Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells

Evidence for the loss of plasminogen receptor KT gene in chicken

The loss of conserved genes has the potential to alter phenotypes drastically. Screening of vertebrate genomes for lineage-specific gene loss events has identified numerous natural knockouts associated with specific phenotypes. We provide evidence for the loss of a multi-exonic plasminogen receptor KT (PLGRKT) protein-encoding gene located on the Z chromosome in chicken. Exons 1 and 2 are entirely missing; remnants of exon 3 and a mostly intact exon 4 are identified in an assembly gap-free region in chicken with conserved synteny across species and verified using transcriptome and genome sequencing. PLGRKT gene disrupting changes are present in representative species from all five galliform families. In contrast to this, the presence of an intact transcriptionally active PLGRKT gene in species such as mallard, swan goose, and Anolis lizard suggests that gene loss occurred in the galliform lineage sometime between 68 and 80 Mya. The presence of galliform specific chicken repeat 1 (CR1) insertion at the erstwhile exon 2 of PLGRKT gene suggests repeat insertion-mediated loss. However, at least nine other independent PLGRKT coding frame disrupting changes in other bird species are supported by genome sequencing and indicate a role for relaxed purifying selection before CR1 insertion. The recurrent loss of a conserved gene with a role in the regulation of macrophage migration, efferocytosis, and blood coagulation is intriguing. Hence, we propose potential candidate genes that might be compensating the function of PLGRKT based on the presence of a C-terminal lysine residue, transmembrane domains, and gene expression patterns.

PTEN in Regulating Hematopoiesis and Leukemogenesis

PTEN is one of the most frequently mutated tumor suppressor genes in human cancers. By counteracting the PI3K/AKT/mTOR pathway, PTEN plays an essential role in regulating hematopoietic stem cells (HSCs) self-renewal, migration, lineage commitment, and differentiation. PTEN also plays important roles in suppressing leukemogenesis, especially T-cell acute lymphoblastic leukemia (T-ALL). Herein, we will review the function of PTEN in regulating hematopoiesis and leukemogenesis and discuss potential therapeutic approaches against leukemia with PTEN mutations.

A Single Metabolite which Modulates Lipid Metabolism Alters Hematopoietic Stem/Progenitor Cell Behavior and Promotes Lymphoid Reconstitution

Fatty acid β-oxidation (FAO), the breakdown of lipids, is a metabolic pathway used by various stem cells. FAO levels are generally high during quiescence and downregulated with proliferation. The endogenous metabolite malonyl-CoA modulates lipid metabolism as a reversible FAO inhibitor and as a substrate for de novo lipogenesis. Here we assessed whether malonyl-CoA can be exploited to steer the behavior of hematopoietic stem/progenitor cells (HSPCs), quiescent stem cells of clinical relevance. Treatment of mouse HSPCs in vitro with malonyl-CoA increases HSPC numbers compared with nontreated controls and ameliorates blood reconstitution capacity when transplanted in vivo, mainly through enhanced lymphoid reconstitution. Similarly, human HSPC numbers also increase upon malonyl-CoA treatment in vitro. These data corroborate that lipid metabolism can be targeted to direct cell fate and stem cell proliferation. Physiological modulation of metabolic pathways, rather than genetic or pharmacological inhibition, provides unique perspectives for stem cell manipulations in health and disease.

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Modulating lipid metabolism with malonyl-CoA increases murine HSPCs in vitro

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Malonyl-CoA promotes lymphoid cell fate of HSPCs in vitro and in vivo

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The myeloid bias in aged HSPCs is partially recovered with malonyl-CoA exposure

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Malonyl-CoA increases the numbers of human HSPCs in vitro

Awakening the HSC: Dynamic Modeling of HSC Maintenance Unravels Regulation of the TP53 Pathway and Quiescence

Hematopoietic stem cells (HSCs) provide all types of blood cells during the entire life of the organism. HSCs are mainly quiescent and can eventually enter the cell cycle to differentiate. HSCs are maintained and tightly regulated in a particular environment. The stem cell niche regulates dormancy and awakening. Deregulations of this interplay can lead to hematopoietic failure and diseases. In this paper, we present a Boolean network model that recapitulates HSC regulation in virtue of external signals coming from the niche. This Boolean network integrates and summarizes the current knowledge of HSC regulation and is based on extensive literature research. Furthermore, dynamic simulations suggest a novel systemic regulation of TP53 in homeostasis. Thereby, our model indicates that TP53 activity is balanced depending on external stimulations, engaging a regulatory mechanism involving ROS regulators and RAS activated transcription factors. Finally, we investigated different mouse models and compared them to in silico knockout simulations. Here, the model could recapitulate in vivo observed behaviors and thus sustains our results.

