High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

Alternative splicing in autism spectrum disorder: Recent insights from mechanisms to therapy

Alternative splicing (AS) is a vital and highly dynamic RNA regulatory mechanism that allows a single gene to generate multiple mRNA and protein isoforms. Dysregulation of AS has been identified as a key contributor to the pathogenesis of autism spectrum disorders (ASD). A comprehensive understanding of aberrant splicing mechanisms and their functional consequences in ASD can help uncover the molecular basis of the disorder and facilitate the development of therapeutic strategies. This review focuses on the major aberrant splicing events and key splicing regulators associated with ASD, highlighting their roles in linking defective splicing to ASD pathogenesis. In addition, a discussion of how emerging technologies, such as long-read sequencing, single-cell sequencing, spatial transcriptomics and CRISPR-Cas systems are offering novel insights into the role and mechanisms of AS in ASD is presented. Finally, the RNA splicing-based therapeutic strategies are evaluated, emphasizing their potential to address unmet clinical needs in ASD treatment.

Expanded T cell clones with lymphoma driver somatic mutations accumulate in refractory celiac disease

Intestinal inflammation continues in a subset of patients with celiac disease despite a gluten-free diet. Here, by applying multi-omic single-cell analysis to duodenal biopsies, we found that low-grade malignancies with lymphoma driver mutations in patients with refractory celiac disease type 2 (RCD2) are comprised by surface CD3-negative (sCD3-) lymphocytes stalled at an innate lymphoid cell (ILC)-progenitor T cell stage undergoing extensive TRA, TRB, and TRD TCR recombination. In people with refractory celiac disease type 1 (RCD1), a disease currently lacking explanation, we identified sCD3+ T cells with lymphoma driver mutations in 6 of 10 individuals with RCD1 and in one of the patients with active, recently diagnosed celiac disease. Furthermore, the mutant T cells formed large TCRαβ clones and displayed inflammatory and cytotoxic molecular profiles. Thus, accumulation of lymphoma driver-mutated T cells and sCD3- progenitors may contribute to chronic, nonresponsive celiac disease.

Memory T Cells in Respiratory Virus Infections: Protective Potential and Persistent Vulnerabilities

Respiratory virus infections, such as those caused by influenza viruses, respiratory syncytial virus (RSV), and coronaviruses, pose a significant global health burden. While the immune system's adaptive components, including memory T cells, are critical for recognizing and combating these pathogens, recurrent infections and variable disease outcomes persist. Memory T cells are a key element of long-term immunity, capable of responding swiftly upon re-exposure to pathogens. They play diverse roles, including cross-reactivity to conserved viral epitopes and modulation of inflammatory responses. However, the protective efficacy of these cells is influenced by several factors, including viral evolution, host age, and immune system dynamics. This review explores the dichotomy of memory T cells in respiratory virus infections: their potential to confer robust protection and the limitations that allow for breakthrough infections. Understanding the underlying mechanisms governing the formation, maintenance, and functional deployment of memory T cells in respiratory mucosa is critical for improving immunological interventions. We highlight recent advances in vaccine strategies aimed at bolstering T cell-mediated immunity and discuss the challenges posed by viral immune evasion. Addressing these gaps in knowledge is pivotal for designing effective therapeutics and vaccines to mitigate the global burden of respiratory viruses.

De novo antibody identification in human blood from full-length single B cell transcriptomics and matching haplotype-resolved germline assemblies

Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells results in whole transcriptome characterization of each cell, as well as highly accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrate binding to measles antigens and neutralization of authentic measles virus.

Transcriptomics in the era of long-read sequencing

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

Long-read RNA sequencing enables full-length chimeric transcript annotation of transposable elements in lung adenocarcinoma

BACKGROUND

Transposable elements (TEs), which constitute nearly half of the human genome, have long been regarded as genomic "dark matter". However, their reactivation in tumor cells, resulting in the production of TE-chimeric transcripts (TCTs), has emerged as a potential driver of cancer progression. The complexity and full extent of these transcripts remain elusive, largely due to the limitations of short-read next-generation sequencing technologies. These methods have struggled to comprehensively capture the diversity and structure of TCTs, particularly those involving short interspersed nuclear elements (SINEs) or closely co-transcribed TEs.

