Spatial transcriptomic data analysis
Spatial transcriptomics provides a molecular view of the organization of complex tissues.
Spatial transcriptomic assays quantify gene expression by spatial location within a tissue. The analysis of spatial transcriptomic data can reveal the spatial organization of a tissue from larger spatial domains down to the cell-type and molecular level.
The research questions addressed by spatial transcriptomics typically involve those of changes in tissue composition in developmental or pathological processes or interactions between cell types in complex tissues such as tumor microenvironments.
Analysis of spatial omics data often incorporates multiple modalities, such as from imaging or single-cell sequencing to enhance both the spatial resolution and the accuracy of cell type detection.
Our team comprises spatial analysis experts with experience in both developing ground-breaking spatial transcriptomics technologies and applying them in various normal tissues and pathologies, such as tumors.
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Spatial transcriptomic technologies
Technologies for spatial transcriptomics, and more broadly spatial omics, are numerous. The most commonly used ones originate from two companies: 10x Genomics and Nanostring. The key differences between these spatial platforms lie in their spatial resolution and the scope of their molecular targets.
Spatial resolution
The lowest-resolution spatial technologies are based on relatively large regions of interest (ROIs), which may be defined based on histological staining or specific molecular markers. In contrast, higher-resolution technologies can achieve single-cell or even subcellular resolutions.
Molecular targets
The molecular capture capabilities of spatial technologies range from probe-based target panels to sequencing-based whole transcriptomes. Some technologies also support multi-omic targeting, such as simultaneous transcript and protein quantification.
Common spatial transcriptomic platforms
The most widely used platform, 10x Genomics Visium, features a grid of spots, each resolving 1-10 cells per spot. Transcripts from these spots are sequenced without being limited to a predefined target panel. A more recent platform from 10x Genomics, the Visium HD, has a significantly higher, subcellular resolution.
A common ROI-based platform is the Nanostring GeoMx, which allows manual or, alternatively, computer-aided selection of captured regions of complex shapes. The output from a GeoMx measurement provides molecular counts for target panels, which may include transcripts or proteins, detected using probes or antibodies, respectively.
Both companies also offer imaging-based platforms with single-cell resolution, the 10x Genomics Xenium and the Nanostring CosMx. Both technologies utilize targeted transcript panels with up to approximately 5000 transcripts.
Spatial transcriptomic analyses
The chosen measurement platform influences the necessary and available data analyses. Notably, the analysis of ROI-based spatial data aligns more closely with that of bulk transcriptomic data, while the analysis of spot-based spatial data has more similarities to that of single-cell transcriptomic data. The analyses discussed below apply particularly to spot-based technologies.
Spatial variability of gene expression
Identifying genes with spatially variable expression levels provides a direct view of the molecular and functional architecture of a tissue. Spatially variable genes are often used as input features for downstream spatial analyses.
Cell type identification
With most spatial transcriptomic technologies, a single spatial spot may contain multiple cells. Identifying which cell types are present and in what proportions within these spots requires a deconvolution analysis. Cell-type deconvolution typically uses reference expression profiles obtained from single-cell RNA-sequencing experiments to estimate the proportions of each cell type at each spot.
Detection of spatial domains
Spatially variable genes or spot-wise cell-type proportions can be used to detect spatial domains, corresponding to tissue substructures at different scales. The expression patterns and cell types within these domains can be further analyzed across samples or time points to study developmental or pathogenic processes.
Cell-cell communication
The spatial organization of cell types reveals which cells are in close proximity to each other. This proximity can be analyzed alongside ligand-receptor expression levels to uncover modes of cell-cell communication relevant to the studied tissue.
Meet some of our spatial analysis experts
I am a bioinformatician with a multidisciplinary background in developing and applying novel spatial and single-cell transcriptomics methods to study the biology and pathology of diverse tissue types.
