Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells

AbstractEfficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

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DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Nature Communications ◽

10.1038/s41467-021-26979-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ronghui Lou ◽

Weizhen Liu ◽

Rongjie Li ◽

Shanshan Li ◽

Xuming He ◽

...

Keyword(s):

Data Mining ◽

In Silico ◽

Deep Neural Network ◽

Learning Models ◽

Spectral Library ◽

Proteome Coverage ◽

Data Independent Acquisition ◽

User Access ◽

Model Training ◽

Egf Signaling

AbstractPhosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

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DeepPhospho: Accelerate DIA phosphoproteome profiling by Deep Learning

10.21203/rs.3.rs-393214/v1 ◽

2021 ◽

Author(s):

Wenqing Shui ◽

Ronghui Lou ◽

Weizhen Liu ◽

Rongjie Li ◽

Shanshan Li ◽

...

Keyword(s):

Neural Network ◽

Data Mining ◽

Deep Learning ◽

Deep Neural Network ◽

Learning Models ◽

Spectral Library ◽

Proteome Coverage ◽

Data Independent Acquisition ◽

User Access ◽

Egf Signaling

Abstract Phosphoproteomics integrating data-independent acquisition (DIA) has enabled deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a novel deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we established a new DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expanded the phosphoproteome coverage while maintaining high quantification performance, which led to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server to facilitate user access to predictions and library generation.

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Use of Linear Ion Traps in Data-Independent Acquisition Methods Benefits Low-Input Proteomics

Analytical Chemistry ◽

10.1021/acs.analchem.1c01885 ◽

2021 ◽

Author(s):

Eva Borràs ◽

Olga Pastor ◽

Eduard Sabidó

Keyword(s):

Ion Traps ◽

Low Input ◽

Data Independent Acquisition ◽

Linear Ion Traps

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Arabidopsis proteome and the mass spectral assay library

Scientific Data ◽

10.1038/s41597-019-0294-0 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Huoming Zhang ◽

Pei Liu ◽

Tiannan Guo ◽

Huayan Zhao ◽

Dalila Bensaddek ◽

...

Keyword(s):

Quantitative Proteomics ◽

Model Organism ◽

Massively Parallel ◽

Spectral Library ◽

Mass Spectral ◽

Novel Proteins ◽

Data Independent Acquisition ◽

Arabidopsis Proteome ◽

Expression Atlas ◽

Targeted Approach

AbstractArabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.

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Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Molecular & Cellular Proteomics ◽

10.1074/mcp.ra119.001714 ◽

2019 ◽

Vol 19 (1) ◽

pp. 181-197 ◽

Cited By ~ 12

Author(s):

Katalin Barkovits ◽

Sandra Pacharra ◽

Kathy Pfeiffer ◽

Simone Steinbach ◽

Martin Eisenacher ◽

...

Keyword(s):

Relative Quantification ◽

Spectral Library ◽

Data Independent Acquisition

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A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients

10.1101/860643 ◽

2019 ◽

Cited By ~ 2

Author(s):

Dorte B. Bekker-Jensen ◽

Ana Martínez del Val ◽

Sophia Steigerwald ◽

Patrick Rüther ◽

Kyle Fort ◽

...

Keyword(s):

Mass Spectrometer ◽

Ion Mobility ◽

Rat Tissues ◽

Label Free ◽

Mass Spectrometers ◽

Proteome Coverage ◽

Data Independent Acquisition ◽

Orbitrap Mass Spectrometer ◽

Peptide Precursors ◽

Free Quantification

ABSTRACTState-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 unique peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 minute LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

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DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

10.1101/2021.04.22.440949 ◽

2021 ◽

Author(s):

Mingxuan Gao ◽

Wenxian Yang ◽

Chenxin Li ◽

Yuqing Chang ◽

Yachen Liu ◽

...

Keyword(s):

Data Analysis ◽

State Of The Art ◽

Data Driven ◽

Spectral Library ◽

Software Suite ◽

High Coverage ◽

Discriminative Models ◽

Data Independent Acquisition ◽

Comprehensive Information ◽

Negative Controls

We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis.

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Global proteomic analyses of human cytotrophoblast differentiation/invasion

Development ◽

10.1242/dev.199561 ◽

2021 ◽

Author(s):

Hao Chen ◽

Katherine E. Williams ◽

Elaine Y. Kwan ◽

Mirhan Kapidzic ◽

Kenisha A. Puckett ◽

...

Keyword(s):

Growth And Development ◽

Second Trimester ◽

Spectral Library ◽

Time Points ◽

Culture Time ◽

Data Independent Acquisition ◽

Molecular Features ◽

New Information ◽

Acquisition Strategy ◽

In Pregnancy

During human pregnancy, cytotrophoblasts (CTBs) from the placenta differentiate into specialized subpopulations that play critical roles in proper fetal growth and development. A subset of these CTBs differentiate along an invasive pathway, penetrating the decidua and anchoring the placenta to the uterus. A critical hurdle in pregnancy is the ability of these cells to migrate, invade, and remodel spiral arteries, ensuring adequate blood flow to nourish the developing fetus. While advances continue in describing the molecular features regulating the differentiation of these cells, assessment of their global proteomic changes at midgestation remain undefined. Here, using sequential window acquisition of all theoretical fragment-ion spectra (SWATH), a data-independent acquisition strategy, we characterized the protein repertoire of second trimester human CTBs during their differentiation towards an invasive phenotype. This mass spectrometry-based approach allowed identification of 3,026 proteins across four culture time points corresponding to sequential stages of differentiation, confirming the expression dynamics of established molecules and offering new information into other pathways involved. The availability of a SWATH CTB global spectral library serves as a beneficial resource for hypothesis generation and a foundation for further understanding of CTB differentiation dynamics.

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Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics

10.1101/152744 ◽

2017 ◽

Cited By ~ 4

Author(s):

Ryan Peckner ◽

Samuel A Myers ◽

Jarrett D Egertson ◽

Richard S Johnson ◽

Jennifer G. Abelin ◽

...

Keyword(s):

Mass Spectrometry ◽

Open Source Software ◽

Software Tool ◽

Nucleotide Polymorphisms ◽

Spectral Library ◽

New Paradigm ◽

Single Nucleotide ◽

Data Independent Acquisition ◽

Targeted Analysis ◽

Peptide Precursors

AbstractMass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

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