Peripheral blood RNA-seq from 96 sporadic ALS cases and 48 controls were examined for diagnostic, prognostic and cohort stratification biomarkers. Biomarkers distinguishing patients with ALS from controls were identified including activation of ferroptosis and immune-related pathways, and differential transcript usage. Machine learning algorithms classified patients with ALS from controls with 78% accuracy yet were unable to accurately predict survival. Four subgroups of patients with ALS with distinct gene expression profiles and immune cell proportions were identified by unsupervised clustering analysis, highlighting heterogeneity of the patients.

This review focuses on the contribution of stereological studies to the understanding of the cellular architectural changes underlying MSA. We provide an overview of brain-wide cellular and volumetric changes in MSA patients and discuss the stereological results in the context of findings from imaging studies and MSA symptomatology. Combining different techniques such as stereology and diagnostic imaging with clinical data allows for a more detailed interdisciplinary understanding of the disease and illuminates the relationship between neuropathological changes and MSA symptomatology.

Pericytes are important brain cells, but existing pericyte markers (e.g. PDGFRβ immunohistochemical staining) are suboptimal for use in human brain sections. We analysed single-cell RNA expression profiles derived from human and mouse brains, and pericyte-enriched expression was notable for SLC6A12 and SLC19A1. In human brain sections, staining with SLC6A12 and SLC19A1 antibodies was far more effective than a PDGFRB antibody at staining pericyte-like cells. The SLC6A12 antibody was particularly well suited for the purpose of labelling pericytes in human brain sections.