservoirs.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) sequence diversity and the presence of archived epitope muta-tions in antibody binding sites are a major obstacle for the clinical application of broadly neutralizing antibodies (bNAbs) against HIV-1. Specifically, it is unclear to what degree the viral reservoir is compartmentalized and if virus susceptibility to antibody neutralization differs across tissues. METHODS: The Last Gift cohort enrolled 7 people with HIV diagnosed with a terminal illness and collected antemortem blood and postmortem tissues across 33 anatomical compartments for near full-length env HIV genome sequencing. Using these data, we applied a Bayesian machine-learning model (Markov chain Monte Carlo-support vector machine) that uses HIV-1 envelope sequences and approximated glycan-occupancy information to quantitatively predict the half-maximal inhib-itory concentrations (IC50) of bNAbs, allowing us to map neutralization resistance pattern across tissue reservoirs. RESULTS: Predicted mean susceptibilities across tissues within participants were relatively homogenous, and the susceptibility pattern observed in blood often matched what was predicted for tissues. However, selected tissues, such as the brain, showed ev-idence of compartmentalized viral populations with distinct neutralization susceptibilities in some participants. Additionally, we found substantial heterogeneity in the range of neutralization susceptibilities across tissues within and between indi-viduals, and between bNAbs within individuals (standard deviation of log2(IC50) >3.4). CONCLUSIONS: Blood-based screening methods to determine viral susceptibility to bNAbs might underestimate the presence of resistant viral variants in tissues. The extent to which these resistant viruses are clinically relevant, that is, lead to bNAb therapeutic failure, needs to be further explored.

A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate edge localization. Fluorescence live cell imaging produces noisy and low-contrast images due to phototoxicity and photobleaching. While phase contrast microscopy is gentle to live cells, it suffers from the halo and shade-off artifacts that cannot be handled by conventional segmentation algorithms. Here, we present a deep learning-based pipeline, termed MARS-Net (Multiple-microscopy-type-based Accurate and Robust Segmentation Network), that utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy, allowing quantitative profiling of cellular morphodynamics. SUMMARY: To accurately segment cell edges and quantify cellular morphodynamics from live-cell imaging data, we developed a deep learning-based pipeline termed MARS-Net (multiple-microscopy-type-based accurate and robust segmentation network). MARS-Net utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy. For effective training on distinct types of live-cell microscopy, MARS-Net comprises a pretrained VGG19 encoder with U-Net decoder and dropout layers. We trained MARS-Net on movies from phase-contrast, spinning-disk confocal, and total internal reflection fluorescence microscopes. MARS-Net produced more accurate edge localization than the neural network models trained with single-microscopy-type datasets. We expect that MARS-Net can accelerate the studies of cellular morphodynamics by providing accurate pixel-level segmentation of complex live-cell datasets.

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation.

Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.

Functional convalescent plasma antibodies and pre-infusion titers shape the early severe COVID-19 immune response.

Transfer of convalescent plasma (CP) had been proposed early during the SARS-CoV-2 pandemic as an accessible therapy, yet trial results worldwide have been mixed, potentially due to the heterogeneous nature of CP. Here we perform deep profiling of SARS-CoV-2-specific antibody titer, Fc-receptor binding, and Fc-mediated functional assays in CP units, as well as in plasma from hospitalized COVID-19 patients before and after CP administration. The profiling results show that, although all recipients exhibit expanded SARS-CoV-2-specific humoral immune responses, CP units contain more functional antibodies than recipient plasma. Meanwhile, CP functional profiles influence the evolution of recipient humoral immunity in conjuncture with the recipient's pre-existing SARS-CoV2-specific antibody titers: CP-derived SARS-CoV-2 nucleocapsid-specific antibody functions are associated with muted humoral immune evolution in patients with high titer anti-spike IgG. Our data thus provide insights into the unexpected impact of CP-derived functional anti-spike and anti-nucleocapsid antibodies on the evolution of SARS-CoV-2-specific response following severe infection.

Computational Interspecies Translation Between Alzheimer's Disease Mouse Models and Human Subjects Identifies Innate Immune Complement, TYROBP, and TAM Receptor Agonist Signatures, Distinct From Influences of Aging.

Mouse models are vital for preclinical research on Alzheimer's disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.

Early cross-coronavirus reactive signatures of humoral immunity against COVID-19.

The introduction of vaccines has inspired hope in the battle against SARS-CoV-2. However, the emergence of viral variants, in the absence of potent antivirals, has left the world struggling with the uncertain nature of this disease. Antibodies currently represent the strongest correlate of immunity against SARS-CoV-2, thus we profiled the earliest humoral signatures in a large cohort of acutely ill (survivors and nonsurvivors) and mild or asymptomatic individuals with COVID-19. Although a SARS-CoV-2­specific immune response evolved rapidly in survivors of COVID-19, nonsurvivors exhibited blunted and delayed humoral immune evolution, particularly with respect to S2-specific antibodies. Given the conservation of S2 across ß-coronaviruses, we found that the early development of SARS-CoV-2­specific immunity occurred in tandem with preexisting common ß-coronavirus OC43 humoral immunity in survivors, which was also selectively expanded in individuals that develop a paucisymptomatic infection. These data point to the importance of cross-coronavirus immunity as a correlate of protection against COVID-19.

