Multiomic Sequencing Solutions To Revolutionize Human Disease Research

WHAT YOU’RE MISSING MATTERS Resolving the mechanisms underpinning human diseases is vital to understand disease phenotypes, identify novel biomarkers, and enable drug discovery. Multiomic approaches — spanning genomics, bulk and single-cell transcriptomics, epigenetics, and proteomics — are crucial to this, providing data to help unravel complex pathways. In disease research, tissue samples are invaluable and scarce resources; maximising the information obtained from these samples is essential. However, legacy multiomic methods require the use of multiple platforms and often involve complex workfl ows, lengthy turnaround times, and considerable costs. Even when combining data from multiple traditional technologies, valuable information is missed from precious research samples, leaving important biological mechanisms unresolved. ASK BOLDER QUESTIONS MAKE NO COMPROMISES LEGACY TECHNOLOGY 3 2+ 3 sequencing libraries sequencing platforms NANOPORE SEQUENCING ULTRARICH DATA sequencing library Structural variation ULTRARICH DATA Nanopore sequencing delivers... Genomic and epigenomic data from a single dataset • Directly analyse native DNA — no PCR required — to uncover genomic variants alongside methlyation status in a single nanopore sequencing run • Capture SNVs, SVs, repeat expansions, CNVs, and native epigenetic modifi cations, including 5mC and 5hmC, from a single library • Phase data with confi dence using long reads, for haplotype-level information • Avoid amplifi cation bias and access regions of the genome missed by traditional methods Powerful transcriptome and epitranscriptome analysis • Go beyond gene-level analysis: gain isoform-level expression information with full-length transcript sequencing and identify fusions with ease using native RNA or cDNA sequencing • With direct RNA sequencing, detect native RNA modifi cations including m6A and characterise isoforms without PCR bias in a single sequencing run • Measure poly-A tail length in cDNA or native RNA High-resolution transcriptome analysis for single cells • Analyse full-length transcripts from single cells to reveal biology hidden by short reads • Shed light on cellular heterogeneity with a comprehensive view of isoform diversity, alternative splicing, expressed variants, and transcripts for nonsense-mediated decay • Seamlessly integrate nanopore sequencing into your single-cell workfl ow — go from cDNA to sequencing-ready library in ~3 hours and perform cell barcode and UMI identifi cation using our EPI2ME™ software …from a single platform sequencing run separate data analyses & complex integration 1 1 1 SNVs Methylation analysis workfl ow Phasing REVEAL MORE BIOLOGY Case study: immunology T helper (Th) lymphocytes are modifi ed by epigenetic mechanisms in response to signalling factors as part of the immune response. However, analysis of epigenetic modifi cations typically involves chemical conversion such as bisulfi te treatment, which cannot distinguish 5mC from 5hmC and signifi cantly damages DNA libraries. Goldsmith et al. used targeted, PCR-free nanopore sequencing to directly profi le methylation in Th cells1. They were able to effi ciently detect both 5mC and 5hmC patterns and distinguish between Th cell subsets, illustrating a potential pathway to identify pathogenic subsets that may play an important role in disease. Case study: neurology Multiple sclerosis (MS) is a chronic neurodegenerative disease. Studies have identifi ed that DNA methylation patterns may be associated with the pathogenesis of MS, suggesting their potential for future drug targets. Si et al. used nanopore sequencing to directly detect methylation in brain samples from mice induced with experimental autoimmune encephalomyelitis (EAE), an animal model of MS2. Comparing to a control mouse group, they found 490 differentially methylated promoters. Several of these genes had been fl agged as relevant in previous MS studies, but methylation data had not been available. With this methylation data and additional metabolomic analyses, they were able to identify ‘a potential link between the dysregulation of promoter methylation and metabolome in the brain of EAE mice’. For detection of multiple modifi ed bases … most techniques require samples to be split, and different modifi ed bases to be detected separately. In the present study, we took advantage of nanopore sequencing’s ability to determine the 5mC and 5hmC simultaneously. Goldsmith et al. bioRxiv (2023)1 benefi ts of concurrent analysis of sequence identity, base modifi cations, real-time, and cost-effective generation of genome-wide data support the application of [nanopore sequencing] in studying the brain and spinal cord in different diseases Si et al. J. Neuroimmunol. (2023)2 Case study: cancer Single-cell sequencing is an important tool in acute myeloid leukaemia (AML) research, enabling the study of the impact of therapies on gene expression and identifi cation of malignant and non-malignant cell populations. Penter et al. developed a targeted single-cell nanopore sequencing method to analyse somatic nuclear mutations, isoform expression, and more in full-length transcripts3. Applying this method to AML bone marrow research samples, the team detected somatic mutations in the myeloid cell population — but also, unexpectedly, in erythroid and megakaryotic cell populations. Additional analysis revealed that these two cell compartments directly differentiate from leukemic clones, prompting the researchers to defi ne two new AML-derived expression clusters for transcriptome-based classifi cation of AML. The authors concluded that their study demonstrates how ‘a focus on myeloid progenitor populations for single cell genomic studies likely misses important AML subpopulations’. Case study: cardiovascular disease Cardiovascular disease is associated with abnormalities in human aortic smooth muscle cell (HASMC) plasticity. Aberrant splicing causes dysregulation of HASMCs and arterial contraction; however, analysis of this variation is limited with short-read sequencing, which cannot sequence full-length isoforms and relies heavily on reference transcripts, thereby missing novel transcripts. Wu et al. used nanopore direct RNA sequencing to investigate alternative splicing events in HASMCs4. Of the full-length transcripts they identifi ed, 75% were unannotated. This included CISD1-u, which was found to be involved in phenotypic switching in HASMCs, providing new insights into the mechanisms of cardiovascular disease. We present a long-read sequencing-based framework for integrative genotyping of single cell profi les that substantially improves the resolution of leukemia and immune cell phenotypes Penter et al. Nat. Commun. (2024)3 Long-read RNA-seq technology directly sequences full-length RNA transcripts, providing opportunities to precisely identify alternative splicing events Wu et al. Commun. Biol. (2023)4BR_1224(EN)_V1_12Mar2024 Why Oxford Nanopore for multiomic sequencing? • Genomic, epigenomic, and transcriptomic data from a single platform • Direct analysis of DNA and RNA — capture base modifi cations and previously hidden variants in regions inaccessible to traditional PCR-based methods • Unrestricted read lengths, from short to ultra-long (>4 Mb achieved) — sequence full-length isoforms, reveal hidden structural variants, and phase haplotypes with ease • Leave long, complex workfl ows behind — utilise simple, streamlined, and scalable end-to-end workfl ows, plus rapid turnaround times with fast library prep and real-time data streaming • Scale to your needs with powerful, fl exible sequencing devices — from multiplexed targeted sequencing to high-depth whole-genome and whole-transcriptome sequencing at the large cohort scale References 1. Goldsmith, C. et al. Single molecule DNA methylation and hydroxymethylation reveal unique epigenetic identity profi les of T helper cells. bioRxiv 527091 (2023). DOI: https://doi.org/10.1101/2023.02.03.527091 2. Si, W. et al. Nanopore sequencing identifi es differentially methylated genes in the central nervous system in experimental autoimmune encephalomyelitis. J. Neuroimmunol. 381:578134 (20