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A guide to spatial transcriptomics

Today, multi-omics studies have been conducted extensively, revealing information about gene expression changes, cellular function and homeostasis. As a result, all of these processes can now be correlated with disease states and progression. However, until very recently, insight into the spatial organisation, differentiation and localization of molecules at genomic and transcriptomic level has been lacking. Historically, conventional tools have never been able to effectively capture such details.

Multi-Omics: Exploring Inside Cells

Spatial organisation is filling the knowledge gap

Recently, technological advances have enabled researchers to go beyond just sequencing the ‘omes’. Spatially resolved transcriptomics was invented by Lundeberg, Frisen and Stahl at KHL Royal Institute of Technology in Sweden in 2016. This technology was able to provide gene expression data for a large number of cells, whilst simultaneously adding positional context. Today, it is possible to analyse tens of thousands of genes localized in a small section of tissue. Spatial information about the molecular characteristics behind these genes helps to unravel the heterogeneity among cells, and offers valuable insights into the cause-and-effect relationships behind certain diseases.

Emerging technologies for genome-wide spatial transcriptomics have the potential to provide detailed molecular maps. They use an on-slide complementary DNA synthesis approach, allowing the capturing of gene expression architecture in intact tissue. This means that information from important processes is retained. Recently, 10x Genomics released their new ‘Visium’ platform – boasting a five-fold increase in resolution compared to the first generation of spatial transcriptomics technologies.

Michael Schnall-Levin, Founding Scientist at 10x Genomics, explained: “Our Visium Spatial Gene Expression technology enables researchers to unravel the biological architecture in normal and diseased tissues by preserving the spatial relationships between cells to provide insights not only at the individual cell level, but at the tissue level as well. Researchers can now map whole transcriptome spatial gene expression across multiple cells for complex tissue samples.”

It is not yet routine for spatial analysis to deliver transcriptome-wide information, but the field is moving rapidly. One of the next hot topics is predicted to be ‘spatial single-cell transcriptomics’, which will be particularly useful for studying different diseases, as they often start with a single cell and spread spatially.

To read a discussion between four spatial genomics experts who share their views on the importance and impact of relevant technology advances, download the ‘Multi-Omics: An Integrative Approach to Biomedical Research’ report:

How does spatial transcriptomics work?

A typical spatial transcriptomics workflow starts by isolating and staining a tissue section of interest. The section is then placed into contact with a slide holding RNA-binding capture probes. The bound RNA is barcoded and used to synthesise complementary DNA, which is then generated into libraries and sequenced. Subsequently, the data is visualised, making it possible to infer where the genes of interest are expressed and in which parts of the tissue section.

Step-by-step: Spatial transcriptomics workflow

  1. All cells are permeabilised.
  2. A dense carpet of specialised probes on the surface of a glass slide capture and bind to the mRNA. Each capture probe contains a spatial barcode, unique to that spot on the slide.
  3. Fluorescent labelling of the mRNA creates a footprint that mirrors the morphology of the tissue.
  4. The tissue that is attached to the slide acts as a template for a reverse transcription reaction, generating a complementary DNA library.
  5. The complementary DNA, with the spatial barcodes incorporated, is cleaved from the surface of the slide and collected for standard sequencing.
  6. The uniquely barcoded capture probes then link the resulting RNA sequences back to their previous spots on the slide – a process called ‘de-multiplexing’.
  7. The RNA data is aligned to a high-resolution microscope image of the tissue section so that it can be mapped back to its point of origin.
  8. The expression of mRNA can then be visualised with a spatial dimension.

Diagram showing the different stages of the spatial transcriptomics workflow – from preparing the tissue section, to the capture probes on the glass slide, to the visualisation of mRNA by mapping back to its point of origin. Image credit: Spatial Transcriptomics

Challenges facing spatial transcriptomics

  • Identifying and validating the right biomarkers for a specific disease
  • Merging sequencing data with imaging readouts
  • Visualising the phenotype of a tissue section
  • Accurately capturing the cell state
  • Identifying the role of a cell in the tissue microenvironment

To address some of these issues, it is necessary to expand the number of assays and analysis tools available to researchers; although it should be ensured that the platforms remain cost-efficient, unbiased and accurate. Moreover, to properly facilitate the comparison of datasets generated across various laboratories at different timepoints, high resolution and large-scale information, measured in a spatial context, are required.

10x Genomics has established the Visium Clinical Translational Research Network to improve workflows assessing the spatial cellular relationship in research studies. It has been recognised that collaboration is crucial for acceleration in this area of science. Therefore, this community was designed to be a space where global researchers can come together to share their knowledge and experiences. Initiatives such as this one are vital for tackling some of the challenges that remain in spatial transcriptomics, and so should be encouraged.

Image credit: Science