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Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed. Graphical Abstract SWATH‐MS combines deep proteome coverage with quantitative consistency and accuracy and is often the method of choice for personalized medicine, drug screens or systems biology. This tutorial provides guidelines on how to set up SWATH‐MS experiments, perform the mass spectrometric measurements and analyse the data.

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis‐ SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood‐based biomarker studies. Synopsis The degree and origins of the abundance variability of 342 human plasma proteins are analyzed by a longitudinal twin design and SWATH mass spectrometry. The results suggest genetic control and longitudinal variation affect protein levels and biological processes to different degrees. We used the highly accurate and reproducible SWATH mass spectrometry technique to quantify 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years. The observed total quantitative variability of human plasma proteome is dissected to its root causes, genes, environment and longitudinal factors. The roles of the heritable, environmental and longitudinal determinants in controlling plasma protein levels are different for different proteins and functional clusters, strongly suggesting that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. We further identified 13 cis ‐SNPs significantly influencing the level of specific plasma proteins as protein quantitative trait loci (pQTLs), and five of them are associated with gene expression QTLs (eQTLs) in human tissues. Graphical Abstract The degree and origins of the abundance variability of 342 human plasma proteins are analyzed by a longitudinal twin design and SWATH mass spectrometry. The results suggest genetic control and longitudinal variation affect protein levels and biological processes to different degrees.

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer , for custom exploration of the HEK293 proteome modularity. Synopsis The study presents an integrated framework for targeted, complex‐centric analysis based on size exclusion chromatography (SEC) and SWATH/DIA mass spectrometry. The workflow facilitates the parallel detection of hundreds of protein complexes and their variants at high selectivity and under error‐control. The presented workflow is based on the concept of complex‐centric proteome profiling and combines size exclusion chromatography (SEC) with SWATH/DIA mass spectrometry. The implementation of the complex‐centric data analysis is supported by the computational framework CCprofiler . Application of the SEC‐SWATH‐MS workflow to HEK293 cells led the detection and quantification of subunit distribution of 462 distinct protein complexes containing 2,127 proteins and identified novel complex variants such as assembly intermediates. The interactive platform, SECexplorer is presented to support custom complex‐centric exploration of SEC‐SWATH‐MS datasets. Graphical Abstract The study presents an integrated framework for targeted, complex‐centric analysis based on size exclusion chromatography (SEC) and SWATH/DIA mass spectrometry. The workflow facilitates the parallel detection of hundreds of protein complexes and their variants at high selectivity and under error‐control.

There remains a significant gap in our quantitative understanding of crosstalk between apoptosis and necroptosis pathways. By employing the SWATH-MS technique, we quantified absolute amounts of up to thousands of proteins in dynamic assembling/deassembling of TNF signaling complexes. Combining SWATH-MS-based network modeling and experimental validation, we found that when RIP1 level is below ∼1000 molecules/cell (mpc), the cell solely undergoes TRADDdependent apoptosis. When RIP1 is above ∼1000 mpc, pro-caspase-8 and RIP3 are recruited to necrosome respectively with linear and nonlinear dependence on RIP1 amount, which well explains the co-occurrence of apoptosis and necroptosis and the paradoxical observations that RIP1 is required for necroptosis but its increase down-regulates necroptosis. Higher amount of RIP1 (>∼46,000 mpc) suppresses apoptosis, leading to necroptosis alone. The relation between RIP1 level and occurrence of necroptosis or total cell death is biphasic. Our study provides a resource for encoding the complexity of TNF signaling and a quantitative picture how distinct dynamic interplay among proteins function as basis sets in signaling complexes, enabling RIP1 to play diverse roles in governing cell fate decisions.

Anticancer peptides (ACPs) are rising as a new strategy for cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer‐generated peptide library inspired by alpha‐lactalbumin, a milk protein with known anticancer properties. A total of 2688 distinct peptides, 5–25 amino acids in length, are generated from alpha‐lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate peptides ALA‐A1 and ALA‐A2. In vitro screening against five human cancer cell lines supports ALA‐A2 as the positive hit. ALA‐A2 selectively kills A549 lung cancer cells in a dose‐dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions‐proteomics and functional validation reveal that ALA‐A2 induces autophagy to mediate lung cancer cell death. This approach to identify ALA‐A2 is time and cost‐effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA‐A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development. This study describes a novel strategy for searching for a new anticancer peptide (ACP) by integrating the computational peptide library of alpha‐lactalbumin with downstream in silico machine learning‐based screening and in vitro experimental validation. The novel ACP, so‐called ALA‐A2, specifically kills lung adenocarcinoma cells through autophagy mediated cell death.

