Single-cell RNA Sequencing Methods Research Articles

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display metabolic and transcriptional diversity along with recalcitrance to antibiotics and host immune defenses. Here, we present an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. BaSSSh-seq captures extensive transcriptional heterogeneity during biofilm compared to planktonic growth. We quantify and visualize transcriptional regulatory networks across heterogeneous biofilm subpopulations and identify gene sets that are associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detects alterations in biofilm metabolism, stress response, and virulence induced by distinct immune cell populations. This work facilitates the exploration of biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

IDEIS: a tool to identify PTPRC/CD45 isoforms from single-cell transcriptomic data.

Single-cell RNA sequencing (scRNA-seq) methods are widely used in life sciences, including immunology. Typical scRNA-seq analysis pipelines quantify the abundance of particular transcripts without accounting for alternative splicing. However, a well-established pan-leukocyte surface marker, CD45, encoded by the PTPRC gene, presents alternatively spliced variants that define different immune cell subsets. Information about some of the splicing patterns in particular cells in the scRNA-seq data can be obtained using isotype-specific DNA oligo-tagged anti-CD45 antibodies. However, this requires generation of an additional sequencing DNA library. Here, we present IDEIS, an easy-to-use software for CD45 isoform quantification that uses single-cell transcriptomic data as the input. We showed that IDEIS accurately identifies canonical human CD45 isoforms in datasets generated by 10× Genomics 5' sequencing assays. Moreover, we used IDEIS to determine the specificity of the Ptprc splicing pattern in mouse leukocyte subsets.

Single-cell RNA sequencing and multiple bioinformatics methods to identify the biomarkers of ischemic stroke to alzheimer’s disease

Ranked as the world’s second leading cause of mortality, ischemic stroke (IS) imposes a significant global burden, marked by its substantial incidence and economic impact across nations and regions, chiefly attributed to its elevated disability rates. Alzheimer’s disease (AD) is linked to a heightened susceptibility to IS. However, few studies have been reported to explain the potential correlation between IS and AD. We investigated the relationship and relevant mechanisms between IS and AD using single-cell RNA sequencing and multiple bioinformatics approaches. By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 15 IS related AD genes. According to the KEGG pathway analysis, the IS related AD disease genes were significantly enriched in the parkinson disease pathway, oxidative phosphorylation pathway, thermogenesis pathway, alzheimer disease pathway and neurodegeneration pathway. Finally, five genes associated with immunity (TXN, HINT1, TOMM7, COX7C, RPL17) were identified in IS related AD. Significantly, it was found that all hub genes were mainly highly expressed in epithelial cells, endothelial cells, monocytes and oligodendrocytes by single cell RNA sequencing. We believe these hub genes may regulate the immune pathway which may contribute to the development of IS related AD. In addition, these genes may be potential targets for the treatment of IS related AD.

Optimizing single-cell RNA sequencing methods for human colon biopsies: droplet-based vs. picowell-based platforms.

Single-cell RNA sequencing (scRNA) has empowered many insights into gastrointestinal microenvironments. However, profiling human biopsies using droplet-based scRNA (D-scRNA) is challenging since it requires immediate processing to minimize epithelial cell damage. In contrast, picowell-based (P-scRNA) platforms permit short-term frozen storage before sequencing. We compared P- and D-scRNA platforms on cells derived from human colon biopsies. Endoscopic rectosigmoid mucosal biopsies were obtained from two adults and conducted D-scRNA (10X Chromium) and P-scRNA (Honeycomb HIVE) in parallel using an individual's pool of single cells (> 10,000 cells/participant). Three experiments were performed to evaluate 1) P-scRNA with cells under specific storage conditions (immediately processed [fresh], vs. frozen at -20C vs. -80C [2 weeks]); 2) fresh P-scRNA versus fresh D-scRNA; and 3) P-scRNA stored at -80C with fresh D-scRNA. Significant recovery of loaded cells was achieved for fresh (80.9%) and -80C (48.5%) P-scRNA and D-scRNA (76.6%), but not -20C P-scRNA (3.7%). However, D-scRNA captures more typeable cells among recovered cells (71.5% vs. 15.8% Fresh and 18.4% -80C P-scRNA), and these cells exhibit higher gene coverage at the expense of higher mitochondrial read fractions across most cell types. Cells profiled using D-scRNA demonstrated more consistent gene expression profiles among the same cell type than those profiled using P-scRNA. Significant intra-cell-type differences were observed in profiled gene classes across platforms. Our results highlight non-overlapping advantages of P-scRNA and D-scRNA and underscore the need for innovation to enable high-fidelity capture of colonic epithelial cells. The platform-specific variation highlights the challenges of maintaining rigor and reproducibility across studies that use different platforms.

