Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Metric multidimensional scaling for large single-cell datasets using neural networks

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics

A fundamental analysis task for single-cell transcriptomics data is clustering with subsequent visualization of cell clusters. The genes responsible for the clustering are only inferred in a subsequent step. Clustering cells and genes together would be the remit of biclustering algorithms, which are often bogged down by the size of single-cell data. Here we present 'Correspondence Analysis based Biclustering on Networks' (CAbiNet) for joint clustering and visualization of single-cell RNA-sequencing data. CAbiNet performs efficient co-clustering of cells and their respective marker genes and jointly visualizes the biclusters in a non-linear embedding for easy and interactive visual exploration of the data.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

BACKGROUND

Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.

RESULTS

Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html .

CONCLUSIONS

Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Unraveling Divergent Transcriptomic Profiles: A Comparative Single-Cell RNA Sequencing Study of Epithelium, Gingiva, and Periodontal Ligament Tissues

The periodontium comprising periodontal ligament (PDL), gingiva, and epithelium play crucial roles in maintaining tooth integrity and function. Understanding tissue cellular composition and gene expression is crucial for illuminating periodontal pathophysiology. This study aimed to identify tissue-specific markers via scRNA-Seq. Primary human PDL, gingiva, and epithelium tissues (n = 7) were subjected to cell hashing and sorting. scRNA-Seq library preparation using 10× Genomics protocol and Illumina sequencing was conducted. The analysis was performed using Cellranger (v3.1.0), with downstream analysis via R packages Seurat (v5.0.1) and SCORPIUS (v1.0.9). Investigations identified eight distinct cellular clusters, revealing the ubiquitous presence of epithelial and gingival cells. PDL cells evolved in two clusters with numerical superiority. The other clusters showed varied predominance regarding gingival and epithelial cells or an equitable distribution of both. The cluster harboring most cells mainly consisted of PDL cells and was present in all donors. Some of the other clusters were also tissue-inherent, while the presence of others was environmentally influenced, revealing variability across donors. Two clusters exhibited genetic profiles associated with tissue development and cellular integrity, respectively, while all other clusters were distinguished by genes characteristic of immune responses. Developmental trajectory analysis uncovered that PDL cells may develop after epithelial and gingival cells, suggesting the inherent PDL cell-dominated cluster as a final developmental stage. This single-cell RNA sequencing study delineates the hierarchical organization of periodontal tissue development, identifies tissue-specific markers, and reveals the influence of environmental factors on cellular composition, advancing our understanding of periodontal biology and offering potential insights for therapeutic interventions.

Integrated Single-Cell Transcriptomic Atlas of Human Kidney Endothelial Cells

Key Points

We created a comprehensive reference atlas of normal human kidney endothelial cells. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney.

Background

Kidney endothelial cells are exposed to different microenvironmental conditions that support specific physiologic processes. However, the heterogeneity of human kidney endothelial cells has not yet been systematically described.

Methods

We reprocessed and integrated seven human kidney control single-cell/single-nucleus RNA sequencing datasets of >200,000 kidney cells in the same process.

Results

We identified five major cell types, 29,992 of which were endothelial cells. Endothelial cell reclustering identified seven subgroups that differed in molecular characteristics and physiologic functions. Mapping new data to a normal kidney endothelial cell atlas allows rapid data annotation and analysis. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney and identified endothelial marker genes that were conserved in humans and mice, as well as differentially expressed genes between corresponding subpopulations. Furthermore, combined analysis of single-cell transcriptome data with public genome-wide association study data showed a significant enrichment of endothelial cells, especially arterial endothelial cells, in BP heritability. Finally, we identified M1 and M12 from coexpression networks in endothelial cells that may be deeply involved in BP regulation.

Conclusions

We created a comprehensive reference atlas of normal human kidney endothelial cells that provides the molecular foundation for understanding how the identity and function of kidney endothelial cells are altered in disease, aging, and between species. Finally, we provide a publicly accessible online tool to explore the datasets described in this work (https://vascularmap.jiangnan.edu.cn ).

