Biology subjects

Campbell, J. D.

Publications and source records attributed to Campbell, J. D..

36 records · Page 2Linked to original sources

bioRxiv2022

Interactive analysis of single-cell data using flexible workflows with SCTK2.0

Analysis 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 Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/499900v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@2a3964org.highwire.dtl.DTLVardef@1debe9org.highwire.dtl.DTLVardef@6b3a2aorg.highwire.dtl.DTLVardef@1b7bf84_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntuitive graphical user interface for interactive analysis of scRNA-seq data C_LIO_LIAllows non-computational users to analyze scRNA-seq data with end-to-end workflows C_LIO_LIProvides interoperability between tools across different programming environments C_LIO_LIProduces HTML reports for reproducibility and easy sharing of results C_LI

bioinformatics↗

bioRxiv2021

Macrophages govern antiviral responses in human lung tissues protected from SARS-CoV-2 infection

The majority of SARS-CoV-2 infections among healthy individuals result in asymptomatic to mild disease. However, the immunological mechanisms defining effective lung tissue protection from SARS-CoV-2 infection remain elusive. Unlike mice solely engrafted with human fetal lung xenograft (fLX), mice co-engrafted with fLX and a myeloid-enhanced human immune system (HNFL mice) are protected against SARS-CoV-2 infection, severe inflammation, and histopathology. Effective control of viral infection in HNFL mice associated with significant macrophage infiltration, and the induction of a potent macrophage-mediated interferon response. The pronounced upregulation of the USP18-ISG15 axis (a negative regulator of IFN responses), by macrophages was unique to HNFL mice and represented a prominent correlate of reduced inflammation and histopathology. Altogether, our work shed light on unique cellular and molecular correlates of lung tissue protection during SARS-CoV-2 infection, and underscores macrophage IFN responses as prime targets for developing immunotherapies against coronavirus respiratory diseases. HIGHLIGHTSO_LIMice engrafted with human fetal lung xenografts (fLX-mice) are highly susceptible to SARS-CoV-2. C_LIO_LICo-engraftment with a human myeloid-enriched immune system protected fLX-mice against infection. C_LIO_LITissue protection was defined by a potent and well-balanced antiviral response mediated by infiltrating macrophages. C_LIO_LIProtective IFN response was dominated by the upregulation of the USP18-ISG15 axis. C_LI

microbiology↗

bioRxiv2021

Functional and structural modifications of influenza antibodies during pregnancy

Pregnancy represents a unique tolerogenic immune state which may alter susceptibility to infection and vaccine-response. Here we characterized humoral immunity to seasonal influenza vaccine strains in pregnant and non-pregnant women. Pregnant women had reduced hemagglutinin subtype-1 (H1)-IgG, IgG1, and IgG2, hemagglutination inhibition and group 1 and 2 stem IgG. However, H1-specific avidity and Fc{gamma}R1 binding increased. Influenza-antibodies in pregnancy had distinct Fc and Fab glycans characterized by di-galactosylation and di-sialylation. In contrast, agalactosylation and bisection were prominent outside of pregnancy. H1-specific Fc-functionality was moderately reduced in pregnancy, although likely compensated by stronger binding to cognate antigen and FcR. Multivariate analysis revealed distinct populations characterized by Fc{gamma}R1 binding, H1-IgG levels, and glycosylation. Pooled sera from pregnant women exhibited longer retention in vivo. Our results demonstrate structural and functional modulation of humoral immunity during pregnancy in an antigen-specific manner towards reduced inflammation, increased retention in circulation, and efficient placental transport.

immunology↗

bioRxiv2021

CpG-adjuvanted stable prefusion SARS-CoV-2 spike protein protected hamsters from SARS-CoV-2 challenge

The COVID-19 pandemic presents an unprecedented challenge to global public health. Rapid development and deployment of safe and effective vaccines are imperative to control the pandemic. In the current study, we applied our adjuvanted stable prefusion SARS-CoV-2 spike (S-2P)-based vaccine, MVC-COV1901, to hamster models to demonstrate immunogenicity and protection from virus challenge. Golden Syrian hamsters immunized intramuscularly with two injections of 1 {micro}g or 5 {micro}g of S-2P adjuvanted with CpG 1018 and aluminum hydroxide (alum) were challenged intranasally with SARS-CoV-2. Prior to virus challenge, the vaccine induced high levels of neutralizing antibodies with 10,000-fold higher IgG level and an average of 50-fold higher pseudovirus neutralizing titers in either dose groups than vehicle or adjuvant control groups. Six days after infection, vaccinated hamsters did not display any weight loss associated with infection and had significantly reduced lung pathology and most importantly, lung viral load levels were reduced to lower than detection limit compared to unvaccinated animals. Vaccination with either 1 g or 5 g of adjuvanted S-2P produced comparable immunogenicity and protection from infection. This study builds upon our previous results to support the clinical development of MVC-COV1901 as a safe, highly immunogenic, and protective COVID-19 vaccine.