Background Cancer cachexia (cancer‐induced muscle wasting) is found in a subgroup of cancer patients leaving the patients with a poor prognosis for survival due to a lower tolerance of the chemotherapeutic drug. The cause of the muscle wasting in these patients is not fully understood, and no predictive biomarker exists to identify these patients early on. Skeletal muscle loss is an inevitable consequence of advancing age. As cancer frequently occurs in old age, identifying and differentiating the molecular mechanisms mediating muscle wasting in cancer cachexia vs. age‐related sarcopenia are a challenge. However, the ability to distinguish between them is critical for early intervention, and simple measures of body weight may not be sufficiently sensitive to detect cachexia early. Methods We used a range of omics approaches: (i) undepleted proteome was quantified using advanced high mass accuracy mass spectrometers in SWATH‐MS acquisition mode; (ii) phospho epitopes were quantified using protein arrays; and (iii) morphology was assessed using fluorescent microscopy. Results We quantified the soluble proteome of muscle biopsies from cancer cachexia patients and compared them with cohorts of cancer patients and healthy individuals with and without age‐related muscle loss (aka age‐related sarcopenia). Comparing the proteomes of these cohorts, we quantified changes in muscle contractile myosins and energy metabolism allowing for a clear identification of cachexia patients. In an in vitro time lapse experiment, we mimicked cancer cachexia and identified signal transduction pathways governing cell fusion to play a pivotal role in preventing muscle regeneration. Conclusions The work presented here lays the foundation for further understanding of muscle wasting diseases and holds the promise of overcoming ambiguous weight loss as a measure for defining cachexia to be replaced by a precise protein signature.

Primary Sjögren’s syndrome (pSS) is a complex heterogeneous disease characterized by a wide spectrum of glandular and extra-glandular manifestations. In this pilot study, a SWATH-MS approach was used to monitor extracellular vesicles-enriched saliva (EVs) sub-proteome in pSS patients, to compare it with whole saliva (WS) proteome, and assess differential expressed proteins between pSS and healthy control EVs samples. Comparison between EVs and WS led to the characterization of compartment-specific proteins with a moderate degree of overlap. A total of 290 proteins were identified and quantified in EVs from healthy and pSS patients. Among those, 121 proteins were found to be differentially expressed in pSS, 82% were found to be upregulated, and 18% downregulated in pSS samples. The most representative functional pathways associated to the protein networks were related to immune-innate response, including several members of S100 protein family, annexin A2, resistin, serpin peptidase inhibitors, azurocidin, and CD14 monocyte differentiation antigen. Our results highlight the usefulness of EVs for the discovery of novel salivary-omic biomarkers and open novel perspectives in pSS for the identification of proteins of clinical relevance that could be used not only for the disease diagnosis but also to improve patients’ stratification and treatment-monitoring. Data are available via ProteomeXchange with identifier PXD025649.

The blood–brain barrier (BBB), which is comprised of brain capillary endothelial cells, plays a pivotal role in the transport of drugs from the blood to the brain. Therefore, an analysis of proteins in the endothelial cells, such as transporters and tight junction proteins, which contribute to BBB function, is important for the development of therapeutics for the treatment of brain diseases. However, gene transfection into the vascular endothelial cells of the BBB is fraught with difficulties, even in vitro. We report herein on the development of lipid nanoparticles (LNPs), in which mRNA is encapsulated in a nano-sized capsule composed of a pH-activated and reductive environment-responsive lipid-like material (ssPalm). We evaluated the efficiency of mRNA delivery into non-polarized human brain capillary endothelial cells, hCMEC/D3 cells. The ssPalm LNPs permitted marker genes (GFP) to be transferred into nearly 100% of the cells, with low toxicity in higher concentration. A proteomic analysis indicated that the ssPalm-LNP had less effect on global cell signaling pathways than a Lipofectamine MessengerMAX/GFP-encoding mRNA complex (LFN), a commercially available transfection reagent, even at higher mRNA concentrations.

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, is characterized by executive dysfunction and memory impairment mediated by the accumulation of extracellular amyloid-β peptide (Aβ) and intracellular hyperphosphorylated tau protein. The hippocampus (HIPP) is essential for memory formation and is involved in early stages of disease. In fact, hippocampal atrophy is used as an early biomarker of neuronal injury and to evaluate disease progression. It is not yet well-understood whether changes in hippocampal volume are due to neuronal or glial loss. The aim of the study was to assess hippocampal atrophy and/or gliosis using unbiased stereological quantification and to obtain hippocampal proteomic profiles related to neurodegeneration and gliosis. Hippocampal volume measurement, stereological quantification of NeuN-, Iba-1- and GFAP-positive cells, and sequential window acquisition of all theoretical mass spectrometry (SWATH-MS) analysis were performed in AD and non-AD cases. Reduced hippocampal volume was identified using the Cavalieri probe, particularly in the CA1 region, where it correlated with neuronal loss and astrogliosis. A total of 102 downregulated and 47 upregulated proteins were identified in the SWATH-MS analysis after restrictive filtering based on an FC > 1.5 and p value < 0.01. The Hsp90 family of chaperones, particularly BAG3 and HSP90AB1, are closely related to astrocytes, indicating a possible role in degrading Aβ and tau through chaperone-mediated autophagy.