Biology subjects

Schneltzer, E.

Publications and source records attributed to Schneltzer, E..

4 recordsLinked to original sources

bioRxiv2026

FERDINAND: An Open Framework for Download and AI-ready Preprocessing of IMPC 2D Radiographs

The International Mouse Phenotyping Consortium (IMPC) maintains a large archive of whole-body dorsoventral radiographs. However, heterogeneity in file formats, metadata completeness, orientation, and image quality limits its reuse for large-scale quantitative analysis. We present FERDINAND (Framework for Extracting and Restructuring Distributed IMPC radiographs into a Normalised, AI-ready Navigable Dataset), an open framework for programmatic access, harmonisation, quality control, and curation of IMPC radiographs. FERDINAND combines metadata retrieval and local radiograph archiving with centre-aware preprocessing, automated exposure screening, orientation standardisation, and clustering-assisted visual curation. Applied to IMPC Data Release 24, FERDINAND organises 104,819 radiographs acquired between February 2012 and June 2025 across ten phenotyping centres and 6,306 single-gene knockout mouse lines, with semi-automated curation recorded for every image. As an independent validation of the harmonised output, a DeepLabCut-based bone-morphometry pipeline showed that FERDINAND-processed radiographs improve data efficiency for landmark detection: at reduced training-set sizes, they achieve better performance than original radiographs and require fewer manual annotations to reach a given level of quality. FERDINAND provides a reproducible foundation for artificial intelligence (AI)-ready analysis and scalable quantitative phenotyping of heterogeneous IMPC and comparable radiograph resources. The complete FERDINAND source code is openly available on GitHub.

bioinformatics↗

bioRxiv2026

EchoVisuALL: From Echocardiography to Gene Discovery

Cardiovascular diseases are a major global health burden, demanding phenotyping frame-works that can match the scale and complexity of contemporary mouse genetics. Here, we introduce EchoVisuALL, an AI-enabled pipeline for automated high-throughput transthoracic echocardiography (TTE) coupling deep-learning-based left-ventricular segmentation with data reporting. Across 65,000 recordings from over 18,000 mice, including single-gene knockouts from the International Mouse Phenotyping Consortium, the framework quantified cardiac morphology and function with minimal operator dependency and high reliability, validated against an expert-curated gold standard dataset. By extracting quantitative parameters across the cardiac cycle, EchoVisuALL in combination with multi-dimensional clustering uncovered nonlinear phenotypic relationships and revealed 37 of 715 genes associated with significant cardiac abnormalities, encompassing well-known human disease genes as well as 12 previously unrecognized candidates, including Cep70, Acot12, Atp8b3, Eea1, Kctd2, and Tspan15. These genotype-phenotype associations are involved in myocardial energetics, membrane biology, and cardiac remodeling. We demonstrate the potential of EchoVisuALL to move beyond image segmentation by delivering a standardized, quantitative foundation for scalable downstream analyses, enabling the discovery of novel cardiac disease genes.

bioinformatics↗

bioRxiv2025

Rethinking ratio-based normalization: A guide towards model-based approaches in heart weight analysis

Heart weight is a critical parameter in cardiology and mouse research, reflecting structural and functional changes linked to cardiac size or hypertrophy and pathophysiological conditions. Normalizing heart weight (HW) to body weight (BW) or tibia length (TL) is a common practice; however, the validity of these ratios has been questioned due to non-proportional relationships between parameters, and this becomes particularly problematic when comparing distinct populations based on such normalized values. Using data from over 25,000 C57BL/6N wildtype mice provided by the International Mouse Phenotyping Consortium (IMPC), we investigated the limitations of ratio-based normalization when comparing different groups, aiming to propose a robust framework for HW analysis. Our findings reveal negligible to weak correlations between HW, BW, and TL across age and sex groups, undermining the validity of ratio-based methods. A modelling study using simulated data demonstrated that ratios could produce misleading results, including reversed or false group differences, when scaling assumptions are violated. Ratios yield accurate and interpretable results only when a truly proportional relationship exists between the variables--specifically, when the regression line passes through the origin--conditions under which ratio-based normalization aligns with outcomes obtained from more robust modelling approaches. These results underscore the superiority of linear models with covariate adjustment and allometric scaling for organ weight analysis, as they more accurately capture biologically relevant scaling relationships. By leveraging the IMPCs large-scale wildtype dataset, we establish the necessity of reassessing normalization practices in quantitative biology traits and propose that ratios should be avoided when comparing normalized values across distinct populations unless key mathematical assumptions are met. This study advances the analytical rigor in phenotyping research, enabling more accurate interpretations of organ mass and function across biological contexts.

physiology↗

bioRxiv2025

Establishing Comprehensive Transthoracic Echocardiography Reference Ranges for Mouse Models: Insights into the Impact of Anesthesia, Sex, and Age

Mouse models play a critical role in cardiology research, offering valuable insights into the molecular mechanisms, genetics, and potential treatments for cardiovascular diseases. However, the ability to transfer findings in mice between studies is limited by the absence of standardized protocols and valid reference values for the assessment of normal cardiac function in mice. This study aims to establish comprehensive transthoracic echocardiography (TTE) reference ranges for mice, particularly focusing on C57BL/6N wildtype controls. The study, which includes data from over 15,000 mice through the International Mouse Phenotyping Consortium (IMPC), highlights how variables such as sex, age, body weight, and anesthesia affect TTE parameters. The findings showed that anesthesia is the primary predictor of variability in cardiac function. Isoflurane and tribromoethanol anesthetized mice presented with modified cardiac function compared to conscious mice. Additionally, we observed minimal sex differences in cardiac morphology and function, except for small variations influenced by anesthesia. The effects of aging on cardiac function were modest, characterized by a decrease in heart rate and subtle changes in ventricular dimensions without evidence of pathological remodeling, likely attributable to disease-free cardiovascular aging. Validation of the reference ranges across multiple mouse strains showed that these values provide a reliable baseline for experiments involving cardiac function in mice. The data underscore the importance of using anesthesia-specific reference values when interpreting TTE results, ensuring robust comparisons in genetic and pharmacological studies. These reference ranges serve as quality assurance tools for future cardiac studies in mice, offering insights into typical TTE parameter values, supporting the detection of experimental perturbations, and contributing to more effective translation of findings from mouse to human.

genetics↗