Abstract:Myelin disruption is a feature of natural aging and Alzheimer’s disease (AD). In the CNS, myelin is produced by oligodendrocytes, which are generated throughout life by oligodendrocyte progenitor cells (OPCs). Here, we examined age-related changes in OPCs in APP/PS1 mice, a model for AD-like pathology, compared with non-transgenic (Tg) age-matched controls. The analysis was performed in the CA1 area of the hippocampus following immunolabeling for NG2 with the nuclear dye Hoescht, to identify OPC and OPC sister… Show more
“…In the dendritically-enriched genes, we also discovered over-representation of astrocyte terms ("astrocyte projection"), which conforms to the known existence of astrocytes and other glial cells around the neuropil regions of neurons [28][29][30] . We performed GO analyses with specialized glial cell signatures from PangloDB 31 , and again confirmed stronger enrichment of glial cell signatures in the dendritic regions, only in the Sprod-corrected data (Sup.…”
Section: Detection Of Spatially Variable Genes Is More Accurate After Sprod Denoisingmentioning
Spatial transcriptomics (ST) technologies provide gene expression close to or even superior to single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, the expression data captured by ST technologies suffer from high noise levels, including but not limited to drop-outs as in regular single-cell RNA-sequencing (scRNA-seq). The extra experimental steps for preserving the spatial locations of sequencing could result in even more severe noises, compared to regular scRNA-seq. Fortunately, such noises could be largely removed by leveraging information from the physical locations of sequencing, and the tissue and cellular organization reflected by corresponding pathology images. In this work, we demonstrated the extensive levels of noise in ST data. We developed a mathematical model, named Sprod, to remove such noises based on latent space and graph learning of matched location and imaging data. We comprehensively validated Sprod and demonstrated its advantages over prior methods for removing drop-outs in scRNA-seq data. We further showed that, after adequately de-noising by Sprod, differential expression analyses, pseudotime analyses, and cell-to-cell interaction inferences yield significantly more informative results. In particular, with Sprod, we discovered 3-4 times more RNA transcripts that were actively transported in mouse hippocampus neurons. We also showed that the tumor cells at the tumor-stroma boundaries demonstrate differential transcriptomic features from the tumor cells in the central regions, caused by their interactions with the stroma/immune cells. Overall, we envision denoising by Sprod to become a key first step to empower ST technologies for biomedical discoveries and innovations.
“…In the dendritically-enriched genes, we also discovered over-representation of astrocyte terms ("astrocyte projection"), which conforms to the known existence of astrocytes and other glial cells around the neuropil regions of neurons [28][29][30] . We performed GO analyses with specialized glial cell signatures from PangloDB 31 , and again confirmed stronger enrichment of glial cell signatures in the dendritic regions, only in the Sprod-corrected data (Sup.…”
Section: Detection Of Spatially Variable Genes Is More Accurate After Sprod Denoisingmentioning
Spatial transcriptomics (ST) technologies provide gene expression close to or even superior to single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, the expression data captured by ST technologies suffer from high noise levels, including but not limited to drop-outs as in regular single-cell RNA-sequencing (scRNA-seq). The extra experimental steps for preserving the spatial locations of sequencing could result in even more severe noises, compared to regular scRNA-seq. Fortunately, such noises could be largely removed by leveraging information from the physical locations of sequencing, and the tissue and cellular organization reflected by corresponding pathology images. In this work, we demonstrated the extensive levels of noise in ST data. We developed a mathematical model, named Sprod, to remove such noises based on latent space and graph learning of matched location and imaging data. We comprehensively validated Sprod and demonstrated its advantages over prior methods for removing drop-outs in scRNA-seq data. We further showed that, after adequately de-noising by Sprod, differential expression analyses, pseudotime analyses, and cell-to-cell interaction inferences yield significantly more informative results. In particular, with Sprod, we discovered 3-4 times more RNA transcripts that were actively transported in mouse hippocampus neurons. We also showed that the tumor cells at the tumor-stroma boundaries demonstrate differential transcriptomic features from the tumor cells in the central regions, caused by their interactions with the stroma/immune cells. Overall, we envision denoising by Sprod to become a key first step to empower ST technologies for biomedical discoveries and innovations.
