Advancing MS-based proteomics toward clinical applications evolves around developing standardized start-to-finish and fit-for-purpose workflows for clinical specimens. Steps along the method design involve the determination and optimization of several bioanalytical parameters such as selectivity, sensitivity, accuracy, and precision. In a joint effort, eight proteomics laboratories belonging to the MSCoreSys initiative including the CLINSPECT-M, MSTARS, DIASyM, and SMART-CARE consortia performed a longitudinal round-robin study to assess the analysis performance of plasma and serum as clinically relevant samples. A variety of LC-MS/MS setups including mass spectrometer models from ThermoFisher and Bruker as well as LC systems from ThermoFisher, Evosep, and Waters Corporation were used in this study. As key performance indicators, sensitivity, precision, and reproducibility were monitored over time. Protein identifications range between 300 and 400 IDs across different state-of-the-art MS instruments, with timsTOF Pro, Orbitrap Exploris 480, and Q Exactive HF-X being among the top performers. Overall, 71 proteins are reproducibly detectable in all setups in both serum and plasma samples, and 22 of these proteins are FDA-approved biomarkers, which are reproducibly quantified (CV < 20% with label-free quantification). In total, the round-robin study highlights a promising baseline for bringing MS-based measurements of serum and plasma samples closer to clinical utility.

In spatial’omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry imaging (MSI)-based spatial ‘omics must compromise between data acquisition speed and biochemical profiling depth. Here, we introduce fast, label-free quantum cascade laser mid-infrared imaging microscopy (QCL-MIR imaging) to guide MSI to high-interest tissue regions as small as kidney glomeruli, cultured multicellular spheroid cores or single motor neurons. Focusing on smaller tissue areas enables extensive spatial lipid identifications by on-tissue tandem-MS employing imaging parallel reaction monitoring-Parallel Accumulation-Serial Fragmentation (iprm-PASEF). QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. Taken together, widely applicable and fast QCL-MIR imaging-based guidance of MSI ensures that more time is available for exploration and validation of new biology by default on-tissue tandem-MS analysis.

Single-cell MALDI mass spectrometry imaging (MSI) of lipids and metabolites >200 Da has recently come to the forefront of biomedical research and chemical biology. However, cell-targeting and metabolome-preserving methods for analysis of low mass, hydrophilic metabolites (<200 Da) in large cell populations are lacking. Here, the PRISM-MS (PRescan Imaging for Small Molecule - Mass Spectrometry) mass-guided MSI workflow is presented, which enables space-efficient single cell lipid and metabolite analysis. In conjunction with giant unilamellar vesicles (GUVs) as MSI ground truth for cell-sized objects and Monte Carlo reference-based consensus clustering for data-dependent identification of cell subpopulations, PRISM-MS enables MSI and on-cell MS2-based identification of low-mass metabolites like amino acids or Krebs cycle intermediates involved in stimulus-dependent cell activation. The utility of PRISM-MS is demonstrated through the characterization of complex metabolome changes in lipopolysaccharide (LPS)-stimulated microglial cells and human-induced pluripotent stem cell-derived microglia. Translation of single cell results to endogenous microglia in organotypic hippocampal slice cultures indicates that LPS-activation involves changes of the itaconate-to-taurine ratio and alterations in neuron-to-glia glutamine-glutamate shuttling. The data suggests that PRISM-MS can serve as a standard method in single cell metabolomics, given its capability to characterize larger cell populations and low-mass metabolites.

The deposition of matrix compounds significantly influences the effectiveness of matrix-assisted laser desorption/ionization (MALDI) Mass Spectrometry Imaging (MSI) experiments, impacting sensitivity, spatial resolution, and reproducibility. Dry deposition methods offer advantages by producing homogeneous matrix layers and minimizing analyte delocalization without the use of solvents. However, refining these techniques to precisely control matrix thickness, minimize heating temperatures, and ensure high-purity matrix layers is crucial for optimizing MALDI-MSI performance. Here, we present a novel approach utilizing low-temperature thermal evaporation (LTE) for organic matrix deposition under reduced vacuum pressure. Our method allows for reproducible control of matrix layer thickness, as demonstrated by linear calibration for two organic matrices, 2,5-dihydroxybenzoic acid (DHB) and 1,5-diaminonaphthalene (DAN). The environmental scanning electron microscopy images reveal a uniform distribution of small-sized matrix crystals, consistently on the sub-micrometer scale, across tissue slides following LTE deposition. Remarkably, LTE serves as an additional purification step for organic matrices, producing very pure layers irrespective of initial matrix purity. Furthermore, stability assessment of MALDI-MSI data from mouse brain sections coated with LTE-deposited DHB or DAN matrix indicates minimal impact on ionization efficiency, signal intensity, and image quality even after storage at −80 °C for 2 weeks, underscoring the robustness of LTE-deposited matrices for MSI applications. Comparative analysis with the spray-coating method highlights several advantages of LTE deposition, including enhanced ionization, reduced analyte diffusion, and improved MSI image quality.

