Vienna Biocenter

Field Asymmetric Ion Mobility Spectrometry (FAIMS) is an indispensable tool in single-cell and low-input proteomics. Here, we show that tuning FAIMS resolution via electrode temperature modulation improves sensitivity. We demonstrate that lowering FAIMS resolution broadens the compensation voltage window, thereby increasing ion transmission. This significantly improves ion counts, quantitative precision and peptide identifications by 25-34%. These results were mirrored when analyzing single cell samples.

Selective autophagy is a fundamental protein quality control pathway that safeguards proteostasis by degrading damaged or surplus cellular components, particularly under stress. This process is orchestrated by selective autophagy receptors (SARs) that recruit specific cargo for degradation. Although significant strides have been made in understanding the molecular framework of selective autophagy, the diversity of SAR repertoires across species remains largely unexplored. Through a comparative interactome analysis across five model organisms, we identified a suite of conserved and lineage-specific SAR candidates. Among these, we validated coupling of ubiquitin to endoplasmic reticulum degradation- and protein rich in the amino acids E, L, K, and S-domain-containing SAR (CESAR) as a conserved SAR critical for proteostasis under heat stress. CESAR specifically facilitates the degradation of ubiquitinated protein aggregates and is indispensable for heat stress tolerance. Altogether, our study establishes a robust pipeline and a rich resource for SAR discovery. It also positions CESAR as a pivotal regulator of proteostasis, with broad implications for improving stress resilience in plants.

Antibiotic resistance is spreading worldwide, associated with more than 5 million deaths annually, yet few new antibiotics have emerged in recent years. This gap highlights the need for alternative strategies that enable the rational design of antibacterial agents. One promising approach is the development of proteolysis-targeting chimeras in bacteria (BacPROTACs), which can hijack endogenous degradation pathways. BacPROTACs tether selected proteins to caseinolytic proteases (Clp) to promote their degradation. Thus, unlike conventional inhibitors, they eliminate their targets, act in a catalytic manner, and can exploit weak binders. Furthermore, BacPROTACs have been shown to reach pathogens in infected cells, exhibiting potent activity against multidrug-resistant Mycobacterium tuberculosis. While challenges remain in cell entry, substrate selectivity, and pharmacokinetic optimization, BacPROTACs offer a promising route to develop next-generation antibiotics.

Abstract Messenger RNA (mRNA) is a central polymer of gene expression, whose ultrastructure and defining regulatory rules remain unclear. To visualize the ultrastructure of mRNA, we here develop Combi-PAINT, a powerful and generalizable method for combinatorial super-resolved DNA-PAINT multiplexing, which we combine with high-efficiency RNA labelling and MINFLUX microscopy. This approach enables the nanometer-precision tracing of mRNA in three dimensions in cells. We use Combi-PAINT to visualize multiple distinct mRNA species inside the human cell nucleus and cytoplasm, revealing their quantitative ultrastructures and transcript-specific molecular patterns. By bridging sequence specificity with nanometer resolution, we provide a new lens for studying the cellular life of mRNA.

The integration of neuroimaging and transcriptomics data has revolutionized our understanding of complex brain phenomena and holds promise for linking molecular level genetic markers to brain dynamics and function. However, the involved data differ strongly across biological scales and acquisition modalities, presenting significant preprocessing and analytical challenges. In this context, multi-modal brain data exploration software offers a low-threshold, holistic, and non-biased approach to hypothesis generation that is complementary to literature search. This review assesses current data exploration tools that combine transcriptomic and neuroimaging data, evaluating them based on species coverage, data modalities, data integration approaches, visualization capabilities, and user data integration. We highlight the potential of these tools by discussing their applicability for example research questions and identify yet unaddressed workflows and promising approaches to support imaging transcriptomics analyses. This comprehensive survey aims to inform researchers in their selection of suitable software for their specific research needs and to guide the development of more effective multi-modal brain data exploration tools.

Tumor cell plasticity drives metastasis and therapy resistance, yet its regulation by oncoprotein dosage dynamics remains poorly understood. In Ewing sarcoma (EwS), variations in EWS::FLI1 (EF) fusion oncoprotein activity have been associated with epithelial-mesenchymal plasticity (EMP). Using degron technology, we precisely modulated endogenous EF in EwS cells and linked phenotypic states to distinct oncoprotein dosages. Strikingly, modest EF depletion promoted a pro-metastatic phenotype that diminished upon near-complete EF loss, revealing a paradoxical effect of submaximal EF inhibition. Nascent RNA-sequencing uncovered distinct gene clusters with heterogenous transcriptional responses to graded EF loss. Genes most sensitive to subtle EF depletion harbored GGAA microsatellites within EF-bound enhancers, while chromatin profiling uncovered candidate cofactors regulating EF-repressed EMP programs. Transient EF depletion followed by rapid restoration, modelling oncoprotein fluctuations, caused persistent dysregulation of genes functionally linked to enhanced extravasation and metastatic burden in preclinical models. This study highlights the therapeutic challenge of incomplete EF elimination, serving a paradigm in which oncoprotein dosage dynamics act as non-genetic drivers of disease progression and reveal novel vulnerabilities of advanced disease.

