Journal: eLife

Article Title: Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex

doi: 10.7554/eLife.52951

Figure Lengend Snippet: ( A ) Overview of experimental approach using Patch-seq. ( B ) Example tdTomato-positive translaminar clone spanning cortical layers 2–6 in an acute cortical slice used for Patch-seq experiments. Overlay of bright field and fluorescence image was performed in Adobe Photoshop. Scale bar: 100 μm. ( C and D ) Box plots showing library size ( C ) and number of genes detected ( D ) for all cells passing quality control criteria (n = 206). ( E ) Density plot of the percent of variance in normalized log-expression values explained by different experimental factors. Each curve corresponds to the variance in gene expression across all genes (n = 12,841 genes) that can be explained by a single variable, with right-shifted curves reflecting variables that explain a higher fraction of the variance. ( F ) T-distributed stochastic neighbor embedding (t-SNE) plots using the top highly variable and correlated genes across all cells (n = 91 genes; n = 87, 22, 84, and 13 cells in layers 2/3, 4, 5, and 6, respectively), colored by layer position. ( G ) Performance of a generalized linear model (GLM) trained to predict region from gene expression data of L2/3 neurons (n = 12,841 genes and 85 cells) with model performance (black dot) compared to the chance-level performance estimated using shuffled data (gray, mean and 95% coverage interval; one-tailed p -value computed from shuffled data, shuffling region). ( H ) Performance of a GLM trained to predict region from gene expression data of L5 neurons (n = 12,841 genes and 77 cells) as described in ( G ). See also and and . Figure 2—source data 1. Gene expression data, related to . Normalized counts, normalized log counts, and metadata for all Patch-seq neurons included in our analysis.

Article Snippet: We fit a binomial GLM (using the glmfit() function in Matlab) containing the relevant linear terms and all possible pairwise interactions: g ( P ) = β 0 + β L ⋅ L + β C ⋅ C + β D ⋅ D + β R ⋅ R + β L C ⋅ L ⋅ C + β L D ⋅ L ⋅ D + β L R ⋅ L ⋅ R + β C D ⋅ C ⋅ D + β C R ⋅ C ⋅ R + β D R ⋅ D ⋅ R where β 0 is a constant term, L is a binary variable representing the lineage relationship (1 for related and 0 for unrelated), C is a binary variable representing the connection type (1 for vertical and 0 for lateral), D is the Euclidean distance between the cells in microns, R is a numeric variable representing the rostrocaudal position of the clone with integer values from 1 (most rostral) to 5 (most caudal), and β i are the corresponding coefficients.

Techniques: Fluorescence, Control, Expressing, Gene Expression, One-tailed Test

Journal: eLife

Article Title: Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex

doi: 10.7554/eLife.52951

Figure Lengend Snippet: Generalized linear model of connectivity. Connectivity was modeled as a binomial response variable with the following predictors: lineage relationship (1 for related, 0 for unrelated), connection type (1 for vertical, 0 for lateral), Euclidean distance between the cells in microns, and rostrocaudal position (a numeric factor from 1 to 5; see Materials and methods). ‘×’ denotes an interaction between two linear terms. Overall χ 2 = 33.5 compared to constant model, p=2.26 × 10 −4 , 1988 error degrees of freedom. The four terms with small p -values are: connection class (connection probability P is lower for unrelated vertical connections, compared to unrelated lateral), Euclidean distance (P decreases with increasing distance for unrelated lateral connections), lineage × connection type ( P is higher for related vertical pairs), and connection type × Euclidean distance (the effect of Euclidean distance on P depends on the type of connection tested).

Article Snippet: We fit a binomial GLM (using the glmfit() function in Matlab) containing the relevant linear terms and all possible pairwise interactions: g ( P ) = β 0 + β L ⋅ L + β C ⋅ C + β D ⋅ D + β R ⋅ R + β L C ⋅ L ⋅ C + β L D ⋅ L ⋅ D + β L R ⋅ L ⋅ R + β C D ⋅ C ⋅ D + β C R ⋅ C ⋅ R + β D R ⋅ D ⋅ R where β 0 is a constant term, L is a binary variable representing the lineage relationship (1 for related and 0 for unrelated), C is a binary variable representing the connection type (1 for vertical and 0 for lateral), D is the Euclidean distance between the cells in microns, R is a numeric variable representing the rostrocaudal position of the clone with integer values from 1 (most rostral) to 5 (most caudal), and β i are the corresponding coefficients.

Techniques: