Non-mammalian Organisms

High-resolution spatial transcriptomic maps beyond human and mouse

The study of biological processes, such as development, regeneration, disease formation, and progression, often utilizes non-typical model organisms rather than the usual mammalian models.

Curio Seeker technology offers flexibility when studying these emerging models. The technology is capable of analyzing any polyadenylated nucleic acid molecules, and the workflow can be applied to fresh frozen tissues from a wide variety of organisms beyond humans, primates, and mice.

Here, explore how researchers have applied Curio Seeker and its foundational technology, Slide-seq, in organisms such as plants, worms, and birds.

Plant seeds

Plant seeds

Single-nucleus atlas of seed-to-seed development in Arabidopsis

Lee et al., bioRxiv, March 2023

Spatial transcriptomic techniques are useful tools to aid in the analysis and annotation of single-cell RNA-seq data sets, especially for cell clusters that are identified de novo and without previously known cell type markers.

In this example, the researchers at the Salk Institute created a single-nucleus transcriptome atlas of seed-to-seed development that encompasses a diverse set of tissues across ten developmental stages from ~800,000 nuclei. 

From the snRNA-seq data, the researchers identified 183 clusters, each corresponding to individual cell types. Initial annotation of each cell type was guided by curated marker genes identified in previous studies. Due to the lack of cell type markers for many organs and cell types within the entire Arabidopsis life cycle, they turned to spatial transcriptomic techniques, including Curio Seeker, to annotate the de novo clusters identified in snRNA-seq.  

The researchers applied the Curio Seeker workflow to a frozen OCT block containing germinating seeds (1.25d seeds). UMAP analysis of the gene expression of Curio Seeker beads revealed 5 major groupings. Spatial mapping of these gene expression signature clusters revealed their correspondence to cotyledons (clusters 0 and 5), root tip region (cluster 1), epidermis (cluster 2), seed coat (cluster 3), and the provasculature (cluster 4). 

Adapted from Figure 6 of Lee et al.: Spatial map of 1.25d germinating seeds showing distinct gene expression clusters that broadly correspond to different cell types.

In summary, single-cell scale spatial transcriptomics with the Curio Seeker Spatial Mapping Kit enabled the researchers to annotate the de novo clusters identified in snRNA-seq data and visualize the locations of cell types identified and where they clustered together in the seeds.

To explore the data set further, check out the exploratory data browser published by the authors here.



Deciphering the stem cell microenvironments that drive whole-body regeneration in Planaria

Benham-Pyle et al., bioRxiv, February 2023

Sequencing-based spatial technology is a powerful tool that can be utilized to explore the cellular and molecular environments of unique biological phenomena, particularly those that are poorly understood such as regeneration. Stem cells, renowned for their regenerative capacity, rely on a specialized microenvironment known as a niche, yet the specific niches crucial for tissue repair or replacement remain unidentified and uncharacterized.

In this example, researchers at Stowers Institute for Medical Research generated a spatial atlas/reference of the freshwater flatworm, Schmidtea mediterranea, at different time-points after amputation, with the goal of identifying cells contributing to regenerative stem cell niches.

Using the spatial maps generated by Slide-seq, the precursor to Curio Seeker, the researchers focused on identifying beads containing transcripts of piwi-1, an established marker of stem cells in planarians. Given the tightly packed arrangement of beads on a Slide-seq puck, the authors predicted that cell types in close proximity to stem cells would have their transcripts co-captured onto the same beads.

A sub-clustering of piwi-1 positive beads revealed that the majority of beads contained mRNA from various cell types, with only 11% dominated by a stem cell signature. Notably, 54.01% of the captured gene signatures were enriched in secretory cells termed hecatonoblasts, while the second most frequent signature, accounting for 22.37% of the captured gene signatures, was intestinal. Probing into these hybrid cell identities, the researchers selected 23 enriched genes from each hybrid cluster. Functional analyses using RNA interference revealed that the knockdown of two intestinal genes or four secretory genes individually resulted in regeneration or survival defects suggesting that the neighboring secretory and intestinal cells express genes that regulate the function of their neighboring stem cells.

Adapted from Figure 1 of Benham-Pyle et al.: (top row) Spatial map and UMAP of cell identities; (bottom row) sub-clustering of beads containing Piwi, a stem cell signature, and additional cell-type signatures.

In summary, spatial transcriptomics utilizing the tightly packed bead monolayer of a Slide-seq enabled the researchers to uncover a pair of differentiated cell types contributing to the stem cell niches in the regenerative freshwater flatworm Schmidtea meditteranea.



Identifying and validating molecular heterogeneities in complex tissue structures

Kasemeier-Kulesa et al., Developmental Dynamics, 2023

Spatial transcriptomics elevates discovery-based research by integrating the spatial dimension into single-cell profiling. This proves particularly advantageous when deciphering the molecular signatures of densely packed cells within intricate tissue environments. 

This type of approach is paramount in understanding the complexities of neural progenitor cell populations within the developing sympathetic nervous system, surmounting obstacles stemming from tissue architecture and molecular diversity. 

Notably, in this example, using chick trunk as a model, Slide-seq was used in uncovering potential molecular markers of the preganglionic neurons (PGNs) in the ventral neural tube, a tissue exhibiting a complex architecture and incredible molecular diversity where multiple motor columns express overlapping proteins in close proximity; and the presympathetic ganglia (SG) that are organized into unique structures comprised of a core of neurons surrounded by a perimeter of their progenitors. Here, spatial transcriptomics provided a potent tool for characterizing and distinguishing these cell populations within their spatial context.

Adapted from Figure 1 of Kasemeier-Kulesa et al.: (top left) schematic of the organization of PGNs and SGs in the chick trunk at sympathetic nervous system development, HH24 and HH28; (top right) UMAP of cell fates from scRNA-seq of chick trunk tissue at HH24; (bottom) Slide-seq puck containing chick trunk tissue at HH24 reveals expression of Sox10 and TH-positive regions.

Using tyrosine hydroxylase (TH) as a known marker, the authors successfully distinguished core neuronal SG beads in their Slide-seq data. However, identifying progenitor cells surrounding the core proved challenging due to their uncommitted state and expression of common NCC markers Sox10 and HNK1. Leveraging the densely packed monolayer of beads on the Slide-seq puck, the authors analyzed beads adjacent to TH+ beads to uncover novel markers of the SG progenitor perimeter.

Similarly, to identify PGNs within the neural tube, the authors presumed the general location of PGN cell bodies and their juxtaposition next to known and unique and overlapping motor columns within the spatial map generated by Slide-seqV2. By analyzing beads within the presumed location, the authors confirmed beads belonging to a motor column fate and those belonging to an alternative fate, i.e., presumptive PGNs, thereby yielding a list of candidate genes as potential novel markers of PGNs.

In summary, the authors utilized both the bead diameter and organization features unique to Slide-seq to predict cell types and uncover potential novel markers. The heightened spatial resolution of Slide-seq facilitated precise mapping of gene expression within intricate tissue structures, surpassing the capabilities of both scRNA-seq and other spatial transcriptomic technologies. This enabled a more efficient and accurate exploration of the authors’ inquiries into vertebrate nervous system development.