Revealing Genetic Dynamics: scRNA-seq Unravels Modifications in Human PDL Cells across In Vivo and In Vitro Environments

The periodontal ligament (PDL) is a highly specialized fibrous tissue comprising heterogeneous cell populations of an intricate nature. These complexities, along with challenges due to cell culture, impede a comprehensive understanding of periodontal pathophysiology. This study aims to address this gap, employing single-cell RNA sequencing (scRNA-seq) technology to analyze the genetic intricacies of PDL both in vivo and in vitro. Primary human PDL samples (n = 7) were split for direct in vivo analysis and cell culture under serum-containing and serum-free conditions. Cell hashing and sorting, scRNA-seq library preparation using the 10x Genomics protocol, and Illumina sequencing were conducted. Primary analysis was performed using Cellranger, with downstream analysis via the R packages Seurat and SCORPIUS. Seven distinct PDL cell clusters were identified comprising different cellular subsets, each characterized by unique genetic profiles, with some showing donor-specific patterns in representation and distribution. Formation of these cellular clusters was influenced by culture conditions, particularly serum presence. Furthermore, certain cell populations were found to be inherent to the PDL tissue, while others exhibited variability across donors. This study elucidates specific genes and cell clusters within the PDL, revealing both inherent and context-driven subpopulations. The impact of culture conditions-notably the presence of serum-on cell cluster formation highlights the critical need for refining culture protocols, as comprehending these influences can drive the creation of superior culture systems vital for advancing research in PDL biology and regenerative therapies. These discoveries not only deepen our comprehension of PDL biology but also open avenues for future investigations into uncovering underlying mechanisms.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

Single-nucleus sequencing of silkworm larval midgut reveals the immune escape strategy of BmNPV in the midgut during the late stage of infection

The midgut is an important barrier against microorganism invasion and proliferation, yet is the first tissue encountered when a baculovirus naturally invades the host. However, only limited knowledge is available how different midgut cell types contribute to the immune response and the clearance or promotion of viral infection. Here, single-nucleus RNA sequencing (snRNA seq) was employed to analyze the responses of various cell subpopulations in the silkworm larval midgut to B. mori nucleopolyhedrovirus (BmNPV) infection. We identified 22 distinct clusters representing enteroendocrine cells (EEs), enterocytes (ECs), intestinal stem cells (ISCs), Goblet cell-like and muscle cell types in the BmNPV-infected and uninfected silkworm larvae midgut at 72 h post infection. Further, our results revealed that the strategies for immune escape of BmNPV in the midgut at the late stage of infection include (1) inhibiting the response of antiviral pathways; (2) inhibiting the expression of antiviral host factors; (3) stimulating expression levels of genes promoting BmNPV replication. These findings suggest that the midgut, as the first line of defense against the invasion of the baculovirus, has dual characteristics of "resistance" and "tolerance". Our single-cell dataset reveals the diversity of silkworm larval midgut cells, and the transcriptome analysis provides insights into the interaction between host and virus infection at the single-cell level.

A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.

scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.

Single-nuclei RNA-seq reveals skin cell responses to Aeromonas hydrophila infection in Chinese longsnout catfish Leiocassis longirostris

As the primary natural barrier that protects against adverse environmental conditions, the skin plays a crucial role in the innate immune response of fish, particularly in relation to bacterial infections. However, due to the diverse functionality and intricate anatomical and cellular composition of the skin, deciphering the immune response of the host is a challenging task. In this study, single nuclei RNA-sequencing (snRNA-seq) was performed on skin biopsies obtained from Chinese longsnout catfish (Leiocassis longirostris), comparing Aeromonas hydrophila-infected subjects to healthy control subjects. A total of 19,581 single nuclei cells were sequenced using 10x Genomics (10,400 in the control group and 9,181 in the treated group). Based on expressed unique transcriptional profiles, 33 cell clusters were identified and classified into 12 cell types including keratinocyte (KC), fibroblast (FB), endothelial cells (EC), secretory cells (SC), immune cells, smooth muscle cells (SMC), and other cells such as pericyte (PC), brush cell (BC), red blood cell (RBC), neuroendocrine cell (NDC), neuron cells (NC), and melanocyte (MC). Among these, three clusters of KCs, namely, KC1, KC2, and KC5 exhibited significant expansion after A. hydrophila infection. Analysis of pathway enrichment revealed that KC1 was primarily involved in environmental signal transduction, KC2 was primarily involved in endocrine function, and KC5 was primarily involved in metabolism. Finally, our findings suggest that neutrophils may play a crucial role in combating A. hydrophila infections. In summary, this study not only provides the first detailed comprehensive map of all cell types present in the skin of teleost fish but also sheds light on the immune response mechanism of the skin following A. hydrophila infection in Chinese longsnout catfish.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Single-cell reference mapping to construct and extend cell-type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

