Caleb Weinreb

Clonal analysis of lineage fate in native haematopoiesis

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate. Here we use transposon tagging to clonally trace the fates of progenitors and stem cells in unperturbed haematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte lineage arises largely independently of other haematopoietic fates. Our data, combined with single-cell RNA sequencing, identify a functional hierarchy of unilineage- and oligolineage-producing clones within the multipotent progenitor population. Finally, our results demonstrate that traditionally defined long-term haematopoietic stem cells are a significant source of megakaryocyte-restricted progenitors, suggesting that the megakaryocyte lineage is the predominant native fate of long-term haematopoietic stem cells. Our study provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

Mapping the vertebrate developmental landscape As embryos develop, numerous cell types with distinct functions and morphologies arise from pluripotent cells. Three research groups have used single-cell RNA sequencing to analyze the transcriptional changes accompanying development of vertebrate embryos (see the Perspective by Harland). Wagner et al. sequenced the transcriptomes of more than 90,000 cells throughout zebrafish development to reveal how cells differentiate during axis patterning, germ layer formation, and early organogenesis. Farrell et al. profiled the transcriptomes of tens of thousands of embryonic cells and applied a computational approach to construct a branching tree describing the transcriptional trajectories that lead to 25 distinct zebrafish cell types. The branching tree revealed how cells change their gene expression as they become more and more specialized. Briggs et al. examined whole frog embryos, spanning zygotic genome activation through early organogenesis, to map cell states and differentiation across all cell lineages over time. These data and approaches pave the way for the comprehensive reconstruction of transcriptional trajectories during development. Science, this issue p. 981, p. eaar3131, p. eaar5780; see also p. 967 Single-cell RNA sequencing reveals cell type trajectories and cell lineage in the developing zebrafish embryo. High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach (TracerSeq) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in embryos lacking the chordin gene. We provide web-based resources for further analysis of the single-cell data.

The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution

Mapping the vertebrate developmental landscape As embryos develop, numerous cell types with distinct functions and morphologies arise from pluripotent cells. Three research groups have used single-cell RNA sequencing to analyze the transcriptional changes accompanying development of vertebrate embryos (see the Perspective by Harland). Wagner et al. sequenced the transcriptomes of more than 90,000 cells throughout zebrafish development to reveal how cells differentiate during axis patterning, germ layer formation, and early organogenesis. Farrell et al. profiled the transcriptomes of tens of thousands of embryonic cells and applied a computational approach to construct a branching tree describing the transcriptional trajectories that lead to 25 distinct zebrafish cell types. The branching tree revealed how cells change their gene expression as they become more and more specialized. Briggs et al. examined whole frog embryos, spanning zygotic genome activation through early organogenesis, to map cell states and differentiation across all cell lineages over time. These data and approaches pave the way for the comprehensive reconstruction of transcriptional trajectories during development. Science, this issue p. 981, p. eaar3131, p. eaar5780; see also p. 967 A single-cell transcriptome analysis of whole frog embryos reveals cell states and provides a map of differentiation over time. INTRODUCTION Metazoan development represents a big jump in complexity compared with unicellular life in two aspects: cell-type differentiation and cell spatial organization. In vertebrate embryos, many distinct cell types appear within just a single day of life after fertilization. Studying the developmental dynamics of all embryonic cell types is complicated by factors such as the speed of early development, complex cellular spatial organization, and scarcity of raw material for conventional analysis. Genetics and experimental embryology have clarified major transcription factors and secreted signaling molecules involved in the specification of early lineages. However, development involves parallel alterations in many cellular circuits, not just a few well-described factors. RATIONALE We recently developed a microfluidics-based single-cell RNA sequencing method capable of efficiently profiling tens of thousands of individual transcriptomes. Building on earlier studies that showed how single-cell transcriptomics can reveal cell states within complex tissues, we reasoned that a series of such measurements from embryos, if collected with sufficient time resolution, could allow reconstruction of developmental cell-state hierarchies. We focused on the western claw-toed frog, Xenopus tropicalis, which serves as one of the best-studied model systems of early vertebrate development. We profiled these embryos from just before the onset of zygotic transcription up to a point at which dozens of distinct cell types have formed encompassing progenitors of most major organs. To establish aspects of development general to vertebrates, we additionally incorporated data from the copublished paper by Wagner et al. on zebrafish embryos, which separated from frogs about 400 million years ago. RESULTS We profiled 136,966 single-cell transcriptomes over the first day of life of Xenopus tropicalis. Our analysis classifies 259 gene expression clusters across 10 time points, which belong to 69 annotated embryonic cell types and capture further substructure. Using a computational approach to link cell states between time points, a resulting cell-state graph agrees well with previous lineage-tracing studies and shows that developmental fate choices can be well approximated by a treelike model. Many cell states are detected considerably earlier than previously understood, thus revealing the earliest events in their differentiation. The data lends clarity to numerous specific developmental processes, such as the developmental origin of the vertebrate neural crest. Through an evolutionary comparison with zebrafish, we identified diverging features of developmental dynamics, including many genes showing cell-type specificity in one organism but not in another. Yet, we also identified conserved patterns in the reuse of transcription factors across lineages and in multilineage priming at fate branch points. The resulting resource is available in an interactive online browser that allows in silico exploration of any gene in any cell state (tinyurl.com/scXen2018). CONCLUSION The approaches and results presented here, along with the copublished paper by Wagner et al., establish the first steps toward a data-driven dissection of developmental dynamics at the scale of entire organisms. They provide a useful, annotated resource for developmental biologists, comprehensively tracking differentiation programs as they unfold on a high-dimensional gene expression landscape. Although demonstrated on model organisms, the same approaches could be transformative to the study of nonmodel organisms by allowing rapid and quantitative description of differentiation processes across the tree of life, opening up a new front in evolutionary biology. Single-cell analysis of whole developing vertebrate embryos. Xenopus embryos at 10 time points over the first day of life were dissociated, barcoded, and sequenced, yielding 136,966 single-cell transcriptomes. These data were clustered and connected over time to reveal a complete view of transcriptional changes in each embryonic lineage and clarify numerous features of early development. hpf, hours postfertilization. Time series of single-cell transcriptome measurements can reveal dynamic features of cell differentiation pathways. From measurements of whole frog embryos spanning zygotic genome activation through early organogenesis, we derived a detailed catalog of cell states in vertebrate development and a map of differentiation across all lineages over time. The inferred map recapitulates most if not all developmental relationships and associates new regulators and marker genes with each cell state. We find that many embryonic cell states appear earlier than previously appreciated. We also assess conflicting models of neural crest development. Incorporating a matched time series of zebrafish development from a companion paper, we reveal conserved and divergent features of vertebrate early developmental gene expression programs.

