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Background: Amino acid metabolism is crucial for inflammatory processes during atherogenesis. The endogenous amino acid homoarginine is a robust biomarker for cardiovascular outcome and mortality with high levels being protective. However, the underlying mechanisms remain elusive. We investigated the effect of homoarginine supplementation on atherosclerotic plaque development with a particular focus on inflammation. Methods: Female ApoE-deficient mice were supplemented with homoarginine (14 mg/L) in drinking water starting 2 weeks before and continuing throughout a 6-week period of Western-type diet feeding. Control mice received normal drinking water. Immunohistochemistry and flow cytometry were used for plaque- and immunological phenotyping. T cells were characterized using mass spectrometry–based proteomics, by functional in vitro approaches, for example, proliferation and migration/chemotaxis assays as well as by super-resolution microscopy. Results: Homoarginine supplementation led to a 2-fold increase in circulating homoarginine concentrations. Homoarginine-treated mice exhibited reduced atherosclerosis in the aortic root and brachiocephalic trunk. A substantial decrease in CD3 + T cells in the atherosclerotic lesions suggested a T-cell–related effect of homoarginine supplementation, which was mainly attributed to CD4 + T cells. Macrophages, dendritic cells, and B cells were not affected. CD4 + T-cell proteomics and subsequent pathway analysis together with in vitro studies demonstrated that homoarginine profoundly modulated the spatial organization of the T-cell actin cytoskeleton and increased filopodia formation via inhibition of Myh9 (myosin heavy chain 9). Further mechanistic studies revealed an inhibition of T-cell proliferation as well as a striking impairment of the migratory capacities of T cells in response to relevant chemokines by homoarginine, all of which likely contribute to its atheroprotective effects. Conclusions: Our study unravels a novel mechanism by which the amino acid homoarginine reduces atherosclerosis, establishing that homoarginine modulates the T-cell cytoskeleton and thereby mitigates T-cell functions important during atherogenesis. These findings provide a molecular explanation for the beneficial effects of homoarginine in atherosclerotic cardiovascular disease.

Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput and robustness, a challenge that we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. In single runs of mammalian cells, a three-plex analysis of tryptic peptides quantified 7,700 proteins per channel. The Lys-N enzyme enables five-plex quantification at MS1 and MS2 level. Because the multiplex channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this feature and confidently quantifies close to 4,000 proteins in single cells with excellent reproducibility, while our workflow currently allows routine analysis of 80 single cells per day. The concept of a stable proteome still holds at this deeper proteome coverage.

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (https://github.com/MannLabs/alphapeptdeep). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition (https://github.com/MannLabs/PeptDeep-HLA).

Bacteria reorganize their physiology upon entry to stationary phase. What part of this reorganization improves starvation survival is a difficult question, because the change in physiology includes a global reorganization of the proteome, envelope and metabolism of the cell. In this work, we used several trade-offs between fast growth and long survival to statistically score over 2000 E. coli proteins for their global correlation with death rate. The combined ranking allowed us to narrow down the set of proteins that positively correlate with survival and validate the causal role of a subset of proteins. Remarkably, we found that important survival genes are related to the cell envelope, i.e., periplasm and outer membrane, because maintenance of envelope integrity of E. coli plays a crucial role during starvation. Our results uncover a new protective feature of the outer membrane that adds to the growing evidence that the outer membrane is not only a barrier that prevents abiotic substances from reaching the cytoplasm, but essential for bacterial proliferation and survival.Standfirst textA trade-off between the two major modes of bacterial lifestyle, growth and starvation can be explained by bacteria investing resources into the cell envelope to make it impermeable to ions, which improves their lifespan but comes at the expense of slowing down growth.HighlightsA trade-off between growth rate and death rate confines fitness of bacteria across environments.Analysis of proteome signatures in 126 conditions across five independent perturbations reveals the cell envelope as a key determinant of death rate.The trade-off can be abolished by changing environment to a low-salt, but osmo-balanced medium where cell envelope integrity is not limiting.

Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Whereas 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 ratiobased 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 minutes and 100,000 proteomes in less than two hours - thousand-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 (DDA) and data-independent acquisition (DIA). Additionally, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is available as an open-source Python package and as a GUI with a one-click installer and can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing1–3. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary4,5. Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions6,7. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material8, posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease.

