Microglia undergo molecular and functional adaptations to dark and light phases in male laboratory mice.

Microglia are increasingly recognized to contribute to brain health and disease. Preclinical studies using laboratory rodents are essential to advance our understanding of the physiological and pathophysiological functions of these cells in the central nervous system. Rodents are nocturnal animals, and they are mostly maintained in a defined light-dark cycle within animal facilities, with many laboratories investigating microglial molecular and functional profiles during the animals' light (sleep) phase. However, only a few studies have considered possible differences in microglial functions between the active and sleep phases. Based on initial evidence suggesting that microglial intrinsic clock genes can affect their phenotypes, we sought to investigate differences in transcriptional, proteotype and functional profiles of microglia between light (sleep) and dark (active) phases, and how these changes are affected in pathological models. We found marked transcriptional and proteotype differences between microglia harvested from male mice during the light or dark phase. Amongst others, these differences related to genes and proteins associated with immune responses, motility, and phagocytosis, which were reflected by functional alterations in microglial synaptic pruning and response to bacterial stimuli. Possibly accounting for such changes, we found RNA and protein regulation in SWI/SNF and NuRD chromatin remodeling complexes between light and dark phases. Importantly, we also show that the time of microglial sample collection influences the nature of microglial transcriptomic changes in a model of immune-mediated neurodevelopmental disorders. Our findings emphasize the importance of considering diurnal factors in studying microglial cells and indicate that implementing a circadian perspective is pivotal for advancing our understanding of their physiological and pathophysiological roles in brain health and disease.

Probabilistic pathway-based multimodal factor analysis.

MOTIVATION: Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. RESULTS: We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes.

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

Proteomic Profiling of Advanced Melanoma Patients to Predict Therapeutic Response to Anti-PD-1 Therapy.

PURPOSE: Despite high clinical need, there are no biomarkers that accurately predict the response of patients with metastatic melanoma to anti-PD-1 therapy. EXPERIMENTAL DESIGN: In this multicenter study, we applied protein depletion and enrichment methods prior to various proteomic techniques to analyze a serum discovery cohort (n = 56) and three independent serum validation cohorts (n = 80, n = 12, n = 17). Further validation analyses by literature and survival analysis followed. RESULTS: We identified several significantly regulated proteins as well as biological processes such as neutrophil degranulation, cell-substrate adhesion, and extracellular matrix organization. Analysis of the three independent serum validation cohorts confirmed the significant differences between responders (R) and nonresponders (NR) observed in the initial discovery cohort. In addition, literature-based validation highlighted 30 markers overlapping with previously published signatures. Survival analysis using the TCGA database showed that overexpression of 17 of the markers we identified correlated with lower overall survival in patients with melanoma. CONCLUSIONS: Ultimately, this multilayered serum analysis led to a potential marker signature with 10 key markers significantly altered in at least two independent serum cohorts: CRP, LYVE1, SAA2, C1RL, CFHR3, LBP, LDHB, S100A8, S100A9, and SAA1, which will serve as the basis for further investigation. In addition to patient serum, we analyzed primary melanoma tumor cells from NR and found a potential marker signature with four key markers: LAMC1, PXDN, SERPINE1, and VCAN.

PIM1 controls GBP1 activity to limit self-damage and to guard against pathogen infection.

Disruption of cellular activities by pathogen virulence factors can trigger innate immune responses. Interferon-γ (IFN-γ)-inducible antimicrobial factors, such as the guanylate binding proteins (GBPs), promote cell-intrinsic defense by attacking intracellular pathogens and by inducing programmed cell death. Working in human macrophages, we discovered that GBP1 expression in the absence of IFN-γ killed the cells and induced Golgi fragmentation. IFN-γ exposure improved macrophage survival through the activity of the kinase PIM1. PIM1 phosphorylated GBP1, leading to its sequestration by 14-3-3σ, which thereby prevented GBP1 membrane association. During Toxoplasma gondii infection, the virulence protein TgIST interfered with IFN-γ signaling and depleted PIM1, thereby increasing GBP1 activity. Although infected cells can restrain pathogens in a GBP1-dependent manner, this mechanism can protect uninfected bystander cells. Thus, PIM1 can provide a bait for pathogen virulence factors, guarding the integrity of IFN-γ signaling.

Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis.

Myelofibrosis is a hematopoietic stem cell disorder belonging to the myeloproliferative neoplasms. Myelofibrosis patients frequently carry driver mutations in either JAK2 or Calreticulin (CALR) and have limited therapeutic options. Here, we integrate ex vivo drug response and proteotype analyses across myelofibrosis patient cohorts to discover targetable vulnerabilities and associated therapeutic strategies. Drug sensitivities of mutated and progenitor cells were measured in patient blood using high-content imaging and single-cell deep learning-based analyses. Integration with matched molecular profiling revealed three targetable vulnerabilities. First, CALR mutations drive BET and HDAC inhibitor sensitivity, particularly in the absence of high Ras pathway protein levels. Second, an MCM complex-high proliferative signature corresponds to advanced disease and sensitivity to drugs targeting pro-survival signaling and DNA replication. Third, homozygous CALR mutations result in high endoplasmic reticulum (ER) stress, responding to ER stressors and unfolded protein response inhibition. Overall, our integrated analyses provide a molecularly motivated roadmap for individualized myelofibrosis patient treatment.

Mapping the dynamic high-density lipoprotein synapse.

BACKGROUND AND AIMS: Heterogeneous high-density lipoprotein (HDL) particles, which can contain hundreds of proteins, affect human health and disease through dynamic molecular interactions with cell surface proteins. How HDL mediates its long-range signaling functions and interactions with various cell types is largely unknown. Due to the complexity of HDL, we hypothesize that multiple receptors engage with HDL particles resulting in condition-dependent receptor-HDL interaction clusters at the cell surface. METHODS: Here we used the mass spectrometry-based and light-controlled proximity labeling strategy LUX-MS in a discovery-driven manner to decode HDL-receptor interactions. RESULTS: Surfaceome nanoscale organization analysis of hepatocytes and endothelial cells using LUX-MS revealed that the previously known HDL-binding protein scavenger receptor B1 (SCRB1) is embedded in a cell surface protein community, which we term HDL synapse. Modulating the endothelial HDL synapse, composed of 60 proteins, by silencing individual members, showed that the HDL synapse can be assembled in the absence of SCRB1 and that the members are interlinked. The aminopeptidase N (AMPN) (also known as CD13) was identified as an HDL synapse member that directly influences HDL uptake into the primary human aortic endothelial cells (HAECs). CONCLUSIONS: Our data indicate that preformed cell surface residing protein complexes modulate HDL function and suggest new theragnostic opportunities.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma.

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency.

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

Apicobasal Surfaceome Architecture Encodes for Polarized Epithelial Functionality and Depends on Tumor Suppressor PTEN.

The loss of apicobasal polarity during the epithelial-to-mesenchymal transition (EMT) is a hallmark of cancer and metastasis. The key feature of this polarity in epithelial cells is the subdivision of the plasma membrane into apical and basolateral domains, with each orchestrating specific intra- and extracellular functions. Epithelial transport and signaling capacities are thought to be determined largely by the quality, quantity, and nanoscale organization of proteins residing in these membrane domains, the apicobasal surfaceomes. Despite its implications for cancer, drug uptake, and infection, our current knowledge of how the polarized surfaceome is organized and maintained is limited. Here, we used chemoproteomic surfaceome scanning to establish proteotype maps of apicobasal surfaceomes and reveal quantitative distributions of, i.e., surface proteases, phosphatases, and tetraspanins as potential key regulators of polarized cell functionality. We show further that the tumor suppressor PTEN regulates polarized surfaceome architecture and uncover a potential role in collective cell migration. Our differential surfaceome analysis provides a molecular framework to elucidate polarized protein networks regulating epithelial functions and PTEN-associated cancer progression.

Calreticulin mutations affect its chaperone function and perturb the glycoproteome.

