Matthias Mann

Metaproteomics analyses require protein sequence databases, yet the microbial proteins present in complex communities are not known in advance. Searching reference catalogs containing hundreds of millions of candidate sequences reduces identification sensitivity, while generating sample-specific databases through metagenomic sequencing adds time and cost. We introduce FastaLake, a computational workflow that uses full-length de novo peptide predictions by AlphaNovo to retrieve complete proteins from broad sequence resources and selects compact databases for conventional searching. In a defined eight-member community, FastaLake approached reference-search peptide yields without knowing its members. Using public sequence catalogs, FastaLake identified about as many peptides as matched metagenomic databases in two stool cohorts and 20–35% more in intestinal fluid at the same nominal 1% peptide false discovery rate. The resulting proteomes reveal personal functional profiles that persist across intestinal regions despite differences in leading species between intestinal fluid and stool. DNA, RNA and protein profiles agreed on broad differences between patients but only weakly on individual functions. All 726 acquisition-specific databases were smaller than the human reference proteome. Compact databases allowed non-specific searches of all 347 CapScan acquisitions, revealing histidine enrichment at peptide C termini in intestinal fluid. FastaLake enables metaproteomics analysis without matched metagenomic sequencing by constructing search databases from sequence resources and de novo peptide sequencing.

Summary: Background: Pre-operative triage of adnexal masses with serum CA-125, HE4, and ultrasound (O-RADS) has limited accuracy, contributing to unnecessary surgery. We develop and validate a first of its kind, biomarker panel-free plasma proteomic classifier that distinguishes malignant from benign adnexal masses from a single blood draw, operating directly on discovery-mode mass spectrometry. Methods: In this multicenter prospective observational study, plasma from women with adnexal masses and from healthy controls at a large urban academic center (discovery and four validation cohorts) and an external academic center from another Urban-suburban area (external validation) was analyzed by data-independent-acquisition mass spectrometry (Orbitrap Astral), quantifying >1,000 proteins per sample. Cancerous (malignant, borderline adnexal mass, or metastasis to ovary) versus benign adnexal masses were classified with ADAPT-MS (Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry), which retrains an ElasticNet model for each sample based on the proteins measured. The primary outcome was discrimination (area under the receiver operating characteristic curve [AUC]). Pre-operative O-RADS scores and serum CA-125 were compared and combined with proteomics on matched subsets. Findings: Using a discovery cohort (n=1062), an ADAPT-MS classifier, validated across five independent cohorts (n=623), achieved a summary AUC of 0.835 (cohort range 0.779–0.904). Among patients with pre-operative O-RADS (n=231), proteomics (AUC 0.852) was comparable to O-RADS alone (0.863) and combining them raised discrimination to 0.922. Proteomics outperformed serum CA-125 (0.915 versus 0.777; n=125) and proteome-derived ROMA-like and two-marker surrogates. Biologically, the classification-relevant proteins are enriched for host response proteins rather than tumor cell associated protein changes, comprising ECM remodeling, complement and innate immune response pathways. This explains good detection of tumor with heterogenous pathology and also metastasis to ovary. Interpretation: A single plasma proteomic measurement interpreted using adaptive machine learning discriminates malignant from benign adnexal masses with good internal and external validation, and is complementary to ultrasound, warranting prospective evaluation as a panel-free diagnostic adjunct to imaging. Funding: Funded by the Max Planck Society for the Advancement of Sciences (MM), an NIH R35 grant (EL), and support from Arthur and Nicole Herbst (EL). Research in context: Evidence before this study: Presence of protein biomarkers for ovarian cancer and in detail, adnexal mass triage is historically present e.g. by CA-125 or HE4. Proteomics studies have shown many more potential protein markers that might be used as biomarkers instead or additional to the presently used ones. Classification algorithms however do currently rely on single or few biomarkers only, limiting sensitivity and specificity. Added value of this study: To our knowledge, this study is the first example of a proteomics classifier that uses ML on discovery-based MS-proteomics data at the single-sample level and thus has the potential for diagnostic use. While in the past, proteomics has been used as a discovery tool and potential markers needed to be subjected to a follow-up assay development, we show by application to 5 different independent validation cohorts that a proteomics classifier based on the recently developed architecture Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry (ADAPT-MS) generalizes in performance between discovery and validation cohorts and can be applied at the single sample level. Implications of all the available evidence: We demonstrate that proteomics can be applied beyond discovery and classify adnexal masses together with ultrasound at an AUC of 0.922. This sets a precedence case for other diagnostic applications of proteomics, both in study design (plasma proteomics is always measured and used to best of ability in discovery and validation), and in single-sample application mode by ADAPT-MS.

Understanding tissue function requires connecting spatial cellular context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We develop multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export, mxDVP achieves >6000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings reveal a spectrum of endocrine heterogeneity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.

