Tanycyte specific biomarkers and uses thereof

Abstract

The present invention provides a biomarker capable of distinguishing pyloric 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. In addition, biomarkers for identifying tanycytes discovered based on the large-scale scRNA-seq dataset of the present invention can accurately identify tanycytes.

Description

Technology Field

[0001] The present invention provides a biomarker capable of distinguishing pyloric cells among various cells of the hypothalamus, derived from a self-constructed large-scale scRNA-seq dataset, and uses thereof. Background Technology

[0003] 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.

[0004] 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.

[0005] 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. Prior art literature

[0007] Harris et al, “Transcriptional regulation of intermediate progenitor cell generation during hippocampal development”, Development, 143, 4620-4630, 2016. The problem to be solved

[0008] The inventors constructed a large-scale integrated scRNA-seq dataset that can serve as a useful resource for investigating cell-type gene expression and cell differentiation trajectories in the mouse hypothalamus after birth, and used this to identify specific biomarkers capable of distinguishing tanycytes and completed the present invention.

[0010] Therefore, the objective of the present invention is to provide a biomarker capable of distinguishing pyloric cells among various cells of the hypothalamus and a use thereof. means of solving the problem

[0012] The present invention provides a biomarker composition for identifying tanycytes comprising one or more biomarkers selected from the group consisting of 1500015010Rik and C4b.

[0013] In addition, the present invention provides a composition for identifying tanycytes comprising a preparation for measuring the level of mRNA of one or more genes selected from the group consisting of 1500015010Rik and C4b or the protein encoded by said gene.

[0014] According to a preferred embodiment of the present invention, a 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.

[0015] According to a preferred embodiment of the present invention, the agent 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.

[0016] In addition, the present invention provides a kit for identifying tanycytes comprising the above composition.

[0017] In addition, the present invention provides a method for identifying tanycytes comprising the step of measuring the level of mRNA of one or more genes selected from the group consisting of 1500015010Rik and C4b or the level of a protein encoded by said gene in a sample.

[0018] According to a preferred embodiment of the present invention, the method further includes the step of classifying as a pyloric cell when the level of the mRNA of the gene or the level of the protein encoded by the gene is higher than the level of the control group. Effects of the invention

[0020] 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. In addition, biomarkers for identifying tanycytes discovered based on the large-scale scRNA-seq dataset of the present invention can accurately identify tanycytes. Brief explanation of the drawing

