Spatial transcriptomics analysis is a cutting-edge and innovative approach for exploring the molecular architecture and cellular composition of tissues. This technology allows the detection of gene expression in situ, providing a spatially resolved view of transcriptomics data that enables a better understanding of cellular interactions within their microenvironment. Its novelty lies in its ability to bridge the gap between traditional histology and molecular profiling, providing new insights into the spatial organization of cells and their gene expression patterns. At Karda Genomics, we offer a comprehensive range of spatial transcriptomics data analysis services to enable our clients to fully explore the applications of this powerful technology in a wide range of fields, including cancer research, developmental biology, neuroscience, and more.
By leveraging the power of spatial transcriptomics analysis, professionals can gain a more complete understanding of the cellular and molecular components that make up biological systems. With its ability to identify cell types and their interactions within the context of the tissue microenvironment, spatial transcriptomics analysis represents a powerful tool for advancing our knowledge of complex biological systems.
Let Karda Genomics be your partner in this exciting field, providing you with the expertise and support you need to unlock the potential of spatial transcriptomics analysis.
Analysis Steps:
- Image processing: converting raw spatial transcriptomics images into usable data.
- Quality control: assessing the quality of the data and filtering out low-quality spots.
- Spot detection: identifying and quantifying the number of RNA molecules in each spot on the image.
- Normalization: adjusting for technical variability between spots to enable meaningful comparisons.
- Gene expression quantification: estimating gene expression levels for each spot using RNA molecule counts.
Possible downstream tasks:
- Spatial mapping: visualizing the spatial distribution of gene expression across the tissue to identify patterns and spatial relationships.
- Cell type identification: using gene expression patterns to identify different cell types within the tissue.
- Differential gene expression analysis: identifying genes that are differentially expressed between cell types or conditions.
- Clustering: grouping spots based on similarity in gene expression patterns to identify distinct cell populations or spatially related areas.
- Spatial trajectory analysis: inferring the developmental trajectory of cells within the tissue to understand how they differentiate or transition between cell states.
- Spatial gene regulatory network inference: inferring regulatory relationships between genes to understand how they control cellular processes in a spatial context.
- Image analysis: processing and analyzing spatial images to visualize the expression patterns of genes within their tissue context, allowing for the identification of cell types and the investigation of spatial relationships between them.
The number of downstream analyses that become available is constantly expanding and Karda Genomics supports all current approaches.