Publications

Chow et al. Imaging cell lineage with a synthetic digital recording system. bioRxiv (2020)

Multicellular development depends on the differentiation of cells into specific fates with precise spatial organization. Lineage history plays a pivotal role in cell fate decisions, but is inaccessible in most contexts. Engineering cells to actively record lineage information in a format readable in situ would provide a spatially resolved view of lineage in diverse developmental processes. Here, we introduce a serine integrase-based recording system that allows in situ readout, and demonstrate its ability to reconstruct lineage relationships in cultured stem cells and flies. The system, termed intMEMOIR, employs an array of independent three-state genetic memory elements that can recombine stochastically and irreversibly, allowing up to 59,049 distinct digital states. intMEMOIR accurately reconstructed lineage trees in stem cells and enabled simultaneous analysis of single cell clonal history, spatial position, and gene expression in Drosophila brain sections. These results establish a foundation for microscopy-readable clonal analysis and recording in diverse systems.

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Cao et al. Sci-fate characterizes the dynamics of gene expression in single cells. Nature Biotechnology (2020)

Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, we present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell-state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.

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Schier. Single-cell biology: beyond the sum of its parts. Nature Methods (2020)

The field of single-cell RNA sequencing (scRNA-seq) has been paired with genomics, epigenomics, spatial omics, proteomics and imaging to achieve multimodal measurements of individual cellular phenotypes and genotypes. In its purest form, single-cell multimodal omics involves the simultaneous detection of multiple traits in the same cell. More broadly, multimodal omics also encompasses comparative pairing and computational integration of measurements made across multiple distinct cells to reconstruct phenotypes. Here I highlight some of the biological insights gained from multimodal studies and discuss the challenges and opportunities in this emerging field.

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Askary et al. In situ readout of DNA barcodes and single base edits facilitated by in vitro transcription. Nature Biotechnology (2020).

Molecular barcoding technologies that uniquely identify single cells are hampered by limitations in barcode measurement.Readout by sequencing does not preserve the spatial organization of cells in tissues, whereas imaging methods preserve spatial structure but are less sensitive to barcode sequence. Here we introduce a system for image-based readout of short (20-basepair) DNA barcodes. In this system, called Zombie, phage RNA polymerases transcribe engineered barcodes in fixed cells. Theresulting RNA is subsequently detected by fluorescent in situ hybridization. Using competing match and mismatch probes,Zombie can accurately discriminate single-nucleotide differences in the barcodes. This method allows in situ readout of densecombinatorial barcode libraries and single-base mutations produced by CRISPR base editors without requiring barcode expressionin live cells. Zombie functions across diverse contexts, including cell culture, chick embryos and adult mouse brain tissue. The ability to sensitively read out compact and diverse DNA barcodes by imaging will facilitate a broad range of barcoding and genomic recording strategies.

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Yin et al. High-throughput single cell sequencing with linear amplification. Molecular Cell (2019).

Conventional methods for single-cell genome sequencing are limited with respect to uniformity and throughput. Here, we describe sci-L3, a single-cell sequencing method that combines combinatorial indexing (sci-) and linear (L) amplification. The sci-L3 method adopts a 3-level (3) indexing scheme that minimizes amplification biases while enabling exponential gains in throughput. We demonstrate the generalizability of sci-L3 with proof-of-concept demonstrations of single-cell whole-genome sequencing (sci-L3-WGS), targeted sequencing (sci-L3-targetseq), and a co-assay of the genome and transcriptome (sci-L3-RNA/DNA). We apply sci-L3-WGS to profile the genomes of >10,000 sperm and sperm precursors from F1 hybrid mice, mapping 86,786 crossovers and characterizing rare chromosome mis-segregation events in meiosis, including instances of whole-genome equational chromosome segregation. We anticipate that sci-L3 assays can be applied to fully characterize recombination landscapes, to couple CRISPR perturbations and measurements of genome stability, and to other goals requiring high-throughput, high-coverage single-cell sequencing.

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Raj et al. Emergence of neuronal diversity during vertebrate brain development. bioRxiv (2019).

