KR-102964156-B1 - Intermediate progenitor cell specific biomarkers and uses thereof

Abstract

The present invention provides a biomarker capable of identifying intermediate precursor cells among various cells of the hypothalamus and uses thereof. The large-scale scRNA-seq dataset of the present invention can further advance the understanding of transcriptional dynamics underlying various cell types, NSC lineages, differentiation, and specialization processes. Furthermore, the biomarkers for identifying intermediate progenitor cells discovered based on the large-scale scRNA-seq dataset of the present invention can accurately identify intermediate progenitor cells.

Inventors

• 임수빈
• 무함마드 주나이드

Assignees

• 아주대학교산학협력단

Dates

Publication Date

20260512

Application Date

20230808

Claims (7)

1. A biomarker composition for identifying hypothalamic intermediate progenitor cells containing Lockd.
2. A composition for identifying hypothalamic intermediate progenitor cells comprising a preparation for measuring the level of mRNA of the Lockd gene or the protein encoded by said gene.
3. A composition according to paragraph 2, wherein the preparation for measuring the mRNA level of the gene comprises sense and antisense primers or probes that bind complementarily to the mRNA of the gene.
4. A composition according to paragraph 2, wherein the preparation for measuring the protein level comprises an antibody or an antigen-binding fragment thereof, or an aptamer, that specifically binds to a protein or a fragment thereof encoded by the gene.
5. A kit for identifying hypothalamic intermediate progenitor cells comprising the composition of claim 2.
6. i) a step of measuring the level of mRNA of the Lockd gene or the protein encoded by said gene in a sample; and ii) a step of classifying as a hypothalamic intermediate precursor cell if the level of the mRNA of the said gene or the protein encoded by the said gene is higher than the level of the control group; A method for identifying hypothalamic intermediate progenitor cells including
7. delete

Description

Intermediate progenitor cell specific biomarkers and uses thereof The present invention provides a biomarker capable of identifying intermediate precursor cells among various cells of the hypothalamus, derived from a self-constructed large-scale scRNA-seq dataset, and uses thereof. Postnatal neurogenesis involves the processes of progenitor cell division, differentiation, and the integration of newly formed cells into the brain. Research over the past decade has shown increasing evidence that neural stem cells (NSCs) may be responsible for the majority of postnatal neurogenesis. These stem cells can produce both neuronal progenitor cells (NPCs) and glial progenitor cells, which in turn differentiate into postmitotic neurons and glial cells, respectively, after division. Research over the past few years has demonstrated that neurogenesis and gliogenesis can occur in the hypothalamus of adult mammals. Although the hypothalamus is a critical brain region playing a vital role in regulating homeostasis and survival-related behaviors, knowledge regarding the intrinsic mechanisms of its development remains limited. Despite years of research, the existence and extent of adult neurogenesis and gliogenesis remain subjects of debate. One possible reason for this discrepancy may be the lack of well-characterized subpopulations of hypothalamic cells, which are particularly central to this process. Studies using techniques such as immunochemistry and in situ hybridization have struggled to identify and evaluate these cell populations and locations due to limitations in the number of available transcriptomes and markers. Therefore, to gain a deeper understanding of cell proliferation in the hypothalamus of postnatal animals, a comprehensive analysis of NSC and progenitor cell populations across the entire hypothalamus is required. High-throughput single-cell RNA sequencing (scRNA-seq) analysis and experimental approaches have been applied to study brain proliferation in depth. In particular, the hypothalamic region has been extensively studied in relation to neurogenesis/gliogenesis and development. Various proliferating cell types, including glial-like cells, neuronal progenitors, astrocyte progenitors, and oligodendrocyte progenitors, have been identified in this region. More recently, a comprehensive single-cell transcriptome atlas of neurogenesis in primates was revealed through the analysis of scRNA-seq data on 207,785 cells from the adult macaque hippocampus. However, cellular and molecular characterization of postnatal mouse hypothalamic neurogenesis/gliogenesis with single-cell resolution is still lacking, possibly due to limited large-scale scRNA-seq datasets with uniform cell-level metadata containing major and minor cell types and other biologically relevant information. Figure 1a shows a schematic representation of the study design, batch correction, and clustering for single-cell RNA-seq of a young adult mouse brain dataset. Figure 1b shows a schematic representation of the study design, batch correction, and clustering for single-cell RNA-seq of a young adult mouse brain dataset. Figure 1c shows a schematic representation of the study design, batch correction, and clustering for single-cell RNA-seq of a young adult mouse brain dataset. Figure 2a illustrates the QC and processing of the integrated data. The heatmap shows the distribution and order of both cells and features according to PCA scores. Figure 2b illustrates the QC and processing of the integrated data. (B) The VizDimReduction plot shows the distribution and order of both cells and features according to PCA scores. (C) The Elbowplot shows the ranking of principal components based on the percentage of variance explained by each. Figure 3a shows the cell type ratios and batch removal of the final merged dataset. The cells of the final merged dataset were grouped by cell type, including the percentage of reads of mitochondrial genes (percent.mt) and the number of unique genes (nFeature). Figure 3b shows the cell type ratios and batch removal of the final merged dataset. The cell count ratios are grouped by study (left) and cell type (right) and further divided by sequencing tool. Figure 3c shows the results of comparing the ratio of cell numbers according to different datasets using different sequencing methods (Retro-seq, Connect-seq). Figure 3d shows the cell type proportions and batch removal of the final merged dataset. It shows the UMAP plots of the merged dataset that were not batch modified (top) and modified (bottom). Cells were grouped by study, age, brain region, sex, and mouse lineage. Figure 4a shows the cell type annotation and analysis of a merged dataset of the hypothalamus of young adult mice. It shows a UMAP plot illustrating neuronal or non-neuronal cells (left) classified by the combined expression of panneural markers (right). Figure 4b shows the cell type annotation and analysis of the merged dataset of the hypothalamus of young