Biology subjects

Faber, C.

Publications and source records attributed to Faber, C..

10 recordsLinked to original sources

bioRxiv2026

A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability

The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 {+/-} 3/48 {+/-} 1 dB for mouse surface-coil datasets and 49 {+/-} 3/44 {+/-} 4 dB for rat array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.

bioengineering↗

bioRxiv2025

Vascular Basement Membrane Laminins Modulate Functional Zonation of Cerebral Microvessels

We investigated whether vascular basement membrane (BM) laminins influence vascular zonation by performing single-cell RNA sequencing on cerebral blood vessels from mice lacking the major vascular laminins in endothelial and smooth muscle BMs, laminin 4 (Lama4-/-) and laminin 5 (Tek-cre:Lama5-/-), and wild-type littermates. Our dataset expands existing cerebral vascular transcriptomic profiles and reveals that Lama4-/- endothelial cells exhibit increased arterial marker expression and reduced postcapillary venule identity. In vitro and in vivo studies indicate that compensatory upregulation of laminin 5 in Lama4-/-vessels enhances expression of junctional proteins (Ocln, Cldn5) and promotes vessel contractility via increased expression of contractile molecules in mural cells. Additionally, loss of Lama4 upregulates expression of large artery markers (Gja4, Dll4, Tgfb2) and results in elevated autotaxin (Enpp2) levels, a key enzyme in lysophosphatidic acid production implicated in stroke. Accordingly, Lama4-/- mice exhibit worsened stroke outcomes, driven not by immune infiltration or junctional defects, but by increased vascular permeability likely mediated by autotaxin and/or activation of resident myeloid cells. Our data suggest that laminin 4/5 ratios in vascular BMs regulate functional zonation between arterioles, capillaries and postcapillary venules by modulating metabolic pathways in endothelial and mural cells, and indirectly influencing resident myeloid cells.

cell biology↗

bioRxiv2025

Titin cleavage impairs cardiac mechanical connectivity to drive diastolic failure and fibrosis

Titin, the largest human protein, serves as the elastic backbone of sarcomeres in muscle cells and imparts passive stiffness to cardiomyocytes. While proteolytic cleavage of elastic titin has been linked to cardiovascular disease, the isolated effects of titin stiffness loss in the living heart remain poorly understood. Here, we developed a knock-in mouse model allowing for the selective cleavage of cardiac titin springs in vivo. Using a time-resolved approach combining MRI, echocardiography, immunofluorescence, and molecular profiling, we demonstrate that titin cleavage does not dilate the heart but induces concentric remodeling, impaired diastolic function, and low-output heart failure. Mechanistically, compromised titin-based restoring forces trigger internal mechanical imbalance and dysconnectivity, which activate fibroblasts and promote extracellular matrix remodeling, revealing titins essential role in maintaining cardiac mechanical homeostasis.

physiology↗

bioRxiv2024

Influence of Contact Map Topology on RNA Structure Prediction

The available sequence data of RNA molecules have highly increased in the past years. Unfortunately, while computational power is still under exponential growth, the computer prediction quality from sequence to final structure is still inferior to the labour intensive experimental work. Although a reliable end-to-end procedure was already developed for proteins since Alphafold2, while its successor AlphaFold3 can also predict RNA, its confidence in particular for novel sequences and folds appear still limited. Another strategy entails two steps: (i) predicting potential contacts in the form of a contact maps from evolutionary data, and (ii) simulating the molecule with a physical force field while using the contact map as restraint. However, the quality of the structure prediction crucially depends on the quality of the contact map. Until now, only the proportion of true positive contacts was considered as a quality characteristic. We propose to also include the distribution of these contacts, and have done so in our recent studies. We observed that the clustering of contacts, as is common for many AI algorithms, has a negative impact on prediction quality. In contrast, a more distributed topology is beneficial. We have applied these findings from computer experiments to current algorithms and introduced a measure of distribution, the Gaussian score.

biophysics↗

bioRxiv2024

Activation mapping in multi-center rat sensory-evoked functional MRI datasets using a unified pipeline.

Functional Magnetic Resonance Imaging (fMRI) in rodents is pivotal for understanding the mechanisms underlying Blood Oxygen Level-Dependent (BOLD) signals and phenotyping animal models of disorders, amongst other applications. Despite its growing use, comparing rodent fMRI results across different research sites remains challenging due to variations in experimental protocols. Here, we aggregated and analyzed 22 sensory-evoked rat fMRI datasets from 12 imaging centers, totaling scans from 220 rats, to assess the consistency of results across diverse protocols. We applied a standardized preprocessing pipeline and evaluated the impact of different hemodynamic response function models on group and individual level activity patterns. Our analysis revealed inter-dataset variability attributed to differences in experimental design, anesthesia protocols, and imaging parameters. We identified robust activation clusters in all (22/22) datasets. The comparison between stock human models implemented in software and rat-specific models showed significant variations in the resulting statistical maps. Our findings emphasize the necessity for standardized protocols and collaborative efforts to improve the reproducibility and reliability of rodent fMRI studies. We provide open access to all datasets and analysis code to foster transparency and further research in the field.

neuroscience↗

bioRxiv2023

Data-driven signal analysis of sensory cortical processing using high-resolution fMRI across different studies

