Formalin-fixed paraffin-embedded (FFPE) samples are widely used in biomedical research, especially in pathology. These samples allow long-term preservation of tissues, making them a valuable resource for studying diseases like cancer. However, one major challenge has limited their use in modern genomics: the quality of RNA extracted from FFPE samples is often degraded, making it hard to use these samples for single-cell studies.
Researchers at the Garvan Institute of Medical Research have developed a new method called snPATHO-seq that allows researchers to extract high-quality transcriptomic data from FFPE samples at the single-nucleus level. Here’s a look at what this means for research and how it compares to other technologies.
What is snPATHO-seq?
snPATHO-seq is a cutting-edge technique that makes it possible to study individual cells from FFPE samples. Instead of requiring fresh or frozen tissue, it focuses on extracting nuclei from FFPE tissues. Nuclei contain enough intact RNA to provide reliable data, even from samples with degraded RNA.
How Does snPATHO-seq Perform?
Researchers compared the performance of snPATHO-seq to other widely-used single-cell sequencing methods, such as the 10x Genomics 3’ and 10x Flex assays. These workflows are typically used with fresh or frozen tissues, which offer better RNA quality. However, the study found that snPATHO-seq delivers comparable results, proving that FFPE samples can still provide meaningful transcriptomic data.
The snPATHO-seq workflow enables nuclei isolation and single-nucleus gene expression detection from human FFPE tissue samples
a Illustration of the snPATHO-seq workflow. Created in BioRender. Wang, T. (2024) BioRender.com/u53s150. b, c Boxplots of the number of UMIs (b) and genes (c) detected per nucleus. The boxes show the UMIs (b) and Genes (c) median and interquartile range. Outliers were shown as dots. d UMAP embedding of unintegrated snRNA-seq data annotated by sample IDs. e UMAP embedding of unintegrated snRNA-seq data split by processing methods. f UMAP embedding of Seurat CCA integrated snRNA-seq data from patient 4411 annotated by cell type. g UMAP embedding of Seruat CCA integrated 4411 snRNA-seq data split by processing methods. h Barplot showing the fraction of cell types detected by different snRNA-seq methods in sample 4411. i Heatmap of the scaled expression of selected cell type markers detected by differential gene expression analyses in 4411 data. The top 200 significantly differentially expressed genes identified in each cell population (if available) were selected by fold change and used for plotting. A gene was considered significantly differentially expressed if the BH-adjusted P value was lower than 0.05. Genes were arranged by hierarchical clustering based on the expression in the FFPE-snPATHO-seq data on the x-axis. Cell types identified by different snRNA-seq workflows were manually arranged on the y-axis. N = 1 sample per protocol.
A Robust Tool for Diverse Samples
Another exciting feature of snPATHO-seq is its versatility. It works across a wide range of tissue types, from healthy to diseased samples, demonstrating that this method is robust for many kinds of research.
Combining snPATHO-seq with Spatial Transcriptomics
The power of snPATHO-seq becomes even greater when paired with spatial transcriptomics tools like FFPE Visium. Spatial transcriptomics allows scientists to map gene expression within the exact locations of cells in a tissue. By combining snPATHO-seq with these spatial technologies, researchers can gain multi-modal insights, revealing both the genetic activity and the spatial organization of cells.
Why This is Important
This breakthrough opens new possibilities for using archived FFPE samples, which are often underutilized. It allows researchers to reanalyze old samples with new techniques, potentially leading to new discoveries in cancer, neurodegenerative diseases, and other fields.
With snPATHO-seq, scientists can unlock the hidden data within preserved tissues, expanding opportunities for personalized medicine and advancing our understanding of complex diseases.
Conclusion
The introduction of snPATHO-seq is a major step forward for genomics research. It demonstrates that even older, preserved tissue samples can provide high-quality transcriptomic data. As more labs adopt this technology, we can expect new breakthroughs from existing FFPE sample collections—bringing us closer to uncovering the mysteries of disease at the single-cell level.
This innovation highlights the power of combining new technology with old samples, making it an exciting time for researchers exploring the frontiers of single-cell genomics.