Biology subjects

Blumenthal, D. B.

Publications and source records attributed to Blumenthal, D. B..

At least 19 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↗

bioRxiv2026

MaSkel and napari-MaSkel: fast morphological skeleton feature extraction from segmentation masks

Summary: Existing workflows for extracting morphological features from network-like biomedical structures often require several heterogeneous tools, which can limit integration and reproducibility. We present MaSkel and napari-MaSkel, open-source Python tools that provide GUI- and CLI-based workflows for 2D and 3D skeletonization and graph-based feature extraction, using a substantially accelerated implementation of the widely used Lee94 thinning algorithm. Availability and Implementation: MaSkel and napari-MaSkel are open-source Python packages available under the MIT license on PyPI and GitHub: https://github.com/bionetslab/maskel/, https://github.com/bionetslab/napari-maskel/. Documentation: https://bionetslab.github.io/maskel/, https://bionetslab.github.io/napari-maskel/. Benchmarking and validation: https://github.com/bionetslab/maskel-evaluations.

bioinformatics↗

bioRxiv2026

POME: Graph-based embeddings for partially observed mixed-type data

Partially observed mixed-type (POM) data, as often encountered in clinical, epidemiological, and phenotypic datasets, are very common in biomedical research. Yet, advanced data analysis and machine learning based on POM data is complicated by their heterogeneous nature and often substantial fractions of missing values. While one promising way to overcome these issues is to compute vector-valued embeddings for POM datasets which can then be used for downstream analyses, existing embedding methods are mostly not designed for POM data. To address this gap, we developed POME (partially observed mixed-type data embeddings), a self-supervised model that yields low-dimensional representations of both samples and variables, using shared concept learning and a bipartite graph representation of the underlying POM data. We validated POME through extensive experiments on three real-world biomedical datasets, with diverse downstream tasks and objectives: POME achieves state-of-the-art imputation performance and produces high-quality patient representations that support not only unsupervised discovery of well-separated and clinically meaningful patients subgroups but also supervised predictive modeling and zero-shot representation space mining for use cases such as adjuvant therapy modality recommendation. POME is available as a Python package on GitHub (https://github.com/bionetslab/POME) and PyPI (https://pypi.org/project/pome-py).

bioinformatics↗

bioRxiv2026

Towards reconstruction of the human interactome from positive and negative experimental evidence

Protein-protein interactions (PPIs) have been detected and reported in the millions, but while they are used in many different contexts for better understanding cellular processes in health and disease, the knowledge of the human PPI network is far from complete, containing many false positive measurements and being highly biased. Both to chart the extent of those problems and to solve them requires not just a knowledge of high-confidence positive interactions, but also likely non-interacting protein pairs. However, this information is typically not reported in PPI studies. We developed a methodology to reconstruct this knowledge from existing PPI data. We reconstruct the experimental search space in which PPI screens have been performed and then create a model that informs how likely a PPI is real given its testing and observation frequency. We argue that negative protein pairs allow us to estimate the error rates of experimental and computational screens. We show how this knowledge could be incorporated for calibration. Finally, we evaluate a simple machine learning approach to PPI prediction and propose how such negative data can be used for training instead of random protein pairs. Together, our results show that reconstructing the experimental search space recovers a largely overlooked layer of information from existing PPI data that can help guide a more accurate and complete mapping of the human interactome.

bioinformatics↗

bioRxiv2026

Benchmarking Graph Neural Networks for Multi-Omics Cancer Subtyping using Methylation and Gene Expression Profiles

Motivation: Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a comprehensive and systematic assessment of model architectures, data modalities, graph structures, and their performance for graph signal classification in the biomedical domain is yet to be performed. In order to close this gap, we conducted a benchmarking study on multiple GNNs on a Protein-Protein Interaction (PPI) network for Kidney Renal Clear Cell Carcinoma and Breast cancer subtype prediction, performing an in-depth investigation of architectures, incorporating skip connections and various data modalities. Results: While none of the GNNs outperforms the structure-agnostic Multi-Layer Perceptron baseline, all of them can handle bimodal data (gene methylation and expression) and offer the ability to gain explainability based on PPIs. We offer practical guidelines for applying GNNs to graph signal processing tasks specifically for cancer classification. Depending on the underlying dataset and PPI structure employed, models on different data modalities outperform others. Overall, we suggest using ChebNet, which tends to outperform the Graph Convolutional Network and the Graph Attention Network in cancer subtype prediction. We recommend using GNN architectures that employ a simple flattening readout layer, as they provide better classification performance and faster training time than those with global average pooling. Additionally, we tested residual connections, but they had only an insignificant impact on classification performance.

bioinformatics↗

bioRxiv2026

Dynamic phosphoproteomics and proteomics uncover Leishmania donovani-driven ferritin hijacking, contributing to the control of iron homeostasis and iron-related oxidative stress

