Optimizing Single-Cell Long-Read Sequencing for Enhanced Isoform Detection in Pancreatic Islets

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenomeand epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

A number of next-generation sequencing (NGS) technologies such as Roche/454, Illumina and AB SOLiD have recently become available. These technologies are capable of generating hundreds of thousands or tens of millions of short DNA sequence reads at a relatively low cost. These NGS technologies, now referred as second-generation sequencing (SGS) technologies, are being utilized for de novo sequencing, genome re-sequencing, and whole genome and transcriptome analysis. Now, new generation of sequencers, based on the 'next-next' or third-generation sequencing (TGS) technologies like the Single-Molecule Real-Time (SMRT™) Sequencer, Heliscope™ Single Molecule Sequencer, and the Ion Personal Genome Machine™ are becoming available that are capable of generating longer sequence reads in a shorter time and at even lower costs per instrument run. Ever declining sequencing costs and increased data output and sample throughput for NGS and TGS sequencing technologies enable the plant genomics and breeding community to undertake genotyping-by-sequencing (GBS). Data analysis, storage and management of large-scale second or TGS projects, however, are essential. This article provides an overview of different sequencing technologies with an emphasis on forthcoming TGS technologies and bioinformatics tools required for the latest evolution of DNA sequencing platforms.

The last two decades have seen tremendous advancements in tools like CRISPR, single-molecule sequencing technologies, optical genome mapping, single-cell omics techniques, etc. Genomic analyses of multiple individuals become important to address medical questions and advances in genomic technologies and methods directly contribute to improved outcomes in disease research and healthcare. Genome-wide association studies conducted in the past inform us about the limitations of technologies in analyzing factors contributing to human traits and diseases using only short-read amplicon sequencing technologies, especially in repetitive genomic stretches where the likelihood of structural variations is higher. While short-read sequencing is a highly evolved technology, the discovery of causative variants with it alone is impeded by its read length and depending on the assay, is limited to gene coding regions, and less penetrative variants are often missed. In a recent report, it was found that only about one in three known genetic disorders in children have been diagnosed successfully with short-read sequencing (SRS, including whole exome and whole genome assays). On the other hand, building evidence that variants occurring in extragenic regions are found to be driving mutations for diseases shows that there is a need to capture both intergenic and extragenic information. Long-read platforms including optical genome mapping and Pacific Biosciences (PacBio) or Oxford Nanopore Technology (ONT) sequencers have advantages like improved contiguity, long-range haplotype phasing, and therefore, identification of structural variants associated with diseases, especially where alleles extend beyond typical read length. They are better suited to capture large structural variations, their genotypes, and copy number alterations but some practical challenges, like lack of base-level resolution for optical mapping, high error rates, cost per sample, low throughput, and resource-heavy informatics analysis for long-read sequencing, prevent routine adoption long-read platforms. Targeted interrogation of the genome in lieu of the whole genome alleviates some of the above challenges. CRISPR-Cas9-based targeting methods offer programmability and thus are being increasingly used for long-read platforms. This thesis discusses novel applications of CRISPR-Cas9 nickase-based experimental methods for targeted interrogation of genomic features on long-read sequencing and optical mapping platforms. First, the linked-pair sequencing strategy enabled by CRISPR-Cas9n chemistry is described. Here, orderly fragmentation of DNA molecules is performed using CRISPR-Cas9n and pairs of sgRNA in such a way that produced fragments share identical termini. This strategy is capable of greater multiplicity and can assemble critical genetic loci effectively. The validity and efficacy of the method for sequencing were first demonstrated on a lambda phage model and Haemophilus influenzae model, and later for sequencing a cancer panel in the human genome containing 100 full-length gene sequences. When the designed linker sequences contained heterozygous genetic variants, long haplotypes could be established. These whole-gene haplotypes enable the study of genetic variants at non-coding regulatory elements and the detection of any allele-specific effects. The use of Cas9n to introduce nicks allowed for preferential ligation of sequencing adapters resulting in significant enrichment of target regions. Second, multicolor whole genome labeling for optical mapping technique, enabled by CRISPR-Cas9n, is described. Here, Cas9n is used to make directed nicks at target sites to incorporate fluorophores. This technique was used in the detection and quantification of known biomarkers (telomeres and D4Z4 units) and in finding the locations of LINE-1 insertions across the genome With Cas9-mediated nick-labeling, it is possible to target and fluorescently label any 20mer or the combination of multiple 20mers across the whole genome, especially in repetitive regions lacking DLE motifs. Custom maps can be generated to enable precise detection of breakpoints and interrogate the repetitive sequences; this enables more in-depth analysis of SVs than was previously possible. Application of this method in the interrogation of exogenic DNA integrations in the host genome for gene therapy validation and safety assessment is also demonstrated. Finally, additional applications of Cas9n chemistry using both long-read sequencing and optical genome mapping platforms together are demonstrated. Multicolor Cas9n-mediated labeling chemistry is leveraged to create high-density optical maps with an increased resolution for use in structural variant analysis, calling breakpoints, and scaffolding genome assemblies. Such high-density optical maps can also be used to study interactions of Cas9 with target DNA.

