Whole-organism lineage tracing by combinatorial and cumulative genome editing

Recording development with single cell dynamic lineage tracing

Every animal grows from a single fertilized egg into an intricate network of cell types and organ systems. This process is captured in a lineage tree: a diagram of every cell's ancestry back to the founding zygote. Biologists have long sought to trace this cell lineage tree in individual organisms and have developed a variety of technologies to map the progeny of specific cells. However, there are billions to trillions of cells in complex organisms, and conventional approaches can only map a limited number of clonal populations per experiment. A new generation of tools that use molecular recording methods integrated with single cell profiling technologies may provide a solution. Here, we summarize recent breakthroughs in these technologies, outline experimental and computational challenges, and discuss biological questions that can be addressed using single cell dynamic lineage tracing.

DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveal Multiple Trajectories of Dendritic Cell Development

A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) in fms-like tyrosine kinase 3 ligand culture from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures and visualize these multidimensional data using developmental interpolated t-distributed stochastic neighborhood embedding (DiSNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.

Development of CRISPR-Cas systems for genome editing and beyond

The development of clustered regularly interspaced short-palindromic repeat (CRISPR)-Cas systems for genome editing has transformed the way life science research is conducted and holds enormous potential for the treatment of disease as well as for many aspects of biotechnology. Here, I provide a personal perspective on the development of CRISPR-Cas9 for genome editing within the broader context of the field and discuss our work to discover novel Cas effectors and develop them into additional molecular tools. The initial demonstration of Cas9-mediated genome editing launched the development of many other technologies, enabled new lines of biological inquiry, and motivated a deeper examination of natural CRISPR-Cas systems, including the discovery of new types of CRISPR-Cas systems. These new discoveries in turn spurred further technological developments. I review these exciting discoveries and technologies as well as provide an overview of the broad array of applications of these technologies in basic research and in the improvement of human health. It is clear that we are only just beginning to unravel the potential within microbial diversity, and it is quite likely that we will continue to discover other exciting phenomena, some of which it may be possible to repurpose as molecular technologies. The transformation of mysterious natural phenomena to powerful tools, however, takes a collective effort to discover, characterize, and engineer them, and it has been a privilege to join the numerous researchers who have contributed to this transformation of CRISPR-Cas systems.

DNA sequencing in high-throughput neuroanatomy

Mapping brain connectivity at single cell resolution is critical for understanding brain structure. For decades, such mapping has been principally approached with microscopy techniques, aiming to visualize neurons and their connections. However, these techniques are often very labor intensive and do not scale well to the complexity of mammalian brains. We recently leveraged the speed and parallelization of DNA sequencing to map the projections of thousands of single neurons in single experiments, and to map cortical mesoscale connectivity in single mice. Here, I review the state of sequencing-based neuroanatomy, and discuss future directions in synaptic connectivity mapping and comparative connectomics.

Molecular recording of mammalian embryogenesis

Ontogeny describes the emergence of complex multicellular organisms from single totipotent cells. This field is particularly challenging in mammals, owing to the indeterminate relationship between self-renewal and differentiation, variation in progenitor field sizes, and internal gestation in these animals. Here we present a flexible, high-information, multi-channel molecular recorder with a single-cell readout and apply it as an evolving lineage tracer to assemble mouse cell-fate maps from fertilization through gastrulation. By combining lineage information with single-cell RNA sequencing profiles, we recapitulate canonical developmental relationships between different tissue types and reveal the nearly complete transcriptional convergence of endodermal cells of extra-embryonic and embryonic origins. Finally, we apply our cell-fate maps to estimate the number of embryonic progenitor cells and their degree of asymmetric partitioning during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems, which will facilitate the construction of a quantitative framework for understanding developmental processes.