METHODS

Leveraging full-length cDNA sequencing technology based on nanopore sequencing platform, we developed a customized pipeline for identifying and quantifying TCTs in 19 lung adenocarcinoma (LUAD) cell lines. The short-read RNA-seq dataset from a LUAD corhort (~ 200 tumor samples) was employed to validate the identified TCTs and explore their association with tumor progression. To assess the functional roles of a specific TCTs, cell migration and cell proliferation assays were performed.

RESULTS

We uncovered 208 unique TCT candidates in the LUAD cell lines. Our approach allowed for the identification of cryptic promoters and terminators within non-transposing TEs. Notably, we identified a chimeric transcript involving MIR_HKDC1, which appears to play a significant role in the progression of LUAD. Furthermore, the expression of these TCTs were associated with poor clinical outcomes in a cohort of LUAD patients, suggesting their potential as novel biomarkers for both LUAD progression and prognosis.

CONCLUSIONS

Our study underscores the application of long-read sequencing to unravel the complex landscape of TCTs in LUAD. We provide a comprehensive characterization of TCTs in LUAD, exploring their potential regulatory roles in cancer progression. These findings contribute to a deeper understanding of the genomic intricacies underlying cancer, and offer new directions for the development of targeted therapies and personalized treatment strategies for LUAD. This research highlights the potential of TCTs as both biomarkers and therapeutic targets in the oncogenesis, offering new insights into the interplay between transposable elements and gene regulation in cancer.

The endogenous antigen-specific CD8+ T cell repertoire is composed of unbiased and biased clonotypes with differential fate commitments

Generating balanced populations of CD8+ effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of how a diverse CD8+ T cell repertoire differentiates remains unclear. We identified several hundred T cell receptor (TCR) clonotypes that constitute the polyclonal response against a single antigen and found that a majority of TCR clonotypes were highly biased toward memory or effector fates. TCR-intrinsic biases were not stochastic and were dominant over environmental cues. Differential gene expression analysis of memory- or effector-biased TCR clonotypes showed bifurcation of differential fates at the early effector stage. Additionally, phylogenetic analysis revealed that memory-biased clonotypes retain their fate preferences in subclonal populations but effector-biased subclones can switch to a memory fate. Our study highlights that the polyclonal CD8+ T cell response is a composite of unbiased and biased clonotypes with varying capacity to incorporate environmental cues in their cell fate decisions.

A triad of somatic mutagenesis converges in self-reactive B cells to cause a virus-induced autoimmune disease

The unexplained association between infection and autoimmune disease is strongest for hepatitis C virus-induced cryoglobulinemic vasculitis (HCV-cryovas). To analyze its origins, we traced the evolution of pathogenic rheumatoid factor (RF) autoantibodies in four HCV-cryovas patients by deep single-cell multi-omic analysis, revealing three sources of B cell somatic mutation converged to drive the accumulation of a large disease-causing clone. A method for quantifying low-affinity binding revealed recurring antibody variable domain combinations created by V(D)J recombination that bound self-immunoglobulin G (IgG) but not viral E2 antigen. Whole-genome sequencing revealed thousands of somatic mutations, numerically comparable to chronic lymphocytic leukemia and normal memory B cells, but with 1-2 corresponding to driver mutations found recurrently in B cell leukemia and lymphoma. V(D)J hypermutation created autoantibodies with compromised solubility in complex with self-IgG. In this virus-induced autoimmune disease, infection promotes a catastrophic confluence of somatic mutagenesis in the descendants of a single B cell.

