The human body is made up of trillions of cells. They display immense cellular variety and a high degree of spatial organization – a product of development that continuously changes due to factors like the environment and age. Understanding the interplay between intricate structural and functional details of our cells is crucial for basic and clinical research. Historically, however, scientists seeking to explore the spatial element of biology were limited by the technology of the day, which prevented them from marrying single cell insights with tissue-wide observations.

Now, within a field of molecular biology called spatial transcriptomics, this barrier is beginning to dissolve. Recognized as Method of the Year by Nature in 2020, spatial transcriptomics employs advanced imaging methods with novel chemistry techniques that stand to dramatically expand our understanding of health and disease.

Location, location, location

To understand disease, we must understand what constitutes a healthy body, and here location matters. During development, all cells carry an identical copy of the genome, and over time the cells differentiate into various types based on cues from their spatial environment. These cues regulate expression of their genome as needed, leading to different transcription patterns and subsequent protein expression in each cell. In this way, cells acquire a phenotype suited to their role in the body– e.g., as a brain cell, a liver cell, or a muscle cell. Throughout an organ or tissue, or even within smaller microenvironments, different cell types coexist in physical spaces. Each region contains an interactive community that, in the cases of acute and chronic illness, can be thrown out of balance resulting in disease symptoms.

Diseases will often trigger changes in specific cell types or parts of the body. For example, cirrhosis of the liver is associated with several cell types found there, neurodegeneration of the brain is thought to be driven, in part, by changes in glial cells, and a cancerous tumor’s microenvironment orchestrates the actions of numerous cell types to encourage tumor growth and metastasis. While progress has been made to elucidate the cell and molecular changes associated with disease, science has only begun to scratch the surface. For poorly understood diseases, scientists may not even know what they’re looking for. To unlock these medical quandaries, scientists need a reference map known as a “cell atlas” that provides the location and identity for all the cells of the human body based on genetic, transcriptomic, proteomic, and metabolomic profiles. Work towards a full human cell atlas is still in progress, but scientists have already been able to use the growing body of data to pinpoint new genetic and cellular hallmarks of disease.

Making these reference maps is no small task since they must reflect massive complexity of the human body. Spatial transcriptomics represents a powerful cell atlasing strategy. It employs both high throughput methods and single cell techniques that enable researchers to begin classifying cells into cell types based on RNA expression. It then relates that information to cell shape, size, function, and interaction with cellular neighbors – all in the context of the whole tissue.

Challenges along the way

Spatial transcriptomics seeks to analyze astronomical numbers of cells and map the differences between them. As such, scientists need technologies that can acquire high-volume, single molecule data at a pace, scale, and level of sensitivity that is practical for mapping an entire organism to generate results that can reliably differentiate healthy and diseased tissue.

However, the need for both subcellular detail and tissue-wide data presents a major challenge for the field. To meet both needs, spatial transcriptomic technologies must maximize on a few key capabilities: the size of the tissue area that can be analyzed per experiment, the number of genes that can be assessed per experiment, the detection efficiency, and the spatial resolution of the image. So far, however, most spatial transcriptomics solutions have been forced to compromise the quality of one or more of these capabilities for the sake of the others. On the other hand, while many single-cell sequencing technologies on the market offer the advantage of genetically profiling many genes at once, these methods lose all spatial information by breaking apart the tissue.

The future of spatial transcriptomics

Certain biological questions, for example, those that would involve atlasing entire organs of healthy and diseased individuals to uncover biomarkers of disease, are currently unattainable for most labs. However, advancements in the field promise to improve our capture of transcriptomic data on a genomic scale and reduce costs, soon making experiments that were previously unimaginable, routine. That’s because new spatial technologies are incorporating more capabilities that elevate the quality of data that can be acquired, such as:

• Multiplexing – assay hundreds or more mRNA species per experiment
• Resolution – single-cell/subcellular resolution that provides insight into mRNA species location and copy number
• Sensitivity – identify cells with lowly expressing genes
• Sample area – ability to analyze whole sections that captures tissue architecture and large volumes of cells

The next generation of spatial transcriptomics tools should excel in each of these capabilities without sacrificing the quality of the others. This will allow scientists to gain a thorough understanding of how cells follow a distinct spatial organization that is fundamental to their functionality, classification, and influence on neighboring cells. Through new spatial data, scientific models of biological systems will be rooted in accurate, detailed maps of actual biological tissue, providing unparalleled insight into biological systems. With these maps, diseases may be addressed, not only based on their molecular mechanisms, but also through systemic understanding of their etiology. The evolution of spatial technologies will not only provide researchers with increasingly transformative insight into tissue-scale basic research and translational medicine, it will also accelerate drug discovery and development.

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