Single-Cell RNA-Seq Analysis of Patient Myeloid-Derived Suppressor Cells and the Response to Inhibition of Bruton's Tyrosine Kinase.

Myeloid-derived suppressor cell (MDSC) levels are elevated in patients with cancer and contribute to reduced efficacy of immune checkpoint therapy. MDSC express Bruton's tyrosine kinase (BTK) and BTK inhibition with ibrutinib, an FDA-approved irreversible inhibitor of BTK, leads to reduced MDSC expansion/function in mice and significantly improves the antitumor activity of anti-PD-1 antibody treatments. Single-cell RNA sequencing (scRNA-seq) was used to characterize the effect of ibrutinib on gene expression of fluorescence-activated cell sorting-enriched MDSC from patients with different cancer types [breast, melanoma, head and neck squamous cell cancer (HNSCC)]. Melanoma patient MDSC were treated in vitro for 4 hours with 5 µmol/L ibrutinib or DMSO, processed for scRNA-seq using the Chromium 10× Genomics platform, and analyzed via the Seurat v4 standard integrative workflow. Baseline gene expression of MDSC from patients with breast, melanoma, and HNSCC cancer revealed similarities among the top expressed genes. In vitro ibrutinib treatment of MDSC from patients with melanoma resulted in significant changes in gene expression. GBP1, IL-1ß, and CXCL8 were among the top downregulated genes whereas RGS2 and ABHD5 were among the top upregulated genes (P < 0.001). Double positive CD14+CD15+ MDSC and PMN-MDSC responded similarly to BTK inhibition and exhibited more pronounced gene changes compared with early MDSC and M-MDSC. Pathway analysis revealed significantly downregulated pathways including TREM1, nitric oxide signaling, and IL-6 signaling (P < 0.004). IMPLICATIONS: scRNA-seq revealed characteristic gene expression patterns for MDSC from different patients with cancer and BTK inhibition led to the downregulation of multiple genes and pathways important to MDSC function and migration.

A Bayesian multivariate mixture model for high throughput spatial transcriptomics.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

Novelty-induced locomotor behavior predicts heroin addiction vulnerability in male, but not female, rats.

RATIONALE: The ongoing rise in opioid use disorder (OUD) has made it imperative to better model the individual variation within the human population that contributes to OUD vulnerability. Using animal models that capture such variation can be a useful tool. Individual variation in novelty-induced locomotion is predictive of substance use disorder (SUD) propensity. In this model, rats are characterized as high-responders (HR) or low-responders (LR) using a median split based on distance travelled during a locomotor test, and HR rats are generally found to exhibit a more SUD vulnerable behavioral phenotype. OBJECTIVES: The HR/LR model has commonly been used to assess behaviors in male rats using psychostimulants, with limited knowledge of the predictive efficacy of this model in females or the use of an opioid as the reward. In the current study, we assessed several behaviors across the different phases of drug addiction (heroin taking, refraining, and seeking) in over 500 male and female heterogeneous stock rats run at two geographically separate locations. Rats were characterized as HRs or LRs within each sex for analysis. RESULTS: Overall, females exhibit a more OUD vulnerable phenotype relative to males. Additionally, the HR/LR model was predictive of OUD-like behaviors in male, but not female rats. Furthermore, phenotypes did not differ in anxiety-related behaviors, reacquisition of heroin-taking, or punished heroin-taking behavior in either sex. CONCLUSIONS: These results emphasize the importance of assessing females in models of individual variation in SUD and highlight limitations in using the HR/LR model to assess OUD propensity.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Migrating Type 2 Dendritic Cells Prime Mucosal Th17 Cells Specific to Small Intestinal Commensal Bacteria.

Dendritic cells (DCs) are professional APCs equipped with MHC-restricted Ags, costimulations, and cytokines that effectively prime and differentiate naive T cells into distinct functional subsets. The immune signals that DCs carry reflect the route of Ag uptake and the innate stimuli they received. In the mucosal tissues, owing to the great variety of foreign Ags and inflammatory cues, DCs are predominantly activated and migratory. In the small intestine, CD4 Th17 cells are abundant and have been shown to be regulated by DCs and macrophages. Using a mouse commensal bacteria experimental model, we identified that the early priming step of commensal-driven Th17 cells is controlled by bona fide Zbtb46-expressing DCs. CCR7-dependent migration of type 2 DCs (DC2s) from the small intestine to the mesenteric lymph nodes (MLNs) is essential for the activation of naive CD4 T cells. The migratory DC2 population in the MLNs is almost exclusively Esam+ cells. Single-cell RNA sequencing highlighted the abundance of costimulatory markers (CD40 and OX40) and chemokines (Ccl22 and Cxcl16) on MLN migratory DCs. Further resolution of MLN migratory DC2s revealed that the Th17-polarizing cytokine IL-6 colocalizes with DC2s expressing CD40, Ccl17, and Ccl22. Thus, early Th17 cell differentiation is initiated by a small subset of migratory DC2s in the gut-draining lymph nodes.