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

Recent development involving 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 less relevant, and that the traditional 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). SCOTCH supports both Nanopore and PacBio sequencing platforms, and is compatible with single-cell library preparation protocols from both 10X Genomics and Parse Biosciences. Through a sub-exon identification strategy with dynamic thresholding and read mapping scores, SCOTCH precisely aligns reads to known isoforms and discover novel isoforms, efficiently addressing ambiguous mapping challenges commonly encountered in long-read single-cell data. Comprehensive simulations and real data analyses across multiple platforms (including 10X Genomics and Parse Bioscience, paired with Illumina or Nanopore sequencing technologies with R9 and R10 flowcells, as well as PacBio sequencing) demonstrated that SCOTCH outperforms existing methods in mapping accuracy, quantification accuracy and novel isoform detection, while also uncovering novel biological insights on transcriptome complexity at the single-cell level.

Full-length mRNA sequencing resolves novel variation in 5' UTR length for genes expressed during human CD4 T-cell activation

Isoform sequencing (Iso-Seq) uses long-read technology to produce highly accurate full-length reads of mRNA transcripts. Visualization of individual mRNA molecules can reveal new details of transcript variation within understudied portions of mRNA, such as the 5' untranslated region (UTR). Differential 5' UTRs may contain motifs, upstream open reading frames (uORFs), and secondary structures that can serve to regulate translation or further indicate changes in promoter usage, where transcriptional control may impact protein expression levels. To begin to explore isoform variation during T-cell activation, we generated the first Iso-Seq reference transcriptome of activated human CD4 T cells. Within this dataset, we discovered many novel splice- and end-variant transcripts. Remarkably, one in every eight genes expressed in our dataset was found to have a notable proportion of transcripts with 5' UTR lengthened by over 100 bp compared to the longest corresponding UTR within the Gencode dataset. Among these end-variant transcripts, two novel isoforms were identified for CXCR5, a chemokine receptor associated with T follicular helper cell (Tfh) function and differentiation. When investigated in a model cell system, these lengthened UTR conferred reduced transcript stability and, for one of these isoforms, short uORFs introduced by the added length altered protein expression kinetics. This study highlights instances in which current reference databases are incomplete relative to the information obtained by long-read sequencing of intact mRNA. Iso-Seq is thus a promising approach to better understanding the plasticity of promoter usage, alternative splicing, and UTR sequences that influence RNA stability and translation efficiency.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Identification of clonally expanded γδ T-cell populations during CAR-T cell therapy

Anti-CD19 Chimeric Antigen Receptor (CAR)-T cell therapies have shown promise for treating B cell malignancies, but the clinical outcome is influenced by both the CAR-T product and the patient's immune system. The role of γδ T cells in the context of CAR-T cell therapy remains poorly understood. This study investigates the transcriptional heterogeneity, clonal expansion and dynamics of γδ T cells in patients undergoing anti-CD19 CAR-T cell therapy. Longitudinal single cell multi-omics analysis was performed on γδ T cells from four patients receiving anti-CD19 CAR-T cell therapy. Single cell RNA-seq, antibody-based protein profiling (AbSeq) and full-length TCRγδ sequences revealed clonally expanded populations displaying plasticity in T cell differentiation, and temporal dynamics of large clones, suggesting ongoing expansion and differentiation. Clonally expanded γδ T cells had heterogeneous gene expression profiles, occupying seven transcriptionally distinct clusters. Analysis of chemokine markers indicated cluster-specific homing tendencies of circulating γδ T cells to peripheral tissues. We found unexpectedly high frequencies of Vδ1 and Vδ3 cells in the blood with distinct gene and protein expression profiles. This analysis provides insights into the dynamic and heterogeneous nature of γδ T cells following anti-CD19 CAR-T cell therapy, contributing valuable information for optimizing CAR-T cell therapies in B cell malignancies.

