Single Nucleus Rna-seq
Single Nucleus Rna-seq - Related Videos
09:54
RNA-Seq
0 Views •
2023
Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.
06:22
Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
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Cited by 15 •
2020
Intra-tumoral heterogeneity is an inherent feature of tumors, including gliomas. We developed a simple and efficient protocol that utilizes a combination of buffers and gradient centrifugation to isolate single nuclei from fresh frozen glioma tissues for single nucleus RNA and ATAC sequencing studies.
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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2025
This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.
06:21
Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq
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Cited by 20 •
2013
A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.
14:49
Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA
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Cited by 30 •
2011
Here we describe a method for preparation of both single read and paired end Illumina mRNA-Seq sequencing libraries for gene expression analysis based on T7 linear RNA amplification. This protocol requires only 10 nanograms of starting total RNA and generates highly consistent libraries representing whole transcripts.
FAQs
During library preparation, RNA from each nucleus receives an identifying barcode before complementary DNA is generated and sequenced. Computational analysis then uses those labels to associate sequence-derived expression measurements with their source nuclei rather than combining signals across the entire specimen. This preserves cell-to-cell variation, which is essential for examining heterogeneous tumors.
Some complex tissues and tumors do not readily yield intact, separable cells during sample preparation. Isolating nuclei provides an alternative material for transcriptomic analysis, allowing researchers to examine gene-expression patterns despite those dissociation challenges. The approach is also compatible with archived or frozen specimens, expanding studies beyond freshly collected tissue.
The resulting expression profiles can be compared across individual nuclei to identify distinct cellular populations within a tumor. This supports separation of malignant cells from surrounding stromal and immune populations based on their gene-expression patterns. Such resolution helps researchers describe tumor composition and examine how different populations contribute to the surrounding microenvironment.
Single-nucleus expression data can reveal gene-expression programs associated with tumor progression or treatment response. Researchers can examine whether particular cellular populations show patterns linked to advancing disease or changing therapeutic sensitivity. Because measurements remain resolved across nuclei, these programs can be connected to specific malignant, stromal, or immune components rather than treated as properties of the tumor as a whole.
A typical workflow begins by isolating nuclei from the specimen, followed by capture and barcoding of nuclear RNA. The labeled RNA is converted into complementary DNA, and the resulting libraries are sequenced. Computational analysis then interprets the sequence data as gene-expression profiles across nuclei, providing the basis for identifying cellular populations and expression programs.
It is particularly useful when investigators need to characterize complex tumors, analyze material that is difficult to dissociate, or work with archived and frozen specimens. Applications include molecular classification, assessment of tumor heterogeneity, characterization of tumor microenvironments, and investigation of expression programs associated with tumor progression or treatment response.