Unveiling the Hidden World of RNA: How Spatial Multi-Omics Revolutionizes Molecular Pathology
Imagine a world where the intricate language of RNA is not just deciphered but visually mapped within the intricate landscape of tissue. This is the groundbreaking vision presented by Rong Fan, PhD, at the AMP conference, where he unveiled how spatial multi-omics is transforming molecular pathology.
Fan's presentation, Decoding RNA Biology in Space: Towards the Future of Molecular Pathology, highlighted a critical challenge: the limitations of traditional histopathology. While pathologists like Fan's collaborator, Mina Xu, MD, can diagnose patients by examining tissue histology images, they often need more molecular information for complex cases. The "holy grail" is to overlay molecular data directly onto these histology images.
This is where spatial omics technology steps in. Over the past decade, it has exploded, with spatial transcriptomics remaining a cornerstone. However, Fan's research group has pushed beyond gene expression, aiming for spatial multi-omics, which encompasses RNA, proteins, and epigenetic features.
Their innovative approach transforms fixed tissue into a reaction chamber, enabling the tagging of RNA, proteins, and epigenetic markers like open chromatin regions and histone modifications. While they've published on spatial epigenomics, Fan focused on RNA biology.
RNA, he reminded the audience, is more than just messenger molecules. Each mRNA molecule undergoes a dynamic lifecycle. Understanding RNA directly in tissue, Fan argued, could unlock deeper biological insights and provide pathologists with enhanced diagnostic precision.
His team adapted a polyadenylation strategy, originally developed by Stephen Quake at Stanford, to add poly(A) tails to RNA molecules in tissue sections. This barcoding technique allows the visualization of various RNA species, including long non-coding RNAs, small non-coding RNAs, and microRNAs. Most notably, it enables the spatial mapping of tRNAs, the RNA bridge connecting mRNA to protein synthesis.
The clinical potential of this molecular richness was demonstrated in a lymphoma case study. A patient with prolonged stomach pain had a biopsy revealing two distinct disease regions. Xu could differentiate between low-grade B cell lymphoma (MALT) and diffuse large B cell lymphoma (DLBCL), but this alone couldn't guide therapy. Low-grade to high-grade disease transformations worsen outcomes, and targeted treatments exist but are toxic.
Fan's team applied their technology, generating spatial clusters and cell-type maps. AI machine learning tools, including the iStar pipeline from the University of Pennsylvania, integrated FFPE histology with spatial transcriptomics, achieving "super-resolved, almost single-cell" data across the tissue.
This resolution enabled previously inaccessible questions. Comparing macrophages in low- and high-grade regions, they found polarization towards the M2 macrophage activation pathway in high-grade lymphoma. These molecular clues could lead to better treatment ideas.
The datasets also contained genomic information. Capturing RNA across transcript lengths revealed single nucleotide variants and copy number alterations, allowing the reconstruction of the evolutionary phylogenetic tree of tumor clones and their spatial context within the tissue.
Fan also highlighted the interrogation of microRNAs, detecting approximately 1,800 human microRNAs, approaching the entire pool of 2,000. Integrated analyses suggested a chronic inflammation-NF-κB activation chain in the patient's tumor, leading to PI3K–AKT pathway activation. This could potentially guide biomarker-driven therapies.
In conclusion, Fan emphasized the broader significance. FFPE samples, the everyday materials of clinical pathology, now yield unprecedented molecular depth. For the first time, human clinical tissue specimens reveal extensive molecular biology information. With emerging tools like spatial multi-omics, we're entering a new era in molecular pathology.
