scRNA-seq Platforms Research Articles

Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster

We explore the understudied role of Y chromosome genes in modulating mating behaviors and interval timing in male fruit flies. Our findings reveal a significant impact of these genes on mating duration, a critical aspect of sexual selection and reproductive success. Through the use of XO males lacking a Y chromosome and RNA interference (RNAi) techniques to knockdown specific Y chromosome genes, we demonstrate that the Y chromosome and its genes WDY and CCY are essential for the generation of Longer-Mating-Duration (LMD) and Shorter-Mating-Duration (SMD) behaviors. Notably, the neuronal knockdown of Ppr-Y, a gene highly expressed in both neuronal and glial cells, leads to profound disruptions in courtship and mating behaviors without affecting fertility. Utilizing the fly SCope scRNA-seq data platform, we identified that several Y chromosome genes, including kl-3, kl-5, WDY, and PRY, are preferentially expressed in fru-positive neurons, suggesting a role in male-specific neuronal populations. Our work not only advances the understanding of the Y chromosome's contribution to complex mating behaviors but also sets the stage for future investigations into the molecular mechanisms underlying sexual dimorphism and reproductive strategies in Drosophila.

Deciphering cellular heterogeneity in Spodoptera frugiperda midgut cell line through single cell RNA sequencing

Using the 10x Genomics Chromium single-cell RNA sequencing (scRNA-seq) platform, we discovered unexpected heterogeneity in an established cell line developed from the midgut of the Fall armyworm, Spodoptera frugiperda, a major global pest. We analyzed the sequences of 18,794 cells and identified ten unique cellular clusters, including stem cells, enteroblasts, enterocytes and enteroendocrine cells, characterized by the expression of specific marker genes. Additionally, these studies addressed an important knowledge gap by investigating the expression of genes coding for respiratory and midgut membrane insecticide targets classified by the Insecticide Resistance Action Committee. Dual-fluorescence tagging method, fluorescence microscopy and fluorescence-activated cell sorting confirmed the expression of midgut cell type-specific genes. Stem cells were isolated from the heterogeneous population of SfMG-0617 cells. Our results, validated by KEGG and Gene Ontology analyses and supported by Monocle 3.0, advance the fields of midgut cellular biology and establish standards for scRNA-seq studies in non-model organisms.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

Comparison of the single-cell and single-nucleus hepatic myeloid landscape within decompensated cirrhosis patients.

A complete understanding of disease pathophysiology in advanced liver disease is hampered by the challenges posed by clinical specimen collection. Notably, in these patients, a transjugular liver biopsy (TJB) is the only safe way to obtain liver tissue. However, it remains unclear whether successful sequencing of this extremely small and fragile tissue can be achieved for downstream characterization of the hepatic landscape. Here we leveraged in-house available single-cell RNA-sequencing (scRNA-seq) and single-nucleus (snRNA-seq) technologies and accompanying tissue processing protocols and performed an in-patient comparison on TJB's from decompensated cirrhosis patients (n = 3). We confirmed a high concordance between nuclear and whole cell transcriptomes and captured 31,410 single nuclei and 6,152 single cells, respectively. The two platforms revealed similar diversity since all 8 major cell types could be identified, albeit with different cellular proportions thereof. Most importantly, hepatocytes were most abundant in snRNA-seq, while lymphocyte frequencies were elevated in scRNA-seq. We next focused our attention on hepatic myeloid cells due to their key role in injury and repair during chronic liver disease. Comparison of their transcriptional signatures indicated that these were largely overlapping between the two platforms. However, the scRNA-seq platform failed to recover sufficient Kupffer cell numbers, and other monocytes/macrophages featured elevated expression of stress-related parameters. Our results indicate that single-nucleus transcriptome sequencing provides an effective means to overcome complications associated with clinical specimen collection and could sufficiently profile all major hepatic cell types including all myeloid cell subsets.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality.

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

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.

Identification of Niche-Specific Gene Signatures between Malignant Tumor Microenvironments by Integrating Single Cell and Spatial Transcriptomics Data.

