Understanding development and stem cells using single cell-based analyses of gene expression

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

Integration of a multi-omics stem cell differentiation dataset using a dynamical model

Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.

Gene Editing in Hematopoietic Stem Cells

Hematopoietic stem cells (HSCs) can be isolated and collected from the body, genetically modified, and expanded ex vivo. The invention of innovative and powerful gene editing tools has provided researchers with great convenience in genetically modifying a wide range of cells, including hematopoietic stem and progenitor cells (HSPCs). In addition to being used to modify genes to study the functional role that specific genes play in the hematopoietic system, the application of gene editing platforms in HSCs is largely focused on the development of cell-based gene editing therapies to treat diseases such as immune deficiency disorders and inherited blood disorders. Here, we review the application of gene editing tools in HSPCs. In particular, we provide a broad overview of the development of gene editing tools, multiple strategies for the application of gene editing tools in HSPCs, and exciting clinical advances in HSPC gene editing therapies. We also outline the various challenges integral to clinical translation of HSPC gene editing and provide the possible corresponding solutions.

Cellular and molecular profiles of larval and adult Xenopus corneal epithelia resolved at the single-cell level

Corneal Epithelial Stem Cells (CESCs) and their proliferative progeny, the Transit Amplifying Cells (TACs), are responsible for homeostasis and maintaining corneal transparency. Owing to our limited knowledge of cell fates and gene activity within the cornea, the search for unique markers to identify and isolate these cells remains crucial for ocular surface reconstruction. We performed single-cell RNA sequencing of corneal cells from larval and adult stages of Xenopus. Our results indicate that as the cornea develops and matures, there is an increase in cellular diversity, which is accompanied by a substantial shift in transcriptional profile, gene regulatory network and cell-cell communication dynamics. Our data also reveals several novel genes expressed in corneal cells and changes in gene expression during corneal differentiation at both developmental time-points. Importantly, we identify specific basal cell clusters in both the larval and adult cornea that comprise a relatively undifferentiated cell type and express distinct stem cell markers, which we propose are the putative larval and adult CESCs, respectively. This study offers a detailed atlas of single-cell transcriptomes in the frog cornea. In the future, this work will be useful to elucidate the function of novel genes in corneal epithelial homeostasis, wound healing and regeneration.

Molecular profiling of stem cell-derived retinal pigment epithelial cell differentiation established for clinical translation

Human embryonic stem cell-derived retinal pigment epithelial cells (hESC-RPE) are a promising cell source to treat age-related macular degeneration (AMD). Despite several ongoing clinical studies, a detailed mapping of transient cellular states during in vitro differentiation has not been performed. Here, we conduct single-cell transcriptomic profiling of an hESC-RPE differentiation protocol that has been developed for clinical use. Differentiation progressed through a culture diversification recapitulating early embryonic development, whereby cells rapidly acquired a rostral embryo patterning signature before converging toward the RPE lineage. At intermediate steps, we identified and examined the potency of an NCAM1⁺ retinal progenitor population and showed the ability of the protocol to suppress non-RPE fates. We demonstrated that the method produces a pure RPE pool capable of maturing further after subretinal transplantation in a large-eyed animal model. Our evaluation of hESC-RPE differentiation supports the development of safe and efficient pluripotent stem cell-based therapies for AMD.

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Transcriptional analysis of hESC-RPE differentiation benchmarked to in vivo cells

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NCAM1 emerges as a cell-surface marker of multipotent neuroepithelial progenitors

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hESC-RPE cells are obtained through a divergence-convergence process


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hESC-RPE further mature in vivo upon subretinal injection into the rabbit eye

Single-cell analysis reveals Comma-1D as a unique cell model for mammary gland development and breast cancer

The mammary epithelial tree contains two distinct populations, luminal and basal. The investigation of how this heterogeneity is developed and how it influences tumorigenesis has been hampered by the need to perform these studies using animal models. Comma-1D is an immortalized mouse mammary epithelial cell line that has unique morphogenetic properties. By performing single-cell RNA-seq studies we found that Comma-1D cultures consist of two main populations with luminal and basal features and a smaller population with mixed lineage and bipotent characteristics. We demonstrated that multiple transcription factors associated with the differentiation of the mammary epithelium in vivo also modulate this process in Comma-1D cultures. Additionally, we found that only cells with luminal features were able to acquire transformed characteristics after an oncogenic HER2 mutant was introduced in their genomes. Overall, our studies characterize at a single-cell level the heterogeneity of the Comma-1D cell line and illustrate how Comma-1D cells can be used as an experimental model to study both the differentiation and the transformation processes in vitro.

