MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Multiplexed phosphoproteomics of low cell numbers using SPARCE

Understanding cellular diversity and disease mechanisms requires a global analysis of proteins and their modifications. While next-generation sequencing has advanced our understanding of cellular heterogeneity, it fails to capture downstream signalling networks. Ultrasensitive mass spectrometry-based proteomics enables unbiased protein-level analysis of low cell numbers, down to single cells. However, phosphoproteomics remains limited to high-input samples due to sample losses and poor reaction efficiencies associated with processing low cell numbers. Isobaric stable isotope labelling is a promising approach for reproducible and accurate quantification of low abundant phosphopeptides. Here, we introduce SPARCE (Streamlined Phosphoproteomic Analysis of Rare CElls) for multiplexed phosphoproteomic analysis of low cell numbers. SPARCE integrates cell isolation, water-based lysis, on-tip TMT labelling, and phosphopeptide enrichment. SPARCE outperforms traditional methods by enhancing labelling efficiency and phosphoproteome coverage. To demonstrate the utility of SPARCE, we analysed four patient-derived glioblastoma stem cell lines, reliably quantifying phosphosite changes from 1000 FACS-sorted cells. This workflow expands the possibilities for signalling analysis of rare cell populations.

The network response to Egf is tissue-specific

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach

MOTIVATION

Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared.

RESULTS

We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications.

AVAILABILITY AND IMPLEMENTATION

The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

MAPK14/p38α shapes the molecular landscape of endometrial cancer and promotes tumorigenic characteristics

The molecular underpinnings of high-grade endometrial carcinoma (HGEC) metastatic growth and survival are poorly understood. Here, we show that ascites-derived and primary tumor HGEC cell lines in 3D spheroid culture faithfully recapitulate key features of malignant peritoneal effusion and exhibit fundamentally distinct transcriptomic, proteomic, and metabolomic landscapes compared with conventional 2D monolayers. Using a genetic screening platform, we identify MAPK14 (which encodes the protein kinase p38α) as a specific requirement for HGEC in spheroid culture. MAPK14/p38α has broad roles in programming the phosphoproteome, transcriptome, and metabolome of HGEC spheroids, yet has negligible impact on monolayer cultures. MAPK14 promotes tumorigenicity in vivo and is specifically required to sustain a sub-population of spheroid cells that is enriched in cancer stemness markers. Therefore, spheroid growth of HGEC activates unique biological programs, including p38α signaling, that cannot be captured using 2D culture models and are highly relevant to malignant disease pathology.

How mass spectrometry can be exploited to study AMPK

AMP-activated protein kinase (AMPK) is a key regulator of metabolism and a recognised target for the treatment of metabolic diseases such as Type 2 diabetes (T2D). Here, we review how mass spectrometry (MS) can be used to study short-term control by AMPK via protein phosphorylation and long-term control due to changes in protein expression. We discuss how MS can quantify AMPK subunit levels in tissues from different species. We propose hydrogen-deuterium exchange (HDX)-MS to investigate molecular mechanisms of AMPK activation and thermoproteomic profiling (TPP) to assess off-target effects of pharmacological AMPK activators/inhibitors. Lastly, because large MS data sets are generated, we consider different approaches that can be used for their interpretation.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

FAS Inhibited Proteomics and Phosphoproteomics Profiling of Colorectal Cancer Spheroids Shows Activation of Ferroptotic Death Mechanism

Colorectal cancer (CRC) is projected to become the third most diagnosed and third most fatal cancer in the United States by 2024, with early onset CRC on the rise. Research is constantly underway to discover novel therapeutics for the treatment of various cancers to improve patient outcomes and survival. Fatty acid synthase (FAS) has become a druggable target of interest for the treatment of many different cancers. One such inhibitor, TVB-2640, has gained popularity for its high specificity for FAS and has entered a phase 1 clinical trial for the treatment of solid tumors. However, the distinct molecular differences that occur upon inhibition of FAS have yet to be understood. Here, we conduct proteomics and phosphoproteomics analyses on HCT 116 and HT-29 CRC spheroids inhibited with either a generation 1 (cerulenin) or generation 2 (TVB-2640) FAS inhibitor. Proteins involved in lipid metabolism and cellular respiration were altered in abundance. It was also observed that proteins involved in ferroptosis─an iron mediated form of cell death─were altered. These results show that HT-29 spheroids exposed to cerulenin or TVB-2640 are undergoing a ferroptotic death mechanism. The data were deposited to the ProteomeXchange Consortium via the PRIDE repository with the identifier PXD050987.

Multilevel Proteomic Profiling of Colorectal Adenocarcinoma Caco-2 Cell Differentiation to Characterize an Intestinal Epithelial Model

Emergent advancements on the role of the intestinal microbiome for human health and disease necessitate well-defined intestinal cellular models to study and rapidly assess host, microbiome, and drug interactions. Differentiated Caco-2 cell line is commonly utilized as an epithelial model for drug permeability studies and has more recently been utilized for investigating host-microbiome interactions. However, its suitability to study such interactions remains to be characterized. Here, we employed multilevel proteomics to demonstrate that both spontaneous and butyrate-induced Caco-2 differentiations displayed similar protein and pathway changes, including the downregulation of proteins related to translation and proliferation and upregulation of functions implicated in host-microbiome interactions, such as cell adhesion, tight junction, extracellular vesicles, and responses to stimuli. Lysine acetylomics revealed that histone protein acetylation levels were decreased along with cell differentiation, while the acetylation in proteins associated with mitochondrial functions was increased. This study also demonstrates that, compared to spontaneous differentiation methods, butyrate-containing medium accelerates Caco-2 differentiation, with earlier upregulation of proteins related to host-microbiome interactions, suggesting its superiority for assay development using this intestinal model. Altogether, this multiomics study emphasizes the controlled progression of Caco-2 differentiation toward a specialized intestinal epithelial-like cell and establishes its suitability for investigating the host-microbiome interactions.