The microtubule-associated protein Tau (encoded by the MAPT gene) is linked to a family of neurodegenerative disorders defined as tauopathies, which are characterized by its brain accumulation in neurofibrillary tangles and neuropil threads. Newly described Tau functions comprise DNA protection, chromatin remodeling, p53 regulation and cell fate modulation, suggesting a role of Tau in oncogenesis. Bioinformatic-supported characterization of Tau in cancer reveals robust expression in bone cancer cells, in particular Ewing sarcoma (EwS) cell lines. EwS is an aggressive cancer caused by a fusion of members of the FET and ETS gene families, primarily EWSR1::FLI1. Here we found that MAPT is a EWSR1::ETS target gene and that higher Tau expression in EwS cells inhibited their migratory and invasive behavior, consistent with a more immobile and proliferative phenotype observed in EwS. Indeed, we report that high Tau expression is associated with improved overall survival of EwS patients. We also show that the sessile but proliferative phenotype of EWSR1::ETS-high cells may result from a modulatory role of Tau on focal adhesion to extracellular matrix proteins. Our data highlight the utility of determining Tau expression as a prognostic factor in EwS as well as the opportunity to target Tau expression as an innovative EwS therapy.

Ewing sarcoma (EwS) cell line culture largely relies on standard techniques, which do not recapitulate physiological conditions. Here, we report on a feasible and cost-efficient EwS cell culture technique with increased physiological relevance employing an advanced medium composition, reduced fetal calf serum, and spheroidal growth. Improved reflection of the transcriptional activity related to proliferation, hypoxia, and differentiation in EwS patient tumors was detected in EwS cells grown in this refined in vitro condition. Moreover, transcriptional signatures associated with the oncogenic activity of the EwS-specific FET::ETS fusion transcription factors in the refined culture condition were shifted from proliferative toward metabolic gene signatures. The herein-presented EwS cell culture technique with increased physiological relevance provides a broadly applicable approach for enhanced in vitro modeling relevant to advancing EwS research and the validity of experimental results.

Human cancer cell lines are the mainstay of cancer research. Recent reports showed that highly mutated adult carcinoma cell lines (mainly HeLa and MCF-7) present striking diversity across laboratories and that long-term continuous culturing results in genomic/transcriptomic heterogeneity with strong phenotypical implications. Here, we hypothesize that oligomutated pediatric sarcoma cell lines mainly driven by a fusion transcription factor, such as Ewing sarcoma (EwS), are genetically and phenotypically more stable than the previously investigated adult carcinoma cell lines. A comprehensive molecular and phenotypic characterization of multiple EwS cell line strains, together with a simultaneous analysis during 12 months of continuous cell culture show that fusion-driven pediatric sarcoma cell line strains are genomically more stable than adult carcinoma strains, display remarkably stable and homogenous transcriptomes, and exhibit uniform and stable drug response. Additionally, the analysis of multiple EwS cell lines subjected to long-term continuous culture reveals that variable degrees of genomic/transcriptomic/phenotypic changes among fusion-driven cell lines, further exemplifying that the potential for reproducibility of in vitro scientific results may be rather understood as a spectrum, even within the same tumor entity.

Background
Feature rankings are crucial in bioinformatics but can be distorted by influential points (IPs), which are often overlooked. This study aims to investigate the impact of IPs on feature rankings and propose IPs detection method

Method
We use a leave-one-out approach to assess each case's influence on feature rankings by comparing rank changes after its removal. The rank changes are measured by a novel rank comparison method that involves using adaptive top-prioritized weights that are adjustable to the distribution of rank changes. Our IP detection method was evaluated on several public datasets.

Results
Our method identified potential IPs in several TCGA gene expression datasets, revealing that IPs can severely distort feature rankings. These rank changes can ultimately affect subsequent analyses such as enriched pathways, suggesting the necessity of IPs detection when deriving feature rankings.

Conclusions
IPs significantly impact feature rankings and subsequent analyses; routine IP detection is necessary yet underutilized. Our method is available in the R package findIPs.

The T-box transcription factor T-bet is known as a master regulator of T-cell response but its role in malignant B cells is not sufficiently explored. Here, we conducted single-cell resolved multi-omics analyses of malignant B cells from patients with chronic lymphocytic leukemia (CLL) and studied a CLL mouse model with genetic knockout of TBX21. We found that T-bet acts as a tumor suppressor in malignant B cells by decreasing their proliferation rate. NF-κB activity induced by inflammatory signals provided by the microenvironment, triggered T-bet expression which impacted on promoter proximal and distal chromatin co-accessibility and controlled a specific gene signature by mainly suppressing transcription. Gene set enrichment analysis identified a positive regulation of interferon signaling, and a negative control of proliferation by T-bet. In line, we showed that T-bet represses cell cycling and is associated with longer overall survival of CLL patients. Our study uncovers a novel tumor suppressive role of T-bet in malignant B cells via its regulation of inflammatory processes and cell cycling which has implications for stratification and therapy of CLL patients. Linking T-bet activity to inflammation explains the good prognostic role of genetic alterations in inflammatory signaling pathways in CLL.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