[0022] 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 adult mice. It shows the relative expression of panneural and nonneural marker genes. Figure 4c shows the cell type annotation and analysis of the merged dataset of the hypothalamus of young adult mice. It shows a UMAP plot showing the normalized expression of Slc17a6 and Slc32a1 after a second iteration of unsupervised clustering in neuronal cells only. Figure 4d shows the cell type annotation and analysis of the merged dataset of the hypothalamus of young adult mice. It shows a UMAP plot illustrating 13 neuronal cell types identified by unsupervised clustering. Figure 4e shows the cell type annotation and analysis of the merged dataset of the hypothalamus of young adult mice. It shows the relative expression of neurotransmitters (Slc17a6, Slc32a1, Gad1, Gad2) and identification marker genes. Figure 5a shows the cell type annotation and analysis of non-neuronal cell clusters. (A) UMAP plot showing 17 non-neuronal cell types identified by unsupervised clustering. (B) Relative expression of cell type-specific genes differentially expressed across 17 non-neuronal cell subtypes. (C) Relative expression of astrocyte subtype-specific genes. Positive and negative markers for each subtype are indicated as "++" and "--", respectively. (D) UMAP plot showing the positive and negative expression of the Mki67 / Hmgb2 / Sox2 genes across all cell types of young adult hypothalamus. (E) Showing the pseudo-chronological order of cells determined by Monocle3 and slingshot and slinghot, showing a single trajectory from "Tany-like ependy" to "Astro3" cells. Figure 5b shows cell type annotation and analysis of non-neuronal cell clusters. (F) Pseudo-chronological order of cells determined by Monocle3 and slingshot and slinghot showing a single trajectory from "Tany-like ependy" to "Astro3" cells. (G) UMAP plot (left) and relative expression (right) of oligodendrocyte subtype-specific genes showing heterogeneity of MO clusters after a second iteration of unsupervised clustering. (H, I) Pseudo-chronological order of cells determined by Monocle3 and slingshot and slinghot showing a single trajectory and heterogeneity in MO differentiation. (J) UMAP plot (left) and relative expression (right) of syndactyly subtype-specific genes in combined hypothalamic syndactyly cells. (K) Shows a UMAP plot showing neuronal and non-neuronal cell subtypes. Figure 6 shows the Sox9 (A) UMAP plot expressing glial-like cells, which illustrates the neurogenic differentiation pathway proposed in previously published literature. (B) The feature plot shows the expression of some neurogenic genes across all Sox9-expressing cells. (C) The violin plot shows Olig2-expressing astrocyte subtype heterogeneity (Olig2-AS). Figure 7a shows the expression of transcriptional heterogeneity during oligodendrocyte maturation. (A) Box plot showing the pseudotime sequence of IPC (blue), OPC (green), NFO (cyan), and MO (red) based on gene expression profiles. (B) Pseudotime gene expression analysis shows gene-specific heterogeneous expression of the MO cluster. Figure 7b shows the expression of transcriptional heterogeneity during oligodendrocyte maturation. It shows the gene expression patterns at each stage during oligodendrocyte maturation. Figure 8a illustrates the label mapping and transfer of the merged dataset for technical validation. (A) A schematic representation showing identified anchors that facilitate the transfer of individual labels between the integrated (reference) and query (laboratory-generated validation) datasets. (B) A UMAP plot showing cell type annotations in the validation dataset. (C) Relative expression of neuronal and non-neuronal gene markers. (D) A UMAP plot showing the actual and predicted cell types of the validation dataset by projecting cells from the validation dataset onto the UMAP embeddings of the integrated dataset. (E) A UMAP plot showing cells from the validation dataset and the integrated dataset in the final UMAP embeddings. Figure 8b shows the label mapping and transfer of the merged dataset for technical validation. It shows the UMAP plot of the final merged (reference and validation) dataset. Cells are grouped by study, sex, mouse lineage, age, brain region, and cell type. Figure 9a shows the QC and processing of the validation dataset. The heatmap shows the distribution and order of both cells and features according to PCA scores. Figure 9b shows the QC and processing of the validation dataset. (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 10a shows a feature plot showing the predicted cell types from the validation dataset after projecting cells onto the integrated UMAP. Figure 10b shows a feature plot showing the predicted cell types from the validation dataset after projecting cells onto the integrated UMAP. Figure 11 shows the expression levels of tanycyte-specific biomarkers. Specific details for implementing the invention

[0023] The present invention will be described in more detail below.

[0025] The “identification” of the present invention includes, without limitation, identifying, selecting, and detecting tanycytes among target samples, and may also include verifying whether tanycytes have been effectively induced to differentiate from progenitor cells.

[0026] The term "biomarker" of the present invention refers to a molecule quantitatively or qualitatively associated with the presence of a biological phenomenon, and the marker of the present invention refers to a gene that serves as a criterion for identifying tanycytes. The marker may be derived from a genomic nucleotide sequence or from an expressed nucleotide sequence (e.g., from RNA, nRNA, mRNA, cDNA, etc.), or from a coded polypeptide. The term includes a nucleic acid sequence complementary to or flanked thereto by the marker sequence, such as a nucleic acid used as a probe or primer pair capable of amplifying the marker sequence.

[0027] The term "expression" in the present invention refers to the production of a protein or nucleic acid. "Protein" is used interchangeably with "polypeptide" or "peptide" and refers to a polymer of amino acid residues, for example, as generally found in proteins in their natural state. "Polynucleotide" or "nucleic acid" refers to a deoxyribonucleotide (DNA) or ribonucleotide (RNA) in the form of a single or double strand. Unless otherwise limited, it also includes known analogs of natural nucleotides that hybridize to nucleic acids in a manner similar to naturally occurring nucleotides. "mRNA" refers to RNA that transmits genetic information (gene-specific base sequences) to ribosomes that determine the amino acid sequence from specific genes during the process of protein synthesis.

[0029] Recently, a reference map of the mouse hypothalamus consisting of 384,925 cells was presented by integrating open scRNA-seq datasets, including data obtained from various stages including the embryonic stage; however, more in-depth molecular characterization is still needed from birth to adult hypothalamic neurogenesis / gliogenesis.

