Biology subjects

Martens, L.

Publications and source records attributed to Martens, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

CD163 and Tim-4 identify resident intestinal macrophages across sub-tissular regions that are spatially regulated by TGF-β

In bodily organs, macrophages are localised in poorly understood tissular and sub-tissular niches associated with defined macrophage ontogeny and activity. In the intestine, a paradigm is emerging that long-lived macrophages are dominantly present in the muscular layer, while highly monocyte-replenished populations are found in the lamina propria beneath the epithelial barrier. Whether longevity is restricted in such a simplified manner has not been well explored. Moreover, the impact of specific gut-associated factors on long-lived macrophage functionality and niche occupancy is unknown. We generated sc-RNA-Seq data from wild-type and Ccr2-/- mice to identify phenotypic features of long-lived macrophage populations in distinct intestinal niches and identified CD163 as a useful marker to distinguish submucosal/muscularis (S/M) from lamina propria (LP) macrophages. Challenging the emerging paradigm, long-lived macrophages, identified by Tim-4 expression, were found in the LP and S/M. Long-lived LP macrophages are restrained in their response to proinflammatory stimulation compared to short-lived populations in the same location, and to the long-lived population within the S/M. Employing a novel Timd4creTgfbr2fl/fl mouse line we demonstrate distinct functions of TGF-{beta} on long-lived macrophages in these two compartments. Importantly, in Timd4creTgfbr2fl/fl mice, zonation of CD163+ macrophages in the S/M was lost, suggesting TGF-{beta} plays an unappreciated role in positioning of macrophages in the tissue. These data highlight the importance of considering ontogeny and niche when assessing the action of key intestinal regulatory signals.

immunology↗

bioRxiv2023

msqrob2PTM: differential abundance and differential usage analysis of MS-based proteomics data at the post-translational modification and peptidoform level

In the era of open-modification search engines, more post-translational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis and ageing. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by the inherently low abundance of many PTMs and the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM, and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both Differential Peptidoform (PTM) Abundance (DPA) and Differential Peptidoform (PTM) Usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform (PTM) intensities, while for DPU, we correct for parent protein abundance by an intermediate normalisation step which calculates the log2-ratio of the peptidoform (PTM) intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

bioinformatics↗

bioRxiv2023

A Simulation Study on High Spatio-Temporal Resolution Acousto-Electrophysiological Neuroimaging

Acousto-electrophysiological neuroimaging is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The regions electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested. This is done by calculating the forward electroencephalography (EEG) response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density. It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within FDA-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.

neuroscience↗

bioRxiv2023

Benchmarking DIA data analysis workflows

Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows using a ground-truth sample, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library-free mode. In spectral library mode we used the independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library-free mode or using in silico predicted libraries, together with Spectronaut in library-free mode, shows the highest sensitivity maintaining a high reproducibility and accuracy. In general, DIA-NN shows the best reproducibility, while the accuracy is comparable for all DIA workflows.

bioinformatics↗

bioRxiv2023

Unipept Desktop 2.0: construction of targeted reference protein databases for proteogenomics analyses

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of proteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is now also possible to perform (meta)proteogenomics analyses. Since the protein reference database now lives on the users local machine, they have complete control over the database used during an analysis. Data does no longer need to be transmitted over the internet, decreasing the time required for an analysis and better safeguarding privacy sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

bioinformatics↗

bioRxiv2022

Quality control for the target decoy approach for peptide identification

Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target-decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/516857v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@fed04borg.highwire.dtl.DTLVardef@11d115dorg.highwire.dtl.DTLVardef@15f1a45org.highwire.dtl.DTLVardef@b5ceab_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins

Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyse the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.

