• Schistosoma mansoni
• genetics
• genomics
• infectious disease
• microbiology
• miracidium
• single-cell atlas
• single-cell sequencing

• Animals
• Schistosoma mansoni/genetics
• Schistosoma mansoni/growth & development
• Larva/growth & development
• Larva/genetics
• Single-Cell Analysis
• Life Cycle Stages/genetics

• 10.35802/098051 Wellcome Trust
• 10.35802/206194 Wellcome Trust
• MR/T020733/1 UK Research and Innovation
• MR/W013568/1 UK Research and Innovation
• Marine Biological Laboratory

• machine learning
• gene regulatory networks
• transcriptomics
• computational models
• rna sequencing

• Humans
• Algorithms
• Computational Biology/methods
• Computer Simulation
• Gene Regulatory Networks
• RNA-Seq/methods
• Sequence Analysis, RNA/methods
• Single-Cell Analysis/methods
• Single-Cell Gene Expression Analysis
• Transcription Factors/metabolism
• Transcription Factors/genetics

• RGPIN-2019-04460 Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada)
• Government of Canada’s New Frontiers in Research Fund (NFRF) [NFRFE-2019-01290]; Canada Foundation for Innovation (CFI) JELF [project 40781]; McGill Initiative in Computational Medicine

• cell clustering
• deep learning
• scRNA-seq
• self-supervised training

• Sequence Analysis, RNA/methods
• Single-Cell Gene Expression Analysis
• Single-Cell Analysis/methods
• Algorithms
• Cluster Analysis
• Gene Expression Profiling/methods

• National Science Foundation of China
• Natural Science Foundation of Tianjin City
• W. W. Smith Charitable Trust grant
• NSF CAREER Award

• Challenges
• Clinical trials
• Gene editing
• HSC
• Perspectives

• Humans
• Gene Editing
• Hematopoietic Stem Cells
• Hematologic Diseases
• Hematopoietic Stem Cell Transplantation

• Biomarkers
• Cornea
• Corneal epithelial stem cells
• Single-cell RNA-Sequencing
• Xenopus

• Animals
• Cornea
• Epithelium, Corneal/metabolism
• Larva/genetics
• Larva/metabolism
• Stem Cells/metabolism
• Xenopus laevis/genetics
• Xenopus laevis/metabolism

• R01 AA010154 NIAAA NIH HHS
• R21 HD104039 NICHD NIH HHS
• R01 EY023979 NEI NIH HHS
• R01 HL126845 NHLBI NIH HHS
• T32 EB019944 NIBIB NIH HHS

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.

•

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

• Large-eyed model
• age-related macular degeneration
• cellular profiling and transcriptome
• cellular therapy
• clinical translation
• differentiation protocol dynamics
• human embryonic stem cell-derived retinal pigment epithelial cell
• retinal progenitor cells
• single-cell RNA sequencing
• subretinal injection

• Breast cancer
• Comma-1D
• Differentiation
• Mammary epithelium
• Oncogenesis
• Stem cells

• CP: Developmental biology
• Shp2
• collagen fibrils
• nerve growth factor
• single-cell RNA sequence
• tendon maturation
• tendon regeneration
• tendon stem/progenitor cells

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.

• RNA-Seq
• endothelial cells
• macrophages
• pulmonary hypertension
• smooth muscle cells
• stem cells

• developmental biology
• embryo development
• microfluidics
• single cell

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.

• Brca1/BRCA1
• Mrc2
• mammary tumor
• single cell RNA-seq
• tumor heterogeneity

• Langevin dynamics
• cross-sectional data
• predictive computational models
• pseudo-longitudinal data

• Nanyang Technological University
• ZonMw, Netherlands Organization for Health Research and Development
• Nationale Politie
• NWO, The Dutch Science Foundation
• EU Horizon 2020 programme

• RNA-seq
• enzymatic dissociation
• gene expression
• myoblast
• transcriptome

• fate specification
• lineage specification
• multipotency
• neural crest
• pluripotency
• single cell transcriptomics

• R03 HD096320 NICHD NIH HHS

• adult stem cells
• fate choice
• heterogeneity
• neural crest
• neural crest-derived stem cells
• single cell analysis

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.

• Candida albicans
• adjacent gene co-expression
• functional clustering
• gene regulation
• genomic organization
• stress response

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.

• RNA sequencing
• Single-cell sequencing
• cancer stem cell
• heterogeneity
• pluripotent stem cell
• tissue-specific stem cell

• RNA-Seq
• TGF-beta
• organoid
• single-cell
• thyroid

• Animals
• Biomarkers/metabolism
• Cell Differentiation
• Gene Expression Profiling
• Gene Expression Regulation
• Iodides/metabolism
• Mice
• Organoids/cytology
• Organoids/metabolism
• Single-Cell Analysis/methods
• Symporters/genetics
• Symporters/metabolism
• Thyroid Gland/cytology
• Thyroid Gland/metabolism
• Transforming Growth Factor beta/antagonists & inhibitors
• Transforming Growth Factor beta/genetics
• Transforming Growth Factor beta/metabolism

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.