Neurogenesis in the vertebrate brain comprises many steps ranging from the proliferation of progenitors to the differentiation and maturation of neurons. Although these processes are highly regulated, the landscape of transcriptional changes and progenitor identities underlying brain development are poorly characterized. Here, we describe the first developmental single-cell RNA-seq catalog of more than 200,000 zebrafish brain cells encompassing 12 stages from 12 hours post-fertilization to 15 days post-fertilization. We characterize known and novel gene markers for more than 800 clusters across these timepoints. Our results capture the temporal dynamics of multiple neurogenic waves from embryo to larva that expand neuronal diversity from ∼20 cell types at 12 hpf to ∼100 cell types at 15 dpf. We find that most embryonic neural progenitor states are transient and transcriptionally distinct from long-lasting neural progenitors of post-embryonic stages. Furthermore, we reconstruct cell specification trajectories for the retina and hypothalamus, and identify gene expression cascades and novel markers. Our analysis reveal that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo, while hypothalamic neural progenitors become progressively distinct with developmental time. These data provide the first comprehensive single-cell transcriptomic time course for vertebrate brain development and suggest distinct neurogenic regulatory paradigms between different stages and tissues.

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Zhou et al. Single-Cell Analysis Reveals Regulatory Gene Expression Dynamics Leading to Lineage Commitment in Early T Cell Development. Cell Systems (2019).

Intrathymic T cell development converts multipotent precursors to committed pro-T cells, silencing progenitor genes while inducing T cell genes, but the underlying steps have remained obscure. Single-cell profiling was used to define the order of regulatory changes, employing single-cell RNA sequencing (scRNA-seq) for full-transcriptome analysis, plus sequential multiplexed single-molecule fluorescent in situ hybridization (seqFISH) to quantitate functionally important transcripts in intrathymic precursors. Single-cell cloning verified high T cell precursor frequency among the immunophenotypically defined “early T cell precursor” (ETP) population; a discrete committed granulocyte precursor subset was also distinguished. We established regulatory phenotypes of sequential ETP subsets, confirmed initial co-expression of progenitor with T cell specification genes, defined stage-specific relationships between cell cycle and differentiation, and generated a pseudotime model from ETP to T lineage commitment, supported by RNA velocity and transcription factor perturbations. This model was validated by developmental kinetics of ETP subsets at population and clonal levels. The results imply that multilineage priming is integral to T cell specification.

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Kim et al. Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior. Cell (2019).

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms—SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)—and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

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Pliner et al. Supervised classification enables rapid annotation of cell atlases. Nature Methods (2019).

Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting. We describe Garnett, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single-cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.

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McFaline-Figueroa et al. A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. Nature Genetics (2019).

Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-β. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete ‘partial’ stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory ‘checkpoints’ in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.

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Packer et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single cell resolution. Science (2019).

During development, a single-cell zygote undergoes repeated cell divisions to produce an embryo that contains many distinct cell types. This sequence of cell divisions is called the organism’s cell lineage. Each cell in the lineage expresses a different set of genes in various quantities (the cell’s transcriptome), thus directing cells to differentiate into specific cell types over time. Identifying these transcriptome changes and understanding how they regulate cell type specification are fundamental challenges in biology. Advances in methods that assay the mRNA content of single cells [e.g., single-cell RNA sequencing (sc-RNA-seq)] have made it possible to directly measure these gene expression patterns for each cell at a whole-organism scale.

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Chen et al. Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair. Nucleic Acids Research (2019).

Non-homologous end-joining (NHEJ) plays an important role in double-strand break (DSB) repair of DNA. Recent studies have shown that the error patterns of NHEJ are strongly biased by sequence context, but these studies were based on relatively few templates. To investigate this more thoroughly, we systematically profiled ∼1.16 million independent mutational events resulting from CRISPR/Cas9-mediated cleavage and NHEJ-mediated DSB repair of 6872 synthetic target sequences, introduced into a human cell line via lentiviral infection. We find that: (i) insertions are dominated by 1 bp events templated by sequence immediately upstream of the cleavage site, (ii) deletions are predominantly associated with microhomology and (iii) targets exhibit variable but reproducible diversity with respect to the number and relative frequency of the mutational outcomes to which they give rise. From these data, we trained a model that uses local sequence context to predict the distribution of mutational outcomes. Exploiting the bias of NHEJ outcomes towards microhomology mediated events, we demonstrate the programming of deletion patterns by introducing microhomology to specific locations in the vicinity of the DSB site. We anticipate that our results will inform investigations of DSB repair mechanisms as well as the design of CRISPR/Cas9 experiments for diverse applications including genome-wide screens, gene therapy, lineage tracing and molecular recording.