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure.

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display vast metabolic and transcriptional diversity along with high recalcitrance to antibiotics and host immune defenses. Investigating the complex heterogeneity within biofilm has been hindered by the lack of a sensitive and high-throughput method to assess stochastic transcriptional activity and regulation between bacterial subpopulations, which requires single-cell resolution. We have developed an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. We validated the ability of BaSSSh-seq to capture extensive transcriptional heterogeneity during biofilm compared to planktonic growth. Application of new computational tools revealed transcriptional regulatory networks across the heterogeneous biofilm subpopulations and identification of gene sets that were associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detected alterations in biofilm metabolism, stress response, and virulence that were tailored to distinct immune cell populations. This work provides an innovative platform to explore biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding.

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

Multimodal Learning for Mapping the Genotype-Phenotype Dynamics.

How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Quantitative characterization of this relationship, however, is challenging due to the vast combinatorial possibilities and dynamic interplay between genotype and phenotype landscapes. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here, we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, enhanced precision in phenotype annotating, and uncovered potential cross-tissue biomarkers that are undetectable through conventional gene expression analysis alone. Moreover, our results revealed that the gene networks are characterized by scale-free patterns and show context-dependent gene-gene interactions, both of which result in significant variations in the topology of the gene network, particularly evident during aging. Finally, utilizing contextualized embeddings, we investigated gene polyfunctionality which illustrates the multifaceted roles that genes play in different biological processes, and demonstrated that for VWF gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.

Spatial and single-cell explorations uncover prognostic significance and immunological functions of mitochondrial calcium uniporter in breast cancer

The mitochondrial calcium uniporter (MCU) is a transmembrane protein facilitating the entry of calcium ions into mitochondria from the cell cytosol. Maintaining calcium balance is crucial for enhancing cellular energy supply and regulating cell death. The interplay of calcium balance through MCU and the sodium-calcium exchanger is known, but its regulation in the breast cancer tumor microenvironment remains elusive. Further investigations are warranted to explore MCU’s potential in BRCA clinical pathology, tumor immune microenvironment, and precision oncology. Our study, employing a multi-omics approach, identifies MCU as an independent diagnostic biomarker for breast cancer (BRCA), correlated with advanced clinical status and poor overall survival. Utilizing public datasets from GEO and TCGA, we discern differentially expressed genes in BRCA and examine their associations with immune gene expression, overall survival, tumor stage, gene mutation status, and infiltrating immune cells. Spatial transcriptomics is employed to investigate MCU gene expression in various regions of BRCA, while spatial transcriptomics and single-cell RNA-sequencing methods explore the correlation between MCUs and immune cells. Our findings are validated through the analysis of 59 BRCA patient samples, utilizing immunohistochemistry and bioinformatics to examine the relationship between MCU expression, clinicopathological features, and prognosis. The study uncovers the expression of key gene regulators in BRCA associated with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators positively correlate with different immune cells in six immune datasets, playing a pivotal role in immune cell infiltration in BRCA. Notably, high MCU performance is linked to CD8 + T cells infiltration in BRCA. Furthermore, pharmacogenomic analysis of BRCA cell lines indicates that MCU inactivation is associated with increased sensitivity to specific small molecule drugs. Our findings suggest that MCU alterations may be linked to BRCA progression, unveiling new diagnostic and prognostic implications for MCU in BRCA. The study underscores MCU's role in the tumor immune microenvironment and cell cycle progression, positioning it as a potential tool for BRCA precision medicine and drug screening.

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

Accumulated evidence demonstrates that long non-coding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood due to the lack of a reference map of lncRNA transcriptome in various cell types. In this study, we employed a targeted single-cell RNA sequencing (scRNA-seq) method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys. 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 (GRNs) 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 provide insights into their potential roles in regulating cellular processes, such as immune response and energy metabolism, during kidney aging. 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/).

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy.

