Neither routine single-cell RNA sequencing nor tissue sample RNA sequencing can provide researchers with precise spatial information. The spatial transcriptome is a technology that analyzes RNA-Seq data at the spatial level to analyze all mRNA in a single tissue section and obtain transcription information at specific locations in the tissue, providing effective data support for research and diagnosis.
CD Genomics can help you generate all transcriptome data from complete tissue samples, so that you can locate and distinguish the active expression of functional genes in specific tissue regions, provide valuable insights for research and diagnosis, and allow scientists to detect gene expression of tissue samples. All gene activity, and map where the activity occurred. The technology has brought new discoveries that will help scientists better understand diseases and biological processes.
The workflow of spatial transcriptome sequencing is mainly divided into two parts: the histology part and the omics part. The histology part includes sample embedding, slicing, fixation, staining and imaging, and records the morphological information of the slice; the omics part includes cDNA synthesis, amplification, adaptor ligation and sequencing, and records the transcript information and spatial location information of the section. Using 10X Genomics spatial transcriptome sequencing technology, each slide used for library construction has four capture areas, where each capture area contains 5000 barcoded spots and each spot has a unique barcode sequence. The cells in the tissue section will release mRNA, and the mRNA that migrates to each spot will be marked with the corresponding barcode sequence, and then the library will be constructed and sequenced. Finally, analyze the data according to the barcode information of the data to determine which data comes from which location, so as to realize the visualization of spatial gene expression.
|In Situ Sequencing
|Analyzes gene expression at the level of individual cells.
|Examines gene expression within intact tissue sections.
|Studies the spatial distribution of gene expression within tissues.
|Provides high cellular resolution, profiling individual cells.
|Provides cellular resolution, but not at single-cell level.
|Provides spatial resolution without single-cell resolution.
|Gene Expression Data
|Captures the expression profile of each individual cell.
|Provides expression data for regions of interest within tissue sections.
|Provides expression data for spatially defined areas of tissues.
|Cellular Heterogeneity Analysis
|Allows the characterization of cell subtypes and states.
|Suitable for studying cellular heterogeneity but lacks single-cell precision.
|Suitable for identifying spatially distinct patterns but not cellular heterogeneity.
|Insight into Tissue Architecture
|Limited insights into tissue architecture and organization.
|Preserves tissue architecture, making it useful for mapping gene expression within the tissue context.
|Provides information about the spatial distribution of genes within tissue regions.
|Identifying cell types, subtypes, and their functional states.
|Mapping gene expression patterns within intact tissues.
|Analyzing gene expression across tissue regions for understanding spatial biology.
|Examples of Use
|Studying cell diversity in tumors, developmental biology.
|Investigating the distribution of specific genes in tissue sections.
|Exploring spatial gene expression in organs, including brain mapping.
|Compatibility with Frozen Tissues
|Suitable for frozen cells.
|Suitable for OCT-embedded samples.
|Suitable for OCT-embedded samples.
|Lacks spatial resolution.
|Offers spatial resolution within tissue sections.
|Provides spatial resolution but not single-cell resolution.
For more information, please contact our technique team.
|Applicable to most tissue types, verified tissues include brain, tumor, kidney, skin, heart, etc.
|From tissue sectioning to library construction, to sequencing, and to data analysis.
|Rich experience in library construction and short-read NGS guarantee high quality.
|Data preprocessing, basic analysis, advanced analysis, and customized analysis.
Tissue section; RNA purification; quality assessment and quantification
Ribosomal RNA Removal
250~300 bp Insert cDNA Library
Illumina Novaseq, PE 150
≥ 80 million read pair per sample
Visualize and preprocess results, and perform custom bioinformatics analysis.
Sample Requirements: Complete tissue section samples. Correct tissue processing and preparation can maintain the morphological quality of tissue sections and the integrity of mRNA transcription.
Sample storage: The prepared tissue section samples can be stored at -80°C for one week, and each slide is stored separately in a sealed container.
Shipping Method: When shipping histological samples, the sample should be stored in a sealed container. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.
Deliverable: raw data as BAM files, coverage summary, QC report, custom bioinformatics analyses.
Data quality control
Differential gene analysis (DGA)
Differentially expressed gene analysis