microbiology↗

bioRxiv2020

Single-cell characterization of subsolid and solid lesions in the lung adenocarcinoma spectrum

Determining the clinical significance of CT scan-detected subsolid pulmonary nodules requires an understanding of the molecular and cellular features that may foreshadow disease progression. We studied the alterations at the transcriptome level in both immune and non-immune cells, utilizing single-cell RNA sequencing, to compare the microenvironment of subsolid, solid, and non-involved lung tissues from surgical resection specimens. This evaluation of early spectrum lung adenocarcinoma reveals a significant decrease in the cytolytic activities of natural killer and natural killer T cells, accompanied by a reduction of effector T cells as well as an increase of CD4+ regulatory T cells in subsolid lesions. Characterization of non-immune cells revealed that both cancer-associated alveolar type 2 cells and fibroblasts contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid lesions through ligand-receptor interactions. These findings suggest a decrement of immune surveillance in subsolid lesions.

cancer biology↗

bioRxiv2020

The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the discovery, prediction, and exploration of mutational signatures

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 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 COSMIC database, embed tumors in two dimensions with UMAP, 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 signature observed in cancer cohorts.

bioinformatics↗

bioRxiv2020

Celda: A Bayesian model to perform bi-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and 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 simultaneous 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. We used Celda to identify transcriptional modules and cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset. Celda identified a population of proliferating T cells and a single plasma cell which were missed by two other clustering methods. Celda identified transcriptional modules that highlighted unique and shared biological programs across cell types. Celda also outperformed a PCA-based approach for gene clustering on simulated data. Overall, Celda presents a novel statistically principled approach towards characterizing transcriptional programs and cellular heterogeneity in single-cell RNA-seq data.

bioinformatics↗

bioRxiv2020

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing 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 the singleCellTK R 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.

bioinformatics↗

bioRxiv2020

Multi-Modal Profiling of Human Fetal Liver-Derived Hematopoietic Stem Cells Reveals the Molecular Signature of Engraftment Potential

The human hematopoietic stem cell (HSC) harbors remarkable regenerative potential that can be harnessed therapeutically. During early development, HSCs in the fetal liver (FL) undergo active expansion while simultaneously retaining robust engraftment capacity, yet the underlying molecular program responsible for their efficient engraftment remains unclear. We profiled 26,407 FL cells at both transcriptional and protein levels including over 7,000 highly enriched and functional FL HSCs to establish a detailed molecular signature of engraftment potential. Integration of transcript and linked cell surface marker expression revealed a generalizable signature defining functional FL HSCs and allowed for the stratification of enrichment strategies with high translational potential. This comprehensive, multi-modal profiling of engraftment capacity connects a critical biological function at a key developmental timepoint with its underlying molecular drivers, serving as a useful resource for the field.

cell biology↗

bioRxiv2020

Development of CpG-adjuvanted stable prefusion SARS-CoV-2 spike antigen as a subunit vaccine against COVID-19

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 is a worldwide health emergency. The immense damage done to public health and economies has prompted a global race for cures and vaccines. In developing a COVID-19 vaccine, we applied technology previously used for MERS-CoV to produce a prefusion-stabilized SARS-CoV-2 spike protein by adding two proline substitutions at the top of the central helix (S-2P). To enhance immunogenicity and mitigate the potential vaccine-induced immunopathology, CpG 1018, a Th1-biasing synthetic toll-like receptor 9 (TLR9) agonist was selected as an adjuvant candidate. S-2P was combined with various adjuvants, including CpG 1018, and administered to mice to test its effectiveness in eliciting anti-SARS-CoV-2 neutralizing antibodies. S-2P in combination with CpG 1018 and aluminum hydroxide (alum) was found to be the most potent immunogen and induced high titer of spike-specific antibodies in sera of immunized mice. The neutralizing abilities in pseudotyped lentivirus reporter or live wild-type SARS-CoV-2 were measured with reciprocal inhibiting dilution (ID50) titers of 5120 and 2560, respectively. In addition, the antibodies elicited were able to cross-neutralize pseudovirus containing the spike protein of the D614G variant, indicating the potential for broad spectrum protection. A marked Th-1 dominant response was noted from cytokines secreted by splenocytes of mice immunized with CpG 1018 and alum. No vaccine-related serious adverse effects were found in the dose-ranging study in rats administered single- or two-dose regimens with up to 50 g of S-2P combined with CpG 1018 alone or CpG 1018 with alum. These data support continued development of CHO-derived S-2P formulated with CpG 1018/alum as a candidate vaccine to prevent COVID-19 disease.

immunology↗

bioRxiv2020

Rapid GMP-compliant expansion of SARS-CoV-2-specific T cells from convalescent donors for use as an allogeneic cell therapy for COVID-19