Targeted barcoding of variable antibody domains and individual transcriptomes of the human B-cell repertoire using Link-Seq

Here, we present Link-Seq, a highly efficient droplet microfluidic method for combined sequencing of antibody-encoding genes and the transcriptome of individual B cells at large scale. The method is based on 3' barcoding of the transcriptome and subsequent single-molecule PCR in droplets, which freely shift the barcode along specific gene regions, such as the antibody heavy- and light-chain genes. Using the immune repertoire of COVID-19 patients and healthy donors as a model system, we obtain up to 91.7% correctly paired immunoglobulin heavy and light chains. Furthermore, we map the V(D)J usage and obtain sensitivities comparable with the current gold-standard 10× Genomics commercial systems while offering full flexibility in experimental setup and significant cost savings. A further unique feature of Link-Seq is the possibility of barcoding multiple target genes in a site-specific manner. Based on the open character of the platform and its conceptual advantages, we expect Link-Seq to become a versatile tool for single-cell analysis, especially for applications requiring additional processing steps that cannot be implemented on commercially available platforms.

NK cell exhaustion in Wilson's disease revealed by single-cell RNA sequencing predicts the prognosis of cholecystitis

Metabolic abnormalities associated with liver disease have a significant impact on the risk and prognosis of cholecystitis. However, the underlying mechanism remains to be elucidated. Here, we investigated this issue using Wilson's disease (WD) as a model, which is a genetic disorder characterized by impaired mitochondrial function and copper metabolism. Our retrospective clinical study found that WD patients have a significantly higher incidence of cholecystitis and a poorer prognosis. The hepatic immune cell landscape using single-cell RNA sequencing showed that the tissue immune microenvironment is altered in WD, mainly a major change in the constitution and function of the innate immune system. Exhaustion of natural killer (NK) cells is the fundamental factor, supported by the upregulated expression of inhibitory receptors and the downregulated expression of cytotoxic molecules, which was verified in clinical samples. Further bioinformatic analysis confirmed a positive correlation between NK cell exhaustion and poor prognosis in cholecystitis and other inflammatory diseases. The study demonstrated dysfunction of liver immune cells triggered by specific metabolic abnormalities in WD, with a focus on the correlation between NK cell exhaustion and poor healing of cholecystitis, providing new insights into the improvement of inflammatory diseases by assessing immune cell function.

Nanopore sequencing: flourishing in its teenage years

Over the past decade, nanopore sequencing has experienced significant advancements and changes, transitioning from an initially emerging technology to a significant instrument in the field of genomic sequencing. However, as advancements in next-generation sequencing technology persist, nanopore sequencing also improves. This paper reviews the developments, applications, and outlook on nanopore sequencing technology. Currently, nanopore sequencing supports both DNA and RNA sequencing, making it widely applicable in areas such as telomere-to-telomere (T2T) genome assembly, direct RNA sequencing (DRS), and metagenomics. The openness and versatility of nanopore sequencing have established it as a preferred option for an increasing number of research teams, signaling a transformative influence on life science research. As the nanopore sequencing technology advances, it provides a faster, more cost-effective approach with extended read lengths, demonstrating the significant potential for complex genome assembly, pathogen detection, environmental monitoring, and human disease research, offering a fresh perspective in sequencing technologies.

Understanding isoform expression by pairing long-read sequencing with single-cell and spatial transcriptomics

RNA isoform diversity, produced via alternative splicing, and alternative usage of transcription start and poly(A) sites, results in varied transcripts being derived from the same gene. Distinct isoforms can play important biological roles, including by changing the sequences or expression levels of protein products. The first single-cell approaches to RNA sequencing-and later, spatial approaches-which are now widely used for the identification of differentially expressed genes, rely on short reads and offer the ability to transcriptomically compare different cell types but are limited in their ability to measure differential isoform expression. More recently, long-read sequencing methods have been combined with single-cell and spatial technologies in order to characterize isoform expression. In this review, we provide an overview of the emergence of single-cell and spatial long-read sequencing and discuss the challenges associated with the implementation of these technologies and interpretation of these data. We discuss the opportunities they offer for understanding the relationships between the distinct variable elements of transcript molecules and highlight some of the ways in which they have been used to characterize isoforms' roles in development and pathology. Single-nucleus long-read sequencing, a special case of the single-cell approach, is also discussed. We attempt to cover both the limitations of these technologies and their significant potential for expanding our still-limited understanding of the biological roles of RNA isoforms.