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Sánchez-Guardado and Lois. Lineage does not regulate the sensory synaptic input of projection neurons in the mouse olfactory bulb. eLife (2019).

Lineage regulates the synaptic connections between neurons in some regions of the invertebrate nervous system. In mammals, recent experiments suggest that cell lineage determines the connectivity of pyramidal neurons in the neocortex, but the functional relevance of this phenomenon and whether it occurs in other neuronal types remains controversial. We investigated whether lineage plays a role in the connectivity of mitral and tufted cells, the projection neurons in the mouse olfactory bulb. We used transgenic mice to sparsely label neuronal progenitors and observed that clonally related neurons receive synaptic input from olfactory sensory neurons expressing different olfactory receptors. These results indicate that lineage does not determine the connectivity between olfactory sensory neurons and olfactory bulb projection neurons.

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Dries et al. Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data. bioRxiv (2019).

The rapid development of novel spatial transcriptomics technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational algorithms and pipelines. Here we present Giotto, a comprehensive, flexible, robust, and open-source pipeline for spatial transcriptomic data analysis and visualization. The data analysis module implements a wide range of algorithms ranging from basic tasks such as data pre-processing to innovative approaches for cell-cell interaction characterization. The data visualization module provides a user-friendly workspace that allows users to interactively visualize, explore and compare multiple layers of information. These two modules can be used iteratively for refined analysis and hypothesis development. We illustrate the functionalities of Giotto by using the recently published seqFISH+ dataset for mouse brain. Our analysis highlights the utility of Giotto for characterizing tissue spatial organization as well as for the interactive exploration of multi- layer information in spatial transcriptomic and imaging data. We find that single-cell resolution spatial information is essential for the investigation of ligand-receptor mediated cell-cell interactions. Giotto is generally applicable and can be easily integrated with external software packages for multi-omic data integration.

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Thyme et al. Phenotypic Landscape of Schizophrenia-Associated Genes Defines Candidates and Their Shared Functions. Cell (2019).

Genomic studies have identified hundreds of candidate genes near loci associated with risk for schizophrenia. To define candidates and their functions, we mutated zebrafish orthologs of 132 human schizophrenia-associated genes. We created a phenotype atlas consisting of whole-brain activity maps, brain structural differences, and profiles of behavioral abnormalities. Phenotypes were diverse but specific, including altered forebrain development and decreased prepulse inhibition. Exploration of these datasets identified promising candidates in more than 10 gene-rich regions, including the magnesium transporter cnnm2 and the translational repressor gigyf2, and revealed shared anatomical sites of activity differences, including the pallium, hypothalamus, and tectum. Single-cell RNA sequencing uncovered an essential role for the understudied transcription factor znf536 in the development of forebrain neurons implicated in social behavior and stress. This phenotypic landscape of schizophrenia-associated genes prioritizes more than 30 candidates for further study and provides hypotheses to bridge the divide between genetic association and biological mechanism.

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Eng et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature (2019).

Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells1–5. Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells—with high accuracy and sub-diffraction-limit resolution—in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand–receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discoverydriven studies of biological processes in situ.

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Cao, Spielmann et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature (2019).

Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.

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Raj et al. Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT. Nature Protocols (2018).

