Single-Cell Transcriptional Analysis

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

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.

Defining the relationship of salivary gland malignancies to novel cell subpopulations in human salivary glands using single nucleus RNA-sequencing

Salivary glands have essential roles in maintaining oral health, mastication, taste and speech, by secreting saliva. Salivary glands are composed of several types of cells, and each cell type is predicted to be involved in the carcinogenesis of different types of cancers including adenoid cystic carcinoma (ACC), acinic cell carcinoma (AciCC), salivary duct carcinoma (SDC), myoepithelial carcinoma (MECA) and other histology. In our study, we performed single nucleus RNA-seq on three human salivary gland samples to clarify the gene expression profile of each complex cellular component of the salivary glands and related these expression patterns to expression found in salivary gland cancers (SGC) to infer cell of origin. By single nucleus RNA-seq, salivary gland cells were stratified into four clusters: acinar cells, ductal cells 1, ductal cells 2 and myoepithelial cells/stromal cells. The localization of each cell group was verified by IHC of each cluster marker gene, and one group of ductal cells was found to represent intercalated ductal cells labeled with HES1. Furthermore, in comparison with SGC RNA-seq data, acinar cell markers were upregulated in AciCC, but downregulated in ACC and ductal cell markers were upregulated in SDC but downregulated in MECA, suggesting that markers of origin are highly expressed in some SGC. Cell type expressions in specific SGC histology are similar to those found in normal salivary gland populations, indicating a potential etiologic relationship.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Highly Multiplexed, Efficient, and Automated Single-Cell MicroRNA Sequencing with Digital Microfluidics

Single-cell microRNA (miRNA) sequencing has allowed for comprehensively studying the abundance and complex networks of miRNAs, which provides insights beyond single-cell heterogeneity into the dynamic regulation of cellular events. Current benchtop-based technologies for single-cell miRNA sequencing are low throughput, limited reaction efficiency, tedious manual operations, and high reagent costs. Here, a highly multiplexed, efficient, integrated, and automated sample preparation platform is introduced for single-cell miRNA sequencing based on digital microfluidics (DMF), named Hiper-seq. The platform integrates major steps and automates the iterative operations of miRNA sequencing library construction by digital control of addressable droplets on the DMF chip. Based on the design of hydrophilic micro-structures and the capability of handling droplets of DMF, multiple single cells can be selectively isolated and subject to sample processing in a highly parallel way, thus increasing the throughput and efficiency for single-cell miRNA measurement. The nanoliter reaction volume of this platform enables a much higher miRNA detection ability and lower reagent cost compared to benchtop methods. It is further applied Hiper-seq to explore miRNAs involved in the ossification of mouse skeletal stem cells after bone fracture and discovered unreported miRNAs that regulate bone repairing.

Single cell transcriptome sequencing of stimulated and frozen human peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) are blood cells that are a critical part of the immune system used to fight off infection, defending our bodies from harmful pathogens. In biomedical research, PBMCs are commonly used to study global immune response to disease outbreak and progression, pathogen infections, for vaccine development and a multitude of other clinical applications. Over the past few years, the revolution in single-cell RNA sequencing (scRNA-seq) has enabled an unbiased quantification of gene expression in thousands of individual cells, which provides a more efficient tool to decipher the immune system in human diseases. In this work, we generate scRNA-seq data from human PBMCs at high sequencing depth (>100,000 reads/cell) for more than 30,000 cells, in resting, stimulated, fresh and frozen conditions. The data generated can be used for benchmarking batch correction and data integration methods, and to study the effect of freezing-thawing cycles on the quality of immune cell populations and their transcriptomic profiles.

scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data

BACKGROUND

Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. Obtaining a perfect clustering result is of central importance for subsequent analyses, but not easy. Additionally, the increase in cell throughput due to the advancement of scRNA-seq protocols exacerbates many computational issues, especially regarding method runtime. To address these difficulties, a new, accurate, and fast method for detecting DEGs in scRNA-seq data is needed.

RESULTS

Here, we propose single-cell minimum enclosing ball (scMEB), a novel and fast method for detecting single-cell DEGs without prior cell clustering results. The proposed method utilizes a small part of known non-DEGs (stably expressed genes) to build a minimum enclosing ball and defines the DEGs based on the distance of a mapped gene to the center of the hypersphere in a feature space.

CONCLUSIONS

We compared scMEB to two different approaches that could be used to identify DEGs without cell clustering. The investigation of 11 real datasets revealed that scMEB outperformed rival methods in terms of cell clustering, predicting genes with biological functions, and identifying marker genes. Moreover, scMEB was much faster than the other methods, making it particularly effective for finding DEGs in high-throughput scRNA-seq data. We have developed a package scMEB for the proposed method, which could be available at https://github.com/FocusPaka/scMEB .

Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes

Ferroptosis is distinct from classic apoptotic cell death characterized by the accumulation of reactive oxygen species (ROS) and lipid peroxides on the cell membrane. Increasing findings have demonstrated that ferroptosis plays an important role in cancer development, but the exploration of ferroptosis in breast cancer is limited. In our study, we aimed to establish a ferroptosis activation-related model based on the differentially expressed genes between a group exhibiting high ferroptosis activation and a group exhibiting low ferroptosis activation. By using machine learning to establish the model, we verified the accuracy and efficiency of our model in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) set and gene expression omnibus (GEO) dataset. Additionally, our research innovatively utilized single-cell RNA sequencing data to systematically reveal the microenvironment in the high and low FeAS groups, which demonstrated differences between the two groups from comprehensive aspects, including the activation condition of transcription factors, cell pseudotime features, cell communication, immune infiltration, chemotherapy efficiency, and potential drug resistance. In conclusion, different ferroptosis activation levels play a vital role in influencing the outcome of breast cancer patients and altering the tumor microenvironment in different molecular aspects. By analyzing differences in ferroptosis activation levels, our risk model is characterized by a good prognostic capacity in assessing the outcome of breast cancer patients, and the risk score can be used to prompt clinical treatment to prevent potential drug resistance. By identifying the different tumor microenvironment landscapes between the high- and low-risk groups, our risk model provides molecular insight into ferroptosis in breast cancer patients.

Cloning a profibrotic stem cell variant in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and rapidly fatal interstitial lung disease marked by the replacement of lung alveoli with dense fibrotic matrices. Although the mechanisms initiating IPF remain unclear, rare and common alleles of genes expressed in lung epithelia, combined with aging, contribute to the risk for this condition. Consistently, single-cell RNA sequencing (scRNA-seq) studies have identified lung basal cell heterogeneity in IPF that might be pathogenic. We used single-cell cloning technologies to generate "libraries" of basal stem cells from the distal lungs of 16 patients with IPF and 10 controls. We identified a major stem cell variant that was distinguished from normal stem cells by its ability to transform normal lung fibroblasts into pathogenic myofibroblasts in vitro and to activate and recruit myofibroblasts in clonal xenografts. This profibrotic stem cell variant, which was shown to preexist in low quantities in normal and even fetal lungs, expressed a broad network of genes implicated in organ fibrosis and showed overlap in gene expression with abnormal epithelial signatures identified in previously published scRNA-seq studies of IPF. Drug screens highlighted specific vulnerabilities of this profibrotic variant to inhibitors of epidermal growth factor and mammalian target of rapamycin signaling as prospective therapeutic targets. This profibrotic stem cell variant in IPF was distinct from recently identified profibrotic stem cell variants in chronic obstructive pulmonary disease and may extend the notion that inappropriate accrual of minor and preexisting stem cell variants contributes to chronic lung conditions.

scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors

Single-cell multi-omics can provide a unique perspective on tumor cellular heterogeneity. Most previous single-cell whole-genome RNA sequencing (scWGS-RNA-seq) methods demonstrate utility with intact cells from fresh samples. Among them, many are not applicable to frozen samples that cannot produce intact single-cell suspensions. We have developed scONE-seq, a versatile scWGS-RNA-seq method that amplifies single-cell DNA and RNA without separating them from each other and hence is compatible with frozen biobanked samples. We benchmarked scONE-seq against existing methods using fresh and frozen samples to demonstrate its performance in various aspects. We identified a unique transcriptionally normal-like tumor clone by analyzing a 2-year frozen astrocytoma sample, demonstrating that performing single-cell multi-omics interrogation on biobanked tissue by scONE-seq could enable previously unidentified discoveries in tumor biology.

Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

Background

Recent technological advances opened the opportunity to simultaneously study gene expression for thousands of individual cells on a genome-wide scale. The experimental accessibility of such single-cell RNA sequencing (scRNAseq) approaches allowed gaining insights into the cell type composition of heterogeneous tissue samples of animal model systems and emerging models alike. A major prerequisite for a successful application of the method is the dissociation of complex tissues into individual cells, which often requires large amounts of input material and harsh mechanical, chemical and temperature conditions. However, the availability of tissue material may be limited for small animals, specific organs, certain developmental stages or if samples need to be acquired from collected specimens. Therefore, we evaluated different dissociation protocols to obtain single cells from small tissue samples of Drosophila melanogaster eye-antennal imaginal discs.

