From Systems to Single Cells: Advancing Network Medicine in Neurological Disorders

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d1204009e105">Neurological diseases are pathologies that usually affect the brain, spinal cord and/or nerves of the nervous system. There are more than 600 such diseases, including a wide and heterogeneous range of conditions, and represent one of the leading causes of disability and mortality worldwide [1]. These diseases often manifest through overlapping symptoms and mixed pathologies that defy rigid diagnostic categories, demanding novel approaches that go beyond traditional brain-centric and phenotype-based frameworks [2]. Alzheimer’s disease (AD) is a clear example of this, where multiple pathologies coexist and the traditional brain-centered definitions fall short of capturing the full picture. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1204009e107">In this context, network medicine offers a paradigm shift by combining principles from network science and systems biology to analyse the complex relationships among biological entities. By considering these features as interconnected elements rather than independent components, it enables the discovery of hidden interactions that were previously overlooked. Specifically, advances in proteomics have facilitated the characterization of physical connections between proteins, simplifying the interactome as the network of protein-protein interactions (PPI) in the cellular environment. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1204009e109">To explore the potential of this approach, we focused on schizophrenia [3], a complex and heterogeneous neuropsychiatric disorder that lacks clear molecular biomarkers. Its clinical variability and diagnostic challenges make it a relevant case study for testing network-based methods [4]. By the characterization of disease modules within the interactome, the assessment of disease - drug proximity, and the use of differential gene expression to refine the selection process, we identified potential candidate drugs for repurposing [5]. The data utilized were previously gathered from the platform of the DISNET project [6], which integrates heterogeneous biomedical data, including both structured and unstructured data from public sources, to facilitate the study of diseases and the identification of candidate drugs for repurposing. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1204009e111">Building on these findings, we also explored the application of network-based methodologies to Alzheimer’s disease, leveraging the granularity of single-cell RNA sequencing (scRNA-seq). This approach enables the construction of cell-type-specific PPI networks, offering a higher-resolution understanding of disease-associated molecular alterations [7]. Rather than treating AD as a unitary entity, our work embraces the view that AD represents a spectrum of overlapping endotypes. By profiling over 400,000 brain cells, we reveal distinct transcriptional and network-level alterations across cell types. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1204009e113">This work explores the potential of network-based methodologies to improve our mechanistic understanding of neurological disorders through these two complementary strategies. On one hand, using global PPI networks we can identify broad molecular mechanisms and therapeutic targets, as we did in schizophrenia. On the other, integrating scRNA-seq enables to map transcriptional dysregulation in a cell-type-specific manner, moving beyond a one-size-fits-all disease model.