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基于抗体优化的低起始量样本改良m6A测序的比较分析

Comparative analysis of improved m6A sequencing based on antibody optimization for low-input samples.

作者信息

Lu Jiafeng, Xia Wenjuan, Li Jincheng, Zhang Liya, Qian Chunfeng, Li Hong, Huang Boxian

机构信息

State Key Laboratory of Reproductive Medicine, Suzhou Municipal Hospital, Suzhou Affiliated Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, Suzhou, 215002, China.

出版信息

Sci Rep. 2025 Jan 7;15(1):1058. doi: 10.1038/s41598-025-85150-8.

DOI:10.1038/s41598-025-85150-8
PMID:39774984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11707076/
Abstract

The most effective method for mapping N6-methyladenosine (mA) is mA RNA immunoprecipitation sequencing (MeRIP-seq). The quality of MeRIP-seq relies on various factors, with the anti-mA antibody being a crucial determinant. However, comprehensive research on anti-mA antibody selection and optimal concentrations for different tissues has been limited. In this study, we optimized the concentration of five different anti-mA antibodies across various tissues. Our findings demonstrated that 5 µg of Millipore antibodies (ABE572 and MABE1006) performed well, starting from 15 µg total RNA from the liver, while 1.25 µg of Cell Signaling Technology antibodies (CST) (#56593) was suitable for low-input total RNA. In summary, we provide a significant guideline for anti-mA antibody selection in MeRIP sequencing for different tissues, especially in the context of low-input RNA.

摘要

用于绘制N6-甲基腺嘌呤（mA）图谱的最有效方法是mA RNA免疫沉淀测序（MeRIP-seq）。MeRIP-seq的质量取决于多种因素，其中抗mA抗体是一个关键决定因素。然而，关于抗mA抗体选择以及不同组织最佳浓度的全面研究一直有限。在本研究中，我们针对各种组织优化了五种不同抗mA抗体的浓度。我们的研究结果表明，5 µg的密理博抗体（ABE572和MABE1006）表现良好，起始肝脏总RNA量为15 µg，而1.25 µg的细胞信号技术公司（CST）抗体（#56593）适用于低起始量总RNA。总之，我们为不同组织的MeRIP测序中抗mA抗体的选择提供了重要指导，尤其是在低起始量RNA的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/31d16cc9520e/41598_2025_85150_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/afc71084e2eb/41598_2025_85150_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/b0d4fdf0a0bd/41598_2025_85150_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/b2bbe93f4601/41598_2025_85150_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/91bccaa1ad82/41598_2025_85150_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/31d16cc9520e/41598_2025_85150_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/afc71084e2eb/41598_2025_85150_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/b0d4fdf0a0bd/41598_2025_85150_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/b2bbe93f4601/41598_2025_85150_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/91bccaa1ad82/41598_2025_85150_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4946/11707076/31d16cc9520e/41598_2025_85150_Fig5_HTML.jpg

相似文献

[1]
Comparative analysis of improved m6A sequencing based on antibody optimization for low-input samples.

Sci Rep. 2025-1-7

[2]
A low-cost, low-input method establishment for m6A MeRIP-seq.

Biosci Rep. 2024-1-31

[3]
Refined RIP-seq protocol for epitranscriptome analysis with low input materials.

PLoS Biol. 2018-9-13

[4]
MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq.

BMC Bioinformatics. 2020-3-14

[5]
mA RNA Immunoprecipitation Followed by High-Throughput Sequencing to Map N-Methyladenosine.

Methods Mol Biol. 2022

[6]
Detection of N6-methyladenosine in SARS-CoV-2 RNA by methylated RNA immunoprecipitation sequencing.

STAR Protoc. 2022-3-18

[7]
Identification and comparison of m6A modifications in glioblastoma non-coding RNAs with MeRIP-seq and Nanopore dRNA-seq.

Epigenetics. 2023-12

[8]
Mapping mA at Individual-Nucleotide Resolution Using Crosslinking and Immunoprecipitation (miCLIP).

Methods Mol Biol. 2017

[9]
Profiling of RNA N6-Methyladenosine methylation reveals the critical role of m6A in betaine alleviating hepatic steatosis.

Sci Rep. 2025-3-1

[10]
trumpet: transcriptome-guided quality assessment of mA-seq data.

BMC Bioinformatics. 2018-7-13

本文引用的文献

[1]
MiR-138 is a potent regulator of the heterogenous MYC transcript population in cancers.

Oncogene. 2022-2

[2]
Tumor-associated macrophages promote PD-L1 expression in tumor cells by regulating PKM2 nuclear translocation in pancreatic ductal adenocarcinoma.

Oncogene. 2022-2

[3]
PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

Nucleic Acids Res. 2021-7-2

[4]
The N6-methyladenosine RNA-binding protein YTHDF1 modulates the translation of TRAF6 to mediate the intestinal immune response.

Nucleic Acids Res. 2021-6-4

[5]
N6-methyladenosine demethyltransferase FTO-mediated autophagy in malignant development of oral squamous cell carcinoma.

Oncogene. 2021-6

[6]
YTHDF2 is a potential target of AML1/ETO-HIF1α loop-mediated cell proliferation in t(8;21) AML.

Oncogene. 2021-6

[7]
METTL3-mediated mA is required for murine oocyte maturation and maternal-to-zygotic transition.

Cell Cycle. 2020-2

[8]
Tissue-specific transcription reprogramming promotes liver metastasis of colorectal cancer.

Cell Res. 2020-1

[9]
Landscape and Regulation of mA and mAm Methylome across Human and Mouse Tissues.

Mol Cell. 2019-10-29

[10]
DART-seq: an antibody-free method for global mA detection.

Nat Methods. 2019-9-23

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