RIMS-seq2
Cytosine methylation is the primary epigenetic modification of DNA in higher eukaryotes. In humans, it predominantly occurs in CpG contexts through the action of DNA methyltransferases (DNMTs), which transfer a methyl group to cytosine to form 5-methylcytosine (m5C)[2]
This modification plays a critical role in processes such as gene expression regulation and chromatin structure. Additionally, aberrant methylation patterns are associated with the development of diseases, including carcinogenesis, establishing methylation as a universally valuable biomarker for diagnosis, prognosis, and therapeutic response [3].
Techniques to detect DNA methylation are categorized based on their underlying principles: enzymatic digestion, affinity enrichment, or chemical conversion. Among these, bisulfite sequencing is the most widely used but reduces sequence complexity and is limited to methylation analysis. Recent methods, such as TAPS and DM-seq, which selectively convert methylated cytosines, have improved data accuracy but require intricate protocols [4].
The RIMS-seq2 approach aims to simplify simultaneous genome and methylome analysis by introducing a controlled chemical deamination step [5].
RIMS-seq2 (Reduced Integration of Methylation and Sequencing 2) is a cutting-edge DNA sequencing technology designed to simultaneously evaluate both the genome and the methylome in a single experimental setup. This innovative method offers a streamlined approach to assess 5-methylcytosines (5mC) without relying on traditional bisulfite sequencing. Unlike conventional methods that degrade DNA and reduce sequencing accuracy, RIMS-seq2 minimizes chemical degradation and improves sequence fidelity. Its ability to capture both genetic and epigenetic information simultaneously makes it a revolutionary tool for multi-omics studies, particularly in fields like cancer biology, neurodegenerative disease research, and developmental biology.[5][6][7][8][9]
History and Development
The development of RIMS-seq2 stems from challenges faced by earlier techniques such as Whole Genome Bisulfite Sequencing (WGBS). These methods were plagued by excessive degradation of DNA, high costs, and limited sequencing accuracy. To address these limitations, a team led by researchers Bo Yan and Laurence Ettwiller at New England Biolabs (NEB) built upon the original RIMS-seq method (developed for bacterial genomes) to create RIMS-seq2 for human genomic research [5]
Published in 2024, the RIMS-seq2 technique introduces significant improvements, including controlled deamination of methylated cytosines that preserve the integrity of DNA while providing a high-resolution view of methylation patterns. This advancement has enabled researchers to apply the method to complex eukaryotic genomes with greater efficiency and accuracy, paving the way for broader applications in human health and disease studies [5]
Methodology
RIMS-seq2 employs a unique approach to sequence-specific methylation detection, integrating a modified deamination protocol with high-throughput sequencing technologies[5]. The methodology can be summarized as follows:
1. Sample Preparation:
The initial step in RIMS-seq2 involves the extraction of high-quality genomic DNA from the target organism or tissue. This DNA must undergo purification processes to ensure that it is free from contaminants such as proteins, lipids, and RNA that could degrade the sample or interfere with subsequent steps. The integrity and quality of the DNA are assessed before proceeding with the following stages. The clean DNA is critical for generating accurate and reproducible sequencing data, as degraded or contaminated DNA can compromise the reliability of the results. Extraction of high-quality genomic DNA from the target organism or tissue. Purification steps to ensure minimal contamination and degradation of the DNA [5].
2. Controlled Deamination Process:
The central innovation of RIMS-seq2 lies in its controlled deamination step. This process selectively targets unmethylated cytosines (C) and converts them into thymine (T), while leaving methylated cytosines (5mC) intact. This reaction is achieved through a mild chemical treatment that prevents the degradation of the DNA, a common issue in traditional bisulfite sequencing. By reading the unmethylated cytosines as thymine during sequencing, researchers can accurately infer the methylation status of each cytosine. This approach significantly reduces the degradation of DNA, ensuring that higher-quality data is obtained compared to bisulfite methods [5].
3. Library Preparation and Sequencing:
After the deamination step, the DNA undergoes standard library preparation, where the DNA is fragmented into smaller pieces (typically 150–300 base pairs). Sequencing adapters are ligated to the ends of these fragments to facilitate amplification and sequencing. The prepared libraries are then subjected to high-throughput sequencing, usually using Illumina platforms. These sequencing platforms generate millions of short DNA reads, which are mapped to a reference genome. This mapping allows for the identification of genetic variants (such as SNPs and small insertions/deletions) as well as the methylation status of cytosines across the genome [5].
