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Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, embed tumors in two dimensions with uniform manifold approximation and projection, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signatures observed in cancer cohorts.Significance:The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization.

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. Using an exploratory analysis of PBMC datasets, we find that some droplets that were originally called “empty” due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a “spongelet” which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

SummaryAnalysis of single-cell RNA-seq (scRNA-seq) data can reveal novel insights into heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analysis. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single Cell Toolkit 2.0 (SCTK2.0), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.Graphical AbstractHighlightsIntuitive graphical user interface for interactive analysis of scRNA-seq dataAllows non-computational users to analyze scRNA-seq data with end-to-end workflowsProvides interoperability between tools across different programming environmentsProduces HTML reports for reproducibility and easy sharing of results

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.

Chronic opioid usage not only causes addiction behavior through the central nervous system, but also modulates the peripheral immune system. However, how opioid impacts the immune system is still barely characterized systematically. In order to understand the immune modulatory effect of opioids in an unbiased way, here we perform single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from opioid-dependent individuals and controls to show that chronic opioid usage evokes widespread suppression of antiviral gene program in naive monocytes, as well as in multiple immune cell types upon stimulation with the pathogen component lipopolysaccharide. Furthermore, scRNA-seq reveals the same phenomenon after a short in vitro morphine treatment. These findings indicate that both acute and chronic opioid exposure may be harmful to our immune system by suppressing the antiviral gene program. Our results suggest that further characterization of the immune modulatory effects of opioid is critical to ensure the safety of clinical opioids., Over 100 million of opioid prescriptions are issued yearly in the USA alone, but the impact of opioid use on the immune system is barely characterized. Here the authors report antiviral immune response is blunted in several types of blood cells from opioid-dependent individuals, and when healthy donor cells are exposed to morphine in a dish.

BackgroundEffects of timing of Convalescent plasma (CP) administration on hospitalized COVID-19 patients are not established.MethodsWe used the National COVID Cohort Collaborative data to perform a retrospective cohort study of hospitalized COVID-19 patients in the United States between 07-01-2020 and 12-19-2020. We stratified patients based on day of CP administration (Day 0, 1, 2, 3 and 4) from COVID-19 diagnosis. We used 35 predictors to frame matched cohorts accounting for clinical and sociodemographic characteristics. We used competing risk survival models to examine the association between CP administration and length of hospital stay with in-hospital death as a competing risk performing Gray’s test on the cumulative incidence function and Cox’s regression on cause specific hazard ratios.ResultsIn a cohort of 4,003 hospitalized COVID-19 patients, 197 (4.9%) received CP within the first 5 days following COVID-19 diagnosis. After adjusting for potential confounding variables, there were no statistically significant associations between day of CP administration and length of hospital stay. Day 0 CP administration signallled lower mortality but was not statistically significant (HR 0.45 [0.19-1.03]).ConclusionsWe found no association between the timing of CP administration and length of stay among hospitalized COVID-19 patients.

BackgroundDrug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects.MethodsUsing a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect.ResultsWe identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients.ConclusionsTogether, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.

Much of the current postsecondary training in core data science fields treats ‘practice’ as something to be relegated to capstone projects or other final preparations before students leave their programs. Here we argue for a paradigm shift, placing a so-called practicum course at the center of a data science program, intentionally organized as a hybrid between an educational classroom and an industry-like environment. As a case study, we detail our experience of the past 5 years developing the Statistics Practicum in Boston University’s M.S. in Statistical Practice (MSSP) program. We describe the motivation, organization, and logistics of our practicum, as well as both successes and challenges we have faced. In particular, the challenge of fairly and effectively assessing student achievement and program impact in this novel setting is discussed.

Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, embed tumors in two dimensions with uniform manifold approximation and projection, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signatures observed in cancer cohorts. Significance: The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization.

Complex biological systems can be understood by dividing them into hierarchies. Each level of such a hierarchy is composed of different subunits which cooperate to perform distinct biological functions. Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and is being used to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform bi-clustering of co-expressed genes into modules and cells into subpopulations. This model can also quantify the relationship between different levels in a biological hierarchy by determining the contribution of each gene in each module, each module in each cell population, and each cell population in each sample. We used Celda to identify transcriptional modules and cell subpopulations in publicly-available peripheral blood mononuclear cell (PBMC) dataset. In addition to the major classes of cell types, Celda also identified a population of proliferating T-cells and a single plasma cell that was missed by other clustering methods in this dataset. Transcriptional modules captured consistency in expression patterns among genes linked to same biological functions. Furthermore, transcriptional modules provided direct insights on cell type specific marker genes, and helped understanding of subtypes of B- and T-cells. Overall, Celda presents a novel principled approach towards characterizing transcriptional programs and cellular and heterogeneity in single-cell data.