Early cross-coronavirus reactive signatures of protective humoral immunity against COVID-19.

The introduction of vaccines has inspired new hope in the battle against SARS-CoV-2. However, the emergence of viral variants, in the absence of potent antivirals, has left the world struggling with the uncertain nature of this disease. Antibodies currently represent the strongest correlate of immunity against COVID-19, thus we profiled the earliest humoral signatures in a large cohort of severe and asymptomatic COVID-19 individuals. While a SARS-CoV-2-specific immune response evolved rapidly in survivors of COVID-19, non-survivors exhibited blunted and delayed humoral immune evolution, particularly with respect to S2-specific antibody evolution. Given the conservation of S2 across ß-coronaviruses, we found the early development of SARS-CoV-2-specific immunity occurred in tandem with pre-existing common ß-coronavirus OC43 humoral immunity in survivors, which was selectively also expanded in individuals that develop paucisymptomatic infection. These data point to the importance of cross-coronavirus immunity as a correlate of protection against COVID-19.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Functional Antibodies in COVID-19 Convalescent Plasma.

In the absence of an effective vaccine or monoclonal therapeutic, transfer of convalescent plasma (CCP) was proposed early in the SARS-CoV-2 pandemic as an easily accessible therapy. However, despite the global excitement around this historically valuable therapeutic approach, results from CCP trials have been mixed and highly debated. Unlike other therapeutic interventions, CCP represents a heterogeneous drug. Each CCP unit is unique and collected from an individual recovered COVID-19 patient, making the interpretation of therapeutic benefit more complicated. While the prevailing view in the field would suggest that it is administration of neutralizing antibodies via CCP that centrally provides therapeutic benefit to newly infected COVID-19 patients, many hospitalized COVID-19 patients already possess neutralizing antibodies. Importantly, the therapeutic benefit of antibodies can extend far beyond their simple ability to bind and block infection, especially related to their ability to interact with the innate immune system. In our work we deeply profiled the SARS-CoV-2-specific Fc-response in CCP donors, along with the recipients prior to and after CCP transfer, revealing striking SARS-CoV-2 specific Fc-heterogeneity across CCP units and their recipients. However, CCP units possessed more functional antibodies than acute COVID-19 patients, that shaped the evolution of COVID-19 patient humoral profiles via distinct immunomodulatory effects that varied by pre-existing SARS-CoV-2 Spike (S)-specific IgG titers in the patients. Our analysis identified surprising influence of both S and Nucleocapsid (N) specific antibody functions not only in direct antiviral activity but also in anti-inflammatory effects. These findings offer insights for more comprehensive interpretation of correlates of immunity in ongoing large scale CCP trials and for the design of next generation therapeutic design.

Humoral signatures of protective and pathological SARS-CoV-2 infection in children.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to spread relentlessly, associated with a high frequency of respiratory failure and mortality. Children experience largely asymptomatic disease, with rare reports of multisystem inflammatory syndrome in children (MIS-C). Identifying immune mechanisms that result in these disparate clinical phenotypes in children could provide critical insights into coronavirus disease 2019 (COVID-19) pathogenesis. Using systems serology, in this study we observed in 25 children with acute mild COVID-19 a functional phagocyte and complement-activating IgG response to SARS-CoV-2, similar to the acute responses generated in adults with mild disease. Conversely, IgA and neutrophil responses were significantly expanded in adults with severe disease. Moreover, weeks after the resolution of SARS-CoV-2 infection, children who develop MIS-C maintained highly inflammatory monocyte-activating SARS-CoV-2 IgG antibodies, distinguishable from acute disease in children but with antibody levels similar to those in convalescent adults. Collectively, these data provide unique insights into the potential mechanisms of IgG and IgA that might underlie differential disease severity as well as unexpected complications in children infected with SARS-CoV-2.

Compromised Humoral Functional Evolution Tracks with SARS-CoV-2 Mortality.

The urgent need for an effective SARS-CoV-2 vaccine has forced development to progress in the absence of well-defined correlates of immunity. While neutralization has been linked to protection against other pathogens, whether neutralization alone will be sufficient to drive protection against SARS-CoV-2 in the broader population remains unclear. Therefore, to fully define protective humoral immunity, we dissected the early evolution of the humoral response in 193 hospitalized individuals ranging from moderate to severe. Although robust IgM and IgA responses evolved in both survivors and non-survivors with severe disease, non-survivors showed attenuated IgG responses, accompanied by compromised Fcɣ receptor binding and Fc effector activity, pointing to deficient humoral development rather than disease-enhancing humoral immunity. In contrast, individuals with moderate disease exhibited delayed responses that ultimately matured. These data highlight distinct humoral trajectories associated with resolution of SARS-CoV-2 infection and the need for early functional humoral immunity.

Deep transfer learning-based hologram classification for molecular diagnostics.

Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH. This limits LDIH utility for point-of-care applications, particularly in resource limited settings. We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance with experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics.

Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging.

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics. Here, we establish a computational framework called HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging. HACKS identifies distinct subcellular protrusion phenotypes based on machine-learning algorithms and reveals their underlying actin regulator dynamics at the leading edge. Using our method, we discover "accelerating protrusion", which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. We validate our finding by pharmacological perturbations and further identify the fine regulation of Arp2/3 and VASP recruitment associated with accelerating protrusion. Our study suggests HACKS can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation.