The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography–mass spectrometry (LC–MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

mpwR is an R package for a standardized comparison of mass spectrometry (MS)-based proteomic label-free workflows recorded by data-dependent or data-independent spectral acquisition. The user-friendly design allows easy access to compare the influence of sample preparation procedures, combinations of liquid chromatography (LC)-MS setups, as well as intra- and inter-software differences on critical performance measures across an unlimited number of analyses. mpwR supports outputs of commonly used software for bottom-up proteomics, such as ProteomeDiscoverer, Spectronaut, MaxQuant, and DIA-NN.

A major evolution from purely clinical diagnoses to biomarker supported clinical diagnosing has been occurring over the past years in neurology. High-throughput methods, such as next-generation sequencing and mass spectrometry-based proteomics along with improved neuroimaging methods, are accelerating this development. This calls for a consensus framework that is broadly applicable and provides a spot-on overview of the clinical validity of novel biomarkers. We propose a harmonized terminology and a uniform concept that stratifies biomarkers according to clinical context of use and evidence levels, adapted from existing frameworks in oncology with a strong focus on (epi)genetic markers and treatment context. We demonstrate that this framework allows for a consistent assessment of clinical validity across disease entities and that sufficient evidence for many clinical applications of protein biomarkers is lacking. Our framework may help to identify promising biomarker candidates and classify their applications by clinical context, aiming for routine clinical use of (protein) biomarkers in neurology.

This exploratory case–control study investigates the synaptic marker beta-synuclein in serum and plasma pTau181 in adults with Down syndrome (DS) with (sDS, n = 14) and without (aDS, n = 47) clinical symptoms of Alzheimer disease (AD) as well as euploid controls (n = 23). Beta-synuclein was higher in aDS and more pronounced in sDS (p < 0.0001), whereas pTau181 was only higher in sDS (p < 0.0001). Both markers showed good discriminatory power (area under the curve > 0.90) to distinguish symptomatic from asymptomatic AD. The data indicate that synaptic alterations belong to the earliest AD-associated events in DS and highlight the value of serum beta-synuclein as a potential early marker of AD.

We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.

The membrane protein seizure 6–like (SEZ6L) is a neuronal substrate of the Alzheimer’s disease protease BACE1, and little is known about its physiological function in the nervous system. Here, we show that SEZ6L constitutive knockout mice display motor phenotypes in adulthood, including changes in gait and decreased motor coordination. Additionally, SEZ6L knockout mice displayed increased anxiety-like behaviour, although spatial learning and memory in the Morris water maze were normal. Analysis of the gross anatomy and proteome of the adult SEZ6L knockout cerebellum did not reveal any major differences compared to wild type, indicating that lack of SEZ6L in other regions of the nervous system may contribute to the phenotypes observed. In summary, our study establishes physiological functions for SEZ6L in regulating motor coordination and curbing anxiety-related behaviour, indicating that aberrant SEZ6L function in the human nervous system may contribute to movement disorders and neuropsychiatric diseases.

Cerebral amyloid angiopathy (CAA) is an age-related condition and a major cause of intracerebral hemorrhage and cognitive decline that shows close links with Alzheimer's disease (AD). CAA is characterized by the aggregation of amyloid-β (Aβ) peptides and formation of Aβ deposits in the brain vasculature resulting in a disruption of the angioarchitecture. Capillaries are a critical site of Aβ pathology in CAA type 1 and become dysfunctional during disease progression. Here, applying an advanced protocol for the isolation of parenchymal microvessels from post-mortem brain tissue combined with liquid chromatography tandem mass spectrometry (LC–MS/MS), we determined the proteomes of CAA type 1 cases (n = 12) including a patient with hereditary cerebral hemorrhage with amyloidosis-Dutch type (HCHWA-D), and of AD cases without microvascular amyloid pathology (n = 13) in comparison to neurologically healthy controls (n = 12). ELISA measurements revealed microvascular Aβ1-40 levels to be exclusively enriched in CAA samples (mean: > 3000-fold compared to controls). The proteomic profile of CAA type 1 was characterized by massive enrichment of multiple predominantly secreted proteins and showed significant overlap with the recently reported brain microvascular proteome of patients with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary cerebral small vessel disease (SVD) characterized by the aggregation of the Notch3 extracellular domain. We found this overlap to be largely attributable to the accumulation of high-temperature requirement protein A1 (HTRA1), a serine protease with an established role in the brain vasculature, and several of its substrates. Notably, this signature was not present in AD cases. We further show that HTRA1 co-localizes with Aβ deposits in brain capillaries from CAA type 1 patients indicating a pathologic recruitment process. Together, these findings suggest a central role of HTRA1-dependent protein homeostasis in the CAA microvasculature and a molecular connection between multiple types of brain microvascular disease.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Proteomic biomarker discovery using formalin-fixed paraffin-embedded (FFPE) tissue requires robust workflows to support the analysis of large cohorts of patient samples. It also requires finding a reasonable balance between achieving a high proteomic depth and limiting the overall analysis time. To this end, we evaluated the merits of online coupling of single-use disposable trap column nanoflow liquid chromatography, high-field asymmetric-waveform ion-mobility spectrometry (FAIMS), and tandem mass spectrometry (nLC–FAIMS–MS/MS). The data show that ≤600 ng of peptide digest should be loaded onto the chromatographic part of the system. Careful characterization of the FAIMS settings enabled the choice of optimal combinations of compensation voltages (CVs) as a function of the employed LC gradient time. We found nLC–FAIMS–MS/MS to be on par with StageTip-based off-line basic pH reversed-phase fractionation in terms of proteomic depth and reproducibility of protein quantification (coefficient of variation ≤15% for 90% of all proteins) but requiring 50% less sample and substantially reducing sample handling. Using FFPE materials from the lymph node, lung, and prostate tissue as examples, we show that nLC–FAIMS–MS/MS can identify 5000–6000 proteins from the respective tissue within a total of 3 h of analysis time.

