Journal: bioRxiv

Article Title: An integrative spatial multi-omic workflow for unified analysis of tumor tissue

doi: 10.1101/2024.10.15.618574

Figure Lengend Snippet: A. Distribution of cell types annotated by nuclei-only and cell-morphology segmentation. Sample annotated by cell type, showing only nuclei which share the same cell type annotations with cell-morphology segmentation. B. IF tile scan conducted post 10x Genomics Xenium data acquisition, showing cells stained for glial fibrillary protein (GFAP, green), actin (Phalloidin, magenta) and nuclei (DAPI, cyan). Scale, 1 mm. Inset indicated in red box, used in C. Scale, 50 µm. C. Structural comparison between polygons generated from expansion-based segmentation (top, left), overlaid with IF (bottom, left) and nuclei-only with overlaid cell-morphology segmentation (top, right), overlaid with IF (bottom, right). Scale, 10 µm. D. Comparison of cell area between expansion-based and cell-morphology segmentation strategies, only considering cells identified by both segmentations. The x-axis is the size of cell area as estimated by cell-morphology and the y-axis is the size of cell area estimated by expansion-based segmentation. Points are colored by the average number of nearest neighbors within 15 µm between the two segmentation strategies. E. Comparison of circular ratio between expansion-based and cell-morphology segmentation strategies for matched cells identified by both segmentations. This ratio for a particular cell mask is calculated by dividing the area of the maximum inscribed circle by the area of minimum binding circle. The x-axis is the ratio based on cell-morphology segmentations, and the y-axis is the ratio based on expansion-based segmentations. Cells are colored by the average number of nearest neighbors within 15 µm between the two segmentation strategies.

Article Snippet: Here, we address these challenges and present Spatial Multi-omics Integration (SMINT), a unified analysis framework for the effective analysis of ST (10x Genomics Xenium, 339 gene custom panel) and SM (MALDI-IMS) data generated from sequential sections of surgically resected low-grade glioma.

Techniques: Staining, Comparison, Generated, Binding Assay

Journal: bioRxiv

Article Title: Spatial host-microbiome sequencing

doi: 10.1101/2022.07.18.500470

Figure Lengend Snippet: (a) Bacterial reference of the mouse gut microbiome. Phylogenetic tree based on metagenomic sequencing of colonic content from SPF mice, representing the 65 species in the mouse gut bacteria reference and colored to highlight taxonomic families and genera. (b) Enhanced annotation performance of the deep learning model. Average Pearson correlation coefficient (y axis) between true and predicted taxonomic labels from all spatial spots ( Methods ) on five taxonomic levels (x axis) when using Kraken2 (orange) or Kraken2 together with the deep learning (DL) model (blue) (y axis) (n = 3). (c) Highly specific mapping of bacterial reads. Overall bacterial alignment rates to the respective reference genomes (y axis, %) for GF (left, n = 3), ASF (middle, n = 3) and SPF (right, n = 3) tissue sections using spatial 16S sequencing. (d) High reproducibility of bacterial abundances in SPF mouse colons by SHM-Seq. Percentage (y axis) of the top 10 most abundant bacteria genera in each of three independent samples of SPF mouse colons (x axis). (e) SHM-seq compares well to 16S rRNA sequencing. Pseudo-bulk abundances of bacteria genera (dot) from SHM-seq (x axis, SPF mice, n = 3) and bulk 16S rRNA sequencing (y axis, SPF mice, n = 3). Top left: Pearson’s r. Shaded areas: 95% confidence interval. (f) Enzymatic (SHM-seq) extraction of bacterial content agrees with established mechanic extraction. Pseudo-bulk abundances of each bacteria genera (dot) from SHM-seq when bacterial wall permeabilization was performed enzymatically (x axis, SPF mice, n = 3) or by mechanical extraction (y axis, SPF mice, n = 3). Shaded areas: 95% confidence interval. (g-i) SHM-Seq agreement with FISH fluorescent signals in ASF mice tissue targeting ASF502 bacteria. (g) Distribution (Box plot, normalized signals per region) and individual measurements (scatter plot, mean signal per region and sample (n = 6)) of scaled normalized ASF502 bacterial counts by FISH (y axis, fluorescence intensity with probe targeting ASF502) and SHM-seq (x axis, read counts) in shared morphological regions of interest (MROIs) ( colors, Method) . Shaded areas: 95% confidence interval. Boxplots: Center black line, median; color-coded box, interquartile range; error bars, 1.5x interquartile range; black dots; outliers. (h) Cross-section of an ASF mouse colon with four regions (red rectangle; 1-4) and their (i) zoom-in images. Colors: tissue (blue), fibers (gray) and ASF502 bacteria (red).

Article Snippet: Here, we bridge this gap, by developing spatial host-microbiome sequencing (SHM-seq, ), a robust all-sequencing based technology that leverages previous advancements in spatial transcriptomics[ , ], and provides histology, spatial RNA-seq and spatial 16S sequencing using readily available instrumentation to profile the host’s expression responses in relation to microbial biogeography.

Techniques: Sequencing, Bacteria, Extraction, Fluorescence