[0030] The objective of the present invention is to identify and extensively characterize the functions of major NSC and NPC cell types during neurogenesis / gliogenesis in the mouse hypothalamus with unprecedented resolution during postnatal developmental stages. The inventors processed and analyzed publicly available scRNA-seq transcriptome data obtained from the mouse hypothalamus using a uniform and optimized informatics pipeline. In this process, the inventors performed an integrated single-cell transcriptome analysis of the postnatal mouse hypothalamus consisting of 294,749 cells and analyzed the cell differentiation trajectories and characteristics during neurogenesis / gliogenesis (Figs. 1a–1c). Analysis of the aforementioned gene expression patterns and specific differentiation trajectories demonstrates the continuity of activation and differentiation processes in the hypothalamus after birth and identified 30 distinct cell types, including all major cell types found in the hypothalamic region, such as ependymal cells, tanycytes, astrocytes, oligodendrocytes, and glial-like cells such as intermediate progenitor cells (IPCs). Furthermore, the inventors discovered the co-expression of the neural progenitor marker Sox2 and the proliferation marker Mki67, confirming the presence of NSCs in the hypothalamus of newborn and young adult mice. The data analysis of the present invention revealed the transcriptional dynamics underlying oligodendrocyte differentiation and the cellular heterogeneity of tanycytes, a type of non-neuronal cell specific to the hypothalamus whose function in the adult brain is not well understood.In addition, to evaluate the technical validity of the integrated dataset, the inventors performed anchor-based mapping and label transfer using a Connect-seq-derived single-nucleus RNA-seq (snRNA-seq) dataset generated in the laboratory from 1,533 cells of the entire hypothalamus and BNST (bed nucleus of the stria terminalis) of CRH-IRES-Cre (CRH-Cre) mice. The large-scale single-cell transcriptome data presented in this invention can serve as a valuable asset for exploring gene expression and cell differentiation in the hypothalamus across various developmental stages.

[0032] Accordingly, the present invention can provide a biomarker composition for identifying tanycytes comprising one or more biomarkers selected from the group consisting of Cdc25c, Cdca2, Cdkn2c, Cdc20, Ckap2, Cdca3, Cenpm, Aurkb, and Emid1.

[0034] In addition, the present invention may provide a composition for identifying tanycytes comprising a preparation for measuring the level of mRNA of one or more genes selected from the group consisting of 1500015010Rik and C4b or the protein encoded by said gene.

[0035] According to a preferred embodiment of the present invention, a preparation for measuring the mRNA level of the gene may include sense and antisense primers or probes that bind complementarily to the mRNA of the gene.

[0036] The above "primer" refers to a short nucleic acid sequence having a short free 3' hydroxyl group, capable of forming base pairs with a complementary template, and functioning as a starting point for template replication. In the present invention, PCR amplification can be performed using the sense and antisense primers of the aforementioned biomarker polynucleotide to identify synechiae cells based on whether a desired product is produced. The PCR conditions and the lengths of the sense and antisense primers can be modified based on those known in the art.

[0037] The term "probe" above refers to a nucleic acid fragment, such as RNA or DNA, ranging from a few bases to hundreds of bases in length, capable of forming a specific binding with mRNA. It is labeled so that the presence or absence of a specific mRNA can be confirmed. The probe can be produced in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, etc. In the present invention, hybridization is performed using a probe complementary to the aforementioned biomarker polynucleotide, and the presence or absence of hybridization can be used to identify pyloric ependymal cells. The selection of a suitable probe and hybridization conditions can be modified based on those known in the art.

[0038] According to a preferred embodiment of the present invention, the preparation for measuring the protein level may comprise 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.

[0039] The term "antibody" above refers to a specific protein molecule directed toward an antigenic site, as is a term known in the art. For the purposes of the present invention, an antibody refers to an antibody that specifically binds to the aforementioned biomarker. Such an antibody can be produced by cloning each gene into an expression vector according to conventional methods to obtain a protein encoded by the biomarker gene, and then producing the obtained protein by conventional methods. This includes partial peptides that can be produced from the protein. The form of the antibody of the present invention is not particularly limited; polyclonal antibodies, monoclonal antibodies, or parts thereof having antigen-binding properties are included in the antibody of the present invention, and all immunoglobulin antibodies are included. Furthermore, the antibody of the present invention includes special antibodies such as humanized antibodies. The antibody against the protein encoded by the biomarker gene of the present invention may be any antibody that can be produced by methods known in the art.