“…However, there is a need for future studies addressing two main issues: firstly, whether these cells remain resident and in a quiescent state in OPC niches, their survival times and the factors involved in their survival, and the potential role of OPC-loaded microparticles; and secondly, the capacity of HOG cells to respond to such demyelinating diseases as multiple sclerosis [ 31 ] and neuromyelitis optica [ 84 ], degenerative diseases such as Alzheimer disease [ 85 ], trauma [ 86 ], vascular diseases, and primary diseases of myelin, such as Alexander disease [ 87 ]. Although the implanted HOG cells can proliferate and differentiate in vitro, questions remain on whether they behave similarly in vivo.…”
Oligodendrocyte precursor cell (OPC) migration is a mechanism involved in remyelination; these cells migrate from niches in the adult CNS. However, age and disease reduce the pool of OPCs; as a result, the remyelination capacity of the CNS decreases over time. Several experimental studies have introduced OPCs to the brain via direct injection or intrathecal administration. In this study, we used the nose-to brain pathway to deliver oligodendrocyte lineage cells (human oligodendroglioma (HOG) cells), which behave similarly to OPCs in vitro. To this end, we administered GFP-labelled HOG cells intranasally to experimental animals, which were subsequently euthanised at 30 or 60 days. Our results show that the intranasal route is a viable route to the CNS and that HOG cells administered intranasally migrate preferentially to niches of OPCs (clusters created during embryonic development and adult life). Our study provides evidence, albeit limited, that HOG cells either form clusters or adhere to clusters of OPCs in the brains of experimental animals.
“…Furthermore, OPCs sense potassium released during neuronal activity through the potassium channels they express, notably Kir4.1, as well as sensing metabolites, including L-lactate, which represent further modes of OPC signal integration [ 12 , 58 ]. Overall, OPC self-renewal is at least partly dependent on neuronal activity and age-related dysregulation of neurotransmission is a potential causative factor in the loss of OPCs and myelin [ 13 , 73 ]. Fig.…”
Section: Myelination Is Regulated By Neuronal Activity and Is Disrupted In Ageing Wmmentioning
White matter (WM) is a highly prominent feature in the human cerebrum and is comprised of bundles of myelinated axons that form the connectome of the brain. Myelin is formed by oligodendrocytes and is essential for rapid neuronal electrical communication that underlies the massive computing power of the human brain. Oligodendrocytes are generated throughout life by oligodendrocyte precursor cells (OPCs), which are identified by expression of the chondroitin sulphate proteoglycan NG2 (Cspg4), and are often termed NG2-glia. Adult NG2+ OPCs are slowly proliferating cells that have the stem cell–like property of self-renewal and differentiation into a pool of ‘late OPCs’ or ‘differentiation committed’ OPCs(COPs) identified by specific expression of the G-protein-coupled receptor GPR17, which are capable of differentiation into myelinating oligodendrocytes. In the adult brain, these reservoirs of OPCs and COPs ensure rapid myelination of new neuronal connections formed in response to neuronal signalling, which underpins learning and cognitive function. However, there is an age-related decline in myelination that is associated with a loss of neuronal function and cognitive decline. The underlying causes of myelin loss in ageing are manifold, but a key factor is the decay in OPC ‘stemness’ and a decline in their replenishment of COPs, which results in the ultimate failure of myelin regeneration. These changes in ageing OPCs are underpinned by dysregulation of neuronal signalling and OPC metabolic function. Here, we highlight the role of purine signalling in regulating OPC self-renewal and the potential importance of GPR17 and the P2X7 receptor subtype in age-related changes in OPC metabolism. Moreover, age is the main factor in the failure of myelination in chronic multiple sclerosis and myelin loss in Alzheimer’s disease, hence understanding the importance of purine signalling in OPC regeneration and myelination is critical for developing new strategies for promoting repair in age-dependent neuropathology.
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