A unique human cord blood CD8+CD45RA+CD27+CD161+ T-cell subset identified by flow cytometric data analysis using Seurat.

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Dramatic genomic alterations, either inducible or in a pathological state, dismantle the core regulatory networks, leading to the activation of normally silent genes. Despite possessing immense therapeutic potential, accurate detection of these transcripts is an ever-challenging task, as it requires prior knowledge of the physiological gene expression levels. Here, we introduce EcTracker, an R-/Shiny-based single-cell data analysis web server that bestows a plethora of functionalities that collectively enable the quantitative and qualitative assessments of bona fide cell types or tissue-specific transcripts and, conversely, the ectopically expressed genes in the single-cell ribonucleic acid sequencing datasets. Moreover, it also allows regulon analysis to identify the key transcriptional factors regulating the user-selected gene signatures. To demonstrate the EcTracker functionality, we reanalyzed the CRISPR interference (CRISPRi) dataset of the human embryonic stem cells differentiated into endoderm lineage and identified the prominent enrichment of a specific gene signature in the SMAD2 knockout cells whose identity was ambiguous in the original study. The key distinguishing features of EcTracker lie within its processing speed, availability of multiple add-on modules, interactive graphical user interface and comprehensiveness. In summary, EcTracker provides an easy-to-perform, integrative and end-to-end single-cell data analysis platform that allows decoding of cellular identities, identification of ectopically expressed genes and their regulatory networks, and therefore, collectively imparts a novel dimension for analyzing single-cell datasets.

The impact of package selection and versioning on single-cell RNA-seq analysis.

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.

Evaluation of Stem-Cell Embryo Models by Integration with a Human Embryo Single-Cell Transcriptome Atlas.

Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of the molecular processes of early development and provided us with the means to capture biological heterogeneity and assess the cellular composition in early embryos. Comparative analysis of the transcriptional landscapes of embryos with single-cell resolution allows us to better understand and improve stem-cell-based embryo models. However, proper comparison between different single-cell datasets acquired by different laboratories and through different technologies is imperative for adequate analysis and findings. In this chapter, we focus on the analysis of human blastoids, which model the blastocyst, and their integrative analysis with human embryo datasets and a 2D in vitro early development model system dataset, which models epiblast, extraembryonic mesoderm, and trophoblast cells.

Single-Cell RNAseq Clustering.

Single-cell RNA sequencing (scRNA-seq) allows the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation organization of a sample. In this chapter, we describe how to approach the clustering of single-cell RNAseq transcriptomics data using various clustering tools, and we provide some information on the limitations affecting the clustering procedure.

Harnessing Single-Cell RNA Sequencing to Identify Dendritic Cell Types, Characterize Their Biological States, and Infer Their Activation Trajectory.

Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce the defense mechanisms the best suited to face the threat. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions and how.To decipher the nature, functions, and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single-cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning in the field. In addition, awareness must be raised on the need for specific, robust, and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories, and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on GitHub. We hope that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types and that it will contribute to establishing high standards in the field.

Single-nuclei RNA sequencing uncovers heterogenous transcriptional signatures in Parkinson's disease associated with nuclear receptor-related factor 1 defect.

Previous studies have found that deficiency in nuclear receptor-related factor 1 (Nurr1), which participates in the development, differentiation, survival, and degeneration of dopaminergic neurons, is associated with Parkinson's disease, but the mechanism of action is perplexing. Here, we first ascertained the repercussion of knocking down Nurr1 by performing liquid chromatography coupled with tandem mass spectrometry. We found that 231 genes were highly expressed in dopaminergic neurons with Nurr1 deficiency, 14 of which were linked to the Parkinson's disease pathway based on Kyoto Encyclopedia of Genes and Genomes analysis. To better understand how Nurr1 deficiency autonomously invokes the decline of dopaminergic neurons and elicits Parkinson's disease symptoms, we performed single-nuclei RNA sequencing in a Nurr1 LV-shRNA mouse model. The results revealed cellular heterogeneity in the substantia nigra and a number of activated genes, the preponderance of which encode components of the major histocompatibility II complex. Cd74, H2-Ab1, H2-Aa, H2-Eb1, Lyz2, Mrc1, Slc6a3, Slc47a1, Ms4a4b, and Ptprc2 were the top 10 differentially expressed genes. Immunofluorescence staining showed that, after Nurr1 knockdown, the number of CD74-immunoreactive cells in mouse brain tissue was markedly increased. In addition, Cd74 expression was increased in a mouse model of Parkinson's disease induced by treatment with 6-hydroxydopamine. Taken together, our results suggest that Nurr1 deficiency results in an increase in Cd74 expression, thereby leading to the destruction of dopaminergic neurons. These findings provide a potential therapeutic target for the treatment of Parkinson's disease.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. RESULTS: We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. CONCLUSION: As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

Discordant Genome Assemblies Drastically Alter the Interpretation of Single-Cell RNA Sequencing Data Which Can Be Mitigated by a Novel Integration Method.

