Multiple sclerosis (MS) is an autoimmune disease affecting the brain and spinal cord that is associated with chronic inflammation leading to demyelination and neurodegeneration. With the recent increase in the number of available therapies for MS, optimal treatment will be based on a personalized approach determined by an individual patient’s prognosis and treatment risks. An integral part of such therapeutic decisions will be the use of molecular biomarkers to predict disability progression, monitor ongoing disease activity, and assess treatment response. This review describes current published findings within the past 3 years in biomarker research in MS, specifically highlighting recent advances in the validation of cerebrospinal fluid biomarkers such as neurofilaments (light and heavy chains), chitinases and chitinase 3-like proteins, soluble surface markers of innate immunity, and oligoclonal immunoglobulin M antibodies. Current research in circulating miRNAs as biomarkers of MS is also discussed. Continued validation and testing will be required before MS biomarkers are routinely applied in a clinical setting.

Human microglia play a pivotal role in neurological diseases, but few targeted therapies that directly modulate microglial state or function exist due to an incomplete understanding of microglial heterogeneity. We use single-cell RNA sequencing to profile live human microglia from autopsies or surgical resections across diverse neurological diseases and central nervous system regions. We observe a central divide between oxidative and heterocyclic metabolism and identify subsets associated with antigen presentation, motility, and proliferation. Specific subsets are enriched in susceptibility genes for neurodegenerative diseases or the disease-associated microglial signature. We validate subtypes in situ with an RNAscope-immunofluorescence pipeline and leverage our dataset as a classification resource, finding that iPSC model systems recapitulate substantial in vivo heterogeneity. Finally, we identify and validate candidates for chemically inducing subtype-specific states in vitro, showing that Camptothecin downregulates the transcriptional signature of disease-enriched subsets and upregulates a signature previously shown to be depleted in Alzheimer's.

Glioblastoma (GBM) diffusely infiltrates the brain and intermingles with non-neoplastic brain cells, including astrocytes, neurons and microglia/myeloid cells. This complex mixture of cell types forms the biological context for therapeutic response and tumor recurrence. We used single-nucleus RNA sequencing and spatial transcriptomics to determine the cellular composition and transcriptional states in primary and recurrent glioma and identified three compositional ‘tissue-states’ defined by cohabitation patterns between specific subpopulations of neoplastic and non-neoplastic brain cells. These tissue-states correlated with radiographic, histopathologic, and prognostic features and were enriched in distinct metabolic pathways. Fatty acid biosynthesis was enriched in the tissue-state defined by the cohabitation of astrocyte-like/mesenchymal glioma cells, reactive astrocytes, and macrophages, and was associated with recurrent GBM and shorter survival. Treating acute slices of GBM with a fatty acid synthesis inhibitor depleted the transcriptional signature of this pernicious tissue-state. These findings point to therapies that target interdependencies in the GBM microenvironment.

Glioblastoma is an aggressive diffusely infiltrating neoplasm that spreads beyond surgical resection margins, where it intermingles with non-neoplastic brain cells. This complex tissue harboring infiltrating glioma and non-neoplastic brain cells is the origin of tumor recurrence. Thus, understanding the cellular and molecular features of the glioma margin is therapeutically and prognostically important. Here, we used single-nucleus RNA sequencing (snRNAseq) of primary and recurrent glioma to define compositional tissue-states that correlate with radiographic and histopathologic features. We found that glioma cells can be clustered into proliferative, astrocyte-like/mesenchymal, and progenitor-like/proneural states in both the primary and post-treatment recurrence settings. We focused on non-neoplastic microenvironment cells including oligodendrocytes, myeloid cells, neurons, and astrocytes - the latter two are under-represented in single-cell RNAseq studies. Cell type-specific signatures of the astrocyte-like/mesenchymal glioma, and a subpopulation of non-neoplastic astrocytes correlated with poor prognosis, the latter correlated with glioma recurrence. Notably, astrocytes were enriched for metabolic and neurodegenerative gene signatures. Leveraging snRNAseq-derived compositional information, we define three tissue-states that correlate with radiographic localization of primary and recurrent glioma. Our findings define a compositional approach to the glioma microenvironment and reveal prognostically and anatomically relevant features paving the way to new mechanistic and therapeutic discoveries.