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Structured Review

Allen Institute for Brain Science scrnaseq data
(A) Bidimensional representation of parameter values transformed using UMAP: each dot represents one individual, and closed lines indicate the convex hulls associated with all the individuals obtained with a given morphology (color-coded accordingly to both the convex hull and the points contained in it). (B) UMAP projection and clustering of CA3 excitatory neurons based on <t>scRNAseq</t> data. Using the Leiden clustering algorithm with a resolution of 0.65 delineated the primary division in the CA3 principal neuron population. Note that CA3 principal cells are primarily composed of a larger population of cells (cluster 1, black) and a second minority population (cluster 2, red). (C) Violin plots of the distributions of maximal conductance values for four different classes of ion channels (potassium, calcium, sodium and hyperpolarization-activated) for the model cells included in the analysis, normalized over the range of allowed variability of each parameter as reported in (black and red indicate thorny and a-thorny cells, respectively). Dashed lines indicate the median of the population, while the upper and lower dotted lines represent the 25th and 75th percentile of the distributions. Most parameter distributions were significantly different between the two cell-types (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001). For the remaining parameters see . (D) Expression levels for cells belonging to cluster 1 (black) or cluster 2 (red) for analogous classes of ion channel genes as shown in (C). Note that expression levels for most Na channel genes were not significantly different while Ca and K channel genes were significantly differentially expressed between the two clusters (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001).
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Images

1) Product Images from "Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons"

Article Title: Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons

Journal: PLoS Computational Biology

doi: 10.1371/journal.pcbi.1010071

(A) Bidimensional representation of parameter values transformed using UMAP: each dot represents one individual, and closed lines indicate the convex hulls associated with all the individuals obtained with a given morphology (color-coded accordingly to both the convex hull and the points contained in it). (B) UMAP projection and clustering of CA3 excitatory neurons based on scRNAseq data. Using the Leiden clustering algorithm with a resolution of 0.65 delineated the primary division in the CA3 principal neuron population. Note that CA3 principal cells are primarily composed of a larger population of cells (cluster 1, black) and a second minority population (cluster 2, red). (C) Violin plots of the distributions of maximal conductance values for four different classes of ion channels (potassium, calcium, sodium and hyperpolarization-activated) for the model cells included in the analysis, normalized over the range of allowed variability of each parameter as reported in (black and red indicate thorny and a-thorny cells, respectively). Dashed lines indicate the median of the population, while the upper and lower dotted lines represent the 25th and 75th percentile of the distributions. Most parameter distributions were significantly different between the two cell-types (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001). For the remaining parameters see . (D) Expression levels for cells belonging to cluster 1 (black) or cluster 2 (red) for analogous classes of ion channel genes as shown in (C). Note that expression levels for most Na channel genes were not significantly different while Ca and K channel genes were significantly differentially expressed between the two clusters (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001).
Figure Legend Snippet: (A) Bidimensional representation of parameter values transformed using UMAP: each dot represents one individual, and closed lines indicate the convex hulls associated with all the individuals obtained with a given morphology (color-coded accordingly to both the convex hull and the points contained in it). (B) UMAP projection and clustering of CA3 excitatory neurons based on scRNAseq data. Using the Leiden clustering algorithm with a resolution of 0.65 delineated the primary division in the CA3 principal neuron population. Note that CA3 principal cells are primarily composed of a larger population of cells (cluster 1, black) and a second minority population (cluster 2, red). (C) Violin plots of the distributions of maximal conductance values for four different classes of ion channels (potassium, calcium, sodium and hyperpolarization-activated) for the model cells included in the analysis, normalized over the range of allowed variability of each parameter as reported in (black and red indicate thorny and a-thorny cells, respectively). Dashed lines indicate the median of the population, while the upper and lower dotted lines represent the 25th and 75th percentile of the distributions. Most parameter distributions were significantly different between the two cell-types (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001). For the remaining parameters see . (D) Expression levels for cells belonging to cluster 1 (black) or cluster 2 (red) for analogous classes of ion channel genes as shown in (C). Note that expression levels for most Na channel genes were not significantly different while Ca and K channel genes were significantly differentially expressed between the two clusters (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001).

Techniques Used: Transformation Assay, Expressing

Related Articles

other:

Article Title: Spatial cell-type enrichment predicts mouse brain connectivity.
Article Snippet: The primary scRNAseq dataset used here contains 200 cell types from the Mouse Brain Atlas (mousebrain.org), which were sampled from 12 locations throughout the mouse cortex.19 A second, confirmatory dataset comprised of 25 cell types, which were sampled from the primary visual cortex, the anterior lateral motor cortex, and the dorsal lateral geniculate complex, uses scRNAseq data made available by the Allen Institute for Brain Science (AIBS).18,20 These cell type densities are min-max normalized to avoid the bias from the cell types’ own artificial scales to create our cell type enrichment features, ensuring that each regional cell type value falls in the range 1⁄20;1 .

Article Title: A branching model of lineage differentiation underpinning the neurogenic potential of enteric glia.
Article Snippet: Adult mouse cortical scRNA-seq data from the Allen Institute for Brain Science was downloaded from https://www.dropbox.com/s/kqsy9tvsklbu7c4/ allen_brain.rds.