Results

We show that a combination of mechanical and chemical dissociation resulted in sufficient high-quality cells. As an alternative, we tested protocols for the isolation of single nuclei, which turned out to be highly efficient for fresh and frozen tissue samples. Eventually, we performed scRNAseq and single-nuclei RNA sequencing (snRNAseq) to show that the best protocols for both methods successfully identified relevant cell types. At the same time, snRNAseq resulted in less artificial gene expression that is caused by rather harsh dissociation conditions needed to obtain single cells for scRNAseq. A direct comparison of scRNAseq and snRNAseq data revealed that both datasets share biologically relevant genes among the most variable genes, and we showed differences in the relative contribution of the two approaches to identified cell types.

Conclusion

We present two dissociation protocols that allow isolating single cells and single nuclei, respectively, from low input material. Both protocols resulted in extraction of high-quality RNA for subsequent scRNAseq or snRNAseq applications. If tissue availability is limited, we recommend the snRNAseq procedure of fresh or frozen tissue samples as it is perfectly suited to obtain thorough insights into cellular diversity of complex tissue.

Click Chemistry Enables Rapid Amplification of Full-Length Reverse Transcripts for Long-Read Third Generation Sequencing

Here we describe the development of a novel click chemistry-based method for the generation and amplification of full-length cDNA libraries from total RNA, while avoiding the need for problematic template-switching (TS) reactions. Compared with prior efforts, our method involves neither random priming nor stochastic cDNA termination, thus enabling amplification of transcripts that were previously inaccessible via related click chemistry-based RNA sequencing techniques. A key modification involving the use of PCR primers containing two overhanging 3'-nucleotides substantially improved the read-through compatibility of the 1,4-disubstituted 1,2,3-triazole-containing cDNA, where such modifications typically hinder amplification. This allowed us to more than double the possible insert size compared with the state-of-the art click chemistry-based technique, PAC-seq. Furthermore, our method performed on par with a commercially available PCR-cDNA RNA sequencing kit, as determined by Oxford Nanopore sequencing. Given the known advantages of PAC-seq, namely, suppression of PCR artifacts, we anticipate that our contribution could enable diverse applications including improved analyses of mRNA splicing variants and fusion transcripts.

Histologically resolved multiomics enables precise molecular profiling of human intratumor heterogeneity

Both the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multiomics in unfixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser microdissection of regions of interests. These microtissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human oral squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multiomics assessments.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

A Primer for Single-Cell Sequencing in Non-Model Organisms

Single-cell sequencing technologies have led to a revolution in our knowledge of the diversity of cell types, connections between biological levels of organization, and relationships between genotype and phenotype. These advances have mainly come from using model organisms; however, using single-cell sequencing in non-model organisms could enable investigations of questions inaccessible with typical model organisms. This primer describes a general workflow for single-cell sequencing studies and considerations for using non-model organisms (limited to multicellular animals). Importantly, single-cell sequencing, when further applied in non-model organisms, will allow for a deeper understanding of the mechanisms between genotype and phenotype and the basis for biological variation.

RNA multimerization as an organizing force for liquid-liquid phase separation

RNA interactions are exceptionally strong and highly redundant. As such, nearly any two RNAs have the potential to interact with one another over relatively short stretches, especially at high RNA concentrations. This is especially true for pairs of RNAs that do not form strong self-structure. Such phenomena can drive liquid-liquid phase separation, either solely from RNA-RNA interactions in the presence of divalent or organic cations, or in concert with proteins. RNA interactions can drive multimerization of RNA strands via both base-pairing and tertiary interactions. In this article, we explore the tendency of RNA to form stable monomers, dimers, and higher order structures as a function of RNA length and sequence through a focus on the intrinsic thermodynamic, kinetic, and structural properties of RNA. The principles we discuss are independent of any specific type of biomolecular condensate, and thus widely applicable. We also speculate how external conditions experienced by living organisms can influence the formation of nonmembranous compartments, again focusing on the physical and structural properties of RNA. Plants, in particular, are subject to diverse abiotic stresses including extreme temperatures, drought, and salinity. These stresses and the cellular responses to them, including changes in the concentrations of small molecules such as polyamines, salts, and compatible solutes, have the potential to regulate condensate formation by melting or strengthening base-pairing. Reversible condensate formation, perhaps including regulation by circadian rhythms, could impact biological processes in plants, and other organisms.

