Lecture
Spatial Transcriptomic and Proteomic Technologies for the Human Cell Atlas: Analytical Considerations, Capabilities, and Applications
- at -
- ICM Saal 2
- Type: Lecture
Lecture description
S. Perera, Cambridge/UK; Prof. S. A. Teichmann, Cambridge/UK
The Human Cell Atlas (HCA) is an international initiative aiming to generate a comprehensive reference map of all human cell types to advance understanding of health and disease (Regev et al., 2017). A central challenge in achieving this goal is the accurate spatial localisation of gene expression within intact tissue architecture. Spatial transcriptomic technologies have therefore become a critical
analytical component of the HCA framework, enabling molecular profiling while preserving spatial context (Ståhl et al., 2016; Moses and Pachter, 2022).
This presentation provides an overview of key imaging-based and sequencing-based spatial transcriptomic technologies currently applied within the HCA, including 10x Genomics Xenium, Visium and Visium HD, RNAscope, and in situ sequencing approaches. Analytical performance is discussed in terms of sensitivity, spatial resolution, multiplexing capacity, tissue compatibility (fresh frozen and FFPE), workflow complexity, and cost. Strengths and limitations of each method are illustrated using examples from diverse human tissues, including cardiovascular, gut, and thymus samples.
In addition, integration of spatial transcriptomics with multiplex spatial proteomic technologies, including the RareCyte Orion platform and Akoya PhenoCycler Fusion, is presented as a complementary strategy to link RNA and protein-level information within the same or consecutive tissue sections. This multimodal framework enables improved cell-type annotation, orthogonal validation of spatial gene expression, and deeper characterisation of tissue microenvironments (Goltsev et al., 2018; Wang et al., 2012).
To support multimodal spatial data interpretation, computational frameworks were applied for celltype classification and cross-platform integration. Tools including CellTypist, TissueTypist and scVI, together with complementary analytical pipelines, enabled robust cell identity mapping and harmonisation of spatial transcriptomic datasets across platforms, facilitating consistent annotation within the Human Cell Atlas framework.
Overall, this contribution highlights how analytical decisions in spatial transcriptomics influence biological interpretation and demonstrates how complementary spatial technologies can be strategically combined within large-scale atlas-building efforts such as the Human Cell Atlas. These considerations are broadly applicable to translational research, pathology, and future multimodal spatial biology studies.
References
Regev, A. et al., 2017. The Human Cell Atlas. eLife, 6, e27041.
Ståhl, P.L. et al., 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), pp.78–82.
Moses, L. and Pachter, L., 2022. Museum of spatial transcriptomics. Nature Methods, 19, pp.534–546.
Goltsev, Y. et al., 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell, 174(4), pp.968–981.
Wang, F. et al., 2012. RNAscope: a novel in situ RNA analysis platform for FFPE tissues. Journal of Molecular Diagnostics, 14(1), pp.22–2
The Human Cell Atlas (HCA) is an international initiative aiming to generate a comprehensive reference map of all human cell types to advance understanding of health and disease (Regev et al., 2017). A central challenge in achieving this goal is the accurate spatial localisation of gene expression within intact tissue architecture. Spatial transcriptomic technologies have therefore become a critical
analytical component of the HCA framework, enabling molecular profiling while preserving spatial context (Ståhl et al., 2016; Moses and Pachter, 2022).
This presentation provides an overview of key imaging-based and sequencing-based spatial transcriptomic technologies currently applied within the HCA, including 10x Genomics Xenium, Visium and Visium HD, RNAscope, and in situ sequencing approaches. Analytical performance is discussed in terms of sensitivity, spatial resolution, multiplexing capacity, tissue compatibility (fresh frozen and FFPE), workflow complexity, and cost. Strengths and limitations of each method are illustrated using examples from diverse human tissues, including cardiovascular, gut, and thymus samples.
In addition, integration of spatial transcriptomics with multiplex spatial proteomic technologies, including the RareCyte Orion platform and Akoya PhenoCycler Fusion, is presented as a complementary strategy to link RNA and protein-level information within the same or consecutive tissue sections. This multimodal framework enables improved cell-type annotation, orthogonal validation of spatial gene expression, and deeper characterisation of tissue microenvironments (Goltsev et al., 2018; Wang et al., 2012).
To support multimodal spatial data interpretation, computational frameworks were applied for celltype classification and cross-platform integration. Tools including CellTypist, TissueTypist and scVI, together with complementary analytical pipelines, enabled robust cell identity mapping and harmonisation of spatial transcriptomic datasets across platforms, facilitating consistent annotation within the Human Cell Atlas framework.
Overall, this contribution highlights how analytical decisions in spatial transcriptomics influence biological interpretation and demonstrates how complementary spatial technologies can be strategically combined within large-scale atlas-building efforts such as the Human Cell Atlas. These considerations are broadly applicable to translational research, pathology, and future multimodal spatial biology studies.
References
Regev, A. et al., 2017. The Human Cell Atlas. eLife, 6, e27041.
Ståhl, P.L. et al., 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), pp.78–82.
Moses, L. and Pachter, L., 2022. Museum of spatial transcriptomics. Nature Methods, 19, pp.534–546.
Goltsev, Y. et al., 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell, 174(4), pp.968–981.
Wang, F. et al., 2012. RNAscope: a novel in situ RNA analysis platform for FFPE tissues. Journal of Molecular Diagnostics, 14(1), pp.22–2