Precision oncology has revolutionized the treatment of ALK-positive lung cancer with targeted therapies. However, an unmet clinical need still to address is the treatment of refractory tumors that contain drug-induced resistant mutations in the driver oncogene or exhibit resistance through the activation of diverse mechanisms. In this study, we established mouse tumor-derived cell models representing the two most prevalent EML4-ALK variants in human lung adenocarcinomas and characterized their proteomic profiles to gain insights into the underlying resistance mechanisms. We showed that Eml4-Alk variant 3 confers a worse response to ALK inhibitors, suggesting its role in promoting resistance to targeted therapy. In addition, proteomic analysis of brigatinib-treated cells revealed the upregulation of SRC kinase, a protein frequently activated in cancer. Co-targeting of ALK and SRC showed remarkable inhibitory effects in both ALK-driven murine and ALK-patient-derived lung tumor cells. This combination induced cell death through a multifaceted mechanism characterized by profound perturbation of the (phospho)proteomic landscape and a synergistic suppressive effect on the mTOR pathway. Our study demonstrates that the simultaneous inhibition of ALK and SRC can potentially overcome resistance mechanisms and enhance clinical outcomes in ALK-positive lung cancer patients. ONE SENTENCE SUMMARY: Co-targeting ALK and SRC enhances ALK inhibitor response in lung cancer by affecting the proteomic profile, offering hope for overcoming resistance and improving clinical outcomes.

The Hippo pathway transducers yes-associated protein (YAP) and WW-domain containing transcription regulator 1 (WWTR1/TAZ) are key regulators of liver tumorigenesis, promoting tumor formation and progression. Although the first inhibitors are in clinical trials, targeting the relevant upstream regulators of YAP/TAZ activity could prove equally beneficial. To identify regulators of YAP/TAZ activity in hepatocarcinoma (HCC) cells, we carried out a proximity labelling approach (BioID) coupled with mass spectrometry. We verified CRK-like proto-oncogene adaptor protein (CRKL) as a new YAP-exclusive interaction partner. CRKL is highly expressed in HCC patients, and its expression is associated with YAP activity as well as poor survival prognosis. In vitro experiments demonstrated CRKL-dependent cell survival and the loss of YAP binding induced through actin disruption. Moreover, we delineated the activation of the JNK/JUN pathway by CRKL, which promoted YAP transcription. Our data illustrate that CRKL not only promoted YAP activity through its binding but also through the induction of YAP transcription by JNK/JUN activation. This emphasizes the potential use of targeting the JNK/JUN pathway to suppress YAP expression in HCC patients.

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

Potency assessment of monoclonal antibodies or corresponding biosimilars in cell-based assays is an essential prerequisite in biopharmaceutical research and development. However, cellular bioassays are still subject to limitations in sample throughput, speed, and often need costly reagents or labels as they are based on an indirect readout by luminescence or fluorescence. In contrast, whole-cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) Mass Spectrometry (MS) has emerged as a direct, fast and label-free technology for functional drug screening being able to unravel the molecular complexity of cellular response to pharmaceutical reagents. However, this approach has not yet been used for cellular testing of biologicals. In this study, we have conceived, developed and benchmarked a label-free MALDI-MS based cell bioassay workflow for the functional assessment of complement-dependent cytotoxicity (CDC) of Rituximab antibody. By computational evaluation of response profiles followed by subsequent m/z feature annotation via fragmentation analysis and trapped ion mobility MS, we identified adenosine triphosphate and glutathione as readily MS-assessable metabolite markers for CDC and demonstrate that robust concentration–response characteristics can be obtained by MALDI-TOF MS. Statistical assay performance indicators suggest that whole-cell MALDI-TOF MS could complement the toolbox for functional cellular testing of biopharmaceuticals.

Multiple myeloma (MM) is a hematological disease exhibiting aberrant clonal expansion of cancerous plasma cells in the bone marrow. The effects of treatments for MM vary between patients, highlighting the importance of developing prognostic models for informed therapeutic decision-making. Most previous models were constructed at the gene level, ignoring the fact that the dysfunction of the pathway is closely associated with disease development and progression. The present study considered two strategies that construct predictive models by taking pathway information into consideration: pathway score method and group lasso using pathway information. The former simply converted gene expression to sample-wise pathway scores for model fitting. We considered three methods for pathway score calculation (ssGSEA, GSVA, and z-scores) and 14 data sources providing pathway information. We implemented these methods in microarray data for MM (GSE136324) and obtained a candidate model with the best prediction performance in interval validation. The candidate model is further compared with the gene-based model and previously published models in two external data. We also investigated the effects of missing values on prediction. The results showed that group lasso incorporating Vax pathway information (Vax(grp)) was more competitive in prediction than the gene model in both internal and external validation. Immune information, including VAX pathways, seemed to be more predictive for MM. Vax(grp) also outperformed the previously published models. Moreover, the new model was more resistant to missing values, and the presence of missing values (<5%) would not evidently deteriorate its prediction accuracy using our missing data imputation method. In a nutshell, pathway-based models (using group lasso) were competitive alternatives to gene-based models for MM. These models were documented in an R package where a missing data imputation method was also integrated to facilitate future validation.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell–cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein–protein cell–cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB’s utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface and programmatically as a knowledge graph. We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Energetic stress compels cells to evolve adaptive mechanisms to adjust their metabolism. Inhibition of mTOR kinase complex 1 (mTORC1) is essential for cell survival during glucose starvation. How mTORC1 controls cell viability during glucose starvation is not well understood. Here we show that the mTORC1 effectors eukaryotic initiation factor 4E binding proteins 1/2 (4EBP1/2) confer protection to mammalian cells and budding yeast under glucose starvation. Mechanistically, 4EBP1/2 promote NADPH homeostasis by preventing NADPH-consuming fatty acid synthesis via translational repression of Acetyl-CoA Carboxylase 1 (ACC1), thereby mitigating oxidative stress. This has important relevance for cancer, as oncogene-transformed cells and glioma cells exploit the 4EBP1/2 regulation of ACC1 expression and redox balance to combat energetic stress, thereby supporting transformation and tumorigenicity in vitro and in vivo. Clinically, high EIF4EBP1 expression is associated with poor outcomes in several cancer types. Our data reveal that the mTORC1-4EBP1/2 axis provokes a metabolic switch essential for survival during glucose starvation which is exploited by transformed and tumor cells.

