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Inside the Lab Microscopy and imaging, Software and hardware, Technology and innovation, Bioinformatics

A Place for Everything in Spatial Biology

Over the past few years, spatial biology tools have taken the life science research community by storm. Among pathologists – who regularly analyze tissue sections and have a keen understanding of the importance of spatial context in diagnostics – this new suite of technologies may not seem revolutionary. But spatial biology promises to transform the practice of pathology just as much as it has the experiments performed in research labs. These tools aim to bring together the best of both worlds, delivering deep molecular profiles in situ from a tissue section.

Imagine the information you might normally glean from a histology slide – and then consider the value of adding a layer of deep insight into the expression of dozens of genes or proteins at single-cell and even subcellular resolution to reveal critical information about cellular and molecular interactions. Already, spatial biology has driven impressive discoveries about the role of immune cells in the tumor microenvironment, the distribution of viral particles in cases of infectious disease, and the progression of neurodegenerative diseases. Though these insights began in research labs, they are already being translated to the practice of pathology.

How does it work?
Spatial biology emerged from diverse technological advances in next-generation sequencing, molecular biology, optics and imaging, proteomics, and bioinformatics. The ability to generate data at higher resolution allowed scientists to finally ask a question that could not previously be answered: how does all of this gene and protein expression play out in the body? There had always been a recognition that spatial context mattered in biology – but, until recently, molecular biology tools required processing samples with methods that made it impossible to map expression data of multiple analytes back to its original location.

Current spatial biology tools build on single-cell analysis, adding important information about natural tissue architecture for the closest view yet to in situ biology. Platforms tend to focus on spatial transcriptomics or spatial proteomics, though some tools can interrogate both. Multiplexing is a key feature of these tools, enabling scientists to measure and spatially resolve tens or hundreds of genes and proteins at the same time from the same sample.

These tools often use in situ hybridization – fluorescently tagging genes in a sample, imaging, removing the tags, and adding new tags to query more genes in an iterative cycle. Some approaches tag genes or proteins with location-embedded barcodes, running the sample through a gene or protein quantification process and mapping the results back to their original location in the sample with those barcodes.

SARS-CoV-2 transcripts of the nucleocapsid protein (yellow) are predominantly found close to the cell nucleus. Interestingly, some cells show polar localization of SARS-CoV-2 transcripts. Transcripts of 80 other genes (marked in different colors) are distributed throughout the cell. Spatial biology technology enabled scientists to compare infected and non-infected cells to understand subcellular gene regulation and how the infection affects neighboring cells over time. Image courtesy of Medical University of Graz, Austria; captured using the Resolve Biosciences Molecular Cartography™ technology.

In the pathology lab
Although spatial biology tools are still a strong focus in research labs, they have already shown substantial downstream utility for pathology laboratories.

For example, some of the earliest spatial biology experiments occurred in oncology, where they helped researchers identify clear signatures based on the tumor microenvironment that can accurately predict cancer progression or whether a certain cancer is likely to respond to a specific immunotherapy (1,2). As these spatial signatures are validated for clinical use, they will offer substantial value by establishing more accurate prognoses or treatment plans for patients.

Spatial biology offers a deeper and more quantitative view of disease propagation than ever before.

In the infectious disease arena, some pathology labs are already using spatial biology tools to map out how infections target specific organs or tissues, monitor the distribution of viral or bacterial material in the body, and pinpoint secondary and tertiary events. This approach was quickly applied to patient samples collected in the COVID-19 pandemic when it became known that SARS-CoV-2 had the ability to spread well beyond the lungs and establish a foothold in other organs and tissues (3). Spatial biology offers a deeper and more quantitative view of disease propagation than ever before.

In the future, I expect the copious research being done in autoimmune diseases to translate to pathology as well. Scientists hope that characterizing interactions between immune cells and other types of cells will provide new insights into disease progression — information that could ultimately enable more accurate diagnoses and prognoses for patients.

Implementing spatial biology
Although it’s never easy for pathologists to implement platforms designed for research labs, many spatial biology systems have been designed specifically to fit into pathology workflows. If you are interested in adding spatial biology to your lab’s capabilities, here are some considerations to help you get started.

  • Genes or proteins? Pathologists often stain tissue sections for specific proteins known to be useful biomarkers of disease. Spatial biology opens the doors to gene expression analysis as well – and, because most platforms currently query one or the other, it will likely be necessary to choose which is better suited to your needs. Although proteins are usually considered the most phenotypically relevant, gene expression offers an earlier view of biological events that may be even more useful.
  • Multiplex capacity. Different applications will require different levels of multiplexing. In pathology labs, the massive multiplexing needed for true biological discovery projects is probably overkill. But more targeted spatial biology tools have a broad range of multiplexing – from a handful of proteins to dozens, or from a few dozen genes to hundreds. On this front, it’s important to consider not just what you need today, but also what you anticipate needing in the coming years.
  • Sensitivity. Applications in which pathologists may need to quantify extremely rare transcripts or proteins are best suited for highly sensitive tools capable of detecting and quantifying even a single analyte in a cell or sample.
  • Sample preservation. Unfortunately, some spatial biology tools still require the processing of an entire sample, destroying it in the analysis workflow. For pathology labs, where experts often want to go back and query the same sample with different techniques, a platform that preserves the original sample is important for the ability to probe different analytes.
  • Resolution. Generally, technologies that directly image a sample can provide higher resolution than those that map data back to a source via barcode. If your target applications would benefit from cellular or subcellular resolution, a technology based on in situ hybridization may be a better fit for your lab.
  • Logistics. Two other factors important for any clinical lab are turnaround time and ease of use. Ultimately, spatial biology tools must deliver results fast enough to have an impact on patient care; their operation must also be straightforward to be incorporated into a clinical lab workflow.

Early adopters of spatial biology in pathology laboratories are already demonstrating the utility of these tools for the entire clinical lab community. From infectious disease to oncology and beyond, spatial technologies offer a deeper, more quantitative view of key genes or proteins to improve diagnosis, prognosis, and treatment selection – ultimately helping pathologists deliver even better patient care.

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  1. S Lu et al., “Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis,” JAMA Oncol, 5, 1195 (2019). PMID: 31318407.
  2. F Finotello, F Eduati, “Multi-omics profiling of the tumor microenvironment: paving the way to precision immuno-oncology,” Front Oncol, 8, 430 (2018). PMID: 30345255.
  3. TM Delorey et al., “COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets,” Nature, [Online ahead of print] (2021). PMID: 33915569.

About the Author

Nachiket Kashikar

Head of Business Development at Resolve Biosciences, Monheim am Rhein, Germany.

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