Spatial Gene Expression for Fresh Frozen_Wuhan biological Optical Valley Science and technology

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Spatial Gene Expression for Fresh Frozen

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Spatial Gene Expression for Fresh Frozen

Product Description

Case Analysis

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Q&A

Principle of the technique

When a frozen tissue slice is placed in the capture region of a Visium chip with spatial barcode and has been HE stained and imaged, the tissue is permeabilised and the intracellular mRNA is released and captured by a probe with oligo-dT on the chip. The captured mRNA starts reverse transcription and the resulting cDNA contains spatial barcode sequences. After constructing a library and sequencing, the mRNA transcribed sequences can be mapped to their original location in the tissue, thus obtaining information about the location of gene expression.

The core of the Visim spatial transcriptome is the microarray: the formal library is constructed with four capture regions, each containing approximately 5,000 spots, each capable of capturing 1-10 cells, with a distance of 100 μm between spots and spot centroids. Each spot contains millions of capture probes that bind to mRNA, and each probe contains a unique barcode (barcode).

Principle of space transcriptome gene expression chip

Sample type: OCT-embedded samples that can be used for tissue slicing (not available for samples such as bone tissue)

Experimental process

Experimental flow of space transcriptome sequencing

Analysis process

　　Space transcriptome sequencing analysis process

Application directions

a. Tumor heterogeneity;

b. Histomorphology;

c. Tissue developmental mechanisms;

d. Response to intervention;

e. Biomarker discovery;

f. Cellular mapping.

Product advantages

a. Comprehensive understanding of disease complexity

b. Discovery of new biomarkers and identification of new cell types and states

c. Locating the spatial structure of cellular profiles

d. Identify spatio-temporal gene expression patterns

Case 1 Human dorsolateral prefrontal cortex transcriptome scale spatial gene expression

Researchers used the 10x Genomics Visium platform to define a spatial map of gene expression in six layers of human dorsolateral prefrontal cortex, identifying extensive layer-enriched expression signatures and precise associations with previous laminar markers. The investigators overlaid layer expression features on large-scale single-core RNA sequencing data, enhancing the spatial annotation of expression-driven clusters. The clinical significance of spatially defined expression was highlighted by integrating neuropsychiatric disease gene sets to demonstratet differential stratum-enriched expression of genes associated with schizophrenia and autism spectrum disorders. Secondly, the researchers developed a data-driven framework to define non-surveillance clusters in spatial transcriptomics data that can be applied to other tissues or brain regions that are less morphologically intact than cortical laminar tissue. Finally, they created a web application for the scientific community to explore this raw and aggregated data to support ongoing neuroscience and spatial transcriptomics research. (http://research.libd.org/spatialLIBD)

Reference：Maynard K R , Collado-Torres L , Weber L M , et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex[J]. Nature Neuroscience, 2021:1-1.

Case 2 Multimodal analysis of the composition and spatial structure of human squamous cell carcinoma

To define the cellular composition and structure of cutaneous squamous cell carcinoma (cSCC), researchers selected a series of human cutaneous squamous cell carcinoma and paired healthy skin samples and used single-cell RNA sequencing combined with spatial transcriptomics and multiplex ion beam imaging to integrate a high-latitude multimodal approach to characterise human cutaneous squamous cell carcinoma and determine tumor heterogeneity and spatial localization. The results show that cSCC exhibits four tumor subpopulations, three of which are of the normal epidermal state type and one cancer-specific tumor-specific keratin-forming (TSK) cell population that is localized to the fibrovascular ecological niche. Integration of single cell and spatial transcriptome data mapping ligand-receptor networks to specific cell types reveals that TSK cells are hubs for intercellular communication. Multiple features of potential immunosuppression were observed, including co-localization of T regulatory cells (Treg) with CD8+ T cells in the compartmentalized tumor stroma. Finally, a combination of single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified an important role for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define the spatial compartmentalization of cSCC tumor and stromal cell subpopulation interactions and the gene networks in which they are involved in tumor cell communication.

Reference：Ji AL,Rubin AJ,Thrane K,et al.Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma[J].Cell,2020,2:497-514.e22.

Case 3 BayesSpace: enabling higher resolution spatial transcriptome analysis

Spatial transcriptome technologies enable comprehensive measurements of transcriptome profiles while preserving spatial context. However, existing methods could not solve the problem of limited technical resolution or efficient use of spatial information. The research team of this article has developed BayesSpace, a fully Bayesian statistical method that uses information from spatial neighbourhoods to enhance the resolution of spatial transcriptome data and perform clustering analysis. The investigators benchmarked BayesSpace against current spatial and non-spatial clustering methods and showed that it improved the identification of transcriptional profiles within different tissues from brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma samples. Using immunohistochemistry and a computer dataset constructed from scRNA-seq data, the results show that BayesSpace resolves tissue structures that are undetectable at native resolution and identifies transcriptional heterogeneity that cannot be identified by histological analysis. The results illustrate the importance of BayesSpace in facilitating the discovery of new insights in biology from spatial transcriptomic datasets.