Purpose
Improvement of patient care is associated with increasing publication numbers in biomedical research. However, such increasing numbers of publications make it challenging for physicians and scientists to screen and process the literature of their respective fields. In this study, we present a comprehensive bibliometric analysis of the evolution of gastrointestinal stromal tumor (GIST) research, analyzing the current state of the field and identifying key open questions going beyond the recent advantages for future studies to assess.

Methods
Using the Web of Science Core Collection, 5040 GIST-associated publications in the years 1984–2022 were identified and analyzed regarding key bibliometric variables using the Bibliometrix R package and VOSviewer software.

Results
GIST-associated publication numbers substantially increased over time, accentuated from year 2000 onwards, and being characterized by multinational collaborations. The main topic clusters comprise surgical management, tyrosine kinase inhibitor (TKI) development/treatment, diagnostic workup, and molecular pathophysiology. Within all main topic clusters, a significant progress is reflected by the literature over the years. This progress ranges from conventional open surgical techniques over minimally invasive, including robotic and endoscopic, resection techniques to increasing identification of specific functional genetic aberrations sensitizing for newly developed TKIs being extensively investigated in clinical studies and implemented in GIST treatment guidelines. However, especially in locally advanced, recurrent, and metastatic disease stages, surgery-related questions and certain specific questions concerning (further-line) TKI treatment resistance were infrequently addressed.

Conclusion
Increasing GIST-related publication numbers reflect a continuous progress in the major topic clusters of the GIST research field. Especially in advanced disease stages, questions related to the interplay between surgical approaches and TKI treatment sensitivity should be addressed in future studies.

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

Venetoclax/azacitidine combination therapy is effective in acute myeloid leukemia (AML) and tolerable for older, multimorbid patients. Despite promising response rates, many patients do not achieve sustained remission or are upfront refractory. Identification of resistance mechanisms and additional therapeutic targets represent unmet clinical needs. By using a genome-wide CRISPR/Cas9 library screen targeting 18,053 protein- coding genes in a human AML cell line, various genes conferring resistance to combined venetoclax/azacitidine treatment were identified. The ribosomal protein S6 kinase A1 (RPS6KA1) was among the most significantly depleted sgRNA-genes in venetoclax/azacitidine- treated AML cells. Addition of the RPS6KA1 inhibitor BI-D1870 to venetoclax/azacitidine decreased proliferation and colony forming potential compared to venetoclax/azacitidine alone. Furthermore, BI-D1870 was able to completely restore the sensitivity of OCI-AML2 cells with acquired resistance to venetoclax/azacitidine. Analysis of cell surface markers revealed that RPS6KA1 inhibition efficiently targeted monocytic blast subclones as a potential source of relapse upon venetoclax/azacitidine treatment. Taken together, our results suggest RPS6KA1 as mediator of resistance towards venetoclax/azacitidine and additional RPS6KA1 inhibition as strategy to prevent or overcome resistance.

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

A significant portion of mammalian proteomes is secreted to the extracellular space to fulfill crucial roles in cell-to-cell communication. To best recapitulate the intricate and multi-faceted crosstalk between cells in a live organism, there is an ever-increasing need for methods to study protein secretion in model systems that include multiple cell types. In addition, post-translational modifications further expand the complexity and versatility of cellular communication. This review aims to summarise recent strategies and model systems that employ cellular co-culture, chemical biology tools, protein enrichment and proteomic methods to characterize the composition and function of cellular secretomes. This is all geared towards gaining better understanding of organismal biology in vivo mediated by secretory signaling.