Lineage commitment of hematopoietic stem cells and progenitors: insights from recent single cell and lineage tracing technologies

Blood production is essential to maintain human health, and even small perturbations in hematopoiesis can cause disease. Hematopoiesis has therefore been the focus of much research for many years. Experiments determining the lineage potentials of hematopoietic stem and progenitor cells (HSPCs) in vitro and after transplantation revealed a hierarchy of progenitor cell states, where differentiating cells undergo lineage commitment-a series of irreversible changes that progressively restrict their potential. New technologies have recently been developed that allow for a more detailed analysis of the molecular states and fates of differentiating HSPCs. Proteomic and lineage-tracing approaches, alongside single-cell transcriptomic analyses, have recently helped to reveal the biological complexity underlying lineage commitment during hematopoiesis. Recent insights from these new technologies were presented by Dr. Marjorie Brand and Dr. Allon Klein in the Summer 2019 ISEH Webinar, and are discussed in this Perspective.

Multi-set Pre-processing of Multicolor Flow Cytometry Data

Flow Cytometry is an analytical technology to simultaneously measure multiple markers per single cell. Ten thousands to millions of single cells can be measured per sample and each sample may contain a different number of cells. All samples may be bundled together, leading to a ‘multi-set’ structure. Many multivariate methods have been developed for Flow Cytometry data but none of them considers this structure in their quantitative handling of the data. The standard pre-processing used by existing multivariate methods provides models mainly influenced by the samples with more cells, while such a model should provide a balanced view of the biomedical information within all measurements. We propose an alternative ‘multi-set’ preprocessing that corrects for the difference in number of cells measured, balancing the relative importance of each multi-cell sample in the data while using all data collected from these expensive analyses. Moreover, one case example shows how multi-set pre-processing may benefit removal of undesired measurement-to-measurement variability and another where class-based multi-set pre-processing enhances the studied response upon comparison to the control reference samples. Our results show that adjusting data analysis algorithms to consider this multi-set structure may greatly benefit immunological insight and classification performance of Flow Cytometry data.

Cell Type Purification by Single-Cell Transcriptome-Trained Sorting

Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.

A single-cell survey of Drosophila blood

Drosophila blood cells, called hemocytes, are classified into plasmatocytes, crystal cells, and lamellocytes based on the expression of a few marker genes and cell morphologies, which are inadequate to classify the complete hemocyte repertoire. Here, we used single-cell RNA sequencing (scRNA-seq) to map hemocytes across different inflammatory conditions in larvae. We resolved plasmatocytes into different states based on the expression of genes involved in cell cycle, antimicrobial response, and metabolism together with the identification of intermediate states. Further, we discovered rare subsets within crystal cells and lamellocytes that express fibroblast growth factor (FGF) ligand branchless and receptor breathless, respectively. We demonstrate that these FGF components are required for mediating effective immune responses against parasitoid wasp eggs, highlighting a novel role for FGF signaling in inter-hemocyte crosstalk. Our scRNA-seq analysis reveals the diversity of hemocytes and provides a rich resource of gene expression profiles for a systems-level understanding of their functions.

A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration

Stem cell heterogeneity is essential for homeostasis in tissue development. This paper establishes a general mathematical framework to model the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The framework generalizes the classical G0 cell cycle model and incorporates the epigenetic states of individual cells represented by a continuous multidimensional variable. In the model, the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, are dependent on their epigenetic states, and the random transitions of epigenetic states between cell cycles are represented by an inheritance probability function that describes the conditional probability of cell state changes. Moreover, the model can be extended to include genotypic changes and describe the process of gene mutation-induced tumor development. The proposed mathematical framework provides a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis

Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during de- velopment, regeneration, homeostasis, and disease is a central goal of mod- ern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality re- duction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single- cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.

GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution

MOTIVATION

Many methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters.

RESULTS

The proposed method, GPseudoClust, is a novel approach that jointly infers pseudotemporal ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with non-parametric Bayesian clustering methods, efficient Markov Chain Monte Carlo sampling and novel subsampling strategies which aid computation. We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings.

AVAILABILITY AND IMPLEMENTATION

An implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/GPseudoClust.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deletion of a conserved Gata2 enhancer impairs haemogenic endothelium programming and adult Zebrafish haematopoiesis

Gata2 is a key transcription factor required to generate Haematopoietic Stem and Progenitor Cells (HSPCs) from haemogenic endothelium (HE); misexpression of Gata2 leads to haematopoietic disorders. Here we deleted a conserved enhancer (i4 enhancer) driving pan-endothelial expression of the zebrafish gata2a and showed that Gata2a is required for HE programming by regulating expression of runx1 and of the second Gata2 orthologue, gata2b. By 5 days, homozygous gata2aΔi4/Δi4 larvae showed normal numbers of HSPCs, a recovery mediated by Notch signalling driving gata2b and runx1 expression in HE. However, gata2aΔi4/Δi4 adults showed oedema, susceptibility to infections and marrow hypo-cellularity, consistent with bone marrow failure found in GATA2 deficiency syndromes. Thus, gata2a expression driven by the i4 enhancer is required for correct HE programming in embryos and maintenance of steady-state haematopoietic stem cell output in the adult. These enhancer mutants will be useful in exploring further the pathophysiology of GATA2-related deficiencies in vivo.

Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for patients with chronic lymphocytic leukemia (CLL), despite extensive heterogeneity in this disease. To define the underlining regulatory dynamics, we analyze high-resolution time courses of ibrutinib treatment in patients with CLL, combining immune-phenotyping, single-cell transcriptome profiling, and chromatin mapping. We identify a consistent regulatory program starting with a sharp decrease of NF-κB binding in CLL cells, which is followed by reduced activity of lineage-defining transcription factors, erosion of CLL cell identity, and acquisition of a quiescence-like gene signature. We observe patient-to-patient variation in the speed of execution of this program, which we exploit to predict patient-specific dynamics in the response to ibrutinib based on the pre-treatment patient samples. In aggregate, our study describes time-dependent cellular, molecular, and regulatory effects for therapeutic inhibition of B cell receptor signaling in CLL, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring.

Ibrutinib, a Bruton tyrosine kinase inhibitor, provides effective treatment for chronic lymphocytic leukemia (CLL). Here, the authors describe time-dependent molecular changes to malignant cells and to the immune system in patients undergoing ibrutinib therapy, with can be used for therapy monitoring.

Discovering Rare Genes Contributing to Cancer Stemness and Invasive Potential by GBM Single-Cell Transcriptional Analysis

Single-cell RNA sequencing presents the sophisticated delineation of cell transcriptomes in many cancer types and highlights the tumor heterogeneity at higher resolution, which provides a new chance to explore the molecular mechanism in a minority of cells. In this study, we utilized publicly available single-cell RNA-seq data to discover and comprehensively dissect rare genes existing in few glioblastoma (GBM) cells. Moreover, we designed a framework to systematically identify 51 rare protein-coding genes (PCGs) and 47 rare long non-coding RNAs (lncRNAs) in GBM. Patients with high expression levels of rare genes like CYB5R2 and TPPP3 had worse overall survival and disease-free survival, implying their potential implication in GBM progression and prognosis. We found that these rare genes tended to be specifically expressed in GBM cancer stem cells, which emphasized their ability to characterize stem-like cancer cells and implied their contribution to GBM growth. Furthermore, rare genes were enriched in a 17-cell subset, which was located in an individual branch of the pseudotime trajectory of cancer progression and exhibited high cell cycle activity and invasive potential. Our study captures the rare genes highly expressed in few cells, deepens our understanding of special states during GBM tumorigenesis and progression such as cancer stemness and invasion, and proposes potential targets for cancer therapy.

Expression of mRNAs for IL-1β, IL-6, IL-10, TNFα, CX3CL1, and TGFβ1 Cytokines in the Brain Tissues: Assessment of Contribution of Blood Cells with and without Perfusion

Cytokines are important regulators of brain function under both normal and pathological conditions. Cytokines can be synthesized by resident cells of the central nervous system (CNS) (vascular endothelium, cells of the blood-brain barrier, parenchymal cells of the CNS) or cells in the lumen of blood vessels, as well as introduced with the bloodstream. The ratio between the quantity of cytokines synthesized in the CNS and those entering it from external sources under various conditions remains poorly understood. In this work, we studied the contribution of mRNAs from non-resident cells to the common pool of cytokine (TNFα, IL-1β, IL-6, IL-10, CX3CL1, and TGFβ1) mRNAs in the rat neocortex, hippocampus, dura matter, pia matter, and choroid plexus. We also evaluated the representation of various populations of resident and non-resident immune cells based on the expression of marker genes (Ncf1, Tbx21, Foxp3, RORγc). The removal of blood by transcardial perfusion led to a decrease in the quantity of the TNFα mRNA in the neocortex and hippocampus and of the IL-1β, IL-6, and IL-10 mRNAs in the dura mater. The mRNA levels of other cytokines in studied structures were not affected by perfusion. Our findings suggest that mRNAs present in the blood can make a significant contribution to the mRNA levels of some cytokines in the CNS; therefore, preliminary perfusion of brain tissue is a necessary stage of experimental design for correct estimation of mRNA content in the brain.

