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.

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.

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.

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.