CRISPR/Cas-based devices for mammalian synthetic biology

Since its first demonstration for mammalian gene editing, CRISPR/Cas technology has been widely adopted in research, industry, and medicine. Beyond indel mutations induced by Cas9 activity, recent advances in CRISPR/Cas have enabled DNA or RNA base editing. In addition, multiple orthogonal methods for the spatiotemporal regulation of CRISPR/Cas activity and repurposed Cas proteins for the visualization and relocation of specific genomic loci in living cells have been described. By harnessing the versatility of CRISPR/Cas-based devices and gene circuits, synthetic biologists are developing memory devices for lineage tracing and technologies for unbiased, high-throughput interrogation of combinatorial gene perturbations. We envision that such approaches will enable researchers to gain deeper insights into the translation of genotypes to phenotypes in healthy and diseased states.

Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice

Fate mapping is a powerful genetic tool for linking stem or progenitor cells with their progeny, and hence for defining cell lineages in vivo. The resolution of fate mapping depends on the numbers of distinct markers that are introduced in the beginning into stem or progenitor cells; ideally, numbers should be sufficiently large to allow the tracing of output from individual cells. Highly diverse genetic barcodes can serve this purpose. We recently developed an endogenous genetic barcoding system, termed Polylox. In Polylox, random DNA recombination can be induced by transient activity of Cre recombinase in a 2.1-kb-long artificial recombination substrate that has been introduced into a defined locus in mice (Rosa26^Polylox reporter mice). Here, we provide a step-by-step protocol for the use of Polylox, including barcode induction and estimation of induction efficiency, barcode retrieval with single-molecule real-time (SMRT) DNA sequencing followed by computational barcode identification, and the calculation of barcode-generation probabilities, which is key for estimations of single-cell labeling for a given number of stem cells. Thus, Polylox barcoding enables high-resolution fate mapping in essentially all tissues in mice for which inducible Cre driver lines are available. Alternative methods include ex vivo cell barcoding, inducible transposon insertion and CRISPR-Cas9-based barcoding; Polylox currently allows combining non-invasive and cell-type-specific labeling with high label diversity. The execution time of this protocol is ~2-3 weeks for experimental data generation and typically <2 d for computational Polylox decoding and downstream analysis.

Stem cell topography splits growth and homeostatic functions in the fish gill

While lower vertebrates contain adult stem cells (aSCs) that maintain homeostasis and drive un-exhaustive organismal growth, mammalian aSCs display mainly the homeostatic function. Here, we use lineage analysis in the medaka fish gill to address aSCs and report separate stem cell populations for homeostasis and growth. These aSCs are fate-restricted during the entire post-embryonic life and even during re-generation paradigms. We use chimeric animals to demonstrate that p53 mediates growth coordination among fate-restricted aSCs, suggesting a hierarchical organisation among lineages in composite organs like the fish gill. Homeostatic and growth aSCs are clonal but differ in their topology; modifications in tissue architecture can convert the homeostatic zone into a growth zone, indicating a leading role for the physical niche defining stem cell output. We hypothesise that physical niches are main players to restrict aSCs to a homeostatic function in animals with fixed adult size.

Organoids required! A new path to understanding human brain development and disease

Our ability to study the developing human brain has recently been dramatically advanced by the development of human 'brain organoids', three-dimensional culture systems that recapitulate selected aspects of human brain development in reductionist, yet complex, tissues in vitro. Here I discuss the promises and challenges this new model system presents.

Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification

Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.

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

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

Developmental origins and oncogenic pathways in malignant brain tumors

Brain tumors such as adult glioblastomas and pediatric high-grade gliomas or medulloblastomas are among the leading causes of cancer-related deaths, exhibiting poor prognoses with little improvement in outcomes in the past several decades. These tumors are heterogeneous and can be initiated from various neural cell types, contributing to therapy resistance. How such heterogeneity arises is linked to the tumor cell of origin and their genetic alterations. Brain tumorigenesis and progression recapitulate key features associated with normal neurogenesis; however, the underlying mechanisms are quite dysregulated as tumor cells grow and divide in an uncontrolled manner. Recent comprehensive genomic, transcriptomic, and epigenomic studies at single-cell resolution have shed new light onto diverse tumor-driving events, cellular heterogeneity, and cells of origin in different brain tumors. Primary and secondary glioblastomas develop through different genetic alterations and pathways, such as EGFR amplification and IDH1/2 or TP53 mutation, respectively. Mutations such as histone H3K27M impacting epigenetic modifications define a distinct group of pediatric high-grade gliomas such as diffuse intrinsic pontine glioma. The identification of distinct genetic, epigenomic profiles and cellular heterogeneity has led to new classifications of adult and pediatric brain tumor subtypes, affording insights into molecular and lineage-specific vulnerabilities for treatment stratification. This review discusses our current understanding of tumor cells of origin, heterogeneity, recurring genetic and epigenetic alterations, oncogenic drivers and signaling pathways for adult glioblastomas, pediatric high-grade gliomas, and medulloblastomas, the genetically heterogeneous groups of malignant brain tumors. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cell Differentiation and Reversion Signaling Pathways > Cell Fate Signaling.