Ferric citrate and apo-transferrin enable erythroblast maturation with β-globin from hemogenic endothelium

Red blood cell (RBC) generation from human pluripotent stem cells (PSCs) offers potential for innovative cell therapy in regenerative medicine as well as developmental studies. Ex vivo erythropoiesis from PSCs is currently limited by the low efficiency of functional RBCs with β-globin expression in culture systems. During induction of β-globin expression, the absence of a physiological microenvironment, such as a bone marrow niche, may impair cell maturation and lineage specification. Here, we describe a simple and reproducible culture system that can be used to generate erythroblasts with β-globin expression. We prepared a two-dimensional defined culture with ferric citrate treatment based on definitive hemogenic endothelium (HE). Floating erythroblasts derived from HE cells were primarily CD45+CD71+CD235a+ cells, and their number increased remarkably upon Fe treatment. Upon maturation, the erythroblasts cultured in the presence of ferric citrate showed high transcriptional levels of β-globin and enrichment of genes associated with heme synthesis and cell cycle regulation, indicating functionality. The rapid maturation of these erythroblasts into RBCs was observed when injected in vivo, suggesting the development of RBCs that were ready to grow. Hence, induction of β-globin expression may be explained by the effects of ferric citrate that promote cell maturation by binding with soluble transferrin and entering the cells.Taken together, upon treatment with Fe, erythroblasts showed advanced maturity with a high transcription of β-globin. These findings can help devise a stable protocol for the generation of clinically applicable RBCs.

An information-theoretic approach to single cell sequencing analysis

BACKGROUND

Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information.

RESULTS

Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types.

CONCLUSIONS

Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Mapping human adult hippocampal neurogenesis with single-cell transcriptomics: Reconciling controversy or fueling the debate?

The notion of exploiting the regenerative potential of the human brain in physiological aging or neurological diseases represents a particularly attractive alternative to conventional strategies for enhancing or restoring brain function. However, a major first question to address is whether the human brain does possess the ability to regenerate. The existence of human adult hippocampal neurogenesis (AHN) has been at the center of a fierce scientific debate for many years. The advent of single-cell transcriptomic technologies was initially viewed as a panacea to resolving this controversy. However, recent single-cell RNA sequencing studies in the human hippocampus yielded conflicting results. Here, we critically discuss and re-analyze previously published AHN-related single-cell transcriptomic datasets. We argue that, although promising, the single-cell transcriptomic profiling of AHN in the human brain can be confounded by methodological, conceptual, and biological factors that need to be consistently addressed across studies and openly discussed within the scientific community.

Reversible transitions between noradrenergic and mesenchymal tumor identities define cell plasticity in neuroblastoma

Noradrenergic and mesenchymal identities have been characterized in neuroblastoma cell lines according to their epigenetic landscapes and core regulatory circuitries. However, their relationship and relative contribution in patient tumors remain poorly defined. We now document spontaneous and reversible plasticity between the two identities, associated with epigenetic reprogramming, in several neuroblastoma models. Interestingly, xenografts with cells from each identity eventually harbor a noradrenergic phenotype suggesting that the microenvironment provides a powerful pressure towards this phenotype. Accordingly, such a noradrenergic cell identity is systematically observed in single-cell RNA-seq of 18 tumor biopsies and 15 PDX models. Yet, a subpopulation of these noradrenergic tumor cells presents with mesenchymal features that are shared with plasticity models, indicating that the plasticity described in these models has relevance in neuroblastoma patients. This work therefore emphasizes that intrinsic plasticity properties of neuroblastoma cells are dependent upon external cues of the environment to drive cell identity.

Review of single-cell RNA-seq data clustering for cell-type identification and characterization

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

MASI enables fast model-free standardization and integration of single-cell transcriptomics data

Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods, in terms of integration, annotation, and speed. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. Finally, we show MASI can annotate approximately one million cells on a personal laptop, making large-scale single-cell data integration more accessible. We envision that MASI can serve as a cheap computational alternative for the single-cell research community.

Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools

Traditional bulk RNA-Seq pipelines do not assess cell-type composition within heterogeneous tissues. Therefore, it is difficult to determine whether conflicting findings among samples or datasets are the result of biological differences or technical differences due to variation in sample collections. This report provides a user-friendly, open source method to assess cell-type composition in bulk RNA-Seq datasets for heterogeneous tissues using published single cell (sc)RNA-Seq data as a reference. As an example, we apply the method to analysis of kidney cortex bulk RNA-Seq data from female (N=8) and male (N=9) baboons to assess whether observed transcriptome sex differences are biological or technical, i.e., variation due to ultrasound guided biopsy collections. We found cell-type composition was not statistically different in female versus male transcriptomes based on expression of 274 kidney cell-type specific transcripts, indicating differences in gene expression are not due to sampling differences. This method of cell-type composition analysis is recommended for providing rigor in analysis of bulk RNA-Seq datasets from complex tissues. It is clear that with reduced costs, more analyses will be done using scRNA-Seq; however, the approach described here is relevant for data mining and meta analyses of the thousands of bulk RNA-Seq data archived in the NCBI GEO public database.

Biologically informed deep learning to query gene programs in single-cell atlases

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review

Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.

Cellular and transcriptional heterogeneity in the intrahepatic biliary epithelium

Epithelial tissues comprise heterogeneous cellular subpopulations, which often compartmentalize specialized functions like absorption and secretion to distinct cell types. In the liver, hepatocytes and biliary epithelial cells (BECs; also called cholangiocytes) are the two major epithelial lineages and play distinct roles in (1) metabolism, protein synthesis, detoxification, and (2) bile transport and modification, respectively. Recent technological advances, including single cell transcriptomic assays, have shed new light on well-established heterogeneity among hepatocytes, endothelial cells, and immune cells in the liver. However, a "ground truth" understanding of molecular heterogeneity in BECs has remained elusive, and the field currently lacks a set of consensus biomarkers for identifying BEC subpopulations. Here, we review long-standing definitions of BEC heterogeneity as well as emerging studies that aim to characterize BEC subpopulations using next generation single cell assays. Understanding cellular heterogeneity in the intrahepatic bile ducts holds promise for expanding our foundational mechanistic knowledge of BECs during homeostasis and disease.

BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin â€" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data, genetic perturbation screens, and direct experimental manipulation of receptor-ligand interactions to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche and revealing species-specific communication pathways. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease.

Cytotoxic innate lymphoid cells sense cancer cell-expressed interleukin-15 to suppress human and murine malignancies

Malignancy can be suppressed by the immune system. However, the classes of immunosurveillance responses and their mode of tumor sensing remain incompletely understood. Here, we show that although clear cell renal cell carcinoma (ccRCC) was infiltrated by exhaustion-phenotype CD8⁺ T cells that negatively correlated with patient prognosis, chromophobe RCC (chRCC) had abundant infiltration of granzyme A-expressing intraepithelial type 1 innate lymphoid cells (ILC1s) that positively associated with patient survival. Interleukin-15 (IL-15) promoted ILC1 granzyme A expression and cytotoxicity, and IL-15 expression in chRCC tumor tissue positively tracked with the ILC1 response. An ILC1 gene signature also predicted survival of a subset of breast cancer patients in association with IL-15 expression. Notably, ILC1s directly interacted with cancer cells, and IL-15 produced by cancer cells supported the expansion and anti-tumor function of ILC1s in a murine breast cancer model. Thus, ILC1 sensing of cancer cell IL-15 defines an immunosurveillance mechanism of epithelial malignancies.

Triku: a feature selection method based on nearest neighbors for single-cell data

Background

Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset.

Results

Triku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes.

Conclusion

Triku is developed in Python 3 and is available at https://github.com/alexmascension/triku.

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

Background

A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.

Results

We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.

Conclusions

We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (https://github.com/PYangLab/scCCESS).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02622-0.

