High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors

Shiau, Cheng-Kai; Lu, Liping; Kieser, Rachel; Fukumura, Kazutaka; Pan, Timothy; Lin, Hsiao‐Yun; Yang, Jie; Tong, Eric L.; Lee, GaHyun; Yan, Yuanqing; Huse, Jason T.; Gao, Ruli

doi:10.1038/s41467-023-39813-7

Cited by 19 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, single-cell analysis, even combined to LRS, is mainly used to investigate the biological complexity of cell types and tissues, especially in cancer, with studies primarily aimed at developing efficient and accurate analytical methods ( Hård et al, 2023 ; Shiau et al, 2023 ; Wu and Schmitz, 2023 ; You et al, 2023 ). At present, published work in single-cell genomics remains firmly in the research space and exploration of clinical relevance, especially in the fields of oncology, immunology, and hematology, is still at an early stage ( Kim N. et al, 2021 ; Lim et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Long read sequencing on its way to the routine diagnostics of genetic diseases

Olivucci,

Iovino,

Innella

et al. 2024

Front. Genet.

View full text Add to dashboard Cite

The clinical application of technological progress in the identification of DNA alterations has always led to improvements of diagnostic yields in genetic medicine. At chromosome side, from cytogenetic techniques evaluating number and gross structural defects to genomic microarrays detecting cryptic copy number variants, and at molecular level, from Sanger method studying the nucleotide sequence of single genes to the high-throughput next-generation sequencing (NGS) technologies, resolution and sensitivity progressively increased expanding considerably the range of detectable DNA anomalies and alongside of Mendelian disorders with known genetic causes. However, particular genomic regions (i.e., repetitive and GC-rich sequences) are inefficiently analyzed by standard genetic tests, still relying on laborious, time-consuming and low-sensitive approaches (i.e., southern-blot for repeat expansion or long-PCR for genes with highly homologous pseudogenes), accounting for at least part of the patients with undiagnosed genetic disorders. Third generation sequencing, generating long reads with improved mappability, is more suitable for the detection of structural alterations and defects in hardly accessible genomic regions. Although recently implemented and not yet clinically available, long read sequencing (LRS) technologies have already shown their potential in genetic medicine research that might greatly impact on diagnostic yield and reporting times, through their translation to clinical settings. The main investigated LRS application concerns the identification of structural variants and repeat expansions, probably because techniques for their detection have not evolved as rapidly as those dedicated to single nucleotide variants (SNV) identification: gold standard analyses are karyotyping and microarrays for balanced and unbalanced chromosome rearrangements, respectively, and southern blot and repeat-primed PCR for the amplification and sizing of expanded alleles, impaired by limited resolution and sensitivity that have not been significantly improved by the advent of NGS. Nevertheless, more recently, with the increased accuracy provided by the latest product releases, LRS has been tested also for SNV detection, especially in genes with highly homologous pseudogenes and for haplotype reconstruction to assess the parental origin of alleles with de novo pathogenic variants. We provide a review of relevant recent scientific papers exploring LRS potential in the diagnosis of genetic diseases and its potential future applications in routine genetic testing.

show abstract

Section: Discussionmentioning

confidence: 99%

Long read sequencing on its way to the routine diagnostics of genetic diseases

Olivucci,

Iovino,

Innella

et al. 2024

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Nevertheless, LongSom detected a large fraction of variants in intronic or even intergenic regions. Last, indels are the most common source of errors in LR scRNA-seq data, whereby they are frequently excluded from the analyses, ours included (Shiau et al 2023). To further improve the genomic analyses of scRNA-seq data, algorithms for filtering technical indels while detecting somatic indels are required, especially as technical indels can lead to false positive somatic SNVs (Ahsan et al 2021) In summary, we proposed a workflow for detecting multiple genetic variants (SNVs, CNAs, fusions) in LR scRNA-seq, enabling clonal heterogeneity reconstruction and clonal genotypes to treatment-resistance phenotype linkage.…”

Section: Discussionmentioning

confidence: 99%

“…Because it is less sensitive to capture bias, we have shown in recent work that LR scRNA-seq is more suited to detect genetic variations than SR scRNA-seq (Dondi et al 2023). Furthermore, LR scRNA-seq can simultaneously detect SNVs, CNAs, fusions, and gene isoform expression in the same cells (Dondi et al 2023;Shiau et al 2023).…”

Section: Introductionmentioning

confidence: 99%

De novo detection of somatic variants in long-read single-cell RNA sequencing data

Dondi,

Borgsmüller,

Ferreira

et al. 2024

Preprint

View full text Add to dashboard Cite

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, possibly leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), copy-number alterations (CNAs), and gene fusions to reconstruct the tumor clonal heterogeneity. For SNV calling, LongSom distinguishes somatic SNVs from germline polymorphisms by reannotating marker gene expression-based cell types using called variants and applying strict filters. Applying LongSom to ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against single-cell and bulk panel DNA-seq data and could not be detected with short-read (SR) scRNA-seq. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo SNVs, CNAs, and fusions detection, thus enabling the study of cancer evolution, clonal heterogeneity, and treatment resistance.

show abstract

“…The scNaST set of tools builds up on scNapBar ( 39 ), using a hybrid approach to assign spatial barcodes to long reads, followed by spatial spot deconvolution, spatial gene expression, transcript classification, and differential transcript usage ( 47 ). Only recently, two toolkits, scNanoGPS (single-cell Nanopore sequencing analysis of Genotypes and Phenotypes Simultaneously) and Scywalker (not peer-reviewed yet), were developed to assign CBs and UMIs as well as calculate gene and transcript-wise expression profiles without complementary single-cell short reads ( 67 , 68 ). scNanoGPS features CB correction that does not rely on a user-provided barcode whitelist; instead, it is generated within the algorithm.…”

Section: Analysis Of Single-cell Long-read Rna-seq Datamentioning

confidence: 99%

Advances in single-cell long-read sequencing technologies

Gupta,

O’Neill,

Wolvetang

et al. 2024

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

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, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

show abstract

High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors

Cited by 19 publications

References 47 publications

Long read sequencing on its way to the routine diagnostics of genetic diseases

Long read sequencing on its way to the routine diagnostics of genetic diseases

De novo detection of somatic variants in long-read single-cell RNA sequencing data

Advances in single-cell long-read sequencing technologies

Contact Info

Product

Resources

About