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The spatial organisation of cells defines the biological functions of tissue ecosystems from development to disease. Recently, an array of technologies have been developed to query gene expression in a spatial context. These include techniques such as employing barcoded oligonucleotides, single-molecule fluorescence in situ hybridization (smFISH), and DNA nanoball (DNB)-patterned arrays. However, resolution and efficiency vary across platforms and technologies. To obtain spatially relevant biological information from spatially resolved transcriptomics, we combined the Stereo-seq workflow with CRISPRclean technology to develop the STOmics-GenX pipeline. STOmics-GenX not only allowed us to reduce genomic, mitochondrial, and ribosomal reads, but also lead to a ∼2.1-fold increase in the number of detected genes when compared to conventional Stereo-seq (STOmics). Additionally, the STOmics-GenX pipeline resulted in an improved detection of cell type specific genes, thereby improving cellular annotations. Most importantly, STOmics-GenX allowed for enhanced detection of clinically relevant biomarkers such as Alpha-fetoprotein (AFP), enabling the identification of two spatially distinct subsets of hepatocytes in hepatocellular carcinoma tissue. Thereby, combining CRISPRclean technology with STOmics not only allowed improved gene detection but also paved the way for spatial precision oncology by improved detection of clinically relevant biomarkers.

FFPE (formalin-fixed, paraffin-embedded) tissue archives are the largest repository of clinically annotated human specimens. Despite numerous advances in technology, current methods for sequencing of FFPE-fixed single-cells are slow, labour intensive, insufficiently sensitive and have a low resolution, making it difficult to fully exploit their enormous research and clinical potential. Here we introduce single nuclei pathology sequencing (snPATHO-Seq), a sensitive and efficient high-throughput platform to profile the transcriptome of single nuclei extracted from formalin-fixed paraffin-embedded (FFPE) samples. snPATHO-Seq combines an optimised nuclei extraction protocol from archival samples with 10x Genomics probe-based technology targeting the whole transcriptome. We performed direct comparison of the Fixed RNA Profiling (FRP) and established 3’ single cell RNA-Sequencing (scRNA-Seq) workflows through a comprehensive bioinformatics analysis of matched fresh and fixed samples derived from the LNCaP prostate cancer cell line. FRP detected 2.1 times more transcripts in the fixed sample than the 3’ kit did in the fresh sample. Low mitochondrial genes detection using the FRP was translated into 99.9 percent of cells passing the QC filters, compared to 81.6 percent of cells using the v3.1 chemistry. We then optimized snPATHO-Seq and applied it to a human breast cancer metastasis to the liver collected at autopsy and preserved in FFPE, a particularly challenging sample type. Remarkably, at 28,000 reads/cell snPATHO-Seq was able to detect a median of 1850 genes/cell and 3,216 UMI counts/cell. Comparison of snPATHO-Seq with spatial transcriptomics data (10x Genomics Visium FFPE v1) derived from an adjacent section of the same sample revealed a strong correlation, validating the accuracy of the snPATHO-Seq data. Gene expression data from snPATHO-Seq was used to predict cell type composition within each spatial transcriptomic location via deconvolution. Overall, snPATHO-Seq enables high quality and sensitivity snRNA-Seq from preserved tissue samples, unlocking the vast archives of FFPE tissues and thereby allowing extensive retrospective clinical genomic studies.