Biology subjects

Koellensperger, G.

Publications and source records attributed to Koellensperger, G..

9 recordsLinked to original sources

bioRxiv2023

MeXpose - A modular imaging pipeline for the quantitative assessment of cellular metal bioaccumulation

We introduce MeXpose, an imaging pipeline for single-cell metallomics by laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOFMS). MeXpose is designed for mechanistic studies on metal exposure unravelling cellular phenotypes and tissue level characteristics of metal bioaccumulation. MeXpose leverages the high-resolution capabilities of low-dispersion laser ablation setups, a standardised approach to quantitative bioimaging, and the toolbox of immunohistochemistry using metal-labelled antibodies for cellular phenotyping. MeXpose further offers the full scope of single-cell metallomics via an extended mass range accessible through ICP-TOFMS instrumentation (covering isotopes from m/z 14-256) and integration of a complete image analysis workflow. This enables studying quantitative metal accumulation in phenotypically characterized tissue at cellular resolution. Metal amounts in the sub-fg range per cell can be absolutely quantified. As a showcase, an ex vivo human skin model exposed to cobalt chloride (CoCl2) was investigated. Metal permeation was studied for the first time at single-cell resolution, showing high bioaccumulation in the epidermal layers and especially in mitotic cells, accumulating cobalt (Co) in the low fg range per cell. In this cellular phenotype, Co accumulation was correlated to DNA damage. While the amount of cobalt was significantly lower in the collagenous matrix of the dermal layer, cells in the vicinity of blood vessels and smooth muscle showed significant Co deposits as well. MeXpose provides unprecedented insights into metal bioaccumulation with the ability to explore novel relationships between metal exposure and cellular responses on a single-cell level, paving the way for advanced toxicological and therapeutic studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/571675v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7a9548org.highwire.dtl.DTLVardef@1deab3dorg.highwire.dtl.DTLVardef@1bc5186org.highwire.dtl.DTLVardef@8c6898_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Homologue Series Detection and Management in LC-MS data with homologueDiscoverer

SummaryUntargeted metabolomics data analysis is highly labor intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series are a particular case of features that can either represent large numbers of noise features, or alternatively represent features of interest with large peak redundancy. Here we present homologueDiscoverer, an R package which allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AvailabilityhomologueDiscoverer is freely available at github https://github.com/kevinmildau/homologueDiscoverer. Contactkevin.mildau@univie.ac.at, christoph.bueschl@boku.ac.at, juergen.zanghellini@univie.ac.at

bioinformatics↗

bioRxiv2022

Fatty sweet symphony: Decoding distinct ganglioside patterns of native and differentiated mesenchymal stem cells by a novel glycolipidomics profiling strategy

Gangliosides are an indispensable glycolipid class concentrated on cell surfaces with a critical role in stem cell differentiation. Nonetheless, owing to the lack of suitable methods for scalable analysis covering the full scope of ganglioside molecular diversity, their mechanistic properties in signaling and differentiation remain undiscovered to a large extent. This work introduces a sensitive and comprehensive ganglioside assay based on liquid chromatography, high-resolution mass spectrometry, and multistage fragmentation. Complemented by an open-source data evaluation workflow, we provide automated in-depth lipid species-level and molecular species-level annotation based on decision rule sets for all major ganglioside classes. Compared to conventional state-of-the-art methods, the presented ganglioside assay offers (1) increased sensitivity, (2) superior structural elucidation, and (3) the possibility to detect novel ganglioside species. A major reason for the highly improved sensitivity is the optimized spectral readout based on the unique capability of two parallelizable mass analyzers for multistage fragmentation. In addition to the significant technological advance, we identified 263 ganglioside species including cell-state-specific markers and previously unreported gangliosides in native and differentiated human mesenchymal stem cells. A general increase of the ganglioside numbers upon differentiation was observed as well as cell-state-specific clustering based on the ganglioside species patterns. By proving the predictive power of gangliosides as ubiquitous cell state-specific markers, we demonstrated the high throughput universal capability of our novel analytical strategy, which comes with new insights on the biological role of gangliosides in stem cell differentiation. Our analytical workflow will pave the way for new ganglioside- and glycolipid-based clusters of differentiation markers to determine stem cell phenotypes.

