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ihc staining with neun  (Proteintech)


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    Proteintech ihc staining with neun
    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons <t>(NeuN)</t> and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under <t>immunohistochemical</t> staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).
    Ihc Staining With Neun, supplied by Proteintech, used in various techniques. Bioz Stars score: 96/100, based on 555 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ihc staining with neun/product/Proteintech
    Average 96 stars, based on 555 article reviews
    ihc staining with neun - by Bioz Stars, 2026-02
    96/100 stars

    Images

    1) Product Images from "Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution"

    Article Title: Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution

    Journal: Nature Communications

    doi: 10.1038/s41467-025-62793-9

    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons (NeuN) and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under immunohistochemical staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).
    Figure Legend Snippet: a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons (NeuN) and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under immunohistochemical staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).

    Techniques Used: Staining, Immunohistochemical staining, Expressing



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    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons <t>(NeuN)</t> and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under <t>immunohistochemical</t> staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).
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    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons <t>(NeuN)</t> and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under <t>immunohistochemical</t> staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).
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    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons <t>(NeuN)</t> and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under <t>immunohistochemical</t> staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).
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    Image Search Results


    a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons (NeuN) and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under immunohistochemical staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).

    Journal: Nature Communications

    Article Title: Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution

    doi: 10.1038/s41467-025-62793-9

    Figure Lengend Snippet: a Between-group sum of squares of factors affecting IT cell type transcriptome variation. Two-sided Anova significance: all p = 0 ( n = 42). b The cellular composition analysis by scCODA show differential subclass in snRNA-seq datasets, highlighting the significant reduction of LINC00507 IT neurons in the ACC. Each dot denotes cells in a subclass in one region compared to all others regions. Only the data with significant output from scCODA will be shown. c Scatterplot of Stereo-seq example (ACC) showing 4 gradient-laminar distributed glutamatergic IT subclasses. Cells are colored by their cell density. The slide size is 1x1cm, and the subclasses are displayed using the cell bin. d Mature neurons (NeuN) and projection neurons (MAP2) distribution. Left, NeuN and MAP2 staining for neuron. Right, laminar distribution of mature and projection neurons under immunohistochemical staining. e The biplot showed the correlation between cortical region function and cell specificity by redundancy analysis (RDA). The angle reflects the correlation between function and cell type, and the length indicates the magnitude of each explicative contribution. A small angle between cell type and function suggests a positive correlation, while a large angle suggests a negative correlation. Here ET neurons show a strong correlation with pain function. f Detailed representation of specific cortical regions relevant to pain perception. Zoom-in Stereo slides view for pain-positive (S1, S1E, PoCG, ACC) and pain-negative (ITG, AG, FPPFC) cortical regions, illustrating laminar organization and the distribution of ET. ET densely distributed in pain relevant regions such as S1, S1E, PoCG, while the region with least correlation with pain were found much less ET. The down left plot denotes the pain correlation in cortical regions. g Violin plots in ACC (left) and other regions (right) show the expression of differential genes in ET, ANOVA q-value adjusted. *** p < 0.001, ** p < 0.01, * p < 0.05. h Dot plot represents the specific pathways of ET neurons in different cortical regions, highlighting their unique characteristics across various areas, highlighting the molecular functions and biological processes of ET in multi-cortical regions. (Hypergeometric test (q-value adjusted)).

    Article Snippet: Coronal sections adjacent to Stereo-seq chips were collected for IHC staining with NeuN (Proteintech 26975-1-AP, 1:10000), GAP43 (Abcam ab75810, 1: 3000), and MAP2 (Proteintech 17490-1-AP, 1:2500) antibodies.

    Techniques: Staining, Immunohistochemical staining, Expressing