Leishmania donovani, the causative agent of visceral leishmaniasis, survives within the parasitophorous vacuole (PV) of mammalian macrophages by extensively rewiring host cellular pathways. Although transcriptional and proteomic changes in infected macrophages have been characterized, the impact on the host phosphoproteome, a pivotal, reversible regulator of signalling, remains largely unexplored. To address this gap, we combined time-resolved quantitative phosphoproteomics and proteomics to map the dynamic response of murine macrophages to L. donovani infection. Early after infection, the parasite rapidly attenuates the macrophage signaling cascades normally triggered by phagocytosis. Between 24 h and 48 h post-infection we observed a progressive de-phosphorylation of proteins involved in innate immunity, apoptosis and other stress-responsive pathways, consistent with a partial shutdown of multiple host kinases. From 24 h onward, a global decline in protein abundance was also detected, most notably within lysosomal network. Strikingly, only five host proteins were consistently up-regulated, suggesting their importance for parasite survival. Ferritin light chain (Ftl1) displayed the largest increase. Immunofluorescence and transmission-electron microscopy revealed that Ftl1 accumulates within the parasitophorous vacuole, colocalizes with the ferritin receptor Ncoa4, and yet fails to undergo degradation, indicating that Leishmania co-opts ferritin as its own intracellular iron-storage compartment. Ferritin is not confined to the PV; it is also internalized by the parasite. Within L. donovani, ferritin accumulates not only in the cytoplasm but also in the nucleus, where it may function both as an iron-storage depot and as an iron buffer that protects the parasite from oxidative damage. Finally, we demonstrate that Leishmania donovani pre-conditions its host macrophage for the iron-rich environments of the liver and spleen by driving the assembly of ferritin particles enriched in ferritin-light chain. This contrasts with L. amazonensis, which, residing in the iron-poor skin, induces ferritin predominated by ferritin-heavy chain (Fth1). Knock-down of Ftl1 did not decrease parasite survival because macrophages compensated for its loss by up-regulating Hspb1, which encodes a protein that limits lipid peroxidation by inhibiting the Fenton reaction. Although this response protects the host cell, it also diminishes iron import, imposing a metabolic cost on Leishmania. Hspb1 represents only one example; additional, as yet unidentified, compensatory pathways are likely activated by the parasite to mitigate the loss of ferritin-light chain. Collectively, our data uncover a previously unknown strategy whereby L. donovani hijacks host ferritin trafficking to create a protected iron reservoir, thereby preventing ferroptosis. This mechanism sets Leishmania, a eukaryotic intracellular parasite, apart from the canonical iron-acquisition tactics employed by bacteria and fungi.

microbiology↗

bioRxiv2026

Bridging the gap between genome-wide association studies and network medicine with GNExT

MotivationA growing volume of large-scale genome-wide association study (GWAS) datasets offers unprecedented power to uncover the genetic determinants of complex traits, but existing web-based platforms for GWAS data exploration provide limited support for interpreting these findings within broader biological systems. Systems medicine is particularly well-suited to fill this gap, as its network-oriented view of molecular interactions enables the integration of genetic signals into coherent network modules, thereby opening opportunities for disease mechanism mining and drug repurposing. ResultsWe introduce GNExT (GWAS Network Exploration Tool), a web-based platform that significantly extends the scope of exploration of variant-level effects and significance beyond those provided by existing solutions. By including MAGMA and Drugst.One, GNExT allows its users to study genetic variants in the context of the latest systems medicine approaches, extending to the identification of potential drug repurposing candidates. Moreover, GNExT advances platform implementation well beyond the current state of the art by offering a highly standardized Nextflow pipeline for data import and preprocessing, allowing researchers to deploy their study results on a sophisticated web interface with minimal implementation overhead. We demonstrate the utility of GNExT using a genome-wide association meta-analysis of human olfactory identification, in which the framework translated isolated GWAS signals to potential pharmacological targets in human olfaction. Furthermore, the deployment of a GNExT instance on European-ancestry Pan-UK Biobank data demonstrates the frameworks scalability, resulting in a comprehensive large-scale resource encompassing thousands of traits and enabling new network medicine-based investigations. Availability and ImplementationThe complete GNExT ecosystem, including the Nextflow preprocessing pipeline, the backend service, and frontend interface, is publicly available on GitHub (https://github.com/dyhealthnet/gnext_nf_pipeline, https://github.com/dyhealthnet/gnext_platform). The public instances of the GNExT platform on olfaction and Pan-UKBB are available under https://olfaction.gnext.gm.eurac.edu and https://panukbb-eur.gnext.gm.eurac.edu.

