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S3-E17.3 – Spatial Transcriptomics and The History of Single Cell Genomics

This episode focuses on the history of single-cell genomics up through spatial transcriptomics and beyond and raises questions about the new things we can learn and the demand these improved techniques place on other elements in the research process.

https://www.science.org/doi/10.1126/science.abm0594

Professors Scott Friedman and Neil Henderson join the Surfers (including the returning Stephen Harrison) to discuss some truly exciting advances in the basic science and technology of defining, diagnosing, and treating NAFLD and NASH. This conversation focuses largely on spatial transcriptomics: its history, what it can tell us today, and how it might improve even further over time. All these places focus on the need for healthy hepatic cells researchers can use with this technology, which may already suggest that cells we once considered “good enough” are not today.

Neil starts this conversation by describing the value of spatial transcriptomics today by using what Scott describes as the “blender analogy” developed by Neil’s colleague Prakash Ramachandran. This starts with days where we would “mash up” tissue together and re-do RNA analysis (in this metaphor, like blending fruit in a blender into a smoothie and trying to taste for the flavors) to a next stage where you can tell what the individual fruits are to spatial transcriptomics today, which is like looking at a fruit tart in three dimensions, seeing where each piece sits in the fruit and the size and nature of spaces between them. As Neil points out, this allows the technology to barcode the spots so the informatics people can work out individual gene expressions.

Roger asks Neil to walk the audience back through the history of how we came to this place technologically. He starts with the early days of single-cell genomics and proceeds through high throughput droplet-based systems to cDNA libraries that enable informaticians to indicate exactly which gene is expressed in which individual cell.

Beyond that, Neil discusses the power of single nuclei sequencing, which provides rich data from frozen tissue and thereby provides greater space for global collaboration and examining tissue that might have been stored for years. In the liver, this has allowed for hepatocyte sequencing, which was not viable previously…and there are more advances yet to come.

At this point, the conversation shifts toward what’s current and tangible as Jörn Schattenberg asks how much variability in tissue samples can be attributed to human differences. Neil describes his group as “nicely quite surprised at how congruent some of the data has been.”

The rest of this conversation centers mostly on sources of tissue on how “healthy” that tissue actually is. In Edinburgh, Neil notes, much of his tissue comes from distal liver sites in patients with colorectal cancer. The sites may be free from cancer but may have effects from earlier chemotherapy and other systemic challenges. Scott wraps up this conversation with the story of a patient where pathologists captured tissue “far away” from the site of a neuroendocrine tumor. Pathologists believed the tissue was healthy, but single-cell sequencing revealed a “huge neuroendocrine cell population.” Such is the value of the new technologies and the challenges they face for researchers to improve other elements of the process.

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