Environmental exposures, stem cells, and cancer

An estimated 70-90% of all cancers are linked to exposure to environmental risk factors. In parallel, the number of stem cells in a tissue has been shown to be a strong predictor of risk of developing cancer in that tissue. Tumors themselves are characterized by an acquisition of "stem cell" characteristics, and a growing body of evidence points to tumors themselves being sustained and propagated by a stem cell-like population. Here, we review our understanding of the interplay between environmental exposures, stem cell biology, and cancer. We provide an overview of the role of stem cells in development, tissue homeostasis, and wound repair. We discuss the pathways and mechanisms governing stem cell plasticity and regulation of the stem cell state, and describe experimental methods for assessment of stem cells. We then review the current understanding of how environmental exposures impact stem cell function relevant to carcinogenesis and cancer prevention, with a focus on environmental and occupational exposures to chemical, physical, and biological hazards. We also highlight key areas for future research in this area, including defining whether the biological basis for cancer disparities is related to effects of complex exposure mixtures on stem cell biology.

A single-cell transcriptome atlas for zebrafish development

The ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome atlas encompasses transcriptional profiles from 44,102 ​cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting atlas is a resource for biologists to generate hypotheses for functional analysis, which we hope integrates with existing efforts to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

Causes and Consequences of Hematopoietic Stem Cell Heterogeneity

Blood and immune cells derive from multipotent hematopoietic stem cells (HSCs). Classically, stem and progenitor populations have been considered discrete homogeneous populations. However, recent technological advances have revealed significant HSC heterogeneity, with evidence for early HSC lineage segregation and the presence of lineage-biased HSCs and lineage-restricted progenitors within the HSC compartment. These and other findings challenge many aspects of the classical view of HSC biology. We review the most recent findings regarding the causes and consequences of HSC heterogeneity, discuss their far-reaching implications, and suggest that so-called continuum-based models may help consolidate apparently divergent experimental observations in this field.

Single-Cell RNA Sequencing with Drop-Seq

Drop-Seq is a low-cost, high-throughput platform to profile thousands of cells by encapsualting them into individual droplets. Uniquely barcoded mRNA capture microparticles and cells are coconfined through a microfluidic device within the droplets where they undergo cell lysis and RNA hybridiztion. After breaking the droplets and pooling the hybridized particles, reverse transcription, PCR, and sequencing in single reactions allow to generate data from thousands of single-cell transcriptomes while maintaining information on the cellular origin of each transcript.

A periodic table of cell types

Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.

New paradigms on hematopoietic stem cell differentiation

Ever since hematopoietic stem cells (HSCs) were first identified half a century ago, their differentiation roadmap has been extensively studied. The classical model of hematopoiesis has long held as a dogma that HSCs reside at the top of a hierarchy in which HSCs possess self-renewal capacity and can progressively give rise to all blood lineage cells. However, over the past several years, with advances in single cell technologies, this developmental scheme has been challenged. In this review, we discuss the evidence supporting heterogeneity within HSC and progenitor populations as well as the hierarchical models revised by novel approaches mainly in mouse system. These evolving views provide further understanding of hematopoiesis and highlight the complexity of hematopoietic differentiation.

SCOUT: A new algorithm for the inference of pseudo-time trajectory using single-cell data

Single cell technology is a powerful tool to reveal intercellular heterogeneity and discover cellular developmental processes. When analyzing the complexity of cellular dynamics and variability, it is important to construct a pseudo-time trajectory using single-cell expression data to reflect the process of cellular development. Although a number of computational and statistical methods have been developed recently for single-cell analysis, more effective and efficient methods are still strongly needed. In this work we propose a new method named SCOUT for the inference of single-cell pseudo-time ordering with bifurcation trajectories. We first propose to use the fixed-radius near neighbors algorithms based on cell densities to find landmarks to represent the cell states, and employ the minimum spanning tree (MST) to determine the developmental branches. We then propose to use the projection of Apollonian circle or a weighted distance to determine the pseudo-time trajectories of single cells. The proposed algorithm is applied to one synthetic and two realistic single-cell datasets (including single-branching and multi-branching trajectories) and the cellular developmental dynamics is recovered successfully. Compared with other popular methods, numerical results show that our proposed method is able to generate more robust and accurate pseudo-time trajectories. The code of the method is implemented in Python and available at https://github.com/statway/SCOUT.

Purification of high-quality RNA from a small number of fluorescence activated cell sorted zebrafish cells for RNA sequencing purposes

Background

Transgenic zebrafish lines with the expression of a fluorescent reporter under the control of a cell-type specific promoter, enable transcriptome analysis of FACS sorted cell populations. RNA quality and yield are key determinant factors for accurate expression profiling. Limited cell number and FACS induced cellular stress make RNA isolation of sorted zebrafish cells a delicate process. We aimed to optimize a workflow to extract sufficient amounts of high-quality RNA from a limited number of FACS sorted cells from Tg(fli1a:GFP) zebrafish embryos, which can be used for accurate gene expression analysis.

