abstract

Abstract

Previously it has been demonstrated that SWATH acquisition provides improved metabolite coverage as compared to traditional data dependent techniques for proteomics and metabolomics biomarker research. Large comprehensive datasets can now be easily generated with high throughput, thus creating the need for better data processing solutions. Here, the OneOmics suite was used to process a SWATH acquisition data set acquired using on the SCIEX ZenoTOF 7600 system, with the goal of quantifying differences in metabolites found in ZDF and healthy rat urine to demonstrate the workflow. The complete integration of data processing applications in the cloud-based platform enabled rapid processing of the study data. The platform features innovative algorithms and a wide range of tools for quantifying metabolites with confidence and exploring the biological relevance of the results.

introduction

Introduction

SWATH acquisition is a data independent acquisition (DIA) workflow that has been demonstrated to improve metabolite coverage over traditional data dependent techniques for untargeted metabolomics.^1,2 The workflow enables creation of a digitized record of the metabolome present in a sample, with full (MS1) and MS/MS scans capturing every detectable analyte in a single injection. The richness of SWATH acquisition data offers numerous data analysis opportunities, one of which is to identify differential metabolites across sample groups. While instrumentation and appropriate methods for collecting metabolomics SWATH acquisition data are well-established, the lack of software tools for processing large-scale metabolomics SWATH acquisition studies remains a challenge for wide-spread adoption of the workflow in laboratories.³

In this work, the OneOmics suite, a cloud-based solution for SWATH acquisition data processing, was used to investigate  analytes present in the urine of  Zucker Diabetic Fatty (ZDF) vs. control Sprague Dawley (SD) rats. ZDF rats are widely used as an animal model of genetic type 2 diabetes. The OneOmics suite facilitated complete end-to-end processing of the metabolomics data set, starting with ion-library driven extraction of analyte peak groups from the SWATH acquisition data. The platform features built-in false discovery rate (FDR) analysis and normalization algorithms to facilitate accurate identification of differentially expressed analytes. Data sets can be further interrogated using multivariate statistical analysis tools and viewed in a biological context with pathway mapping (Figure 1).

Figure 1. Overview of the metabolomics workflow for SWATH acquisition data processing in the OneOmics suite. Following collection of variable window SWATH acquisition data using the SCIEX ZenoTOF 7600 system, data files were uploaded to the OneOmics suite for processing. Experimental meta data was entered, and then the Extractor and Assembler Apps were used to extract and quantify metabolites across the samples. Additional apps were used to visualize the results and perform statistical analysis to determine the metabolite differences between ZDF vs. SD rat urine.

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Key features of the OneOmics suite for metabolomics data processing

• OneOmics suite enables fast and confident identification of differential metabolites across experiment groups in large-scale metabolomics SWATH acquisition studies
• The entire processing workflow, from meta data management to examining biological significance of results, is supported in the platform
• OneOmics suite features specific algorithms for metabolomics SWATH acquisition peak extraction and scoring, FDR analysis, and normalization, along with data dashboards for rapid assessment of data quality and results
• Enriched analytes can be mapped to biological pathways using the Bioreviews App