Structural variation (SV) is typically defined as variation within the human genome that exceeds 50 base pairs (bp). SV may be copy number neutral or it may involve duplications, deletions, and complex rearrangements. Recent studies have shown SV to be associated with many human diseases. However, studies of SV have been challenging due to technological constraints. With the advent of third generation (long-read) sequencing technology, exploration of longer stretches of DNA not easily examined previously has been made possible. In the present study, we utilized third generation (long-read) sequencing techniques to examine SV in the EGFR landscape of four haplotypes derived from two human samples. We analyzed the EGFR gene and its landscape (+/- 500,000 base pairs) using this approach and were able to identify a region of non-coding DNA with over 90% similarity to the most common activating EGFR mutation in non-small cell lung cancer. Based on previously published Alu-element genome instability algorithms, we propose a molecular mechanism to explain how this non-coding region of DNA may be interacting with and impacting the stability of the EGFR gene and potentially generating this cancer-driver gene. By these techniques, we were also able to identify previously hidden structural variation in the four haplotypes and in the human reference genome (hg38). We applied previously published algorithms to compare the relative stabilities of these five different EGFR gene landscape haplotypes to estimate their relative potentials to generate the EGFR exon 19, 15 bp canonical deletion. To our knowledge, the present study is the first to use the differences in genomic architecture between targeted cancer-linked phased haplotypes to estimate their relative potentials to form a common cancer-linked driver mutation.

Accurate long-read sequencing allows assembly of the duplicated RHD and RHCE genes harboring variants relevant to blood transfusion

Neurodegenerative diseases exhibit chronic progressive lesions in the central and peripheral nervous systems with unclear causes. The search for pathogenic mutations in human neurodegenerative diseases has benefited from massively parallel short-read sequencers. However, genomic regions, including repetitive elements, especially with high/low GC content, are far beyond the capability of conventional approaches. Recently, long-read single-molecule DNA sequencing technologies have emerged and enabled researchers to study genomes, transcriptomes, and metagenomes at unprecedented resolutions. The identification of novel mutations in unresolved neurodegenerative disorders, the characterization of causative repeat expansions, and the direct detection of epigenetic modifications on naive DNA by virtue of long-read sequencers will further expand our understanding of neurodegenerative diseases. In this article, we review and compare 2 prevailing long-read sequencing technologies, Pacific Biosciences and Oxford Nanopore Technologies, and discuss their applications in neurodegenerative diseases.

Marine cone snail venoms represent a vast library of bioactive peptides with proven potential as research tools, drug leads, and therapeutics. In this study, a transcriptome library of four different organs, namely radular sheath, venom duct, venom gland, and salivary gland, from piscivorous Conus striatus was constructed and sequenced using both Illumina next-generation sequencing (NGS) and PacBio third-generation sequencing (TGS) technologies. A total of 428 conotoxin precursor peptides were retrieved from these transcriptome data, of which 413 conotoxin sequences assigned to 13 gene superfamilies, and 15 conotoxin sequences were classified as unassigned families. It is worth noting that there were significant differences in the diversity of conotoxins identified from the NGS and TGS data: 82 conotoxins were identified from the NGS datasets while 366 conotoxins from the TGS datasets. Interestingly, we found point mutations in the signal peptide sequences of some conotoxins with the same mature sequence. Therefore, TGS broke the traditional view of the conservation of conotoxin signal peptides and the variability of mature peptides obtained by NGS technology. These results shed light on the integrated NGS and TGS technologies to mine diverse conotoxins in Conus species, which will greatly contribute to the discovery of novel conotoxins and the development of new marine drugs.

BackgroundRenal fibrosis, characterized by the abnormal accumulation of extracellular matrix in renal tissue and progressive loss of kidney function, is posing a significant challenge in clinical treatment. While several therapeutic options exist, effective treatments remain limited. Inonotus obliquus (Chaga), a traditional medicinal mushroom, has shown promising effects in chronic kidney disease (CKD), yet its cellular and molecular mechanisms remain largely unexplored.MethodsWe analysed the chemical composition of Chaga using UPLC-MS and predicted its biological targets using PubChem and Swiss Target Prediction. We used single-cell RNA sequencing to study cellular responses in a mouse model of folic acid-induced renal fibrosis, complemented by spatial transcriptomics to map cellular location patterns. Histological assessment was performed using H&E and Masson trichrome staining.ResultsFor the first time, we employed single-cell RNA sequencing technology to investigate Chaga treatment in renal fibrosis. Histological analysis revealed that Chaga treatment significantly reduced renal tubular damage scores [from 5.00 (5.00, 5.00) to 2.00 (2.00, 2.00), p < 0.05] and decreased collagen deposition area (from 11.40% ± 3.01% to 4.06% ± 0.45%, p < 0.05) at day 14. Through analysis of 82,496 kidney cells, we identified 30 distinct cell clusters classified into eight cell types. Key findings include the downregulation of pro-inflammatory M1 macrophages and upregulation of anti-inflammatory M2 macrophages, alongside decreased T cell responses. Single-cell sequencing revealed differential gene expression in proximal tubular subpopulations associated with reduced fibrosis. Pathway and network pharmacology analyses of 60 identified compounds in Chaga and their 675 predicted targets suggested potential effects on immune and fibrotic pathways, particularly affecting Tregs and NKT cells. Cell-to-cell communication analyses revealed potential interactions between proximal tubular cells, macrophages, and T Cells, providing insights into possible mechanisms by which Chaga may ameliorate renal fibrosis.ConclusionOur study provided new insights into the potential therapeutic effects of Chaga in renal fibrosis through single-cell sequencing analysis. Our findings suggest that Chaga may represent a promising candidate for renal fibrosis treatment, though further experimental validation is needed to establish its clinical application.