Results

We evaluated two suitable RNA isolation kits (the RNAqueous micro and the RNeasy plus micro kit) and determined that sorting cells directly into lysis buffer is a critical step for success. For low cell numbers, this ensures direct cell lysis, protects RNA from degradation and results in a higher RNA quality and yield. We showed that this works well up to 0.5× dilution of the lysis buffer with sorted cells. In our sort settings, this corresponded to 30,000 and 75,000 cells for the RNAqueous micro kit and RNeasy plus micro kit respectively. Sorting more cells dilutes the lysis buffer too much and requires the use of a collection buffer. We also demonstrated that an additional genomic DNA removal step after RNA isolation is required to completely clear the RNA from any contaminating genomic DNA. For cDNA synthesis and library preparation, we combined SmartSeq v4 full length cDNA library amplification, Nextera XT tagmentation and sample barcoding. Using this workflow, we were able to generate highly reproducible RNA sequencing results.

Conclusions

The presented optimized workflow enables to generate high quality RNA and allows accurate transcriptome profiling of small populations of sorted zebrafish cells.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5608-2) contains supplementary material, which is available to authorized users.

Rate of Progression through a Continuum of Transit-Amplifying Progenitor Cell States Regulates Blood Cell Production

The nature of cell-state transitions during the transit-amplifying phases of many developmental processes-hematopoiesis in particular-is unclear. Here, we use single-cell RNA sequencing to demonstrate a continuum of transcriptomic states in committed transit-amplifying erythropoietic progenitors, which correlates with a continuum of proliferative potentials in these cells. We show that glucocorticoids enhance erythrocyte production by slowing the rate of progression through this developmental continuum of transit-amplifying progenitors, permitting more cell divisions prior to terminal erythroid differentiation. Mechanistically, glucocorticoids prolong expression of genes that antagonize and slow induction of genes that drive terminal erythroid differentiation. Erythroid progenitor daughter cell pairs have similar transcriptomes with or without glucocorticoid stimulation, indicating largely symmetric cell division. Thus, the rate of progression along a developmental continuum dictates the absolute number of erythroid cells generated from each transit-amplifying progenitor, suggesting a paradigm for regulating the total output of differentiated cells in numerous other developmental processes.

Revolutionizing immunology with single-cell RNA sequencing

The immune system is composed of a complex hierarchy of cell types that protect the organism against disease and maintain homeostasis. Identifying heterogeneity of immune cells is the key to understanding the immune system. Advanced single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing our ability to study immunology. By measuring transcriptomes at the single-cell level, scRNA-seq enables identification of cellular heterogeneity in far greater detail than conventional methods. In this review, we introduce the existing scRNA-seq technologies and present their strengths and weaknesses. We also discuss potential applications and future innovations of scRNA-seq in immunology.

Chasing Mavericks: The quest for defining developmental waves of hematopoiesis

Hematopoiesis is the process by which mature blood and immune cells are produced from hematopoietic stem and progenitor cells (HSCs and HSPCs). The last several decades of research have shed light on the origin of HSCs, as well as the heterogeneous pools of fetal progenitors that contribute to lifelong hematopoiesis. The overarching concept that hematopoiesis occurs in dynamic, overlapping waves throughout development, with each wave contributing to both continuous and developmentally limited cell types, has been solidified over the years. However, recent advances in our ability to track the production of hematopoietic cells in vivo have challenged several long-held dogmas on the origin and persistence of distinct hematopoietic cell types. In this review, we highlight emerging concepts in hematopoietic development and identify unanswered questions.

Maps of variability in cell lineage trees

New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.

Nanoscale tweezers for single-cell biopsies

Much of the functionality of multicellular systems arises from the spatial organization and dynamic behaviours within and between cells. Current single-cell genomic methods only provide a transcriptional 'snapshot' of individual cells. The real-time analysis and perturbation of living cells would generate a step change in single-cell analysis. Here we describe minimally invasive nanotweezers that can be spatially controlled to extract samples from living cells with single-molecule precision. They consist of two closely spaced electrodes with gaps as small as 10-20 nm, which can be used for the dielectrophoretic trapping of DNA and proteins. Aside from trapping single molecules, we also extract nucleic acids for gene expression analysis from living cells without affecting their viability. Finally, we report on the trapping and extraction of a single mitochondrion. This work bridges the gap between single-molecule/organelle manipulation and cell biology and can ultimately enable a better understanding of living cells.