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing

Inferring the cell types in single-cell RNA-sequencing (scRNA-seq) data is of particular importance for understanding the potential cellular mechanisms and phenotypes occurring in complex tissues, such as the tumor-immune microenvironment (TME). The sparsity and noise of scRNA-seq data, combined with the fact that immune cell types often occur on a continuum, make cell typing of TME scRNA-seq data a significant challenge. Several single-label cell typing methods have been put forth to address the limitations of noise and sparsity, but accounting for the often overlapped spectrum of cell types in the immune TME remains an obstacle. To address this, we developed a new scRNA-seq cell-typing method, Cell-typing using variance Adjusted Mahalanobis distances with Multi-Labeling (CAMML). CAMML leverages cell type-specific weighted gene sets to score every cell in a dataset for every potential cell type. This allows cells to be labelled either by their highest scoring cell type as a single label classification or based on a score cut-off to give multi-label classification. For single-label cell typing, CAMML performance is comparable to existing cell typing methods, SingleR and Garnett. For scenarios where cells may exhibit features of multiple cell types (e.g., undifferentiated cells), the multi-label classification supported by CAMML offers important benefits relative to the current state-of-the-art methods. By integrating data across studies, omics platforms, and species, CAMML serves as a robust and adaptable method for overcoming the challenges of scRNA-seq analysis.

Benchmarking UMI-based single-cell RNA-seq preprocessing workflows

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.

RESULTS

Here, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis.

CONCLUSIONS

In summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

GdClean: removal of Gadolinium contamination in mass cytometry data

MOTIVATION

Mass cytometry (Cytometry by Time-Of-Flight, CyTOF) is a single-cell technology that is able to quantify multiplex biomarker expressions and is commonly used in basic life science and translational research. However, the widely used Gadolinium (Gd)-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) scanning in clinical practice can lead to signal contamination on the Gd channels in the CyTOF analysis. This Gd contamination greatly affects the characterization of the real signal from Gd-isotope-conjugated antibodies, severely impairing the CyTOF data quality and ruining downstream single-cell data interpretation.

RESULTS

We first in-depth characterized the signals of Gd isotopes from a control sample that was not stained with Gd-labeled antibodies but was contaminated by Gd isotopes from GBCAs, and revealed the collinear intensity relationship across Gd contamination signals. We also found that the intensity ratios of detected Gd contamination signals to the reference Gd signal were highly correlated with the natural abundance ratios of corresponding Gd isotopes. We then developed a computational method named by GdClean to remove the Gd contamination signal at the single-cell level in the CyTOF data. We further demonstrated that the GdClean effectively cleaned up the Gd contamination signal while preserving the real Gd-labeled antibodies signal in Gd channels. All of these shed lights on the promising applications of the GdClean method in preprocessing CyTOF datasets for revealing the true single-cell information.

AVAILABILITY AND IMPLEMENTATION

The R package GdClean is available on GitHub at https://github.com/JunweiLiu0208/GdClean.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single cell RNA sequencing techniques and applications in research of ovary development and related diseases

The ovary is a highly organized composite of germ cells and various types of somatic cells, whose communications dictate ovary development to generate functional oocytes. The differences between individual cells might have profound effects on ovary functions. Single cell RNA sequencing techniques are promising approaches to explore the cell type composition of organisms, the dynamics of transcriptomes, the regulatory network between genes and the signaling pathways between cell types at the single cell resolution. In this review, we provide an overview of the currently available single cell RNA sequencing techniques including Smart-seq2 and Drop-seq, as well as their applications in biological and clinical research to provide a better understanding on the molecular mechanisms underlying ovary development and associated diseases.

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

BACKGROUND

The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it is usually necessary to generate further specific datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare-cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class.

RESULTS

We demonstrate the effectiveness of our method for three independent use cases, each consisting of already published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8635). This use case was designed to take a larger imbalance ratio (~1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (~1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single-cell capture procedures and the impact of "less" rare-cell types. The third dataset refers to the murine data of the Allen Brain Atlas, including more than 1 million cells. For validation purposes only, all datasets have also been analyzed traditionally using common data analysis approaches, such as the Seurat workflow.

CONCLUSIONS

In comparison to baseline testing without oversampling, our approach identifies rare-cells with a robust precision-recall balance, including a high accuracy and low false positive detection rate. A practical benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis in R and Python is publicly available at FairdomHub, as well as GitHub, and can easily be transferred to identify other rare-cell types.

Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape

Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has cataloged software tools for analyzing scRNA-seq data. With the number of tools in the database passing 1000, we provide an update on the state of the project and the field. This data shows the evolution of the field and a change of focus from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find that open science practices reward developers with increased recognition and help accelerate the field.