Calreticulin (CALR) is an endoplasmic reticulum (ER)-retained chaperone that assists glycoproteins in obtaining their structure. CALR mutations occur in patients with myeloproliferative neoplasms (MPNs), and the ER retention of CALR mutants (CALR MUT) is reduced due to a lacking KDEL sequence. Here, we investigate the impact of CALR mutations on protein structure and protein levels in MPNs by subjecting primary patient samples and CALR-mutated cell lines to limited proteolysis-coupled mass spectrometry (LiP-MS). Especially glycoproteins are differentially expressed and undergo profound structural alterations in granulocytes and cell lines with homozygous, but not with heterozygous, CALR mutations. Furthermore, homozygous CALR mutations and loss of CALR equally perturb glycoprotein integrity, suggesting that loss-of-function attributes of mutated CALR chaperones (CALR MUT) lead to glycoprotein maturation defects. Finally, by investigating the misfolding of the CALR glycoprotein client myeloperoxidase (MPO), we provide molecular proof of protein misfolding in the presence of homozygous CALR mutations.

Decoding Functional High-Density Lipoprotein Particle Surfaceome Interactions.

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport (RCT) and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDL's journey throughout the body, its functions are mediated through interactions with cell surface receptors on different cell types. To characterize and better understand the functional interplay between HDL particles and tissue, we analyzed the surfaceome-residing receptor neighborhoods with which HDL potentially interacts. We applied a combination of chemoproteomic technologies including automated cell surface capturing (auto-CSC) and HATRIC-based ligand-receptor capturing (HATRIC-LRC) on four different cellular model systems mimicking tissues relevant for RCT. The surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAECs) revealed the main currently known HDL receptor scavenger receptor B1 (SCRB1), as well as 155 shared cell surface receptors representing potential HDL interaction candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF-modulated surfaceome of HAEC using the auto-CSC technology. VEGF-A treatment led to the remodeling of the surfaceome of HAEC cells, including the previously reported higher surfaceome abundance of SCRB1. In total, 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using the HATRIC-LRC technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK). Through RNA interference, we revealed its contribution to endothelial HDL binding and uptake. Furthermore, subsequent proximity ligation assays (PLAs) demonstrated the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence binding and uptake of HDL.

Poxviruses package viral redox proteins in lateral bodies and modulate the host oxidative response.

All poxviruses contain a set of proteinaceous structures termed lateral bodies (LB) that deliver viral effector proteins into the host cytosol during virus entry. To date, the spatial proteotype of LBs remains unknown. Using the prototypic poxvirus, vaccinia virus (VACV), we employed a quantitative comparative mass spectrometry strategy to determine the poxvirus LB proteome. We identified a large population of candidate cellular proteins, the majority being mitochondrial, and 15 candidate viral LB proteins. Strikingly, one-third of these are VACV redox proteins whose LB residency could be confirmed using super-resolution microscopy. We show that VACV infection exerts an anti-oxidative effect on host cells and that artificial induction of oxidative stress impacts early and late gene expression as well as virion production. Using targeted repression and/or deletion viruses we found that deletion of individual LB-redox proteins was insufficient for host redox modulation suggesting there may be functional redundancy. In addition to defining the spatial proteotype of VACV LBs, these findings implicate poxvirus redox proteins as potential modulators of host oxidative anti-viral responses and provide a solid starting point for future investigations into the role of LB resident proteins in host immunomodulation.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.

BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks.

The molecular nanoscale organization of the surfaceome is a fundamental regulator of cellular signaling in health and disease. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would unlock theranostic opportunities to target protein communities and the possibility to engineer extracellular signaling. Here, we develop an optoproteomic technology termed LUX-MS that enables the targeted elucidation of acute protein interactions on and in between living cells using light-controlled singlet oxygen generators (SOG). By using SOG-coupled antibodies, small molecule drugs, biologics and intact viral particles, we demonstrate the ability of LUX-MS to decode ligand receptor interactions across organisms and to discover surfaceome receptor nanoscale organization with direct implications for drug action. Furthermore, by coupling SOG to antigens we achieved light-controlled molecular mapping of intercellular signaling within functional immune synapses between antigen-presenting cells and CD8+ T cells providing insights into T cell activation with spatiotemporal specificity. LUX-MS based decoding of surfaceome signaling architectures thereby provides a molecular framework for the rational development of theranostic strategies.

Reproducible Determination of High-Density Lipoprotein Proteotypes.

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Elucidation of host-virus surfaceome interactions using spatial proteotyping.

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.