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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Davis Electronic Theses and Dissertations (2021)

Droplet-based microfluidic techniques in combination with continued sequencing advancement has revolutionized many areas of cell based research. In particular, microfluidics have allowed research in genomics to examine cellular structures such as developing embryos at a resolution previously unobtainable. The introduction of single-cell RNA sequencing (scRNA-seq) technologies and bioinformatics pipelines has enabled many breakthrough discoveries such as targets for immune system regulation, the Human Cell Atlas, and cell development trajectories and their associated gene expressions. scRNA-seq is a rapidly growing approach for answering questions related to cell populations and their behaviors, responses, and peculiarities in important areas such as cancer, autoimmune deficiencies, and many other diseases. The answers that scRNA-seq can provide guide new therapeutics and the many new emerging technologies in single-cell genomics such as VDJ analysis and CITE-Seq have much more to offer. VDJ single-cell analysis targets the transcriptomic and immune receptor qualities and clonotypes of T and B cells. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows for the quantification of proteins with the usage of DNA-barcoded antibodies. These techniques along with many others are applied to bioinformatics processes and create additional data and necessary techniques for downstream analysis. These many emerging technologies come with a variety of challenges in computational biology and bioinformatics. Data from single-cell analysis are often complex, containing many forms of metadata such as clonotype, cell types or subtypes, and one or more treatments. Translational bioinformatics is a crucial step for allowing specialized researchers to easily interact with the data and ask directed questions. In addition, tissue and cell samples can vary widely in cell type contents, expression patterns, treatments and extraction. Though many methods exist for cell identification, these often are not one size fits all. In the following chapters two particular applications and methods will be discussed. The first is a method that uses generalized linear models and custom gene signature sets to identify cell types and shows improvement on existing methods. The second is an application that uses the R programming language package called Shiny that is popular for interactive data visualization. The application presented enables more complex visualizations than other available tools based on categorical and continuous variables in the dataset.

    Cover page: Assessing the Current, Future Landscape of scRNA-seq Data Exploration and Cell Type Modeling
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2020)
    Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions.
      Cover page: PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2022)

      Objective

      Single-cell RNA sequencing (scRNA-seq) is a powerful technology that can be applied to the cells populating the whole knee in the study of joint pathology. The knee contains cells embedded in hard structural tissues, cells in softer tissues and membranes, and immune cells. This creates a technical challenge in preparing a viable and representative cell suspension suitable for use in scRNA-seq in minimal time, where under-digestion may exclude cells in hard tissues, over-digestion may damage soft tissue cells, and prolonged digestion may induce phenotypic drift. We developed a rapid two-stage digestion protocol to overcome these difficulties.

      Design

      A two-stage digest consisting of first collagenase IV, an intermediate cell recovery, then collagenase II on the remaining hard tissue. Cells were sequenced on the 10x Genomics platform.

      Results

      We observed consistent cell numbers and viable single cell suspensions suitable for scRNA-seq analysis. Comparison of contralateral knees and separate mice showed reproducible cell yields and gene expression patterns by similar cell-types. A diverse collection of structural and immune cells were captured with a majority from immune origins. Two digestions were necessary to capture all cell-types.

      Conclusions

      The knee contains a diverse mixture of stromal and immune cells that may be crucial for the study of osteoarthritis. The two-stage digestion presented here reproducibly generated highly viable and representative single-cell suspension for sequencing from the whole knee. This protocol facilitates transcriptomic studies of the joint as a complete organ.
        Creative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2021)
        Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.
          Cover page: Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples.
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2019)
          Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through uniform resource locators (URLs) published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed easy to install, and 28% of the tools failed to be installed at all because of problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software.
            Cover page: Challenges and recommendations to improve the installability and archival stability of omics computational tools.
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