The tumor microenvironment significantly affects the transcriptomic states of tumor cells. Single-cell RNA sequencing (scRNA-seq) helps elucidate the transcriptomes of individual cancer cells and their neighboring cells. However, cell dissociation results in the loss of information on neighboring cells. To address this challenge and comprehensively assess the gene activity in tissue samples, it is imperative to integrate scRNA-seq with spatial transcriptomics. In our previous study on physically interacting cell sequencing (PIC-seq), we demonstrated that gene expression in single cells is affected by neighboring cell information. In the present study, we proposed a strategy to identify niche-specific gene signatures by harmonizing scRNA-seq and spatial transcriptomic data. This approach was applied to the paired or matched scRNA-seq and Visium platform data of five cancer types: breast cancer, gastrointestinal stromal tumor, liver hepatocellular carcinoma, uterine corpus endometrial carcinoma, and ovarian cancer. We observed distinct gene signatures specific to cellular niches and their neighboring counterparts. Intriguingly, these niche-specific genes display considerable dissimilarity to cell type markers and exhibit unique functional attributes independent of the cancer types. Collectively, these results demonstrate the potential of this integrative approach for identifying novel marker genes and their spatial relationships.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Chemoenzymatic Measurement of LacNAc in Single-Cell Multiomics Reveals It as a Cell-Surface Indicator of Glycolytic Activity of CD8+ T Cells.

Despite the rich information about the physiological state of a cell encoded in the dynamic changes of cell-surface glycans, chemical methods to capture specific glycan epitopes at the single-cell level are quite limited. Here, we report a chemoenzymatic method for the single-cell detection of N-acetyllactosamine (LacNAc) by labeling LacNAc with a specific DNA barcode. The chemoenzymatic labeling does not alter the transcriptional status of immune cells and is compatible with multiple scRNA-seq platforms. Integrated analysis of LacNAc and the transcriptome of T cells at the single-cell level reveals that the amount of cell-surface LacNAc is significantly upregulated in activated CD8+ T cells but maintained at basal levels in resting CD8+ T cells (i.e., naive and central memory T cells). Further analysis confirms that LacNAc levels are positively correlated with the glycolytic activity of CD8+ T cells during differentiation. Taken together, our study demonstrates the feasibility of the chemoenzymatic detection of cell-surface glycan in single-cell RNA sequencing-based multiomics with TCR sequence and cell-surface epitope information (i.e., scTCR and CITE-seq), and provides a new way to characterize the biological role of glycan in diverse physiological states.

Flexible and high-throughput approach for capturing large number of single cells simultaneously using microwell-based technology

Abstract Single-cell RNA-seq is a powerful method for studying complex cell populations. As opportunities grow for single-cell multiomics methods in translational research, there is an increased need for high-throughput approaches capable of efficiently capturing and analyzing large numbers of cells simultaneously. Current assays on scRNA-seq platforms have limited throughput due to the low cell recovery and high multiplet rates with high cell inputs. Here we demonstrate the feasibility of profiling the single-cell gene expression of hundreds of thousands of cells in a single experiment using an eight-lane microwell based cartridge. We also increased the cell label diversity of cell capture beads to enable high cell loading with a lower rate of barcode collisions. To show the range of cell capture capabilities of the eight-lane microwell cartridge, we loaded different numbers of cells per lane ranging from 100 to over 40,000 cells. To evaluate the multiplet rates and sensitivity, we labeled two different cell types with antibody-based sample multiplexing prior to cell capture. Cell multiplet rates were also assessed through Sample Tag analysis. Detection of Sample Tags used showed no contamination of Sample Tag between samples. In addition, less than 0.5% barcode collision rate was detected from 360,000 viable cells captured with a bead. Targeted mRNA profiling had high correlation (R 2> 0.95) of gene expression across cell capture lanes. This study demonstrates that the eight-lane microwell-based cartridge technology provides flexibility and high-throughput cell capture of thousands of cells and could be readily extended to other existing single-cell technologies such as surface protein marker detection and immune profiling.

Single cell multi-omics technologies enable human immunogenomics monitoring

Abstract Single-cell RNA-seq (scRNAseq) can investigate transcriptomic or targeted gene expression, including V(D)J and surface proteins to provide insights into mechanism discovering and therapeutic monitoring. Multiple scRNAseq technologies have recently been developed and emerged as a major driver of progress in biomedical research. Here we used three scRNAseq platforms, BD Rhapsody, Fluent PIPseq, and Parse Evercode, to monitor immunogenomics variation of certain immune cells from four hematopoietic stem cell transplant recipients. Our results identified major signature genes as potential biomarkers for stem cell transplantation outcomes. We compared an array of metrics from the three platforms in parallel to assess applications, unique advantages, and cost-effectiveness. We conclude that the BD Rhapsody system is the most comprehensive multi-omics platform for immunogenomics analyses of tens of thousands of cells, while Fluent PIPseq & Pasre Evercode have unique features and potential for larger scale single cell multi-omics profiling with Parse enabling up to one million cells per experiment. supported by 5P01CA049605-32 (EHM) from NIH, K00CA245728 (CSB) from NCI, and OPP1113682 (HTM) from the Bill and Melinda Gates Foundation.

