Single-nucleus analysis reveals microenvironment-specific neuron and glial cell enrichment in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a complicated neurodegenerative disease. Neuron-glial cell interactions are an important but not fully understood process in the progression of AD. We used bioinformatic methods to analyze single-nucleus RNA sequencing (snRNA-seq) data to investigate the cellular and molecular biological processes of AD. METHOD: snRNA-seq data were downloaded from Gene Expression Omnibus (GEO) datasets and reprocessed to identify 240,804 single nuclei from healthy controls and patients with AD. The cellular composition of AD was further explored using Uniform Manifold Approximation and Projection (UMAP). Enrichment analysis for the functions of the DEGs was conducted and cell development trajectory analyses were used to reveal underlying cell fate decisions. iTALK was performed to identify ligand-receptor pairs among various cell types in the pathological ecological microenvironment of AD. RESULTS: Six cell types and multiple subclusters were identified based on the snRNA-seq data. A subcluster of neuron and glial cells co-expressing lncRNA-SNHG14, myocardin-related transcription factor A (MRTFA), and MRTFB was found to be more abundant in the AD group. This subcluster was enriched in mitogen-activated protein kinase (MAPK)-, immune-, and apoptosis-related pathways. Through molecular docking, we found that lncRNA-SNHG14 may bind MRTFA and MRTFB, resulting in an interaction between neurons and glial cells. CONCLUSIONS: The findings of this study describe a regulatory relationship between lncRNA-SNHG14, MRTFA, and MRTFB in the six main cell types of AD. This relationship may contribute to microenvironment remodeling in AD and provide a theoretical basis for a more in-depth analysis of AD.

Comparative transcriptome findings reveal the neuroinflammatory network and potential biomarkers to early detection of ischemic stroke.

INTRODUCTION: Ischemic stroke accounts for 70-80% of all stroke cases, leading to over two million people dying every year. Poor diagnosis and late detection are the major causes of the high death and disability rate. METHODS: In the present study, we used the middle cerebral artery occlusion (MCAO) rat model and applied comparative transcriptomic analysis, followed by a systematic advanced bioinformatic analysis, including gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA). We aimed to identify novel biomarkers for the early detection of ischemic stroke. In addition, we aimed to delineate the molecular mechanisms underlying the development of ischemic stroke, in which we hoped to identify novel therapeutic targets for treating ischemic stroke. RESULTS: In the comparative transcriptomic analysis, we identified 2657 differentially expressed genes (DEGs) in the brain tissue of the MCAO model. The gene enrichment analysis highlighted the importance of these DEGs in oxygen regulation, neural functions, and inflammatory and immune responses. We identified the elevation of angiopoietin-2 and leptin receptor as potential novel biomarkers for early detection of ischemic stroke. Furthermore, the result of IPA suggested targeting the inflammasome pathway, integrin-linked kinase signaling pathway, and Th1 signaling pathway for treating ischemic stroke. CONCLUSION: The results of the present study provide novel insight into the biomarkers and therapeutic targets as potential treatments of ischemic stroke.

Identification of vasospasm biomarkers for cerebral hemorrhage via bio-informatics analysis.

