Spatial genomics is a cutting-edge field that combines genomics and spatial analysis to investigate the role of genomic features in disease at single-cell resolution.
Introduction to Spatial Genomics
Spatial genomics is a field of study that focuses on analyzing the spatial organization of genomic features within intact tissues. It involves the simultaneous analysis of various molecular components, including genomic DNA and RNA, through transcriptomic analysis and epigenetic modifications within their spatial context. These techniques aim to reveal the spatial relationships between the different genomic elements and provide insights into the organization and function of single cells within tissues, enabling the molecular connection of a particular genotype to its phenotype.
Cancer research has benefited greatly from these methods. The cancer genome exhibits high levels of diversity in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations like Copy Number Alterations (CNAs) over larger genomic regions. Most SNPs and CNAs are found in non-coding regions of the genome, and they influence the regulation of oncogenes and tumor suppressors in a cancer-specific way.
One of the additional common features of this disease is the accumulation of non-coding driver mutations during the progression of the disease, giving rise to genomic instability and neoplastic development. In this context, spatial genomics can provide information about the effects of these non-coding changes, as well as the epigenetic modifications that might also affect chromatin organization, promoting the development of cancer.
The most applied spatial genomics technology is spatial transcriptomics. There are two methods for profiling transcriptomes while preserving spatial information: imaging-based methods like in situ hybridization and in situ sequencing, and sequencing-based methods, which extract mRNAs from the tissue and profile them using next-generation sequencing (NGS) techniques.
Some spatial multi-omics techniques have been developed as well; the most common approach is the integration of spatial transcriptome and proteomics. Nevertheless, there is a new method for co-mapping the transcriptome and the epigenome, allowing the study of chromatin accessibility, histone modifications, and mRNA expression in the same tissue section. These techniques have been applied to mouse and human brain samples, providing valuable insights into epigenetic and transcriptional dynamics within tissues.
The power of single-cell resolution
Single-cell technologies (e.g., scRNA-seq) are valuable techniques for analyzing gene expression in individual cells. By capturing the transcriptome of single-cell populations, scRNA-seq enables the exploration of genotype-phenotype relationships at a detailed level. These approaches have been merged with spatial biology to achieve a higher grade of complexity by identifying the spatial organization of tissues at a cellular resolution. Spatial Genomics’ GenePS is one example of new technologies that take advantage of this single-cell spatial analysis.
The investigation conducted by Shah and colleagues (2016) was instrumental in advancing the field of spatial biology in this direction. In their work, they aimed to uncover the spatial organization of hippocampus tissues at a cellular level using single-cell gene expression profiles. To achieve that, they employed the cutting-edge 3D multiplexed imaging technique, seqFISH, which allowed them to quantify and cluster up to 249 genes in a staggering 16.958 cells; they were able to identify distinct transcriptional states and investigate whether the hippocampus is divided into unique sub-regions.
Mapping the blueprint of health
Spatial transcriptomics has become one of the foremost techniques in cancer research for characterizing the tumor microenvironment. It efficiently analyzes various cell types and states within intact tumor samples and yields valuable insights into spatial heterogeneity. Innovative approaches, including solid phase capture methods like 10X Visium, have been implemented to profile the tumor microenvironment across different tumor types.
Omics eBook Compilation of the top interviews, articles, and news in the last year. Downloading the latest edition techniques offers higher throughput and is ideal for translational applications through the combination of histopathology and NGS methods. Coupled histological and spatial transcriptomics data has also been implemented to train machine-learning algorithms aimed at predicting histopathological annotations and local gene expression in breast cancer. Furthermore, Slide-Seq and High Definition Spatial Transcriptomics (HDST) have effectively mapped gene expression at the single-cell level in cryopreserved Her2+ breast cancer specimens.
Each method has unique advantages. The Nanostring GeoMX DSP system can detect oligonucleotide RNA probes and labeled antibodies to profile protein spatial distribution in non-small cell lung cancer tissues, identify biomarkers for PD-1 checkpoint blockade treatment outcome, and map cancer-associated fibroblast programs and their proximity to immune infiltrates in pancreatic ductal adenocarcinoma. Other methods exist that offer subcellular resolution mapping of pre-selected markers in intact tumor samples.
Case study: Bio-Techne
The first company to create a spatial genomics platform was Bio-Techne, through its acquired brand Advanced Cell Diagnostics (ACD). ACD developed the RNAscope technology, an advanced RNA in situ hybridization technique used for spatial genomics studies. This pioneering spatial genomics product from ACD laid the foundation for further advancements in the field.
The RNAscope assay (2011) serves as the cornerstone of ACD’s product portfolio. With its unique “double Z” probe design, this assay allows for the spatial analysis of mRNA and long non-coding RNA (lncRNA) targets that are longer than 300 nucleotides in any tissue and species.
The BaseScope assay (2016), built upon the same technology, was developed to detect RNA molecules that were previously challenging to identify within the tissues. It enables the specific detection of exon junctions, splice variants, highly homologous sequences, short targets, and point mutations.
In July 2021, ACD expanded its RNAscope(tm) HiPlex platform, a more advanced in situ hybridization (ISH) tool that allows for the investigation of essential gene expression patterns at a single-cell resolution. These technologies were used by the U.S. Centers for Disease Control and Prevention (CDC) to visualize SARS-CoV-2 RNA within autopsied tissues from suspected COVID-19 patients directly.
Challenges and prospects
Spatial Genomics’ market size is projected to reach USD 948.2 million by 2030, growing at an annual rate of 15.2%. This is the result of the growing need for new technologies that can examine the spatial arrangement of the genome. Despite all the challenges associated with the development of these high-throughput technologies, the field of spatial genomics holds significant promise. Some potential areas of future improvement include refining spatial resolution, enhancing Multi-Omic integration, and profiling single cells and their clinical applications.