Developmental Biology

Building high-resolution spatial transcriptomic atlases for studying development

Proper development of organisms requires precise spatial and temporal orchestration of gene expression from a multitude of cell types. 

In the following, explore how Curio Seeker and its foundational technique, Slide-seq, enable researchers to study the intricate interplay between cells in their native spatial context during various developmental processes.

Additionally, explore another collection of Curio Seeker and Slide-seq studies on non-mammalian model organisms to see how researchers applied these techniques to study developmental processes beyond human and mouse models.



Building 3D spatial transcriptome maps of complete mouse embryos

Kumar et al., Nature Genetics, July 2023.

While Slide-seq analysis produces a spatial transcriptomic map in 2D from a tissue section, it can be used to create 3D spatial maps of a tissue by aligning multiple 2D maps from serial sections. 

In this study, researchers at the Max Planck Institute demonstrated such an approach and constructed 3D transcriptome maps of whole mouse embryos during early mouse organogenesis. Specifically, they obtained >10 sections, separated by 20-30 micron thickness, per mouse embryo at different early embryonic ages. 

They built a computational tool called sc3D to align the individual spatial transcriptomic map from each section to create the 3D map of an embryo. Visualization of the 3D map and comparison of the cell states in E8.5 and E9 embryo data revealed how cell states changed spatially and temporally, and showed the spatial distribution and morphologies of emerging tissues at the onset of organogenesis.

Adapted from Figure 1 of Kumar et al.: Serial sections analyzed with Slide-seq from multiple embryos at each stage for constructing 3D spatial maps of whole embryos.

Adapted from Figure 1 of Kumar et al.: Computational tool sc3D recreating a 3D map of cell states (brain, heart, neural tube, somites, NMPs – neuromesodermal progenitors, PSM – pre-somitic mesoderm) of an E8.5 (left) and E9 (right) embryo from Slide-seq data showing the spatial and temporal changes that occur within the developing embryo. 

3D visualization of the embryo sections revealed alignment of cell states and spatial locations of marker genes. For example, Rax, a marker of the developing eye, exhibited spatial gene expression confinement between E8.5 and E9.5, defining the future optic cup.

Adapted from Figure 2 of Kumar et al.: Slide-seq analysis recapitulated known gene markers in embryo development. Leftmost column: a schematic of the embryo. Rest of the columns: normalized gene expression of different marker genes.

Next, the authors combined the Slide-seq data with an annotated single-cell reference to identify and locate the cell states within the developing embryo. This analysis showed that Slide-seq accurately identified and located the expected positions of cell states.

Adapted from Extended Data Figure 2 of Kumar et al.: Single-cell annotated reference overlaid on Slide-seq data enables the researchers to identify and locate cell states within the developing embryo.

To facilitate the interactive exploration of the 3D embryo spatial transcriptome maps by the research community, the authors built sc3D-Viewer, a data visualization browser. Check out the movies showing the visualization of the 3D embryo maps in the supplementary data accompanying the manuscript.

Screen capture of Supplementary Movie 1 of Kumar et al.: sc3D-viewer for visualization of 3D mouse embryo transcriptome map built from Slide-seq data from serial sections.

In summary, the authors demonstrated the utility of Slide-seq to build high-resolution 3D transcriptome atlases and provided tools for registering and visualizing Slide-seq data from multiple serial sections. Through the study of the 3D maps of the mouse embryos, they were able to identify regionalized gene expression and differentiation trajectories in space, particularly in neural tube formation and patterning. 

An in-depth presentation of this study is available in this webinar by Dr. Abhishek Kumar, the lead author of the study.



High-resolution spatiotemporal map of the ovulating mouse ovary

Mantri et al., PNAS, Dec 2023.

Ovulation is essential for reproductive success but little is known about the spatial changes that occur during follicle development and how the location of a follicle affects the likelihood of ovulation. 

In their PNAS paper, Mantri et al. used the Curio Seeker Spatial Mapping Kit to generate a spatially resolved hierarchical map of the cell types and their phenotypes in immature and preovulatory mouse ovaries during hormone-induced ovulation. 

The authors assessed three immature mouse ovaries and seven representative mouse ovaries undergoing hormone-controlled ovulation over eight well-defined time points between ovulation induction and follicle rupture. Using the deconvolution algorithm RCTD with a scRNA-seq reference, the researchers assigned the most likely cell type to each spatial transcriptome (as defined by a spatially indexed bead on the Curio Seeker tile). The main groups of cells identified include granulosa cells, mesenchymal cells, oocytes, endothelial cells, epithelial cells, immune cells, and erythrocytes. The single-cell scaled resolution provided by the Curio Seeker kit enabled the researchers to further identify mural cells located along the follicular walls and cumulus cells surrounding the oocytes at each stage during follicle maturation and ovulation.

