Biology subjects

Esser-Skala, W.

Publications and source records attributed to Esser-Skala, W..

3 recordsLinked to original sources

bioRxiv2025

Differences between in vivo and ex vivo hematopoietic model systems modulate the outcomes of genetic perturbations.

Ex vivo experimental models are extensively used as reductionist models, yet the impact of model choice on molecular states and perturbation responses has not been systematically evaluated. Here, we compared the outcomes of genetic perturbations in hematopoietic stem cells between in vivo and ex vivo model systems. This revealed strong baseline differences characterized by a reduced interferon response and elevated growth and metabolism signatures in ex vivo models, a pattern recapitulated in various orthogonal human and mouse systems. We further found substantial differences between perturbation effects between in vivo and ex vivo models, with some perturbations showing opposite effects. Finally, we evaluated whether AI-based perturbation prediction models can predict in vivo perturbation effects from ex vivo perturbation effects, which proved challenging. Together, our findings reveal systematic biases in ex vivo models, demonstrate how these biases influence perturbation outcomes, suggest approaches to enhance ex vivo systems, and provide a test case for computational prediction of perturbation effects.

systems biology↗

bioRxiv2025

Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens

Cardiac fibrosis is mediated by the persistent activity of myofibroblasts, which differentiate from resident cardiac fibroblasts in response to tissue damage and stress signals. The signaling pathways and transcription factors regulating fibrotic transformation have been thoroughly studied. In contrast, the roles of chromatin factors in myofibroblast differentiation and their contribution to pathogenic cardiac fibrosis remain poorly understood. Here, we combined bulk and single-cell CRISPR screens to characterize the roles of chromatin factors in the fibrotic transformation of primary cardiac fibroblasts. We uncover strong regulators of fibrotic states including Srcap and Kat5 chromatin remodelers. We confirm that these factors are required for functional processes underlying fibrosis including collagen synthesis and cell contractility. Using chromatin profiling in perturbed cardiac fibroblasts, we demonstrate that pro-fibrotic chromatin complexes facilitate the activity of well-characterized pro-fibrotic transcription factors. Finally, we show that KAT5 inhibition alleviates fibrotic responses in patient-derived human fibroblasts.

cell biology↗

bioRxiv2023

Reliable interpretability of biology-inspired deep neural networks

Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.

bioinformatics↗