Leveraging the power of long reads for targeted sequencing

Long-read sequencing technologies have improved the contiguity and, as a result, the quality of genome assemblies by generating reads long enough to span and resolve complex or repetitive regions of the genome. Several groups have shown the power of long reads in detecting thousands of genomic and epigenomic features that were previously missed by short-read sequencing approaches. While these studies demonstrate how long reads can help resolve repetitive and complex regions of the genome, they also highlight the throughput and coverage requirements needed to accurately resolve variant alleles across large populations using these platforms. At the time of this review, whole-genome long-read sequencing is more expensive than short-read sequencing on the highest throughput short-read instruments; thus, achieving sufficient coverage to detect low-frequency variants (such as somatic variation) in heterogenous samples remains challenging. Targeted sequencing, on the other hand, provides the depth necessary to detect these low-frequency variants in heterogeneous populations. Here, we review currently used and recently developed targeted sequencing strategies that leverage existing long-read technologies to increase the resolution with which we can look at nucleic acids in a variety of biological contexts.

Transcriptome Assembly at Single-Cell Resolution with Beaver

Emerging single-cell RNA sequencing techniques (scRNA-seq) has enabled the study of cellular transcriptome heterogeneity, yet accurate reconstruction of full-length transcripts at single-cell resolution remains challenging due to high dropout rates and sparse coverage. While meta-assembly approaches offer promising solutions by integrating information across multiple cells, current methods struggle to balance consensus assembly with cell-specific transcriptional signatures. Here, we present Beaver, a cell-specific transcript assembler designed for short-read scRNA-seq data. Beaver implements a transcript fragment graph to organize individual assemblies and designs an efficient dynamic programming algorithm that searches for candidate full-length transcripts from the graph. Beaver in-corporates two random forest models trained on 51 meticulously engineered features that accurately estimate the likelihood of each candidate transcript being expressed in individual cells. Our experiments, performed using both real and simulated Smart-seq3 scRNA-seq data, firmly show that Beaver substantially outperforms existing meta-assemblers and single-sample assemblers. At the same level of sensitivity, Beaver achieved 32.0%-64.6%, 13.5%-36.6%, and 9.8%-36.3% higher precision in average compared to meta-assemblers Aletsch, TransMeta, and PsiCLASS, respectively, with similar improvements over single-sample assemblers Scallop2 (10.1%-43.6%) and StringTie2 (24.3%-67.0%). Beaver is freely available at https://github.com/Shao-Group/beaver . Scripts that reproduce the experimental results of this manuscript are available at https://github.com/Shao-Group/beaver-test .

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.

Fine mapping of RNA isoform diversity using an innovative targeted long-read RNA sequencing protocol with novel dedicated bioinformatics pipeline

BACKGROUND

Solving the structure of mRNA transcripts is a major challenge for both research and molecular diagnostic purposes. Current approaches based on short-read RNA sequencing and RT-PCR techniques cannot fully explore the complexity of transcript structure. The emergence of third-generation long-read sequencing addresses this problem by solving this sequence directly. However, genes with low expression levels are difficult to study with the whole transcriptome sequencing approach. To fix this technical limitation, we propose a novel method to capture transcripts of a gene panel using a targeted enrichment approach suitable for Pacific Biosciences and Oxford Nanopore Technologies platforms.

RESULTS

We designed a set of probes to capture transcripts of a panel of genes involved in hereditary breast and ovarian cancer syndrome. We present SOSTAR (iSofOrmS annoTAtoR), a versatile pipeline to assemble, quantify and annotate isoforms from long read sequencing using a new tool specially designed for this application. The significant enrichment of transcripts by our capture protocol, together with the SOSTAR annotation, allowed the identification of 1,231 unique transcripts within the gene panel from the eight patients sequenced. The structure of these transcripts was annotated with a resolution of one base relative to a reference transcript. All major alternative splicing events of the BRCA1 and BRCA2 genes described in the literature were found. Complex splicing events such as pseudoexons were correctly annotated. SOSTAR enabled the identification of abnormal transcripts in the positive controls. In addition, a case of unexplained inheritance in a family with a history of breast and ovarian cancer was solved by identifying an SVA retrotransposon in intron 13 of the BRCA1 gene.