Lineage relationships among the large number of heterogeneous cell types generated during development are difficult to reconstruct in a high-throughput manner. We recently established a method, scGESTALT, that combines cumulative editing of a lineage barcode array by CRISPR–Cas9 with large-scale transcriptional profiling using droplet-based singlecell RNA sequencing (scRNA-seq). The technique generates edits in the barcode array over multiple timepoints using Cas9 and pools of single-guide RNAs (sgRNAs) introduced during early and late zebrafish embryonic development, which distinguishes it from similar Cas9 lineage-tracing methods. The recorded lineages are captured, along with thousands of cellular transcriptomes, to build lineage trees with hundreds of branches representing relationships among profiled cell types. Here, we provide details for (i) generating transgenic zebrafish; (ii) performing multi-timepoint barcode editing; (iii) building scRNA-seq libraries from brain tissue; and (iv) concurrently amplifying lineage barcodes from captured single cells. Generating transgenic lines takes 6 months, and performing barcode editing and generating single-cell libraries involve 7 d of hands-on time. scGESTALT provides a scalable platform to map lineage relationships between cell types in any system that permits genome editing during development, regeneration, or disease.

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Cao et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science (2018).

Although we can increasingly measure transcription, chromatin, methylation, and other aspects of molecular biology at single-cell resolution, most assays survey only one aspect of cellular biology. Here we describe sci-CAR, a combinatorial indexing–based coassay that jointly profiles chromatin accessibility and mRNA (CAR) in each of thousands of single cells. As a proof of concept, we apply sci-CAR to 4825 cells, including a time series of dexamethasone treatment, as well as to 11,296 cells from the adult mouse kidney. With the resulting data, we compare the pseudotemporal dynamics of chromatin accessibility and gene expression, reconstruct the chromatin accessibility profiles of cell types defined by RNA profiles, and link cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells.

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Pliner et al. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Molecular Cell (2018).

Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here, we introduce Cicero, an algorithm that identifies co-accessible pairs of DNA elements using single-cell chromatin accessibility data and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs"-they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD1 in myoblasts exhibit early opening in a PBX1- and MEIS1 -dependent manner. Our strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

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Cusanovich, Hill et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. Cell (2018).

We applied a combinatorial indexing assay, sciATAC-seq, to profile genome-wide chromatin accessibility in ~100,000 single cells from 13 adult mouse tissues. We identify 85 distinct patterns of chromatin accessibility, most of which can be assigned to cell types, and ~400,000 differentially accessible elements. We use these data to link regulatory elements to their target genes, to define the transcription factor grammar specifying each cell type, and to discover in vivo correlates of heterogeneity in accessibility within cell types. We develop a technique for mapping single cell gene expression data to single-cell chromatin accessibility data, facilitating the comparison of atlases. By intersecting mouse chromatin accessibility with human genome-wide association summary statistics, we identify cell-type-specific enrichments of the heritability signal for hundreds of complex traits. These data define the in vivo landscape of the regulatory genome for common mammalian cell types at single-cell resolution.

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Shah et al. Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH. Cell (2018).

Visualization of the transcriptome and the nuclear organization in situ has been challenging for single-cell analysis. Here, we demonstrate a multiplexed single-molecule in situ method, intron seqFISH, that allows imaging of 10,421 genes at their nascent transcription active sites in single cells, followed by mRNA and lncRNA seqFISH and immunofluorescence. This nascent transcriptome-profiling method can identify different cell types and states with mouse embryonic stem cells and fibroblasts. The nascent sites of RNA synthesis tend to be localized on the surfaces of chromosome territories, and their organization in individual cells is highly variable. Surprisingly, the global nascent transcription oscillated asynchronously in individual cells with a period of 2 hr in mouse embryonic stem cells, as well as in fibroblasts. Together, spatial genomics of the nascent transcriptome by intron seqFISH reveals nuclear organizational principles and fast dynamics in single cells that are otherwise obscured.

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McKenna et al. FlashFry: a fast and flexible tool for large-scale CRISPR target design. BMC Biology (2018).

Genome-wide knockout studies, noncoding deletion scans, and other large-scale studies require a simple and lightweight framework that can quickly discover and score thousands of candidate CRISPR guides targeting an arbitrary DNA sequence. While several CRISPR web applications exist, there is a need for a highthroughput tool to rapidly discover and process hundreds of thousands of CRISPR targets. Here, we introduce FlashFry, a fast and flexible command-line tool for characterizing large numbers of CRISPR target sequences. With FlashFry, users can specify an unconstrained number of mismatches to putative offtargets, richly annotate discovered sites, and tag potential guides with commonly used on-target and off-target scoring metrics. FlashFry runs at speeds comparable to commonly used genome-wide sequence aligners, and output is provided as an easy-to-manipulate text file. FlashFry is a fast and convenient command-line tool to discover and score CRISPR targets within large DNA sequences..