A Cd9+Cd271+ stem/progenitor population and the SHP2 pathway contribute to neonatal-to-adult switching that regulates tendon maturation

Tendon maturation lays the foundation for postnatal tendon development, its proper mechanical function, and regeneration, but the critical cell populations and the entangled mechanisms remain poorly understood. Here, by integrating the structural, mechanical, and molecular properties, we show that post-natal days 7-14 are the crucial transitional stage for mouse tendon maturation. We decode the cellular and molecular regulatory networks at the single-cell level. We find that a nerve growth factor (NGF)-secreting Cd9⁺Cd271⁺ tendon stem/progenitor cell population mainly prompts conversion from neonate to adult tendon. Through single-cell gene regulatory network analysis, in vitro inhibitor identification, and in vivo tendon-specific Shp2 deletion, we find that SHP2 signaling is a regulator for tendon maturation. Our research comprehensively reveals the dynamic cell population transition during tendon maturation, implementing insights into the critical roles of the maturation-related stem cell population and SHP2 signaling pathway during tendon differentiation and regeneration.

Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Examining the Development of Chronic Thromboembolic Pulmonary Hypertension at the Single-Cell Level

BACKGROUND

The mechanism of chronic thromboembolic pulmonary hypertension (CTEPH) is known to be multifactorial but remains incompletely understood.

METHODS

In this study, single-cell RNA sequencing, which facilitates the identification of molecular profiles of samples on an individual cell level, was applied to investigate individual cell types in pulmonary endarterectomized tissues from 5 patients with CTEPH. The order of single-cell types was then traced along the developmental trajectory of CTEPH by trajectory inference analysis, and intercellular communication was characterized by analysis of ligand-receptor pairs between cell types. Finally, comprehensive bioinformatics tools were used to analyze possible functions of branch-specific cell types and the underlying mechanisms.

RESULTS

Eleven cell types were identified, with immune-related cell types (T cells, natural killer cells, macrophages, and mast cells) distributed in the left (early) branch of the pseudotime tree, cancer stem cells, and CRISPLD2+ cells as intermediate cell types, and classic disease-related cell types (fibroblasts, smooth muscle cells, myofibroblasts, and endothelial cells) in the right (later) branch. Ligand-receptor interactions revealed close communication between macrophages and disease-related cell types as well as between smooth muscle cells and fibroblasts or endothelial cells. Moreover, the ligands and receptors were significantly enriched in key pathways such as the PI3K/Akt signaling pathway. Furthermore, highly expressed genes specific to the undefined cell type were significantly enriched in important functions associated with regulation of endoplasmic reticulum stress.

CONCLUSIONS

This single-cell RNA sequencing analysis revealed the order of single cells along a developmental trajectory in CTEPH as well as close communication between different cell types in CTEPH pathogenesis.

Single-Cell Sequencing to Unveil the Mystery of Embryonic Development

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

Dissecting the heterogeneity and tumorigenesis of BRCA1 deficient mammary tumors via single cell RNA sequencing

Background: BRCA1 plays critical roles in mammary gland development and mammary tumorigenesis. And loss of BRCA1 induces mammary tumors in a stochastic manner. These tumors present great heterogeneity at both intertumor and intratumor levels.

Methods: To comprehensively elucidate the heterogeneity of BRCA1 deficient mammary tumors and the underlying mechanisms for tumor initiation and progression, we conducted bulk and single cell RNA sequencing (scRNA-seq) on both mammary gland cells and mammary tumor cells isolated from Brca1 knockout mice.

Results: We found the BRCA1 deficient tumors could be classified into four subtypes with distinct molecular features and different sensitivities to anti-cancer drugs at the intertumor level. Whereas within the tumors, heterogeneous subgroups were classified mainly due to the different activities of cell proliferation, DNA damage response/repair and epithelial-to-mesenchymal transition (EMT). Besides, we reconstructed the BRCA1 related mammary tumorigenesis to uncover the transcriptomes alterations during this process via pseudo-temporal analysis of the scRNA-seq data. Furthermore, from candidate markers for BRCA1 mutant tumors, we discovered and validated one oncogene Mrc2, whose loss could reduce mammary tumor growth in vitro and in vivo.

