slide-seq of mouse hippocampus  (Vizgen Inc)

Journal: Nature Communications

Article Title: Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

doi: 10.1038/s41467-023-43120-6

Figure Lengend Snippet: a Schematic showing the cortical impact site and microglia activation. b Spatio-temporal trajectory of microglial activation at 3 days post-TBI, as predicted by our PSTS algorithm, running from the hypothalamus (node 4), through the thalamus (node 2) and hippocampus (node 3) and then the cortical penumbra regions adjacent to the lesion core (nodes 1). Colour-coded pseudo-time-space values (ranging from 0 to 1 ) reflect microglia-related gene expression changes through the tissue space. c Clustering results for TBI Visium ST data ( n = 2442 spots). d Transition genes positively (blue) or negatively (red) correlated with the predicted trajectory for microglia activation (extracted by Spearman correlation test of pseudo-time-space values; adjusted p -value < 0.05 and correlation coefficient >0.3 or <−0.3). e Enrichment analysis of upregulated transition genes revealing significant pathways related to microglia activation, inflammation and neural injury. f Experimental validation of the spatio-temporal trajectory for microglia (green) activation following TBI; cell nuclei are shown in blue. Imaging was performed across five different brain regions of interest (ROIs; from one brain per time point), equivalent to the trajectory nodes, from sham (uninjured) controls and five different time points post-TBI. Note the changes in microglia abundance and morphology across cluster nodes and time. g Density plots illustrating changes in microglia cell body size (proxy for activation) over time (top) and space (bottom; 3 days post-TBI only). h Changes in microglia density over time and space for all ROIs ( n = 4 biological replicates per time point; error bars show SEM. i Variograms depicting the autocorrelation of PSTS/pseudotime values for each spot. Plots show the spatial variance in PSTS/pseudotime values produced by Slingshot, Monocle 3 and PSTS. Lower values of the semi-variance Matheron estimator indicate higher PSTS/pseudotime continuity in the spatial context, and thus a more likely trajectory (see “Methods”); PSTS semi-variance is indicated by the red dashed line. j Spatial branching patterns for microglia activation using different trajectory analysis methods. Only PSTS predicted a trajectory leading to the penumbra regions rather than the core (where microglia are mostly absent; see inset and also Figs. and .

Article Snippet: Slide-seq data for the mouse hippocampus containing 47,573 cells and 20,572 genes were downloaded from website ( https://www.dropbox.com/s/cs6pii5my4p3ke3/mouse_hippocampus_reference.rds?dl=0 ), (accessed February 2022).

Techniques: Activation Assay, Gene Expression, Biomarker Discovery, Imaging, Produced

Journal: Nature Communications

Article Title: Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

doi: 10.1038/s41467-023-43120-6

Figure Lengend Snippet: a Overview of the stLearn SCTP algorithm, which uses spatial location and ligand-receptor (LR) co-expression to predict interactions in multiple spatial technologies: (1) spatial neighbourhoods are scored for LR co-expression, (2) background spatial co-expression is determined by randomly pairing genes (default 1000 pairs) with equivalent expression levels to LR pair, (3) significant spots of spatial LR co-expression are determined by comparison to the random background, (4) counting of cell type co-occurrence in neighbourhoods of significant LR co-expression, with and without permutation of cell type information, and (5) cell types with significant co-localisation in regions of LR co-expression are predicted as interacting. b stLearn SCTP results for the top-ranked LR pair Gas6-Axl in seqFISH+ data from mouse cortex. c Enlarged panel of the boxed area in b , showing the subventricular zone; black arrows connect interacting cells, and chord plot summarises predicted CCIs facilitated by Gas6-Axl . d Scatter plot highlighting the top predicted LR pair by stLearn SCTP ( Gas6-Axl ), with the number of significant cells on the y -axis and LR pairs on the x -axis. e Mouse hippocampus Slide-seq data annotated by cluster. f Cells binned by spatial location, with bins representing mixtures of cells similar to Visium data. Bins are represented as pie charts showing the breakdown of cell types. g Significant co-expressing spots for the top-ranked ligand-receptor pair Apoe-Lrp1 , illustrating that SCTP can scale to a large number of cells by binning. h Visium ST data from human breast cancer, with each spot coloured by the dominant cell type, as predicted by deconvolution. Red boxes correspond to Ductal Carcinoma In Situ (DCIS), and yellow boxes show regions highlighted in i and j . i , DCIS regions showing significant SCTP predictions for a highly-ranked LR pair ( GPC3-IGF1R ), overlayed as arrows, where the receiving spot expresses the receptor and the output spot expresses the ligand. j Network diagram of SCTP-predicted CCI results for GPC3-IGF1R . Zoomed-in images of interacting spots (from yellow boxes 1 and 2 in h and i ) are shown on the edges, connecting relevant cell types in the graph.

