Journal: Nature

Article Title: Single Molecule Imaging of Transcription Dynamics in Somatic Stem Cells

doi: 10.1038/s41586-020-2432-4

Figure Lengend Snippet: a , Representative images of CMP in different states. Scale bar = 10μm. b , Gating scheme for assigning CMP to transcriptional states. See for details on the gating strategy. tSNE plot demonstrates the proximity of states to one another and to immunophenotypic GMP and MEP. Images and analyses derived from experimental datasets reported in and , Frequency distribution of transcriptional bursting for each gene in each transcriptional state. x-axis is the number of active alleles. d , (top) Schematic of “states” being the consequence of simple transcriptional noise of the LES state (right) versus truly separate transcriptional states (right) that require transition events (edges). (bottom) Time dependent behavior of simulated cells in a noise only (gray) or state transition system (red) shown as a bivariate plot of PU.1 copy number versus Gata1+Gata2 copy number. T indicates the amount of elapsed simulation time as a fraction of the final time. (e-f) , Gillespie simulations of state transitions, modulating half-life alone. If a transition to another state occurs by noise alone, the cell only changes the mRNA half-life of the mRNA defining that state. e , Endpoint states reached in the simulations (n=10,000) and f , 1000 representative simulation trajectories, color coded on the final endpoint state. Each panel is a different factor change in the mRNA half-life, with the left-most panel as the reference (i.e. the half-lives used in ), (second panel from left), 3X (second from right), and 4X (right-most).

Article Snippet: tSNE maps of primary KL cells were generated in MATLAB with the ‘tsne’ function using the mature and nascent mRNA values/cell for each gene as variables.

Techniques: Derivative Assay

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