Conclusion: Our study provides a useful resource for better understanding of mammary tumorigenesis induced by BRCA1 deficiency.

Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a 'baseline' method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Transcriptome analysis of gene expression changes upon enzymatic dissociation in skeletal myoblasts

Single-cell RNA-sequencing analysis is one of the most effective tools for understanding specific cellular states. The use of single cells or pooled cells in RNA-seq analysis requires the isolation of cells from a tissue or culture. Although trypsin or more recently cold-active protease (CAP) has been used for cell dissociation, the extent to which the gene expression changes are suppressed has not been clarified. To this end, we conducted detailed profiling of the enzyme-dependent gene expression changes in mouse skeletal muscle progenitor cells, focusing on the enzyme treatment time, amount and temperature. We found that the genes whose expression was changed by the enzyme treatment could be classified in a time-dependent manner and that there were genes whose expression was changed independently of the enzyme treatment time, amount and temperature. This study will be useful as reference data for genes that should be excluded or considered for RNA-seq analysis using enzyme isolation methods.

Neural crest multipotency and specification: power and limits of single cell transcriptomic approaches

The neural crest is a unique population of multipotent cells forming in vertebrate embryos. Their vast cell fate potential enables the generation of a diverse array of differentiated cell types in vivo. These include, among others, connective tissue, cartilage and bone of the face and skull, neurons and glia of the peripheral nervous system (including enteric nervous system), and melanocytes. Following migration, these derivatives extensively populate multiple germ layers. Within the competent neural border ectoderm, an area located at the junction between the neural and non-neural ectoderm during embryonic development, neural crest cells form in response to a series of inductive secreted cues including BMP, Wnt, and FGF signals. As cells become progressively specified, they express transcriptional modules conducive with their stage of fate determination or cell state. Those sequential states include the neural border state, the premigratory neural crest state, the epithelium-to-mesenchyme transitional state, and the migratory state to end with post-migratory and differentiation states. However, despite the extensive knowledge accumulated over 150 years of neural crest biology, many key questions remain open, in particular the timing of neural crest lineage determination, the control of potency during early developmental stages, and the lineage relationships between different subpopulations of neural crest cells. In this review, we discuss the recent advances in understanding early neural crest formation using cutting-edge high-throughput single cell sequencing approaches. We will discuss how this new transcriptomic data, from 2017 to 2021, has advanced our knowledge of the steps in neural crest cell lineage commitment and specification, the mechanisms driving multipotency, and diversification. We will then discuss the questions that remain to be resolved and how these approaches may continue to unveil the biology of these fascinating cells.

Between Fate Choice and Self-Renewal-Heterogeneity of Adult Neural Crest-Derived Stem Cells

Stem cells of the neural crest (NC) vitally participate to embryonic development, but also remain in distinct niches as quiescent neural crest-derived stem cell (NCSC) pools into adulthood. Although NCSC-populations share a high capacity for self-renewal and differentiation resulting in promising preclinical applications within the last two decades, inter- and intrapopulational differences exist in terms of their expression signatures and regenerative capability. Differentiation and self-renewal of stem cells in developmental and regenerative contexts are partially regulated by the niche or culture condition and further influenced by single cell decision processes, making cell-to-cell variation and heterogeneity critical for understanding adult stem cell populations. The present review summarizes current knowledge of the cellular heterogeneity within NCSC-populations located in distinct craniofacial and trunk niches including the nasal cavity, olfactory bulb, oral tissues or skin. We shed light on the impact of intrapopulational heterogeneity on fate specifications and plasticity of NCSCs in their niches in vivo as well as during in vitro culture. We further discuss underlying molecular regulators determining fate specifications of NCSCs, suggesting a regulatory network including NF-κB and NC-related transcription factors like SLUG and SOX9 accompanied by Wnt- and MAPK-signaling to orchestrate NCSC stemness and differentiation. In summary, adult NCSCs show a broad heterogeneity on the level of the donor and the donors' sex, the cell population and the single stem cell directly impacting their differentiation capability and fate choices in vivo and in vitro. The findings discussed here emphasize heterogeneity of NCSCs as a crucial parameter for understanding their role in tissue homeostasis and regeneration and for improving their applicability in regenerative medicine.