Article Title: Reverse engineering neuron type-specific and type-orthogonal splicing-regulatory networks using single-cell transcriptomes
Article Snippet: To investigate the rich diversity of AS regulation across neuron types, we leveraged the scRNA-seq data from 21,154 mouse neocortical cells generated by the Allen Institute for Brain Science .

Article Title: A branching model of lineage differentiation underpinning the neurogenic potential of enteric glia
Article Snippet: Adult mouse cortical scRNA-seq data from the Allen Institute for Brain Science was downloaded from https://www.dropbox.com/s/kqsy9tvsklbu7c4/allen_brain.rds .

Article Title: Neuronal identity defines α-synuclein and tau toxicity
Article Snippet: Human multiple cortical areas scRNA-seq , Allen Institute for Brain Science , , , https://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq.

Article Title: Spatial cell-type enrichment predicts mouse brain connectivity.
Article Snippet: We used previously computed regional densities for 200 neuronal and non-neuronal cell types from publicly available scRNA-seq data from Zeisel et al.19 and in situ hybridization data from the Allen Institute for Brain Science 17 using the MISS algorithm16 (Figure 1i).

Sequencing:

Article Title: Searching for the cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer’s disease
Article Snippet: .. The scRNAseq data used to generate the cell-type maps come from Yao, et al. for the Allen Institute for Brain Science (AIBS), which sequenced approximately 1.3 million individual cells sampled comprehensively throughout the neocortex and hippocampal formation at 10x sequencing depth . ..

Article Title: Searching for the cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer's disease.
Article Snippet: .. The scRNAseq data used to generate the cell-type maps come from Yao, et al. for the Allen Institute for Brain Science (AIBS), which sequenced approximately 1.3 million individual cells sampled comprehensively throughout the neocortex and hippocampal formation at 10x sequencing depth52. ..



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Image Search Results


Chodl mRNA is highly expressed in quiescent SCs (A) Chodl gene expression in various cell types in the mouse. Data were explored through the Tabula Muris data portal. (B) Relative expression of the Chodl gene in SCs, bladder cells, and a non-annotated cell population in (A). (C) UMAP-embedding of the scRNA-seq data on SCs isolated from non-injured muscles and injured muscles at 5 dpi in 2-month-old mice. (D) Violin plots show subcluster-specific gene expression ( Pax7 , Myog , Mki67 ) and enrichment of Chodl in the QSCs and ASCs. Colored by cluster identity. Abbreviations: QSC, quiescent SCs; SSC, self-renewal SCs; ASC, activated SCs; PSC, proliferating SCs; CSC, committed SCs; DSC, differentiating SCs. (E) qRT-PCR analysis of Chodl expression level in mouse QSC, ASC, and myoblast. n = 3 mice. (F) Immunoblot analysis showing CHODL expression in mouse myoblasts during myogenic differentiation. Relative band densitometry was calculated using ImageJ. (G) qRT-PCR analysis of Chodl in TA muscles post CTX injury in the mouse. n = 4 mice. Data are represented as mean ± SEM. The p values in E and G were analyzed using one-way ANOVA and Tukey post hoc test; ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001.

Journal: iScience

Article Title: Chondrolectin regulates the sublaminar localization and regenerative function of muscle satellite cells in mice

doi: 10.1016/j.isci.2026.115703

Figure Lengend Snippet: Chodl mRNA is highly expressed in quiescent SCs (A) Chodl gene expression in various cell types in the mouse. Data were explored through the Tabula Muris data portal. (B) Relative expression of the Chodl gene in SCs, bladder cells, and a non-annotated cell population in (A). (C) UMAP-embedding of the scRNA-seq data on SCs isolated from non-injured muscles and injured muscles at 5 dpi in 2-month-old mice. (D) Violin plots show subcluster-specific gene expression ( Pax7 , Myog , Mki67 ) and enrichment of Chodl in the QSCs and ASCs. Colored by cluster identity. Abbreviations: QSC, quiescent SCs; SSC, self-renewal SCs; ASC, activated SCs; PSC, proliferating SCs; CSC, committed SCs; DSC, differentiating SCs. (E) qRT-PCR analysis of Chodl expression level in mouse QSC, ASC, and myoblast. n = 3 mice. (F) Immunoblot analysis showing CHODL expression in mouse myoblasts during myogenic differentiation. Relative band densitometry was calculated using ImageJ. (G) qRT-PCR analysis of Chodl in TA muscles post CTX injury in the mouse. n = 4 mice. Data are represented as mean ± SEM. The p values in E and G were analyzed using one-way ANOVA and Tukey post hoc test; ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001.

Article Snippet: To gain insight into the expression pattern of Chodl across various tissues, we conducted a comprehensive analysis using the scRNA-seq data publicly available from Tabula Muris , Our analysis revealed that Chodl mRNA was mainly enriched in SCs, bladder cells, and a non-annotated cell population, while not detected in other cell types, including fibro-adipogenic progenitors (FAPs) ( A and 1B).

Techniques: Gene Expression, Expressing, Isolation, Muscles, Quantitative RT-PCR, Western Blot, Cell Characterization