systems biology↗

bioRxiv2022

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores

MotivationInferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, correct taxonomic identification is crucial when identifying different viral strains with high sequence homology - considering, e.g., the different epidemiological characteristics of the various strains of SARS-CoV-2. Additionally, many viruses mutate frequently, further complicating the correct assignment of virus proteomic samples. ResultsWe present PepGM, a probabilistic graphical for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence score, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on species level, which PepGM clearly indicates by lower confidence scores. Availability and ImplementationPepGM is written in Python and embedded into a Snakemake workflow. Its is available at https://github.com/BAMeScience/PepGM

bioinformatics↗

bioRxiv2022

Trans-synaptic dwelling of SARS-CoV-2 particles perturbs neural synapse organization and function

SARS-CoV-2 infection is associated with short- and long-term neurological and psychiatric complications, referred to as neuroCOVID. These symptoms are relatively heterogenous and fluctuating, hampering the discovery of molecular mechanisms underlying viro-induced brain perturbations. Here, we show that the human cerebral cortex poorly supports SARS-CoV-2 dissemination using post-mortem COVID-19 patient samples, ex vivo organotypic cultures of human brain explants and stem cell-derived cortical organoids. Despite restricted infection, the sole exposure of neural cells to SARS-CoV-2 particles is sufficient to induce significant perturbations on neural synapse organization associated to electrical activity dysfunction. Single-organoid proteomics revealed that exposure to SARS-CoV-2 is associated to trans-synaptic proteins upregulation and unveiled that incoming virions dwell at LPHN3/FLRT3-containing synapses. Our study provides new mechanistic insights on the origin of SARS-CoV-2-induced neurological disorders. One-Sentence SummarySARS-CoV-2 modulates neural plasticity and electrical activity as viral particles lodge at the trans-synaptic interface.

microbiology↗

bioRxiv2022

Immunopeptidomics-based design of highly effective mRNA vaccine formulations against Listeria monocytogenes

Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and increasing antibiotic resistance no clinically approved vaccine against Listeria is available. Attenuated Listeria strains offer protection and are tested as antitumor vaccine vectors, but would benefit from a better knowledge on immunodominant vector antigens. To identify novel antigens, we screened for Listeria epitopes presented on the surface of infected human cell lines by mass spectrometry-based immunopeptidomics. In between more than 15,000 human self-peptides, we detected 68 Listeria epitopes from 42 different bacterial proteins, including several known antigens. Peptide epitopes presented on different cell lines were often derived from the same bacterial surface proteins, classifying these antigens as potential vaccine candidates. Encoding these highly presented antigens in lipid nanoparticle mRNA vaccine formulations resulted in specific CD8+ T-cell responses and high levels of protection in vaccination challenge experiments in mice. Our results pave the way for the development of a clinical mRNA vaccine against Listeria and aid to improve attenuated Listeria vaccines and vectors, demonstrating the power of immunopeptidomics for next-generation bacterial vaccine development.

immunology↗

bioRxiv2022

FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, co-expression analysis becomes complex. To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4,210 interactions between 1,039 understudied proteins. Our findings showcase FAVAs capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

bioinformatics↗

bioRxiv2022

An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia

The holistic nature of omics studies makes them ideally suited to generate hypotheses on health and disease. Sequencing-based genomics and mass spectrometry (MS)-based proteomics are linked through epigenetic regulation mechanisms. However, epigenomics is currently mainly focused on DNA methylation status using sequencing technologies, while studying histone posttranslational modifications (hPTMs) using MS is lagging, partly because reuse of raw data is impractical. Yet, targeting hPTMs using epidrugs is an established promising research avenue in cancer treatment. Therefore, we here present the most comprehensive MS-based preprocessed hPTM atlas to date, including 21 T-cell acute lymphoblastic leukemia (T-ALL) cell lines. We present the data in an intuitive and browsable single licensed Progenesis QIP project and provide all essential quality metrics, allowing users to assess the quality of the data, edit individual peptides, try novel annotation algorithms and export both peptide and protein data for downstream analyses, exemplified by the PeptidoformViz tool. This data resource sets the stage for generalizing MS-based histone analysis and provides the first reusable histone dataset for epidrug development.

cancer biology↗

bioRxiv2022

A panoramic perspective on human phosphosites

Protein phosphorylation is the most common post-translational reversible modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics datasets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false positive sites using five complementary site properties. We then describe the context of these P-sites in terms of protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we also assess structural dynamics of P-sites in their phosphorylated and unphosphorylated state. Our study shows that the residues that gets phosphorylated are more flexible than their equivalent non-phosphorylated residues. Our structural and biophysical analyses of P-sites in solvent inaccessible (buried) regions of proteins show that these sites are primarily found in multi-site phospho-proteins, where highly dynamic structural transitions can occur upon binding with another protein. Finally, our analysis of the biophysical properties of P-site mutations shows that P-site mutations that occur in structurally rigid regions are more often involved in disease.

bioinformatics↗

bioRxiv2022

Non-linearities and Timescales in Temporal Interference Deep Brain Stimulation

In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency. It was previously seen that for the same input intensity, the neurons do not react to a purely sinusoidal signal. This study aims to get a better understanding of the mechanism underlying TI neuromodulation, which is largely unknown. To this end, single-compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares different neuron models to get insight into which models are able to reproduce the experimental observations. It was found that integrate- and-fire models do not entirely reflect the experimental behavior while the Hodgkin-Huxley and Frankenhaeuser-Huxley model do reflect this behavior. Changing the characteristics of the ion gates in the Frankenhaeuser-Huxley model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The model results show that TI stimulation is not qualitatively impacted by nonlinearities in the current-voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper makes advances both in terms of biophysical insight into the neuron as well as the insight in computational modelling of TI stimulation.

neuroscience↗

bioRxiv2021

C/EBPδ-induced epigenetic changes control the dynamic gene transcription of S100A8 and S100A9

The proinflammatory alarmins S100A8 and S100A9 are among the most abundant proteins in neutrophils and monocytes but completely silenced after differentiation to macrophages. The molecular mechanisms of the extraordinarily dynamic transcriptional regulation of s100a8 and s100a9 genes, however, are only barely understood. Using an unbiased genome-wide CRISPR/Cas9 knockout based screening approach in immortalized murine monocytes we identified the transcription factor C/EBP{delta} as a central regulator of S100A8 and S100A9 expression. S100a8 and S100a9 expression was further controlled by the C/EBP{delta}-antagonists ATF3 and FBXW7. We confirmed the clinical relevance of this regulatory network in subpopulations of human monocytes in a clinical cohort of cardiovascular patients. Moreover, we identified specific C/EBP{delta}-binding sites within s100a8 and s100a9 promoter regions, and demonstrated that C/EBP{delta}-dependent JMJD3-mediated demethylation of H3K27me3 is indispensable for their expression. Overall, our work uncovered C/EBP{delta} as a novel regulator of S100A8 and S100A9 expression. Therefore, C/EBP{delta} represents a promising target for modulation of inflammatory conditions that are characterised by S100A8 and S100A9 overexpression.

cell biology↗

bioRxiv2021

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

bioinformatics↗

bioRxiv2021

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify Major Histocompatibility Complex-presented peptides on every cell that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the non-tryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS2PIP and retention time predictions by DeepLC, have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS2PIP was tailored towards tryptic peptides, we have here retrained MS2PIP to include non-tryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides, but also yield further improvements for tryptic peptides. We show that the integration of new MS2PIP models, DeepLC, and Percolator in one software package, MS2Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS2Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Integration of immunopeptide MS2PIP models, DeepLC, and Percolator into MS2Rescore thus allows substantial improved identification of novel epitopes from existing immunopeptidomics workflows.

bioinformatics↗

bioRxiv2021

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here, we present a spatial proteogenomic atlas of the healthy human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and bile-duct macrophages. We also uncover the respective spatially-resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that bile-duct macrophages are induced by local lipid exposure, while Kupffer cells crucially depend on their crosstalk with hepatic stellate cells via the evolutionarily-conserved ALK1-BMP9/10 axis.

immunology↗