SACall: A Neural Network Basecaller for Oxford Nanopore Sequencing Data Based on Self-Attention Mechanism

Highly portable Oxford Nanopore sequencer producing long reads in real-time at low cost has made many breakthroughs in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. In this paper, we developed an end-to-end basecaller, SACall, based on convolution layers, transformer self-attention layers and a CTC decoder. In SACall, the convolution layers are used to downsample the signals and capture the local patterns. To achieve the contextual relevance of signals, self-attention layers are adopted to calculate the similarity of the signals at any two positions in the raw signal sequence. Finally, the CTC decoder generates the DNA sequence by a beam search algorithm. We use a benchmark consisting of nine isolated genomes to test the quality of different basecallers including SACall, Albacore, and Guppy. The performances of basecallers are evaluated from the perspective of read accuracy, assembly quality, and consensus accuracy. Among most of the genomes in the test benchmark, the reads basecalled by SACall have fewer errors than the reads basecalled by other basecallers. When assembling the basecalled reads of each genome, the assembly from SACall basecalled reads achieves a higher assembly identity. In addition, there are fewer errors in the polished assembly from reads basecalled by SACall compared to those basecalled by Albacore and Guppy. In general, SACall outperforms the Nanopore official basecallers Albacore and Guppy in the benchmark. Moreover, SACall is an open-source and freely available basecaller, which gives a chance for researchers to train their own basecalling models on specific data and basecall Nanopore reads.

Single-Cell Sequencing to Unveil the Mystery of Embryonic Development

Embryonic development is a fundamental physiological process that can provide tremendous insights into stem cell biology and regenerative medicine. In this process, cell fate decision is highly heterogeneous and dynamic, and investigations at the single-cell level can greatly facilitate the understanding of the molecular roadmap of embryonic development. Rapid advances in the technology of single-cell sequencing offer a perfectly useful tool to fulfill this purpose. Despite its great promise, single-cell sequencing is highly interdisciplinary, and successful applications in specific biological contexts require a general understanding of its diversity as well as the advantage versus limitations for each of its variants. Here, the technological principles of single-cell sequencing are consolidated and its applications in the study of embryonic development are summarized. First, the technology basics are presented and the available tools for each step including cell isolation, library construction, sequencing, and data analysis are discussed. Then, the works that employed single-cell sequencing are reviewed to investigate the specific processes of embryonic development, including preimplantation, peri-implantation, gastrulation, and organogenesis. Further, insights are provided on existing challenges and future research directions.

Mapping Gene Expression in the Spatial Dimension

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.

A virtual sequencer reveals the dephasing patterns in error-correction code DNA sequencing

An error-correction code (ECC) sequencing approach has recently been reported to effectively reduce sequencing errors by interrogating a DNA fragment with three orthogonal degenerate sequencing-by-synthesis (SBS) reactions. However, similar to other non-single-molecule SBS methods, the reaction will gradually lose its synchronization within a molecular colony in ECC sequencing. This phenomenon, called dephasing, causes sequencing error, and in ECC sequencing, induces distinctive dephasing patterns. To understand the characteristic dephasing patterns of the dual-base flowgram in ECC sequencing and to generate a correction algorithm, we built a virtual sequencer in silico. Starting from first principles and based on sequencing chemical reactions, we simulated ECC sequencing results, identified the key factors of dephasing in ECC sequencing chemistry and designed an effective dephasing algorithm. The results show that our dephasing algorithm is applicable to sequencing signals with at least 500 cycles, or 1000-bp average read length, with acceptably low error rate for further parity checks and ECC deduction. Our virtual sequencer with our dephasing algorithm can further be extended to a dichromatic form of ECC sequencing, allowing for a potentially much more accurate sequencing approach.

The cell as a bag of RNA

Genomic sequencing has provided insight into the genetic characterization of many organisms, and we are now seeing sequencing technologies turned towards phenotypic characterization of cells, tissues, and whole organisms. In particular, single-cell transcriptomic techniques are revolutionizing certain aspects of cell biology and enabling fundamental discoveries about cellular diversity, cell state, and cell type identity. I argue here that much of this progress depends on abstracting one's view of the cell to regard it as a 'bag of RNA'.

Illuminating Neural Circuits in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder and there is currently no cure. Neural circuit dysfunction is the fundamental mechanism underlying the learning and memory deficits in patients with AD. Therefore, it is important to understand the structural features and mechanisms underlying the deregulated circuits during AD progression, by which new tools for intervention can be developed. Here, we briefly summarize the most recently established cutting-edge experimental approaches and key techniques that enable neural circuit tracing and manipulation of their activity. We also discuss the advantages and limitations of these approaches. Finally, we review the applications of these techniques in the discovery of circuit mechanisms underlying β-amyloid and tau pathologies during AD progression, and as well as the strategies for targeted AD treatments.

Marrow adipogenic lineage precursor: A new cellular component of marrow adipose tissue

Bone marrow mesenchymal stromal cells are a highly heterogenic cell population containing mesenchymal stem cells as well as other cell types. With the advance of single cell transcriptome analysis, several recent reports identified a prominent subpopulation of mesenchymal stromal cells that specifically express adipocyte markers but do not contain lipid droplets. We name this cell type marrow adipogenic lineage precursor, MALP, and consider it as a major cellular component of marrow adipose tissue. Here, we review the discovery of MALPs and summarize their unique features and regulatory roles in bone. We further discuss how these findings advance our understanding of bone remodeling, mesenchymal niche regulation of hematopoiesis, and marrow vasculature maintenance.