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

Type I interferons (IFNs) play a central role not only in innate immunity against viral infection, but also in the antitumour response, e.g. through a direct impact on cell proliferation. Particularly for cancer arising in the context of chronic inflammation, constant exposure to IFNs may constitute a strong selective pressure during tumour evolution. Expansion of neoplastic subclones resistant to the antiproliferative effects of IFNs may contribute to immunoediting of tumours, leading to more aggressive disease. Experimental evidence for this development of IFN-insensitivity has been scarce and its molecular mechanism is unclear. In this study we demonstrate that six weeks exposure of cells to IFN-β in vitro reduces their sensitivity to its antiproliferative effects, and that this phenotype was stable for up to four weeks. Furthermore, we observed substantial differences in cellular sensitivity to growth inhibition by IFN-β in a panel of ten different liver cancer cell lines, most prominently in a pair of highly dedifferentiated cell lines, and least in cells from well-differentiated tumours. In both, long-term IFN selection and in dedifferentiated tumour cell lines, we found IFNAR2 expression to be substantially reduced, suggesting the receptor complex to be a sensitive target amenable to immunoediting. Beyond new insights into possible molecular processes in tumour evolution, these findings might prove valuable for the development of biomarkers allowing to stratify tumours for their sensitivity to IFN treatment in the context of patient tailored therapies.

Insufficient vacuum stability of matrix chemicals is a major limitation in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) of large tissue sample cohorts. Here, we designed and synthesized the photo-cleavable caged molecule 4,5-dimethoxy-2-nitrobenzyl-2,5-dihydroxyacetophenone (DMNB-2,5-DHAP) and employed it for lipid MALDI-MSI of mouse brain tissue sections. DMNB-2,5-DHAP is vacuum-stable in a high vacuum MALDI ion source for at least 72 h. Investigation of the uncaging process suggested that the built-in laser (355 nm) in the MALDI ion source promoted the in situ generation of 2,5-DHAP. A caging group is used for the first time in designing a MALDI matrix that is vacuum-stable, uncaged upon laser irradiation during the measurement process, and that boosts lipid ion intensity with MALDI-2 laser-induced postionization.

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co‐regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss‐of‐function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

Motivation
Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows.
Results
The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets.
Availability and implementation
The R package MsQuality is available through Bioconductor at bioconductor.org/packages/MsQuality.

Infection with certain cutaneous human papillomaviruses (HPV), in conjunction with chronic ultraviolet (UV) exposure, are the major cofactors of non-melanoma skin cancer (NMSC), the most frequent cancer type worldwide. Cutaneous squamous cell carcinomas (SCCs) as well as tumors in general represent three-dimensional entities determined by both temporal and spatial constraints. Whole tissue proteomics is a straightforward approach to understand tumorigenesis in better detail, but studies focusing on different progression states toward a dedifferentiated SCC phenotype on a spatial level are rare. Here, we applied an innovative proteomic workflow on formalin-fixed, paraffin-embedded (FFPE) epithelial tumors derived from the preclinical animal model Mastomys coucha. This rodent is naturally infected with its genuine cutaneous papillomavirus and closely mimics skin carcinogenesis in the context of cutaneous HPV infections in humans. We deciphered cellular networks by comparing diverse epithelial tissues with respect to their differentiation level and infection status. Our study reveals novel regulatory proteins and pathways associated with virus-induced tumor initiation and progression of SCCs. This approach provides the basis to better comprehend the multistep process of skin carcinogenesis.

Erythropoietin (Epo) ensures survival and proliferation of colony-forming unit erythroid (CFU-E) progenitor cells and their differentiation to hemoglobin-containing mature erythrocytes. A lack of Epo-induced responses causes embryonic lethality, but mechanisms regulating the dynamic communication of cellular alterations to the organismal level remain unresolved. By time-resolved transcriptomics and proteomics, we show that Epo induces in CFU-E cells a gradual transition from proliferation signature proteins to proteins indicative for differentiation, including heme-synthesis enzymes. In the absence of the Epo receptor (EpoR) in embryos, we observe a lack of hemoglobin in CFU-E cells and massive iron overload of the fetal liver pointing to a miscommunication between liver and placenta. A reduction of iron-sulfur cluster-containing proteins involved in oxidative phosphorylation in these embryos leads to a metabolic shift toward glycolysis. This link connecting erythropoiesis with the regulation of iron homeostasis and metabolic reprogramming suggests that balancing these interactions is crucial for protection from iron intoxication and for survival.

Summary
Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. MSPypeline provides a platform for importing MaxQuant output tables, generating quality control reports, data preprocessing including normalization and performing exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions.
Availability and implementation
The source code is available under the MIT license at https://github.com/siheming/mspypeline with documentation at mspypeline.readthedocs.io. Benchmark mass spectrometry data are available on ProteomeXchange (PXD025792).

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors.

