Journal: bioRxiv

Article Title: CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models

doi: 10.1101/2024.11.20.619750

Figure Lengend Snippet: a, Distribution of staining types in CellBinDB. b, Distribution of tissue types in CellBinDB, where tissue types with less than 10 samples are included in other, for details, see supplementary table 1. c, Examples of CellBinDB images and their two types of (semantic and instance) ground truth annotations, with 10 μ m scale bar from left to right columns 1, 2: ssDNA, columns 3, 4: DAPI, columns 5, 6: H&E, columns 7, 8: mIF, column 9: 10x Genomics DAPI, column 10: 10x Genomics H&E. The first row is the original microscope image, followed by the semantic annotation mask and instance annotation mask. d, Scatter plot of t-SNE demonstrates the diverse spread of data by different staining types and sources. e, Scatter plot of t-SNE demonstrates the diversity of CellBinDB compared to previous datasets. f, The number of manual and semi-automatic annotations in CellBinDB. g, The dataset annotation process includes four steps: 1.model annotation, 2.annotation team modification/re-annotation(depends on the model annotation results), 3.expert review, go to the next step if the annotations are correct, otherwise return to the second step for modification, 4. add the original image and the two masks to the dataset.

Article Snippet: WSIs are generated by 1. a STOmics Microscope Go Optical equipped with Scanner Version 1.2.2, using 10×/0.75 NA and 20X/0.5 NA objective and Go Optical Scanner Ximea Mc124 for grayscale images and Go Optical Scanner Ximea Mc050 for RGB images.

Techniques: Staining, Microscopy, Modification

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

Journal: Cell Reports

Article Title: A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators

doi: 10.1016/j.celrep.2025.115240

Figure Lengend Snippet: Single-cell RNA-seq and cluster annotation of wheat root tips (A) UMAP visualization of the three replicates in our scRNA-seq experiment and corresponding atlas metrics. (B) Expression of cell type markers across each cluster. Dot diameter, proportion of cluster cells in a cluster expressing a given gene; color, mean expression across cells in that cluster. (C) Sankey plot showing annotations transferred from Arabidopsis ( Ath ), rice ( Osa ), maize ( Zma ), and single-nuclei wheat (sn Tae ) to our wheat atlas ( Tae ) and corresponding q value. (D and E) Annotated UMAPs with cell type (D) and cell state (E) annotations. Please note that cluster 6 was manually annotated as pericycle based on evidence from STOmics Stereo-seq data and known pericycle marker genes and was therefore marked with an asterisk.

Article Snippet: To experimentally validate the predicted annotations of our soil-grown wheat root meristem atlas obtained from the orthology-based mapping approach, we next optimized and implemented an untargeted spatial transcriptomics (ST) technology called STOmics Stereo-seq , on the same samples as collected for scRNA-seq experiment (see for experimental and analysis details).

Techniques: RNA Sequencing, Expressing, Marker

Journal: Cell Reports

Article Title: A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators

doi: 10.1016/j.celrep.2025.115240

Figure Lengend Snippet: scRNA-seq-derived marker gene expression patterns in STOmics Stereo-seq root sections (A) A cross-section of wheat root apical meristem with major cell types annotated. (B–F) UMAP feature plot and STOmics Stereo-seq visualization of marker genes from epidermis (B), cortex (C), phloem (D), xylem (E), and root cap (F).

Article Snippet: To experimentally validate the predicted annotations of our soil-grown wheat root meristem atlas obtained from the orthology-based mapping approach, we next optimized and implemented an untargeted spatial transcriptomics (ST) technology called STOmics Stereo-seq , on the same samples as collected for scRNA-seq experiment (see for experimental and analysis details).

Techniques: Derivative Assay, Marker, Gene Expression

Journal: Cell Reports

Article Title: A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators

doi: 10.1016/j.celrep.2025.115240

Figure Lengend Snippet: Tissue-specific markers conserved across Arabidopsis , wheat, rice, and maize or unique to the monocot clade (A) UpSet plot showing the intersections of orthologous groups of xylem markers across Arabidopsis , wheat, rice, and maize. (B–E) Feature plots of a xylem-specific marker across species. (F and G) Spatial expression in STOmics Stereo-seq data (F) and ternary plot showing genome asymmetry information (G) of the same xylem-specific marker in the wheat root meristem. (H) UpSet plot showing the intersections of orthologous groups of cortex markers across Arabidopsis , wheat, rice, and maize. (I–L) Feature plots of a cortex-specific marker unique to monocots. (M and N) Spatial expression in STOmics Stereo-seq data (M) and ternary plot showing genome asymmetry information (N) of the same cortex-specific marker in the wheat root meristem.

Article Snippet: To experimentally validate the predicted annotations of our soil-grown wheat root meristem atlas obtained from the orthology-based mapping approach, we next optimized and implemented an untargeted spatial transcriptomics (ST) technology called STOmics Stereo-seq , on the same samples as collected for scRNA-seq experiment (see for experimental and analysis details).

Techniques: Marker, Expressing

Journal: Cell Reports

Article Title: A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators

doi: 10.1016/j.celrep.2025.115240

Figure Lengend Snippet:

Article Snippet: To experimentally validate the predicted annotations of our soil-grown wheat root meristem atlas obtained from the orthology-based mapping approach, we next optimized and implemented an untargeted spatial transcriptomics (ST) technology called STOmics Stereo-seq , on the same samples as collected for scRNA-seq experiment (see for experimental and analysis details).

Techniques: Recombinant, Generated, Gene Expression, Software, Marker