The evaluation, application, and expansion of 16s amplicon metagenomics

Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken 2, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data. My evaluation of 16S methods should improve 16S amplicon analyses by advocating for the modernization of computational tools; my analysis of infant nasopharyngeal microbiomes lays groundwork for future predictive models for childhood disease and longitudinal microbiomic studies; my analysis of gut microbes illuminates the mechanisms through which bacteria can mediate cardiovascular health. Taken together, the research I present here represents a significant contribution to 16S metagenomics and its application to epidemiology, clinical nutritional science

A Novel Quantitative Approach for Eliminating Sample-To-Sample Variation Using a Hue Saturation Value Analysis Program

Objectives: As computing technology and image analysis techniques have advanced, the practice of histology has grown from a purely qualitative method to one that is highly quantified. Current image analysis software is imprecise and prone to wide variation due to common artifacts and histological limitations. In order to minimize the impact of these artifacts, a more robust method for quantitative image analysis is required. Methods and Results: Here we present a novel image analysis software, based on the hue saturation value color space, to be applied to a wide variety of histological stains and tissue types. By using hue, saturation, and value variables instead of the more common red, green, and blue variables, our software offers some distinct advantages over other commercially available programs. We tested the program by analyzing several common histological stains, performed on tissue sections that ranged from 4 µm to 10 µm in thickness, using both a red green blue color space and a hue saturation value color space. Conclusion: We demonstrated that our new software is a simple method for quantitative analysis of histological sections, which is highly robust to variations in section thickness, sectioning artifacts, and stain quality, eliminating sample-to-sample variation

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

I will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design. The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. We demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.NIH U01CA22041

Genesis and growth of extracellular vesicle-derived microcalcification in atherosclerotic plaques

Clinical evidence links arterial calcification and cardiovascular risk. Finite-element modelling of the stress distribution within atherosclerotic plaques has suggested that subcellular microcalcifications in the fibrous cap may promote material failure of the plaque, but that large calcifications can stabilize it. Yet the physicochemical mechanisms underlying such mineral formation and growth in atheromata remain unknown. Here, by using three-dimensional collagen hydrogels that mimic structural features of the atherosclerotic fibrous cap, and high-resolution microscopic and spectroscopic analyses of both the hydrogels and of calcified human plaques, we demonstrate that calcific mineral formation and maturation results from a series of events involving the aggregation of calcifying extracellular vesicles, and the formation of microcalcifications and ultimately large calcification zones. We also show that calcification morphology and the plaque’s collagen content – two determinants of atherosclerotic plaque stability - are interlinked

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Abstract Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools

<i>CSH</i> is consistent between individuals.

<p>Apoe−/− mouse innominate arteries stained with MAC3 antibody for detection of macrophages. RGB1(threshold1) was optimized for Cross Section 1, and overestimates the positive area when applied to Cross Section 2. RGB2(threshold2) was optimized for Cross Section 2, and underestimates the positive area when applied to Cross Section 1. <i>CSH</i> was able to effectively use a single HSV threshold on both cross sections. In the overlays between the HSV and RGB1 and RGB2, yellow area shows where there is agreement between the HSV method and the RGB method. Green area in the overlays may indicate false positive area reported by the RGB method, while red area in the overlays may represent false negative area reported by the RGB method.</p

<i>CSH</i> processing on an infarcted mouse heart.

<p>A) An infarcted mouse heart section cut 8 µm thick. Muscle tissue is scarlet red, while collagen fibers appear blue, and necrotic regions are purple-black. Insets show enlarged areas of muscle, collagen and necrotic region. B) The same mouse heart, post-processing by <i>CSH</i>. The areas that <i>CSH</i> determined as collagen are blue, and the areas that <i>CSH</i> determined as muscle are red. The background is yellow. C) A plot of the pixels from the original heart image mapped to HSV space. The gray arrows indicate the direction from which this 3-D graph will be displayed in the following 2-D images. D) A plot of the pixels from the original image in the Hue-Saturation plane. The borders collagen and the muscle rectangular thresholds are visible at Hue = {200, 300, 385}. E) A plot of the pixels from the original image in the Hue-Value plane. F) A plot of the pixels from the original image in the Value-Saturation plane. This graph most clearly shows the different shapes of the collagen peak (blue) and the muscle peak (red).</p

Analytic performance across diverse section thicknesses.

<p>A) A section of infarcted mouse heart cut to 4 µm and stained with Masson's trichrome. Descending from the original image, we see the RGB binary image, the HSV binary image, a density map of the pixels mapped to the RGB color space, and a density map of the pixels mapped to the HSV color space. B) A section of infarcted mouse heart cut to 6 µm and stained with Masson's trichrome. C) A section of infarcted mouse heart cut to 8 µm and stained with Masson's trichrome. D) For each of four experimental hearts and each of the three section thicknesses, the area identified as muscle is plotted next to the area identified as collagen using the RGB method. Because each heart has a different size infarction, these results for each heart are normalized as a percentage of the measured area in the 6 µm sample. As the section thickness increases, RGB analysis decreases the perceived collagen area, despite analyzing adjacent sections of heart. E) For each of four experimental hearts and each of the three section thicknesses, the area identified as muscle is plotted next to the area identified as collagen using the HSV method. Because each heart has a different size infarction, these results for each heart are normalized as a percentage of the measured area in the 6 µm sample. There is no discernible change in perceived muscle or collagen area as the section thickness increases when using the HSV method.</p

<i>CSH</i> is a powerful tool for a variety of stains.

<p>Common histological stains, displaying the fidelity of <i>CSH</i>. (Top panels): A mouse aorta stained with alkaline phosphatase (ALP, red) for detection of early calcification, with Gill's hematoxylin as counterstaining (purple), which depicts advanced calcification. ALP stain is scarlet red (denoted “A” in the top left panel), while hematoxylin is a shade of purple (denoted “H” in the top left panel). Visually, the hematoxylin interferes with the ALP, making it difficult to see where the ALP stain begins and ends. We analyzed the section for ALP-positive area using both CSH and an RGB-based method. (Bottom panels): A mouse liver stained with picrosirius red staining visualized using polarized light microscopy for detection of fibrosis. We analyzed the section using both CSH and an RGB-based method. The RGB method was unable to register the brightest parts of the stain as positive (gray), and falsely interpreted stain artifacts as positive areas (green in both “Merge” images).</p