Abstract 2454: Identification of colorectal cancer stem cells from single cell RNA sequencing

Abstract Cancer stem cells (CSCs) play a critical role in metastasis, relapse and therapy resistance in colorectal cancer (CRC). Although the normal lineage of cell development in the intestine has identified many of the genes involved in the induction and maintenance of pluripotency, recent studies of CSCs suggest significant heterogeneity and lack of common surface markers for identification. In this study, we perform single cell RNA sequencing (scRNA-seq) to extract a single cell stemness signature (SCS_sig) for colorectal CSCs identification and as a potential predictor of relapse after surgical resection. A total of 26 fresh surgery specimens (6 colon tumor, 3 colon matched normal, 9 liver metastasis, 8 liver matched normal) from neoadjuvant chemotherapy (NAC) treated patients with single-cell transcriptome generated using 10X Chromium scRNA-seq and Illumina platforms were analyzed. Following extensive quality control and batch effect correction, we identified 20k epithelial cells using canonical epithelial cell marker genes (EPCAM, KRT8 and KRT18) from a total of 111,200 cells, with mean 1113 epithelial cell per sample (excluding liver normal samples).To obtain the CSCs profiling signature, we first annotated a ‘gold-standard’ set of CSC that expressed all canonical colon CSC marker genes (LGR5, ASCL2, EPHB2, PROM1, AXIN2, LEFTY1, RNF43, CD44 and SLC12A2), identifying 346 CSCs (1.71% of epithelial cells). Published stemness signatures were significantly enriched in the gold standard stem cells (false discovery rate (FDR) < 0.05). Then we selected the 50 most significantly up-regulated genes in this gold standard set (relative to other epithelial cells) as a single-cell stemness signature (named SCS_sig hereafter). Interesting, we saw that in every tumor there was a continuum of stemness, rather than distinct stem and differentiated populations. The stemness state by SCS_sig was positively correlated with less differentiated state calculated by CytoTRACE score (r = 0.76, p< 0.001), a computational method that predicts the differentiation state of cells from scRNA-seq data. The SCS_sig was significantly higher in tumor cells compared to normal cells in our cohort as well as the previously published SMC cohort (fold change = 2.52 and p < 0.001, fold change = 1.44 and p < 0.001, respectively). There was notable heterogeneity in the fraction of stem-like tumor cells with in 15 tumor samples, with the tumor with greatest CSC percentage having the shortest time to relapse. The SCS_sig generated from stem-like cells can identify the stemness state of epithelial cells in scRNAseq data. A signature score is more simple and robust than using few or panel of CSCs marker genes to identify single CSC because of the reads dropout in scRNA-seq. Further investigation of the SCS_sig as a predictive and prognostic biomarker in CRC is ongoing in larger patient cohorts. Citation Format: Kangyu Lin, Mohammad Zeineddine, Saikat Chowdhury, Nicholas Hornstein, Sendurai Mani, Scott Kopetz, John Paul Shen. Identification of colorectal cancer stem cells from single cell RNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2454.

Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow.

The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.In this chapter, we describe how cell subpopulation discovery algorithms, integrated into rCASC, could be efficiently executed on cloud-HPC infrastructure. To achieve this task, we focus on the StreamFlow framework which provides container-native runtime support for scientific workflows in cloud/HPC environments.

Abstract B031: The effects of tissue processing and microfluidic factors on single-cell RNA-sequencing data quality in colorectal tissues

Abstract Our HTAN (Human Tumor Atlas Network) study presented in Chen et al. documented single-cell RNA-sequencing (scRNA-seq) data on 128 human colonic specimens for mapping out distinct paths for polyp to cancer transformations. What we have not detailed is our optimizations on the droplet-based scRNA-seq approach that generate high quality data from challenging tissues such as the colonic epithelium. In this work, we provide a systematic dissection of tissue processing and microfluidic encapsulation factors that affect data quality generated on colonic epithelial cells using an open droplet-based scRNA-seq platform (inDrops). We optimized our process based on these factors to significantly decrease dying cell bodies and ambient RNA from being co-encapsulated with true cells, which led to the development of the current scRNA-seq protocol used for human specimens. We have also developed pre-filtering data metrics to score data quality stemming from degree of ambient contamination. We envision that these experimental optimizations reduce the amount of introduced artifacts, leading to more rigorous biological interpretations. Citation Format: Deronisha L. Arceneaux, Zhengyi Chen, Joey Simmons, Bob Chen, Cody Heiser, Yanwen Xu. The effects of tissue processing and microfluidic factors on single-cell RNA-sequencing data quality in colorectal tissues [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr B031.