biochemistry↗

bioRxiv2022

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS is quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB, and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5% to 4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

systems biology↗

bioRxiv2021

Benchmarking feature quality assurance strategies for non-targeted metabolomics

Automated data pre-processing (DPP) forms the basis of any liquid chromatography-high resolution mass spectrometry-driven non-targeted metabolomics experiment. However, current strategies for quality control of this important step have rarely been investigated or even discussed. We exemplified how reliable benchmark peak lists could be generated for eleven publicly available datasets acquired across different instrumental platforms. Moreover, we demonstrated how these benchmarks can be utilized to derive performance metrics for DPP and tested whether these metrics can be generalized for entire datasets. Relying on this principle, we cross-validated different strategies for quality assurance of DPP, including manual parameter adjustment, variance of replicate injection-based metrics, unsupervised clustering performance, automated parameter optimization, and deep learning-based classification of chromatographic peaks. Overall, we want to highlight the importance of assessing DPP performance on a regular basis.

bioinformatics↗

bioRxiv2021

Thermodynamic genome-scale metabolic modeling of metallodrug resistance in colorectal cancer

BackgroundMass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. MethodsMeasuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. ResultsNormalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. ConclusionsHere we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

cancer biology↗

bioRxiv2020

A combined flow injection/reversed phase chromatography - high resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C- internal standards

We propose a fully automated novel workflow for lipidomics based on flow injection-followed by liquid chromatography high resolution mass spectrometry (FI/LC-HRMS). The workflow combined in-depth characterization of the lipidome achieved via reversed phase LC-HRMS with absolute quantification as obtained by a high number of lipid species-specific- and/or retention time (RT) matched/class-specific calibrants. The lipidome of 13C labelled yeast (LILY) provided a cost efficient, large panel of internal standards covering triacylglycerols (TG), steryl esters (SE), free fatty acids (FA), diacylglycerols (DG), sterols (ST), ceramides (Cer), hexosyl ceramides (HexCer), phosphatidylglycerols (PG), phosphatidylethanolamines (PE), phosphatidic acids (PA), cardiolipins (CL), phosphatidylinositols (PI), phosphatidylserines (PS), phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and lysophosphatidylethanolamines (LPE). In order to exploit the full potential of isotopically enriched biomass, LILY was absolutely quantified on demand via reversed isotope dilution analysis using FI-HRMS. Subsequent LC-HRMS analysis integrated different calibration strategies including lipid species-specific standards for >90 lipids. Extensive measures on quality control allowed to rank the calibration strategies and to automatically selected the calibration strategy of highest metrological order for the respective lipid species. Overall, the workflow enabled a streamlined analysis pipeline (identification and quantification in separate analytical runs) and provided validation tools together with absolute concentration values for >350 lipids in human plasma on a species level with an analytical run-time of less than 25 min per sample. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/367987v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@127472borg.highwire.dtl.DTLVardef@1b17e98org.highwire.dtl.DTLVardef@1ee6ea6org.highwire.dtl.DTLVardef@a28477_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Benchmarking non-targeted metabolomics using yeast derived libraries

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is the essential discovery tool in metabolomics. Up to date, standardization and validation remain a challenge. Community wide accepted, cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts, derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of > 200 metabolites, reproducibly recovered in ethanolic extracts by orthogonal LCHRMS methods, different fermentations (over three years) and different laboratories. More specifically, compound identification was based on accurate mass, matching retention times, and MS/MS spectra as compared to authentic standards and internal databases. The library includes metabolites from the classes of 1) organic acids and derivatives (2) nucleosides, nucleotides and analogues, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds and (7) benzoids at expected concentrations ranges of sub-nM to {micro}M. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the Human metabolome data base (HMDB). A large fraction of metabolites was found to be stable for several years when stored at -80{degrees}C. Thus, the yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments, but enabled in-house routines for instrumental performance tests. Finally, the benchmark material opens new avenues for batch to batch corrections in large scale non-targeted metabolomics studies.

biochemistry↗

bioRxiv2019

STAT3-dependent systems-level analysis reveals PDK4 as an independent predictor of biochemical recurrence in prostate cancer

Prostate cancer (PCa) has a broad spectrum of clinical behaviour, hence biomarkers are urgently needed for risk stratification. We previously described the protective effect of STAT3 in a prostate cancer mouse model. By utilizing a gene co-expression network in addition to laser microdissected proteomics from human and murine prostate FFPE samples, we describe STAT3-induced downregulation of the TCA cycle/OXPHOS in PCa on transcriptomic and proteomic level. We identify pyruvate dehydrogenase kinase 4 (PDK4), a key regulator of the TCA cycle, as a promising independent prognostic marker in PCa. PDK4 predicts disease recurrence independent of diagnostic risk factors such as grading, staging and PSA level. Furthermore, PDK4 expression is causally linked to type 2 diabetes mellitus, which is known to have a protective effect on PCa. We conclude that this effect is related to PDK4 expression and that PDK4 loss could serve as a biomarker for PCa with dismal prognosis.

cancer biology↗