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UMI-count modeling and differential expression analysis for single-cell RNA sequencing

ABSTRACT: UMI-count modeling and differential expression analysis for single-cell RNA sequencing

PROVIDER: PRJNA559293 | ENA |

REPOSITORIES: ENA

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Project description:Purpose: To analyze the sensitivity and specificity of the AmpFISH method, we sequenced the NIH3T3 cell line via UMI-RNAseq experiments. Methods:NIH3T3 cells were grown in DMEM (Dulbecco’s Modified Eagle Medium, Gibco) supplemented with 10% FBS (Fetal Bovine Serum, Sigma), 50U/ml Penicillin and 50 mg/ml streptomycin (Gibco,cat.no.15070) at 37℃ with 5% CO2. Cells were treated with 0.25% trypsin solution (HyClone, No.SH42605.01) when they reached ~106 cells/ml. Then, the cells were washed with 1X PBS, and then mixed with 1ml TRIzol solution (ThermoFish, No.15596029), and snap-frozen with dry ice. Total RNA was qualitatively and quantitatively evaluated as follows: (1) the RNA sample was initially qualitatively evaluated using 1% agarose gel electrophoresis for possible contamination and degradation; (2) RNA purity and concentration were then examined using NanoPhotometer spectrophotometer; (3) RNA integrity and quantity were finally measured using RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system. After library preparation and pooling of different samples, the samples were subjected to Illumina sequencing. The libraries were sequenced using the Illumina NovaSeq 6000 Platform for 6G raw data and generated 150nt pair-end reads. UMI sequences on each read were identified by UMI-tools (1.0.0), and reads with UMIs were used for the subsequent analysis. To identify the duplicated reads, UMIs were initially removed from the UMI reads, and the remaining parts of each read were mapped to the reference genome using Hisat2. Reads that mapped to the same location on the reference genome were identified as duplicated reads. Then, the UMIs on each read were recalled, and the duplicated reads with the same UMI were identified as non-natural duplications, which were subsequently removed from the processed data. HTSeq v0.6.1 was used to count the read numbers mapped to each gene. Then, the FPKM of each gene was calculated based on the length of the gene, and the read count was mapped to the gene. Conclusions:AmpFISH provides convenient and versatile tools for sensitive RNA/DNA detection and to gain useful information on cellular molecules using simple workflows

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UMI-count modeling and differential expression analysis for single-cell RNA sequencing

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