MAPK14 /p38α Shapes the Molecular Landscape of Endometrial Cancer and promotes Tumorigenic Characteristics

The molecular underpinnings of H igh G rade E ndometrial C arcinoma (HGEC) metastatic growth and survival are poorly understood. Here we show that ascites-derived and primary tumor HGEC cell lines in 3D spheroid culture faithfully recapitulate key features of malignant peritoneal effusion and exhibit fundamentally distinct transcriptomic, proteomic and metabolomic landscapes when compared with conventional 2D monolayers. Using genetic screening platform we identify MAPK14 (which encodes the protein kinase p38α) as a specific requirement for HGEC in spheroid culture. MAPK14 /p38α has broad roles in programing the phosphoproteome, transcriptome and metabolome of HGEC spheroids, yet has negligible impact on monolayer cultures. MAPK14 promotes tumorigenicity in vivo and is specifically required to sustain a sub-population of spheroid cells that is enriched in cancer stemness markers. Therefore, spheroid growth of HGEC activates unique biological programs, including p38α signaling, that cannot be captured using 2D culture models and are highly relevant to malignant disease pathology.

A complex interplay of intra- and extracellular factors regulates the outcome of fetal- and adult-derived MLL-rearranged leukemia

Infant and adult MLL1/KMT2A-rearranged (MLLr) leukemia represents a disease with a dismal prognosis. Here, we present a functional and proteomic characterization of in utero-initiated and adult-onset MLLr leukemia. We reveal that fetal MLL::ENL-expressing lymphomyeloid multipotent progenitors (LMPPs) are intrinsically programmed towards a lymphoid fate but give rise to myeloid leukemia in vivo, highlighting a complex interplay of intra- and extracellular factors in determining disease subtype. We characterize early proteomic events of MLL::ENL-mediated transformation in fetal and adult blood progenitors and reveal that whereas adult pre-leukemic cells are mainly characterized by retained myeloid features and downregulation of ribosomal and metabolic proteins, expression of MLL::ENL in fetal LMPPs leads to enrichment of translation-associated and histone deacetylases signaling proteins, and decreased expression of inflammation and myeloid differentiation proteins. Integrating the proteome of pre-leukemic cells with their secretome and the proteomic composition of the extracellular environment of normal progenitors highlights differential regulation of Igf2 bioavailability, as well as of VLA-4 dimer and its ligandome, upon initiation of fetal- and adult-origin leukemia, with implications for human MLLr leukemia cells' ability to communicate with their environment through granule proteins. Our study has uncovered opportunities for targeting ontogeny-specific proteomic vulnerabilities in in utero-initiated and adult-onset MLLr leukemia.

Comparative Hippocampal Proteome and Phosphoproteome in a Niemann-Pick, Type C1 Mouse Model Reveal Insights into Disease Mechanisms

Niemann-Pick disease, type C (NPC) is a neurodegenerative, lysosomal storage disorder in individuals carrying two mutated copies of either the NPC1 or NPC2 gene. Consequently, impaired cholesterol recycling and an array of downstream events occur. Interestingly, in NPC, the hippocampus displays lysosomal lipid storage but does not succumb to progressive neurodegeneration as significantly as other brain regions. Since defining the neurodegeneration mechanisms in this disease is still an active area of research, we use mass spectrometry to analyze the overall proteome and phosphorylation pattern changes in the hippocampal region of a murine model of NPC. Using 3 week old mice representing an early disease time point, we observed changes in the expression of 47 proteins, many of which are consistent with the previous literature. New to this study, changes in members of the SNARE complex, including STX7, VTI1B, and VAMP7, were identified. Furthermore, we identified that phosphorylation of T286 on CaMKIIα and S1303 on NR2B increased in mutant animals, even at the late stage of the disease. These phosphosites are crucial to learning and memory and can trigger neuronal death by altering protein-protein interactions.

A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics

Multiplex proteomics using isobaric labeling tags has emerged as a powerful tool for the simultaneous relative quantification of peptides and proteins across multiple experimental conditions. However, the quantitative accuracy of the approach is largely compromised by ion interference, a phenomenon that causes fold changes to appear compressed. The degree of compression is generally unknown, and the contributing factors are poorly understood. In this study, we thoroughly characterized ion interference at the MS2 level using a defined two-proteome experimental system with known ground-truth. We discovered remarkably poor agreement between the apparent precursor purity in the isolation window and the actual level of observed reporter ion interference in MS2 scans-a discrepancy that we found resolved by considering cofragmentation of peptide ions hidden within the spectral "noise" of the MS1 isolation window. To address this issue, we developed a regression modeling strategy to accurately predict reporter ion interference in any dataset. Finally, we demonstrate the utility of our procedure for improved fold change estimation and unbiased PTM site-to-protein normalization. All computational tools and code required to apply this method to any MS2 TMT dataset are documented and freely available.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).