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within thesingleCellTKR package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

Chimeric antigen receptor (CAR) T-cell therapy has produced remarkable anti-tumor responses in patients with B-cell malignancies. However, clonal kinetics and transcriptional programs that regulate the fate of CAR-T cells after infusion remain poorly understood. Here we perform TCRB sequencing, integration site analysis, and single-cell RNA sequencing (scRNA-seq) to profile CD8+ CAR-T cells from infusion products (IPs) and blood of patients undergoing CD19 CAR-T immunotherapy. TCRB sequencing shows that clonal diversity of CAR-T cells is highest in the IPs and declines following infusion. We observe clones that display distinct patterns of clonal kinetics, making variable contributions to the CAR-T cell pool after infusion. Although integration site does not appear to be a key driver of clonal kinetics, scRNA-seq demonstrates that clones that expand after infusion mainly originate from infused clusters with higher expression of cytotoxicity and proliferation genes. Thus, we uncover transcriptional programs associated with CAR-T cell behavior after infusion., Understanding factors that impact CAR T cell expansion in the clinic is crucial to improving its therapeutic success. Here the authors document heterogeneity in the clonal dynamics of CAR-T cells by tracking individual clones using the endogenous TCR and integration sites and provide further insights into the role of transcriptional states in clonal kinetics.

Animal model studies highlight the role of innate-like lymphocyte populations in the early inflammatory response and subsequent parasite control following Plasmodium infection. IFN-γ production by these lymphocytes likely plays a key role in the early control of the parasite and disease severity. Analyzing human innate-like T cell and NK cell responses following infection with Plasmodium has been challenging because the early stages of infection are clinically silent. To overcome this limitation, we examined blood samples from a controlled human malaria infection (CHMI) study in a Tanzanian cohort, in which volunteers underwent CHMI with a low or high dose of Plasmodium falciparum sporozoites. The CHMI differentially affected NK, NKT (invariant NKT), and mucosal-associated invariant T cell populations in a dose-dependent manner, resulting in an altered composition of this innate-like lymphocyte compartment. Although these innate-like responses are typically thought of as short-lived, we found that changes persisted for months after the infection was cleared, leading to significantly increased frequencies of mucosal-associated invariant T cells 6 mo postinfection. We used single-cell RNA sequencing and TCR αβ-chain usage analysis to define potential mechanisms for this expansion. These single-cell data suggest that this increase was mediated by homeostatic expansion-like mechanisms. Together, these data demonstrate that CHMI leads to previously unappreciated long-lasting alterations in the human innate-like lymphocyte compartment. We discuss the consequences of these changes for recurrent parasite infection and infection-associated pathologies and highlight the importance of considering host immunity and infection history for vaccine design.

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cell’s native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

Leigh syndrome is a fatal neurometabolic disorder caused by defects in mitochondrial function. mTOR inhibition with rapamycin attenuates disease progression in a mouse model of Leigh syndrome (Ndufs4 KO mouse); however, the mechanism of rescue is unknown. Here we identify PKC downregulation as a key event mediating the beneficial effects of rapamycin treatment of Ndufs4 KO mice. Assessing the impact of rapamycin on the brain proteome and phosphoproteome of Ndufs4 KO mice we find that rapamycin restores mitochondrial protein levels, inhibits signaling through both mTOR complexes, and reduces the abundance and activity of multiple protein kinase C (PKC) isoforms. Administration of PKC inhibitors increases survival, delays neurological deficits, prevents hair loss, and decreases inflammation in Ndufs4 KO mice. Thus, PKC may be a viable therapeutic target for treating severe mitochondrial disease. Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Leigh syndrome is a fatal neurometabolic disorder caused by defects in mitochondrial function. mTOR inhibition with rapamycin attenuates disease progression in a mouse model of Leigh syndrome (Ndufs4 KO mouse); however, the mechanism of rescue is unknown. Here we assessed the impact of rapamycin on the brain proteome and phosphoproteome of Ndufs4 KO mice. We report that rapamycin remodels the brain proteome to alter mitochondrial structure, inhibits signaling through both mTOR complexes, and inhibits multiple protein kinase C (PKC) isoforms. Administration of PKC inhibitors was sufficient to increase survival, delay neurological deficits, and prevent hair loss in Ndufs4 KO mice. Thus, PKC may be a viable therapeutic target for treating severe mitochondrial disease.One Sentence SummaryProteomic and phosphoproteomic analysis of mouse brain identifies PKC as a key target to treat mitochondrial disease

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny.

