SnPATHO-seq: unlocking the FFPE archives for single nucleus RNA profiling [snRNA-seq]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1117352 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR29202617_1.fastq.gz | Fastqsanger.gz | |||
| SRR29202617_2.fastq.gz | Fastqsanger.gz | |||
| SRR29202618_1.fastq.gz | Fastqsanger.gz | |||
| SRR29202618_2.fastq.gz | Fastqsanger.gz |
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Project description:FFPE tissue samples have long been considered challenging for sc-/sn-RNA sequencing assays. In this study, we developed a novel snRNA-seq workflow, snPATHO-seq, for FFPE tissue samples utilising isolated high-quality nuclei from FFPE samples and 10X Genomics FLEX chemistry. The snPATHO-seq demonstrated consistent performance with the established 10X Genomics 3' assay. In addition, it allows more accurate single-nucleus and spatial transcriptomics integration with the FFPE Visium dataset generated from the same FFPE block. We envision this new protocol can facilitate the transcriptomic interpretation of millions of FFPE archives generated and simplify the sample collection process and data generation.
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Project description:FFPE tissue samples have long been considered challenging for sc-/sn-RNA sequencing assays. In this study, we developed a novel snRNA-seq workflow, snPATHO-seq, for FFPE tissue samples utilising isolated high-quality nuclei from FFPE samples and 10X Genomics FLEX chemistry. The snPATHO-seq demonstrated consistent performance with the established 10X Genomics 3' assay. In addition, it allows more accurate single-nucleus and spatial transcriptomics integration with the FFPE Visium dataset generated from the same FFPE block. We envision this new protocol can facilitate the transcriptomic interpretation of millions of FFPE archives generated and simplify the sample collection process and data generation.
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Project description:snPATHO-seq: unlocking the FFPE archives for single nucleus RNA profiling [Visium]
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Project description:Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.
2022-08-31 | GSE211797 | GEO
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2025-04-22 | E-MTAB-13651 | biostudies-arrayexpress
Project description:Spliceosomal snRNA are key components of small nuclear ribonucleoprotein particles (snRNPs), the building blocks of the spliceosome. The biogenesis of snRNPs is a complex process involving multiple cellular and subcellular compartments, the details of which are yet to be described. In short, the snRNA is exported to the cytoplasm as 3‘-end extended precursor (pre-snRNA), where it acquires a heptameric Sm ring. The SMN complex which catalyses this step, recruits Sm proteins and assembles them around the pre-snRNA at the single stranded Sm site. After additional modification, the complex is re-imported into the nucleus where the final maturation step occurs. Our modeling suggests that during the cytoplasmic stage of maturation pre-snRNA assumes a compact secondary structure containing Near Sm site Stem (NSS) which is not compattible with the formation of the Sm ring. To validate our in silico predictions we employed selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq) on U2 snRNA in vivo, ex vivo and in vitro, and U4 pre-snRNA in vitro. For the in vivo experiment HeLa cells were incubated for 10 min at 37°C with NAI or DMSO to final concentration 200 mM. RNA was isolated using Trizol (Sigma) and 200 µl chloroform and precipitated with ethanol at -20°C overnight. For the ex vivo experiment, RNA was isolated from HeLa cells after Protease K treatment at room temperature for 45 min. After incubation, RNA was isolated using equilibrated phenol/chloroform/isoamyl alcohol buffered by folding buffer (110 mM HEPES pH 8.0, 110 mM KCl, 11 mM MgCl2) and cleaned on a PD-10 column according to the manufacturer’s instructions. Isolated RNA was treated with 100mM NAI or DMSO for 10 min at 37°C. For the in vitro experiment, U2WT and U4 pre-snRNA were transcribed by T7 polymerase followed by DNase I (30 min at 37 °C) and Proteinase K (30 min at 37°C) treatments. U2 snRNA was purified on 30 kDa Amicon columns, folded for 30 min at 37°C in 57 mM MgCl2 and incubated with 100 mM NAI at 37°C for 10 min. DMSO was used as a negative control. U4 pre-snRNA was purified on Superdex 200 Increase 10/300GL, folded for 30 min at 37°C in 60 mM MgCl2 and incubated with 100 mM NAI at 37°C for 10 min. DMSO was used as a negative control. All prepared RNA samples (in vitro, ex vivo, in vivo) were used for reverse transcription with the gene-specific primer 5’-CGTTCCTGGAGGTACTGCAA for U2 snRNA and 5’- AAAAATTCAGTCTCCG for U4 pre-snRNA. We used SHAPE MaP buffer (50 mM Tris-HCl pH 8.0, 75 mM KCl, 10 mM DTT, 0.5 mM dNTP, 6 mM MnCl2) and SuperScript II (Invitrogen). Amplicons for snRNAs were generated using gene-specific forward and reverse primers. Importantly, the primers include Nextera adaptors required for downstream library construction. PCR reaction products were cleaned using Monarch PCR&DNA Clean-up Kits. Remaining Illumina adaptor sequences were added using the PCR MasterMix and index primers provided in the NexteraXT DNA Library Preparation Kit (Illumina) according to the manufacturer’s protocol. Libraries were quantified using Qubit (Invitrogen) and BioAnalyzer (Agilent). Amplicons were sequenced on a NextSeq 500/550 platform using a 150 cycle mid-output kit. All sequencing data was analyzed using the ShapeMapper 2 analysis pipeline1. The ‘—amplicon’ and ‘—primers’ flags were used, along with sequences of gene-specific handles PCR primers, to ensure primer binding sites are excluded from reactivity calculations. Default read-depth thresholds of 5000x were used. Analysis of statistically significant reactivity differences between ex vivo and in vivo-determined SHAPE reactivities was performed using the DeltaSHAPE automated analysis tool and default settings2. 1. Busan, S. & Weeks, K.M. Accurate detection of chemical modifications in RNA by mutational profiling (MaP) with ShapeMapper 2. RNA 24, 143-148 (2018). 2. Smola, M.J., Rice, G.M., Busan, S., Siegfried, N.A. & Weeks, K.M. Selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis. Nat Protoc 10, 1643-69 (2015).
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