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data

Motivation

Normalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression for library size (LS), allowing the variance and other properties of the gene-specific expression distribution to be non-constant in LS. This often results in reduced power and increased false discoveries in downstream analyses, a problem which is exacerbated by the high proportion of zeros present in most datasets.

Results

To address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing, sample heterogeneity and varying zero proportions, leading to improved performance in downstream analyses in a number of settings.

Availability and implementation

The R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. The Dino package is further archived and freely available on Zenodo at https://doi.org/10.5281/zenodo.4897558.

Supplementary information

Supplementary data are available at Bioinformatics online.

The effect of methanol fixation on single-cell RNA sequencing data

Background

Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome.

Results

We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result.

Conclusions

Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07744-6.

Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.

High-resolution single-cell transcriptomics reveals heterogeneity of self-renewing hair follicle stem cells

Multipotent bulge stem cells (SCs) fuel the hair follicle (HF) cyclic growth during adult skin homeostasis, but their intrinsic molecular heterogeneity is not well understood. These hair follicle stem cells (HFSCs) engage in bouts of self-renewal, migration and differentiation during the hair cycle. Here, we perform high-resolution single-cell RNA sequencing (scRNA-seq) of HFSCs sorted as CD34+ /K14-H2BGFP+ from mouse skin at mid-anagen, the self-renewal stage. We dissect the transcriptomic profiles and unravel population-specific transcriptional heterogeneity. Unsupervised clustering reveals five major HFSC populations, which distinguished by known markers associated with both the bulge and the outer root sheath (ORS) underneath. These populations include quiescent bulge, ORS cellular states and proliferative cells. Lineage trajectory analysis predicted the prospective differentiation path of these cellular states and their corresponding self-renewing subpopulations. The bulge population itself can be further sub-divided into distinct subpopulations that can be mapped to the upper, mid and lower bulge regions, and present a decreasing quiescence score. Gene set enrichment analysis (GSEA) revealed new markers and suggested potentially distinct functions of the ORS and bulge subpopulations. This included communications between the upper bulge subpopulation and sensory nerves and between the upper ORS and skin vasculature, as well as enrichment of a bulge subset in cell migratory functions. The lower ORS enriched genes may potentially enable nutrients passing from the surrounding fat and vasculature cells towards the proliferating hair matrix cells. Thus, we provide a comprehensive account of HFSC molecular heterogeneity during their self-renewing stage, which enables future HF functional studies.

SIEVE: identifying robust single cell variable genes for single-cell RNA sequencing data

Single-cell RNA-seq data analysis generally requires quality control, normalization, highly variable genes screening, dimensionality reduction and clustering. Among these processes, downstream analysis including dimensionality reduction and clustering are sensitive to the selection of highly variable genes. Though increasing number of tools for selecting the highly variable genes have been developed, an evaluation of their performances and a general strategy are lack. Here, we compare the performance of nine commonly used methods for screening variable genes by using single-cell RNA-seq data from hematopoietic stem/progenitor cells and mature blood cells, and find that SCHS outperforms other methods regarding to reproducibility and accuracy. However, this method prefers the selection of highly expressed genes. We further propose a new strategy SIEVE (SIngle-cEll Variable gEnes) by multiple rounds of random sampling, therefore minimizing the stochastic noise and identifying a robust set of variable genes. Moreover, SIEVE recovers lowly expressed genes as variable genes and substantially improves the accuracy of single cell classification, especially for the methods with lower reproducibility. The SIEVE software is freely available at https://github.com/YinanZhang522/SIEVE.

Effective ribosomal RNA depletion for single-cell total RNA-seq by scDASH

A decade since its invention, single-cell RNA sequencing (scRNA-seq) has become a mainstay technology for profiling transcriptional heterogeneity in individual cells. Yet, most existing scRNA-seq methods capture only polyadenylated mRNA to avoid the cost of sequencing non-messenger transcripts, such as ribosomal RNA (rRNA), that are usually not of-interest. Hence, there are not very many protocols that enable single-cell analysis of total RNA. We adapted a method called DASH (Depletion of Abundant Sequences by Hybridisation) to make it suitable for depleting rRNA sequences from single-cell total RNA-seq libraries. Our analyses show that our single-cell DASH (scDASH) method can effectively deplete rRNAs from sequencing libraries with minimal off-target non-specificity. Importantly, as a result of depleting the rRNA, the rest of the transcriptome is significantly enriched for detection.