[0040] The term "aptamer" above refers to a single-stranded oligonucleotide, which is also called a chemical antibody because it has a function similar to an antibody, and is a nucleic acid molecule that has binding activity to a specific target molecule. The aptamer can have various three-dimensional structures depending on its base sequence and can have a high affinity for a specific substance, such as in an antigen-antibody reaction. The aptamer can inhibit the activity of a specific target molecule by binding to it.

[0041] The aptamers of the present invention may be RNA, DNA, modified nucleic acids, or mixtures thereof, and may be in a linear or cyclic form, but are not limited thereto. Aptamers having binding activity to each of the three types of biomarker proteins may be prepared by a person skilled in the art according to known methods by referring to each base sequence.

[0042] The nucleotide sequence of the preparation used in the present invention for measuring the expression level of a biomarker gene is interpreted to include a sequence that exhibits substantial identity with a sequence that specifically binds to the biomarker gene, provided that variations having biologically equivalent activity are taken into account. The "substantial identity" refers to a sequence that exhibits at least 60% identity, more specifically 70% identity, even more specifically 80% identity, and most specifically 90% identity when a specific sequence is aligned with any other sequence to correspond as much as possible and the aligned sequence is analyzed using an algorithm commonly used in the art.

[0044] In addition, the present invention can provide a kit for identifying tanycytes comprising the above composition.

[0045] The kit of the present invention may be composed of one or more other component compositions, solutions, or devices suitable for commonly used expression level analysis methods. For example, a kit for measuring protein expression levels may include a substrate, a suitable buffer solution, a secondary antibody labeled with a chromogenic enzyme or fluorescent substance, a chromogenic substrate, etc., for the immunological detection of antibodies.

[0046] The kit of the present invention may include a sample extraction means for obtaining a sample from an evaluation subject. The sample extraction means may include a needle or a syringe, etc. The kit may include a sample collection container for receiving the extracted sample, which may be a liquid, gas, or semi-solid. The kit may further include instructions for use. The sample may be any body sample in which a biomarker may be present or secreted. For example, the sample may be urine, feces, hair, sweat, saliva, body fluid, blood, cerebrospinal fluid, cells, or tissue. Measurement of the biomarker in the sample may be performed on the whole sample or on the processed sample. The kit of the present invention may be manufactured in a number of separate packages or compartments.

[0048] In addition, the present invention may provide a method for identifying tanycytes comprising the step of measuring the level of mRNA of one or more genes selected from the group consisting of 1500015010Rik and C4b or the level of a protein encoded by said gene in a sample.

[0049] According to a preferred embodiment of the present invention, the method may additionally include the step of classifying as a pyloric cell when the level of the mRNA of the gene or the level of the protein encoded by the gene is higher than the level of the control group.

[0051] The present invention will be described in more detail below through examples. These examples are intended solely to illustrate the present invention, and it is obvious to those skilled in the art that the scope of the present invention is not to be interpreted as being limited by these examples.

[0053] Method

[0054] 1. Mouse

[0055] To generate validation data, CRH-IRES-Cre (CRH-Cre; stock number, 012704) mice were purchased from The Jackson Laboratory. All procedures involving the mice were approved by the Animal Laboratory Management Committee of Ajou University and the Animal Laboratory Management Committee of DGIST.

[0057] 2. PRV (Pseudorabies Virus)

[0058] To proliferate PRV180, PK15 cells (American Type Culture Collection) were infected with the virus using infection multiplicity of 0.1 to 0.01. After infection, the cells exhibited significant cytopathic effects (~2 days). They were scraped and harvested, and the cellular material was frozen using liquid nitrogen and then rapidly thawed in a 37°C water bath. After three freeze-thaw cycles, the cell debris was removed by centrifugation twice at 1000g for 5 minutes, and the supernatant was used for experiments. The titer of the virus stock was determined using a standard plaque assay for PK15 cells, and the titer was expressed in plaque-forming units (pfu).

[0060] 3. Stereotaxic Injections

[0061] All injections were performed under inhalation anesthesia with 2% isoflurane. Briefly, 1 μL of PRV (PRVB180; 1–1.5 x 10^6 pfu) was placed in a 1 μL syringe and bilaterally injected into the brains of CRH-Cre mice at a rate of 100 nL / min using a Stereotaxic Alignment System (David Kopf Instruments). The needle was inserted into the PVN based on a stereotaxic atlas of the brain. After recovery, the animals were housed in isolation for a regular 12-hour dark / light cycle, and food and water were provided freely.

