OBJECTIVE: Our objective was to determine whether altered naive CD4 T-cell biology contributes to development of disease progression in secondary progressive multiple sclerosis (SPMS). METHODS: We compared the naive CD4 T-cell gene expression profiles of 19 patients with SPMS and 14 healthy controls (HCs) using a whole-genome microarray approach. We analyzed surface protein expression of critical genes by flow cytometry after T-cell receptor (TCR) stimulation of naive CD4 T cells isolated from HCs and patients with SPMS. RESULTS: Hierarchical clustering segregated patients with SPMS into 2 subgroups: SP-1, which had a short duration of relapsing-remitting multiple sclerosis (MS), and SP-2, which had a long duration of relapsing-remitting MS. SP-1 patients upregulated numerous immune genes, including genes within TCR and toll-like receptor (TLR) signaling pathways. SP-2 patients showed immune gene downregulation in comparison with HCs. We identified an SP-1-specific transcriptional signature of 3 genes (TLR4, TLR2, and chemokine receptor 1), and these genes had higher surface protein expression in SP-1 than in SP-2. After TCR stimulation for 48 hours, only SP-1 showed a progressive linear increase in TLR2 and TLR4 protein expression. CONCLUSIONS: Differences in naive CD4 T-cell biology, notably of TCR and TLR signaling pathways, identified patients with MS with more rapid conversion to secondary progression, a critical determinant of long-term disability in MS.

Multiple sclerosis is a chronic inflammatory disease of the CNS¹. Astrocytes contribute to the pathogenesis of multiple sclerosis², but little is known about the heterogeneity of astrocytes and its regulation. Here we report the analysis of astrocytes in multiple sclerosis and its preclinical model experimental autoimmune encephalomyelitis (EAE) by single-cell RNA sequencing in combination with cell-specific Ribotag RNA profiling, assay for transposase-accessible chromatin with sequencing (ATAC-seq), chromatin immunoprecipitation with sequencing (ChIP-seq), genome-wide analysis of DNA methylation and in vivo CRISPR-Cas9-based genetic perturbations. We identified astrocytes in EAE and multiple sclerosis that were characterized by decreased expression of NRF2 and increased expression of MAFG, which cooperates with MAT2α to promote DNA methylation and represses antioxidant and anti-inflammatory transcriptional programs. Granulocyte-macrophage colony-stimulating factor (GM-CSF) signalling in astrocytes drives the expression of MAFG and MAT2α and pro-inflammatory transcriptional modules, contributing to CNS pathology in EAE and, potentially, multiple sclerosis. Our results identify candidate therapeutic targets in multiple sclerosis.

Microglia play critical roles in brain development, homeostasis, and neurological disorders. Here, we report that human microglial-like cells (iMGLs) can be differentiated from iPSCs to study their function in neurological diseases, like Alzheimer's disease (AD). We find that iMGLs develop in vitro similarly to microglia in vivo, and whole-transcriptome analysis demonstrates that they are highly similar to cultured adult and fetal human microglia. Functional assessment of iMGLs reveals that they secrete cytokines in response to inflammatory stimuli, migrate and undergo calcium transients, and robustly phagocytose CNS substrates. iMGLs were used to examine the effects of Aβ fibrils and brain-derived tau oligomers on AD-related gene expression and to interrogate mechanisms involved in synaptic pruning. Furthermore, iMGLs transplanted into transgenic mice and human brain organoids resemble microglia in vivo. Together, these findings demonstrate that iMGLs can be used to study microglial function, providing important new insight into human neurological disease.

Cell-cell interactions control the physiology and pathology of the central nervous system (CNS). To study astrocyte cell interactions in vivo, we developed rabies barcode interaction detection followed by sequencing (RABID-seq), which combines barcoded viral tracing and single-cell RNA sequencing (scRNA-seq). Using RABID-seq, we identified axon guidance molecules as candidate mediators of microglia-astrocyte interactions that promote CNS pathology in experimental autoimmune encephalomyelitis (EAE) and, potentially, multiple sclerosis (MS). In vivo cell-specific genetic perturbation EAE studies, in vitro systems, and the analysis of MS scRNA-seq datasets and CNS tissue established that Sema4D and Ephrin-B3 expressed in microglia control astrocyte responses via PlexinB2 and EphB3, respectively. Furthermore, a CNS-penetrant EphB3 inhibitor suppressed astrocyte and microglia proinflammatory responses and ameliorated EAE. In summary, RABID-seq identified microglia-astrocyte interactions and candidate therapeutic targets.

Cell-cell interactions in the central nervous system play important roles in neurologic diseases. However, little is known about the specific molecular pathways involved, and methods for their systematic identification are limited. Here, we developed a forward genetic screening platform that combines CRISPR-Cas9 perturbations, cell coculture in picoliter droplets, and microfluidic-based fluorescence-activated droplet sorting to identify mechanisms of cell-cell communication. We used SPEAC-seq (systematic perturbation of encapsulated associated cells followed by sequencing), in combination with in vivo genetic perturbations, to identify microglia-produced amphiregulin as a suppressor of disease-promoting astrocyte responses in multiple sclerosis preclinical models and clinical samples. Thus, SPEAC-seq enables the high-throughput systematic identification of cell-cell communication mechanisms.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.