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types.

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.

Determination of the nanoparticle- and cell-specific toxicological mechanisms in 3D liver spheroids using scRNAseq analysis

Engineered nanomaterials (ENMs) are commonly used in consumer products, allowing exposure to target organs such as the lung, liver, and skin that could lead to adverse health effects in humans. To better reflect on toxicological effects in liver cells, it is important to consider the contribution of hepatocyte morphology, function, and intercellular interactions in a dynamic 3D microenvironment. Herein, we used a 3D liver spheroid model containing hepatocyte and Kupffer cells (KCs) to study the effects of three different material compositions, namely vanadium pentoxide (V2O5), titanium dioxide (TiO2), or graphene oxide (GO). Additionally, we used single-cell RNA sequencing (scRNAseq) to determine the nanoparticle (NP) and cell-specific toxicological responses. A general finding was that hepatocytes exhibit more variation in gene expression and adaptation of signaling pathways than KCs. TNF-α production tied to the NF-κB pathway was a commonly affected pathway by all NPs while impacts on the metabolic function of hepatocytes were unique to V2O5. V2O5 NPs also showed the largest number of differentially expressed genes in both cell types, many of which are related to pro-inflammatory and apoptotic response pathways. There was also evidence of mitochondrial ROS generation and caspase-1 activation after GO and V2O5 treatment, in association with cytokine production. All considered, this study provides insight into the impact of nanoparticles on gene responses in key liver cell types, providing us with a scRNAseq platform that can be used for high-content screening of nanomaterial impact on the liver, for use in biosafety and biomedical applications.

Time-resolved single-cell RNA-seq using metabolic RNA labelling

Single-cell RNA genomics technologies are revolutionizing biomedical science by profiling single cells with unprecedented resolution, providing fundamental insights into the role of different cellular states and intercellular heterogeneity in health and disease. The combination of single-cell RNA sequencing (scRNA-seq) with metabolic RNA labelling approaches now enables time-resolved monitoring of transcriptional responses for thousands of genes in thousands of individual cells in parallel. This facilitates and accelerates direct characterization of the temporal dimension of biological processes, which has been largely missing in current data. In this Primer, we provide an overview of the various metabolic RNA labelling approaches and their combination with currently available scRNA-seq and multi-omics platforms. We summarize the main challenges in the design of such experiments and discuss the various applications of time-resolved scRNA-seq in vitro and in vivo. We outline the computational tools and challenges to the analyses of the temporal dynamics of transcriptional responses at the single-cell level. We discuss the prospect of integrating data obtained by the respective time-resolved scRNA-seq approaches with complementary methods to elucidate gene regulatory networks that underlie molecular mechanisms. Finally, we discuss open questions and challenges in the field and give our thoughts for future development and applications. The combination of single-cell RNA sequencing with metabolic RNA labelling enables a time-resolved view of transcriptional responses in individual cells. In this Primer, Erhard and Saliba et al. discuss metabolic labelling approaches and how to assess the temporal dynamics of transcriptional responses in different conditions.

TAS-Seq is a robust and sensitive amplification method for bead-based scRNA-seq

Single-cell RNA-sequencing (scRNA-seq) is valuable for analyzing cellular heterogeneity. Cell composition accuracy is critical for analyzing cell–cell interaction networks from scRNA-seq data. However, droplet- and plate-based scRNA-seq techniques have cell sampling bias that could affect the cell composition of scRNA-seq datasets. Here we developed terminator-assisted solid-phase cDNA amplification and sequencing (TAS-Seq) for scRNA-seq based on a terminator, terminal transferase, and nanowell/bead-based scRNA-seq platform. TAS-Seq showed high tolerance to variations in the terminal transferase reaction, which complicate the handling of existing terminal transferase-based scRNA-seq methods. In murine and human lung samples, TAS-Seq yielded scRNA-seq data that were highly correlated with flow-cytometric data, showing higher gene-detection sensitivity and more robust detection of important cell–cell interactions and expression of growth factors/interleukins in cell subsets than 10X Chromium v2 and Smart-seq2. Expanding TAS-Seq application will improve understanding and atlas construction of lung biology at the single-cell level.