[0063] 4. Processing and Integration of scRNA-seq Datasets

[0064] Ten scRNA-seq datasets were obtained from independent studies using NCBI GEO further processed with the Seurat package in R (v. 4.2.1). Standard informatics pipelines were applied to each dataset for preprocessing to standardize cell-level metadata and for cell clustering. Cells with more than 15% mitochondrial-related reads and more than 2,500 or fewer than 100 unique features were filtered out. A total of 152,524 cells were included for further analysis, and the variable characteristics of these cells were identified after normalization. Seurat's FindIntegrateionAnchors function was applied to merged cells to find common anchors between variables. The number of principal components (PCs) in the datasets was determined using Elbow plots prior to cell clustering (Figs. 2a, 2b). Cells in the merged datasets were clustered into distinct cell populations using the FindClusters function (resolution = 0.25). The generated Louvain clusters were visualized as 2D UMAP representations and manually annotated with known cell types using standard neuronal and non-neural marker genes and signatures selected from the original publication.

[0066] 5. Characterization and Analysis of Cell Subpopulations

[0067] To identify diverse populations of excitatory and inhibitory neurons, neuronal clusters in the merged data were subset, and a second dimensionality reduction and clustering were performed using the FindVariableFeatures(), ScaleData(), RunPCA(), and RunUMAP() functions of the Seurat package (resolution = 0.3). The unique clusters of neurons are Slc17a6 , Slc32a1 , Cck , Gad1 and Gad2They were annotated with known marker genes such as [mentioned]. Similarly, synovial cells and mature oligodendrocytes were subset by performing secondary dimensionality reduction and clustering to characterize cellular heterogeneity and pseudo-chronological trajectories.

[0069] 6. Doctor Time Analysis

[0070] Pseudotime trajectory analyses were performed on 'Tany-like ependy', 'Tany', 'Astro1', 'Astro2', 'Astro3', 'Myl-OL', 'MOL', and 'EMOL' cells using Monocle378-80 Alpha. The standard Monocle workflow was applied to these cells using the importCDS and reduceDimension functions. These functions perform normalization, dimensionality reduction, visualization, and differential representation analysis by calculating a size factor that controls the variability of library composition efficiency between cells. Additionally, pseudotime trajectories derived from Monocle3 were validated using the slingshot package in R (v. 2.6.0)70,81,82. The "slingshot" function, which displays the relative position of each cell along the trajectory, was used to obtain UMAP trajectory interference based on pseudotime values.

[0072] 7. Separation of a single nucleus

[0073] Connect-seq experiments were performed by modifying nuclear separation as previously described, and adult 8-week-old CRH-Cre mice were injected with PRVB180. Three days later, the mice were euthanized by cervical dislocation, and the brains were rapidly removed. The hypothalamus was carefully microscopically microdissected and immediately placed in nuclear separation medium (0.25 M sucrose, 25 mM KCl, 5 mM MgCl2, 10 mM TrisCl, dithiothreitol, 0.1% Triton). The tissue was Doounce homogenized to allow for the mechanical separation of nuclei from cells. To facilitate the visualization of the nuclei, the nucleic acid stain Hoechst 33342 (5 μM, Life Technologies) was added to the medium. The samples were washed, resuspended in nuclear storage buffer (sucrose, 5 mM MgCl2, and 10 mM TrisCl), and filtered. The solution and samples were kept cold throughout the protocol. Then, single nuclei infected with PRV were isolated based on the fluorescence emitted by TK-GFP using flow cytometry (FACSAria II; BD Biosciences) according to the previously described method.

[0075] 8. Single nuclear capture

[0076] Single nuclei classified by FACS were loaded into independent single channels of the Chromium Controller (10x Genomics) single-cell platform. Briefly, 10,000 single nuclei were loaded for capture using the Chromium Single Cell 3' Reagent Kit, v2 Chemistry (10x Genomics). After capture and lysis, complementary DNA was synthesized and amplified (14 cycles) according to the manufacturer's protocol (10x Genomics). An Illumina sequencing library was constructed using the amplified cDNA and sequenced on a single lane of the NovaSeq 6000 (Illumina). Illumina basecall files (*.bcl) were converted to FASTQ using Cell Ranger v.1.3 (10x Genomics) with bcl2fastq v.2.17.1.14 for FASTQ generation and alignment. Then, the FASTQ file was aligned to the mm10 genome and transcriptome using the Cell Ranger v.1.3 pipeline, which generates a 'gene versus cell' expression matrix.