Inferring population dynamics from single-cell RNA-sequencing time series data

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.

Using Genotype Abundance to Improve Phylogenetic Inference

Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times. This motivates strategies to incorporate genotype abundance information in phylogenetic inference. In this article, we synthesize a stochastic process model with standard sequence-based phylogenetic optimality, and show that tree estimation is substantially improved by doing so. Our method is validated with extensive simulations and an experimental single-cell lineage tracing study of germinal center B cell receptor affinity maturation.

Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements

Determining cell lineage and function is critical to understanding human physiology and pathology. Although advances in lineage tracing methods provide new insight into cell fate, defining cellular diversity at the mammalian level remains a challenge. Here, we develop a genome editing strategy using a cytidine deaminase fused with nickase Cas9 (nCas9) to specifically target endogenous interspersed repeat regions in mammalian cells. The resulting mutation patterns serve as a genetic barcode, which is induced by targeted mutagenesis with single-guide RNA (sgRNA), leveraging substitution events, and subsequent read out by a single primer pair. By analyzing interspersed mutation signatures, we show the accurate reconstruction of cell lineage using both bulk cell and single-cell data. We envision that our genetic barcode system will enable fine-resolution mapping of organismal development in healthy and diseased mammalian states.

Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics

Lineage tracing provides key insights into the fate of individual cells in complex organisms. Although effective genetic labeling approaches are available in model systems, in humans, most approaches require detection of nuclear somatic mutations, which have high error rates, limited scale, and do not capture cell state information. Here, we show that somatic mutations in mtDNA can be tracked by single-cell RNA or assay for transposase accessible chromatin (ATAC) sequencing. We leverage somatic mtDNA mutations as natural genetic barcodes and demonstrate their utility as highly accurate clonal markers to infer cellular relationships. We track native human cells both in vitro and in vivo and relate clonal dynamics to gene expression and chromatin accessibility. Our approach should allow clonal tracking at a 1,000-fold greater scale than with nuclear genome sequencing, with simultaneous information on cell state, opening the way to chart cellular dynamics in human health and disease.

Expanding the CRISPR Toolbox in Zebrafish for Studying Development and Disease

The study of model organisms has revolutionized our understanding of the mechanisms underlying normal development, adult homeostasis, and human disease. Much of what we know about gene function in model organisms (and its application to humans) has come from gene knockouts: the ability to show analogous phenotypes upon gene inactivation in animal models. The zebrafish (Danio rerio) has become a popular model organism for many reasons, including the fact that it is amenable to various forms of genetic manipulation. The RNA-guided CRISPR/Cas9-mediated targeted mutagenesis approaches have provided powerful tools to manipulate the genome toward developing new disease models and understanding the pathophysiology of human diseases. CRISPR-based approaches are being used for the generation of both knockout and knock-in alleles, and also for applications including transcriptional modulation, epigenome editing, live imaging of the genome, and lineage tracing. Currently, substantial effort is being made to improve the specificity of Cas9, and to expand the target coverage of the Cas9 enzymes. Novel types of naturally occurring CRISPR systems [Cas12a (Cpf1); engineered variants of Cas9, such as xCas9 and SpCas9-NG], are being studied and applied to genome editing. Since the majority of pathogenic mutations are single point mutations, development of base editors to convert C:G to T:A or A:T to G:C has further strengthened the CRISPR toolbox. In this review, we provide an overview of the increasing number of novel CRISPR-based tools and approaches, including lineage tracing and base editing.

Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging

Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.

Nucleic Acid-Barcoding Technologies: Converting DNA Sequencing into a Broad-Spectrum Molecular Counter

The emergence of high-throughput DNA sequencing technologies sparked a revolution in the field of genomics that has rippled into many branches of the life and physical sciences. The remarkable sensitivity, specificity, throughput, and multiplexing capacity that are inherent to parallel DNA sequencing have since motivated its use as a broad-spectrum molecular counter. A key aspect of extrapolating DNA sequencing to non-traditional applications is the need to append nucleic-acid barcodes to entities of interest. In this review, we describe the chemical and biochemical approaches that have enabled nucleic-acid barcoding of proteinaceous and non-proteinaceous materials and provide examples of downstream technologies that have been made possible by DNA-encoded molecules. As commercially available high-throughput sequencers were first released less than 15 years ago, we believe related applications will continue to mature and close by proposing new frontiers to support this assertion.

Precision public health to inhibit the contagion of disease and move toward a future in which microbes spread health

Antimicrobial resistance continues to outpace the development of new chemotherapeutics. Novel pathogens continue to evolve and emerge. Public health innovation has the potential to open a new front in the war of "our wits against their genes" (Joshua Lederberg). Dense sampling coupled to next generation sequencing can increase the spatial and temporal resolution of microbial characterization while sensor technologies precisely map physical parameters relevant to microbial survival and spread. Microbial, physical, and epidemiological big data could be combined to improve prospective risk identification. However, applied in the wrong way, these approaches may not realize their maximum potential benefits and could even do harm. Minimizing microbial-human interactions would be a mistake. There is evidence that microbes previously thought of at best "benign" may actually enhance human health. Benign and health-promoting microbiomes may, or may not, spread via mechanisms similar to pathogens. Infectious vaccines are approaching readiness to make enhanced contributions to herd immunity. The rigorously defined nature of infectious vaccines contrasts with indigenous "benign or health-promoting microbiomes" but they may converge. A "microbial Neolithic revolution" is a possible future in which human microbial-associations are understood and managed analogously to the macro-agriculture of plants and animals. Tradeoffs need to be framed in order to understand health-promoting potentials of benign, and/or health-promoting microbiomes and infectious vaccines while also discouraging pathogens. Super-spreaders are currently defined as individuals who play an outsized role in the contagion of infectious disease. A key unanswered question is whether the super-spreader concept may apply similarly to health-promoting microbes. The complex interactions of individual rights, community health, pathogen contagion, the spread of benign, and of health-promoting microbiomes including infectious vaccines require study. Advancing the detailed understanding of heterogeneity in microbial spread is very likely to yield important insights relevant to public health.

Exploiting Molecular Barcodes in High-Throughput Cellular Assays

Multiplexing strategies, which greatly increase the number of simultaneously measured parameters in single experiments, are now being widely implemented by both the pharmaceutical industry and academic researchers. Color has long been used to identify biological signals and, when combined with molecular barcodes, has substantially enhanced the depth of multiplexed sample characterization. Moreover, the recent advent of DNA barcodes has led to an explosion of innovative cell sequencing approaches. Novel barcoding strategies also show great promise for encoding spatial information in transcriptomic studies, and for precise assessment of molecular abundance. Both color- and DNA-based barcodes can be conveniently analyzed with either a microscope or a cytometer, or via DNA sequencing. Here we review the basic principles of several technologies used to create barcodes and detail the type of samples that can be identified with such tags.

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.

Is it possible to reconstruct an accurate cell lineage using CRISPR recorders?

