Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues. 14/18

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is important to understand molecular mechanisms of cancer immunotherapy and drug targets. Current experimental methods such as immunohistochemistry allow researchers to investigate a small number of cells or a limited number of ligand-receptor pairs at tissue scale with limited cellular resolution. In this work, we developed a powerful experimental and analytical pipeline that allows for the genome-wide discovery and targeted validation of cellular communication. By profiling thousands of genes, spatial transcriptomic and single-cell RNA sequencing data show genes that are possibly involved in interactions. The expression of the candidate genes could be visualized by single-molecule in situ hybridization and droplet digital PCR. We developed a computational pipeline called STRISH that enables us to quantitatively model cell-cell interactions by automatically scanning for local expression of RNAscope data to recapitulate an interaction landscape across the whole tissue. Furthermore, we showed the strong correlation of microscopic RNAscope imaging data analyzed by STRISH with the gene expression values measured by droplet digital PCR. We validated the unique ability of this approach to discover new cell-cell interactions in situ through analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma. We expect that the approach described here will help to discover and validate ligand receptor interactions in different biological contexts such as immune-cancer cell interactions within a tumor.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

Clear cell renal cell carcinoma (ccRCC) is globally the most prevalent renal cancer. The cells of origin in ccRCC have been identified as proximal tubular epithelial cells (PTEC); however, the transcriptomic pathways resulting in the transition from normal to malignant PTEC state have remained unclear. Immunotherapy targeting checkpoints have revolutionized the management of ccRCC, but a sustained clinical response is achieved in only a minority of ccRCC patients. This indicates that our understanding of the mechanisms involved in the malignant transition and resistance to immune checkpoint therapy in ccRCC is unclear. This review examines recent single-cell transcriptomics studies of ccRCC to clarify the transition of PTEC in ccRCC development, and the immune cell types, states, and interactions that may limit the response to targeted immune therapy, and finally suggests stromal cells as key drivers in recurrent and locally invasive ccRCC. These and future single-cell transcriptomics studies will continue to clarify the cellular milieu in the ccRCC microenvironment, thus defining actional clinical, therapeutic, and prognostic characteristics of ccRCC.

Chronic kidney disease (CKD) is a persistent impairment of kidney function. Genome-wide association studies (GWAS) have revealed multiple genetic loci associated with CKD susceptibility but the complete genetic basis is not yet clear. Since CKD shares risk factors with cardiovascular diseases and diabetes, there may be pleiotropic loci at play but may go undetected when using single phenotype GWAS. Here, we used multi-phenotype GWAS in the Norfolk Island isolate (n = 380) to identify new loci associated with CKD. We performed a principal components analysis on different combinations of 29 quantitative traits to extract principal components (PCs) representative of multiple correlated phenotypes. GWAS of a PC derived from glomerular filtration rate, serum creatinine, and serum urea identified a suggestive peak (pmin = 1.67 × 10–7) that mapped to KCNIP4. Inclusion of other secondary CKD measurements with these three kidney function traits identified the KCNIP4 locus with GWAS significance (pmin = 1.59 × 10–9). Finally, we identified a group of two SNPs with increased minor allele frequencies as potential functional variants. With the use of genetic isolate and the PCA-based multi-phenotype GWAS approach, we have revealed a potential pleotropic effect locus for CKD. Further studies are required to assess functional relevance of this locus.

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules–peritubular capillaries by screening for co-expression of ligand–receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (padj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand–receptor interactions were identified within glomeruli and regions of proximal tubules–peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing–based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.