Nov 14, 2025 | 2025, Cells, Computational and structural biotechnology journal, Journal publications
Abstract
The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.
Nov 14, 2025 | 2025, Cells, Journal publications
Abstract
Synchronized oscillatory fluctuations in intracellular calcium concentration across extended neuronal networks represent a functional indicator of connectivity and signal coordination. In this study, a model of human immature neurons (differentiated from LUHMES precursors) has been used to establish a robust protocol for generating reproducible intracellular Ca2+ oscillations in both two-dimensional monolayers and three-dimensional spheroids. Oscillatory activity was induced by defined ionic conditions in combination with potassium channel blockade. It was characterized by stable frequencies of approximately 0.2 Hz and high synchronization indices across millimeter-scale cultures. These properties were consistently reproduced in independent experiments and across laboratories. Single-cell imaging confirmed that oscillations were coordinated throughout large cell populations. Pharmacological interventions demonstrated that neither excitatory nor inhibitory chemical synaptic transmission influenced oscillatory dynamics. Gap junction blockers completely disrupted synchronization, while leaving individual cell activity unaffected. Functional dye-transfer assays provided additional evidence for electrical coupling. This was further supported by connexin-43 expression profiles and immunostaining. Collectively, these findings indicate that synchronized Ca2+ oscillations in LUHMES cultures are mediated by gap junctional communication rather than by conventional synaptic mechanisms. This system offers a practical platform for studying fundamental principles of network coordination and for evaluating pharmacological or toxicological modulators of intercellular coupling. Moreover, it may provide a relevant human-based model to explore aspects of neuronal maturation and to assess compounds with potential neurodevelopmental toxicity.
Nov 14, 2025 | 2025, Cells, Journal publications
Abstract
Human microglia are central regulators and actors in brain infections and neuro-inflammatory pathologies. However, access to such cells is limited, and studies systematically mapping the spectrum of their inflammatory states are scarce. Here, we generated microglia-like cells (MGLCs) from human induced pluripotent stem cells and characterized them as a robust, accessible model system for studying inflammatory activation. We validated lineage identity through transcriptome profiling, revealing selective upregulation of microglial signature genes and enrichment of microglia/macrophage-related gene sets. MGLCs displayed distinct morphologies and produced stimulus- and time-dependent cytokine secretion profiles upon exposure to diverse inflammatory stimuli, including pro-inflammatory cytokines (TNFα, interferon-γ) and agonists of the Toll-like receptors TLR2 (FSL-1), TLR3 (Poly(I:C)), TLR4 (lipopolysaccharide, LPS), and TLR7 (imiquimod). Transcriptome profiling and bioinformatics analysis revealed distinct activation signatures. Functional assays demonstrated stimulus-specific engagement of NFκB and JAK-STAT signaling pathways. The shared NFκB nuclear translocation response of TLR ligands and TNFα was reflected in overlapping transcriptome profiles: they shared modules (e.g., oxidative stress response and TNFα-related signaling) identified by weighted gene co-expression network analysis. Finally, the potential consequences of microglia activation for neighboring cells were studied on the example of microglia-astrocyte crosstalk. The capacity of MGLC supernatants to stimulate astrocytes was measured by quantifying astrocytic NFκB translocation. MGLCs stimulated with FSL-1, LPS, or Poly(I:C) indirectly activated astrocytes via a strictly TNFα-dependent mechanism, highlighting the role of soluble mediators in the signal propagation. Altogether, this platform enables a dissection of microglia activation states and multi-parametric characterization of subsequent neuroinflammation.
Nov 14, 2025 | 2025, Altex, Journal publications
Abstract
Next generation risk assessment (NGRA) strategies use animal-free new approach methodologies (NAMs) to generate information concerning chemical hazard, toxicokinetics (ADME), and exposure. The information from these major pillars of data gathering is used to inform risk assessment and classification decisions. While the required types of data are widely agreed upon, the processes for data collection, integration and reporting, as well as several decisions on the depth and granularity of required data, are poorly standardized. Here, we present the Alternative Safety Profiling Algorithm (ASPA), a broad-purpose, transparent, and reproducible risk assessment workflow that allows documentation and integration of all types of information required for NGRA. ASPA aims to make safety assessments fully traceable for the recipient (e.g., a regulator), delineating which steps and decisions have led to the final outcome, and why certain decisions were made. An overarching objective of ASPA is to ensure that identical data input yields identical outcomes in the hands of independent assessors. Therefore, ASPA is not just a data gathering workflow; it also considers data interdependencies and requires precise justification of intermediate decisions. This includes the monitoring and assessment of uncertainties. To assist users, the ASPA-assist software was developed. It formalizes the reporting process in a reproducible and standardized fashion. By guiding an operator step-by-step through the ASPA workflow, a complete and comprehensive report is assembled, whereby all data, methods, operator activities and intermediate decisions are recorded. Practical examples illustrating the broader applicability of ASPA across various regulations and problem formulations are provided through case studies.
Nov 14, 2025 | 2025, Briefings in Bioinformatics, Journal publications
Abstract
A fundamental goal of biological research is to determine the interactions and functional relationships between genes and their coded proteins that drive biological responses. Understanding the response of the global transcriptome in the context of pathogenesis and drug-related adversities can reveal gene–response relationships that contribute to biogical insights and more accurate and reliable mechanism-based safety assessments. Although transcriptomic data provide a framework to systematically determine gene activity, their high dimensionality and complexity can make interpretation and analysis challenging. Gene co-expression analysis addresses these difficulties in analyzing transcriptomics data by first constructing networks of genes that are co-expressed across treatments, reducing complexity, and then inferring biological relevance and gene–pathology associations for each network. Variation in gene expression in bulk tissue helps define co-expression relationships, but the cell type heterogeneity, inherent to bulk tissue, can also complicate biological interpretations. Consequently, interpretation of some tissue gene co-expression patterns may be subject to the confounding influence of variations in cellular composition obscuring intra-cell-type-specific co-expression network responses. In this review, we highlight methods designed to capture cell type–specific co-expression patterns and discuss their potential utility for understanding mechanisms of toxicity and pathogenesis.
Nov 14, 2025 | 2025, Journal publications, Toxicology and Applied Pharmacology
Abstract
Effective drug discovery relies on combining target knowledge with functional assays and multi-omics data to understand chemicals’ molecular actions. However, the relationship between changes in cell morphology and gene expression deregulation over the duration of exposure and across cell lines following chemical exposition remains unclear. To explore this, we analyzed Cell Painting and L1000 data for 106 compounds across three cell lines from osteoblast, lung, and breast tumors (U2OS, A549, and MCF7) at three time points (6 h, 24 h, 48 h) using a 10 μM concentration. Following chemical exposure, we observed significant and specific differences in the spatial organization of cellular structures and components over time and across cell lines in the Cell Painting data, whereas transcriptomic responses showed less pronounced variability. Using Weighted Gene Co-expression Network Analysis (WGCNA) and enrichment analysis, we identified connections between cell morphology and gene deregulation for chemicals with similar biological effects (e.g., HDAC and CDK inhibitors). These findings suggest that while Cell Painting shows distinct patterns, both technologies offer complementary insights into compound-induced cellular changes, enhancing drug discovery and chemical risk assessment.