Nucleic Acids Analysis

Nucleic acids are natural biopolymers of nucleotides that store, encode, transmit and express genetic information, which play central roles in diverse cellular events and diseases in living things. The analysis of nucleic acids and nucleic acids-based analysis have been widely applied in biological studies, clinical diagnosis, environmental analysis, food safety and forensic analysis. During the past decades, the field of nucleic acids analysis has been rapidly advancing with many technological breakthroughs. In this review, we focus on the methods developed for analyzing nucleic acids, nucleic acids-based analysis, device for nucleic acids analysis, and applications of nucleic acids analysis. The representative strategies for the development of new nucleic acids analysis in this field are summarized, and key advantages and possible limitations are discussed. Finally, a brief perspective on existing challenges and further research development is provided.

Optimization of on-bead emulsion polymerase chain reaction based on single particle analysis

Emulsion polymerase chain reaction (ePCR) enables parallel amplification of millions of different DNA molecules while avoiding bias and chimeric byproducts, essential criteria for applications including next generation sequencing, aptamer selection, and protein-DNA interaction studies. Despite these advantages, ePCR remains underused due to the lack of optimal starting conditions, straightforward methods to evaluate success, and guidelines for tuning the reaction. This knowledge has been elusive for bulk emulsion generation methods, such as stirring and vortexing, the only methods that can emulsify libraries of ≥10⁸ sequences within minutes, because these emulsions have not been characterized in ways that preserve the heterogeneity that defines successful ePCR. Our study quantifies the outcome of ePCR from conditions specified in the literature using single particle analysis, which preserves this heterogeneity. We combine ePCR with magnetic microbeads and quantify the amplification yield via qPCR and the proportion of clonal and saturated beads via flow cytometry. Our single particle level analysis of thousands of beads resolves two key criteria that define the success of ePCR: 1) whether the target fraction of 20% clonal beads predicted by the Poisson distribution is achieved, and 2) whether those beads are partially or maximally covered by amplified DNA. We found that among the two concentrations of polymerase tested, only the higher one, which is 20-fold more than the concentration recommended for conventional PCR, could yield sufficient PCR products. Dramatic increases in the concentrations of reverse primer and nucleotides recommended in literature gave no measurable change in outcome. We thus provide evidence-based starting conditions for effective and economical ePCR for real DNA libraries and a straightforward workflow for evaluating the success of tuning ePCR prior to downstream applications.

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. We have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ∼77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

Single-cell analysis reveals immune landscape in kidneys of patients with chronic transplant rejection

Rationale: Single-cell RNA sequencing (scRNA-seq) has provided an unbiased assessment of specific profiling of cell populations at the single-cell level. Conventional renal biopsy and bulk RNA-seq only average out the underlying differences, while the extent of chronic kidney transplant rejection (CKTR) and how it is shaped by cells and states in the kidney remain poorly characterized. Here, we analyzed cells from CKTR and matched healthy adult kidneys at single-cell resolution.

Methods: High-quality transcriptomes were generated from three healthy human kidneys and two CKTR biopsies. Unsupervised clustering analysis of biopsy specimens was performed to identify fifteen distinct cell types, including major immune cells, renal cells and a few types of stromal cells. Single-sample gene set enrichment (ssGSEA) algorithm was utilized to explore functional differences between cell subpopulations and between CKTR and normal cells.

Results: Natural killer T (NKT) cells formed five subclasses, representing CD4+ T cells, CD8+ T cells, cytotoxic T lymphocytes (CTLs), regulatory T cells (Tregs) and natural killer cells (NKs). Memory B cells were classified into two subtypes, representing reverse immune activation. Monocytes formed a classic CD14+ group and a nonclassical CD16+ group. We identified a novel subpopulation [myofibroblasts (MyoF)] in fibroblasts, which express collagen and extracellular matrix components. The CKTR group was characterized by increased numbers of immune cells and MyoF, leading to increased renal rejection and fibrosis.

Conclusions: By assessing functional differences of subtype at single-cell resolution, we discovered different subtypes that correlated with distinct functions in CKTR. This resource provides deeper insights into CKTR biology that will be helpful in the diagnosis and treatment of CKTR.

Systematic comparison of single-cell and single-nucleus RNA-sequencing methods

The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling-selecting representative methods based on their usage and our expertise and resources to prepare libraries-including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.

Using Single-Cell and Spatial Transcriptomes to Understand Stem Cell Lineage Specification During Early Embryo Development

Embryonic development and stem cell differentiation provide a paradigm to understand the molecular regulation of coordinated cell fate determination and the architecture of tissue patterning. Emerging technologies such as single-cell RNA sequencing and spatial transcriptomics are opening new avenues to dissect cell organization, the divergence of morphological and molecular properties, and lineage allocation. Rapid advances in experimental and computational tools have enabled researchers to make many discoveries and revisit old hypotheses. In this review, we describe the use of single-cell RNA sequencing in studies of molecular trajectories and gene regulation networks for stem cell lineages, while highlighting the integratedexperimental and computational analysis of single-cell and spatial transcriptomes in the molecular annotation of tissue lineages and development during postimplantation gastrulation.

Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells, the true identity of progenitors and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing large scale single cell transcriptome analysis, we computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice. One identified subpopulation is a unique cell type that expresses adipocyte markers but contains no lipid droplets. As non-proliferative precursors for adipocytes, they exist abundantly as pericytes and stromal cells that form a ubiquitous 3D network inside the marrow cavity. Functionally they play critical roles in maintaining marrow vasculature and suppressing bone formation. Therefore, we name them marrow adipogenic lineage precursors (MALPs) and conclude that they are a newly identified component of marrow adipose tissue.

Cell-type diversity and regionalized gene expression in the planarian intestine

Proper function and repair of the digestive system are vital to most animals. Deciphering the mechanisms involved in these processes requires an atlas of gene expression and cell types. Here, we applied laser-capture microdissection (LCM) and RNA-seq to characterize the intestinal transcriptome of Schmidtea mediterranea, a planarian flatworm that can regenerate all organs, including the gut. We identified hundreds of genes with intestinal expression undetected by previous approaches. Systematic analyses revealed extensive conservation of digestive physiology and cell types with other animals, including humans. Furthermore, spatial LCM enabled us to uncover previously unappreciated regionalization of gene expression in the planarian intestine along the medio-lateral axis, especially among intestinal goblet cells. Finally, we identified two intestine-enriched transcription factors that specifically regulate regeneration (hedgehog signaling effector gli-1) or maintenance (RREB2) of goblet cells. Altogether, this work provides resources for further investigation of mechanisms involved in gastrointestinal function, repair and regeneration.

Single-cell omics in ageing: a young and growing field

Organismal ageing results from interlinked molecular changes in multiple organs over time. The study of ageing at the molecular level is complicated by varying decay characteristics and kinetics-both between and within organs-driven by intrinsic and extracellular factors. Emerging single-cell omics methods allow for molecular and spatial profiling of cells, and probing of regulatory states and cell-fate determination, thus providing promising tools for unravelling the heterogeneous process of ageing and making it amenable to intervention. These new strategies are enabled by advances in genomic, epigenomic and transcriptomic technologies. Combined with methods for proteome and metabolome analysis, single-cell techniques provide multidimensional, integrated data with unprecedented detail and throughput. Here, we provide an overview of the current state, and perspectives on the future, of this emerging field. We discuss how single-cell approaches can advance understanding of mechanisms underlying organismal ageing and aid in the development of interventions for ageing and ageing-associated diseases.

Single-Cell Analysis of Foot-and-Mouth Disease Virus

With the rapid development of single-cell technologies, the mechanisms underlying viral infections and the interactions between hosts and viruses are starting to be explored at the single-cell level. The foot-and-mouth-disease (FMD) virus (FMDV) causes an acute and persistent infection that can result in the break-out of FMD, which can have serious effects on animal husbandry. Single-cell techniques have emerged as powerful approaches to analyze virus infection at the resolution of individual cells. In this review, the existing single-cell studies examining FMDV will be systematically summarized, and the central themes of these studies will be presented.

Human Spermatogonial Stem Cells Scrutinized under the Single-Cell Magnifying Glass

Spermatogonial stem cells (SSCs) are essential for adult spermatogenesis. Recently, Wang et al. (2018), Guo et al. (2018), and Hermann et al. (2018) used single-cell RNA sequencing to define and molecularly characterize human testicular cell populations, including spermatogonial subsets with characteristics of human SSCs.

Non-templated addition and template switching by Moloney murine leukemia virus (MMLV)-based reverse transcriptases co-occur and compete with each other

Single-cell RNA-Seq (scRNA-Seq) has led to an unprecedented understanding of gene expression and regulation in individual cells. Many scRNA-Seq approaches rely upon the template switching property of Moloney murine leukemia virus (MMLV)-type reverse transcriptases. Template switching is believed to happen in a sequential process involving nontemplated addition of three protruding nucleotides (+CCC) to the 3′-end of the nascent cDNA, which can then anneal to the matching rGrGrG 3′-end of the template-switching oligo (TSO), allowing the reverse transcriptase (RT) to switch templates and continue copying the TSO sequence. In this study, we present a detailed analysis of template switching biases with respect to the RNA template, specifically of the role of the sequence and nature of its 5′-end (capped versus noncapped) in these biases. Our findings confirmed that the presence of a 5′-m⁷G cap enhances template switching efficiency. We also profiled the composition of the nontemplated addition in the absence of TSO and observed that the 5′-end of RNA template influences the terminal transferase activity of the RT. Furthermore, we found that designing new TSOs that pair with the most common nontemplated additions did little to improve template switching efficiency. Our results provide evidence suggesting that, in contrast to the current understanding of the template switching process, nontemplated addition and template switching are concurrent and competing processes.

