Imagine being able to see the entire molecular landscape of a disease, right within the tissue where it's happening. That's the promise of spatial multi-omics, and it's poised to revolutionize how we understand and treat diseases. Dr. Rong Fan from Yale University recently presented a compelling vision of this future at the AMP conference, showcasing how these cutting-edge technologies are transforming molecular pathology.
Dr. Fan's presentation, titled "Decoding RNA Biology in Space: Toward the Future of Molecular Pathology," addressed a critical bottleneck in traditional pathology: its reliance on visual interpretation of tissue samples. While skilled pathologists like his collaborator, Dr. Mina Xu, can glean a wealth of information from a single tissue slide, complex cases often demand more. As Dr. Fan explained, "She diagnoses patients by looking at the tissue histology image, and she can tell me so much about just a single tissue slide..." But when things get tricky, "that requires a lot more molecular information…she has to order a lot more IHC, but potentially also some genetic testing as well." The ultimate goal, he proposed, is to directly overlay molecular data onto the very same histology image, creating a comprehensive molecular map.
This is where spatial omics comes in. This rapidly evolving field aims to do just that – to map the location of molecules within a tissue sample. "Over the past 10 years, we have seen the explosion of the technology," Dr. Fan noted. Spatial transcriptomics, which focuses on mapping gene expression, is a key tool. But Dr. Fan's lab is pushing the boundaries even further, pioneering what he calls spatial multi-omics. This involves analyzing not just gene expression, but also proteins, epigenetic modifications, and even DNA methylation, all within the same tissue section. Think of it as adding multiple layers of information to a map, revealing a much richer and more nuanced picture.
His lab's innovative approach essentially turns the fixed tissue sample into a miniature reaction chamber. "Much more versatile," he explained. This allows them to tag a wide range of molecules, going beyond just RNA and proteins to include crucial epigenetic features like open chromatin regions, histone modification marks, and DNA methylation. They've already published several studies demonstrating the power of spatial epigenomics and other modalities. However, at the AMP conference, he focused specifically on the role of RNA biology.
RNA, as Dr. Fan reminded the audience, is far more than just a messenger molecule carrying genetic information. "From your freshman biology class, you already learn RNA is not just the messenger. Every messenger RNA molecule has very dynamic life cycle," he said. Understanding the diverse roles and dynamic behavior of different RNA molecules directly within the tissue could unlock deeper biological insights and provide pathologists with unprecedented diagnostic accuracy. And this is the part most people miss: it's not just about what genes are expressed, but how they're being regulated and where they're being expressed that matters.
To achieve this, Dr. Fan's team adapted an ingenious in-tissue polyadenylation strategy originally developed by Stephen Quake at Stanford. This technique allows them to add poly(A) tails to all the RNA molecules present in the tissue section, regardless of their type. By barcoding these molecules, they can then identify and map a wide variety of RNA species, including long non-coding RNAs, small non-coding RNAs, and even microRNAs. Most impressively, the method enables the spatial mapping of tRNAs – "the bridge between the RNA and the protein synthesis." Dr. Fan emphasized the significance, noting that "Turns out tRNA was the first noncoding RNA discover and recognized by the Nobel Prize." But here's where it gets controversial... Some researchers argue that focusing on tRNAs might be overemphasized, as their role in disease is still not fully understood compared to other RNA types.
The clinical potential of this molecular richness became strikingly clear through a lymphoma case study. A patient experiencing prolonged stomach pain underwent a biopsy that revealed two distinct regions of disease. Dr. Xu, the pathologist, was able to distinguish between low-grade B cell lymphoma (MALT) and diffuse large B cell lymphoma (DLBCL). However, this distinction alone wasn't enough to guide treatment decisions. Transformations from low-grade to high-grade disease can significantly worsen patient outcomes, and while effective targeted therapies exist, they often come with significant toxicity.
Dr. Fan's team applied their spatial multi-omics technology to these samples, generating spatial clusters and cell-type maps. Critically, they used AI machine learning tools, including the iStar pipeline from the University of Pennsylvania, to integrate FFPE histology with spatial transcriptomics. This allowed them to achieve what Dr. Fan called "super resolve, almost single cell" data across the tissue. This means they could analyze the molecular characteristics of individual cells within the tissue, providing a level of detail previously unattainable.
This unprecedented resolution allowed them to explore previously inaccessible questions. For example, by comparing macrophages in the low-grade and high-grade regions, they discovered that macrophages in the high-grade lymphoma were "much more polarized to the M2 macrophage alternative activation pathway." They also identified additional pathway differences that were particularly intriguing. These molecular clues, Dr. Fan explained, "can potentially give rise to better treatment ideas." This highlights the potential of spatial multi-omics to identify new therapeutic targets and personalize treatment strategies.
Furthermore, the same datasets yielded valuable genomic information. Because their approach captures RNA across the entire length of transcripts, they could extract "a lot of genomic operation information," including single nucleotide variants and copy number alterations. This allowed them to reconstruct the evolutionary phylogenetic tree of the different tumor clones and, crucially, map these clones back to their precise spatial location within the tissue. This provides insights into how the tumor evolved and spread within the patient.
Dr. Fan also emphasized their ability to analyze microRNAs, noting that they detected approximately 1,800 human microRNAs – "approaching the whole pool" – of the 2,000 known human microRNAs in a cell. Integrated analyses suggested a mechanistic chain in this patient's tumor involving chronic inflammation, NF-κB activation, and ultimately the activation of the PI3K–AKT pathway. "Some of the patients might respond to PI3K–AKT," he noted, highlighting the potential for biomarker-driven therapeutic interventions. And this is the part most people miss... Identifying these pathways could lead to repurposing existing drugs for new applications.
In conclusion, Dr. Fan reflected on the broader implications of this work. FFPE samples – the standard, everyday materials used in clinical pathology – can now yield an unprecedented depth of molecular information. "For the first time, the human clinical tissue specimen…you can see so much molecular biology information," he stated. "Nowadays, with the emerging tools like what I showed you today and many others, we’re at the beginning of the new era in molecular pathology." He painted a picture of a future where pathologists have access to a wealth of molecular data, allowing them to make more accurate diagnoses, predict treatment responses, and ultimately improve patient outcomes. What do you think? Is spatial multi-omics truly the future of molecular pathology, or are there still significant hurdles to overcome before it becomes a mainstream clinical tool? Share your thoughts in the comments below!