INTRODUCTION: When introduced into polyclonal T cells, chimeric antigen receptors (CAR) redirect specificity of the engineered T cells to an antigen recognized by the CAR. We conducted a phase I/II clinical trial of treatment of relapsed and refractory CD19-positive B cell malignancies using a defined formulation of CD4+ and CD8+ CD19-specific CAR-T cells (NCT01865617). Little is known about the transcriptional heterogeneity of CAR-T cells in the infused product and their clonal kinetics after adoptive transfer. METHODS: To understand the factors that impact clonal CAR-T cell behavior in vivo, we performed TCRBV sequencing and single cell transcriptional profiling (10X Genomics) on CD8+ CAR-T cells isolated from infused products and the blood of treated patients. TCRBV sequencing was performed on 0.8 to 1.5 million cells from the infused product and 700-65,000 CAR-T cells from blood after CAR-T infusion. For single-cell RNA sequencing (scRNAseq), we obtained paired 5' gene expression and V(D)J data from individual CAR-T cells isolated from infused products and from the blood at the peak of in vivo expansion, after contraction, and at a late time point. RESULTS: High-throughput sequencing of the TCRBV genes revealed that CAR-T cells were polyclonal in the infused products, and during in vivo expansion and contraction, and at late times (≥ 3 months) after adoptive transfer. We evaluated the diversity of the TCRBV repertoire using the Shannon entropy index, and found that clonal diversity was highest in the infused product and declined at later time points after adoptive transfer. Loss of diversity after adoptive transfer was due to both expansion of higher frequency CAR-T cell clones and loss of low-frequency clones. We identified distinct CAR-T cell clones in the infused product and in blood at multiple time points after infusion that exhibited different kinetics of expansion and contraction. To examine the transcriptional programs that regulate the fate of CAR-T cells after infusion, we performed scRNAseq on CD8+ CAR-T cells, and found transcriptional heterogeneity in the infused products, which declined in CD8+ CAR-T cells isolated from patient blood after adoptive transfer. Gene set enrichment analysis showed that the infused products expressed higher levels of genes associated with hypoxia, glycolysis, and proliferation, and lower levels of genes associated with cytotoxicity compared to CAR-T cells isolated after adoptive transfer. In the infused product, genes associated with cytotoxicity were expressed at higher levels in CAR-T cells harboring clonotypes that were subsequently represented at relatively higher levels in vivo after adoptive transfer. CONCLUSIONS: There is transcriptional heterogeneity in the infused product and distinct CAR-T cell clones exhibit different kinetics of expansion and contraction after infusion. A better understanding of the kinetics of clonal expansion of CAR-T cells after adoptive transfer may provide insight into strategies to improve CAR-T cell immunotherapy. Disclosures Turtle: Nektar Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Precision Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Eureka Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Caribou Biosciences: Membership on an entity's Board of Directors or advisory committees; Juno/Celgene: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding.

We develop a ldquoplug-inrdquo kernel estimator for the differential entropy that is consistent even if the kernel width tends to zero as quickly as 1/N, where N is the number of independent and identically distributed (i.i.d.) samples. Thus, accurate density estimates are not required for accurate kernel entropy estimates; in fact, it is a good idea when estimating entropy to sacrifice some accuracy in the quality of the corresponding density estimate.

Single-cell transcriptomic profiling enables the unprecedented interrogation of gene expression heterogeneity in rare cell populations that would otherwise be obscured in bulk RNA sequencing experiments. The stochastic nature of transcription is revealed in the bimodality of single-cell transcriptomic data, a feature shared across single-cell expression platforms. There is, however, a paucity of computational tools that take advantage of this unique characteristic. We present a new methodology to analyze single-cell transcriptomic data that models this bimodality within a coherent generalized linear modeling framework. We propose a two-part, generalized linear model that allows one to characterize biological changes in the proportions of cells that are expressing each gene, and in the positive mean expression level of that gene. We introduce the cellular detection rate, the fraction of genes turned on in a cell, and show how it can be used to simultaneously adjust for technical variation and so-called “extrinsic noise” at the single-cell level without the use of control genes. Our model permits direct inference on statistics formed by collections of genes, facilitating gene set enrichment analysis. The residuals defined by such models can be manipulated to interrogate cellular heterogeneity and gene-gene correlation across cells and conditions, providing insights into the temporal evolution of networks of co-expressed genes at the single-cell level. Using two single-cell RNA-seq datasets, including newly generated data from Mucosal Associated Invariant T (MAIT) cells, we show how model residuals can be used to identify significant changes across biologically relevant gene sets that are missed by other methods and characterize cellular heterogeneity in response to stimulation.

© 2014 The Author. Published by Oxford University Press. All rights reserved. We consider statistical inference for potentially heterogeneous patterns of association characterizing the expression of bio-molecular pathways across different biologic conditions. We discuss a modeling approach based on Gaussian-directed acyclic graphs and provide computational and methodological details needed for posterior inference. Our application finds motivation in reverse phase protein array data from a study on acute myeloid leukemia, where interest centers on contrasting refractory versus relapsed patients. We illustrate the proposed method through both synthetic and case study data.

Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.

Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0844-5) contains supplementary material, which is available to authorized users.