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

A current trend in proteomics is to acquire data in a “single-shot” by LC–MS/MS because it simplifies workflows and promises better throughput and quantitative accuracy than schemes that involve extensive sample fractionation. However, single-shot approaches can suffer from limited proteome coverage when performed by data dependent acquisition (ssDDA) on nanoflow LC systems. For applications where sample quantities are not scarce, this study shows that high proteome coverage can be obtained using a microflow LC–MS/MS system operating a 1 mm i.d. × 150 mm column, at a flow-rate of 50 μL/min and coupled to an Orbitrap HF-X mass spectrometer. The results demonstrate the identification of ∼9 000 proteins from 50 μg of protein digest from Arabidopsis roots, 7 500 from mouse thymus, and 7 300 from human breast cancer cells in 3 h of analysis time in a single run. The dynamic range of protein quantification measured by the iBAQ approach spanned 5 orders of magnitude and replicate analysis showed that the median coefficient of variation was below 20%. Together, this study shows that ssDDA by μLC–MS/MS is a robust method for comprehensive and large-scale proteome analysis and which may be further extended to more rapid chromatography and data independent acquisition approaches in the future.

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the "high-performance secretome protein enrichment with click sugars" (hiSPECS) method. To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS-induced neuroinflammation and to establish the cell type-resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer-linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type-specific biomarkers for CNS diseases.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported1, advances in mass-spectrometry-based proteomics2 have enabled increasingly comprehensive identification and quantification of the human proteome3,4,5,6. However, there have been few comparisons across species7,8, in stark contrast with genomics initiatives9. Here we use an advanced proteomics workflow—in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system—for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Microglial dysfunction is a key pathological feature of Alzheimer's disease (AD), but little is known about proteome-wide changes in microglia during the course of AD and their functional consequences. Here, we performed an in-depth and time-resolved proteomic characterization of microglia in two mouse models of amyloid β (Aβ) pathology, the overexpression APPPS1 and the knock-in APP-NL-G-F (APP-KI) model. We identified a large panel of Microglial Aβ Response Proteins (MARPs) that reflect heterogeneity of microglial alterations during early, middle and advanced stages of Aβ deposition and occur earlier in the APPPS1 mice. Strikingly, the kinetic differences in proteomic profiles correlated with the presence of fibrillar Aβ, rather than dystrophic neurites, suggesting that fibrillar Aβ may trigger the AD-associated microglial phenotype and the observed functional decline. The identified microglial proteomic fingerprints of AD provide a valuable resource for functional studies of novel molecular targets and potential biomarkers for monitoring AD progression or therapeutic efficacy.

The protease beta-site APP cleaving enzyme 1 (BACE1) has fundamental functions in the nervous system. Its inhibition is a major therapeutic approach in Alzheimer's disease, because BACE1 cleaves the amyloid precursor protein (APP), thereby catalyzing the first step in the generation of the pathogenic amyloid beta (Aβ) peptide. Yet, BACE1 cleaves numerous additional membrane proteins besides APP. Most of these substrates have been identified in vitro, but only few were further validated or characterized in vivo. To identify BACE1 substrates with in vivo relevance, we used isotope label-based quantitative proteomics of wild type and BACE1-deficient (BACE1 KO) mouse brains. This approach identified known BACE1 substrates, including Close homolog of L1 and contactin-2, which were found to be enriched in the membrane fraction of BACE1 KO brains. VWFA and cache domain-containing protein 1 (CACHD)1 and MAM domain-containing glycosylphosphatidylinositol anchor protein 1 (MDGA1), which have functions in synaptic transmission, were identified and validated as new BACE1 substrates in vivo by immunoblots using primary neurons and mouse brains. Inhibition or deletion of BACE1 from primary neurons resulted in a pronounced inhibition of substrate cleavage and a concomitant increase in full-length protein levels of CACHD1 and MDGA1. The BACE1 cleavage site in both proteins was determined to be located within the juxtamembrane domain. In summary, this study identifies and validates CACHD1 and MDGA1 as novel in vivo substrates for BACE1, suggesting that cleavage of both proteins may contribute to the numerous functions of BACE1 in the nervous system.