The analysis of large data sets within and across preclinical studies has always posed a particular challenge in terms of data volume and method heterogeneity between studies. Recent developments in machine learning (ML) and artificial intelligence (AI) allow to address these challenges in complex macro- and microscopic data sets. Because of their complex data structure, functional magnetic resonance imaging (fMRI) measurements are perfectly suited to develop such ML/AI frameworks for data-driven analyses. These approaches have the potential to reveal patterns, including temporal kinetics, in blood-oxygen-level-dependent (BOLD) time series with a reduced workload. However, the typically poor signal-to-noise ratio (SNR) and low temporal resolution of fMRI time series have so far hampered such advances. Therefore, we used line scanning fMRI measurements with high SNR and high spatio-temporal resolution obtained from three independent studies and two imaging centers with heterogeneous study protocols. Unbiased time series clustering techniques were applied for the analysis of somatosensory information processing during electrical paw and optogenetic stimulation. Depending on the similarity formulation, our workflow revealed multiple patterns in BOLD time series. It produced consistent outcomes across different studies and study protocols, demonstrating the generalizability of the data-driven method for cross-study analyzes. Further, we introduce a statistical analysis that is entirely based on cluster distribution. Using this method, we can reproduce previous findings including differences in temporal BOLD characteristics between two stimulation modalities. Our data-driven approach proves high sensitivity, robustness, reproducibility, and generalizability and further quickly provides highly detailed insight into characteristics of BOLD time series. Therefore, it holds great potential for further applications in fMRI data including whole-brain task and resting-state fMRI, which can support fMRI routines. Furthermore, the analytic framework can be used for datasets that have a time-dependent data structure to integrate study results and create robust and generalizable datasets, despite different study protocols.

neuroscience↗

bioRxiv2023

Community structure of heritable viruses in a Drosophila-parasitoids complex

The diversity and phenotypic impacts related to the presence of heritable bacteria in insects have been extensively studied in the last decades. On the contrary, heritable viruses have been overlooked for several reasons, including technical ones. This is regrettable because of the size of this gap knowledge and because case study indicate that viruses may have profound impact on the functionning of individuals and communities. Additionally, the factors that may shape viral communities are poorly known, except in some very specific viral-insect systems. Here we analyze the community structure of heritable viruses in a multi-hosts-multi-parasitoids community. Drosophilidae and their larval and pupal parasitoids were sampled in two locations, two years and two seasons. After two lab generations, putative DNA and RNA viruses were purified and sequenced. Our analysis revealed the presence of 53 viruses (including 37 new viruses), the great majority of which were RNA viruses. The "species" factor was by far the most significant one, explaining more than 50% of the variance in viral structure. Additionally, parasitoids had a higher number of heritable viruses compared to their hosts, suggesting that this lifestyle favours the association with viruses. Finally, our community-level survey challenged previous interpretation concerning the host range of some previously described viruses.

evolutionary biology↗

bioRxiv2023

Distinct Functional cerebral Hypersensitivity networks during incisional and inflammatory pain in rats

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions, especially with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal pain neuroimaging studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. Yet, the challenges of identifying neuroimaging signatures at a pain entity- and modality-specific level are manifold. Therefore, we developed a comprehensive framework for brain network analysis in disease-specific pain models combining functional MRI with graph-theory and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus (mechanical vs. electrical) modality processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. In short, graph-theoretical analyses revealed distinct Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA, resulting in impaired discrimination of stimulus modalities in both pain models compared to control conditions (CTR). Such specific neuroimaging signatures are an important step toward identifying novel pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic options, which ultimately can be translated to the clinic.

neuroscience↗

bioRxiv2021

Highly resolved spatial transcriptomics for detection of rare events in cells

Single-cell spatial transcriptomics technologies leveraged the potential to transcriptionally landscape sophisticated reactions in cells. Current methods to delineate such complex interplay lack the flexibility in rapid target adaptation and are particularly restricted in detecting rare transcripts. We developed a multiplex single-cell RNA In-situ hybridization technique, called Molecular Cartography (MC) that can be easily tailored to specific applications and, by providing unprecedented sensitivity, specificity and resolution, is particularly suitable in tracing rare events at a subcellular level. Using a SARS-CoV-2 infection model, MC allows the discernment of single events in host-pathogen interactions, dissects primary from secondary responses, and illustrates differences in antiviral signaling pathways affected by SARS-CoV-2, simultaneously in various cell types.

molecular biology↗

bioRxiv2021

A preclinical randomized multicenter trial of anti-IL-17A treatment for acute ischemic stroke

Multiple consensus statements have called for preclinical randomized controlled trials (pRCT) to improve translation in stroke research. Here, we investigated the efficacy of IL-17A neutralizing antibodies in a multicentric pRCT using a murine stroke model. C57/Bl.6 mice were subjected to transient middle cerebral artery occlusion (tMCAO). Mice were randomly allocated (1:1). Either anti-IL-17A (500 {micro}g) or isotype antibody (500 {micro}g) were administered 1 h after tMCAO. Primary analysis of infarct volumes was done by MRI after three days. Secondary analysis included mortality, neurological score, neutrophil infiltration and the impact of the gut microbiome on treatment effects. Out of 136 mice, 109 mice were included in the analysis. Mixed model analysis revealed that the IL-17A neutralization significantly reduced infarct sizes (anti IL-17A: 61.77 {+/-} 31.04 mm3; IgG control: 75.66 {+/-} 34.79 mm3; p=0.01). Secondary outcome measures showed a decrease in mortality (Hazard Ratio=3.43, 95% CI = 1.157 - 10.18; p=0.04) and neutrophil invasion into ischemic cortices. There was no difference in the neurological score. The analysis of the gut microbiome showed significant differences between centers. Taken together, this is the first positive pRCT in an ischemia reperfusion model. It suggests IL-17A neutralization as a potential target in stroke.

neuroscience↗