An overview of human single-cell RNA sequencing studies in neurobiological disease

Walter, Thomas; Suter, Robert; Ayad, Nagi G.

doi:10.1016/j.nbd.2023.106201

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…With the rapid development of the scRNA-seq technique, it is possible to analyze a variety of tumors, including CC, at the single-cell level. This will bring great convenience in the understanding and exploration of TIME and individualized therapy [21][22]. TIME is a survival system for tumor cells composed of cellular and non-cellular components, and tumor microenvironment is one of the most important internal factors affecting tumor occurrence and development [23] .…”

Section: Discussionmentioning

confidence: 99%

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

Qian,

Xieyidai,

Han

et al. 2023

Preprint

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Objective: The objective of this study is to comprehensively investigate the communication network within the tumor immune microenvironment (Tumor Immune Microenvironment, TIME) of cervical carcinoma (CC). This involves elucidating the intricate relationships among cells to gain a profound understanding of the interactions between immune cells and tumor cells，as well as the assessment of neutrophil differentiation characteristics and the selection of prognostic genes, the primary goal is to establish a risk model with the ability to predict patients' immune responses and prognosis. Additionally, this model seeks to uncover innovative diagnostic and therapeutic targets for cervical carcinoma, thereby furnishing clinicians with dependable strategies for treatment. Methods: Using single-cell RNA sequencing data (scRNA-seq) obtained from CC samples (E-MTAB-11948), this study employed the Seurat(4.3.0) package to integrate data, remove batch effects, and annotate cell types. A cell communication network was constructed using the iTAKL(0.1.0) package for the analysis of intercellular communication. Neutrophil subpopulations were analyzed utilizing the Monocle2(2.26.0) package to discern various cellular states and conduct pathway analysis using KEGG/GO annotations. Results: we successfully distinguished and further categorized 32 cell populations into 9 major cell types, encompassing T cells, B cells, Mast cells, Neutrophils, Epithelial cells, Endothelial cells, Monocytes, Fibroblasts, and Smooth muscle cells. Furthermore, we ascertained that five subgroups of Neutrophils, each representing diverse differentiation states, exhibit close associations with immune regulatory and metabolic pathways.From our analysis of intersecting genes in the TCGA-CESC dataset, we successfully identified four prognostic genes: C5AR1, HSPA5, CXCL2, and OLR1. The stability of our prognostic risk model has been reiterated through internal and external validation, demonstrating its high consistency, differentiation, and clinical applicability. Notably, the CIRBESORT analysis divulged diminished immune cell content within the tumor immune microenvironment of the high-risk group, correlating with an unfavorable prognosis. Low expression of C5AR1 and high expression of HSPA5, CXCL2, and OLR1 were significantly associated with shorter survival and poorer prognosis.Conclusion: This study elucidated the intricate regulatory network governing the immune microenvironment in CC and comprehensively analyzed intercellular interactions.highlighting the significant roles of C5AR1, HSPA5, CXCL2, and OLR1 in predicting patient prognosis and responsiveness to immunotherapy. These findings offer novel insights and potential strategies for identifying fresh treatment targets in CC. Conclusion: This study elucidated the regulatory network of immune microenvironment in CC, and analyzed the interaction between cells. the key roles of C5AR1, HSPA5, CXCL2 and OLR1 in predicting patient prognosis and response to immunotherapy were revealed. This provides new insights and possible strategies for finding new CC treatment targets.

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Section: Discussionmentioning

confidence: 99%

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

Qian,

Xieyidai,

Han

et al. 2023

Preprint

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“…Hence, these datasets and atlases present opportunities to uncover connections that were challenging to discern across a diverse array of fields. Sc/snRNA-seqs are also very useful in the investigation of brain tumors, neurodegeneration, or psychiatric disorders ( Antunes and Martins-de-Souza, 2023 ; Pozojevic and Spielmann, 2023 ; Walter et al, 2023 ). Since even small changes in the transcriptome can be detected, it can help to identify the involvement of different cellular subtypes in diseases or outline time-dependent changes which would be otherwise undetectable.…”

Section: Genomic Methods In Traumatic Brain Injury Researchmentioning

confidence: 99%

Reactive gliosis in traumatic brain injury: a comprehensive review

Amlerova,

Chmelova,

Anderova

et al. 2024

Front. Cell. Neurosci.

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Traumatic brain injury (TBI) is one of the most common pathological conditions impacting the central nervous system (CNS). A neurological deficit associated with TBI results from a complex of pathogenetic mechanisms including glutamate excitotoxicity, inflammation, demyelination, programmed cell death, or the development of edema. The critical components contributing to CNS response, damage control, and regeneration after TBI are glial cells–in reaction to tissue damage, their activation, hypertrophy, and proliferation occur, followed by the formation of a glial scar. The glial scar creates a barrier in damaged tissue and helps protect the CNS in the acute phase post-injury. However, this process prevents complete tissue recovery in the late/chronic phase by producing permanent scarring, which significantly impacts brain function. Various glial cell types participate in the scar formation, but this process is mostly attributed to reactive astrocytes and microglia, which play important roles in several brain pathologies. Novel technologies including whole-genome transcriptomic and epigenomic analyses, and unbiased proteomics, show that both astrocytes and microglia represent groups of heterogenic cell subpopulations with different genomic and functional characteristics, that are responsible for their role in neurodegeneration, neuroprotection and regeneration. Depending on the representation of distinct glia subpopulations, the tissue damage as well as the regenerative processes or delayed neurodegeneration after TBI may thus differ in nearby or remote areas or in different brain structures. This review summarizes TBI as a complex process, where the resultant effect is severity-, region- and time-dependent and determined by the model of the CNS injury and the distance of the explored area from the lesion site. Here, we also discuss findings concerning intercellular signaling, long-term impacts of TBI and the possibilities of novel therapeutical approaches. We believe that a comprehensive study with an emphasis on glial cells, involved in tissue post-injury processes, may be helpful for further research of TBI and be the decisive factor when choosing a TBI model.

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hPSCs-derived brain organoids for disease modeling, toxicity testing and drug evaluation

Xie,

Bai,

Hou

et al. 2025

Experimental Neurology

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An overview of human single-cell RNA sequencing studies in neurobiological disease

Cited by 9 publications

References 36 publications

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

Reactive gliosis in traumatic brain injury: a comprehensive review

hPSCs-derived brain organoids for disease modeling, toxicity testing and drug evaluation

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