Methods and Application of Spatial Transcriptomics
InquiryAlthough spatial transcriptomics technologies, vary greatly in the number of genes that can be detected and the size of tissues that can be detected, we focus on technologies that enable transcriptome-level detection across tissue regions. The main spatial transcriptome technologies: 1) NGS-based technologies that encode location information onto transcripts prior to NGS sequencing; 2) imaging-based approaches, including in situ sequencing (ISS) - transcripts are amplified and sequenced in tissue, and ISH-based approaches -imaging probes are sequentially hybridized in the tissue. These different techniques can be thought of as converging on a gene expression matrix that captures the transcriptome at each point (i.e., a pixel, a cell, or a group of cells).
1. NGS technology-based approach
Spatial transcriptomics (ST) technology published in 2016 can obtain spatially resolved whole transcriptome information. In 2018, ST technology was acquired and further developed by 10x Genomics, named "10x Visium". The 10x Visium assay has improved resolution (55µm diameter) and runtime. Instead of printing regionally barcoded RT primers on a slide, Slide-seq captures mRNA using random barcoded beads placed on the slide. Shortly after the Slide-seq method was published, another technique using smaller barcoded beads was released, named High Resolution Spatial Transcriptome Technology (HDST). Recently, a method for spatial sequencing (DBiT-seq) using deterministic barcodes in tissue has been developed, which is based on a microfluidic approach to deliver barcodes to the surface of tissue slides to achieve 10 μm pixel size resolution . Stereo-seq uses randomly barcoded DNA nanospheres to achieve nanoscale resolution. Seq-scope has enabled spatial barcoding at subcellular resolution, which can be used to visualize nuclear and cytoplasmic transcription. Nanostring GeoMX DSP technology places data capture in a circular region of interest (ROI), which is irradiated with UV light to release a photocrackable gene tag for sequencing quantification.
In all NGS-based methods, spatially barcoded RNA is collected and sequenced. The barcode of each reads is used to map the spatial location, while the rest of the sequenced reads are mapped to the genome to identify the transcript source.
10x Visium (10xgenomics.com)
2. Imaging-based methods
ISS-based methods directly read out the sequence of transcripts within tissues. Specifically, RNA is reverse transcribed, amplified by rolling circles, and sequenced. BaristaSeq is another gap-filling padlock-based method that increases the read length to 15 bases. STARmap uses barcoded padlock probes that hybridize to the target and avoids the reverse transcription (RT) step by adding a second primer that targets the site next to the padlock probe. This approach avoids the efficiency barriers of cDNA conversion and reduces noise by adding a second hybridization step. The methods mentioned so far are based on a priori knowledge of the target, FISSEQ is an off-target method that captures all kinds of RNAs. Although untargeted amplification can lead to optical crowding and reduced sensitivity, the recently developed expanded sequencing (ExSeq) has demonstrated its utility for untargeted ISS in tissues.
Overview of ISS technologies. (Asp M., et al., 2020)
ISH-based methods are the second class of imaging-based methods, which are based on ISH technology and detect target sequences by hybridization of complementary fluorescent probes. smFISH utilizes multiple short oligonucleotide probes (approximately 20 bp) to target different regions of the same mRNA transcript. Although smFISH has high sensitivity and subcellular spatial resolution, it can only target a few genes at a time. seqFISH is a multiplexed smFISH method that detects a single transcript multiple times through successive rounds of hybridization, imaging, and probe stripping. However, increasing the number of hybridization rounds requires increasing the number of smFISH probes, which makes seqFISH expensive and time-consuming. In 2015, MERFISH technology was released, which can identify the copy number and spatial localization of thousands of RNAs in a single cell. It utilizes techniques such as combinatorial labeling, sequential imaging to increase detection throughput, and binary barcodes to counteract single-molecule labeling and detection errors.