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

Systematic Analysis of Functionally Related Gene Clusters in the Opportunistic Pathogen, Candida albicans

The proper balance of gene expression is essential for cellular health, organismal development, and maintaining homeostasis. In response to complex internal and external signals, the cell needs to modulate gene expression to maintain proteostasis and establish cellular identity within its niche. On a genome level, single-celled prokaryotic microbes display clustering of co-expressed genes that are regulated as a polycistronic RNA. This phenomenon is largely absent from eukaryotic microbes, although there is extensive clustering of co-expressed genes as functional pairs spread throughout the genome in Saccharomyces cerevisiae. While initial analysis demonstrated conservation of clustering in divergent fungal lineages, a comprehensive analysis has yet to be performed. Here we report on the prevalence, conservation, and significance of the functional clustering of co-regulated genes within the opportunistic human pathogen, Candida albicans. Our analysis reveals that there is extensive clustering within this organism-although the identity of the gene pairs is unique compared with those found in S. cerevisiae-indicating that this genomic arrangement evolved after these microbes diverged evolutionarily, rather than being the result of an ancestral arrangement. We report a clustered arrangement in gene families that participate in diverse molecular functions and are not the result of a divergent orientation with a shared promoter. This arrangement coordinates the transcription of the clustered genes to their neighboring genes, with the clusters congregating to genomic loci that are conducive to transcriptional regulation at a distance.

Single-cell Sequencing in the Field of Stem Cells

Variation and heterogeneity between cells are the basic characteristics of stem cells. Traditional sequencing analysis methods often cover up this difference. Single-cell sequencing technology refers to the technology of high-throughput sequencing analysis of genomes at the single-cell level. It can effectively analyze cell heterogeneity and identify a small number of cell populations. With the continuous progress of cell sorting, nucleic acid extraction and other technologies, single-cell sequencing technology has also made great progress. Encouraging new discoveries have been made in stem cell research, including pluripotent stem cells, tissue-specific stem cells and cancer stem cells. In this review, we discuss the latest progress and future prospects of single-cell sequencing technology in the field of stem cells.

Single-Cell Trajectory Inference Guided Enhancement of Thyroid Maturation In Vitro Using TGF-Beta Inhibition

The thyroid gland regulates metabolism and growth via secretion of thyroid hormones by thyroid follicular cells (TFCs). Loss of TFCs, by cellular dysfunction, autoimmune destruction or surgical resection, underlies hypothyroidism. Recovery of thyroid hormone levels by transplantation of mature TFCs derived from stem cells in vitro holds great therapeutic promise. However, the utilization of in vitro derived tissue for regenerative medicine is restricted by the efficiency of differentiation protocols to generate mature organoids. Here, to improve the differentiation efficiency for thyroid organoids, we utilized single-cell RNA-Seq to chart the molecular steps undertaken by individual cells during the in vitro transformation of mouse embryonic stem cells to TFCs. Our single-cell atlas of mouse organoid systematically and comprehensively identifies, for the first time, the cell types generated during production of thyroid organoids. Using pseudotime analysis, we identify TGF-beta as a negative regulator of thyroid maturation in vitro. Using pharmacological inhibition of TGF-beta pathway, we improve the level of thyroid maturation, in particular the induction of Nis expression. This in turn, leads to an enhancement of iodide organification in vitro, suggesting functional improvement of the thyroid organoid. Our study highlights the potential of single-cell molecular characterization in understanding and improving thyroid maturation and paves the way for identification of therapeutic targets against thyroid disorders.

Minimum Spanning vs. Principal Trees for Structured Approximations of Multi-Dimensional Datasets

Construction of graph-based approximations for multi-dimensional data point clouds is widely used in a variety of areas. Notable examples of applications of such approximators are cellular trajectory inference in single-cell data analysis, analysis of clinical trajectories from synchronic datasets, and skeletonization of images. Several methods have been proposed to construct such approximating graphs, with some based on computation of minimum spanning trees and some based on principal graphs generalizing principal curves. In this article we propose a methodology to compare and benchmark these two graph-based data approximation approaches, as well as to define their hyperparameters. The main idea is to avoid comparing graphs directly, but at first to induce clustering of the data point cloud from the graph approximation and, secondly, to use well-established methods to compare and score the data cloud partitioning induced by the graphs. In particular, mutual information-based approaches prove to be useful in this context. The induced clustering is based on decomposing a graph into non-branching segments, and then clustering the data point cloud by the nearest segment. Such a method allows efficient comparison of graph-based data approximations of arbitrary topology and complexity. The method is implemented in Python using the standard scikit-learn library which provides high speed and efficiency. As a demonstration of the methodology we analyse and compare graph-based data approximation methods using synthetic as well as real-life single cell datasets.