I co-developed the spatial transcriptomics method that was later commercialized by 10x Genomics as Visium—the most widely used technology for spatially resolved expression studies today. I also developed VASA-Seq, a new method for single-cell total transcriptome sequencing.
Beyond method development, I have extensive experience analyzing spatial and single-cell data and supporting researchers across various fields in designing and applying such experiments.
I am a senior bioinformatics scientist with over 10 years of experience in analyzing a wide range of next-generation sequencing (NGS) data types, including spatial transcriptomics, single-cell RNA-seq, bulk RNA-seq, ChIP-seq, CUT&Tag, ATAC-seq, single-cell ATAC-seq, MeDIP-seq, and BS-seq.
With a background in both mathematics and biology, I am well-equipped to analyze and interpret complex biological datasets. My work spans various fields, with significant contributions in immunology and oncology research.
I am a bioinformatics scientist specializing in oncology and human health data analysis, with over five years of experience in handling various omics data types. This includes bulk data (whole-exome, RNA-seq) and single-cell and spatial data (scRNA-seq, spatial transcriptomics, single-cell proteomics), as well as expertise in the experimental techniques to generate these types of data.
Throughout my career, I have successfully applied omics analysis to cancer and aging-related diseases, securing public grants, publishing in journals, and presenting at international conferences. I am highly motivated to solve complex challenges and advance healthcare research.
Learn more
References and customer cases
- Investigating a Parkinson's disease treatment's mechanism of action using bulk and spatial transcriptomics (Herantis Pharma)
- Deciphering progression mechanisms in chronic kidney disease with single-nucleus and spatial transcriptomics (University of Geneva)
Selected publications from our team members
- Punzon-Jimenez, P. et al. (2024). Effect of aging on the human myometrium at single-cell resolution. Nature communications, 15(1), 945. https://doi.org/10.1038/s41467-024-45143-z
- Kiviaho, A. et al. (2024). Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer. Nature communications, 15(1), 9949. https://doi.org/10.1038/s41467-024-54364-1
- Caronni, N. et al. (2023). IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer. Nature, 623(7986), 415–422. https://doi.org/10.1038/s41586-023-06685-2
- Andersson, A. et al. (2021). Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nature communications, 12(1), 6012. https://doi.org/10.1038/s41467-021-26271-2
- Yoosuf, N. et al. (2020). Identification and transfer of spatial transcriptomics signatures for cancer diagnosis. Breast cancer research : BCR, 22(1), 6. https://doi.org/10.1186/s13058-019-1242-9
- Vickovic, S. et al. (2019). High-definition spatial transcriptomics for in situ tissue profiling. Nature methods, 16(10), 987–990. https://doi.org/10.1038/s41592-019-0548-y
- Asp, M. et al. (2019). A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart. Cell, 179(7), 1647–1660.e19. https://doi.org/10.1016/j.cell.2019.11.025
- Berglund, E. et al. (2018). Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nature communications, 9(1), 2419. https://doi.org/10.1038/s41467-018-04724-5
- Salmén, F. et al. (2018). Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections. Nature protocols, 13(11), 2501–2534. https://doi.org/10.1038/s41596-018-0045-2
- Lundmark, A. et al. (2018). Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics. Scientific reports, 8(1), 9370. https://doi.org/10.1038/s41598-018-27627-3
- Giacomello, S. et al. (2017). Spatially resolved transcriptome profiling in model plant species. Nature plants, 3, 17061. https://doi.org/10.1038/nplants.2017.61
- Asp, M. et al. (2017). Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Scientific reports, 7(1), 12941. https://doi.org/10.1038/s41598-017-13462-5
- Navarro, J. F. et al. (2017). ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics (Oxford, England), 33(16), 2591–2593. https://doi.org/10.1093/bioinformatics/btx211
- Ståhl, P. L. et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science (New York, N.Y.), 353(6294), 78–82. https://doi.org/10.1126/science.aaf2403
- Jemt, A. et al. (2016). An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries. Scientific reports, 6, 37137. https://doi.org/10.1038/srep37137
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