Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons

Advances in single-cell long-read sequencing technologies

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.

Advances in long-read single-cell transcriptomics

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction of R10 flowcells by Oxford Nanopore, we propose that previous computational methods designed to handle high sequencing error rates are no longer relevant, and that the prevailing approach using short reads to compile "barcode space" (candidate barcode list) to de-multiplex long reads are no longer necessary. Instead, computational methods should now shift focus on harnessing the unique benefits of long reads to analyze transcriptome complexity. In this context, we introduce a comprehensive suite of computational methods named Single-Cell Omics for Transcriptome CHaracterization (SCOTCH). Our method is compatible with the single-cell library preparation platform from both 10X Genomics and Parse Biosciences, facilitating the analysis of special cell populations, such as neurons, hepatocytes and developing cardiomyocytes. We specifically re-formulated the transcript mapping problem with a compatibility matrix and addressed the multiple-mapping issue using probabilistic inference, which allows the discovery of novel isoforms as well as the detection of differential isoform usage between cell populations. We evaluated SCOTCH through analysis of real data across different combinations of single-cell libraries and sequencing technologies (10X + Illumina, Parse + Illumina, 10X + Nanopore_R9, 10X + Nanopore_R10, Parse + Nanopore_R10), and showed its ability to infer novel biological insights on cell type-specific isoform expression. These datasets enhance the availability of publicly available data for continued development of computational approaches. In summary, SCOTCH allows extraction of more biological insights from the new advancements in single-cell library construction and sequencing technologies, facilitating the examination of transcriptome complexity at the single-cell level.

Therapeutic strategies for aberrant splicing in cancer and genetic disorders

Accurate pre-mRNA splicing is essential for proper protein translation; however, aberrant splicing is commonly observed in the context of cancer and genetic disorders. Notably, in genetic diseases, these splicing abnormalities often play a pivotal role. Substantial challenges persist in accurately identifying and classifying disease-induced aberrant splicing, as well as in development of targeted therapeutic strategies. In this review, we examine prevalent forms of aberrant splicing and explore potential therapeutic approaches aimed at addressing these splicing-related diseases. This summary contributes to a deeper understanding of the complexities about aberrant splicing and provide a foundation for the development of effective therapeutic interventions in the field of genetic disorders and cancer.

Long-read RNA sequencing identifies region- and sex-specific C57BL/6J mouse brain mRNA isoform expression and usage

Alternative splicing (AS) contributes to the biological heterogeneity between species, sexes, tissues, and cell types. Many diseases are either caused by alterations in AS or by alterations to AS. Therefore, measuring AS accurately and efficiently is critical for assessing molecular phenotypes, including those associated with disease. Long-read sequencing enables more accurate quantification of differentially spliced isoform expression than short-read sequencing approaches, and third-generation platforms facilitate high-throughput experiments. To assess differences in AS across the cerebellum, cortex, hippocampus, and striatum by sex, we generated and analyzed Oxford Nanopore Technologies (ONT) long-read RNA sequencing (lrRNA-Seq) C57BL/6J mouse brain cDNA libraries. From >85 million reads that passed quality control metrics, we calculated differential gene expression (DGE), differential transcript expression (DTE), and differential transcript usage (DTU) across brain regions and by sex. We found significant DGE, DTE, and DTU across brain regions and that the cerebellum had the most differences compared to the other three regions. Additionally, we found region-specific differential splicing between sexes, with the most sex differences in DTU in the cortex and no DTU in the hippocampus. We also report on two distinct patterns of sex DTU we observed, sex-divergent and sex-specific, that could potentially help explain sex differences in the prevalence and prognosis of various neurological and psychiatric disorders in future studies. Finally, we built a Shiny web application for researchers to explore the data further. Our study provides a resource for the community; it underscores the importance of AS in biological heterogeneity and the utility of long-read sequencing to better understand AS in the brain.

Humanized brain organoids-on-chip integrated with sensors for screening neuronal activity and neurotoxicity

The complex structure and function of the human central nervous system that develops from the neural tube made in vitro modeling quite challenging until the discovery of brain organoids. Human-induced pluripotent stem cells-derived brain organoids offer recapitulation of the features of early human neurodevelopment in vitro, including the generation, proliferation, and differentiation into mature neurons and micro-macroglial cells, as well as the complex interactions among these diverse cell types of the developing brain. Recent advancements in brain organoids, microfluidic systems, real-time sensing technologies, and their cutting-edge integrated use provide excellent models and tools for emulation of fundamental neurodevelopmental processes, the pathology of neurological disorders, personalized transplantation therapy, and high-throughput neurotoxicity testing by bridging the gap between two-dimensional models and the complex three-dimensional environment in vivo. In this review, we summarize how bioengineering approaches are applied to mitigate the limitations of brain organoids for biomedical and clinical research. We further provide an extensive overview and future perspectives of the humanized brain organoids-on-chip platforms with integrated sensors toward brain organoid intelligence and biocomputing studies. Such approaches might pave the way for increasing approvable clinical applications by solving their current limitations.