Probing the RNA-Binding Proteome from Yeast to Man: Major Advances and Challenges

RNA-binding proteins are important for core cellular processes such as mRNA transcription, splicing, transport, translation, and degradation. Recently, hundreds of novel RNA-binders have been identified in vivo in various organisms and cell types. We discuss the RNA interactome capture technique which enabled this boost in identifying new RNA-binding proteins in eukaryotes. A focus of this chapter, however, is the presentation of different challenges and problems that need to be addressed to be able to understand the conserved mRNA-bound proteomes from yeast to man.

Seq-Well: A Sample-Efficient, Portable Picowell Platform for Massively Parallel Single-Cell RNA Sequencing

Seq-Well is a low-cost picowell platform that can be used to simultaneously profile the transcriptomes of thousands of cells from diverse, low input clinical samples. In Seq-Well, uniquely barcoded mRNA capture beads and cells are co-confined in picowells that are sealed using a semipermeable membrane, enabling efficient cell lysis and mRNA capture. The beads are subsequently removed and processed in parallel for sequencing, with each transcript's cell of origin determined via the unique barcodes. Due to its simplicity and portability, Seq-Well can be performed almost anywhere.

Hematopoietic Cytokine Gene Duplication in Zebrafish Erythroid and Myeloid Lineages

Hematopoiesis is a precisely orchestrated process regulated by the activity of hematopoietic cytokines and their respective receptors. Due to an extra round of whole genome duplication during vertebrate evolution in teleost fish, zebrafish have two paralogs of many important genes, including genes involved in hematopoiesis. Importantly, these duplication events brought increased level of complexity in such cases, where both ligands and receptors have been duplicated in parallel. Therefore, precise understanding of binding specificities between duplicated ligand-receptor signalosomes as well as understanding of their differential expression provide an important basis for future studies to better understand the role of duplication of these genes. However, although many recent studies in the field have partly addressed functional redundancy or sub-specialization of some of those duplicated paralogs, this information remains to be scattered over many publications and unpublished data. Therefore, the focus of this review is to provide an overview of recent findings in the zebrafish hematopoietic field regarding activity, role and specificity of some of the hematopoietic cytokines with emphasis on crucial regulators of the erythro-myeloid lineages.

Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis

Single-cell RNA sequencing (scRNA-seq) has been tremendously developed in the past decade owing to overcoming challenges associated with isolation of massive quantities of single cells. Previously, cell heterogeneity and low quantities of available biological material posed significant difficulties to scRNA-seq. Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of normal and malignant hematopoietic cells; this heterogeneity has often been ignored in omics studies. The application of scRNA-seq has profoundly changed our comprehension of many biological phenomena, including organ development and carcinogenesis. Hematopoiesis, is actually a maturation process for more than ten distinct blood and immune cells, and is thought to be critically involved in hematological homeostasis and in sustaining the physiological functions. However, aberrant hematopoiesis directly leads to hematological malignancy, and a deeper understanding of malignant hematopoiesis will provide deeper insights into diagnosis and prognosis for patients with hematological malignancies. Here, we aim to review the recent technical progress and future prospects for scRNA-seq, as applied in physiological and malignant hematopoiesis, in efforts to further understand the hematopoietic hierarchy and to illuminate personalized therapy and precision medicine approaches used in the clinical treatment of hematological malignancies.

Disease-relevant transcriptional signatures identified in individual smooth muscle cells from healthy mouse vessels

Vascular smooth muscle cells (VSMCs) show pronounced heterogeneity across and within vascular beds, with direct implications for their function in injury response and atherosclerosis. Here we combine single-cell transcriptomics with lineage tracing to examine VSMC heterogeneity in healthy mouse vessels. The transcriptional profiles of single VSMCs consistently reflect their region-specific developmental history and show heterogeneous expression of vascular disease-associated genes involved in inflammation, adhesion and migration. We detect a rare population of VSMC-lineage cells that express the multipotent progenitor marker Sca1, progressively downregulate contractile VSMC genes and upregulate genes associated with VSMC response to inflammation and growth factors. We find that Sca1 upregulation is a hallmark of VSMCs undergoing phenotypic switching in vitro and in vivo, and reveal an equivalent population of Sca1-positive VSMC-lineage cells in atherosclerotic plaques. Together, our analyses identify disease-relevant transcriptional signatures in VSMC-lineage cells in healthy blood vessels, with implications for disease susceptibility, diagnosis and prevention.

Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis

Background

Single-cell RNA-seq (scRNA-seq) has recently emerged as a revolutionary and powerful tool for biomedical research. However, there have been relatively few studies using scRNA-seq in the field of kidney study.

Summary

scRNA-seq achieves gene expression profiling at single-cell resolution in contrast with the conventional methods of gene expression profiling, which are based on cell population and give averaged values of gene expression of the cells. Single-cell transcriptomic analysis is crucial because individual cells of the same type are highly heterogeneous in gene expression, which reflects the existence of subpopulations, different cellular states, or molecular dynamics, of the cells, and should be resolved for further insights. In addition, gene expression analysis of tissues or organs that usually comprise multiple cell types or subtypes results in data that are not fully applicable to any given cell type. scRNA-seq is capable of identifying all cell types and subtypes in a tissue, including those that are new or present in small quantity. With these unique capabilities, scRNA-seq has been used to dissect molecular processes in cell differentiation and to trace cell lineages in development. It is also used to analyze the cells in a lesion of disease to identify the cell types and molecular dynamics implicated in the injury. With continuous technical improvement, scRNA-seq has become extremely high throughput and cost effective, making it accessible to all laboratories. In the present review article, we provide an overall review of scRNA-seq concerning its history, improvements, and applications. In addition, we describe the available studies in which scRNA-seq was employed in the field of kidney research. Lastly, we discuss other potential uses of scRNA-seq for kidney research.

Key Message

This review article provides general information on scRNA-seq and its various uses. Particularly, we summarize the studies in the field of kidney diseases in which scRNA-seq was used and discuss potential additional uses of scRNA-seq for kidney research.

Defining Cell Identity with Single-Cell Omics

Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation-most notably in the emergence and evolution of tumors. Recent technical advances in single-cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

Global and targeted approaches to single-cell transcriptome characterization

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell's expression status.

Hematopoietic Hierarchy - An Updated Roadmap

The classical roadmap of hematopoietic hierarchy has been proposed for nearly 20 years and has become a dogma of stem cell research for most types of adult stem cells, including hematopoietic stem cells (HSCs). However, with the development of new technologies such as omics approaches at single-cell resolution, recent studies in vitro and in vivo have suggested that heterogeneity is a common feature of HSCs and their progenies. While these findings broaden our understanding of hematopoiesis, they also challenge the well-accepted hematopoietic hierarchy roadmap. Here, we review recent advances in the hematopoiesis field and provide an updated view to incorporate these new findings as well as to reflect on the complexity of HSCs and their derivatives in development and adulthood.

Establishment of regulatory elements during erythro-megakaryopoiesis identifies hematopoietic lineage-commitment points

BACKGROUND

Enhancers and promoters are cis-acting regulatory elements associated with lineage-specific gene expression. Previous studies showed that different categories of active regulatory elements are in regions of open chromatin, and each category is associated with a specific subset of post-translationally marked histones. These regulatory elements are systematically activated and repressed to promote commitment of hematopoietic stem cells along separate differentiation paths, including the closely related erythrocyte (ERY) and megakaryocyte (MK) lineages. However, the order in which these decisions are made remains unclear.

RESULTS

To characterize the order of cell fate decisions during hematopoiesis, we collected primary cells from mouse bone marrow and isolated 10 hematopoietic populations to generate transcriptomes and genome-wide maps of chromatin accessibility and histone H3 acetylated at lysine 27 binding (H3K27ac). Principle component analysis of transcriptional and open chromatin profiles demonstrated that cells of the megakaryocyte lineage group closely with multipotent progenitor populations, whereas erythroid cells form a separate group distinct from other populations. Using H3K27ac and open chromatin profiles, we showed that 89% of immature MK (iMK)-specific active regulatory regions are present in the most primitive hematopoietic cells, 46% of which contain active enhancer marks. These candidate active enhancers are enriched for transcription factor binding site motifs for megakaryopoiesis-essential proteins, including ERG and ETS1. In comparison, only 64% of ERY-specific active regulatory regions are present in the most primitive hematopoietic cells, 20% of which containing active enhancer marks. These regions were not enriched for any transcription factor consensus sequences. Incorporation of genome-wide DNA methylation identified significant levels of de novo methylation in iMK, but not ERY.

CONCLUSIONS

Our results demonstrate that megakaryopoietic profiles are established early in hematopoiesis and are present in the majority of the hematopoietic progenitor population. However, megakaryopoiesis does not constitute a "default" differentiation pathway, as extensive de novo DNA methylation accompanies megakaryopoietic commitment. In contrast, erythropoietic profiles are not established until a later stage of hematopoiesis, and require more dramatic changes to the transcriptional and epigenetic programs. These data provide important insights into lineage commitment and can contribute to ongoing studies related to diseases associated with differentiation defects.

Whole-organism clone tracing using single-cell sequencing

Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development, monitoring the heritable expression of genetically encoded fluorescent proteins and, more recently, using next-generation sequencing technologies that exploit somatic mutations, microsatellite instability, transposon tagging, viral barcoding, CRISPR-Cas9 genome editing and Cre-loxP recombination. Single-cell transcriptomics provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.

SpatialDE: identification of spatially variable genes

Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here, we develop SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA sequencing data. SpatialDE also implements “automatic expression histology”, a spatial gene clustering approach that enables expression-based tissue histology.

