Dataset Information

Single-cell transcriptomics reveals ependymal subtypes related to cytoskeleton dynamics as the core driver of syringomyelia pathological development.

ABSTRACT: Syringomyelia is a common clinical lesion associated with cerebrospinal fluid flow abnormalities. By a reversible model with chronic extradural compression to mimic human canalicular syringomyelia, we explored the spatiotemporal pathological alterations during syrinx development. The most dynamic alterations were observed in ependymal cells (EPCs), oligodendrocyte lineage, and microglia, as a response to neuroinflammation. Among different cell types, EPC subtypes experienced obvious dynamic alterations, which were accompanied by ultrastructural changes involving the ependymal cytoskeleton, cilia, and dynamic injury in parenchyma primarily around the central canal, corresponding to the single-cell transcripts. After effective decompression, the syrinx resolved with the recovery of pathological damage and overall neurological function, implying that for syringomyelia in the early stage, there was still endogenous repair potential coexisting with immune microenvironment imbalance. Ependymal remodeling and cilia restoration might be important for better resolution of syringomyelia and parenchymal injury recovery.

SUBMITTER: Lu C

PROVIDER: S-EPMC10232665 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single-cell transcriptomics reveals ependymal subtypes related to cytoskeleton dynamics as the core driver of syringomyelia pathological development.

Lu Chunli C Wu Xianming X Wang Xinyu X Xiao Zhifeng Z Ma Longbing L Dai Jianwu J Jian Fengzeng F

iScience 20230511 6

Syringomyelia is a common clinical lesion associated with cerebrospinal fluid flow abnormalities. By a reversible model with chronic extradural compression to mimic human canalicular syringomyelia, we explored the spatiotemporal pathological alterations during syrinx development. The most dynamic alterations were observed in ependymal cells (EPCs), oligodendrocyte lineage, and microglia, as a response to neuroinflammation. Among different cell types, EPC subtypes experienced obvious dynamic alte ...[more]

PMID: 37275526

Similar Datasets

Project description:BackgroundSyringomyelia was a rare condition characterized by the formation of fluid-filled cysts (syrinx) within the spinal cord, resulting in sensory and motor dysfunction. Currently, there was no satisfactory treatment for syringomyelia. Ependymal cells were integral to water transport and may represent a promising therapeutic target.MethodsInduction of syringomyelia occurred in 8-week old female rats followed by histological analyses at 3-, 7-, 14-, 30-, 60-, 180-, and 365-days later. Scanning electron microscope (SEM) and transmission electron microscope (TEM) were performed to visualize cilia on rat central canal membrane cells at 30-day post-induction. Syringomyelia was induced via compression at T12-T13 using a sterile cotton ball. Each rat underwent MRI scanning one day before induction and one day prior to sacrifice. In vivo magnetic resonance imaging (MRI) was utilized to measure syrinx enlargement in eight-week-old syringomyelia rats. Histological Analysis and immunofluorescence staining were performed for changes of cilia, neurons, expression of AQP4 and infiltration of immune cells into spinal tissue.ResultsIn the current study, the cell junctions between ependymal cells of syringomyelia rats were absent, and the cilia on ependymal cells were reduced significantly on day 30 post syringomyelia. The number of ependymal cells kept increasing lasting for 1-2 months and begin to decrease. Edema and vacuolation in the spinal cord tissue are significant in syringomyelia rats. Furthermore, AQP4 expression was elevated in astrocytes of syringomyelia rats, and IBA1+ immune cells infiltrated spinal tissue. Furthermore, neuronal necrosis began in the acute stage of syringomyelia, and reached its peak one month later. Pathological changes in axonal rupture at anterior commissure (connection of the left and right white matter) could be observed in syringomyelia spinal tissue.ConclusionsThese findings underscored the significance of cilia on ependymal cells and the evolving microenvironment post-syringomyelia, providing valuable insights for clinical treatment strategies for this condition.

Project description:Uterine leiomyomas are common benign smooth muscle tumors that impose a major burden on women's health. Recent sequencing studies have revealed recurrent and mutually exclusive mutations in leiomyomas, suggesting the involvement of molecularly distinct pathways. In this study, we explored transcriptional differences among leiomyomas harboring different genetic drivers, including high mobility group AT-hook 2 (HMGA2) rearrangements, mediator complex subunit 12 (MED12) mutations, biallelic inactivation of fumarate hydratase (FH), and collagen, type IV, alpha 5 and collagen, type IV, alpha 6 (COL4A5-COL4A6) deletions. We also explored the transcriptional consequences of 7q22, 22q, and 1p deletions, aiming to identify possible target genes. We investigated 94 leiomyomas and 60 corresponding myometrial tissues using exon arrays, whole genome sequencing, and SNP arrays. This integrative approach revealed subtype-specific expression changes in key driver pathways, including Wnt/β-catenin, Prolactin, and insulin-like growth factor (IGF)1 signaling. Leiomyomas with HMGA2 aberrations displayed highly significant up-regulation of the proto-oncogene pleomorphic adenoma gene 1 (PLAG1), suggesting that HMGA2 promotes tumorigenesis through PLAG1 activation. This was supported by the identification of genetic PLAG1 alterations resulting in expression signatures as seen in leiomyomas with HMGA2 aberrations. RAD51 paralog B (RAD51B), the preferential translocation partner of HMGA2, was up-regulated in MED12 mutant lesions, suggesting a role for this gene in the genesis of leiomyomas. FH-deficient leiomyomas were uniquely characterized by activation of nuclear factor erythroid 2-related factor 2 (NRF2) target genes, supporting the hypothesis that accumulation of fumarate leads to activation of the oncogenic transcription factor NRF2. This study emphasizes the need for molecular stratification in leiomyoma research and possibly in clinical practice as well. Further research is needed to determine whether the candidate biomarkers presented herein can provide guidance for managing the millions of patients affected by these lesions.

Project description:BackgroundParkinson's Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored.ObjectivesThe objectives of this study were (1) to describe the transcriptomics characteristics of three PD progression subtypes identified by AI, and (2) to evaluate if gene expression data can be used to predict disease subtype at baseline.DesignThis is a retrospective longitudinal cohort study utilizing the Parkinson's Progression Markers Initiative (PPMI) database.MethodsWhole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values.ResultsOur study identified differentially expressed genes (DEGs) that were uniquely associated with Parkinson's disease (PD) progression subtypes. Importantly, these DEGs had not been previously linked to PD. Gene-pathway analysis revealed both distinct and shared characteristics between the subtypes. Notably, two subtypes displayed opposite expression patterns for pathways involved in immune response alterations. In contrast, the third subtype exhibited a more unique profile characterized by increased expression of genes related to detoxification processes. All three subtypes showed a significant modulation of pathways related to the regulation of gene expression, metabolism, and cell signaling. ML revealed that the progression subtype with the worst prognosis can be predicted at baseline with 0.877 AUROC, yet the contribution of gene expression was marginal for the prediction of the subtypes.ConclusionThis study provides novel information regarding the transcriptomics profiles of PD progression subtypes, which may foster precision medicine with relevant indications for a finer-grained diagnosis and prognosis.

Dataset Information

Single-cell transcriptomics reveals ependymal subtypes related to cytoskeleton dynamics as the core driver of syringomyelia pathological development.

Publications

Single-cell transcriptomics reveals ependymal subtypes related to cytoskeleton dynamics as the core driver of syringomyelia pathological development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets