IceR improves proteome coverage and data completeness in global and single-cell proteomics

AbstractLabel-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

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IceR improves proteome coverage and data completeness in global and single-cell proteomics

10.1101/2020.11.01.363101 ◽

2020 ◽

Author(s):

Mathias Kalxdorf ◽

Torsten Müller ◽

Oliver Stegle ◽

Jeroen Krijgsveld

Keyword(s):

Single Cell ◽

Large Scale ◽

Missing Values ◽

Peptide Identification ◽

R Package ◽

Protein Quantification ◽

Developmental Trajectory ◽

Label Free ◽

Proteomics Data ◽

Data Completeness

AbstractLabel-free proteomics by data-dependent acquisition (DDA) enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR, an efficient and user-friendly quantification workflow that combines high identification rates of DDA with low missing value rates similar to DIA. Specifically, IceR uses ion current information in DDA data for a hybrid peptide identification propagation (PIP) approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. We demonstrate greatly improved quantification sensitivity on published plasma and single-cell proteomics data, enhancing the number of reliably quantified proteins, improving discriminability between single-cell populations, and allowing reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

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MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Molecular & Cellular Proteomics ◽

10.1074/mcp.ra120.002105 ◽

2020 ◽

Vol 19 (10) ◽

pp. 1706-1723 ◽

Cited By ~ 1

Author(s):

Ting Huang ◽

Meena Choi ◽

Manuel Tzouros ◽

Sabrina Golling ◽

Nikhil Janak Pandya ◽

...

Keyword(s):

Large Scale ◽

Missing Values ◽

High Efficiency ◽

Protein Quantification ◽

Label Free ◽

Unbalanced Designs ◽

Biological Mixtures ◽

Proteome Discoverer ◽

General Statistical ◽

Differentially Abundant Proteins

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

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PANDA: A comprehensive and flexible tool for proteomics data quantitative analysis

10.1101/332957 ◽

2018 ◽

Author(s):

Cheng Chang ◽

Chaoping Guo ◽

Yuqing Ding ◽

Kaikun Xu ◽

Mingfei Han ◽

...

Keyword(s):

Quantitative Proteomics ◽

Computation Time ◽

Peptide Identification ◽

High Accuracy ◽

Protein Quantification ◽

Label Free ◽

Proteomics Data ◽

Flexible Tool ◽

Accuracy And Precision ◽

User Friendly

ABSTRACTSummaryAs the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on two complex da-tasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop ap-plication tool with user-friendly interfaces.AvailabilityPANDA is freely available for download at https://sourceforge.net/projects/panda-tools/[email protected] and [email protected]

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A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2001906117 ◽

2020 ◽

Vol 117 (31) ◽

pp. 18412-18423 ◽

Cited By ~ 2

Author(s):

Chia-Chen Hsu ◽

Jiabao Xu ◽

Bas Brinkhof ◽

Hui Wang ◽

Zhanfeng Cui ◽

...

Keyword(s):

Stem Cells ◽

Stem Cell ◽

Single Cell ◽

Raman Spectra ◽

Large Scale ◽

Developmental Stages ◽

Single Cells ◽

Classification Model ◽

Label Free ◽

Machine Learning Classification

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

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Label-free single-cell protein quantification using a drop-based mix-and-read system

Scientific Reports ◽

10.1038/srep12756 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 22

Author(s):

Alireza Abbaspourrad ◽

Huidan Zhang ◽

Ye Tao ◽

Naiwen Cui ◽

Haruichi Asahara ◽

...

Keyword(s):

Single Cell ◽

Single Cell Protein ◽

Protein Quantification ◽

Cell Protein ◽

Label Free

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PolySTest: Robust statistical testing of proteomics data with missing values improves detection of biologically relevant features

10.1101/765818 ◽

2019 ◽

Cited By ~ 1

Author(s):

Veit Schwämmle ◽

Christina E Hagensen ◽

Adelina Rogowska-Wrzesinska ◽

Ole N. Jensen

Keyword(s):

Mass Spectrometry ◽

Large Scale ◽

Missing Values ◽

Statistical Tests ◽

Ground Truth ◽

Statistical Testing ◽

Molecular Networks ◽

Proteomics Data ◽

Biologically Relevant ◽

Data Browsing

AbstractStatistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss Test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss Test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10%-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss Test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

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Missing Value Imputation using XGboost for Label-Free Mass Spectrometry-Based Proteomics Data

10.1101/2021.04.08.438945 ◽

2021 ◽

Author(s):

Jian Song ◽

Changbin Yu

Keyword(s):

Mass Spectrometry ◽

High Performance ◽

Missing Values ◽

Mean Squared Error ◽

Learning Algorithm ◽

Pearson Correlation ◽

Data Matrix ◽

Label Free ◽

Proteomics Data ◽

Benchmark Datasets

AbstractThe label-free mass spectrometry-based proteomics data inevitably suffer from the problem of missing values. The existence of missing values prevents the downstream analyses which need a complete data matrix. Our motivation is to introduce the state-of-art machine learning algorithm XGboost to realize a method of imputation which can improve the accuracy of imputation. But in practical, XGboost has many parameters need to be tuned to deliver on its potential high performance. Although cross validation may find the best parameters, it is much time-consuming. Alternatively, we empirically determined the parameters to two kinds of base learners of XGboost. To explore the robustness and performance of XGboost based imputation with predetermined parameters, we conducted tests on three benchmark datasets. As a comparative, six common imputation methods were also experimented in terms of normalized root mean squared error and Pearson correlation coefficient. The comparative experimental results indicated that the XGboost based imputation method using the linear base learner is competitive to or out-performs its competitors, including the random forest based imputation, by achieving smaller imputation errors and better structure preservation under the empirical parameters for the three benchmark datasets.

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FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

eLife ◽

10.7554/elife.58783 ◽

2020 ◽

Vol 9 ◽

Author(s):

Christoph N Schlaffner ◽

Konstantin Kahnert ◽

Jan Muntel ◽

Ruchi Chauhan ◽

Bernhard Y Renard ◽

...

Keyword(s):

Protein Modification ◽

Large Scale ◽

Software Tool ◽

Label Free ◽

Anaphase Promoting Complex ◽

Proteomics Data ◽

Single Experiment ◽

Post Translational Modifications ◽

Data Independent Acquisition ◽

Modified Peptides

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

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Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler

10.1101/2020.09.24.311605 ◽

2020 ◽

Author(s):

Matthew The ◽

Lukas Käll

Keyword(s):

Missing Values ◽

Shotgun Proteomics ◽

Protein Quantification ◽

Protein Abundance ◽

Posterior Distributions ◽

Label Free ◽

Differential Abundance ◽

Complicated Process ◽

Python Package ◽

Different Parts

AbstractProtein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail and (5) verify that the hyperparameter estimations are sensible.

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MaxQuant software for ion mobility enhanced shotgun proteomics

10.1101/651760 ◽

2019 ◽

Cited By ~ 6

Author(s):

Nikita Prianichnikov ◽

Heiner Koch ◽

Scarlet Koch ◽

Markus Lubeck ◽

Raphael Heilig ◽

...

Keyword(s):

Ion Mobility ◽

Retention Time ◽

Signal Intensity ◽

Feature Detection ◽

Dynamic Range ◽

Shotgun Proteomics ◽

Detection Algorithm ◽

Protein Quantification ◽

Label Free ◽

Proteomics Data

SummaryIon mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

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