Advances in sequencing and assembly technology have led to the creation of genome assemblies for a wide variety of non-model organisms. The rapid production and proliferation of updated, novel assembly versions can create vexing problems for researchers when multiple-genome assembly versions are available at once, requiring researchers to work with more than one reference genome. Multiple-genome assemblies are especially problematic for researchers studying the genetic makeup of individual cells, as single-cell RNA sequencing (scRNAseq) requires sequenced reads to be mapped and aligned to a single reference genome. Using the Astyanax mexicanus, this study highlights how the interpretation of a single-cell dataset from the same sample changes when aligned to its two different available genome assemblies. We found that the number of cells and expressed genes detected were drastically different when aligning to the different assemblies. When the genome assemblies were used in isolation with their respective annotations, cell-type identification was confounded, as some classic cell-type markers were assembly-specific, whilst other genes showed differential patterns of expression between the two assemblies. To overcome the problems posed by multiple-genome assemblies, we propose that researchers align to each available assembly and then integrate the resultant datasets to produce a final dataset in which all genome alignments can be used simultaneously. We found that this approach increased the accuracy of cell-type identification and maximised the amount of data that could be extracted from our single-cell sample by capturing all possible cells and transcripts. As scRNAseq becomes more widely available, it is imperative that the single-cell community is aware of how genome assembly alignment can alter single-cell data and their interpretation, especially when reviewing studies on non-model organisms.

Methods for Isolating and Defining Single-Cell Transcriptomes of Tissue-Resident Human NK Cells.

Natural killer (NK) cells are innate lymphocytes that control tumors and microbial infections. Human NK cells are transcriptomically and phenotypically heterogeneous. The site where NK cells develop and reside determines their phenotype and effector functions. Our current knowledge about human NK cells is primarily from blood- and bone marrow-derived NK cells. The major limitation in formulating organ-specific clinical therapy is the knowledge gap on how tissue-resident NK cells develop, home, and function. Thus, it is crucial to define the transcriptomic profiles and the transcriptional regulation of tissue-resident NK cells. The major challenges in studying tissue-resident NK cells include their total number and the complexity of the tissue. Additionally, during isolation, keeping them viable and naïve without activation are challenging tasks. Here, we provide methods for isolating and performing transcriptomic analyses of NK cells at the individual cell level. Single-cell RNA sequencing provides a higher resolution of cellular heterogeneity and a better understanding of cell-cell interactions within the microenvironment. Using these methods, we can efficiently identify distinct populations of NK cells in tissues and define their unique transcriptomic profiles.

Generation of Centered Log-Ratio Normalized Antibody-Derived Tag Counts from Large Single-Cell Sequencing Datasets.

Recent developments in single-cell analysis has provided the ability to assay >50 surface-level proteins by combining oligo-conjugated antibodies with sequencing technology. These methods, such as CITE-seq and REAP-seq, have added another modality to single-cell analysis, enhancing insight across many biological subdisciplines. While packages like Seurat have greatly facilitated analysis of single-cell protein expression, the practical steps to carry out the analysis with increasingly larger datasets have been fragmented. In addition, using data visualizations, I will highlight some details about the centered log-ratio (CLR) normalization of antibody-derived tag (ADT) counts that may be overlooked. In this method chapter, I provide detailed steps to generate CLR-normalized CITE-seq data using cloud computing from a large CITE-seq dataset.

Analysis of Single-Cell Transcriptome Data in Drosophila.

The fly Drosophila is a versatile model organism that has led to fascinating biological discoveries. In the past few years, Drosophila researchers have used single-cell RNA-sequencing (scRNA-seq) to gain insights into the cellular composition, and developmental processes of various tissues and organs. Given the success of single-cell technologies a variety of computational tools and software packages were developed to enable and facilitate the analysis of scRNA-seq data. In this book chapter we want to give guidance on analyzing droplet-based scRNA-seq data from Drosophila. We will initially describe the preprocessing commonly done for Drosophila, point out possible downstream analyses, and finally highlight computational methods developed using Drosophila scRNA-seq data.

Seurat Spleen: A Pathognomonic Sign in Angiography.

Splenic injury commonly occurs following abdominal trauma and can result in severe complications and death if it goes unrecognized. The Seurat spleen is a term used to describe the angiographic appearance of splenic injury following blunt trauma, given its resemblance to the pointillistic artwork of French neo-impressionist Georges Seurat. We present a case of a 43-year-old man who presented following a motor vehicle collision and was found to have multiple punctate foci of contrast extravasation in the spleen consistent with the Seurat spleen angiographic sign. This angiographic pattern can be used as a pathognomonic sign to identify splenic injury, with early identification crucial to preventing further complications of the injury.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

Single-Nucleus Sequencing of an Entire Mammalian Heart: Cell Type Composition and Velocity.