This conceptual understanding article is part of a series where we explore the recognition and conversion between representations of the electric field concept; in this article, we present the vector field plot’s case. We conducted a study with higher education introductory and upper-division Physics students in a large private Mexican university, intending to learn how students recognize the electric field’s main characteristics in the vector field plot independently and as a source or target representation in conversion processes. We used the theory of registers of semiotic representations and a phenomenographic approach as a framework to analyze data. We explored students’ recognition and conversion abilities through interpretation and construction tasks. We found that the main difficulties of interpreting and constructing the vector field plot are related to the representation’s surface features, often delimited by physical considerations. In the interpretation tasks, this was observed as the interpretation of the physical symmetry, while in construction tasks, it was evident by the confinement of vectors and other associated difficulties. In conversion processes, we observed that there might be an interaction between the source and the target representation, which may result in difficulties in interpreting the vector field plot. We recommend that higher education introductory and upper-division electricity and magnetism instructors and physics education researchers be aware of the difficulties that some interpretation and construction tasks may present to students learning the electric field concept.

The automotive industry is undergoing a profound transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends are increasingly blurring the boundaries between the automotive sector and other industries. The pace of adaptation to electrification varies considerably between regions and firms. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms’ ability to leverage cross-industry diversification contributes to their competitive advantage in this evolving landscape. We develop a country-level product space covering all industries, and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts which are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. First, we examine this relationship across all industrial sectors to establish general patterns of path dependency, diversification and capability formation. Then, we focus specifically on the electric vehicle (EV) transition. It is argued that new strengths in vehicles and aluminium products in the EU will generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade. In contrast, these sectors are expected to generate only 1.6 and 4.5 new strengths, respectively, in already diversified China. However, a different pattern emerges when these country-level results are compared to the firm-level product space. Countries such as South Korea, China, the US and Canada show the greatest potential for diversification into EV-related products. Established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation will depend on the ability of regions to mobilize existing industrial capabilities, particularly in related sectors such as machinery and electronic equipment.

The von Hippel-Lindau tumor suppressor (VHL) is a component of a ubiquitin ligase complex that controls cellular responses to hypoxia. Endogenous VHL is also utilized by proteolysis-targeting chimera (PROTAC) protein degraders, a promising class of anti-cancer agents. VHL is broadly essential for cell proliferation, yet it is a key tumor suppressor in renal cell carcinoma. To understand the functional consequences of VHL loss, and to identify targeted approaches for the elimination of VHL null cells, we have used genome-wide CRISPR-Cas9 screening in human renal epithelial cells. We find that, upon VHL loss, the HIF1A/ARNT complex is the central inhibitor of cellular fitness, suppressing mitochondrial respiration, and that VHL null cells show HIF1A-dependent molecular vulnerabilities that can be targeted pharmacologically. Combined VHL/HIF1A inactivation in breast and esophageal cancer cells can also provide resistance to ARV-771, a VHL-based bromodomain degrader that has anti-cancer activity. HIF1A stabilization can thus provide opportunities for early intervention in neoplastic VHL clones, and the VHL-HIF1A axis may be relevant for the development of resistance to the emerging class of PROTAC-based cancer therapies.

Neutrophils are vital innate immune cells shown to infiltrate glioblastomas, however we currently lack the molecular understanding of their functional states within the tumour niche. Given that neutrophils are known to display a prominent discordance between mRNA and protein abundance, we developed ultra-sensitive mini-bulk and single cell proteomic (SCP) workflows to study the heterogeneity of peripheral blood and tumour associated neutrophils (TAN) from patients with glioblastoma. Mini-bulk analysis enabled a deeper protein coverage of circulating immature, mature and TAN populations, defining signatures of maturity and demonstrating that TANs resemble mature circulating neutrophils. Analysis of the SCP data results in the detection of >1100 proteins from a single TAN providing a detailed characterization of neutrophil subsets in glioblastoma. Our approach shows evidence of pathogenic and anti-tumorigenic clusters and discovers cell states invisible to scRNAseq, opening new opportunities to selectively target pro-tumoural neutrophil states.