CONCLUSIONS

We have validated a new protocol for the enrichment of transcripts of interest using probes adapted to the ONT and PacBio platforms. This protocol allows a complete description of the alternative structures of transcripts, the estimation of their expression and the identification of aberrant transcripts in a single experiment. This proof-of-concept opens new possibilities for RNA structure exploration in both research and molecular diagnostics.

Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to pneumococcal vaccination

Single-cell RNA sequencing (scRNA-seq) can resolve transcriptional features from individual cells, but scRNA-seq techniques capable of resolving the variable regions of B cell receptors (BCRs) remain limited, especially from widely-used 3'-barcoded libraries. Here, we report a method that can recover paired, full-length variable region sequences of BCRs from 3'-barcoded scRNA-seq libraries. We first verify this method (B3E-seq) can produce accurate, full-length BCR sequences. We then apply this method to profile B cell responses elicited against the capsular polysaccharide of Streptococcus pneumoniae serotype 3 (ST3) by glycoconjugate vaccines in five infant rhesus macaques. We identify BCR features associated with specificity for the ST3 antigen which are present in multiple vaccinated monkeys, indicating a convergent response to vaccination. These results demonstrate the utility of our method to resolve key features of the B cell repertoire and profile antigen-specific responses elicited by vaccination.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

Generating barcodes for nanopore sequencing data with PRO

DNA barcodes, short and unique DNA sequences, play a crucial role in sample identification when processing many samples simultaneously, which helps reduce experimental costs. Nevertheless, the low quality of long-read sequencing makes it difficult to identify barcodes accurately, which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing run. Here, we present a comprehensive study of the generation of barcodes and develop a tool, PRO, that can be used for selecting optimal barcode sets and demultiplexing. We formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically NP-complete. For practical applications, we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing accuracy. Specifically, the maximum size of the barcode kits designed by PRO is 2,292, which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies (ONT). We validated the performance of PRO on a simulated nanopore dataset with high error rates. The demultiplexing accuracy of PRO reached 98.29% for a barcode kit of size 2,922, 4.31% higher than that of Guppy, the official demultiplexing tool. When the size of the barcode kit generated by PRO is the same as the official size provided by ONT, both tools show superior and comparable demultiplexing accuracy.

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types

Key Points

Background

Accumulated evidence demonstrates that long noncoding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood because of the lack of a reference map of lncRNA transcriptome in various cell types.

Methods

In this study, we used a targeted single-cell RNA sequencing method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys.

Results

Through tissue-specific clustering analysis, we identified cell type–specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type– and age-specific expression patterns of lncRNAs suggest that lncRNAs may have a potential role in regulating cellular processes, such as immune response and energy metabolism, during kidney aging.

Conclusions

Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).

Long-read single-cell sequencing reveals the transcriptional landscape of spermatogenesis in obstructive azoospermia and Sertoli cell-only patients

BACKGROUND

High-throughput single-cell RNA sequencing (scRNA-seq) is widely used in spermatogenesis. However, it only reveals short reads in germ and somatic cells, limiting the discovery of novel transcripts and genes.

AIM

This study shows the long-read transcriptional landscape of spermatogenesis in obstructive azoospermia (OA) and Sertoli cell-only patients.

DESIGN

Single cells were isolated from testicular biopsies of OA and non-obstructive azoospermia (NOA) patients. Cell culture was identified by comparing PacBio long-read single-cell sequencing (OA n = 3, NOA n = 3) with short-read scRNA-seq (OA n = 6, NOA n = 6). Ten germ cell types and eight somatic cell types were classified based on known markers.

