MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

Phenotype Classification using Proteome Data in a Data-Independent Acquisition Tensor Format

A novel approach for phenotype prediction is developed for data-independent acquisition (DIA) mass spectrometric (MS) data without the need for peptide precursor identification using existing DIA software tools. The first step converts the DIA-MS data file into a new file format called DIA tensor (DIAT), which can be used for the convenient visualization of all the ions from peptide precursors and fragments. DIAT files can be fed directly into a deep neural network to predict phenotypes such as appearances of cats, dogs, and microscopic images. As a proof of principle, we applied this approach to 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in distinguishing malignant from benign samples. We further applied a refined model to classify thyroid nodules. Deep learning based on 492 training samples achieved an accuracy of 91.7% in an independent cohort of 216 test samples. This approach surpassed the deep-learning model based on peptide and protein matrices generated by OpenSWATH. In summary, we present a new strategy for DIA data analysis based on a novel data format called DIAT, which enables facile two-dimensional visualization of DIA proteomics data. DIAT files can be directly used for deep learning for biological and clinical phenotype classification. Future research will interpret the deep-learning models emerged from DIAT analysis.

DrawAlignR: An Interactive Tool for Across Run Chromatogram Alignment Visualization

Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here a novel and interactive tool for cross-run chromatogram alignment visualization (DrawAlignR) of data-independent acquisition (DIA) data is presented. Furthermore, a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm is developed. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm. DrawAlignR is an open-source web application using R Shiny that can be hosted using the source-code available at https://github.com/Roestlab/DrawAlignR.

Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.