Post-infusion PD-1+CD8+CAR-T cells identify patients responsive to CD19-CAR-T therapy in non-Hodgkin's lymphoma.

Chimeric antigen receptor T cell therapy (CAR-T) has revolutionized treatment for relapsed/refractory (r/r) B-cell non-Hodgkin's lymphoma (NHL). Robust biomarkers and a complete understanding of CAR-T cell function in the post-infusion phase remain limited. Here we used a 37-color spectral flow cytometry panel to perform high dimensional single cell analysis of post-infusion samples in 26 patients treated with CD28 co-stimulatory domain containing commercial CAR-T (CD28-CAR-T) for NHL and focused on computationally gated CD8+ CAR-T cells. We found that the presence of post-infusion PD-1+ CD8+ CAR-T cells at the Day 14 timepoint highly correlated with the ability to achieve complete response (CR) by 6 months. Further analysis identified multiple subtypes of CD8+ PD-1+ CAR-T cells including PD-1+ TCF1+ stem-like CAR-T cells and PD-1+ TIM3+ effector-like CAR-T cells that correlated with improved clinical outcomes such as response and progression free survival. Additionally, we identified a subset of PD-1+ CD8+ CAR+ T cells with effector-like function that was increased in patients who achieved a CR and was associated with Grade 3 or higher immune effector cell-associated neurotoxicity syndrome. Here we identified robust biomarkers of response to CD28-CAR-T and highlight the importance of PD-1 positivity in CD8+ CAR-T cells post-infusion in achieving CR.

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naïve controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naïve controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naïve controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI 1 ) and after (MRI 2 ) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naïve controls no difference was found between MRI 1 and MRI 2 . Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

mRNA vaccines against SARS-CoV-2 induce divergent antigen-specific T-cell responses in patients with lung cancer.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is highly transmissible and evades pre-established immunity. Messenger RNA (mRNA) vaccination against ancestral strain spike protein can induce intact T-cell immunity against the Omicron variant, but efficacy of booster vaccination in patients with late-stage lung cancer on immune-modulating agents including anti-programmed cell death protein 1(PD-1)/programmed death-ligand 1 (PD-L1) has not yet been elucidated. METHODS: We assessed T-cell responses using a modified activation-induced marker assay, coupled with high-dimension flow cytometry analyses. Peripheral blood mononuclear cells (PBMCs) were stimulated with various viral peptides and antigen-specific T-cell responses were evaluated using flow cytometry. RESULTS: Booster vaccines induced CD8+ T-cell response against the ancestral SARS-CoV-2 strain and Omicron variant in both non-cancer subjects and patients with lung cancer, but only a marginal induction was detected for CD4+ T cells. Importantly, antigen-specific T cells from patients with lung cancer showed distinct subpopulation dynamics with varying degrees of differentiation compared with non-cancer subjects, with evidence of dysfunction. Notably, female-biased T-cell responses were observed. CONCLUSION: We conclude that patients with lung cancer on immunotherapy show a substantial qualitative deviation from non-cancer subjects in their T-cell response to mRNA vaccines, highlighting the need for heightened protective measures for patients with cancer to minimize the risk of breakthrough infection with the Omicron and other future variants.

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.

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Multiplexed assay of variant effect reveals residues of functional importance in the BRCA1 coiled-coil and serine cluster domains.

Delineating functionally normal variants from functionally abnormal variants in tumor suppressor proteins is critical for cancer surveillance, prognosis, and treatment options. BRCA1 is a protein that has many variants of uncertain significance which are not yet classified as functionally normal or abnormal. In vitro functional assays can be used to identify the functional impact of a variant when the variant has not yet been categorized through clinical observation. Here we employ a homology-directed repair (HDR) reporter assay to evaluate over 300 missense and nonsense BRCA1 variants between amino acid residues 1280 and 1576, which encompasses the coiled-coil and serine cluster domains. Functionally abnormal variants tended to cluster in residues known to interact with PALB2, which is critical for homology-directed repair. Multiplexed results were confirmed by singleton assay and by ClinVar database variant interpretations. Comparison of multiplexed results to designated benign or likely benign or pathogenic or likely pathogenic variants in the ClinVar database yielded 100% specificity and 100% sensitivity of the multiplexed assay. Clinicians can reference the results of this functional assay for help in guiding cancer treatment and surveillance options. These results are the first to evaluate this domain of BRCA1 using a multiplexed approach and indicate the importance of this domain in the DNA repair process.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data.

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data.

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.

LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity.

Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.

Long-Term Impact of Daily E-cigarette Exposure on the Lungs of Asthmatic Mice.