: Analyses on the cellular level are indispensable to expand our understanding of complex tissues like the mammalian heart. Single-nucleus sequencing (snRNA-seq) allows for the exploration of cellular composition and cell features without major hurdles of single-cell sequencing. We used snRNA-seq to investigate for the first time an entire adult mammalian heart. Single-nucleus quantification and clustering led to an accurate representation of cell types, revealing 24 distinct clusters with endothelial cells (28.8%), fibroblasts (25.3%), and cardiomyocytes (22.8%) constituting the major cell populations. An additional RNA velocity analysis allowed us to study transcription kinetics and was utilized to visualize the transitions between mature and nascent cellular states of the cell types. We identified subgroups of cardiomyocytes with distinct marker profiles. For example, the expression of Hand2os1 distinguished immature cardiomyocytes from differentiated cardiomyocyte populations. Moreover, we found a cell population that comprises endothelial markers as well as markers clearly related to cardiomyocyte function. Our velocity data support the idea that this population is in a trans-differentiation process from an endothelial cell-like phenotype towards a cardiomyocyte-like phenotype. In summary, we present the first report of sequencing an entire adult mammalian heart, providing realistic cell-type distributions combined with RNA velocity kinetics hinting at interrelations.

Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.

Cardiac ischemia leads to the loss of myocardial tissue and the activation of a repair process that culminates in the formation of a scar whose structural characteristics dictate propensity to favorable healing or detrimental cardiac wall rupture. To elucidate the cellular processes underlying scar formation, here we perform unbiased single-cell mRNA sequencing of interstitial cells isolated from infarcted mouse hearts carrying a genetic tracer that labels epicardial-derived cells. Sixteen interstitial cell clusters are revealed, five of which were of epicardial origin. Focusing on stromal cells, we define 11 sub-clusters, including diverse cell states of epicardial- and endocardial-derived fibroblasts. Comparing transcript profiles from post-infarction hearts in C57BL/6J and 129S1/SvImJ inbred mice, which displays a marked divergence in the frequency of cardiac rupture, uncovers an early increase in activated myofibroblasts, enhanced collagen deposition, and persistent acute phase response in 129S1/SvImJ mouse hearts, defining a crucial time window of pathological remodeling that predicts disease outcome.

Transcriptional and Spatial Resolution of Cell Types in the Mammalian Habenula.

The habenula complex is appreciated as a critical regulator of motivated and pathological behavioral states via its output to midbrain nuclei. Despite this, transcriptional definition of cell populations that comprise both the medial habenular (MHb) and lateral habenular (LHb) subregions in mammals remain undefined. To resolve this, we performed single-cell transcriptional profiling and highly multiplexed in situ hybridization experiments of the mouse habenula complex in naive mice and those exposed to an acute aversive stimulus. Transcriptionally distinct neuronal cell types identified within the MHb and LHb, were spatially defined, differentially engaged by aversive stimuli, and had distinct electrophysiological properties. Cell types identified in mice also displayed a high degree of transcriptional similarity to those previously described in zebrafish, highlighting the well-conserved nature of habenular cell types across the phylum. These data identify key molecular targets within habenular cell types and provide a critical resource for future studies.

Super-Enhancer Redistribution as a Mechanism of Broad Gene Dysregulation in Repeatedly Drug-Treated Cancer Cells.

Cisplatin is an antineoplastic drug administered at suboptimal and intermittent doses to avoid life-threatening effects. Although this regimen shortly improves symptoms in the short term, it also leads to more malignant disease in the long term. We describe a multilayered analysis ranging from chromatin to translation-integrating chromatin immunoprecipitation sequencing (ChIP-seq), global run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and ribosome profiling-to understand how cisplatin confers (pre)malignant features by using a well-established ovarian cancer model of cisplatin exposure. This approach allows us to segregate the human transcriptome into gene modules representing distinct regulatory principles and to characterize that the most cisplatin-disrupted modules are associated with underlying events of super-enhancer plasticity. These events arise when cancer cells initiate without ultimately ending the program of drug-stimulated death. Using a PageRank-based algorithm, we predict super-enhancer regulator ISL1 as a driver of this plasticity and validate this prediction by using CRISPR/dCas9-KRAB inhibition (CRISPRi) and CRISPR/dCas9-VP64 activation (CRISPRa) tools. Together, we propose that cisplatin reprograms cancer cells when inducing them to undergo near-to-death experiences.

Visualization of Single Cell RNA-Seq Data Using t-SNE in R.

Single cell RNA sequencing (scRNA-seq) is a powerful tool to analyze cellular heterogeneity, identify new cell types, and infer developmental trajectories, which has greatly facilitated studies on development, immunity, cancer, neuroscience, and so on. Visualizing of scRNA-Seq data is fundamental and essential because it is critical to biological interpretation. Although principal component analysis (PCA) is used for visualizing scRNA-seq at early studies, t-Distributed Stochastic Neighbor embedding (t-SNE), an unsupervised nonlinear dimensionality reduction technique, is widely used nowadays due to its advantage in visualization of scRNA-seq data. Here, we detailed the process of visualization of single-cell RNA-seq data using t-SNE via Seurat, an R toolkit for single cell genomics.

IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis.

BACKGROUND: In single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant. FINDINGS: To accelerate this process, we have developed IKAP-an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology. CONCLUSIONS: By tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.