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Spinal muscular atrophy (SMA) is the leading genetic cause of infant mortality, characterized by progressive neuromuscular degeneration resulting from mutations in the survival motor neuron (SMN1) gene. The availability of disease-modifying therapies for SMA therapies highlights the pressing need for easily accessible and cost-effective blood biomarkers to monitor treatment response and for better disease management. Additionally, the wide implementation of newborn genetic screening programs in Western countries enables presymptomatic diagnosis of SMA and immediate treatment administration. However, the absence of monitoring and prognostic blood biomarkers for neurodegeneration in SMA hinders effective disease management. Neurofilament light protein (NfL) is a promising biomarker of neuroaxonal damage in SMA and reflects disease progression in children with SMA undergoing treatment. Recently, the European Medicines Agency issued a letter of support endorsing the potential utilization of NfL as a biomarker of pediatric neurological diseases, including SMA. Within this review, we comprehensively assess the potential applications of NfL as a monitoring biomarker for disease severity and treatment response in pediatric-onset SMA. We provide reference ranges for normal levels of serum based NfL in neurologically healthy children aged 0-18 years. These reference ranges enable accurate interpretation of NfL levels in children and can accelerate the implementation of NfL into clinical practice.

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer’s disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluidbased biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluidbased biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development., QC 20220810