4. Bioinformatic Analysis:
Once the sequencing data is obtained, bioinformatic analysis plays a crucial role in interpreting the results. Specialized software tools are used to align the sequence reads to a reference genome and identify genetic variants. Additionally, these tools help integrate the methylation data with genomic data, enabling the identification of differentially methylated regions (DMRs). By correlating these methylation changes with genetic variations, researchers can uncover insights into gene regulation, disease mechanisms, and how genetic and epigenetic factors influence one another [5].
5. Validation:
To ensure the accuracy and reproducibility of the data, RIMS-seq2 includes a validation step. This is typically done by using external controls and benchmark datasets to compare the results obtained from RIMS-seq2 with established techniques such as bisulfite sequencing or methylation-specific PCR. The method has been shown to produce high-quality, reproducible data while reducing the costs and time associated with traditional bisulfite sequencing. By validating the methylation calls and ensuring that the data is accurate, RIMS-seq2 becomes a reliable tool for genomic and epigenomic research [5].
Applications
RIMS-seq2 has broad applications in various research fields, including:
Cancer Research:
Mapping methylation changes associated with tumor progression and metastasis. Identifying epigenetic biomarkers for early diagnosis and prognosis [5].
Neurodegenerative Diseases:
Studying DNA methylation in Alzheimer’s, Parkinson’s, and other neurological conditions. Understanding how epigenetic dysregulation contributes to disease mechanisms [5].
Developmental Biology:
Investigating methylation dynamics during embryogenesis and cell differentiation. Exploring how methylation influences gene expression in stem cells [5].
Multi-omics Studies:
RIMS-seq2 integrates with transcriptomics and proteomics to provide a holistic view of cellular processes. Enables the study of genome-epigenome interactions in complex diseases [5].
Precision Medicine:
Personalized therapies targeting specific methylation patterns in cancer or genetic disorders [5].
Limitations of the RIMS-seq2 Technique
RIMS-seq2 is a breakthrough technique, streamlining the integration of methylation and genomic data in a single experiment while addressing several shortcomings of bisulfite sequencing. Nevertheless, its reliance on high-quality samples, computational intensity, and incomplete validation across diverse organisms and applications present hurdles that researchers must navigate. As the methodology continues to evolve, ongoing improvements and adaptations are expected to expand its usability and reliability, ensuring its place as a valuable tool in the field of epigenomics [5][11][12]
However, like all scientific methodologies, it has inherent limitations that could impact its applicability and effectiveness in certain contexts [5]. Below is a comprehensive discussion of these limitations:
Bias in Methylation Detection
While RIMS-seq2 eliminates the extensive degradation caused by bisulfite sequencing, the technique still introduces a bias related to the efficiency of controlled deamination. The chemical conversion of unmethylated cytosines to uracil may not be entirely complete, leading to false negatives. Additionally, methylation in non-CpG contexts (e.g., CHG or CHH) remains less characterized with this method.[5]
DNA Sample Quality Requirements
RIMS-seq2 requires high-quality, intact DNA to achieve reliable results. Although it reduces the destructive steps of bisulfite sequencing, degraded samples such as those from FFPE tissue, ancient DNA, or low-input samples might still pose challenges.[5]
Limited Validation Across Organisms
Most of the validation and applications of RIMS-seq2 described in the article focus on the human genome and bacterial genomes. While promising for multi-omics in humans, its effectiveness in other species, particularly those with large, complex, or repetitive genomes (e.g., plants), has not been fully validated.[5]
Computational Burden
RIMS-seq2 generates vast datasets that integrate genome and methylome information. The complexity of processing and analyzing such integrated data is a limitation, especially for laboratories without advanced computational resources or expertise.[5]
Unexplored Long-Read Applications
RIMS-seq2 has been optimized for short-read sequencing platforms. However, the rise of long-read sequencing technologies such as those from PacBio or Oxford Nanopore offers advantages in resolving structural variations and repetitive regions.[5]
See also
References
- ↑ Valente, Ana; Vieira, Luís; Silva, Maria João; Ventura, Célia (2023-01-01). "The Effect of Nanomaterials on DNA Methylation: A Review". Nanomaterials. 13 (12): 1880. doi:10.3390/nano13121880. ISSN 2079-4991. PMC 10305477 Check
|pmc=value (help). PMID 37368308 Check|pmid=value (help). - ↑ Simpson, Jared T; Workman, Rachael E; Zuzarte, P C; David, Matei; Dursi, L J; Timp, Winston (2017-04-01). "Detecting DNA cytosine methylation using nanopore sequencing". Nature Methods. 14 (4): 407–410. doi:10.1038/nmeth.4184. ISSN 1548-7091. PMID 28218898.
