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An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

ABSTRACT: Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.

SUBMITTER: Gutierrez DB

PROVIDER: S-EPMC6469931 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

Gutierrez Danielle B DB Gant-Branum Randi L RL Romer Carrie E CE Farrow Melissa A MA Allen Jamie L JL Dahal Nikesh N Nei Yuan-Wei YW Codreanu Simona G SG Jordan Ashley T AT Palmer Lauren D LD Sherrod Stacy D SD McLean John A JA Skaar Eric P EP Norris Jeremy L JL Caprioli Richard M RM

Journal of proteome research 20180830 10

Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, ...[more]

PMID: 30114907

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Project description:BACKGROUND:Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected. RESULTS:We present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics. CONCLUSIONS:Our cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.

Dataset Information

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

Publications

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

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