Spatial Transcriptomics Thrives on New Approaches

New platforms, methods, and data analysis promise to expand this technology’s applications in basic science and medicine

Many biologists seek signals of gene expression and study the structure of tissues. Sequencing RNA (RNA-seq) reveals gene expression, and techniques evolved into single-cell RNA-seq (scRNA-seq), which uncovers the gene expression in just one cell. For spatial information about organisms, scientists use microscopy. To put together these two methods, scientists developed spatial transcriptomics, which reveals gene expression and its location in a sample. This new technology caused so much excitement that a Nature journal named it the method of the year in 2020.1

Traditional methods of RNA-seq turn a sample into a soup of sorts before analyzing the results of gene expression. Consequently, all spatial information disappears. In spatial transcriptomics, labels attach to specific transcripts and imaging determines their locations. It’s far easier to describe the process than to do it. It’s even more complicated to make it user-friendly, but that’s just what must happen to expand the use of spatial transcriptomics into more labs, and maybe even into clinics.

Abbey Cutchin
Abbey Cutchin, 10x Genomics

With spatial transcriptomics, scientists can “explore the whole transcriptome across an entire tissue section in an unbiased way,” says Abbey Cutchin, associate director, market development, oncology at 10x Genomics. “For example, if we are studying how immune cells interact with cancer, spatial transcriptomics can provide clarity on how these cells are infiltrating the tumor.” Plus, spatial transcriptomics can “facilitate biomarker discovery that may inform prognostic or diagnostic indicators as well as identify biomarkers that are indicative of a therapeutic response or resistance,” Cutchin notes. Spatial transcriptomics could be crucial to a full understanding of various forms of cancer. In solid cancers, for instance, a tumor consists “of diverse cell types that often communicate in highly structured manners both spatially and temporally,” note two UCLA scientists.2 “Unlocking such complex spatial structures enables us to understand how tumor cells communicate with each other, escape immune surveillance, develop drug-resistance, and eventually metastasize.” Recent advances in the technology behind spatial transcriptomics make it far easier to use. In addition, a range of research projects promise to make this method more powerful than biologists might have imagined even a few years ago. That power comes from improvements in the spatial-transcriptomics platforms and the analysis of the data.

Simplifying the process plus multiplexing

mouse liver image
Using the Rebus Esper instrument, a research team—including Adam Gracz, PhD, and his colleagues at Emory University, plus scientists at Rebus Biosystems—imaged this mouse liver. Cell nuclei were labeled with DAPI (dark blue) and the colored spots identify four gene transcripts selected from a 30-gene panel. section.

Instead of scientists cobbling together a spatial-transcriptomics platform in their labs, it’s far easier to buy one. One option is the Rebus Esper platform. Using just a 20x objective combined with synthetic aperture optics (SAO), this platform can capture information on transcripts in hundreds of thousands of cells in a single run. SAO uses a circular arrangement of interfering lasers that create a 3D pattern that illuminates targets. Combining this technology with proprietary algorithms, the Esper provides 81-nanometer pixel resolution. The platform labels transcripts using microfluidics and chemistry.

This system requires little expertise. A sample only needs to be sectioned, placed in a fluidics cell, and loaded on the platform. That’s the kind of operation that is required to make spatial transcriptomics accessible to more scientists.

Lars Borm
Lars Borm, Karolinska Institute

In addition, Rebus keeps expanding the ways this platform can be used. The latest advance to the Esper is the ability to use enhanced electric (EEL) FISH, which was developed in Sten Linnarsson’s lab at the Karolinska Institute in Sweden. EEL FISH is a method of analyzing gene expression that “can rapidly process large tissue samples without compromising spatial resolution.”3 As Tarif Awad, vice president for scientific affairs at Rebus Biosystems, says, “We are in the process of transferring and automating this method, which can increase plexing levels to hundreds to thousands of transcripts for the Rebus Esper Platform.”

