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

Abstract

Recent advances in mass spectrometry–based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 min and 100,000 proteomes in less than 2 h, a 1000-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition and data-independent acquisition. In addition, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a graphical user interface with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Graphical Abstract

Highlights

• •

  Novel algorithmic concept for label-free quantification.

• •

  Linear dependence of execution time on number of samples.

• •

  Quantifies up to 100,000 samples in less than 2 h.

• •

  Higher accuracy and robustness than state-of-the-art MaxLFQ.

In Brief

Quantification of protein intensities is a crucial and challenging task in mass spectrometry–based proteomics. With directLFQ, we introduce an efficient algorithm that massively decreases execution time for large sample numbers while maintaining high accuracy. As it scales linearly with the number of samples, directLFQ unlocks high-quality quantification for arbitrarily large sample sizes. This is especially important for clinical proteomics as well as the rising field of single cell proteomics.