Targeted PCR amplification and high-throughput sequencing (amplicon sequencing) of 16S rRNA gene fragments is widely used to profile microbial communities. New long-read sequencing technologies can sequence the entire 16S rRNA gene, but higher error rates have limited their attractiveness when accuracy is important. Here we present a high-throughput amplicon sequencing methodology based on PacBio circular consensus sequencing and the DADA2 sample inference method that measures the full-length 16S rRNA gene with single-nucleotide resolution and a near-zero error rate. In two artificial communities of known composition, our method recovered the full complement of full-length 16S sequence variants from expected community members without residual errors. The measured abundances of intra-genomic sequence variants were in the integral ratios expected from the genuine allelic variants within a genome. The full-length 16S gene sequences recovered by our approach allowed Escherichia coli strains to be correctly classified to the O157:H7 and K12 sub-species clades. In human fecal samples, our method showed strong technical replication and was able to recover the full complement of 16S rRNA alleles in several E. coli strains. There are likely many applications beyond microbial profiling for which high-throughput amplicon sequencing of complete genes with single-nucleotide resolution will be of use.

The first generation sequencing technology based on the dideoxynucleotide (ddNTP) chain termination method proposed by Sanger was gradually eliminated due to its high cost, low sequencing read length and cumbersome process. The second generation of sequencing technology, called High-throughput sequencing (HTS), which was then developed, still has the problem of fixed read length. In recent years, the third generation sequencing technology represented by SMRT technology and Nanopore sequencing technology has gradually become popular. Compared with the previous two generations of sequencing technology, the most significant advantage of the third generation sequencing technology is its ability to carry out single molecule sequencing. In this process, the infinite length of nucleic acid sequence can be determined theoretically without the help of PCR amplification. This paper first introduces the basic principles, advantages and disadvantages of third-generation sequencing, and introduces in detail the CRISPR/Cas family-mediated SMRT technology and Nanopore sequencing technology in the third-generation sequencing technology. Finally, the research progress and prospects of the combination of the third generation sequencing technology and gene editing technology in the future are analyzed.

BackgroundLung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing. Emerging long-read sequencing technologies are a promising alternative to study somatic structural variants, however there is no current consensus on how to process data and call somatic events. In this study, we preformed whole genome sequencing of lung cancer and matched non-tumour samples using long and short read sequencing to comprehensively benchmark three sequence aligners and seven structural variant callers comprised of generic callers (SVIM, Sniffles2, DELLY in generic mode and cuteSV) and somatic callers (Severus, SAVANA, nanomonsv and DELLY in somatic modes).ResultsDifferent combinations of aligners and variant callers influenced somatic structural variant detection. The choice of caller had a significant influence on somatic structural variant detection in terms of variant type, size, sensitivity, and accuracy. The performance of each variant caller was assessed by comparing to somatic structural variants identified by short-read sequencing. When compared to somatic structural variants detected with short-read sequencing, more events were detected with long-read sequencing. The mean recall of somatic variant events identified by long-read sequencing was higher for the somatic callers (72%) than generic callers (53%). Among the somatic callers when using the minimap2 aligner, SAVANA and Severus achieved the highest recall at 79.5% and 79.25% respectively, followed by nanomonsv with a recall of 72.5%.ConclusionLong-read sequencing can identify somatic structural variants in clincal samples. The longer reads have the potential to improve our understanding of cancer development and inform personalized cancer treatment.

Single Cell Genotype-Phenotype Analysis Demonstrates That Post-MPN AML Is Driven By Clonal Evolution and Loss of Transcriptional Heterogeneity within Stem Cell Compartments

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

BackgroundWith the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.ResultsIn this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.ConclusionsIn summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology through the comprehensive identification and quantification of full-length mRNA isoforms. Despite great promise, challenges remain in the widespread implementation of LRS technologies for RNA-based applications, including concerns about low coverage, high sequencing error, and robust computational pipelines. Although much focus has been placed on defining mRNA exon composition and structure with LRS data, less careful characterization has been done of the ability to assess the terminal ends of isoforms, specifically, transcription start and end sites. Such characterization is crucial for completely delineating full mRNA molecules and regulatory consequences. However, there are substantial inconsistencies in both start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. Here, we describe the specific challenges of identifying and quantifying mRNA terminal ends with LRS technologies and how these issues influence biological interpretations of LRS data. We then review recent experimental and computational advances designed to alleviate these problems, with ideal use cases for each approach. Finally, we outline anticipated developments and necessary improvements for the characterization of terminal ends from LRS data.