Dynamics of living cells in a cytomorphological state space

Cells are nonequilibrium systems that exchange matter and energy with the environment to sustain their metabolic needs. The nonequilibrium nature of this system presents considerable challenges to developing a general theory describing its behavior; however, when studied at appropriate spatiotemporal scales, the behavior of ensembles of nonequilibrium systems can resemble that of a system at equilibrium. Here we apply this principle to a population of cells within a cytomorphological state space and demonstrate that cellular transition dynamics within this space can be described using equilibrium formalisms. We use this framework to map the effective energy landscape underlying the cytomorphological state space of a population of mouse embryonic fibroblasts (MEFs) and identify topographical nonuniformity in this space, indicating nonuniform occupation of cytomorphological states within an isogenic population. The introduction of exogenous apoptotic agents fundamentally altered this energy landscape, inducing formation of additional energy minima that correlated directly with changes in sensitivity to apoptosis induction. An equilibrium framework allows us to describe the behavior of an ensemble of single cells, suggesting that although cells are complex nonequilibrium systems, the application of formalisms derived from equilibrium thermodynamics can provide insight into the basis of nongenetic heterogeneities within cell populations, as well as the relationship between cytomorphological and functional heterogeneity.

Innovative Molecular Testing Strategies for Adjunctive Investigations in Hemostasis and Thrombosis

Clinicians and scientists in the fields of hemostasis and thrombosis have been among those first to integrate new molecular strategies for the purpose of enhancing disease diagnosis and treatment. The molecular diagnosis and introduction of gene therapy approaches for hemophilia are obvious examples of this tendency. In this review, the authors summarize information concerning three molecular technologies that have reached various stages of translational potential for their incorporation into the clinical management of disorders of hemostasis. Chromatin conformation assays are now being used to capture structural knowledge of long-range genomic interactions that can alter patterns of gene expression and contribute to quantitative trait pathogenesis. Liquid biopsies in various forms are providing opportunities for early cancer detection, and in the context of tumor-educated platelets, as described here, can also characterize tumor type and the extent of tumor progression. This technology is already being trialed in patients with unprovoked venous thrombosis to assess the potential for occult malignancies. Lastly, advances in single cell transcriptome analysis, provide opportunities to definitively determine molecular events in rare cells, such as antigen-specific regulatory T cells, within the context of heterogeneous cell populations.

Technological Advances in Multiscale Analysis of Single Cells in Biomedicine

Single-cell analysis has recently received significant attention in biomedicine. With the advances in super-resolution microscopy, fluorescence labeling, and nanoscale biosensing, new information may be obtained for the design of cancer diagnosis and therapeutic interventions. The discovery of cellular heterogeneity further stresses the importance of single-cell analysis to improve our understanding of disease mechanism and to develop new strategies for disease treatment. To this end, many studies are exploited at the single-cell level for high throughput, highly parallel, and quantitative analysis. Technically, microfluidics are also designed to facilitate single-cell isolation and enrichment for downstream detection and manipulation in a robust, sensitive, and automated manner. Further achievements are made possible by consolidating optically label-free, electrical, and molecular sensing techniques. Moreover, these technologies are coupled with computing algorithms for high throughput and automated quantitative analysis with a short turnaround time. To reflect on how the technological developments have advanced single-cell analysis, this mini-review is aimed to offer readers an introduction to single-cell analysis with a brief historical development and the recent progresses that have enabled multiscale analysis of single-cells in the last decade. The challenges and future trends are also discussed with the view to inspire forthcoming technical developments.

Simultaneous profiling of gene expression and chromatin accessibility in single cells

Profiling multiple omic layers in a single cell enables the discovery and analysis of biological phenomena that are not apparent from analysis of mono-omic data. While methods for multi-omic profiling have been reported, their adoption has been limited due to high cost and complex workflows. Here, we present a simple method for joint profiling of gene expression and chromatin accessibility in tens to hundreds of single cells. We assess the quality of resulting single cell ATAC- and RNA-seq data across three cell types, examine the link between accessibility and expression at the CD3G and FTH1 loci in human primary T cells and monocytes, and compare the accuracy of clustering solutions for mono-omic and combined data. The new method allows biological laboratories to perform simultaneous profiling of gene expression and chromatin accessibility using standard reagents and instrumentation. This technique, in conjunction with other advances in multi-omic profiling, will enable highly-resolved cell state classification and more specific mechanistic hypothesis generation than is possible with mono-omic analysis.