Journal: Horticulture Research

Article Title: Uncovering the genetic architecture of pungency, carotenoids, and flavor in Capsicum chinense via TWAS-mGWAS integration and spatial transcriptomics

doi: 10.1093/hr/uhaf243

Figure Lengend Snippet: Spatially resolved transcriptomic analysis of two contrasting C. chinense accessions, PI 656271 and PI 660973, was performed using Stereo-seq on 5-day post-anthesis (5-dpa) fruits to investigate tissue-specific gene expression patterns. Panel A illustrates the application of spatial transcriptomics to map gene activity within intact fruit tissues. Panel B presents a Uniform Manifold Approximation and Projection (UMAP) plot of spatial transcriptomic spots, where each dot represents an individual spatial location, and colors indicate distinct transcriptional clusters across the fruit sections. Panels C and D display the differential spatial expression of AP2 (APETALA2) transcription factors and PPR (Pentatricopeptide Repeat) genes, respectively, within Modules 1 and 2—gene modules identified as highly spatially correlated and differentially expressed between the two accessions. These spatial expression profiles reveal zone-specific regulatory networks involved in the biosynthesis of capsaicinoids, carotenoids, and volatile metabolites, offering insights into the tissue-level transcriptional control of fruit quality traits in C. chinense .

Article Snippet: Stereo-seq transcriptomics was conducted using the BGI Stereo-seq kit (catalog 211ST114, STOmics), following the manufacturer’s instructions with minor modifications [ ].

Techniques: Gene Expression, Activity Assay, Expressing, Control

Journal: bioRxiv

Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

doi: 10.1101/2024.11.04.621811

Figure Lengend Snippet: a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and STOmics Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.

Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

Techniques: Slice Preparation, Staining, Derivative Assay, Expressing, Marker

Journal: bioRxiv

Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

doi: 10.1101/2024.11.04.621811

Figure Lengend Snippet: a , Illustration of the five different brain bregma used for STOmics Stereo-seq in accordance with the Visium data set. Representative H&E stains are shown for each Bregma. Since Stereo-seq does not support H&E stains directly from the sequenced tissue slices, an adjacent (directly before or after) tissue slice was prepared and stained before running the spatial transcriptomics experiments. b , From left to right and per brain bregma (top to bottom): integrated UMAP representation of all cleaned Stereo-seq spot clusters using the bin200 resolution, pie charts and per replicate spatial projections of the final annotated spot clusters. Cluster names and colors were assigned in accordance with the Visium data set (cf. Methods). c , Distribution of four main quality control features across the cleaned spots and per Stereo-seq brain replicate at bin200 resolution.

Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

Techniques: Staining, Control