Transcriptional Differences in Lipid-Metabolizing Enzymes in Murine Sebocytes Derived from Sebaceous Glands of the Skin and Preputial Glands

Sebaceous glands are adnexal structures, which critically contribute to skin homeostasis and the establishment of a functional epidermal barrier. Sebocytes, the main cell population found within the sebaceous glands, are highly specialized lipid-producing cells. Sebaceous gland-resembling tissue structures are also found in male rodents in the form of preputial glands. Similar to sebaceous glands, they are composed of lipid-specialized sebocytes. Due to a lack of adequate organ culture models for skin sebaceous glands and the fact that preputial glands are much larger and easier to handle, previous studies used preputial glands as a model for skin sebaceous glands. Here, we compared both types of sebocytes, using a single-cell RNA sequencing approach, to unravel potential similarities and differences between the two sebocyte populations. In spite of common gene expression patterns due to general lipid-producing properties, we found significant differences in the expression levels of genes encoding enzymes involved in the biogenesis of specialized lipid classes. Specifically, genes critically involved in the mevalonate pathway, including squalene synthase, as well as the sphingolipid salvage pathway, such as ceramide synthase, (acid) sphingomyelinase or acid and alkaline ceramidases, were significantly less expressed by preputial gland sebocytes. Together, our data revealed tissue-specific sebocyte populations, indicating major developmental, functional as well as biosynthetic differences between both glands. The use of preputial glands as a surrogate model to study skin sebaceous glands is therefore limited, and major differences between both glands need to be carefully considered before planning an experiment.

MLG: multilayer graph clustering for multi-condition scRNA-seq data

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present multilayer graph clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale benchmarking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.

RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data

The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets.

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools is the high-dimensional, high-sparsity, high-noise and large-scale characteristics of single-cell RNA-seq data. Deep learning technologies match the characteristics of single-cell RNA-seq data perfectly and offer unprecedented promise. Here, we give a systematic review for most popular single-cell RNA-seq analysis methods and tools based on deep learning models, involving the procedures of data preprocessing (quality control, normalization, data correction, dimensionality reduction and data visualization) and clustering task for downstream analysis. We further evaluate the deep model-based analysis methods of data correction and clustering quantitatively on 11 gold standard datasets. Moreover, we discuss the data preferences of these methods and their limitations, and give some suggestions and guidance for users to select appropriate methods and tools.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Our understanding of protective versus pathological immune responses to SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses revealed widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, including prominent hyperactivation signatures in neutrophils and NK cells. We also identified chromatin accessibility changes at NF-κB binding sites within cytokine gene loci as a potential mechanism for the striking lack of pro-inflammatory cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrated that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Motivation

Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data.

Results

Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.

Availability and implementation

The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-cell RNA-seq landscape midbrain cell responses to red spotted grouper nervous necrosis virus infection

Viral nervous necrosis (VNN) is an acute and serious fish disease caused by nervous necrosis virus (NNV) which has been reported massive mortality in more than fifty teleost species worldwide. VNN causes damage of necrosis and vacuolation to central nervous system (CNS) cells in fish. It is difficult to identify the specific type of cell targeted by NNV, and to decipher the host immune response because of the functional diversity and highly complex anatomical and cellular composition of the CNS. In this study, we found that the red spotted grouper NNV (RGNNV) mainly attacked the midbrain of orange-spotted grouper (Epinephelus coioides). We conducted single-cell RNA-seq analysis of the midbrain of healthy and RGNNV-infected fish and identified 35 transcriptionally distinct cell subtypes, including 28 neuronal and 7 non-neuronal cell types. An evaluation of the subpopulations of immune cells revealed that macrophages were enriched in RGNNV-infected fish, and the transcriptional profiles of macrophages indicated an acute cytokine and inflammatory response. Unsupervised pseudotime analysis of immune cells showed that microglia transformed into M1-type activated macrophages to produce cytokines to reduce the damage to nerve tissue caused by the virus. We also found that RGNNV targeted neuronal cell types was GLU1 and GLU3, and we found that the key genes and pathways by which causes cell cytoplasmic vacuoles and autophagy significant enrichment, this may be the major route viruses cause cell death. These data provided a comprehensive transcriptional perspective of the grouper midbrain and the basis for further research on how viruses infect the teleost CNS.