Adapted from Figure 1 of Mantri et al.: Curio Seeker data from three immature mouse ovaries (top left) and seven representative mouse ovaries over eight well-defined time points throughout hormone-induced ovulation. GC: Granulosa cell M: Mesenchyme cell.

Through unsupervised clustering of gene expression of the beads associated with mural granulosa cells, the authors found four types of gene signatures in the immature mouse ovary, corresponding to preantral, antral, atretic, and mitotic follicle phenotypes. Subsequently, they segmented each follicle in the spatial map and assigned each follicle to one of the types (preantral, antral, atretic) based on the granulosa cell signatures, revealing that most follicles in immature ovaries belong to the atretic type, and most antral follicles were located along the edge of the ovary (Figure 2B and D of Mantri et al.).

Adapted from Figure 2 of Mantri et al.: Curio Seeker generated spatial transcriptome maps of an immature mouse ovary, revealing spatial localization patterns of different follicle types based on gene expression signatures of the granulosa cell types.

Next, by comparing Curio Seeker data acquired along the different time points post hormone induction, the authors identified genes in mural granulosa cells that were differentially expressed during the follicle maturation process and clustered genes into groups based on their temporal expression patterns, and subsequently visualized their spatial localization.

Adapted from Figure 3 of Mantri et al.: Curio Seeker analysis identified temporally differentiated genes in mural granulosa cells and revealed their corresponding spatial localization during follicle maturation induced by hormone hCG (human chorionic gonadotropin). Left top and bottom: Down-regulated genes and up-regulated genes, respectively. Right top and bottom: spatial localization of representative down-regulated gene (Comp) and represented up-regulated gene (Edn2).

The authors then looked into the gene expression changes during the transition to the luteal phase and identified two distinct types of ovulating follicles based on their transcriptomic signatures that may be associated with the divergent fate of ovulation versus premature luteinization.

Adapted from Figure 5 of Mantri et al.: Curio Seeker data of a preovulatory ovary at 12 hours post hCG induction. Left: highlight of the different granulosa cell types. Middle: Spatial localization of representative gene markers of mural granulosa cells (top) and lytic-like granulosa cells (bottom). Right: Spatial localization of representative gene markers associated with follicle rupture.

In summary, utilizing Curio Seeker, the authors provided a set of high-resolution spatiotemporal atlases of immature and ovulating mouse ovaries and revealed new insights into how different cell types in the ovary coordinate temporally and spatially during the processes of follicular maturation and ovulation. Such knowledge can potentially contribute to advancing therapeutic development in reproductive medicine. 

An in-depth presentation of this study is available in this webinar by Dr. Iwijn de Vlaminck, principal investigator of the study.


Studying spermatogenesis using spatial transcriptomic maps of human and mouse testes

Chen et al., Cell Rep, Nov 2021.

Spermatogenesis, the production of sperms, is critical to male fertility. Previous studies with single-cell RNA-seq (scRNA-seq) revealed new knowledge in the molecular programs behind spermatogenesis but did not provide a complete picture as the spatial context was missing.

In this study, the authors utilized Slide-seq to build atlases of mouse and human testes to study spermatogenesis in the native context of a seminiferous tubule, the spatially confined functional unit of spermatogenesis.

Adapted from Figure 1 of Chen et al.: Slide-seq generated spatial atlas of the mouse testis, showing cell type annotation (C & D), pseudotime reconstruction of germ cell development trajectory (E), and digital segmentation of the seminiferous tubules (F). 

Comparison of mouse and human testes atlases uncovered differences in the spatial structure of spermatogonial microenvironment, suggesting differences in the regulatory mechanisms in spermatogenesis between the two species.

Adapted from Figure 4 of Chen et al.: Slide-seq analysis revealed differences in spatial structures in mouse versus human testes.

Furthermore, the authors compared the testes of leptin-deficient diabetic mice versus matching wild-type, and found disruption in spatial structures of seminiferous tubules in the diseased mice, revealing potential mechanisms behind male infertility induced by diabetes.

Adapted from Figure 5 of Chen et al.: Slide-seq analysis revealed disruptions in spatial structures in seminiferous tubules in diabetic mice.

In summary, high-resolution spatial analysis using Slide-seq enabled the authors to build a spatial atlas of the mouse and human testes in normal and diseased models, revealing new insights into the spermatogenesis process previously not obtainable with single-cell RNA-seq alone.