A scheme depicting the main principle of smFISH: multiple fluorescently labeled probes tile the length of the mRNA. (Haimovich G., et al., 2018)
For ISS-based and ISH-based methods, image processing is used to generate gene expression matrices. To obtain cell-level matrices, either small regions are manually segmented or the images are systematically segmented using computational methods. While these may not correspond to true physical boundaries, they accomplish the task of assigning each mRNA to a cell. Alternatively, data analysis can start at the individual pixel level and incorporate gene expression data to depict cells.
Application of spatial transcriptomics technology
Since spatial transcriptome technology provides an unbiased image of spatial composition, it has been used to generate tissue atlases that provide a valuable resource as a reference.
In neurobiology: spatial transcriptomics-based approaches have established detailed maps of the entire mouse brain or specific regions such as visual cortex, primary motor cortex, middle temporal gyrus, hypothalamus preoptic area, hippocampus and cerebellum. Related studies have identified spatial patterns of known schizophrenia- and autism-related genes in the analysis of the dorsolateral prefrontal cortex, thus suggesting a mechanism for genetic susceptibility to schizophrenia.
In developmental biology: time-resolved spatial transcriptome mapping has helped to elucidate the spatial dynamics of cardiac development, spermatogenesis and intestinal development. Similarly, comprehensive studies of the human endometrium during the proliferative and secretory phases of the menstrual cycle have identified a role for WNT and Notch signaling in regulating secretory epithelial cell differentiation. These atlases have been the focus of several projects to provide an effective resource for the research community and are supported by the Human Cell Atlas project and the Allen Institute for Brain Science.
In addition to normal development and physiology, spatial transcriptomics is well suited to study disorders of tissue structure in disease. Spatial transcriptomics is able to identify mechanisms that play a role in cancer, i.e., altered tissue structure of normal physiological function. With the growing recognition of the importance of the tumor microenvironment, spatial transcriptomics has been used to study its relationship with different states of cancer cells. In particular, spatial transcriptomics enables the study of molecular features between cancer and normal tissues. For example, immunoregulatory cancer cell states have been identified in squamous cell carcinoma of the skin. Spatial transcriptomics has also provided insights into mechanisms of tissue dysregulation in neurodegenerative diseases (including Alzheimer's disease and amyotrophic lateral sclerosis), infectious and inflammatory processes (such as leprosy, influenza, and sepsis), and rheumatic diseases (including rheumatoid arthritis and spondyloarthritis).
The future of spatial transcriptomics technology
In 2018, single-cell transcriptome technology was named Breakthrough Technology of the Year by Science; in 2019, single-cell multi-omics was named Technology of the Year by Nature Methods, signaling that single-cell multi-omics research will become a trend, in which single-cell combined with spatial transcriptome research makes single-cell research in three-dimensional space a new hot spot. Spatial transcriptome technology was named Technology of the Year by Nature Methods in 2020, further confirming that this technology has great potential for development, and that subsequent spatial transcriptome technologies will change the way we understand complex tissues in various research fields.
Spatial transcriptomics has been widely used and promoted to discover disease factors, build spatial atlases, and paint spatial blueprints, but its potential goes far beyond that. For example, in studies of intercellular communication, interactions between different cell types are inferred from transcriptomic data and known ligand-receptor complexes; however, ongoing interactions between individual cells are difficult to capture immediately. Spatially adjacent cells are more likely to interact with each other, both in tissues and in the culture environment, and this is where the spatial transcriptome comes into play; so the introduction of spatial transcriptomics into the study of cell-to-cell communication is worth looking forward to.
In addition, single-cell omics technologies have facilitated the development of spatial transcriptomics in many ways, such as providing marker genes from cell typing, which in turn can assist single-cell omics in distinguishing subpopulations using spatial location information. Furthermore, since image-based spatial research methods can provide subcellular views to observe molecular behavior within individual cells, this enables analysis of gene-gene interactions, gene regulatory networks, and more.
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