研究目的
To develop a new method, Smart-3SEQ, for quantifying transcript abundance accurately with small amounts of total RNA and degraded RNA samples, and to enable gene expression profiling of single cells from archival FFPE tissue using laser-capture microdissection.
研究成果
Smart-3SEQ is a highly cost-effective method for gene expression profiling that is robust to degraded RNA and compatible with FFPE tissue. Combined with LCM, it enables unprecedented studies of small cell populations and single cells, revealing new macrophage phenotypes in the tumor microenvironment.
研究不足
LCM is labor-intensive, limiting the number of cells that can be collected. RNA quality from FFPE material decreases with time, affecting the quality of data from decades-old samples. The method cannot report information about splicing or genotypes unless near the end of the gene and cannot detect nonpolyadenylated transcripts.
1:Experimental Design and Method Selection:
The study combines the template-switching SMART method and protocol optimizations of Smart-seq2 with the streamlined 3′ end–targeting approach of 3SEQ, incorporating unique molecular identifiers to increase the accuracy of transcript counting with low input amounts.
2:Sample Selection and Data Sources:
The study uses reference RNAs (ERCC Mixes 1 and 2, Human Brain Reference RNA, and Universal Human Reference RNA) and FFPE tissue samples from a mastectomy specimen with ductal carcinoma in situ (DCIS).
3:List of Experimental Equipment and Materials:
Includes ArcturusXT LCM System, CapSure HS LCM Cap, RNeasy FFPE Kit, Agilent Bioanalyzer, Illumina NextSeq 500, and MiSeq instruments.
4:Experimental Procedures and Operational Workflow:
RNA is fragmented, reverse transcribed with an oligo(dT) primer, and sequenced using Smart-3SEQ. Laser-capture microdissection is used to isolate single cells and bulk samples from FFPE tissue.
5:Data Analysis Methods:
Reads are aligned to reference sequences using NovoAlign and STAR, and transcript abundances are quantified using featureCounts. Differential gene expression is analyzed with DESeq2.
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