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data

Abstract Motivation Single-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies. Results Force-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and captures complex population topologies. We describe SPRING, a pipeline for data filtering, normalization and visualization using force-directed layouts and show that it reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells and cells of the upper airway epithelium. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. We provide SPRING as an interactive web-tool with an easy to use GUI. Availability and implementation https://kleintools.hms.harvard.edu/tools/spring.html, https://github.com/AllonKleinLab/SPRING/. Supplementary information Supplementary data are available at Bioinformatics online.

Population snapshots predict early haematopoietic and erythroid hierarchies

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

Fundamental limits on dynamic inference from single-cell snapshots

Significance 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.

A single cell hematopoietic landscape resolves eight lineage trajectories and defects in Kit mutant mice

Hematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation causes a multitude of diseases. However, the differentiation journeys toward specific hematopoietic lineages remain ill defined, and system-wide disease interpretation remains challenging. Here, we have profiled 44 802 mouse bone marrow HSPCs using single-cell RNA sequencing to provide a comprehensive transcriptional landscape with entry points to 8 different blood lineages (lymphoid, megakaryocyte, erythroid, neutrophil, monocyte, eosinophil, mast cell, and basophil progenitors). We identified a common basophil/mast cell bone marrow progenitor and characterized its molecular profile at the single-cell level. Transcriptional profiling of 13 815 HSPCs from the c-Kit mutant (W41/W41) mouse model revealed the absence of a distinct mast cell lineage entry point, together with global shifts in cell type abundance. Proliferative defects were accompanied by reduced Myc expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

Emergence of the erythroid lineage from multipotent hematopoiesis

Red cell formation begins with the hematopoietic stem cell, but the manner by which it gives rise to erythroid progenitors, and their subsequent developmental path, remain unclear. Here we combined single-cell transcriptomics of murine hematopoietic tissues with fate potential assays to infer a continuous yet hierarchical structure for the hematopoietic network. We define the erythroid differentiation trajectory as it emerges from multipotency and diverges from 6 other blood lineages. With the aid of a new flow-cytometric sorting strategy, we validated predicted cell fate potentials at the single cell level, revealing a coupling between erythroid and basophil/mast cell fates. We uncovered novel growth factor receptor regulators of the erythroid trajectory, including the proinflammatory IL-17RA, found to be a strong erythroid stimulator; and identified a global hematopoietic response to stress erythropoiesis. We further identified transcriptional and high-purity FACS gates for the complete isolation of all classically-defined erythroid burst-forming (BFU-e) and colony-forming progenitors (CFU-e), finding that they express a dedicated transcriptional program, distinct from that of terminally-differentiating erythroblasts. Intriguingly, profound remodeling of the cell cycle is intimately entwined with CFU-e developmental progression and with a sharp transcriptional switch that extinguishes the CFU-e stage and activates terminal differentiation. Underlying these results, our work showcases the utility of theoretic approaches linking transcriptomic data to predictive fate models, providing key insights into lineage development in vivo.

3D RNA from evolutionary couplings

Non-protein-coding RNAs are ubiquitous in cell physiology, with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved long non-protein-coding RNAs of currently unkown function have been identified. When available, knowledge of their 3D structure is very helpful in elucidating the function of these RNAs. However, despite some outstanding structure elucidation of RNAs using X-ray crystallography, NMR and cryoEM, learning RNA 3D structures remains low-throughput. RNA structure prediction in silico is a promising alternative approach and works well for double-helical stems, but full 3D structure determination requires tertiary contacts outside of secondary structures that are difficult to infer from sequence information. Here, based only on information from RNA multiple sequence alignments, we use a global statistical sequence probability model of co-variation in a pairs of nucleotide positions to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical nucleotide interactions with 65-95% true positive prediction accuracy. Importantly, we infer many long-range tertiary contacts, including non-Watson-Crick interactions, where secondary structure elements assemble in 3D. When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 – 10 Å rmsd deviation in atom positions, with potential for further refinement by molecular dynamics. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we present evolutionary couplings suitable for folding simulations for 180 RNAs of unknown structure, available at https://marks.hms.harvard.edu/ev_rna/. We anticipate that this approach can help shed light on the structure and function of non-protein-coding RNAs as well as 3D-structured mRNAs.