Journal: Cell

Article Title: High-throughput mapping of long-range neuronal projection using in situ sequencing

doi: 10.1016/j.cell.2019.09.023

Figure Lengend Snippet: The laminar organization of projection neurons in the auditory cortex. (A) The mean projection patterns of clusters corresponding to the indicated major classes of neurons. Line thickness indicates projection strength normalized to the strongest projection for that class. Blue arrows indicate projections to contralateral brain areas and black arrows indicate projections to ipsilateral brain areas. (B) The sequenced projection neurons from a brain (XC9) are color-coded by class identities and plotted at their locations in the cortex. The top and bottom of the cortex are indicated by the red and blue dashed lines, respectively. The laminae and their boundaries are marked. Scale bar = 100 μm. Inset: histograms of the laminar depths of each class of projection neurons in the pooled BARseq dataset. (C) Hierarchical clustering of single-cell projection data. Top: dendrogram of the hierarchical structure of the clusters. Middle: the mean projection patterns of the corresponding leaf clusters. Bottom: The laminar distribution of the corresponding leaf clusters. Individual neurons are superimposed on top of the distribution plots (light grey). Neurons whose cluster identity were less confident were marked in gray. The number of cells that belong to each leaf cluster is indicated below. Neurons of subcluster 25 were likely misidentified PT-l neurons (see STAR Methods). (D) t-SNE plot of the projection neurons. The neurons are color-coded by their first level subcluster identities post-hoc. (E) The normalized entropy of nodes/leaves (y-axis) in the indicated clustering hierarchy (x-axis). Grey bars indicate mean ± stdev of all nodes/leaves of a specific hierarchy. See also Fig. S4–S6.

Article Snippet: Spectral clustering was performed using a MATLAB implementation of the algorithm ( https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering ). t-SNE ( van der Maaten and Hinton, 2008 ) was performed using a MATLAB implementation of the standard t-SNE ( https://lvdmaaten.github.io/tsne/ ) using the log projection data as inputs.

Techniques:

Journal: Cell

Article Title: High-throughput mapping of long-range neuronal projection using in situ sequencing

doi: 10.1016/j.cell.2019.09.023

Figure Lengend Snippet: Subtypes of IT neurons defined by gene expression in the auditory cortex. (A) Histograms of the log normalized expression of the indicated marker genes in the indicated clusters obtained from single-cell RNAseq in the auditory cortex. The dendrograms show distances of mean gene expression among transcriptomic clusters (left) and distances of mean projection pattern (right) obtained through BARseq and FISH. (B) t-SNE plot of the gene expression of neurons color-coded by cluster identity as in (A). (C) MetaNeighbor comparison of neuronal clusters obtained in the auditory cortex to those in the visual cortex from Tasic et al. (2018). (D) Projections (left) and the expression of genes (right) of neurons obtained using combination of BARseq and FISH are shown on a log scale. Projection areas are the same as in Fig. 4B, except that each cortical area is divided into upper (u) and lower (l) layers. (E) Distributions of laminar positions of neurons. Individual neurons (red) are superimposed on the smoothed distribution (black). See also Fig. S7.

Article Snippet: Spectral clustering was performed using a MATLAB implementation of the algorithm ( https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering ). t-SNE ( van der Maaten and Hinton, 2008 ) was performed using a MATLAB implementation of the standard t-SNE ( https://lvdmaaten.github.io/tsne/ ) using the log projection data as inputs.

Techniques: Expressing, Marker