Uncovering cellular networks in branching morphogenesis using single-cell transcriptomics

Single-cell RNA-sequencing (scRNA-seq) and related technologies to identify cell types and measure gene expression in space, in time, and within lineages have multiplied rapidly in recent years. As these techniques proliferate, we are seeing an increase in their application to the study of developing tissues. Here, we focus on single-cell investigations of branching morphogenesis. Branched organs are highly complex but typically develop recursively, such that a given developmental stage theoretically contains the entire spectrum of cell identities from progenitor to terminally differentiated. Therefore, branched organs are a highly attractive system for study by scRNA-seq. First, we provide an update on advances in the field of scRNA-seq analysis, focusing on spatial transcriptomics, computational reconstruction of differentiation trajectories, and integration of scRNA-seq with lineage tracing. In addition, we discuss the possibilities and limitations for applying these techniques to studying branched organs. We then discuss exciting advances made using scRNA-seq in the study of branching morphogenesis and differentiation in mammalian organs, with emphasis on the lung, kidney, and mammary gland. We propose ways that scRNA-seq could be used to address outstanding questions in each organ. Finally, we highlight the importance of physical and mechanical signals in branching morphogenesis and speculate about how scRNA-seq and related techniques could be applied to study tissue morphogenesis beyond just differentiation.

Mapping Distinct Bone Marrow Niche Populations and Their Differentiation Paths

The bone marrow microenvironment is composed of heterogeneous cell populations of non-hematopoietic cells with complex phenotypes and undefined trajectories of maturation. Among them, mesenchymal cells maintain the production of stromal, bone, fat, and cartilage cells. Resolving these unique cellular subsets within the bone marrow remains challenging. Here, we used single-cell RNA sequencing of non-hematopoietic bone marrow cells to define specific subpopulations. Furthermore, by combining computational prediction of the cell state hierarchy with the known expression of key transcription factors, we mapped differentiation paths to the osteocyte, chondrocyte, and adipocyte lineages. Finally, we validated our findings using lineage-specific reporter strains and targeted knockdowns. Our analysis reveals differentiation hierarchies for maturing stromal cells, determines key transcription factors along these trajectories, and provides an understanding of the complexity of the bone marrow microenvironment.

The Cellular Diversity and Transcription Factor Code of Drosophila Enteroendocrine Cells

Enteroendocrine cells (EEs) in the intestinal epithelium have important endocrine functions, yet this cell lineage exhibits great local and regional variations that have hampered detailed characterization of EE subtypes. Through single-cell RNA-sequencing analysis, combined with a collection of peptide hormone and receptor knockin strains, here we provide a comprehensive analysis of cellular diversity, spatial distribution, and transcription factor (TF) code of EEs in adult Drosophila midgut. We identify 10 major EE subtypes that totally produced approximately 14 different classes of hormone peptides. Each EE on average co-produces approximately 2-5 different classes of hormone peptides. Functional screen with subtype-enriched TFs suggests a combinatorial TF code that controls EE cell diversity; class-specific TFs Mirr and Ptx1 respectively define two major classes of EEs, and regional TFs such as Esg, Drm, Exex, and Fer1 further define regional EE identity. Our single-cell data should greatly facilitate Drosophila modeling of EE differentiation and function.

Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis.

Single-cell RNA sequencing (scRNA-seq) enables an unparalleled ability to map the heterogeneity of dynamic multicellular processes, such as tissue development, tumor growth, wound response and repair, and inflammation. Multiple methods have been developed to order cells along a pseudotime axis that represents a trajectory through such processes using the concept that cells that are closely related in a lineage will have similar transcriptomes. However, time series experiments provide another useful information source to order cells, from earlier to later time point. By introducing a novel use of biological pathway prior information, our Tempora algorithm improves the accuracy and speed of cell trajectory inference from time-series scRNA-seq data as measured by reconstructing known developmental trajectories from three diverse data sets. By analyzing scRNA-seq data at the cluster (cell type) level instead of at the single-cell level and by using known pathway information, Tempora amplifies gene expression signals from one cell using similar cells in a cluster and similar genes within a pathway. This approach also reduces computational time and resources needed to analyze large data sets because it works with a relatively small number of clusters instead of a potentially large number of cells. Finally, it eases interpretation, via operating on a relatively small number of clusters which usually represent known cell types, as well as by identifying time-dependent pathways. Tempora is useful for finding novel insights in dynamic processes.

