Biology subjects

Hartebrodt, A.

Publications and source records attributed to Hartebrodt, A..

9 recordsLinked to original sources

bioRxiv2026

Concurrent de novo inference of cell types and gene regulatory networks with NetMap

Existing methods for GRN inference based on single-cell RNA-sequencing (scRNA-seq) data typically require prior cell type calling and then infer GRNs at cell type resolution. This risks propagation of errors from cell annotation to GRN inference. Moreover, it makes existing methods blind to regulatory programs that are shared between different cell types, as well as to subtle differences in regulatory activity between cells of the same type. To address these limitations, we present NetMap, a pipeline that jointly infers cell-level GRNs and clusters cells based on shared regulatory programs, combining state-of-the-art single-cell autoencoders and explainable artificial intelligence methods. NetMap outperforms existing methods on synthetic data with a known ground-truth GRN and is able to reliably detect biologically plausible regulons on real data. Moreover, requiring only a small set of user-provided marker genes, NetMap can be used for GRN structure-informed probabilistic cell type assignment. We demonstrate the utility of this approach using PBMC data, where we achieve near perfect clustering and cell type recovery performance compared with manually created cell type labels. Finally, we applied NetMap to a data set with naive, activated and exhausted T-cells, and were able to verify that NetMap retrieves relevant T-cell state signatures from the data, with two approaches. NetMap can be installed as a python package from https://github.com/bionetslab/netmap and is compatible with the scverse system. Scripts to reproduce our results can be found at https://github.com/bionetslab/netmap-evaluation.

bioinformatics↗

bioRxiv2025

AlterNet: Alternative splicing-aware gene regulatory networkinference

Gene regulatory networks (GRNs) help decode biological systems by identifying how genes interact and regulate cellular processes. However, conventional GRN inference methods operate at the gene-level, overlooking transcript-level variability introduced by alternative splicing (AS). In this work, we present AlterNet, the first GRN inference and annotation pipeline which considers isoform of transcription factors as distinct regulators in a GRN. AlterNet builds on the GRNBoost2 inference algorithm, and includes a transcript plausibility and annotation workflow. We applied AlterNet to expression data from heart tissue, including samples from donors with normal heart function and from patients with different types of cardiomyopathy. The resulting isoform-level GRNs uncovered highly relevant regulatory interactions not detectable at the gene-level. Overall, AlterNet infers transcript-level regulatory network which enable the discovery of novel, biologically relevant regulatory interactions that remain hidden in gene-level GRNs. The source code of AlterNet is available on GitHub https://github.com/bionetslab/AlterNet, and can be installed as a Python package.

bioinformatics↗

bioRxiv2025

SignifiKANTE: Efficient P-value computation for gene regulatory networks

Gene regulatory networks (GRNs) are graph-based representations of regulatory relationships between transcription factors and target genes. Various tools exist to infer GRNs from gene expression data, but since this task is computationally intensive, statistical significance estimates are often omitted. While permutation-based empirical P-value computation methods are relatively straightforward to implement, they are prohibitively expensive when applied to popular regression-based GRN inference methods and realistically sized datasets. To address this bottleneck, we developed SignifiKANTE. SignifiKANTE is based on the key insight that the background count distributions of groups of target genes may be highly similar, even if their expression vectors show distinct behavior. Relying on this insight, SignifiKANTE employs gene clustering based on the 1-Wasserstein distance to create a small, constant number of background distributions which enables the simultaneous computation of approximate empirical P-values for multiple target genes. This reduces runtime by orders of magnitudes (for some datasets, from several weeks to few hours), without compromising faithfulness of the obtained P-values. SignifiKANTE extends the popular GRN inference package Arboreto and is available as a Python package on GitHub (https://github.com/bionetslab/SignifiKANTE) and PyPI (https://pypi.org/project/signifikante/).

bioinformatics↗

bioRxiv2025

TNF-α disrupts the malate-aspartate shuttle, driving metabolic rewiring in iPSC-derived enteric neural lineages from Parkinson's Disease patients

Gastrointestinal (GI) dysfunction emerges years before motor symptoms in Parkinsons disease (PD), implicating the enteric nervous system (ENS) in early disease progression. However, the mechanisms linking the PD hallmark protein, -synuclein (-syn), to ENS dysfunction - and whether these mechanisms are influenced by inflammation - remains elusive. Using iPSC-derived enteric neural lineages from patients with -syn triplications, we reveal that TNF- increases mitochondrial--syn interactions, disrupts the malate-aspartate shuttle, and forces a metabolic shift toward glutamine oxidation. These alterations drive mitochondrial dysfunction, characterizing metabolic impairment under cytokine stress. Interestingly, targeting glutamate metabolism with Chicago Sky Blue 6B restores mitochondrial function, reversing TNF--driven metabolic disruption. Our findings position the ENS as a central player in PD pathogenesis, establishing a direct link between cytokines, -syn accumulation, metabolic stress and mitochondrial dysfunction. By uncovering a previously unrecognized metabolic vulnerability in the ENS, we highlight its potential as a therapeutic target for early PD intervention.