Chimeric fusion transcription factors are oncogenic hallmarks of several devastating cancer entities including pediatric sarcomas, such as Ewing sarcoma (EwS) and alveolar rhabdomyosarcoma (ARMS). Despite their exquisite specificity, these driver oncogenes have been considered largely undruggable due to their lack of enzymatic activity.
Here, we show in the EwS model that – capitalizing on neomorphic DNA-binding preferences – the addiction to the respective fusion transcription factor EWSR1-FLI1 can be leveraged to express therapeutic genes.
We genetically engineered a de novo enhancer-based, synthetic and highly potent expression cassette that can elicit EWSR1-FLI1-dependent expression of a therapeutic payload as evidenced by episomal and CRISPR-edited genomic reporter assays. Combining in silico screens and immunohistochemistry, we identified GPR64 as a highly specific cell surface antigen for targeted transduction strategies in EwS. Functional experiments demonstrated that anti-GPR64-pseudotyped lentivirus harboring our expression cassette can specifically transduce EwS cells to promote the expression of viral thymidine kinase sensitizing EwS for treatment to otherwise relatively non-toxic (Val)ganciclovir and leading to strong anti-tumorigenic, but no adverse effects in vivo. Further, we prove that similar vector designs can be applied in PAX3-FOXO1-driven ARMS, and to express immunomodulatory cytokines, such as IL-15 and XCL1, in tumor entities typically considered to be immunologically ‘cold’.
Collectively, these results generated in pediatric sarcomas indicate that exploiting, rather than suppressing, the neomorphic functions of chimeric transcription factors may open inroads to innovative and personalized therapies, and that our highly versatile approach may be translatable to other cancers addicted to oncogenic transcription factors with unique DNA-binding properties.

Undifferentiated small round cell sarcomas (SRCSs) of bone and soft tissue comprise a heterogeneous group of highly aggressive tumours associated with a poor prognosis, especially in metastatic disease. SRCS entities mainly occur in the third decade of life and can exhibit striking disparities regarding preferentially affected sex and tumour localization. SRCSs comprise new entities defined by specific genetic abnormalities, namely EWSR1–non-ETS fusions, CIC-rearrangements or BCOR genetic alterations, as well as EWSR1–ETS fusions in the prototypic SRCS Ewing sarcoma. These gene fusions mainly encode aberrant oncogenic transcription factors that massively rewire the transcriptome and epigenome of the as yet unknown cell or cells of origin. Additional mutations or copy number variants are rare at diagnosis and, depending on the tumour entity, may involve TP53, CDKN2A and others. Histologically, these lesions consist of small round cells expressing variable levels of CD99 and specific marker proteins, including cyclin B3, ETV4, WT1, NKX3-1 and aggrecan, depending on the entity. Besides locoregional treatment that should follow standard protocols for sarcoma management, (neo)adjuvant treatment is as yet ill-defined but generally follows that of Ewing sarcoma and is associated with adverse effects that might compromise quality of life. Emerging studies on the molecular mechanisms of SRCSs and the development of genetically engineered animal models hold promise for improvements in early detection, disease monitoring, treatment-related toxicity, overall survival and quality of life.

Purpose
The relevance of the subfamily A members of ATP-binding cassette (ABCA) transporters as biomarkers of risk and response is emerging in different tumors, but their mechanisms of action have only been partially defined. In this work, we investigated their role in Ewing sarcoma (EWS), a pediatric cancer with unmet clinical issues.
Methods
The expression of ABC members was evaluated by RT-qPCR in patients with localized EWS. The correlation with clinical outcome was established in different datasets using univariate and multivariate statistical methods. Functional studies were conducted in cell lines from patient-derived xenografts (PDXs) using gain- or loss-of-function approaches. The impact of intracellular cholesterol levels and cholesterol lowering drugs on malignant parameters was considered.
Results
We found that ABCA6, which is usually poorly expressed in EWS, when upregulated became a prognostic factor of a favorable outcome in patients. Mechanistically, high expression of ABCA6 impaired cell migration and increased cell chemosensitivity by diminishing the intracellular levels of cholesterol and by constitutive IGF1R/AKT/mTOR expression/activation. Accordingly, while exposure of cells to exogenous cholesterol increased AKT/mTOR activation, the cholesterol lowering drug simvastatin inhibited IGF1R/AKT/mTOR signaling and prevented Ser166 phosphorylation of MDM2. This, in turn, favored p53 activation and enhanced pro-apoptotic effects of doxorubicin.
Conclusions
Our study reveals that ABCA6 acts as tumor suppressor in EWS cells via cholesterol-mediated inhibition of IGF1R/AKT/MDM2 signaling, which promotes the pro-apoptotic effects of doxorubicin and reduces cell migration. Our findings also support a role of ABCA6 as biomarker of EWS progression and sustains its assessment for a more rational use of statins as adjuvant drugs.

Ewing sarcoma (EwS) is a highly aggressive tumor of bone and soft tissues that mostly affects children and adolescents. The pathognomonic oncofusion EWSR1::FLI1 transcription factor drives EwS by orchestrating an oncogenic transcription program through de novo enhancers. By integrative analysis of thousands of transcriptomes representing pan-cancer cell lines, primary cancers, metastasis, and normal tissues, we identify a 32-gene signature (ESS32 [Ewing Sarcoma Specific 32]) that stratifies EwS from pan-cancer. Among the ESS32, LOXHD1, encoding a stereociliary protein, is the most highly expressed gene through an alternative transcription start site. Deletion or silencing of EWSR1::FLI1 bound upstream de novo enhancer results in loss of the LOXHD1 short isoform, altering EWSR1::FLI1 and HIF1α pathway genes and resulting in decreased proliferation/invasion of EwS cells. These observations implicate LOXHD1 as a biomarker and a determinant of EwS metastasis and suggest new avenues for developing LOXHD1-targeted drugs or cellular therapies for this deadly disease.