bioinformatics↗

bioRxiv2026

Towards automated gating of clinical flow cytometry data

Flow cytometry analysis is widespread practice in cell biology, immunology and hematology. Cell populations of interest are typically identified by consecutively examining the expression levels of antigen marker pairs. Since this manual gating process lacks standardization and is time-consuming, several machine learning (ML) methods for automated gating of flow cytometry data have been proposed in recent years. However, their translation into routine workflows has been limited. To address this, we developed the Python package FLAG-X ("flow cytometry automated gating toolbox"), which supports two novel workflows that integrate manual with ML-based gating, using labeled and unlabeled training data. We selected state-of-the-art ML methods developed for automated gating for inclusion in FLAG-X, based on their gating performance in comparison to manual expert annotations. FLAG-X provides a unified interface for top-performing methods and enables seamless integration with standard software for manual gating by exporting results as FCS files. To demonstrate its practical utility, we applied FLAG-X to representative cases from clinical practice. FLAG-X is available at https://anaconda.org/channels/bioconda/packages/flagx/overview. Author summaryIn our research, we work with flow cytometry data, a common laboratory technique to measure the expression of specific antigens in individual cells of a patient sample. In everyday clinical diagnostics or research, experts use dedicated software tools to "manually" inspect these data and often select a subset of cells for further analysis, e.g. B cells for Lymphoma subtype classification. However, this manual procedure, known as gating, can be time-consuming and is not standardized such that outcomes may vary based on the clinician carrying out the cell selection. Automated, machine learning-based methods developed to mitigate those problems are rarely used in routine clinical work. In this study, we set out to close this gap by developing novel workflows that allow clinicians to more easily integrate (semi-)automated approaches into their existing workflows. To do this, we we developed a software package that brings together top-performing methods, functionality to handle clinical flow cytometry data and is designed to be compatible with with existing tools for manual analysis. We evaluated our workflows and toolbox using realistic clinical scenarios and considered practical requirements for their use. By integrating automation into familiar workflows, we strive to make flow cytometry gating both faster and more consistent to support wider use of computational methods in clinical diagnostics.

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

Quantifying distribution shifts in single-cell data with scXMatch

A basic task that frequently arises when analyzing single-cell data is to assess if there is a global distribution shift between the data profiles of cells from two different conditions. Widely used approaches to address this task such as visual inspection of two-dimensional representations or clustering-based workflows lack a solid statistical underpinning and are notoriously unstable and prone to confirmation bias. To promote more rigorous analysis, we here present the scverse-compatible Python tool scXMatch. scXMatch is based on a non-parametric graph-based test to quantify distribution shifts in arbitrary data spaces for which a suitable distance measure is available. We evaluated scXMatch on single-cell gene expression, chromatin accessibility, and imaging-derived cell morphology data, showing that it can robustly detect distribution shifts for different types of single-cell data. scXMatch thus aims to set a new standard in the single-cell biology field, replacing easy-to-manipulate semi-manual distribution shift quantification workflows by principled statistical testing.

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↗

bioRxiv2024

Identification of transcriptional regulators using a combined disease module identification and prize-collecting Steiner tree approach

Transcription factors play important roles in maintaining normal biological function, and their dys-regulation can lead to the development of diseases. Identifying candidate transcription factors involved in disease pathogenesis is thus an important task for deriving mechanistic insights from gene expression data. We developed Transcriptional Regulator Identification using Prize-collecting Steiner trees (TRIPS), a workflow for identifying candidate transcriptional regulators from case-control expression data. In the first step, TRIPS combines the results of differential expression analysis with a disease module identification step to retrieve perturbed subnetworks comprising an expanded gene list. TRIPS then solves a prize-collecting Steiner tree problem on a gene regulatory network, thereby identifying candidate transcriptional modules and transcription factors. We compare TRIPS to relevant methods using publicly available disease datasets and show that the proposed workflow can recover known disease-associated transcription factors with high precision. Network perturbation analyses demonstrate the reliability of TRIPS results. We further evaluate TRIPS on Alzheimers disease, diabetic kidney disease, and prostate cancer single-cell omics datasets. Overall, TRIPS is a useful approach for prioritizing transcriptional mechanisms for further downstream analyses.

bioinformatics↗

bioRxiv2023

DataSAIL: Data Splitting Against Information Leakage

Information Leakage is an increasing problem in machine learning research. It is a common practice to report models with benchmarks, comparing them to the state-of-the-art performance on the test splits of datasets. If two or more dataset splits contain identical or highly similar samples, a model risks simply memorizing them, and hence, the true performance is overestimated, which is one form of Information Leakage. Depending on the application of the model, the challenge is to find splits that minimize the similarity between data points in any two splits. Frequently, after reducing the similarity between training and test sets, one sees a considerable drop in performance, which is a signal of removed Information Leakage. Recent work has shown that Information Leakage is an emerging problem in model performance assessment. This work presents DataSAIL, a tool for splitting biological datasets while minimizing Information Leakage in different settings. This is done by splitting the dataset such that the total similarity of any two samples in different splits is minimized. To this end, we formulate data splitting as a Binary Linear Program (BLP) following the rules of Disciplined Quasi-Convex Programming (DQCP) and optimize a solution. DataSAIL can split one-dimensional data, e.g., for property prediction, and two-dimensional data, e.g., data organized as a matrix of binding affinities between two sets of molecules, accounting for similarities along each dimension and missing values. We compute splits of the MoleculeNet benchmarks using DeepChem, the LoHi splitter, GraphPart, and DataSAIL to compare their computational speed and quality. We show that DataSAIL can impose more complex learning tasks on machine learning models and allows for a better assessment of how well the model generalizes beyond the data presented during training.

bioinformatics↗