Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Ammar, Constantin; Schessner, Julia; Willems, Sander; Michaelis, André C.; Mann, Matthias

doi:10.1016/j.mcpro.2023.100581

Cited by 13 publications

(7 citation statements)

References 41 publications

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“…Since normalization is crucial for reliable quantification by compensating for differences between injected peptide amounts, we removed rows from the DIA-NN report that contained peptides identified in ME samples before re-normalizing our datasets. Next, we examined different ways of data normalization for low-input DIA data, including MaxLFQ from the R package of DIA-NN (26), iq normalization (39) and the recently published directLFQ package in Python (40) ( Fig. S6A ).…”

Section: Resultsmentioning

confidence: 99%

“…This indicates that DIA-ME can detect biologically relevant consequences of mild treatments in single cells. Of note, these results were reliant on proper normalization to ensure unaffected protein quantification, where the recently published directLFQ algorithm (40) showed the best quantitative accuracy among several tested normalization strategies ( Fig. S6 ).…”

Section: Discussionmentioning

confidence: 98%

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Enhanced feature matching in single-cell proteomics characterizes response to IFN-γ and reveals co-existence of different cell states

Krull,

Ali,

Krijgsveld

2024

Preprint

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Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here present DIA-ME, a data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We evaluate the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we observe co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to- implement strategy for single-cell proteomics.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 98%

Enhanced feature matching in single-cell proteomics characterizes response to IFN-γ and reveals co-existence of different cell states

Krull,

Ali,

Krijgsveld

2024

Preprint

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“…We previously described the use of these controls with median, Log 2 normalization, and batch adjustment of peptide signal in a recent quantitative proteomics dataset focused on the phenotypic assessment of Alzheimer’s disease in CSF 73 in additional studies of aging and disease listed on PanoramaWeb in the “Reference Information” section. In recent years, we have most often used median normalization at the peptide-level 78 but have also examined maxLFQ 79 and directLFQ 80 for normalization of peptide and protein-level data. Although originally developed for RNA datasets, we have found, as other groups have indicated, that linear models for microarrays (LIMMA) 81,82 and ComBat 83,84 reduce the contribution of technical variation in proteomics datasets.…”

Section: Discussionmentioning

confidence: 99%

A framework for quality control in quantitative proteomics

Tsantilas,

Merrihew,

Robbins

et al. 2024

Preprint

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A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

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“…Data-independent acquisition (DIA) is becoming increasingly popular in bottom-up proteomics as specialized instrumentation and software become readily available (reviewed in refs − ). The performance and quantification accuracy of DIA workflows are typically benchmarked using mixtures of total proteome digests from 2 to 4 species, as first described by Kuharev et al and later introduced as the LFQbench package by Navarro et al , Analyzing the series of samples with predefined fold changes between individual proteomes allows comprehensive assessment of the quantification accuracy at the proteome-wide scale. − …”

Section: Introductionmentioning

confidence: 99%

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

Jumel,

Shevchenko

2024

J. Proteome Res.

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We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

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Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Cited by 13 publications

References 41 publications

Enhanced feature matching in single-cell proteomics characterizes response to IFN-γ and reveals co-existence of different cell states

Enhanced feature matching in single-cell proteomics characterizes response to IFN-γ and reveals co-existence of different cell states

A framework for quality control in quantitative proteomics

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

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