METHODS

PacBio long-read single-cell sequencing, short-read scRNA-seq, polymerase chain reaction.

RESULTS

A total of 130 426 long-read transcripts (100 517 novel transcripts and 29 909 known transcripts) and 49 508 long-read transcripts (26 002 novel transcripts and 23 506 known transcripts) have been detected in OA and NOA patients, respectively. Moreover, 36 373 and 1642 new genes are identified in OA and NOA patients, respectively. Importantly, specific expressions of long-read transcripts were detected in germ and stomatic cells during normal spermatogenesis.

CONCLUSION

We have identified total full-length transcripts in OA and NOA, and new genes were found. Furthermore, specific expressed full-length transcripts were detected, and the genomic structure of transcripts was mapped in different cell types. These findings may provide valuable information on human spermatogenesis and the treatment of male infertility.

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.

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

RNA isoforms influence cell identity and function. However, a comprehensive brain isoform map was lacking. We analyze single-cell RNA isoforms across brain regions, cell subtypes, developmental time points and species. For 72% of genes, full-length isoform expression varies along one or more axes. Splicing, transcription start and polyadenylation sites vary strongly between cell types, influence protein architecture and associate with disease-linked variation. Additionally, neurotransmitter transport and synapse turnover genes harbor cell-type variability across anatomical regions. Regulation of cell-type-specific splicing is pronounced in the postnatal day 21-to-postnatal day 28 adolescent transition. Developmental isoform regulation is stronger than regional regulation for the same cell type. Cell-type-specific isoform regulation in mice is mostly maintained in the human hippocampus, allowing extrapolation to the human brain. Conversely, the human brain harbors additional cell-type specificity, suggesting gain-of-function isoforms. Together, this detailed single-cell atlas of full-length isoform regulation across development, anatomical regions and species reveals an unappreciated degree of isoform variability across multiple axes.

The complexities of T-cell dysfunction in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by profound alterations and defects in the T-cell compartment. This observation has gained renewed interest as T-cell treatment strategies, which are successfully applied in more aggressive B-cell malignancies, have yielded disappointing results in CLL. Despite ongoing efforts to understand and address the observed T-cell defects, the exact mechanisms and nature underlying this dysfunction remain largely unknown. In this review, we examine the supporting signals from T cells to CLL cells in the lymph node niche, summarize key findings on T-cell functional defects, delve into potential underlying causes, and explore novel strategies for reversing these deficiencies. Our goal is to identify strategies aimed at resolving CLL-induced T-cell dysfunction which, in the future, will enhance the efficacy of autologous T-cell-based therapies for CLL patients.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Helper T cell immunity in humans with inherited CD4 deficiency

CD4+ T cells are vital for host defense and immune regulation. However, the fundamental role of CD4 itself remains enigmatic. We report seven patients aged 5-61 years from five families of four ancestries with autosomal recessive CD4 deficiency and a range of infections, including recalcitrant warts and Whipple's disease. All patients are homozygous for rare deleterious CD4 variants impacting expression of the canonical CD4 isoform. A shorter expressed isoform that interacts with LCK, but not HLA class II, is affected by only one variant. All patients lack CD4+ T cells and have increased numbers of TCRαβ+CD4-CD8- T cells, which phenotypically and transcriptionally resemble conventional Th cells. Finally, patient CD4-CD8- αβ T cells exhibit intact responses to HLA class II-restricted antigens and promote B cell differentiation in vitro. Thus, compensatory development of Th cells enables patients with inherited CD4 deficiency to acquire effective cellular and humoral immunity against an unexpectedly large range of pathogens. Nevertheless, CD4 is indispensable for protective immunity against at least human papillomaviruses and Trophyrema whipplei.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Minor introns impact on hematopoietic malignancies