Article Snippet: Slide-seq data for the mouse hippocampus containing 47,573 cells and 20,572 genes were downloaded from website ( https://www.dropbox.com/s/cs6pii5my4p3ke3/mouse_hippocampus_reference.rds?dl=0 ), (accessed February 2022).

Techniques: Expressing, Comparison, In Situ

Journal: Nature Communications

Article Title: Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

doi: 10.1038/s41467-023-43120-6

Figure Lengend Snippet: a Schematic showing stSME integration of three data types (imaging morphology (I), gene expression (G) and spatial location/distance (D). stSME finds biologically relevant reference spots, to then adjust existing spots, or predict gene expression for new spots (pseudo-spots) by imputation. b Rescue of dropout (zero values; blue arrows) by stSME for gene markers of the Cornu Ammonis (CA) 3 ( Lhfpl1 ) and dentate gyrus (DG; Pla2g2f ) regions of the mouse hippocampus. Note that the imputation is specific to biologically relevant spots. c Effects of imputation on library size (total gene counts per spot; top), and the number of spots with missing values (bottom). d Simulation approach assessing stSME imputation performance using mouse brain Visium ST data. Louvain clustering was performed with imputed values after randomly removing 20% of values from the original (log transformed UMI counts) data as a ’leave-out’ validation strategy. Note that clusters without stSME imputation are much noisier, and also that the hippocampal CA1 (cluster 6) and CA3 (cluster 17) sub-regions could not be separated (white arrows). e Box plot showing poorer clustering results when stSME is not used, as assessed by adjusted Rand index (ARI; data was randomly subsampled 80% from 2702 spots of a brain section, with a total of n = 10 simulations). ARI was calculated using the full data clustering results as the reference. f Robustness and performance of stSME imputation method for the top-2000 highly variable genes (HVGs) across two replicate sections of the Visium human breast cancer ST dataset (10x Genomics; Block A, sections 1 and 2; see “Methods” section for details). Data points are the spatial autocorrelation (Moran’s I index) for the same set of imputed HVGs in section 1 ( x -axis) and section 2 ( y -axis); colour coding reflects sparsity of the gene in the original UMI count matrix. g Imputation of gene expression in regions without data (i.e. array gaps) improves tissue coverage and clustering in human breast cancer samples. Bottom images show zoomed-in displays of boxed DCIS boundary region, showing cluster location and expression of breast cancer markers SFRP2 and MGP (abundant in DCIS).

Article Snippet: Slide-seq data for the mouse hippocampus containing 47,573 cells and 20,572 genes were downloaded from website ( https://www.dropbox.com/s/cs6pii5my4p3ke3/mouse_hippocampus_reference.rds?dl=0 ), (accessed February 2022).

Techniques: Imaging, Gene Expression, Transformation Assay, Biomarker Discovery, Blocking Assay, Expressing

Journal: Science Advances

Article Title: Accurate inference of genome-wide spatial expression with iSpatial

doi: 10.1126/sciadv.abq0990

Figure Lengend Snippet: ( A ) UMAPs showing the expression levels of representative cell type markers in raw STARmap (top panels) and iSpatial inferred (bottom panels) data. Excitatory neuron ( Slc17a7 ), inhibitory neuron ( Gad1 ), oligodendrocyte ( Plp1 ), astrocyte ( Aqp4 ), and endothelial cell ( Cldn5 ). ( B ) The spatial expression of cortex layer markers in the raw STARmap (top panels) and inferred by iSpatial (middle panels) compared with the ISH data from the ABA (bottom panels). Layer information: “L1 to L6,” the six cortical layers; “cc,” corpus callosum; “HPC,” hippocampus. ( C ) The UMAP and spatial expression of Tac2 and Serpinb2 in the raw MERFISH (top panels) and inferred by iSpatial (middle panels) compared with the ISH data from the ABA (bottom panels).

Article Snippet: The Slide-seq V2 of mouse hippocampus was downloaded from the Broad Institute single-cell portal website ( https://singlecell.broadinstitute.org/single_cell/study/SCP815 ).

Techniques: Expressing