Cell lineages provide the framework for understanding how cell fates are decided during development. Describing cell lineages in most organisms is challenging; even a fruit fly larva has ~50,000 cells and a small mammal has >1 billion cells. Recently, the idea of applying CRISPR to induce mutations during development, to be used as heritable markers for lineage reconstruction, has been proposed by several groups. While an attractive idea, its practical value depends on the accuracy of the cell lineages that can be generated. Here, we use computer simulations to estimate the performance of these approaches under different conditions. We incorporate empirical data on CRISPR-induced mutation frequencies in Drosophila. We show significant impacts from multiple biological and technical parameters - variable cell division rates, skewed mutational outcomes, target dropouts and different sequencing strategies. Our approach reveals the limitations of published CRISPR recorders, and indicates how future implementations can be optimised.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

A Regeneration Toolkit

The ability of animals to replace injured body parts has been a subject of fascination for centuries. The emerging importance of regenerative medicine has reinvigorated investigations of innate tissue regeneration, and the development of powerful genetic tools has fueled discoveries into how tissue regeneration occurs. Here, we present an overview of the armamentarium employed to probe regeneration in vertebrates, highlighting areas where further methodology advancement will deepen mechanistic findings.

Single cell transcriptomics: moving towards multi-omics

Single-cell multi-omics analysis helps characterize multiple layers of molecular features at a single-cell scale to provide insights into cellular processes and functions.

Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries

The immunology field has invested great efforts and ingenuity to characterize the various immune cell types and elucidate their functions. However, accumulating evidence indicates that current technologies and classification schemes are limited in their ability to account for the functional heterogeneity of immune processes. Single-cell genomics hold the potential to revolutionize the way we characterize complex immune cell assemblies and study their spatial organization, dynamics, clonal distribution, pathways, function, and crosstalks. In this Perspective, we consider recent and forthcoming technological and analytical advances in single-cell genomics and the potential impact of those advances on the future of immunology research and immunotherapy.

The dynamics of adaptive genetic diversity during the early stages of clonal evolution

The dynamics of genetic diversity in large clonally evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones and mathematical modelling of mutational dynamics to understand adaptive diversity changes during experimental evolution of Saccharomyces cerevisiae under nitrogen and carbon limitation. We find that, despite differences in beneficial mutational mechanisms and fitness effects, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in adaptive diversity follows, caused by highly fit double-mutant 'jackpot' clones that are fed from exponentially growing single mutants, a process closely related to the classic Luria-Delbrück experiment. The diversity crash is likely to be a general feature of asexual evolution with clonal interference; however, both its timing and magnitude are stochastic and depend on the population size, the distribution of beneficial fitness effects and patterns of epistasis.

Combined Numerical and Experimental Investigation of Localized Electroporation-Based Cell Transfection and Sampling

Localized electroporation has evolved as an effective technology for the delivery of foreign molecules into cells while preserving their viability. Consequently, this technique has potential applications in sampling the contents of live cells and the temporal assessment of cellular states at the single-cell level. Although there have been numerous experimental reports on localized electroporation-based delivery, a lack of a mechanistic understanding of the process hinders its implementation in sampling. In this work, we develop a multiphysics model that predicts the transport of molecules into and out of the cell during localized electroporation. Based on the model predictions, we optimize experimental parameters such as buffer conditions, electric field strength, cell confluency, and density of nanochannels in the substrate for successful delivery and sampling via localized electroporation. We also identify that cell membrane tension plays a crucial role in enhancing both the amount and the uniformity of molecular transport, particularly for macromolecules. We qualitatively validate the model predictions on a localized electroporation platform by delivering large molecules (bovine serum albumin and mCherry-encoding plasmid) and by sampling an exogeneous protein (tdTomato) in an engineered cell line.

Single-cell mapping of lineage and identity in direct reprogramming

Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present 'CellTagging', a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a 'dead-end' state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.

Illumination of neural development by in vivo clonal analysis

Single embryonic and adult neural stem cells (NSCs) are characterized by their self-renewal and differentiation potential. Lineage tracing via clonal analysis allows for specific labeling of a single NSC and tracking of its progeny throughout development. Over the past five decades, a plethora of clonal analysis methods have been developed in tandem with integration of chemical, genetic, imaging and sequencing techniques. Applications of these approaches have gained diverse insights into the heterogeneous behavior of NSCs, lineage relationships between cells, molecular regulation of fate specification and ontogeny of complex neural tissues. In this review, we summarize the history and methods of clonal analysis as well as highlight key findings revealed by single-cell lineage tracking of stem cells in developing and adult brains across different animal models.

Quantification of somatic mutation flow across individual cell division events by lineage sequencing

Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations.

Applications of CRISPR-Cas in Bioengineering, Biotechnology, and Translational Research

CRISPR technology is rapidly evolving, and the scope of CRISPR applications is constantly expanding. CRISPR was originally employed for genome editing. Its application was then extended to epigenome editing, karyotype engineering, chromatin imaging, transcriptome, and metabolic pathway engineering. Now, CRISPR technology is being harnessed for genetic circuits engineering, cell signaling sensing, cellular events recording, lineage information reconstruction, gene drive, DNA genotyping, miRNA quantification, in vivo cloning, site-directed mutagenesis, genomic diversification, and proteomic analysis in situ. It has also been implemented in the translational research of human diseases such as cancer immunotherapy, antiviral therapy, bacteriophage therapy, cancer diagnosis, pathogen screening, microbiota remodeling, stem-cell reprogramming, immunogenomic engineering, vaccine development, and antibody production. This review aims to summarize the key concepts of these CRISPR applications in order to capture the current state of play in this fast-moving field. The key mechanisms, strategies, and design principles for each technological advance are also highlighted.

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

DNA-based memory devices for recording cellular events

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.

Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT

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 single-cell 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.

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.

Acceleration of cancer science with genome editing and related technologies

Genome editing includes various edits of the genome, such as short insertions and deletions, substitutions, and chromosomal rearrangements including inversions, duplications, and translocations. These variations are based on single or multiple DNA double-strand break (DSB)-triggered in cellulo repair machineries. In addition to these "conventional" genome editing strategies, tools enabling customized, site-specific recognition of particular nucleic acid sequences have been coming into wider use; for example, single base editing without DSB introduction, epigenome editing with recruitment of epigenetic modifiers, transcriptome engineering using RNA editing systems, and in vitro detection of specific DNA and RNA sequences. In this review, we provide a quick overview of the current state of genome editing and related technologies that multilaterally contribute to cancer science.

Cellular barcoding: lineage tracing, screening and beyond

Cellular barcoding is a technique in which individual cells are labeled with unique nucleic acid sequences, termed barcodes, so that they can be tracked through space and time. Cellular barcoding can be used to track millions of cells in parallel, and thus is an efficient approach for investigating heterogeneous populations of cells. Over the past 25 years, cellular barcoding has been used for fate mapping, lineage tracing and high-throughput screening, and has led to important insights into developmental biology and gene function. Driven by plummeting sequencing costs and the power of synthetic biology, barcoding is now expanding beyond traditional applications and into diverse fields such as neuroanatomy and the recording of cellular activity. In this review, we discuss the fundamental principles of cellular barcoding, including the underlying mathematics, and its applications in both new and established fields.

Bartender: a fast and accurate clustering algorithm to count barcode reads

Motivation

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.

Results

In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.

Availability and implementation

Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.

Contact

sasha.levy@stonybrook.edu or song.wu@stonybrook.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Control of Lineage-Specific Gene Expression by Functionalized gRNA Barcodes

Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.

Rainbow-Seq: Combining Cell Lineage Tracing with Single-Cell RNA Sequencing in Preimplantation Embryos

We developed the Rainbow-seq technology to trace cell division history and reveal single-cell transcriptomes. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA sequencing (RNA-seq) experiment to simultaneously decode the lineage marker genes and read single-cell transcriptomes. We triggered lineage tracking in each blastomere at the 2-cell stage, observed microscopically inequivalent contributions of the progeny to the two embryonic poles at the blastocyst stage, and analyzed every single cell at either 4- or 8-cell stage with deep paired-end sequencing of full-length transcripts. Although lineage difference was not marked unequivocally at a single-gene level, it became clear when the transcriptome was analyzed as a whole. Moreover, several groups of novel transcript isoforms with embedded repeat sequences exhibited lineage difference, suggesting a possible link between DNA demethylation and cell fate decision. Rainbow-seq bridged a critical gap between division history and single-cell RNA-seq assays.

The continuously evolving CRISPR barcoding toolbox

Two articles recently described the development of CRISPR technologies that have the potential to fundamentally transform the barcoding and tracing of mammalian cells.