Lars Borm—a PhD student in Linnarsson’s lab and co-developer of EEL—uses this assay to study mouse and human brains. He points out that the EEL on the Rebus Esper will work very well together. He plans to use this combination of technologies to explore brain development and possibly neurodegenerative diseases.

Tarif Awad
Tarif Awad, VP, Rebus Biosystems

Recently, Rebus modified the Esper chemistry to study the adult human brain. “This challenging sample type has high levels of lipofuscin, which appear as highly fluorescent pigmented granules that accumulate in different tissues with age,” Awad explains. “Lipofuscin generates a strong background that interferes with the detection of RNA molecules.” So, the modified assay reduces lipofuscin levels through a combination of chemistry and photobleaching. Awad points out that neurosurgeon Ethan Winkler at the UCSF Weill Institute for Neurosciences and his colleagues used this method to “perform spatial transcriptomics analysis of human brain samples to study arteriovenous malformations.”4

Adding other measurements

To get an even broader view of biology, more than RNA can be analyzed. “Some markers can be lowly expressed and can be post-transcriptionally regulated,” Cutchin says. “To get a complete understanding and characterize cells, proteins are key analytes to analyze.”

10x Genomics Visium Image
This image created with the 10x Genomics’ Visium platform shows hematoxylin and eosin staining, plus protein and gene expression overlaid on a single tissue section.

With 10x Genomics’ Visium platform, gene and protein expression can be analyzed simultaneously in a tissue section. Plus, whole transcriptome analysis allows unbiased discovery. Cutchin points out that “an unbiased, whole transcriptome approach on Visium may be taken to discover key biological signatures across an entire tissue section.” Next, “a targeted gene expression approach can be taken to probe deeper into cellular interactions at subcellular scale with our Xenium in situ platform launching later in 2022,” Cutchin says.

Visium is also useful in creating cell atlases. One international team of scientists used this platform to map the human endometrium.5 “The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones,” the scientists reported. “We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures.” The scientists suggest that such information might “provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma.”

Other examples show that 10x Genomics’ technology can be applied to a wide range of research questions. For example, scientists in the pain neurobiology research group at the University of Texas at Dallas used the Visium platform to study nociceptors, which detect pain. Nociceptor expert Theodore Price, migraine expert Gregory Dussor, and their colleagues used this platform to identify the transcriptomic signatures of nociceptors in humans. From this work, the scientists mapped potential drug targets that could alleviate pain. The team reported: “This comprehensive spatial characterization of human nociceptors might open the door to development of better treatments for acute and chronic pain disorders.”6

Other commercial platforms

The broad interest in spatial transcriptomics drives the instrument industry to make even more commercial platforms available. Two examples are the RNAscope from Advanced Cell Diagnostics (ACD; a Bio-techne brand) and Vizgen’s MERSCOPE.

In 2011, ACD introduced its RNAscope ISH (in situ hybridization) technology. The kits for the RNAscope platform can be used with a range of sample types, including formalin-fixed, paraffin-embedded samples, fresh-frozen, or fixed frozen tissues, as well as cultured cells. Then, a four-step process takes samples from preparation through the collection of data, which can be gathered with fluorescent or brightfield microscopy. Then, various open-source image-processing programs—including CellProfiler, Imagej, and QuPath—can be used to analyze the data. ACD endorses this platform for studies in cancer, cell and gene therapy, immunology and neurosciences. Plus, this method can be used to measure RNA and more. As an example, ACD scientists reported on using RNAscope to simultaneously analyze gene and protein expression in various tissues, including samples from human tumors.7

Vizgen’s MERSCOPE performs spatial transcriptomics with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Vizgen notes that this platform allows analysis at a range of resolutions, from a whole section to sub-cellular imaging. As an example, two Vizgen scientists described using MERSCOPE to map receptors in a mouse brain.8 As the scientists reported: “The map encompasses three full coronal slices (and three biological replicates per slice). It contains the exact positions of 554,802,908 RNA transcripts from 483 genes within 734,696 cells.” The researchers added that the analysis provides 100-nanometer resolution.

