Osteopontin drives neuroinflammation and cell loss in MAPT-N279K frontotemporal dementia patient neurons

Frontotemporal dementia (FTD) is an incurable group of early-onset dementias that can be caused by the deposition of hyperphosphorylated tau in patient brains. However, the mechanisms leading to neurodegeneration remain largely unknown. Here, we combined single-cell analyses of FTD patient brains with a stem cell culture and transplantation model of FTD. We identified disease phenotypes in FTD neurons carrying the MAPT-N279K mutation, which were related to oxidative stress, oxidative phosphorylation, and neuroinflammation with an upregulation of the inflammation-associated protein osteopontin (OPN). Human FTD neurons survived less and elicited an increased microglial response after transplantation into the mouse forebrain, which we further characterized by single nucleus RNA sequencing of microdissected grafts. Notably, downregulation of OPN in engrafted FTD neurons resulted in improved engraftment and reduced microglial infiltration, indicating an immune-modulatory role of OPN in patient neurons, which may represent a potential therapeutic target in FTD.

Multi-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte state

Huntington disease (HD) is an incurable neurodegenerative disease characterized by neuronal loss and astrogliosis. One hallmark of HD is the selective neuronal vulnerability of striatal medium spiny neurons. To date, the underlying mechanisms of this selective vulnerability have not been fully defined. Here, we employed a multi-omic approach including single nucleus RNAseq (snRNAseq), bulk RNAseq, lipidomics, HTT gene CAG repeat length measurements, and multiplexed immunofluorescence on post-mortem brain tissue from multiple brain regions of HD and control donors. We defined a signature of genes that is driven by CAG repeat length and found it enriched in astrocytic and microglial genes. Moreover, weighted gene correlation network analysis showed loss of connectivity of astrocytic and microglial modules in HD and identified modules that correlated with CAG-repeat length which further implicated inflammatory pathways and metabolism. We performed lipidomic analysis of HD and control brains and identified several lipid species that correlate with HD grade, including ceramides and very long chain fatty acids. Integration of lipidomics and bulk transcriptomics identified a consensus gene signature that correlates with HD grade and HD lipidomic abnormalities and implicated the unfolded protein response pathway. Because astrocytes are critical for brain lipid metabolism and play important roles in regulating inflammation, we analyzed our snRNAseq dataset with an emphasis on astrocyte pathology. We found two main astrocyte types that spanned multiple brain regions; these types correspond to protoplasmic astrocytes, and fibrous-like - CD44-positive, astrocytes. HD pathology was differentially associated with these cell types in a region-specific manner. One protoplasmic astrocyte cluster showed high expression of metallothionein genes, the depletion of this cluster positively correlated with the depletion of vulnerable medium spiny neurons in the caudate nucleus. We confirmed that metallothioneins were increased in cingulate HD astrocytes but were unchanged or even decreased in caudate astrocytes. We combined existing genome-wide association studies (GWAS) with a GWA study conducted on HD patients from the original Venezuelan cohort and identified a single-nucleotide polymorphism in the metallothionein gene locus associated with delayed age of onset. Functional studies found that metallothionein overexpressing astrocytes are better able to buffer glutamate and were neuroprotective of patient-derived directly reprogrammed HD MSNs as well as against rotenone-induced neuronal death in vitro. Finally, we found that metallothionein-overexpressing astrocytes increased the phagocytic activity of microglia in vitro and increased the expression of genes involved in fatty acid binding. Together, we identified an astrocytic phenotype that is regionally-enriched in less vulnerable brain regions that can be leveraged to protect neurons in HD.

Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic and targetable tissue states

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.

Huntington disease oligodendrocyte maturation deficits revealed by single-nucleus RNAseq are rescued by thiamine-biotin supplementation

The complexity of affected brain regions and cell types is a challenge for Huntington's disease (HD) treatment. Here we use single nucleus RNA sequencing to investigate molecular pathology in the cortex and striatum from R6/2 mice and human HD post-mortem tissue. We identify cell type-specific and -agnostic signatures suggesting oligodendrocytes (OLs) and oligodendrocyte precursors (OPCs) are arrested in intermediate maturation states. OL-lineage regulators OLIG1 and OLIG2 are negatively correlated with CAG length in human OPCs, and ATACseq analysis of HD mouse NeuN-negative cells shows decreased accessibility regulated by OL maturation genes. The data implicates glucose and lipid metabolism in abnormal cell maturation and identify PRKCE and Thiamine Pyrophosphokinase 1 (TPK1) as central genes. Thiamine/biotin treatment of R6/1 HD mice to compensate for TPK1 dysregulation restores OL maturation and rescues neuronal pathology. Our insights into HD OL pathology spans multiple brain regions and link OL maturation deficits to abnormal thiamine metabolism.

Cell type-specific changes identified by single-cell transcriptomics in Alzheimer's disease

The rapid advancement of single-cell transcriptomics in neurology has allowed for profiling of post-mortem human brain tissue across multiple diseases. Over the past 3 years, several studies have examined tissue from donors with and without diagnoses of Alzheimer's disease, highlighting key changes in cell type composition and molecular signatures associated with pathology and, in some cases, cognitive decline. Although all of these studies have generated single-cell/nucleus RNA-seq or ATAC-seq data from the full array of major cell classes in the brain, they have each focused on changes in specific cell types. Here, we synthesize the main findings from these studies and contextualize them in the overall space of large-scale omics studies of Alzheimer's disease. Finally, we touch upon new horizons in the field, in particular advancements in high-resolution spatial interrogation of tissue and multi-modal efforts-and how they are likely to further advance mechanistic and target-selection studies on Alzheimer's disease.

USING NEURAL NETWORK TO UNCOVER CELL TYPES THROUGH SNRNA SEQUENCING

The advent of single-nucleus RNA-sequencing (snRNAseq) has allowed for the exploration of genetic signatures of the numerous cells in the brain. In particular, snRNAseq data can provide new insights into how many neurodegenerative diseases, such as Alzheimer’s Disease, alter cells in the brain. One major challenge with analyzing snRNAseq data is the lack of a systematic way to classify the various cell types across different datasets. To address this challenge, we developed a general classifier (“DeepSeq”) that uses state-of-the-art deep learning approaches. We trained our model on multiple snRNAseq datasets derived from post-mortem brain tissue in individuals with and without clinical diagnosis of Alzheimer’s Disease from the ROSMAP cohorts. The two snRNAseq datasets contained 70,064 nuclei and 170,275 nuclei. The two studies employed different clustering techniques, and identified 44 and 18 putative cell types. To map these disparate cluster identities across datasets, we extracted the most relevant genes and trained two separate networks, one on each dataset. We then validated each classifier separately on the holdout cells. The resulting classifier accuracy were 87% and 94%. To map clusters across datasets, we then applied each classifier to the other dataset. Both classifiers yielded mappings that reflected the overall biology, correctly categorizing the nuclei into broad and fine cell type classes. Although validation on additional datasets would expand the generality of this approach, our results show that DeepSeq is an easily implementable classification tool that can assign identity to nuclei in new snRNAseq datasets without the need for preprocessing or cross-batch alignment.