Journal: Communications Biology

Article Title: Advancing spatial cellular communication inference with ligand diffusion and transport model

doi: 10.1038/s42003-025-09413-w

Figure Lengend Snippet: a Spatial annotation of the dataset, including labeled regions (left), aggregated compartments (middle), and the matched H&E image (right). Each spot has a diameter of approximately 55 μm, as specified by 10x Genomics. b Top row: spatial expression of the ligand TGFB1 and its cognate receptors TGFBR2 , TGFBR1 and ACVR1B . Bottom row: total received signals predicted by SCILD for L-R pairs TGFB1- > TGFBR2, TGFB1- > TGFBR1, and TGFB1- > ACVR1B. c Same as ( b ) for the receptor ERBB2 and its cognate ligands HBEGF , AREG , and TGFA .

Article Snippet: The 10x Visium Human Breast Cancer dataset was downloaded from https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 and denoised using Sprod ( https://github.com/yunguan-wang/SPROD ) .

Techniques: Labeling, Expressing

Journal: Bioinformatics

Article Title: BISON: bi-clustering of spatial omics data with feature selection

doi: 10.1093/bioinformatics/btaf495

Figure Lengend Snippet: HBC 10x Visium data: (a) The spatial domains identified by BISON and competing methods. (b) Heatmap of gene groups identified by BISON. Pattern 0 represents the non-DGs.

Article Snippet: The mouse olfactory bulb (MOB) spatial transcriptomics (ST) data were obtained from the public domain through the Spatial Research Lab ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ), and the human breast cancer (HBC) 10x Visium data were obtained from the public domain through 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: This review considers the task of spatial clustering on eight spatially resolved transcriptomics datasets. A. The human DLPFC dataset consisted of 12 neuronal samples from three subjects, each with four sample sections. The displayed sample ID is 151510. B. The mouse brain dataset was obtained from 10X Visium, and each spot contained more than one cell. The entire dataset and its cropped section are available in the Squidpy package. C. The human breast cancer dataset was downloaded from 10X Visium, with manual annotations acquired from the SEDR package. D. The dataset of the olfactory bulbs of mouse was derived from Slide-seqV2 technology, and the particular section Puck_200127_15 was used. E. The Stereo-seq-based dataset of the olfactory bulbs of mouse was obtained from the original paper, and the special section was obtained from the results of SEDR analysis. F. The mouse liver dataset obtained from the Vizgen MERFISH Mouse Liver Map. The special slice is the Liver1Slice1 that measures 347 genes across over > 300,000 liver cells in a single mouse liver slice. G. The pre-processed subset of seqFISH data acquired from Squidpy package. H. The 10x Xenium dataset from the Xenium breast cancer tumor microenvironment Datbase. The related pre-processed data can be gained from Squidpy as well.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Derivative Assay

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: Clustering performance of eight benchmarking methods on the human DLPFC dataset. A. The mean adjusted Rand index (ARI) of each clustering method was determined (Upper). The variance of each clustering method. GraphST had the highest average ARI and the lowest variance (Middle). The ARI of each spatial clustering algorithm was calculated on 12 samples of the human DLPFC. The eight methods are ranked from 1 to 8 in order from the highest to the lowest ARI (Lower). B. The Normalized Mutual Information (NMI) of each clustering method was determined (Upper). The variance of each clustering method. Spatial-MGCN had the highest average NMI and the lowest variance (Middle). The NMI of each spatial clustering algorithm was calculated on 12 samples of the human DLPFC. The eight methods are ranked from 1 to 8 in order from the highest to the lowest NMI (Lower).

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: The ARI and NMI scores of eight spatial clustering methods on three selected DLPFC samples. A, B, C. Three samples were obtained from three different subjects participating in the human DLPFC project. Each subject provided four sections of human brain tissues, and we selected one section from each to compare their ARI values and NMI. Samples 151510 and 151676 were annotated manually into seven clusters, while sample 151672 contained only five groups in the ground truth. The black segment visually represents the standard deviation.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Standard Deviation

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: The identified spatial domains of eight compared methods on three selected slices. A, B, C. The cell-type clustering task was performed on three samples (151570, 151672, and 151676) by using eight spatial clustering methods: SpaGCN, conST, DeepST, CCST, STAGATE, GraphST, SpaceFlow and Spatial-MGCN. These samples corresponded to the three subjects in the human DLPFC dataset. A comparison was made with the ground truth to assess the clustering performance of each baseline.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Comparison

Journal: Frontiers in Cell and Developmental Biology

Article Title: Multiplexed spatial transcriptomics methods and the application of expansion microscopy

doi: 10.3389/fcell.2024.1378875

Figure Lengend Snippet: NGS-based sequencing with region capture by the VISIUM platform. (A) A capture area consists of 5,000 small spots in which capture probes are fixed to the bottom of the wells. (B) Structure of the capture probes. (C) mRNAs are captured inside the spots by the capture probes. Reverse transcription is performed, and second strand synthesis provides cDNA that is then amplified by PCR. (D) Amplified oligonucleotides are prepared for Illumina sequencing by the attachment of various oligonucleotides (TruSeq Read two sequence, indices, P5 and P7). These can be processed by the Illumina sequencer to read out the spatial barcode and UMI for spatial mapping and the RNA coding sequences for RNA identification. This data enables reconstruction of the cellular RNA map. Created with BioRender.com.

Article Snippet: , intratumor heterogeinity of meningiomas to identify targeted therapy , human biopsies , 10x Genomics VISIUM.

Techniques: Sequencing, Reverse Transcription, Amplification, Illumina Sequencing

Journal: Frontiers in Cell and Developmental Biology

Article Title: Multiplexed spatial transcriptomics methods and the application of expansion microscopy

doi: 10.3389/fcell.2024.1378875

Figure Lengend Snippet: Comparison of VISIUM, Slide-seqVs, stereo-seq and seq-scope.

Article Snippet: , intratumor heterogeinity of meningiomas to identify targeted therapy , human biopsies , 10x Genomics VISIUM.

Techniques: Comparison

Journal: Frontiers in Cell and Developmental Biology

Article Title: Multiplexed spatial transcriptomics methods and the application of expansion microscopy

doi: 10.3389/fcell.2024.1378875

Figure Lengend Snippet: Selection of Studies applying spatial transcriptomics technology.

Article Snippet: , intratumor heterogeinity of meningiomas to identify targeted therapy , human biopsies , 10x Genomics VISIUM.

Techniques: Selection, Electron Microscopy

Journal: Frontiers in Cell and Developmental Biology

Article Title: Multiplexed spatial transcriptomics methods and the application of expansion microscopy

doi: 10.3389/fcell.2024.1378875

Figure Lengend Snippet: Cell type and tissue compatibility of spatial transcriptomics methods with and without ExM technology.

Article Snippet: , intratumor heterogeinity of meningiomas to identify targeted therapy , human biopsies , 10x Genomics VISIUM.

Techniques: Cell Culture, Derivative Assay

Journal: Frontiers in Cell and Developmental Biology

Article Title: Multiplexed spatial transcriptomics methods and the application of expansion microscopy

doi: 10.3389/fcell.2024.1378875

Figure Lengend Snippet: Comparison of ISS methods, ISH methods and NGS-based sequencing with region capture.

Article Snippet: , intratumor heterogeinity of meningiomas to identify targeted therapy , human biopsies , 10x Genomics VISIUM.

Techniques: Comparison, Sequencing, RNA Detection, Microscopy, Amplification, Hybridization, Multiplexing, In Situ, Microarray, Cell Culture