Analyses of metabolic compounds inside cells or tissues provide high information content since they represent the endpoint of biological information flow and are a snapshot of the integration of many regulatory processes. However, quantification of the abundance of metabolites requires their careful extraction. We present a comprehensive study comparing ten extraction protocols in four human sample types (liver tissue, bone marrow, HL60, and HEK cells) aiming to detect and quantify up to 630 metabolites of different chemical classes. We show that the extraction efficiency and repeatability are highly variable across protocols, tissues, and chemical classes of metabolites. We used different quality metrics including the limit of detection and variability between replicates as well as the sum of concentrations as a global estimate of analytical repeatability of the extraction. The coverage of extracted metabolites depends on the used solvents, which has implications for the design of measurements of different sample types and metabolic compounds of interest. The benchmark dataset can be explored in an easy-to-use, interactive, and flexible online resource (R/shiny app MetaboExtract:) for context-specific selection of the optimal extraction method. Furthermore, data processing and conversion functionality underlying the shiny app are accessible as an R package.

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer’s, Parkinson’s disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app “MetaboExtract”. Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

Oligodendrogliomas are defined at the molecular level by the presence of an IDH mutation and codeletion of chromosomal arms 1p and 19q. In the past, case reports and small studies described gliomas with sarcomatous features arising from oligodendrogliomas, so called oligosarcomas. Here, we report a series of 24 IDH-mutant oligosarcomas from 23 patients forming a distinct methylation class. The tumors were recurrences from prior oligodendrogliomas or developed de novo. Precursor tumors of 12 oligosarcomas were histologically and molecularly indistinguishable from conventional oligodendrogliomas. Oligosarcoma tumor cells were embedded in a dense network of reticulin fibers, frequently showing p53 accumulation, positivity for SMA and CALD1, loss of OLIG2 and gain of H3K27 trimethylation (H3K27me3) as compared to primary lesions. In 5 oligosarcomas no 1p/19q codeletion was detectable, although it was present in the primary lesions. Copy number neutral LOH was determined as underlying mechanism. Oligosarcomas harbored an increased chromosomal copy number variation load with frequent CDKN2A/B deletions. Proteomic profiling demonstrated oligosarcomas to be highly distinct from conventional CNS WHO grade 3 oligodendrogliomas with consistent evidence for a smooth muscle differentiation. Expression of several tumor suppressors was reduced with NF1 being lost frequently. In contrast, oncogenic YAP1 was aberrantly overexpressed in oligosarcomas. Panel sequencing revealed mutations in NF1 and TP53 along with IDH1/2 and TERT promoter mutations. Survival of patients was significantly poorer for oligosarcomas as first recurrence than for grade 3 oligodendrogliomas as first recurrence. These results establish oligosarcomas as a distinct group of IDH-mutant gliomas differing from conventional oligodendrogliomas on the histologic, epigenetic, proteomic, molecular and clinical level. The diagnosis can be based on the combined presence of (a) sarcomatous histology, (b) IDH-mutation and (c) TERT promoter mutation and/or 1p/19q codeletion, or, in unresolved cases, on its characteristic DNA methylation profile.

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation.
We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest.
Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Background
IDH-mutant gliomas are separate based on the codeletion of the chromosomal arms 1p and 19q into oligodendrogliomas IDH-mutant 1p/19q-codeleted and astrocytomas IDH-mutant. While nuclear loss of ATRX expression excludes 1p/19q codeletion, its limited sensitivity prohibits to conclude on 1p/19q status in tumors with retained nuclear ATRX expression.

Methods
Employing mass spectrometry based proteomic analysis in a discovery series containing 35 fresh frozen and 72 formalin fixed and paraffin embedded tumors with established IDH and 1p/19q status, potential biomarkers were discovered. Subsequent validation immunohistochemistry was conducted on two independent series (together 77 oligodendrogliomas IDH-mutant 1p/19q-codeleted and 92 astrocytomas IDH-mutant).

Results
We detected highly specific protein patterns distinguishing oligodendroglioma and astrocytoma. In these patterns, high HIP1R and low vimentin levels were observed in oligodendroglioma while low HIP1R and high vimentin levels occurred in astrocytoma. Immunohistochemistry for HIP1R and vimentin expression in 35 cases from the FFPE discovery series confirmed these findings. Blinded evaluation of the validation cohorts predicted the 1p/19q status with a positive and negative predictive value as well as an accuracy of 100% in the first cohort and with a positive predictive value of 83%; negative predictive value of 100% and an accuracy of 92% in the second cohort. Nuclear ATRX loss as marker for astrocytoma increased the sensitivity to 96% and the specificity to 100%.

Conclusions
We demonstrate that immunohistochemistry for HIP1R, vimentin, and ATRX predict 1p/19q status with 100% specificity and 95% sensitivity and therefore, constitutes a simple and inexpensive approach to the classification of IDH-mutant glioma.