The majority of microbes on earth, whether they live in the ocean, the soil or in animals, are not growing, but instead struggling to survive starvation1–6. Some genes and environmental conditions affecting starvation survival have been identified7–13, but despite almost a century of study14–16, we do not know which processes lead to irreversible loss of viability, which maintenance processes counteract them and how lifespan is determined from the balance of these opposing processes. Here, we used time-lapse microscopy to capture and characterize the cell death process of E. coli during carbon starvation for the first time. We found that a lack of nutrients results in the collapse of ion homeostasis, triggering a positive-feedback cascade of osmotic swelling and membrane permeabilization that ultimately results in lysis. Based on these findings, we hypothesized that ion transport is the major energetic requirement for starving cells and the primary determinant of the timing of lysis. We therefore developed a mathematical model that integrates ion homeostasis and ‘cannibalistic’ nutrient recycling from perished cells16,17 to predict lifespan changes under diverse conditions, such as changes of cell size, medium composition, and prior growth conditions. Guided by model predictions, we found that cell death during starvation could be dramatically slowed by replacing inorganic ions from the medium with a non-permeating osmoprotectant, removing the cost of ion homeostasis and preventing lysis. Our quantitative and predictive model explains how survival kinetics are determined in starvation and elucidates the mechanistic underpinnings of starvation survival.

Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs). In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.

While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis of fold changes is the standard method, but often, a doubly differential analysis of changes of changes is required. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene. EmpiRe is a quantitative approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements and used to quantify feature fold changes and their directions. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data. It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS) on distribution-free simulations and experimentally validated splicing events. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, quantitative and fast approach with high reliability and an excellent trade-off between sensitivity and precision in (doubly) differential analyses.

1AbstractMass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of proteins in a wide range of biological and biomedical applications. Protein changes need to be reliably derived from a large number of measured peptide intensities and their corresponding fold changes. These fold changes vary considerably for a given protein.Numerous instrumental setups aim to reduce this variability, while current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe (github.com/zimmerlab/MS-EmpiRe), which explicitly accounts for the noise underlying peptide fold changes. We derive dataset-specific, intensity-dependent empirical error distributions, which are used for individual weighing of peptide fold changes to detect differentially abundant proteins. The method requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. In a recently published proteome-wide benchmarking dataset, MS-EmpiRe doubles the number of correctly identified changing proteins at a correctly estimated FDR cutoff in comparison to state-of-the-art tools. We confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe provides rapid processing (< 2min) and is an easy to use, general-purpose tool.

To break down organismal fitness into molecular contributions, costs and benefits of cellular components must be analyzed in all phases of the organism's life cycle. Here, we establish the required quantitative approach for the death phase of the model bacterium Escherichia coli. We show that in carbon starvation, an exponential decay of viability emerges as a collective phenomenon, with viable cells recycling nutrients from cell carcasses to maintain viability. The observed collective death rate is determined by the maintenance rate of viable cells and the amount of nutrients recovered from dead cells. Using this relation, we study the cost of a wasteful enzyme during starvation and the benefit of the stress response sigma factor RpoS. While the enzyme increases maintenance and thereby the death rate, RpoS improves biomass recycling, decreasing the death rate. Our approach thus enables quantitative analyses of how cellular components affect the survival of non-growing cells.

To break down organismal fitness into molecular contributions, physiological costs and benefits of cellular components must be analyzed in all phases of the organism’s life cycle. For the model bacterium Escherichia coli, quantitative approaches to dissect its physiology are available for growth, but not for starvation conditions. We uncover that the exponential decay of viability during carbon starvation is a physiological steady state, in which death rate is quantitatively determined by the ratio of maintenance flux per viable cell to nutrient yield per perished cell. This fundamental relation permits quantitative insights into how environments and genetic elements affect bacterial survival, as exemplified by a study of the cost of a wasteful enzyme and the benefit of the stress response sigma factor rpoS. While the enzyme increases the maintenance flux and thereby the death rate, rpoS improves biomass recycling, decreasing the death rate. Our quantitative approach enables detailed analyses of how cellular components affect survival, and thus fitness, of non-growing cells.

Spectral libraries play a central role in the analysis of data independent acquisition (DIA) proteomics experiments. DIA experiments require spectral libraries, as most current methods cannot apply traditional peptide identification via database searching on DIA data. A central assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of an unmodified peptide in a particular parent ion charge state (peptide charge pair). However, we find that this is often not the case. We analyze a heterogeneous dataset of 440.000 MaxQuant - preprocessed peptide spectra from a QToF mass spectrometer, stemming from over 100 different LC-MS/MS runs. The dataset corresponds to 10.580 peptide charge pairs, which have each been measured and identified at least 20 times. We demonstrate that the same charged and unmodified peptide can fragment in multiple reproducible ways, even within the same LC-MS/MS run. We integrate multiple reference CIPs (MCIPs) in our model library and observe a >99% coverage of replicate fragmentation spectra for 95% of peptide charge pairs (using up to four CIPs). Using a single CIP (as in current spectral library approaches), we find >99% coverage for only 50% of the peptide charge pairs. Our approach achieves substantially greater sensitivity in comparison to the popular SpectraST library generation tool. Using randomized decoy spectra, we demonstrate that identification accuracy of the MCIP approach is improved by up to 12% compared to a single CIP approach. We test the MCIP approach on a SWATH data set and observe a ~30% increase in peptide recognition. We conclude that including MCIPs in spectral library approaches would yield increased sensitivity without compromising the false discovery rate.