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Raj et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nature Biotechnology (2018).

The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method, GESTALT, used CRISPR-Cas9 barcode editing for large-scale lineage tracing, but was restricted to early development and did not identify cell types. Here we present scGESTALT, which combines the lineage recording capabilities of GESTALT with cell-type identification by single-cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points, capturing lineage information from later stages of development. Sequencing of ∼60,000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data, we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types, brain regions, and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease.

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Farrell et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science (2018).

During embryogenesis, cells acquire distinct fates by transitioning through transcriptional states. To uncover these transcriptional trajectories during zebrafish embryogenesis, we sequenced 38,731 cells and developed URD, a simulated diffusion-based computational reconstruction method. URD identified the trajectories of 25 cell types through early somitogenesis, gene expression along them, and their spatial origin in the blastula. Analysis of Nodal signaling mutants revealed that their transcriptomes were canalized into a subset of wild-type transcriptional trajectories. Some wild-type developmental branchpoints contained cells expressing genes characteristic of multiple fates. These cells appeared to trans-specify from one fate to another. These findings reconstruct the transcriptional trajectories of a vertebrate embryo, highlight the concurrent canalization and plasticity of embryonic specification, and provide a framework to reconstruct complex developmental trees from single-cell transcriptomes.

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Pandey et al. Comprehensive Identification and Spatial Mapping of Habenular Neuronal Types Using Single-Cell RNA-Seq. Current Biology (2018).

The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types.We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of 13,000 habenular cells with 43 cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions.

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Hill et al. On the Design of CRISPR-based Single-Cell Molecular Screens. Nature Methods (2018).

Several groups recently coupled CRISPR perturbations and single-cell RNA-seq for pooled genetic screens. We demonstrate that vector designs of these studies are susceptible to ∼50% swapping of guide RNA–barcode associations because of lentiviral template switching. We optimized a published alternative, CROP-seq, in which the guide RNA also serves as the barcode, and here confirm that this strategy performs robustly and doubled the rate at which guides are assigned to cells to 94%.

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Cusanovich, Reddington, Garfield et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature (2018).

Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq), we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2-4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6-8 h after egg laying (predominantly stage 10-11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10-12 h after egg laying (predominantly stage 13), when each of the embryo's more than 20,000 cells are undergoing terminal differentiation. Our results show that there is spatial heterogeneity in the accessibility of the regulatory genome before gastrulation, a feature that aligns with future cell fate, and that nuclei can be temporally ordered along developmental trajectories. During mid-embryogenesis, tissue granularity emerges such that individual cell types can be inferred by their chromatin accessibility while maintaining a signature of their germ layer of origin. Analysis of the data reveals overlapping usage of regulatory elements between cells of the endoderm and non-myogenic mesoderm, suggesting a common developmental program that is reminiscent of the mesendoderm lineage in other species. We identify 30,075 distal regulatory elements that exhibit tissue-specific accessibility. We validated the germ-layer specificity of a subset of these predicted enhancers in transgenic embryos, achieving an accuracy of 90%. Overall, our results demonstrate the power of shotgun single-cell profiling of embryos to resolve dynamic changes in the chromatin landscape during development, and to uncover the cis-regulatory programs of metazoan germ layers and cell types.

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Gagnon, Obbad, Schier. The primary role of zebrafish nanog is in extra-embryonic tissue. Development (2018).

