Editorial: Understanding the heterogeneity and spatial brain environment of neurodegenerative diseases through conventional and future methods

Frontiers in Cellular Neuroscience, 2023 · DOI: 10.3389/fncel.2023.1211273 · Published: May 23, 2023

Simple Explanation

Neurodegenerative diseases (ND) lead to cognitive, psychiatric, and motor issues due to neuron loss. These diseases persist for years, causing health deterioration and eventually death. By 2050, over 60s are estimated to be 22% of the population, with dementia significantly impacting the elderly. The causes and progression of common ND like Alzheimer's and Parkinson's are not well-known, and disease trajectories vary among patients, complicating diagnosis and treatment. ND presents a major challenge for modern medicine. Omics sciences can explain a disease's biological continuum by integrating various data layers. Analyzing data from parallel technologies allows identification of alterations at different levels, from transcripts to cellular metabolic processes.

Study Duration

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Participants

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Evidence Level

Editorial

Key Findings

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Proteomics studies are a promising method for biomarker discovery in neurodegenerative diseases.
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One study used a proteomic approach in rats to analyze protein expression in the hippocampus during early seizure events. The results showed that complex macromolecular assembly and cell deaths are modulated on day one.
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Another study identified potential biomarkers for predicting cognitive impairment after aSAH using mass spectrometry, validating NCAM2, NPTXR, NRXN2, RELN, and CNTN2.

Research Summary

This editorial discusses the heterogeneity and spatial brain environment of neurodegenerative diseases and highlights the potential of omics approaches in understanding these diseases. It emphasizes the challenges in studying neurodegenerative diseases due to their complexity and heterogeneity, advocating for a holistic, systems-level approach using multi-omics data integration. The editorial also summarizes four papers that explore the application of omics in neurodegenerative diseases, including proteomics studies, human cell models of the blood-brain barrier, and methods for isolating glial cells.

Practical Implications

Precision Medicine

Multi-omics data can be used to identify sub-phenotypes and personalize treatment for neurodegenerative diseases.

Biomarker Discovery

Omics approaches, especially proteomics, can help in screening for potential biomarkers and druggable targets for ND.

Understanding Pathogenic Pathways

Integration of different sets of features through multi-omics can help in understanding the pathogenic pathways underlying different disease phenotypes.

Study Limitations

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High analytical variance in multi-omics data.
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Limitations in experimental design for multi-omics studies.
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Integrating multi-omics data is still a signiﬁcant challenge in precision medicine.

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