In the intricate orchestration of the central dogma, pre-mRNA splicing plays a crucial role in the post-transcriptional process that transforms DNA into mature mRNA. Widely acknowledged as a pivotal RNA processing step, it significantly influences gene expression and alters the functionality of gene product proteins. Although U2-dependent spliceosomes efficiently manage the removal of over 99% of introns, a distinct subset of essential genes undergo splicing with a different intron type, denoted as minor introns, using U12-dependent spliceosomes. Mutations in spliceosome component genes are now recognized as prevalent genetic abnormalities in cancer patients, especially those with hematologic malignancies. Despite the relative rarity of minor introns, genes containing them are evolutionarily conserved and play crucial roles in functions such as the RAS-MAPK pathway. Disruptions in U12-type minor intron splicing caused by mutations in snRNA or its regulatory components significantly contribute to cancer progression. Notably, recurrent mutations associated with myelodysplastic syndrome (MDS) in the minor spliceosome component ZRSR2 underscore its significance. Examination of ZRSR2-mutated MDS cells has revealed that only a subset of minor spliceosome-dependent genes, such as LZTR1, consistently exhibit missplicing. Recent technological advancements have uncovered insights into minor introns, raising inquiries beyond current understanding. This review comprehensively explores the importance of minor intron regulation, the molecular implications of minor (U12-type) spliceosomal mutations and cis-regulatory regions, and the evolutionary progress of studies on minor, aiming to provide a sophisticated understanding of their intricate role in cancer biology.

Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants

Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. The CRISPR‒Cas system has emerged as a pivotal tool for genome engineering, enabling the precise incorporation of specific genetic variations, including VUSs, into DNA to facilitate their functional characterization. Additionally, the integration of CRISPR‒Cas technology with sequencing tools allows the high-throughput evaluation of mutations, transforming uncertain genetic data into actionable insights. This allows researchers to comprehensively study the functional consequences of point mutations, paving the way for enhanced understanding and increasing application to precision medicine. This review summarizes the current genome editing tools utilizing CRISPR‒Cas systems and their combination with sequencing tools for functional genomics, with a focus on point mutations.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

De novo antibody discovery in human blood from full-length single B cell transcriptomics and matching haplotyped-resolved germline assemblies

Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully-phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells resulted in whole transcriptome characterization of each cell, as well as highly-accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrated binding to measles antigens and neutralization of measles live virus.

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection

Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector-fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.

Quantifying transcriptome diversity: a review

Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

TKSM: highly modular, user-customizable, and scalable transcriptomic sequencing long-read simulator

MOTIVATION

Transcriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold-standard datasets hinders the benchmarking of such tools. Therefore, the simulation of LR sequencing is an important and practical alternative. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs).

RESULTS

We present TKSM, a modular and scalable LR simulator, designed so that each RNA modification step is targeted explicitly by a specific module. This allows the user to assemble a simulation pipeline as a combination of TKSM modules to emulate a specific sequencing design. Additionally, the input/output of all the core modules of TKSM follows the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps.

AVAILABILITY AND IMPLEMENTATION

TKSM is available as an open source software at https://github.com/vpc-ccg/tksm.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Improving the RNA velocity approach with single-cell RNA lifecycle (nascent, mature and degrading RNAs) sequencing technologies

We presented an experimental method called FLOUR-seq, which combines BD Rhapsody and nanopore sequencing to detect the RNA lifecycle (including nascent, mature, and degrading RNAs) in cells. Additionally, we updated our HIT-scISOseq V2 to discover a more accurate RNA lifecycle using 10x Chromium and Pacbio sequencing. Most importantly, to explore how single-cell full-length RNA sequencing technologies could help improve the RNA velocity approach, we introduced a new algorithm called 'Region Velocity' to more accurately configure cellular RNA velocity. We applied this algorithm to study spermiogenesis and compared the performance of FLOUR-seq with Pacbio-based HIT-scISOseq V2. Our findings demonstrated that 'Region Velocity' is more suitable for analyzing single-cell full-length RNA data than traditional RNA velocity approaches. These novel methods could be useful for researchers looking to discover full-length RNAs in single cells and comprehensively monitor RNA lifecycle in cells.