As one of the providers of spatial transcriptomic data analysis, CD Genomics uses bioinformatics to help you rapidly and accurately fully characterize tissue and structure at single-cell or subcellular resolution, providing a solid foundation for mechanistic understanding of many biological processes in health and disease. Our high-quality data analysis platform will be used to generate high-quality analysis results in a fast analysis cycle.
Research Advances in Spatial Transcriptomics
Human and animal tissues are composed of heterogeneous cell types that are organized and interact in a highly structured manner. However, bulk and single-cell sequencing technologies remove cells from their original microenvironment, resulting in a loss of spatial information. Spatial transcriptomics combines gene expression profiles with spatial information, allowing researchers to gain insight into the spatial organization and functional relationships of cells within tissues. This innovative approach has revolutionized our understanding of tissue structure, intercellular interactions, and the role of spatial organization in a variety of biological processes. There are various computational approaches to analyze spatial transcriptomic data for various purposes, as well as spatial multimodalomics and its potential for application in disease tissue.
Fig. 1. Map of spatial transcriptomics and related technological developments. (Yue L, et al, 2023)
We Can Help Our Clients With:
Spatial transcriptome analysis has different applications in various fields of biological research. CD Genomics combines gene expression data with spatial context and aims to provide valuable insights into tissue development, disease pathology and therapeutic strategies.
Developmental biology: elucidating spatially specific gene expression patterns for tissue morphogenesis and cell fate determination. By analyzing gene expression at different developmental stages and spatial locations, researchers can identify the key regulators and signaling pathways involved in tissue patterns and organogenesis.
Tumor biology: characterize tumor heterogeneity, identify different tumor microenvironments, and explore tumor-immune cell interactions. This information can guide the development of targeted therapies and personalized treatment strategies.
Neuroscience: allow researchers to study gene expression within specific brain regions and cell types while retaining spatial information. By combining spatial with transcriptomics and other imaging techniques, researchers can map gene expression patterns to brain atlases, thereby facilitating the identification of cell types and their connectivity.
Plant biology: spatially regulated genes involved in processes such as growth, development and response to environmental stimuli can be identified. This information contributes to the understanding of plant organogenesis, cell differentiation and adaptation mechanisms.
CD Genomics Data Analysis Pipeline:
CD Genomics provides comprehensive analytical services for spatial transcriptome research using state-of-the-art technology and a team of highly skilled experts. Our services cover the entire workflow of spatial transcriptomics, from sample preparation to data analysis, ensuring accurate and reliable results.
(1) Sample Preparation
CD Genomics provides comprehensive guidance and support in sample collection, preservation and preparation to ensure optimal RNA integrity and preservation of spatial information.
(2) Spatial Transcriptome Analysis
CD Genomics employs advanced spatial transcriptomics technologies, including Slide-seqV2, spatial transcriptomics (ST or Visium) and other cutting-edge methods to capture genome-wide gene expression data while preserving spatial context.
(3) Data Generation and Quality Control
Using high-throughput sequencing platforms, CD Genomics generates large amounts of transcriptomic data from spatially resolved samples. We employ stringent quality control measures to ensure data reliability and accuracy. This includes assessing sequencing quality, evaluating library complexity, and implementing a robust bioinformatics pipeline for data pre-processing.
(4) Bioinformatics Analysis
CD Genomics uses a wide range of bioinformatics tools and algorithms to analyze spatial transcriptome data. Our experts perform data normalization, dimensionality reduction, clustering, differential gene expression analysis, and spatial visualization to provide comprehensive and interpretable results. We apply advanced computational techniques to reveal spatial patterns, identify cell types, and explore the dynamics of gene expression within tissues.
Spatial Transcriptomics Analysis Content:
| Pre-Processing |
Typically involves image alignment, transcript site identification and cell segmentation |
| Downstream Analysis |
Batch effect correction |
| Downscaling and (spatial) clustering |
| Spatial cell type annotation |
| scRNA- seq data |
| Spatially variable genes |
| Gene patterns |
| Spatial regions |
| Inter-cellular interactions |
| Gene - gene interactions |
| Spatial trajectories |
How It Works
CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.
With state-of-the-art technology, experience and a team of experts, CD Genomics offers comprehensive solutions for spatial transcriptome data generation, analysis and interpretation. By leveraging our expertise and strengths, researchers can discover new insights and accelerate discoveries in a variety of biological research areas, from developmental biology to tumor biology and neuroscience. If you are interested in our services, please contact us for more detailed information.
Reference
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Yue L, Liu F, Hu J, et al. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches[J]. Computational and Structural Biotechnology Journal, 2023.
