snPATHO-seq: unlocking the FFPE archives for single nucleus RNA profiling
Vallejo, A. F.; Harvey, K.; Wang, T.; Wise, K.; Butler, L. M.; Polo, J.; Plummer, J. T.; Swarbrick, A.; Martelotto, L. G.
Show abstract
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.
Matching journals
The top 13 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues 96%
- Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples 94%
- High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes 94%
Similar papers in this journal
- Multi-sample Full-length Transcriptome Analysis of 22 Breast Cancer Clinical Specimens with Long-Read Sequencing 93%
- Targeted Transcriptome Analysis using Synthetic Long Read Sequencing Uncovers Isoform Reprograming in the Progression of Colon Cancer 92%
- HybriSeq: Probe-based Device-free Single-cell RNA Profiling 92%
Similar papers in this journal
- Towards resolution of the intron retention paradox in breast cancer 93%
- Integrative multi-omic sequencing reveals the MMTV-Myc mouse model mimics human breast cancer heterogeneity 92%
- Mutational landscape of pure ductal carcinoma in situ and associations with disease prognosis and response to radiotherapy 92%
Similar papers in this journal
- Genome profiles of lymphovascular breast cancer cells reveal multiple clonally differentiated outcomes with multi-regional LCM and G&T-seq 94%
- An Omic and Multidimensional Spatial Atlas from Serial Biopsies of an Evolving Metastatic Breast Cancer 93%
- Evaluation of homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.