This is not an exhaustive list of commercial platforms for spatial transcriptomics, and the field moves so fast that new methods and platforms will continue to hit the market. Consequently, the range of applications will continue to expand, as well.

Musing on brain health and beyond

Many scientists envision applying spatial transcriptomics to healthcare. Certainly, that can and will be done in many ways. For example, one international team of scientists developed a method called multi-modal structured embedding (MUSE) and described how it could be used to characterize various diseased tissues.9

MUSE is a mathematical method that reveals heterogeneity in tissue by combining imaging and single-cell transcripts. These scientists applied MUSE to various methods of spatial transcriptomics and imaging modalities.

As the authors reported: “MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues.” This method also revealed interesting information in diseased tissues. For example, MUSE found heterogeneity in the precursor to amyloid protein in patients with Alzheimer’s disease. Such information might help scientists learn more about the development of this neurodegenerative disease and others.

More ways to measure

Despite all of the advances in platforms, methods, and analysis, spatial transcriptomics is just getting started. For example, data expert Yuan Luo of Northwestern University and his colleagues noted that “designing new approaches is still necessary to take full advantage of the spatial information.”10 They added: “As the field has achieved transcriptome-wide sequencing, spatial transcriptomics data quality is still limited by reduced coverage and low cellular resolution.”

In some cases, though, the resolution can be improved without changing a spatial-transcriptomics platform. As an example, computational biologist Rapheal Gottardo of the Fred Hutchinson Cancer Research Center and his colleagues applied a Bayesian statistical method—known as BayesSpace—to analyze scRNA-seq data and showed that this method of analysis enhanced the spatial resolution.11

A variety of other mathematical and computational methods are already being used in spatial transcriptomics, and will continue to be. One of the key methods is artificial intelligence (AI). As an example, one team of scientists from Finland, Sweden, and the UK, combined AI with spatial transcriptomics to study prostate cancer.12 The morphological heterogeneity of this cancer makes it difficult to distinguish between conditions that do and do not require treatment. This team stained samples from prostate-cancer patients with hematoxylin and eosin and used the Visium platform to analyze the locations of gene expression. This allowed for a combination of morphological and genomic characteristics to be analyzed. Using a neural-network algorithm, the researchers extracted morphological features of the samples. Then, factor analysis produced genetic profiles from the spatial transcriptomics data. “The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes,” the scientists reported. “This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions.”

In fact, heterogeneity creates diagnostic challenges in many cancers, and a combination of spatial transcriptomics and artificial intelligences promises more accurate diagnoses and improvements in treatments. For instance, Niyaz Yoosuf, director of computational biology at BioInvent International in Sweden, and his colleagues used spatial transcriptomics and machine learning to study breast cancer.13 This team used publicly available spatial-transcriptomics datasets derived from patients with breast cancer to train and test a machine-learning method. “Identifying tumor heterogeneity in breast cancer regions is crucial for determining specific disease states and for starting suitable treatments early,” the researchers noted. “Our application of a standard machine learning method to [spatial transcriptomic] data clearly distinguished healthy and diseased areas in the tissue.”

No doubt, tomorrow’s spatial-transcriptomics will address ongoing challenges in this method. As a result, it remains to be seen just how widely this technology will be used in biology and medicine. One thing that is certain, spatial transcriptomics already pushes ahead many scientific endeavors—opening new opportunities to see where genes get expressed and what that might mean.

