Journal: bioRxiv

Article Title: The DLX/Notch axis is necessary for spatiotemporal regulation of neural cell fate

doi: 10.1101/2025.09.28.679022

Figure Lengend Snippet: (A) Schematic diagram illustrating the sub-anatomical compartments within the embryonic telencephalon, including the ventricular zone (VZ), sub-ventricular zone (SVZ), and mantle zone (MZ). (B) Expression of sub-anatomical markers across sections from E12.5 (left), E13.5 2x coronal, 1x sagittal (middle), E14.5 (right). From left to right column: VZ markers, SVZ markers, and MZ markers. From top to bottom row: telencephalon markers (VZ: Fabp7 , SVZ: St18 , MZ: Dcx ); dorsal telencephalon markers (VZ: Pax6 , SVZ: Eomes , MZ: Tbr1 ), and ventral telencephalon markers (VZ/SVZ: Ascl1, Olig2 , MZ: Nkx2-1 ). (C) Cell type classification following unsupervised clustering and manual annotation. WT sections (top row) and Dlx1/Dlx2 -/- sections (bottom row) for E12.5 (left), E13.5 (2x coronal, 1x sagittal) (middle) and E14.5 (right). v/dNP: ventral/dorsal neural progenitors, v/dIP: ventral/dorsal intermediate progenitors, ThalNeur: thalamic neuron, HypothalNeur: hypothalamic neuron. (D) UMAP plots showing clustering and cell type classification for WT and Dlx1/Dlx2 -/- sections. Legend as shown in (C). (E) Dot plot showing the mean expression of cluster markers in cells in each cluster from (C). Dot sizes denote the fraction of cells expressing the corresponding markers. (F) Bar plot showing the percentage of each cell type in WT and Dlx1/Dlx2 -/- E12.5-E14.5 spatial transcriptomics dataset, summarised based on age and genotype. Legend in (C). (G) Volcano plot showing the differential expression analyses comparing VZ and SVZ of WT vs Dlx1/Dlx2 -/- GE. Thresholds for differentially expressed genes were set at FDR<0.05 and fold change > 1 or < -1. (H) Gene ontology analysis results of all differentially expressed genes in VZ and SVZ of Dlx1/Dlx2 -/- GE.

Article Snippet: Samples were prepared and processed in accordance with manufacturer’s instructions (BGI STOmics Stereo-seq Transcriptomics set for Chip-on-a-slide user manual version B) using the STOmics Transcriptomics kit (STOmics Cat# 111KT114).

Techniques: Expressing, Quantitative Proteomics

Journal: bioRxiv

Article Title: Multiscale Spatial Transcriptomic Atlas of Human Basal Ganglia Cell-Type and Cellular Community Organization

doi: 10.64898/2025.12.02.691876

Figure Lengend Snippet: ( A ) Overview of study design, donor and sample quality and metadata, tissue sampling strategy, and adjacent-section workflows. ( B ) Stereo-seq dataset from an anterior basal ganglia section (donor case ID 2724): left, spatial map with cell-type assignments; right, UMAP embedding resolving 10 major cell classes from the same section. ( C ) MERFISH+ dataset from an adjacent anterior section. Left, spatial map with cell-type assignments; right, UMAP embedding resolving 10 major cell classes corresponding to Stereo-seq in ( B ). ( D–G , progressively greater magnification) Multi-scale Stereo-seq enlarged views of the boxed region in ( B ), illustrating the resolution span from centimeter-scale tissue anatomy to micron-scale detection of individual transcripts, with corresponding cell-type labels ( D, E ), cell-contour segmentation ( F ), and single-cell detail ( G ). ( H–K , progressively greater magnification) Multi-scale MERFISH+ enlarged views of the boxed region in ( C ), showing transcripts for selected genes (DRD1, DRD2, PENK, CCK, SST, CHAT, MBP) (color coded) and other genes (gray coded) that define cell-types at micron-scale resolution. See color keys at ( K ). Scale bars as indicated

Article Snippet: To fit the 2 cm × 3 cm Stereo-seq chips (STOmics/Complete Genomics, 111ST13231-CG), each large brain section was trimmed and/or subdivided within the cryostat to approximately 18 mm × 28 mm dimensions ( ).