The role of the zebrafish transcription factor Nanog has been controversial. It has been suggested that Nanog is primarily required for the proper formation of the extra-embryonic yolk syncytial layer (YSL) and only indirectly regulates gene expression in embryonic cells. In an alternative scenario, Nanog has been proposed to directly regulate transcription in embryonic cells during zygotic genome activation. To clarify the roles of Nanog, we performed a detailed analysis of zebrafish nanog mutants. Whereas zygotic nanog mutants survive to adulthood, maternal-zygotic (MZnanog) and maternal mutants exhibit developmental arrest at the blastula stage. In the absence of Nanog, YSL formation and epiboly are abnormal, embryonic tissue detaches from the yolk, and the expression of dozens of YSL and embryonic genes is reduced. Epiboly defects can be rescued by generating chimeric embryos of MZnanog embryonic tissue with wild-type vegetal tissue that includes the YSL and yolk cell. Notably, cells lacking Nanog readily respond to Nodal signals and when transplanted into wild-type hosts proliferate and contribute to embryonic tissues and adult organs from all germ layers. These results indicate that zebrafish Nanog is necessary for proper YSL development but is not directly required for embryonic cell differentiation.

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Cao, Packer et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science (2017).

To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.

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Shah et al. seqFISH Accurately Detects Transcripts in Single Cells and Reveals Robust Spatial Organization in the Hippocampus. Neuron (2017).

We recently applied multiplexed seqFISH to profile expressions of hundreds of genes at the singlecell level in situ (Shah et al., 2016) and provided a map of spatial heterogeneity within each subregion, reconciling previously contradictory descriptions of CA1 at lower spatial resolutions. The accompanying Matters Arising paper from Cembrowski and Spruston questions the spatial organization described for CA1 and raise concerns that the results were determined only by high expression, non-barcoded genes. In response, we show that the same robust spatial structure is observed when only lower average abundance genes measured by barcoded seqFISH are used. In fact, many genes with low average abundance are informative of cell states because they can be expressed strongly in specific subpopulation of cells. Our discussion highlights the importance of single-cell in situ analysis in resolving cellular and spatial heterogeneities otherwise lost in population-averaged measurements. This Matters Arising Response paper addresses the Cembrowski and Spruston (2017) Matters Arising paper, published concurrently in this issue of Neuron.

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Frieda, Linton, Hormoz et al. Synthetic recording and in situ readout of lineage information in single cells. Nature (2017).

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.

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Shah et al. In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus. Neuron (2016).

Identifying the spatial organization of tissues at cellular resolution from single-cell gene expression profiles is essential to understanding biological systems. Using an in situ 3D multiplexed imaging method, seqFISH, we identify unique transcriptional states by quantifying and clustering up to 249 genes in 16,958 cells to examine whether the hippocampus is organized into transcriptionally distinct subregions. We identified distinct layers in the dentate gyrus corresponding to the granule cell layer and the subgranular zone and, contrary to previous reports, discovered that distinct subregions within the CA1 and CA3 are composed of unique combinations of cells in different transcriptional states. In addition, we found that the dorsal CA1 is relatively homogeneous at the single cell level, while ventral CA1 is highly heterogeneous. These structures and patterns are observed using different mice and different sets of genes. Together, these results demonstrate the power of seqFISH in transcriptional profiling of complex tissues.

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Coskun & Cai. Dense transcript profiling in single cells by image correlation decoding. Nature Methods (2016).

Sequential barcoded fluorescent in situ hybridization (seqFISH ) allows large numbers of molecular species to be accurately detected in single cells, but multiplexing is limited by the density of barcoded objects. We present correlation FISH (corrFISH ), a method to resolve dense temporal barcodes in sequential hybridization experiments. Using corrFISH , we quantified highly expressed ribosomal protein genes in single cultured cells and mouse thymus sections, revealing cell-type-specific gene expression.

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McKenna, Findlay, Gagnon et al. Whole-organism lineage tracing by combinatorial and cumulative gene editing. Science (2016).

Multicellular systems develop from single cells through distinct lineages. However, current lineage-tracing approaches scale poorly to whole, complex organisms. Here, we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 target sites, marks cells and enables the elucidation of lineage relationships via the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable and that thousands of lineage-informative barcode alleles can be generated. By sampling hundreds of thousands of cells from individual zebrafish, we find that most cells in adult organs derive from relatively few embryonic progenitors. In future analyses, genome editing of synthetic target arrays for lineage tracing (GESTALT) can be used to generate large-scale maps of cell lineage in multicellular systems for normal development and disease.

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Cusanovich et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science (2015).

Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated before biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from more than 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas.

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