Molecular transitions in early progenitors during human cord blood hematopoiesis

Hematopoietic stem cells (HSCs) give rise to diverse cell types in the blood system, yet our molecular understanding of the early trajectories that generate this enormous diversity in humans remains incomplete. Here, we leverage Drop-seq, a massively parallel single-cell RNA sequencing (scRNA-seq) approach, to individually profile 20,000 progenitor cells from human cord blood, without prior enrichment or depletion for individual lineages based on surface markers. Our data reveal a transcriptional compendium of progenitor states in human cord blood, representing four committed lineages downstream from HSC, alongside the transcriptional dynamics underlying fate commitment. We identify intermediate stages that simultaneously co-express "primed" programs for multiple downstream lineages, and also observe striking heterogeneity in the early molecular transitions between myeloid subsets. Integrating our data with a recently published scRNA-seq dataset from human bone marrow, we illustrate the molecular similarity between these two commonly used systems and further explore the chromatin dynamics of "primed" transcriptional programs based on ATAC-seq. Finally, we demonstrate that Drop-seq data can be utilized to identify new heterogeneous surface markers of cell state that correlate with functional output.

Fundamental limits on dynamic inference from single-cell snapshots

Seeing a snapshot of individuals at different stages of a dynamic process can reveal what the process would look like for a single individual over time. Biologists apply this principle to infer temporal sequences of gene expression states in cells from measurements made at a single moment in time. However, the sparsity and high dimensionality of single-cell data have made inference difficult using formal approaches. Here, we apply recent innovations in spectral graph theory to devise a simple and asymptotically exact algorithm for inferring the unique dynamic solution under defined approximations and apply it to data from bone marrow stem cells.

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.

Single cell analysis of normal and leukemic hematopoiesis

The hematopoietic system is well established as a paradigm for the study of cellular hierarchies, their disruption in disease and therapeutic use in regenerative medicine. Traditional approaches to study hematopoiesis involve purification of cell populations based on a small number of surface markers. However, such population-based analysis obscures underlying heterogeneity contained within any phenotypically defined cell population. This heterogeneity can only be resolved through single cell analysis. Recent advances in single cell techniques allow analysis of the genome, transcriptome, epigenome and proteome in single cells at an unprecedented scale. The application of these new single cell methods to investigate the hematopoietic system has led to paradigm shifts in our understanding of cellular heterogeneity in hematopoiesis and how this is disrupted in disease. In this review, we summarize how single cell techniques have been applied to the analysis of hematopoietic stem/progenitor cells in normal and malignant hematopoiesis, with a particular focus on recent advances in single-cell genomics, including how these might be utilized for clinical application.

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis

The success of marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of well-defined cell surface markers specific for diverse progenitor populations. An inherent problem with this approach is that the presence of specific cell surface markers does not directly reflect the transcriptional state of a cell. Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in zebrafish and order cells along their differentiation trajectory, based on their global transcriptional differences. Within the population of transcriptionally similar stem and progenitor cells, our analysis reveals considerable cell-to-cell differences in their probability to transition to another committed state. Once fate decision is executed, the suppression of transcription of ribosomal genes and upregulation of lineage-specific factors coordinately controls lineage differentiation. Evolutionary analysis further demonstrates that this haematopoietic programme is highly conserved between zebrafish and higher vertebrates.

Single Cell Transcriptomics, Mega-Phylogeny, and the Genetic Basis of Morphological Innovations in Rhizaria

The innovation of the eukaryote cytoskeleton enabled phagocytosis, intracellular transport, and cytokinesis, and is largely responsible for the diversity of morphologies among eukaryotes. Still, the relationship between phenotypic innovations in the cytoskeleton and their underlying genotype is poorly understood. To explore the genetic mechanism of morphological evolution of the eukaryotic cytoskeleton, we provide the first single cell transcriptomes from uncultured, free-living unicellular eukaryotes: the polycystine radiolarian Lithomelissa setosa (Nassellaria) and Sticholonche zanclea (Taxopodida). A phylogenomic approach using 255 genes finds Radiolaria and Foraminifera as separate monophyletic groups (together as Retaria), while Cercozoa is shown to be paraphyletic where Endomyxa is sister to Retaria. Analysis of the genetic components of the cytoskeleton and mapping of the evolution of these on the revised phylogeny of Rhizaria reveal lineage-specific gene duplications and neofunctionalization of α and β tubulin in Retaria, actin in Retaria and Endomyxa, and Arp2/3 complex genes in Chlorarachniophyta. We show how genetic innovations have shaped cytoskeletal structures in Rhizaria, and how single cell transcriptomics can be applied for resolving deep phylogenies and studying gene evolution in uncultured protist species.