DNA methylation controls stemness of astrocytes in health and ischaemia.

Astrocytes are the most abundant cell type in the mammalian brain and provide structural and metabolic support to neurons, regulate synapses and become reactive after injury and disease. However, a small subset of astrocytes settles in specialized areas of the adult brain where these astrocytes instead actively generate differentiated neuronal and glial progeny and are therefore referred to as neural stem cells1-3. Common parenchymal astrocytes and quiescent neural stem cells share similar transcriptomes despite their very distinct functions4-6. Thus, how stem cell activity is molecularly encoded remains unknown. Here we examine the transcriptome, chromatin accessibility and methylome of neural stem cells and their progeny, and of astrocytes from the striatum and cortex in the healthy and ischaemic adult mouse brain. We identify distinct methylation profiles associated with either astrocyte or stem cell function. Stem cell function is mediated by methylation of astrocyte genes and demethylation of stem cell genes that are expressed later. Ischaemic injury to the brain induces gain of stemness in striatal astrocytes7. We show that this response involves reprogramming the astrocyte methylome to a stem cell methylome and is absent if the de novo methyltransferase DNMT3A is missing. Overall, we unveil DNA methylation as a promising target for regenerative medicine.

High-throughput scNMT protocol for multiomics profiling of single cells from mouse brain and pancreatic organoids.

Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth. For complete details on the use and execution of this protocol, please refer to Clark (2019), Clark et al. (2018), and Clark et al., 2018, Hagemann-Jensen et al., 2020a, Hagemann-Jensen et al., 2020b.