neuroscience↗

bioRxiv2025

Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65

As most proteins interact with other proteins to perform their respective functions, methods to computationally predict these interactions have been developed. However, flawed evaluation schemes and data leakage in test sets have obscured the fact that sequence-based protein-protein interaction (PPI) prediction is still an open problem. Recently, methods achieving better-than-random performance on leakage-free PPI data have been proposed. Here, we show that the use of ESM-2 protein embeddings explains this performance gain irrespective of model architecture. We compared the performance of models with varying complexity, per-protein, and per-token embeddings, as well as the influence of self- or cross-attention, where all models plateaued at an accuracy of 0.65. Moreover, we show that the tested sequence-based models cannot implicitly learn a contact map as an intermediate layer. These results imply that other input types, such as structure, might be necessary for producing reliable PPI predictions.

bioinformatics↗

bioRxiv2025

Identifying transcription factors driving cell differentiation

Cellular differentiation is a fundamental biological process at the core of development and growth in multicellular organisms. Understanding it at the level of transcriptomic regulation opens the possibility for novel biological insights, for steering cell development for therapeutic purposes, and for developing targeted therapies for diseases. While many methods exist that infer developmental trajectories from single-cell RNA sequencing data, only few can effectively determine which biological mechanisms drive differentiation along such trajectories. To close this gap, we developed SwitchTFI (switch transcription factor identification), a method to identify differentiation-driving regulatory mechanisms and the transcription factors (TFs) that play a key role in them. SwitchTFI infers a cell state transition-specific gene regulatory network (GRN) from a user-provided baseline GRN via decision stump learning and permutation-based significance tests. Key TFs are then extracted by ranking them according to their centralities in the transition GRN. We show that SwitchTFI can identify TFs known to be involved in pancreatic endocrinogenesis and in erythrocyte differentiation and that it outperforms competitor methods with respect to the functional coherence of the predicted driver TFs. SwitchTFI is available as a Python package at https://github.com/bionetslab/SwitchTFI.

bioinformatics↗

bioRxiv2024

High-accuracy prediction of 5-year progression-free melanoma survival using spatial proteomics and few-shot learning on primary tissue

Emerging techniques in imaging-based spatial proteomics (ISP) enable in-depth insights into the architecture and protein abundance of tissue(s). Explainable machine learning (xML) models promise to yield substantial advances in ISP data-based diagnosis and prognosis. However, a clinical application of these new possibilities predicting the course of a tumor has not been suggested yet. Here, we use a few-shot learning workflow on histological multi-antigen images to predict 5-year progression-free survival (PFS) in melanoma. We address the problem of a relatively small cohort (n = 22), by utilizing a pre-trained convolutional neural network (CNN) model, which we further pre-train on a proxy task for which more samples were available (n = 39) before fine-tuning for PFS prediction. Our approach yielded a model achieving an accuracy of more than 90%, outperforming baseline models trained on clinical data by around 10%. Using an xML technique, we identified immune infiltration and proteins associated with tumor progression as crucial predictors. This indicated that our models PFS predictions are not only highly accurate but also grounded in a relevant biological background.

bioinformatics↗

bioRxiv2024

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are non-Hodgkin lymphomas caused by malignant T cells which migrate to the skin. The cancerous T cells lead to rash-like lesions which can be difficult to distinguish from inflammatory skin conditions like atopic dermatitis (AD) and psoriasis (PSO). To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting spatially resolved protein abundance maps were then analyzed via scoring functions to quantify heterogeneity of the individual cells neighborhoods within spatial graphs inferred from the cells positions in the tissue samples (available as a Python package at https://github.com/bionetslab/SHouT). Our analyses reveal several characteristic patterns of skin tissue organization in CTCL, including a combination of increased local entropy and egophily as characteristic properties of spatial T cell neighborhoods in CTCL as compared to AD and PSO. These results could not only pave the way for high-precision diagnosis of CTCL, but may also facilitate further insights into cellular disease mechanisms.

bioinformatics↗

bioRxiv2023

Decoil: Reconstructing extrachromosomal DNA structural heterogeneity from long-read sequencing data

Circular extrachromosomal DNA (ecDNA) is a form of oncogene amplification found across cancer types and associated with poor outcome in patients. EcDNA can be structurally complex and contain rearranged DNA sequences derived from multiple chromosome locations. As the structure of ecDNA can impact oncogene regulation and may indicate mechanisms of its formation, disentangling it at high resolution from sequencing data is essential. Even though methods have been developed to identify and reconstruct ecDNA in cancer genome sequencing, it remains challenging to resolve complex ecDNA structures, in particular amplicons with shared genomic footprints. We here introduce Decoil, a computational method which combines a breakpoint-graph approach with LASSO regression to reconstruct complex ecDNA and deconvolve co-occurring ecDNA elements with overlapping genomic footprints from long-read nanopore sequencing. Decoil outperforms de-novo assembly and alignment-based methods in simulated longread sequencing data for both simple and complex ecDNAs. Applying Decoil on whole genome sequencing data uncovered different ecDNA topologies and explored ecDNA structure heterogeneity in neuroblastoma tumors and cell lines, indicating that this method may improve ecDNA structural analyzes in cancer.

cancer biology↗