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

A single cell transcriptional atlas of early synovial joint development

Synovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage-tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

A Rainbow Reporter Tracks Single Cells and Reveals Heterogeneous Cellular Dynamics among Pluripotent Stem Cells and Their Differentiated Derivatives

Single-cell transcriptomic approaches have found molecular heterogeneities within populations of pluripotent stem cells (PSCs). A tool that tracks single-cell lineages and their phenotypes longitudinally would reveal whether heterogeneity extends beyond molecular identity. Hence, we generated a stable Cre-inducible rainbow reporter human PSC line that provides up to 18 unique membrane-targeted fluorescent barcodes. These barcodes enable repeated assessments of single cells as they clonally expand, change morphology, and migrate. Owing to the cellular resolution of this reporter, we identified subsets of PSCs with enhanced clonal expansion, synchronized cell divisions, and persistent localization to colony edges. Reporter expression was stably maintained throughout directed differentiation into cardiac myocytes, cortical neurons, and hepatoblasts. Repeated examination of neural differentiation revealed self-assembled cortical tissues derive from clonally dominant progenitors. Collectively, these findings demonstrate the broad utility and easy implementation of this reporter line for tracking single-cell behavior.

Recent advances of single-cell RNA sequencing technology in mesenchymal stem cell research

Mesenchymal stem cells (MSCs) are multipotent stromal cells with great potential for clinical applications. However, little is known about their cell heterogeneity at a single-cell resolution, which severely impedes the development of MSC therapy. In this review, we focus on advances in the identification of novel surface markers and functional subpopulations of MSCs made by single-cell RNA sequencing and discuss their participation in the pathophysiology of stem cells and related diseases. The challenges and future directions of single-cell RNA sequencing in MSCs are also addressed in this review.

DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data

MOTIVATION

Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging.

RESULTS

We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data.

AVAILABILITY AND IMPLEMENTATION

DensityPath software is available at https://github.com/ucasdp/DensityPath.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Examining the co-expression, transcriptome clustering and variation using fuzzy cluster network of testicular stem cells and pluripotent stem cells compared with other cell types

Stem cells are crucial in the field of tissue regeneration and developmental biology. Embryonic stem cells (ESCs) which are pluripotent in nature are derived from the inner cell mass of blastocyst. The gene expression profiles of ESCs and Induced pluripotent stem cells (iPSCs) were compared to identify the differences. Spermatogonial stem cells (SSCs) are also known as Germ-line stem cells (GSCs) present in testis is having the capability of producing the sperm in their whole lifetime. Therefore can be reprogrammed into pluripotent cells called male germline pluripotent cells (gPSCs). It is very difficult to interpret the larger genomic data sets which are available in public databases without high computational facilities. In order to identify the similar groups We studied the co-expression, clustering of the transcriptome and variation of the transcriptome of the GSCs, gPSCs, ESCs and other cell types using fuzzy clustering using AutoSOME. The series matrix file with GSE ID GSE11274 was retrieved and subjected to the various normalization methods, corresponding rows and columns were clustered using p values, ensemble runs, and different running modes. Transcriptome analysis using the proposed approach intuitively and consistently characterized the variation in cell-cell significantly. Collectively, our results suggest that the GSCs and the ESCs displayed differential gene expression profiles, and the GSCs possessed the potential to acquire pluripotency based on the high expression of epigenetic factors and transcription factors. These data may provide novel insights into the reprogramming mechanism of GSCs.

Single-Cell Transcriptomes Distinguish Stem Cell State Changes and Lineage Specification Programs in Early Mammary Gland Development

The mammary gland consists of cells with gene expression patterns reflecting their cellular origins, function, and spatiotemporal context. However, knowledge of developmental kinetics and mechanisms of lineage specification is lacking. We address this significant knowledge gap by generating a single-cell transcriptome atlas encompassing embryonic, postnatal, and adult mouse mammary development. From these data, we map the chronology of transcriptionally and epigenetically distinct cell states and distinguish fetal mammary stem cells (fMaSCs) from their precursors and progeny. fMaSCs show balanced co-expression of factors associated with discrete adult lineages and a metabolic gene signature that subsides during maturation but reemerges in some human breast cancers and metastases. These data provide a useful resource for illuminating mammary cell heterogeneity, the kinetics of differentiation, and developmental correlates of tumorigenesis.

