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Series GSE174518 Query DataSets for GSE174518
Status Public on Jun 01, 2023
Title Nm-Mut-seq: The base-resolution quantitative method for mapping transcriptome-wide 2’-O-methylations
Organisms Homo sapiens; synthetic construct
Experiment type Expression profiling by high throughput sequencing
Other
Summary 2’-O-methylation (Nm) is a prevalent post-transcriptional RNA modification present in many cellular RNAs and plays a critical role in modulating both the physical properties and regulation of eukaryotic RNAs. Studies of Nm modifications in RNA have long been hampered by a lack of effective mapping methods. Previously reported approaches can work well for detecting Nm modifications on abundant RNAs, but face challenges when applied to low-abundant RNAs, such as mRNA, lack stoichiometric information, and are challenged by issues of RNA sample degradation due to chemical treatment. Here, we present Nm-Mut-seq, a mutation signature-based Nm mapping method, which uses a custom reverse transcriptase (RT) that installs mutations at Am, Cm, and Gm-modified sites (Um is undetectable by this method). Our work provides a much-needed approach to detect Nm at base resolution in low abundant RNAs and to estimate the stoichiometry of each modified site transcriptome-wide.
 
Overall design [dataset1] 15 samples (Sample 1-15): Duplicates or Triplicates for "Input" and "Treated" samples in Nm-Mut-seq to detect Nm in total RNA and polyA+ RNA from wild-type HeLa and HepG2 cells. Note that RNA 3'-adaptor sequence is 5’rApp-NNNNN ATCACG AGATCGGAAGAGCACACGTCT-3SpC3; RNA 5'-adaptor sequence is 5’-GUUCAGAGUUCUACAGUCCGACGAUC NNNNN-3'.

[datase2] 20 samples (Sample 16-35): Duplicates for "Input" and "Treated" samples in Nm-Mut-seq to depict Nm calibration curves for estimating Nm methylation fractions.

[datase3] 18 samples (Sample 36-53): Triplicates for Nm-Mut-seq samples to reveal Nm methylation level change in total RNA and polyA+ RNA from siFBL or siFTSJ3 HepG2 cells versus siControl. Note that 5’rApp-NNNNN ATCACG AGATCGGAAGAGCACACGTCT-3SpC3 is for siFBL polyA+ RNA samples (Sample 48, 49, and 50); 5’rApp-NNNNN TTAGGC AGATCGGAAGAGCACACGTCT-3SpC3 is for all the rest samples in dataset3 (Sample 36-47, and Sample 51-53). RNA 5'-adaptor sequence is always 5’-GUUCAGAGUUCUACAGUCCGACGAUC NNNNN-3' for Sample 36-53.
 
Contributor(s) Chen L, Zhang L, Ye C, Dickinson B, He C
Citation(s) 37316584
Submission date May 17, 2021
Last update date Nov 06, 2023
Contact name Li-Sheng Zhang
E-mail(s) zhangls@ust.hk
Phone (852) 3469 2392
Organization name The University of Chicago
Department Department of Chemistry
Lab Prof. Chuan He Lab
Street address 929 E 57th St Rm E313
City Chicago
State/province IL
ZIP/Postal code 60637
Country USA
 
Platforms (2)
GPL24676 Illumina NovaSeq 6000 (Homo sapiens)
GPL26526 Illumina NovaSeq 6000 (synthetic construct)
Samples (53)
GSM5318810 Sample 1: HeLa_Total_RNA_Input
GSM5318811 Sample 2: HeLa_Total_RNA_Treated_rep1
GSM5318812 Sample 3: HeLa_Total_RNA_Treated_rep2
Relations
BioProject PRJNA730412
SRA SRP320050

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE174518_HeLa_WT_polyA-RNA_Nm.xlsx 410.7 Kb (ftp)(http) XLSX
GSE174518_Hela_WT_rRNA.xlsx 50.9 Kb (ftp)(http) XLSX
GSE174518_HepG2_WT_polyA-RNA_Nm.xlsx 437.2 Kb (ftp)(http) XLSX
GSE174518_HepG2_rRNA_siFBL.xlsx 43.5 Kb (ftp)(http) XLSX
GSE174518_HepG2_siFBL_polyA-RNA_Nm.xlsx 254.1 Kb (ftp)(http) XLSX
GSE174518_HepG2_siFTSJ3_polyA-RNA_Nm.xlsx 23.9 Kb (ftp)(http) XLSX
GSE174518_Nm_Calibration-curves.xlsx 134.1 Kb (ftp)(http) XLSX
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