Background: Precision oncology has largely relied on genomic and transcriptomic sequencing to guide therapy selection in patients with advanced cancer. However, these molecular layers do not fully capture the functional state of tumors, as proteins largely represent the active effectors of cellular processes and signaling pathways. Integrating proteomic and phosphoproteomic measurements with genomic data has therefore emerged as an important objective in translational oncology. In routine clinical workflows, tumor biopsies are frequently processed using DNA/RNA extraction kits such as the Qiagen AllPrep system, which generates a protein-rich flowthrough that is typically discarded. Methods: Here we systematically evaluated whether the residual protein fraction generated during Qiagen AllPrep DNA/RNA extraction can serve as a reliable source for mass spectrometry–based proteomics in clinical tumor biopsies. Within the Copenhagen Prospective Personalized Oncology (CoPPO) precision oncology trial, residual protein fractions from metastatic cancer biopsies were recovered, precipitated, and processed using optimized workflows for liquid chromatography–mass spectrometry. We established procedures for protein recovery, digestion, and peptide cleanup, and evaluated multiple acquisition strategies including data-dependent acquisition (DDA) and data-independent acquisition (DIA). We further assessed sample stability during long-term storage, analytical reproducibility, scalability, and compatibility with phosphoproteomics. Results: Optimized single-shot workflows consistently generated deep proteomes from AllPrep-derived biopsy material, quantifying more than 10,000 protein groups in DIA analyses. Protein integrity was preserved in samples stored at − 80 °C for up to five years. The resulting proteomes retained tissue-associated biological signatures and quantified protein products corresponding to approximately 71% of genes represented on the FoundationOne ^® CDx panel. Phosphoproteomic analyses identified more than 10,000 phosphorylation sites associated with key oncogenic signaling pathways including AKT1, BRAF, and EGFR. Implementation of short liquid chromatography gradients enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality. Conclusions: Residual protein fractions from Qiagen AllPrep DNA/RNA extraction provide a scalable resource for deep proteomic and phosphoproteomic profiling from the same biopsy used for genomic and transcriptomic analyses. This workflow enables integrated proteogenomic characterization from a single clinical sample and provides a framework for integrated proteogenomic studies in precision oncology.

Mass spectrometry (MS)-based proteomics now routinely profiles proteomes at scale, but extracting biological insight and integrating complementary modalities remains challenging. Here, we present AlphaPeptTools, a Python package for the analysis of MS-proteomics data built on the AnnData data structure of the scverse. AlphaPeptTools implements scalable and modular strategies for MS-data ingestion, quality control, preprocessing, and statistical analysis while seamlessly integrating with the scverse ecosystem, unlocking multi-level, spatial, and multimodal analyses.

Mitochondrial DNA (mtDNA) mutations accumulate with age, but their mechanistic contribution to aging remains unclear. The classical mtDNA mutator mouse expresses a proofreading-deficient mtDNA polymerase (POLG ^D257A ) and accumulates mtDNA mutations across all tissues leading to premature aging. However, this model cannot resolve whether the aging phenotype results from systemic dysfunction or cell-intrinsic effects of somatic mtDNA mutations. To overcome this limitation, we generated Polg ^iMut mice allowing spatial and temporal control of POLG ^D257A expression. We demonstrate here that mtDNA mutations induced in cardiomyocytes cause progressive contractile dysfunction and respiratory chain deficiency in the heart without accompanying systemic pathology. Proteomic analyses link cardiac mosaic respiratory chain dysfunction to a progressive immune response, characterized by up-regulation of antigen-processing proteins and immune cell infiltration. In contrast, longevity-associated pathways are suppressed and uncoupled from mitochondrial and immune alterations, indicating distinct regulatory mechanisms. These findings demonstrate that mtDNA mutations can drive cardiac dysfunction and reveal a mechanistic link between mitochondrial dysfunction, immune responses, and aging.

BioStudies – one package for all the data supporting a study

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thousands of proteins and transcripts in the same cell, separating RNA from protein by tip-based C18 capture and pairing full-length RNA sequencing with latest-generation mass spectrometry. In HeLa cells, transcript and protein abundances agree on the broad ranking within a cell (r = 0.45), but do not co-vary across the population (r = 0.038). In pluripotency transitions, only a third of matched transcripts and proteins change synchronously, yet the transcription factors defining each state stay tightly co-regulated. Transcript variance is several-fold larger than protein variance, reflecting transcriptional bursting and mRNA sampling noise. The proteome is thus the stable, low-noise definition of cell state, while the transcriptome marks cellular transitions; consequently, the proteome defines cell-state from far fewer cells.

A large fraction of the uncharacterized proteome consists of microproteins. Viruses, rich in microproteins with potent activities, provide a unique system for exploring their functions and evolution. Here, we systematically annotate viral microproteins through a phenotype-first approach: construction of a >50,000-element small ORF library spanning the microprotein-coding potential of human-infecting viruses, followed by broad screens that enrich candidates for high-resolution profiling by Perturb-seq, mass spectrometry, AI-guided structure prediction, and mechanistic studies. This atlas identified >2,000 active microproteins that modulate host pathways through diverse mechanisms, including molecular mimicry, hijacking of key regulators, and altering subcellular localization. We further show the oncogenic potential of BNLF2b, an uncharacterized Epstein-Barr virus microprotein linked to nasopharyngeal carcinoma. Three principles emerge: viral microproteins are diverse and often multifunctional, show widespread convergence toward shared functions, and serve as reservoirs of evolutionary innovation. These findings elucidate how viral microproteins occupy a large functional landscape, establish them as versatile tools for host manipulation, and provide a framework for functional annotation of uncharacterized proteins, contributing toward predictive systems virology.