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

Single-cell analysis of 5-aminolevulinic acid intraoperative labeling specificity for glioblastoma.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor, and resection is a key part of the standard of care. In fluorescence-guided surgery (FGS), fluorophores differentiate tumor tissue from surrounding normal brain. The heme synthesis pathway converts 5-aminolevulinic acid (5-ALA), a fluorogenic substrate used for FGS, to fluorescent protoporphyrin IX (PpIX). The resulting fluorescence is believed to be specific to neoplastic glioma cells, but this specificity has not been examined at a single-cell level. The objective of this study was to determine the specificity with which 5-ALA labels the diversity of cell types in GBM. The authors performed single-cell optical phenotyping and expression sequencing-version 2 (SCOPE-seq2), a paired single-cell imaging and RNA sequencing method, of individual cells on human GBM surgical specimens with macroscopically visible PpIX fluorescence from patients who received 5-ALA prior to surgery. SCOPE-seq2 allowed the authors to simultaneously image PpIX fluorescence and unambiguously identify neoplastic cells from single-cell RNA sequencing. Experiments were also conducted in cell culture and co-culture models of glioma and in acute slice cultures from a mouse glioma model to investigate cell- and tissue-specific uptake and secretion of 5-ALA and PpIX. SCOPE-seq2 analysis of human GBM surgical specimens revealed that 5-ALA treatment resulted in labeling that was not specific to neoplastic glioma cells. The cell culture further demonstrated that nonneoplastic cells could be labeled by 5-ALA directly or by PpIX secreted from surrounding neoplastic cells. Acute slice cultures from mouse glioma models showed that 5-ALA preferentially labeled GBM tumor tissue over nonneoplastic brain tissue with significant labeling in the tumor margins, and that this contrast was not due to blood-brain barrier disruption. Together, these findings support the use of 5-ALA as an indicator of GBM tissue but question the main advantage of 5-ALA for specific intracellular labeling of neoplastic glioma cells in FGS. Further studies are needed to systematically compare the performance of 5-ALA to that of potential alternatives for FGS.

Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics.

Head and neck squamous cell carcinoma (HNSCC) is one of the most frequent cancers worldwide. The main risk factors are consumption of tobacco products and alcohol, as well as infection with human papilloma virus. Approved therapeutic options comprise surgery, radiation, chemotherapy, targeted therapy through epidermal growth factor receptor inhibition, and immunotherapy, but outcome has remained unsatisfactory due to recurrence rates of ~50% and the frequent occurrence of second primaries. The availability of the human genome sequence at the beginning of the millennium heralded the omics era, in which rapid technological progress has advanced our knowledge of the molecular biology of malignant diseases, including HNSCC, at an unprecedented pace. Initially, microarray-based methods, followed by approaches based on next-generation sequencing, were applied to study the genetics, epigenetics, and gene expression patterns of bulk tumors. More recently, the advent of single-cell RNA sequencing (scRNAseq) and spatial transcriptomics methods has facilitated the investigation of the heterogeneity between and within different cell populations in the tumor microenvironment (e.g., cancer cells, fibroblasts, immune cells, endothelial cells), led to the discovery of novel cell types, and advanced the discovery of cell-cell communication within tumors. This review provides an overview of scRNAseq, spatial transcriptomics, and the associated bioinformatics methods, and summarizes how their application has promoted our understanding of the emergence, composition, progression, and therapy responsiveness of, and intercellular signaling within, HNSCC.

Cardiac Development at a Single-Cell Resolution.

Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.

Single-Cell Long-Read Sequencing Enables the Dissection of BTK Subclonal Dynamics in CLL Treatment

Introduction: BTK inhibition is an effective frontline therapy for patients with CLL. However, patients who develop BTK C481S mutations relapse quickly. We recently published a longitudinal study investigating subclonal evolution in 38 patients receiving BTKi treatment for CLL. Of the 38 patients, 6 developed a subclone containing a BTK C481S mutation identified in the whole-exome sequencing (WES) data at the time of relapse. To understand whether a BTK C481S mutation alone can drive relapse or if an additional CLL-relevant mutation is required, we investigate these subclones on a single-cell level using PacBio Multiplexed Arrays Sequencing (MAS-seq). MAS-seq generates single-cell, long-read RNA sequencing data, which is highly accurate and provides coverage across the entire mRNA transcript. Methods: For each patient, B cells were isolated from a pre-BTKi treatment sample and a sample taken at the time of relapse. Single-cell cDNA library preps were generated using the 10X Genomics Chromium platform. The cDNA was then sequenced using MAS-Seq. After processing the MAS-seq data using PacBio's Isoseq pipeline, Seurat was used to perform a single-cell analysis for each sample. We used scBayes, a Bayesian-statistical approach developed in our lab (Qiao et al., Genome Research, in review), to assign subclone identity to individual cells based on the expression of previously identified somatic mutations. The transcript-wide coverage of MAS-seq increases the likelihood of coverage at the somatic mutation sites compared to traditional single-cell RNA sequencing methods. Results: We have sequenced and analyzed samples from 4 patients in this study (pt 1-4). In the pretreatment sample of pt 1, two subclones containing different TP53 mutations were present. At the time of relapse, a BTK C481S subclone evolved from one of these TP53 subclones, becoming the dominant clonal population. No other CLL-relevant mutations were detected in this patient. Pts 2-4 had an additional CLL-relevant mutation evolve with the BTK C481S mutation in the dominant subclone at the time of relapse. In pt 2, a subclone with an NFKBIE and the BTK mutation evolved from a cell population where BRAF, BCOR, and EGFR2 mutations were present. The BTK C481S subclone in Pt 3 also had new KMT2D and CREBBP mutations. This subclone evolved from a cell population with an ASXL1 and SF3B1 mutation. In pts 1-3, the BTK subclone was only seen at the time of relapse. Pt 4 did not have any CLL-relevant mutations identified before treatment. In a sample taken 6 months before relapse, a BTK C481S subclone is visible at a low frequency with no other CLL-relevant mutations. At the time of relapse, a DICER1 variant, a gene recently implicated in CLL (Knisbacher et al., Nature Genetics, 2022), evolved from the BTK subclone and is seen at the time of relapse as the dominant subclone. Furthermore, the full-transcript sequencing at the single-cell level enabled us to identify a second BTK C481S subclone present at the time of relapse and determine that it was independent of the subclone containing both the BTK C481S and DICER1 mutations. This second BTK subclone had no other CLL-relevant mutations and remained at a very low frequency. Conclusion: Single-cell long-read RNA sequencing via MAS-seq provides transcript-wide coverage, which enables genotyping at somatic mutation sites. Using this method, we found that the dominant relapse clones all contained a BTK C481S mutation, which either co-evolved with additional CLL-relevant mutations (3/4 patients) or occurred on the background of existing TP53 mutations (1/4 patients).