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Chemical perturbations can be readily applied to primary cancer samples at large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a chemical compound has affinities to multiple proteins. To computationally infer specific molecular dependencies of individual cancers from their ex vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Through integrating a drug-kinase profiling dataset and several drug response datasets, our method, DepInfeR, correctly identified known protein kinase dependencies, including the EGFR dependence of HER2+ breast cancer cell lines, the FLT3 dependence of acute myeloid leukemia (AML) with FLT3-ITD mutations and the differential dependencies on the B-cell receptor pathway in the two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a detailed map of the kinase dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.

Pathological microglia activation can promote neuroinflammation in many neurodegenerative diseases, and it has therefore emerged as a potential therapeutic target. Increasing evidence suggests alterations in lipid metabolism as modulators and indicators in microglia activation and its effector functions. Yet, how lipid dynamics in activated microglia is affected by inflammatory stimuli demands additional investigation to allow development of more effective therapies. Here, we report an extensive matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) whole cell fingerprinting workflow to investigate inflammation-associated lipid patterns in SIM-A9 microglial cells. By combining a platform of three synergistic MALDI MS technologies we could detect substantial differences in lipid profiles of lipopolysaccharide (LPS)- stimulated and unstimulated microglia-like cells leading to the identification of 21 potential inflammation-associated lipid markers. LPS-induced lipids in SIM-A9 microglial cells include phosphatidylcholines, lysophosphatidylcholines (LysoPC), sphingolipids, diacylglycerols and triacylglycerols. Moreover, MALDI MS-based cell lipid fingerprinting of LPS-stimulated SIM-A9 microglial cells pre-treated with the non-selective histone deacetylase inhibitor suberoylanilide hydroxamic acid revealed specific modulation of LPS-induced-glycerolipids and LysoPC(18:0) with a significant reduction of microglial inflammation response. Our study introduces MALDI MS as a complementary technology for fast and label-free investigation of stimulus-dependent changes in lipid patterns and their modulation by pharmaceutical agents.

Motivation
Omics data are broadly used to get a snapshot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signalling networks.

Results
We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of transcriptomics, phosphoproteomics and metabolomics data, either from bulk or single-cell experiments. FUNKI also features different options to visualize the results and run post-analyses, and is mirrored as a scripted version in R.

Availability and implementation
FUNKI is a free and open-source application built on R and Shiny, available at https://github.com/saezlab/ShinyFUNKI and https://saezlab.shinyapps.io/funki/.

Motivation
First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference.

Results
We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R’s shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.
Availability and implementation
MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

BCL-2 inhibition has been shown to be effective in acute myeloid leukemia (AML) in combination with hypomethylating agents or low-dose cytarabine. However, resistance and relapse represent major clinical challenges. Therefore, there is an unmet need to overcome resistance to current venetoclax-based strategies. We performed high-throughput drug screening to identify effective combination partners for venetoclax in AML. Overall, 64 antileukemic drugs were screened in 31 primary high-risk AML samples with or without venetoclax. Gilteritinib exhibited the highest synergy with venetoclax in FLT3 wild-type AML. The combination of gilteritinib and venetoclax increased apoptosis, reduced viability, and was active in venetoclax-azacitidine–resistant cell lines and primary patient samples. Proteomics revealed increased FLT3 wild-type signaling in specimens with low in vitro response to the currently used venetoclax-azacitidine combination. Mechanistically, venetoclax with gilteritinib decreased phosphorylation of ERK and GSK3B via combined AXL and FLT3 inhibition with subsequent suppression of the antiapoptotic protein MCL-1. MCL-1 downregulation was associated with increased MCL-1 phosphorylation of serine 159, decreased phosphorylation of threonine 161, and proteasomal degradation. Gilteritinib and venetoclax were active in an FLT3 wild-type AML patient-derived xenograft model with TP53 mutation and reduced leukemic burden in 4 patients with FLT3 wild-type AML receiving venetoclax-gilteritinib off label after developing refractory disease under venetoclax-azacitidine. In summary, our results suggest that combined inhibition of FLT3/AXL potentiates venetoclax response in FLT3 wild-type AML by inducing MCL-1 degradation. Therefore, the venetoclax-gilteritinib combination merits testing as a potentially active regimen in patients with high-risk FLT3 wild-type AML.

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands-on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500–1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end-to-end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost-effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non-clinical applications.

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

Computational and mathematical models are key to obtain a system-level understanding of biological processes, but their limitations have to be clearly defined to allow their proper application and interpretation. Crowdsourced benchmarks in the form of challenges provide an unbiased assessment of methods, and for the past decade, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) organized more than 15 systems biology challenges. From transcription factor binding to dynamical network models, from signaling networks to gene regulation, from whole-cell models to cell-lineage reconstruction, and from single-cell positioning in a tissue to drug combinations and cell survival, the breadth is broad. To celebrate the 5-year anniversary of Cell Systems, we review the genesis of these systems biology challenges and discuss how interlocking the forward- and reverse-modeling paradigms allows to push the rim of systems biology. This approach will persist for systems levels approaches in biology and medicine.

Multi‐omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi‐Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network‐level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi‐omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi‐omics studies.