Techniques: Sampling

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

Journal: Research

Article Title: Application of Spatial Omics in the Cardiovascular System

doi: 10.34133/research.0628

Figure Lengend Snippet: Schematic principle and workflow of spatial omics in cardiovascular research. (A) Spatial transcriptomics: Mapped gene expression patterns in cardiovascular tissues, identifying specific gene signatures in different heart regions. Sequencing-based methods are illustrated using the principles of Visium as an example, while imaging-based methods are represented by the principles of Xenium. Slight variations in principles may exist across different technologies. (B) Spatial proteomics: Analyzed protein distributions and interactions, revealing protein networks and spatial organization within cardiac tissues. Antibody-dependent spatial MS methods are illustrated using the principles of GeoMx DSP as an example, while cyclic antibody imaging-based methods are represented by the principles of PCF and CODEX. Slight variations in principles may exist across different technologies. (C) Spatial metabolomics: Provided high-resolution metabolite profiling, revealing metabolic alterations and spatial distributions in cardiovascular tissues. (D) Spatial epigenomics: Profiled chromatin accessibility, histone modifications, and DNA methylation patterns, providing insights into epigenetic landscapes in cardiac cells. (E) Spatial genomics: Studied the spatial arrangement of genetic material within cell nuclei, revealing the 3D genome organization and its impact on cellular function. Created with BioRender.

Article Snippet: In addition, the sequencing-based STOmics Spatial Transcriptomics platform utilizes advanced sequencing technologies to precisely capture and analyze transcriptomic information in its spatial context [ ].

Techniques: Gene Expression, Sequencing, Imaging, Spatial Proteomics, DNA Methylation Assay, Cell Function Assay

Journal: Research

Article Title: Application of Spatial Omics in the Cardiovascular System

doi: 10.34133/research.0628

Figure Lengend Snippet: Summary of the commonly used method of spatial omics technologies

Article Snippet: In addition, the sequencing-based STOmics Spatial Transcriptomics platform utilizes advanced sequencing technologies to precisely capture and analyze transcriptomic information in its spatial context [ ].

Techniques: Sequencing, Imaging, Formalin-fixed Paraffin-Embedded, Expressing, In Situ, High Throughput Screening Assay, Spatial Proteomics, Multiplexing, Fluorescence, Microscopy, Modification

Journal: Research

Article Title: Application of Spatial Omics in the Cardiovascular System

doi: 10.34133/research.0628

Figure Lengend Snippet: Comparative summary of sample preparation for spatial omics technologies

Article Snippet: In addition, the sequencing-based STOmics Spatial Transcriptomics platform utilizes advanced sequencing technologies to precisely capture and analyze transcriptomic information in its spatial context [ ].

Techniques: Sample Prep, Preserving, Modification, Spatial Proteomics, Imaging, Mass Cytometry

Journal: Research

Article Title: Application of Spatial Omics in the Cardiovascular System

doi: 10.34133/research.0628

Figure Lengend Snippet: Schematic view of cardiovascular diseases using spatial transcriptomics. (A) Atherosclerosis: Showed high macrophage infiltration and molecular signatures related to plaque stability within atherosclerotic plaques. (B) Myocardial infarction: Displayed distinct gene expression patterns in infarcted and peri-infarct zones, highlighting molecular responses to ischemic injury. (C) Cardiomyopathy: Revealed fibrosis-related genes and altered cellular composition in HCM tissues. (D) Heart failure: Uncovered region-specific gene expression changes that contribute to the progression of HF. (E) Aortic dissection: Highlighted distinct molecular profiles in dissection sites, providing insights into pathophysiology and potential therapeutic targets. (F) Cardiac myxoma: Identified tumor-specific gene signatures and interactions within the tumor microenvironment. CM, cardiomyocyte; FB, fibroblast; Mac, macrophage; SMC, smooth muscle cell; ETC, EC-like tumor cells; MTC, MSC-like tumor cells. Created with http://BioRender.com .

Article Snippet: In addition, the sequencing-based STOmics Spatial Transcriptomics platform utilizes advanced sequencing technologies to precisely capture and analyze transcriptomic information in its spatial context [ ].

Techniques: Gene Expression, Dissection, Biomarker Discovery

Journal: Research

Article Title: Application of Spatial Omics in the Cardiovascular System

doi: 10.34133/research.0628

Figure Lengend Snippet: Summary of spatial transcriptomics in the research of cardiovascular diseases

Article Snippet: In addition, the sequencing-based STOmics Spatial Transcriptomics platform utilizes advanced sequencing technologies to precisely capture and analyze transcriptomic information in its spatial context [ ].

Techniques: Expressing, Gene Expression, Marker, Infection, Activation Assay, Transformation Assay