 

References
1. Marx, V. Method of the year: spatially resolved transcriptomics. Nature Methods 18:9–14 (2021).
2. Li, X., Wang, C-Y.From bulk, single-cell to spatial RNA sequencing. International Journal of Oral Science 13(1):36. (2021).
3. Borm, L.E., Albiach, A.M., Mannens, C.C.A, et al. Scalable in situ single-cell profiling by electrophoretic capture of mRNA. bioRxiv (2022).
4. Winkler, E.A., Kim, C.N., Ross, J.M., et al. A single-cell atlas of the normal and
malformed human brain vasculature. Science 375(6584): eabi7377. (2022).
5 Garcia-Alonso, L., Handfield, L-F., Roberts, K., et al. Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro. Nature 53:1698–1711. (2021).
6. Tavares-Ferreira, D., Shiers, S., Ray , P.R., et al. Spatial transcriptomics of dorsal root ganglia identifies molecular signatures of human nociceptors. Science Translational Medicine 14(632):eabj8186. (2022).
7. Dikshit, A., Zong, H., Anderson, C., et al. Simultaneous visualization of RNA and protein expression in tissue using a combined RNAscope™ in situ hybridization and immunofluorescence protocol. Methods in Molecular Biology 2148:301–312. (2020).
8. Emaneul, G., He, J. Using MERSCOPE to generate a cell atlas of the mouse brain that includes lowly expressed genes. Microscopy Today 29(6):16–19. (2021).
9. Bao, F., Deng, Y., Wan, S., et al. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature Biotechnology doi: 10.1038/s41587-
022-01251-z (online ahead of print)(2022).
10. Zeng, Z., Li, Y., Li, Y., et al. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biology 23:83. (2022).
11. G. Zhao, E., Stone, M.R., Ren, X., et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology 39:1375–1384. (2021).
12. Chelebian, E., Avenel, C., Kartasalo, K., et al. Morphological features extracted by AI associated with spatial transcriptomics in prostate cancer. Cancers (Basel) 13(19):4837 (2021).
13. Yoosuf, N., Navarro, J.F., Salmén, F. et al. Identification and transfer of spatial
transcriptomics signatures for cancer diagnosis. Breast Cancer Research 22, 6 (2020).

 

Mike May, is a freelance writer and editor with more than 30 years of experience. He earned an M.S. in biological engineering from the University of Connecticut and a PhD in neurobiology and behavior from Cornell University. He worked as an associate editor at American Scientist, and he is the author of more than 1,000 articles for clients that include GEN, Nature, Science, Scientific American and many others. In addition, he served as the editorial director of many publications, including several Nature Outlooks and Scientific American Worldview.

Spatial Trends
Up until recently, our understanding of single cell gene expression has been limited. The technology at the cornerstone of the field, scRNA-seq, lacks a critical piece of information—the cell’s location. But the latest advance in single cell technology—spatial transcriptomics—is hoping to overcome that limitation.

Knowing where a cell is located within the context of a tissue can uncover new biology. A cell’s surroundings can reveal information about the cell’s relationship to its environment, such as other cells that may contact the cell of interest and communication or signaling between cells.

The field of cancer biology has jumped at the opportunity to use spatial to answer long standing questions in the field. Spatial Transcriptomics is well suited to deepen the understanding of cancers by dissecting intratumoral heterogeneity. There are, already, hundreds of publications using this technology to investigate different aspects of tumor biology.

COVID-19 researchers have harnessed the power of spatial transcriptomics to study the pathogenesis of a SARS-CoV-2 infection. For example, David Ting’s group at Massachusetts General Hospital used NanoString’s spatial platform, the GeoMX DSP, to dissect the gene expression of lung cells infected by SARS-CoV-2 and tease apart their expression from their neighboring, non-infected, cells.

Another area to benefit from spatial technology is cell atlas construction. This past May, BGI-Research’s Stereo-Seq (SpaTial Enhanced REsolution Omics sequencing) technology was used to build four panoramic spatial atlases: mouse organogenesis, developing Drosophila embryos, zebrafish embryogenesis, and Arabidopsis leaves.

As spatial transcriptomics gains a foothold in research labs across the globe, one looming question is how easily spatial will translate into the clinic. Another is whether the technology will expand into multi-omics analysis. For some researchers, spatial holds the potential of past genomics advances like next-generation sequencing. But time will tell if the relatively new technology has the ability to start a genomics revolution.

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