[0078] 9. Connect-seq derived data

[0079] This dataset was used for validation purposes to evaluate the technical validity of the integrated dataset and was processed using a uniform informatics pipeline as before. For cell clustering, the resolution was set to 0.25, and eight cell clusters were identified using specific marker genes such as Agt, Pecam1, Itm2a, Mki67, C1qa, Mog, Syp, and Pdgfra. These manually annotated cell types were compared to cell types predicted by Seurat's 'reference-based' integration and 'label transfer' methods using the FindTransferAnchors() and TransferData() functions, respectively (Figs. 8D and 10). Next, cells in the validation dataset with a predicted cell type score ('predicted.celltype.score') less than 0.5 were filtered, the remaining cells were merged with the integrated dataset, and cell-level attributes were visualized in the final UMAP (Figs. 8a, 8b).

[0081] 10. Data and Code Availability

[0082] The R script used to generate the integrated dataset, perform validation dataset analysis, and generate related plots is available on Figshare (DOI:https: / / doi.org / 10.6084 / m9.figshare.21981251.v1). The single-cell RNA-Seq dataset used to generate the 'integrated' dataset is available on NCBI GEO with accession numbers GSE87544, GSE132355, GSE93374, GSE113576, GSE126836, GSE74672, GSE125065, GSE14692, GSE1392, and the scRNA-seq dataset of the VMHvl hypothalamic region is accessible at https: / / doi.org / 10.17632 / ypx3sw2f7c.1 (Mendley Data). The final dataset containing both the integration and validation datasets can be found on Figshare (https: / / doi.org / 10.6084 / m9.figshare.21981251.v1).

[0084] Generation and analysis of integrated scRNA-seq data in the hypothalamus region after birth

[0085] The inventors collected 10 publicly available mouse scRNA-seq datasets from various hypothalamic brain regions, including the arch hypothalamus and mid-hypothalamus (ARCME), lateral hypothalamus (LH), medial preoptic area / anterior hypothalamus (MPOA), mid-hypothalamus (Mid-hypo), ventromedial hypothalamus (VPH), ventromedial hypothalamus (VMHvl), and bed nucleus of the striatum junction (BNST), from P14 to 14 weeks at NCBI GEO. The datasets were obtained from mice of various lineages, including C57BL / 6, C57 X D2F1, Agrp-Cre X Ai6, Nr5a1-Cre X Ai14, and CRH-Cre. Here, a uniform informatics pipeline was used for reanalysis, including quality control, standardization of cell-level metadata, mapping, and label transfer to the validation datasets (Figs. 1a–1c). To facilitate the reuse of the integrated data, cell-level attributes such as study, age, brain region, sex, and mouse strain for each dataset were manually curated, and standardized text was used for metadata. After integrating and correcting for technical batch effects that may occur in various study and experimental protocols, principal component (PC) analysis (Figs. 2a, 2b) and dimensionality reduction were performed using Uniform Manifold Approximation and Projection (UMAP). A total of 294,749 cells from the integrated data were filtered using mitochondrial-related read counts and unique feature counts (Fig. 3a). From this data, 152,524 cells that passed QC were subsequently clustered into 18 cell populations in the first iteration of clustering using cell type-specific markers. The cells were relatively evenly distributed across the dataset and major cell types (Fig. 3b).To determine whether virus-tracking-based sequencing approaches such as Retro-seq and Connect-seq identify varying proportions of cells compared to more commonly used droplet-based tools such as 10X27 and Drop-seq, cell proportions based on major cell types were calculated across various sequencing platforms (Fig. 3c). The dataset derived from Retro-seq had relatively fewer QC-passing neurons and more oligodendrocytes and astrocytes, whereas the relative proportions of each cell type in the dataset derived from Connect-seq were similar among cells in the datasets obtained from droplet-based sequencing platforms. [Fig. 3d] shows QC-passing cells colored by different cell-level attributes before and after batch removal.