Single-cell RNA sequencing of developing mouse mammary epithelia reveals the timing of lineage specification. Giraddi et al. find that fetal mammary stem cells co-express factors that define distinct lineages in their progeny and bear functionally relevant metabolic program signatures that change with differentiation and are resurrected in human breast cancers and metastases.

Single-Cell Expression Variability Implies Cell Function

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

Random forest based similarity learning for single cell RNA sequencing data

Motivation

Genome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore, obtaining accurate cell–cell similarities from scRNA-seq data is a critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal.

Results

Here, we present RAFSIL, a random forest based approach to learn cell–cell similarities from scRNA-seq data. RAFSIL implements a two-step procedure, where feature construction geared towards scRNA-seq data is followed by similarity learning. It is designed to be adaptable and expandable, and RAFSIL similarities can be used for typical exploratory data analysis tasks like dimension reduction, visualization and clustering. We show that our approach compares favorably with current methods across a diverse collection of datasets, and that it can be used to detect and highlight unwanted technical variation in scRNA-seq datasets in situations where other methods fail. Overall, RAFSIL implements a flexible approach yielding a useful tool that improves the analysis of scRNA-seq data.

Availability and implementation

The RAFSIL R package is available at www.kostkalab.net/software.html

Supplementary information

Supplementary data are available at Bioinformatics online.

The new technologies of high-throughput single-cell RNA sequencing

A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Brown Adipose Tissue Development and Metabolism

Brown adipose tissue is well known to be a thermoregulatory organ particularly important in small rodents and human infants, but it was only recently that its existence and significance to metabolic fitness in adult humans have been widely realized. The ability of active brown fat to expend high amounts of energy has raised interest in stimulating thermogenesis therapeutically to treat metabolic diseases related to obesity and type 2 diabetes. In parallel, there has been a surge of research aimed at understanding the biology of rodent and human brown fat development, its remarkable metabolic properties, and the phenomenon of white fat browning, in which white adipocytes can be converted into brown like adipocytes with similar thermogenic properties. Here, we review the current understanding of the developmental and metabolic pathways involved in forming thermogenic adipocytes, and highlight some of the many unknown functions of brown fat that make its study a rich and exciting area for future research.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Modeling Mammalian Commitment to the Neural Lineage Using Embryos and Embryonic Stem Cells

Early mammalian embryogenesis relies on a large range of cellular and molecular mechanisms to guide cell fate. In this highly complex interacting system, molecular circuitry tightly controls emergent properties, including cell differentiation, proliferation, morphology, migration, and communication. These molecular circuits include those responsible for the control of gene and protein expression, as well as metabolism and epigenetics. Due to the complexity of this circuitry and the relative inaccessibility of the mammalian embryo in utero, mammalian neural commitment remains one of the most challenging and poorly understood areas of developmental biology. In order to generate the nervous system, the embryo first produces two pluripotent populations, the inner cell mass and then the primitive ectoderm. The latter is the cellular substrate for gastrulation from which the three multipotent germ layers form. The germ layer definitive ectoderm, in turn, is the substrate for multipotent neurectoderm (neural plate and neural tube) formation, representing the first morphological signs of nervous system development. Subsequent patterning of the neural tube is then responsible for the formation of most of the central and peripheral nervous systems. While a large number of studies have assessed how a competent neurectoderm produces mature neural cells, less is known about the molecular signatures of definitive ectoderm and neurectoderm and the key molecular mechanisms driving their formation. Using pluripotent stem cells as a model, we will discuss the current understanding of how the pluripotent inner cell mass transitions to pluripotent primitive ectoderm and sequentially to the multipotent definitive ectoderm and neurectoderm. We will focus on the integration of cell signaling, gene activation, and epigenetic control that govern these developmental steps, and provide insight into the novel growth factor-like role that specific amino acids, such as L-proline, play in this process.

Assembling the adipose organ: adipocyte lineage segregation and adipogenesis in vivo

Adipose tissue is composed of anatomically distinct depots that mediate several important aspects of energy homeostasis. The past two decades have witnessed increased research effort to elucidate the ontogenetic basis of adipose form and function. In this Review, we discuss advances in our understanding of adipose tissue development with particular emphasis on the embryonic patterning of depot-specific adipocyte lineages and adipocyte differentiation in vivo Micro-environmental cues and other factors that influence cell identity and cell behavior at various junctures in the adipocyte lineage hierarchy are also considered.