COVID-19 disease caused by the SARS-CoV-2 virus is characterized by dysregulation of effector T cells and accumulation of exhausted T cells. T cell responses to viruses can be corrected by adoptive cellular therapy using donor-derived virus-specific T cells. Here we show that SARS-CoV-2-exposed blood donations contain CD4 and CD8 memory T cells specific for SARS-CoV-2 spike, nucleocapsid and membrane antigens. These peptides can be used to isolate virus-specific T cells in a GMP-compliant process. These T cells can be rapidly expanded using GMP-compliant reagents for use as a therapeutic product. Memory and effector phenotypes are present in the selected virus-specific T cells, but our method rapidly expands the desirable central memory phenotype. A manufacturing yield ranging from 1010 to 1011 T cells can be obtained within 21 days culture. Thus, multiple therapeutic doses of virus-specific T cells can be rapidly generated from convalescent donors for treatment of COVID-19 patients One Sentence SummaryCD4+ and CD8+ T cells specific for SARS-CoV-2 can be isolated from convalescent donors and rapidly expanded to therapeutic doses at GMP standard, maintaining the desired central memory phenotype required for protective immune responses against severe COVID-19 infections.

immunology↗

bioRxiv2020

Whole genome doubling confers unique genetic vulnerabilities on tumor cells

Whole genome doubling (WGD) occurs early in tumorigenesis and generates genetically unstable tetraploid cells that fuel tumor development. Cells that undergo WGD (WGD+) must adapt to accommodate their abnormal tetraploid state; however, the nature of these adaptations, and whether they confer vulnerabilities that can subsequently be exploited therapeutically, is unclear. Using sequencing data from [~]10,000 primary human cancer samples and essentiality data from [~]600 cancer cell lines, we show that WGD gives rise to common genetic traits that are accompanied by unique vulnerabilities. We reveal that WGD+ cells are more dependent on spindle assembly checkpoint signaling, DNA replication factors, and proteasome function than WGD- cells. We also identify KIF18A, which encodes for a mitotic kinesin, as being specifically required for the viability of WGD+ cells. While loss of KIF18A is largely dispensable for accurate chromosome segregation during mitosis in WGD- cells, its loss induces dramatic mitotic errors in WGD+ cells, ultimately impairing cell viability. Collectively, our results reveal new strategies to specifically target WGD+ cancer cells while sparing the normal, non-transformed WGD- cells that comprise human tissue.

cancer biology↗

bioRxiv2019

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

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 cells 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.

bioinformatics↗

bioRxiv2019

scruff: An R/Bioconductor package for preprocessing single-cell RNA-sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.\n\nResultsHere we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package.\n\nConclusionsscruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

bioinformatics↗

bioRxiv2018

Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution

The human bronchial epithelium is composed of multiple, distinct cell types that cooperate to perform functions, such as mucociliary clearance, that defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, the precise effects of this exposure on specific cell types are not well-understood. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never- and six current smokers. Unsupervised analyses identified thirteen cell clusters defined by unique combinations of nineteen distinct gene sets. Expression of a set of toxin metabolism genes localized to ciliated cells from smokers. Smoking-induced airway remodeling was characterized by a loss of club cells and extensive goblet cell hyperplasia. Finally, we identified a novel peri-goblet epithelial subpopulation in smokers that expressed a marker of bronchial premalignant lesions. Our data demonstrates that smoke exposure drives a complex landscape of cellular and molecular alterations in the human bronchial epithelium that may contribute to the onset of smoking-associated lung diseases.

genomics↗

bioRxiv2018

Molecular Subtyping reveals Immune Alterations associated with Progression of Bronchial Premalignant Lesions

Bronchial premalignant lesions (PMLs) are precursors of lung squamous cell carcinoma, but have variable outcome, and we lack tools to identify and treat PMLs at highest risk for progression to invasive cancer. Profiling endobronchial biopsies of PMLs obtained from high-risk smokers by RNA-Seq identified four PML subtypes with differences in epithelial and immune processes. One molecular subtype (Proliferative) is enriched with dysplastic lesions and exhibits up-regulation of metabolic and cell cycle pathways and down-regulation of ciliary processes. RNA-Seq profiles from normal-appearing uninvolved large airway brushings could identify subjects with Proliferative lesions with high specificity. Expression of interferon signaling and antigen processing/presentation pathways are decreased in progressive/persistent Proliferative lesions and immunofluorescence indicates a depletion of innate and adaptive immune cells in these lesions. Molecular biomarkers measured in PMLs or the uninvolved airway can enhance histopathological grading and suggests that immunoprevention strategies may be effective in intercepting the progression of PMLs to lung cancer.

cancer biology↗

bioRxiv2018

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

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.

bioinformatics↗

bioRxiv2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

cancer biology↗