[0086] The inventors first distinguished non-neuronal and neuronal cells using known panneural genes (Snap25, Syp, Elval2, Slc17a6, and Slc32a1) and standard nonneural genes (Olig1, Cldn5, C1qa, Sox9, and Mustn1) (Figs. 4a, 4b). To characterize various neuronal populations in the postnatal hypothalamus, the expression patterns of markers for excitatory and inhibitory neurons were investigated. These were Slc17a6 for glutamatergic neurons and Slc32a1, Gad1, and Gad2 for GABAergic neurons (Fig. 4c). Among the 13 identified neuronal clusters, there were 4 glutamatergic clusters, 8 GABAergic clusters, and 1 cluster that was neither glutamatergic nor GABAergic but was Cck+ (Figs. 4c, 4d). Overall, within the identified neuronal population, clusters can be identified by a unique combination of genes expressed at different levels (Fig. 4e).

[0087] Next, a total of 17 clusters of non-neuronal cells were identified (Figs. 5A-B). Oligodendrocytes were detected using markers such as Olig1, Pdgfra, Fyn, and Mobp, all of which exhibit gene expression gradients during postnatal development (Figs. 5a, 6 and 7a, 7b). These cells also include oligodendrocyte progenitor cells (OPCs), newly formed oligodendrocytes (NFOs), and myelinated oligodendrocytes (MOs). The inventors also identified three distinct subtypes of astrocytes (Astro1, 2, and 3) expressing Agt at varying levels. The Astro1 cell cluster exhibits high expression of Gfap, while Astro2 cells express Unc13c and Rgcc highly. The Astro3 cell cluster shows high expression of C1qa and low expression of Unc13c34,35 (Fig. 5a). Tanycytes (Tany) and tanycyte-like ependymal cells (Tanylike ependy) were also detected using the expression levels of Rax and Ccdc153, respectively. In addition, the inventors discovered other distinct clusters that could be easily identified as macrophages (Mac: Mrc1+), microglia (Micro: Cx3cr1+), two endothelial cell populations (Endo1 / 2: Pecam1+), vascular leptomeningeal cells or VLMCs (Dcn+), and a single erythrocyte cluster (Eryth: Alas2+) (Fig. 5a). These findings regarding major non-neuronal cells obtained from the hypothalamus at various postnatal stages are in close agreement with previous scRNA-seq analyses of the mouse brain.

[0089] subgroups of glial cells

[0090] The inventors next aimed to identify and characterize subpopulations of glial cells in the hypothalamus. Tany-like ependymal cells, tanycytes, and astrocytes all express the canonical marker gene Gfap and the neural progenitor marker genes Sox2 and Sox9. To study the differentiation and specialization processes of these glial-like cell populations, subtype designation and trajectory analysis tools were applied using monocle3 and slingshot, and it was found that the pseudo-chronology of cells increased in 'Tany-like ependy' (Fig. 6A). The conversion of cells into mature astrocytes (Astro3) suggests that tany-like ependymal cells differentiate into astrocytes in the postnatal / adult hypothalamus (Figs. 5a, 5b and Fig. 6A). Interestingly, the inventors further discovered a subgroup of Olig2+ astrocytes (Fig. 6C), which supports recent immunohistochemical studies that found highly abundant Olig2-expressing astrocytes in specific regions of the adult mouse brain, and whose function in the postnatal or adult hypothalamus has not yet been discovered.

[0092] Subgroup of proliferating progenitor cells

[0093] The inventors also discovered two proliferating progenitor populations (IPCs and OPCs). Intermediate progenitor cells (IPCs) are a population of NSCs that play a crucial role in adult neurogenesis in the hypothalamus by differentiating into excitatory and / or inhibitory neurons. IPCs can be identified by the expression of Mki67 (proliferation marker gene), Sox2 (neural progenitor cell marker gene), and Hmgb2, which encodes a chromatin-associated transcriptional activator important for transitioning NSCs from a quiescent state to a proliferative state (Fig. 5a). These subpopulations of cells expressing Sox9, Sox2, and Mki67 / Hmgb2 in the hypothalamic region may undergo cell division processes before, during, and after nuclear division, as suggested by recent studies that discovered the presence of proliferative NSCs in these hypothalamic regions after birth. Oligodendrocyte progenitor cells (OPCs) are another type of neural progenitor cell that generates oligodendrocytes. In particular, these cells express Pdgfra, which regulates cell proliferation, differentiation, and migration processes through the Wnt and Notch signaling pathways (Fig. 5a), which indicates a potential role in the regulation of adult oligosylformation in the hypothalamus after birth.