INTRODUCTION: Although the greater popularity of electronic cigarettes (EC) among asthmatics is alarming, there is limited knowledge of the long-term consequences of EC exposure in asthmatics. AIMS AND METHODS: Mild asthmatic C57/BL6J adult male and female mice were established by intranasal insufflation with three combined allergens. The asthmatic and age and sex-matched' naïve mice were exposed to air, nicotine-free (propylene glycol [PG]/vegetable glycerin [VG]-only), or PG/VG+Nicotine, 4 hours daily for 3 months. The effects of EC exposure were accessed by measuring cytokines in bronchoalveolar lavage, periodic acid-schiff (PAS) staining, mitochondrial DNA copy numbers (mtCN), and the transcriptome in the lung. Significance was false discovery rate <0.2 for transcriptome and 0.05 for the others. RESULTS: In asthmatic mice, PG/VG+Nicotine increased PAS-positive cells and IL-13 compared to mice exposed to air and PG/VG-only. In naïve mice exposed to PG/VG+Nicotine and PG/VG-only, higher INF-γ was observed compared to mice exposed only to air. PG/VG-only and PG/VG+Nicotine had significantly higher mtCN compared to air exposure in asthmatic mice, while the opposite pattern was observed in non-asthmatic naïve mice. Different gene expression patterns were profoundly found for asthmatic mice exposed to PG/VG+Nicotine compared to PG/VG-only, including genes involved in mitochondrial dysfunction, oxidative phosphorylation, and p21-activated kinase (PAK) signaling. CONCLUSIONS: This study provides experimental evidence of the potential impact of nicotine enhancement on the long-term effects of EC in asthmatics compared to non-asthmatics. IMPLICATIONS: The findings from this study indicate the potential impact of EC in asthmatics by addressing multiple biological markers. The long-term health outcomes of EC in the susceptible group can be instrumental in supporting policymaking and educational campaigns and informing the public, healthcare providers, and EC users about the underlying risks of EC use.

Selective suppression of de novo SARS-CoV-2 vaccine antibody responses in patients with cancer on B cell-targeted therapy.

We assessed vaccine-induced antibody responses to the SARS-CoV-2 ancestral virus and Omicron variant before and after booster immunization in 57 patients with B cell malignancies. Over one-third of vaccinated patients at the pre-booster time point were seronegative, and these patients were predominantly on active cancer therapies such as anti-CD20 monoclonal antibody. While booster immunization was able to induce detectable antibodies in a small fraction of seronegative patients, the overall booster benefit was disproportionately evident in patients already seropositive and not receiving active therapy. While ancestral virus- and Omicron variant-reactive antibody levels among individual patients were largely concordant, neutralizing antibodies against Omicron tended to be reduced. Interestingly, in all patients, including those unable to generate detectable antibodies against SARS-CoV-2 spike, we observed comparable levels of EBV- and influenza-reactive antibodies, demonstrating that B cell-targeting therapies primarily impair de novo but not preexisting antibody levels. These findings support rationale for vaccination before cancer treatment.

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Investigating the effects of chronic low-dose radiation exposure in the liver of a hypothermic zebrafish model.

Mankind's quest for a manned mission to Mars is placing increased emphasis on the development of innovative radio-protective countermeasures for long-term space travel. Hibernation confers radio-protective effects in hibernating animals, and this has led to the investigation of synthetic torpor to mitigate the deleterious effects of chronic low-dose-rate radiation exposure. Here we describe an induced torpor model we developed using the zebrafish. We explored the effects of radiation exposure on this model with a focus on the liver. Transcriptomic and behavioural analyses were performed. Radiation exposure resulted in transcriptomic perturbations in lipid metabolism and absorption, wound healing, immune response, and fibrogenic pathways. Induced torpor reduced metabolism and increased pro-survival, anti-apoptotic, and DNA repair pathways. Coupled with radiation exposure, induced torpor led to a stress response but also revealed maintenance of DNA repair mechanisms, pro-survival and anti-apoptotic signals. To further characterise our model of induced torpor, the zebrafish model was compared with hepatic transcriptomic data from hibernating grizzly bears (Ursus arctos horribilis) and active controls revealing conserved responses in gene expression associated with anti-apoptotic processes, DNA damage repair, cell survival, proliferation, and antioxidant response. Similarly, the radiation group was compared with space-flown mice revealing shared changes in lipid metabolism.

Novel high-risk acute myeloid leukemia subgroup with ERG amplification and Biallelic loss of TP53.

ETS-related gene (ERG) amplification, observed in 4-6% of acute myeloid leukemia (AML), is associated with unfavorable prognosis. To determine coincident effects of additional genomic abnormalities in AML with ERG amplification (ERGamp), we examined 11 ERGamp cases of 205 newly diagnosed AML using chromosomal microarray analysis and next generation sequencing. ERGamp cases demonstrated a distinct pattern of high genetic complexity: loss of 5q, chromothripsis and TP53 loss of function variants. Remarkably, allelic TP53 loss or loss of heterozygosity (LOH) co-occurring with TP53 inactivating mutation dramatically effected ERGamp tumor patient outcome. In the presence of homozygous TP53 loss of function, ERGamp patients demonstrated no response to induction chemotherapy with median overall survival (OS) of 3.8 months (N = 9). Two patients with heterozygous loss of TP53 function underwent alloSCT without evidence of relapse at one year. Similarly, a validation TCGA cohort, 6 of the 8 ERGamp cases with TP53 loss of function demonstrated median OS of 2.5 months. This suggests that with TP53 mutant ERGamp AML, successive loss of the second TP53 allele, typically by 17p deletion or LOH identifies a specific high-risk subtype of AML patients who are resistant to standard induction chemotherapy and need novel approaches to avert the very poor prognosis.

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.

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.

Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19.

Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al., 2018). Based on a connection between the envelope model and PLS, we develop an envelope-based partial PLS estimator that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Root-n consistency and asymptotic normality are established for this estimator. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics. The efficient estimation leads to a clear scientific interpretation of the results.

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.