BACKGROUND: The incidence of cerebral hemorrhage has rapidly increased over time, and vascular dysfunction has a significant influence on the pathogenesis and outcome of these patients. This is also the case for vasospasm in cerebral hemorrhage, but there is no method to assess this. We conducted this study to find molecular biomarkers of vasospasm in cerebral hemorrhage patients. METHODS: Raw data of GSE37924 was downloaded from the Gene Expression Omnibus (GEO) database, including 66 samples with cerebral vasospasm and 62 samples without cerebral vasospasm. Differentially expressed genes (DEGs) between samples with cerebral vasospasm and those without cerebral vasospasm were analyzed using the limma package in R software. To determine the functions of DEGs, we conducted functional enrichment analysis of DEGs through the clusterProfiler package in R. The protein-protein interaction (PPI) network of DEGs was constructed through STRING (https://string-db.org/) and generated via Cytoscape software. To understand the correlation between DEGs and immune-related genes, immune-related cerebral vasospasm genes were obtained via intersecting immune-related genes and cerebral vasospasm DEGs. We also compared the infiltration of 28 immune cells between cases with cerebral vasospasm and those without cerebral vasospasm. Finally, we constructed a model to perform the validation experiments. RESULTS: Of the DEGs, there were 24 upregulated and 21 downregulated genes in the vasospasm samples compared to the no-vasospasm samples. Functional enrichment analysis showed that these genes play key roles in several biological processes and signaling pathways such as the bone morphogenetic protein (BMP) signaling pathway, cellular response to BMP stimulus, natural killer cell chemotaxis, negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway, MHC protein complex binding, and receptor ligand activity, among others. CCL4, HLA-DQA1, IGF2, NTS, and so on were the significant immune-related genes. Furthermore, the immune cell infiltration results showed that there were differences between patients with vasospasm and those without vasospasm. Finally, we found that CCL4 had significantly higher expression in patients with vasospasm than those without vasospasm. CONCLUSIONS: CCL4 is an important regulator of vascular dysfunction in cerebral hemorrhage.

Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network.

BACKGROUND: The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing transcranial Doppler (TCD) images. METHODS: Overall, 278 patients who underwent cerebral vascular TCD and cerebral angiography were enrolled and classified into stenosis and non-stenosis groups based on cerebral angiography findings. Manual measurements were performed on TCD images. The patients were divided into a training set and a test set, and the CNN architecture was used to classify TCD images. The diagnostic accuracies of manual measurements, CNNs, and TCD parameters for MCA stenosis were calculated and compared. RESULTS: Overall, 203 patients without stenosis and 75 patients with stenosis were evaluated. The sensitivity, specificity, and area under the curve (AUC) for manual measurements of MCA stenosis were 0.80, 0.83, and 0.81, respectively. After 24 iterations of the running model in the training set, the sensitivity, specificity, and AUC of the CNNs in the test set were 0.84, 0.86, and 0.80, respectively. The diagnostic value of CNNs differed minimally from that of manual measurements. Two parameters of TCD, peak systolic velocity and mean flow velocity, were higher in patients with stenosis than in those without stenosis; however, their diagnostic values were significantly lower than those of CNNs (P < 0.05). CONCLUSIONS: The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.

[Diagnostic value of 64-slice spiral computed tomographic angiography in vertebral artery stenosis: a meta-analysis].

OBJECTIVE: To evaluate the diagnostic value of 64-slice spiral computed tomographic angiography (CTA) in vertebral artery stenosis through a meta-analysis of the relevant data. METHODS: A database search of Cochrane Library, PubMed, EBSCO, CBM-disc and CNKI was performed to identify the relevant English and Chinese language articles with such keywords as 64-slice computer tomography, angiography and vertebral artery stenosis. Quality evaluation, heterogeneity test and sensitivity and specificity to the qualified original data were conducted. Summary receiver operating characteristic (SROC) curve, the area under curve (AUC) and diagnostic odds ratio (DROC) were also calculated. RESULTS: A total of 4 studies were eligible for meta-analysis. Among them, 1 was graded as A and 3 were graded as B. No heterogeneity was found based upon a fixed effect model. For vertebral artery stenosis > or = 50%, the pooled weighted sensitivity, specificity, DROC and SROC AUC was 0.98 (0.94 -1.00), 0.93 (0.89 -0.96), 526.33 and 0.9899 respectively; while for vertebral artery stenosis > or = 50%, the parameters were 0.98 (0.91 - 1.00), 0.97 (0.94 -0.99), 838.40 and 0.9932 respectively. CONCLUSIONS: 64-slice spiral CTA has such a high level of accuracy, sensitivity and specificity in the non-invasive diagnosis of vertebral artery stenosis.