• clustering
• data analysis
• graph theory
• minimum spanning trees
• principal trees
• single-cell transcriptomics
• trajectory inference

• Diffusion analysis
• Kidney development
• Lineage tracing
• Lung development
• Mammary gland development
• Mechanical stress
• Morphodynamics
• Spatial transcriptomics

• bone marrow
• differentiation
• scRNA-seq
• stromal cell
• transcription factor

• Drosophila midgut
• Esg
• Mirr
• Notch
• Ptx1
• asymmetric cell division
• classification
• enteroendocrine cells
• intestinal stem cell
• neuropeptide

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.

• Algorithms
• Animals
• Cells, Cultured
• Computational Biology/methods
• Gene Expression Profiling/methods
• Humans
• Mice
• Myoblasts/metabolism
• RNA/genetics
• RNA/metabolism
• Reproducibility of Results
• Sequence Analysis, RNA/methods
• Single-Cell Analysis/methods
• Software

• P41 GM103504 NIGMS NIH HHS
• National Center for Research Resources

• Demultiplex
• Multiplets
• Phony cell type
• Rare cell type
• Sample barcoding
• Single cell RNA

• Bayes Theorem
• Humans
• Molecular Typing/methods
• Sequence Analysis, RNA
• Single-Cell Analysis

• P01 AI106684 NIAID NIH HHS
• U01 DK062420 NIDDK NIH HHS
• R01 HL137709 NHLBI NIH HHS
• UL1 TR001857 NCATS NIH HHS

• Articular cartilage
• Chondrocyte
• Gdf5
• Interzone
• Ligament
• Meniscus
• Single cell RNA-Seq
• Synovial joint development
• Synovium

• Animals
• Cell Differentiation
• Cell Lineage
• Chondrocytes/cytology
• Chondrocytes/metabolism
• Collagen Type II/genetics
• Collagen Type II/metabolism
• Embryo, Mammalian/cytology
• Embryo, Mammalian/metabolism
• Embryonic Development/genetics
• Growth Differentiation Factor 5/deficiency
• Growth Differentiation Factor 5/genetics
• In Situ Hybridization
• Mesenchymal Stem Cells/cytology
• Mesenchymal Stem Cells/metabolism
• Mice
• Mice, Inbred C57BL
• Mice, Knockout
• SOX9 Transcription Factor/genetics
• SOX9 Transcription Factor/metabolism
• Sequence Analysis, RNA
• Single-Cell Analysis/methods
• Stem Cells/cytology
• Stem Cells/metabolism
• Synovial Membrane/cytology

• R25 GM109441 NIGMS NIH HHS
• R35 GM124725 NIGMS NIH HHS
• T32 GM007057 NIGMS NIH HHS
• National Institutes of Health
• Johns Hopkins University
• Maryland Stem Cell Research Fund

• clonal expansion
• directed differentiation
• human pluripotent stem cells
• morphogenesis
• neurogenesis
• phenotypic heterogeneity
• single-cell analysis

• Cell Differentiation
• Cell Engineering
• Cell Line
• Cell Proliferation
• Cell Tracking
• Clone Cells
• Genes, Reporter
• Germ Layers/cytology
• Humans
• Kinetics
• Models, Biological
• Morphogenesis
• Pluripotent Stem Cells/cytology

• R01 HL128362 NHLBI NIH HHS
• T32 EB001650 NIBIB NIH HHS
• R01 HL146868 NHLBI NIH HHS
• R01 HL128368 NHLBI NIH HHS
• U54 DK107979 NIDDK NIH HHS
• P30 DK017047 NIDDK NIH HHS
• DP2 HL137188 NHLBI NIH HHS
• R01 HL141187 NHLBI NIH HHS
• R01 HL142624 NHLBI NIH HHS

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.

• Mesenchymal stem cells
• Single-cell RNA sequencing
• Pathophysiology
• Novel surface markers

• Biology
• Mass Spectrometry
• Models, Molecular
• Proteins

• R01 DK110520 NIDDK NIH HHS
• R01 HD085901 NICHD NIH HHS
• R35 GM122480 NIGMS NIH HHS

• Cell lineage differentiation
• Cellular plasticity
• Pancreas
• Single-cell RNA-seq
• Trans-differentiation
• β Cell

• AutoSOME
• Clustering
• ESCs
• Stem cells
• Transcriptome
• gPSCs

• Animals
• Cluster Analysis
• Computational Biology
• Embryonic Stem Cells/cytology
• Gene Regulatory Networks/genetics
• Genetic Variation/genetics
• Male
• Mice
• Neural Stem Cells/cytology
• Pluripotent Stem Cells/cytology
• Testis/cytology
• Transcriptome

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.

• cell fates
• cell states
• epithelial lineage specification
• heterogeneity
• mammary gland development
• single-cell RNA sequencing
• stem cells

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.

• airway epithelial cell
• cell-to-cell variation
• dermal fibroblast
• induced pluripotent stem cell
• lymphoblastoid cell line
• scRNA-seq
• single-cell RNA sequencing
• single-cell expression variability

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.

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.

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

Supplementary data are available at Bioinformatics online.

• cell atlas
• cell fate engineering
• cell typing
• classification
• cross-species
• direct conversion
• directed differentiation
• single cell RNA-Seq

• Adipogenesis
• Beige adipocyte
• Brite adipocyte
• Brown adipose tissue
• Development
• Glucose and lipid metabolism
• Lineage tracing
• Progenitor cells
• Thermogenesis
• Ucp1

• L-proline
• amino acids
• definitive ectoderm
• early primitive ectoderm-like cells
• embryonic stem cell
• neural
• neurectoderm
• primitive ectoderm