Exploring tissue architecture using spatial transcriptomics

Anjali Rao, Dalia Barkley, Gustavo S França, Itai Yanai, Anjali Rao, Dalia Barkley, Gustavo S França, Itai Yanai

Abstract

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

Figures

Fig. 1 ∣. The technologies of spatial…
Fig. 1 ∣. The technologies of spatial transcriptomics provide a gene expression matrix.
a, NGS-based spatial transcriptomic methods barcode transcripts according to their location in a lattice of spots. b,In situ sequencing approaches directly read out the transcript sequence within the tissue. c,In situ hybridization methods detect target sequences by hybridization of complementary fluorescent probes. d, The product of spatial transcriptomics is the gene expression matrix - where the rows and columns correspond to genes and locations.
Fig. 2 ∣. Exploratory data analysis using…
Fig. 2 ∣. Exploratory data analysis using spatial transcriptomic datasets.
a. Schematic of exploratory data analysis operations of spatial transcriptomic datasets. Characterize: Depicted are spots characterized to be composed of proportions of cell type ɑ, β and γ and gene sets annotated with functional terms. Cluster: Clusters of spots are shown in a lower dimensional space and mapped onto the tissue, and co-expressed gene sets are shown within a gene-gene correlation matrix. Select: A subset of spots can be selected based on histological information, or a subset of spatially variable genes may be selected for analysis. Relate: The relationship between gene sets found to have a spatial overlap as well as adjacent spots and clusters can be examined using the relate operation. Score: Spots scored for gene set expression generate a spatial pattern, while gene profiles can be obtained by summarizing the expression of a subset of spots. b. Operational paths for analysis. Composition: Spots are scored for cell type-specific gene expression profiles from scRNA-Seq data and characterized to identify the composition of the tissue region. Co-localization: Co-varying genes are identified by clustering and spots are scored for the expression of these gene sets to identify a pattern of overlapping spatial expression. A co-localization is described by relating the distance between these spots. Communication: Transcriptionally similar spots are identified by clustering and characterized according to their resident cell types. A subset of receptor and ligand pairs are selected for analysis. Receptors and ligands expressed in cell type α and cell type β, respectively, suggests a relationship between them.
Fig. 3 ∣. Hypothesis generation and testing…
Fig. 3 ∣. Hypothesis generation and testing using spatial transcriptomics.
a, Spatial transcriptomics can be used for hypothesis generation in various experimental contexts. Examples of spatial transcriptomic datasets include normal tissue (atlas), a developmental or disease time-course, and perturbation experiments (genetic, drug or infection). Following data collection, exploratory data analysis may generate observations - requiring validation - that lead to a hypothesis. b, Spatial transcriptomics for hypothesis testing. A well-powered experimental design that uses spatial transcriptomics can test formulated hypotheses. These can be further tested using clinical data, in vivo or in vitro models.

Source: PubMed

Bevorstehende klinische Studien

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