- ↑ Weber, Michael; Hellmann, Ines; Stadler, Michael B; Ramos, Liliana; Pääbo, Svante; Rebhan, Michael; Schübeler, Dirk (2007-04-01). "Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome". Nature Genetics. 39 (4): 457–466. doi:10.1038/ng1990. ISSN 1061-4036. PMID 17334365.
- ↑ Shiraki, Yutaka; Shibata, Naohiro; Doi, Yohei; Arakawa, Yoshichika (2004-01-02). "Escherichia coliProducing CTX-M-2 β-Lactamase in Cattle, Japan". Emerging Infectious Diseases. 10 (1): 69–75. doi:10.3201/eid1001.030219. ISSN 1080-6040. PMC 3322752. PMID 15078599.
- ↑ 5.00 5.01 5.02 5.03 5.04 5.05 5.06 5.07 5.08 5.09 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20 5.21 Yan, Bo; Wang, Duan; Ettwiller, Laurence (2024-07-23). "Simultaneous assessment of human genome and methylome data in a single experiment using limited deamination of methylated cytosine". Genome Research. 34 (6): 904–913. doi:10.1101/gr.278294.123. ISSN 1549-5469. PMC 11293541 Check
|pmc=value (help). PMID 38858087 Check|pmid=value (help). - ↑ Yan, Bo; Wang, Duan; Vaisvila, Romualdas; Sun, Zhiyi; Ettwiller, Laurence (2022). "Methyl-SNP-seq reveals dual readouts of methylome and variome at molecule resolution while enabling target enrichment". Genome Research. 32 (11–12): 2079–2091. doi:10.1101/gr.277080.122. ISSN 1549-5469. PMC 9808626 Check
|pmc=value (help). PMID 36332968 Check|pmid=value (help). - ↑ Gokhman, David; Lavi, Eitan; Prüfer, Kay; Fraga, Mario F.; Riancho, José A.; Kelso, Janet; Pääbo, Svante; Meshorer, Eran; Carmel, Liran (2014-05-02). "Reconstructing the DNA Methylation Maps of the Neandertal and the Denisovan". Science. 344 (6183): 523–527. Bibcode:2014Sci...344..523G. doi:10.1126/science.1250368. ISSN 0036-8075. PMID 24786081.
- ↑ Rand, Arthur C; Jain, Miten; Eizenga, Jordan M; Musselman-Brown, Audrey; Olsen, Hugh E; Akeson, Mark; Paten, Benedict (2017-04-15). "Mapping DNA methylation with high-throughput nanopore sequencing". Nature Methods. 14 (4): 411–413. doi:10.1038/nmeth.4189. ISSN 1548-7091. PMC 5704956. PMID 28218897.
- ↑ Olova, Nelly; Krueger, Felix; Andrews, Simon; Oxley, David; Berrens, Rebecca V.; Branco, Miguel R.; Reik, Wolf (2018-12-11). "Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data". Genome Biology. 19 (1): 33. doi:10.1186/s13059-018-1408-2. ISSN 1474-760X. PMC 5856372. PMID 29544553.
- ↑ Ramazi, Shahin; Dadzadi, Maedeh; Sahafnejad, Zahra; Allahverdi, Abdollah (2023). "Epigenetic regulation in lung cancer". MedComm. 4 (6): e401. doi:10.1002/mco2.401. ISSN 2688-2663. PMC 10600507 Check
|pmc=value (help). PMID 37901797 Check|pmid=value (help). - ↑ Weber, Michael; Davies, Jonathan J; Wittig, David; Oakeley, Edward J; Haase, Michael; Lam, Wan L; Schübeler, Dirk (2005-08-01). "Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells". Nature Genetics. 37 (8): 853–862. doi:10.1038/ng1598. ISSN 1061-4036. PMID 16007088.
- ↑ Vaisvila, Romualdas; Ponnaluri, V.K. Chaithanya; Sun, Zhiyi; Langhorst, Bradley W.; Saleh, Lana; Guan, Shengxi; Dai, Nan; Campbell, Matthew A.; Sexton, Brittany S.; Marks, Katherine; Samaranayake, Mala; Samuelson, James C.; Church, Heidi E.; Tamanaha, Esta; Corrêa, Ivan R. (2021-07-01). "Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA". Genome Research. 31 (7): 1280–1289. doi:10.1101/gr.266551.120. ISSN 1088-9051. PMC 8256858 Check
|pmc=value (help). PMID 34140313 Check|pmid=value (help).
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