Androgen conspires with the CD8+ T cell exhaustion program and contributes to sex bias in cancer.

Sex bias exists in the development and progression of nonreproductive organ cancers, but the underlying mechanisms are enigmatic. Studies so far have focused largely on sexual dimorphisms in cancer biology and socioeconomic factors. Here, we establish a role for CD8+ T cell-dependent antitumor immunity in mediating sex differences in tumor aggressiveness, which is driven by the gonadal androgen but not sex chromosomes. A male bias exists in the frequency of intratumoral antigen-experienced Tcf7/TCF1+ progenitor exhausted CD8+ T cells that are devoid of effector activity as a consequence of intrinsic androgen receptor (AR) function. Mechanistically, we identify a novel sex-specific regulon in progenitor exhausted CD8+ T cells and a pertinent contribution from AR as a direct transcriptional transactivator of Tcf7/TCF1. The T cell-intrinsic function of AR in promoting CD8+ T cell exhaustion in vivo was established using multiple approaches including loss-of-function studies with CD8-specific Ar knockout mice. Moreover, ablation of the androgen-AR axis rewires the tumor microenvironment to favor effector T cell differentiation and potentiates the efficacy of anti-PD-1 immune checkpoint blockade. Collectively, our findings highlight androgen-mediated promotion of CD8+ T cell dysfunction in cancer and imply broader opportunities for therapeutic development from understanding sex disparities in health and disease.

Treatment with soluble CD24 attenuates COVID-19-associated systemic immunopathology.

BACKGROUND: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain-containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. METHODS: Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. RESULTS: Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8+ T cells, CD4+ T cells, and CD56+ natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. CONCLUSIONS: Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19.

TREATMENT WITH SOLUBLE CD24 ATTENUATES COVID-19-ASSOCIATED SYSTEMIC IMMUNOPATHOLOGY.

BACKGROUND: SARS-CoV-2 causes COVID-19 through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns (DAMPs) and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) is able to blunt the broad inflammatory response induced by DAMPs in multiple models. A recent randomized phase III trial evaluating the impact of CD24Fc in patients with severe COVID-19 demonstrated encouraging clinical efficacy. METHODS: We studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial (NCT04317040) collected before and after treatment with CD24Fc or placebo. We performed high dimensional spectral flow cytometry analysis of peripheral blood mononuclear cells and measured the levels of a broad array of cytokines and chemokines. A systems analytical approach was used to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. FINDINGS: Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found systemic hyper-activation of multiple cellular compartments in the placebo group, including CD8+ T cells, CD4+ T cells, and CD56+ NK cells. By contrast, CD24Fc-treated patients demonstrated blunted systemic inflammation, with a return to homeostasis in both NK and T cells within days without compromising the ability of patients to mount an effective anti-Spike protein antibody response. A single dose of CD24Fc significantly attenuated induction of the systemic cytokine response, including expression of IL-10 and IL-15, and diminished the coexpression and network connectivity among extensive circulating inflammatory cytokines, the parameters associated with COVID-19 disease severity. INTERPRETATION: Our data demonstrates that CD24Fc treatment rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19. FUNDING: NIH.

A Bayesian multivariate mixture model for skewed longitudinal data with intermittent missing observations: An application to infant motor development.

In studies of infant growth, an important research goal is to identify latent clusters of infants with delayed motor development-a risk factor for adverse outcomes later in life. However, there are numerous statistical challenges in modeling motor development: the data are typically skewed, exhibit intermittent missingness, and are correlated across repeated measurements over time. Using data from the Nurture study, a cohort of approximately 600 mother-infant pairs, we develop a flexible Bayesian mixture model for the analysis of infant motor development. First, we model developmental trajectories using matrix skew-normal distributions with cluster-specific parameters to accommodate dependence and skewness in the data. Second, we model the cluster-membership probabilities using a Pólya-Gamma data-augmentation scheme, which improves predictions of the cluster-membership allocations. Lastly, we impute missing responses from conditional multivariate skew-normal distributions. Bayesian inference is achieved through straightforward Gibbs sampling. Through simulation studies, we show that the proposed model yields improved inferences over models that ignore skewness or adopt conventional imputation methods. We applied the model to the Nurture data and identified two distinct developmental clusters, as well as detrimental effects of food insecurity on motor development. These findings can aid investigators in targeting interventions during this critical early-life developmental window.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

SUMMARY: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network-Based Discovery of Opioid Use Vulnerability in Rats Using the Bayesian Stochastic Block Model.

Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. The behavioral relationship between opioid exposure and development of opioid use disorder (OUD) varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. However, effective pre-clinical identification of these sub-populations remains challenging due to the complex multivariate measurements employed in animal models of OUD. In this study, we propose a novel non-linear network-based data analysis workflow that employs seven behavioral traits to identify opioid use sub-populations and assesses contributions of behavioral variables to opioid vulnerability and resiliency. Through this analysis workflow we determined how behavioral variables across heroin taking, refraining and seeking interact with one another to identify potentially heroin resilient and vulnerable behavioral sub-populations. Data were collected from over 400 heterogeneous stock rats in two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To enter the analysis workflow, we integrated data from different cohorts of rats and removed possible batch effects. We then constructed a rat-rat similarity network based on their behavioral patterns and implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We identified three statistically distinct clusters corresponding to distinct behavioral sub-populations, vulnerable, resilient and intermediate for heroin use, refraining and seeking. We implement this analysis workflow as an open source R package, named mlsbm.

Household Food Security and Infant Adiposity.

OBJECTIVES: Food insecurity has been associated with obesity, but previous studies are inconsistent and few included infants. We examined associations between household food security and infant adiposity and assessed the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP) as effect modifiers. We hypothesized that infants from food-insecure households would have greater adiposity, with attenuation by WIC and SNAP. METHODS: We repeatedly measured 666 infants from the southeastern United States in 2013-2017. We categorized households as high, marginal, low, or very low using the US Household Food Security Survey Module. Outcomes were BMI z score, subscapular and triceps skinfold-for-age z score, the sum of subscapular and triceps skinfolds, the ratio of subscapular and triceps skinfolds, and BMI z score ≥1 (at risk for overweight). We used covariate-adjusted repeated-measures linear and logistic regressions. RESULTS: Of infants, 68.6% were Black and 60.5% had household incomes <$20 000. Interactions between food security and WIC and/or SNAP were not significant. Compared with infants from high food security households, infants from very low food security households had higher BMI z scores (0.18 U; 95% confidence interval [CI] 0.01 to 0.35), higher subscapular skinfold-for-age z scores (0.31 U; 95% CI 0.04 to 0.59), a higher sum of subscapular and triceps skinfolds (0.53 mm; 95% CI 0.002 to 1.07), and greater odds of being at risk for overweight (odds ratio 1.55; 95% CI 1.14 to 2.10). Infants from low food security households had greater odds of being at risk for overweight (odds ratio 1.72; 95% CI 1.17 to 2.10). CONCLUSIONS: In larger and longer studies, researchers should examine food security and adiposity in young children.

Associations of maternal diet with infant adiposity at birth, 6 months and 12 months.

OBJECTIVES: To assess associations between maternal prenatal diet quality and infant adiposity. DESIGN: The design was a prospective birth cohort. SETTING: We used data from the Nurture study, a cohort of women and their infants residing in the southeastern USA. PARTICIPANTS AND EXPOSURE ASSESSMENT: Between 2013 and 2015, we enrolled 860 women between 20 and 36 weeks' gestation. After reconsenting at delivery and excluding women with implausible calorie intakes, we measured dietary intake using the Block food frequency questionnaire, and assessed diet quality using a modified Alternate Healthy Eating Index 2010 (AHEI-2010), which assessed intake of 10 food categories, including fruits, vegetables, whole grains, nuts/legumes, fats, meats, beverages and sodium (excluding alcohol). OUTCOMES: We assessed birth weight for gestational age z-score, small and large for gestational age, low birth weight and macrosomia. Outcomes at 6 and 12 months were weight-for-length z-score, sum of subscapular and triceps skinfold thickness (SS+TR) and subscapular-to-triceps skinfold ratio (SS:TR). RESULTS: Among mothers, 70.2% were black and 20.9% were white; less than half (45.2%) reported having a high school diploma or less. Among infants, 8.7% were low birth weight and 8.6% were small for gestational age. Unadjusted estimates showed that a higher AHEI-2010 score, was associated with a higher birth weight for gestational z-score (ß=0.01; 95% CI 0.002 to 0.02; p=0.02) and a greater likelihood of macrosomia (OR=1.04; 95% CI 1.004 to 1.09; p=0.03). After adjustment, maternal diet quality was not associated with infant adiposity at birth, 6 or 12 months. CONCLUSIONS: Although poor maternal diet quality during pregnancy was not associated with infant adiposity in our study, maternal diet during pregnancy may still be an important and modifiable factor of public health importance.

GAIL: An interactive webserver for inference and dynamic visualization of gene-gene associations based on gene ontology guided mining of biomedical literature.

In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining and provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. We evaluate the utility and performance of GAIL with applications to gene signatures associated with systemic lupus erythematosus and breast cancer. Results show that GAIL allows effective interrogation and visualization of gene-gene networks and their subnetworks, which facilitates biological understanding of gene-gene associations. GAIL is available at http://chunglab.io/GAIL/.