[0095] Subgroup of oligodendrocytes

[0096] The subcategories of MO (myelinating oligodendrocytes) revealed three clusters: (1) early maturing oligodendrocytes (EMOL) expressing Egr2 (Early Growth Response 2; also known as Krox20) during oligodendrocyte maturation, (2) mature oligodendrocytes (MOL) expressing late oligodendrocyte differentiation genes, e.g., Klk6 and Anxa5, and (3) myelinating oligodendrocytes (Myl-OL) expressing genes involved in myelin formation such as Ctps, Mog, Plp1, and Mobp46,47 (Fig. 5b and Figs. 7a, 7b). Trajectory analysis of the oligodendrocyte population and MO subtype revealed intermediate stages of differentiation (Fig. 5b and Figs. 7a, 7b), consistent with previously reported subpopulations of oligodendrocytes expressing intermediate expression of Olig2 and / or Sox2.

[0098] Subgroup of synovial cells

[0099] Tanycytes and related cells in the ventricular region of the hypothalamus are strong candidate stem / progenitor cells expressing transcription factors such as genes encoding intermediate filament proteins, including Rax, Sox2, Sox9, and GFAP (Fig. 6B). To investigate the transcriptional heterogeneity of tanycytes subtypes, the inventors divided tanycytes into four major subtypes—α1, α2, β1, and β2—based on gene expression using subtype-specific markers, as shown in Fig. 5b. Interestingly, the inventors discovered a cell cluster expressing S100b, which is known to be involved in the regulation and migration of astrocytes. An integrated large-scale scRNA-seq dataset (Fig. 5b) with standardized cell-level metadata including annotations of major and minor cell populations overall can help improve the understanding of transcriptome dynamics of neuronal and non-neuronal cells, including proliferative progenitor cells exhibiting differentiation efficacy in the hypothalamus of postnatal and young adult mice.

[0101] Create validation dataset

[0102] To further evaluate the technical validity of the integrated data, a Connect-seq-derived single-nuclear RNA-seq (snRNA-seq) dataset consisting of 1,533 cells ('validation' dataset) was generated and analyzed using a previously applied processing workflow (Fig. 8a and Figs. 9a, 9b). Using known marker genes, the inventors identified a total of eight cell clusters, including IPC (Mki67+), OPC (Pdgfra+), Astro (Agt+), Endo1 (Pecam+), Endo2 (Itm2a+), Mac (C1qa+), and MO (Mog+), as well as one neuron cluster expressing Syp (Fig. 8a BC). Cell type classifications from the integrated dataset were transferred to the validation dataset (Fig. 8a) by applying anchor-based multimodal reference mapping to generate 'predicted' annotations (Fig. 8a). The integrated mapping dataset helps identify previously mixed cell types in the unsupervised analysis of the validation dataset (Figs. 10a, 10b). The integrated and validation datasets were merged into a final dataset consisting of 153,826 cells with standardized cell-level attributes including study, sex, mouse lineage, age, brain region, and cell type (Figs. 8a, 8b).

[0104] Selection of new markers

[0105] The inventors used the analytical function of Seurat's FindAllMarkers feature to detect differentially expressed genes in tanycytes. This feature compares gene expression levels between different cell populations using statistical methods such as logistic regression models. By applying this approach to single-cell RNA sequencing data, they were able to accurately identify 1500015010Rik and C4b as genes showing significant expression differences between tanycytes and other cells (Fig. 11).

Claims

Claim 1 A biomarker composition for identifying hypothalamic tanycytes containing 1500015010Rik. Claim 2 A composition for identifying hypothalamic tanycytes comprising a preparation for measuring the level of mRNA of the 1500015010Rik gene or the protein encoded by said gene. Claim 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. Claim 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. Claim 5 A kit for identifying hypothalamic tanycytes comprising the composition of claim 2. Claim 6 A method for identifying hypothalamic tanycytes comprising: i) a step of measuring the level of mRNA of the 1500015010Rik gene or the protein encoded by the said gene in a sample; and ii) a step of classifying as hypothalamic tanycytes if the level of mRNA of the said gene or the protein encoded by the said gene is higher than the level of a control group. Claim 7 delete

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