Single-cell RNA sequencing and multiple bioinformatics methods to identify the immunity and ferroptosis-related biomarkers of SARS-CoV-2 infections to ischemic stroke.

Since December 2019, Coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant morbidity and mortality worldwide. There is an increased risk of ischemic stroke (IS) associated with COVID-19. However, few studies have been reported to explain the potential correlation between COVID-19 and IS. We investigated the relationship and relevant mechanisms between COVID-19 and IS using single-cell RNA sequencing and multiple bioinformatics approaches. By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 73 COVID-19-related IS genes. According to the KEGG pathway analysis, the COVID-19-related IS disease genes were significantly enriched in the hematopoietic cell lineage pathway, ribosome pathway, COVID-19 pathway and primary immunodeficiency pathway. Finally, three genes associated with immunity (B4GALT5, CRISPLD2, F5) and two genes associated with ferroptosis (ACSL1, CREB5) were identified up-regulated in COVID-19-related IS. Significantly, it was found that all five genes were highly expressed in monocytes by single cell RNA sequencing. We believe these genes (B4GALT5, CRISPLD2, F5, ACSL1, CREB5) may regulate the immune response and ferroptosis of multiple immune cells, mainly including monocytes, which may contribute to the development of COVID-19-related IS. In addition, these genes may be potential targets for the treatment of COVID-19-related IS.

LAST-seq: single-cell RNA sequencing by direct amplification of single-stranded RNA without prior reverse transcription and second-strand synthesis

Existing single-cell RNA sequencing (scRNA-seq) methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert single-stranded RNA into double-stranded DNA prior to amplification, with the limited RT/SSS efficiency compromising RNA detectability. Here, we develop a new scRNA-seq method, Linearly Amplified Single-stranded-RNA-derived Transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA molecules without prior RT/SSS. LAST-seq offers a high single-molecule capture efficiency and a low level of technical noise for single-cell transcriptome analyses. Using LAST-seq, we characterize transcriptional bursting kinetics in human cells, revealing a role of topologically associating domains in transcription regulation.

Application of single-cell RNA sequencing methods to develop B cell targeted treatments for autoimmunity.

The COVID-19 pandemic coincided with several transformative advances in single-cell analysis. These new methods along with decades of research and trials with antibody therapeutics and RNA based technologies allowed for highly effective vaccines and treatments to be produced at astonishing speeds. While these tools were initially focused on models of infection, they also show promise in an autoimmune setting. Self-reactive B cells play important roles as antigen-presenting cells and cytokine and autoantibody producers for many autoimmune diseases. Yet, current therapies to target autoreactive B cells deplete all B cells irrespective of their pathogenicity. Development of self-reactive B cell targeting therapies that would spare non-pathogenic B cells are needed to treat disease while allowing effective immune responses to other ailments. Single-cell RNA sequencing (scRNA-seq) approaches will aid in identification of the pathogenic self-reactive B cells operative in autoimmunity and help with development of more favorable precision targeted therapies.

Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease.

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencingtechnologies.