Whole-organism lineage tracing by combinatorial and cumulative genome editing

Inference of single-cell phylogenies from lineage tracing data using Cassiopeia

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia.

Inclusion of temporal information in single cell transcriptomics

Single cell transcriptomics has emerged as a powerful method for dissecting cell type diversity and for understanding mechanisms of cell fate decisions. However, inclusion of temporal information remains challenging, since each cell can be measured only once by sequencing analysis. Here, we discuss recent progress and current efforts towards inclusion of temporal information in single cell transcriptomics. Even from snapshot data, temporal dynamics can be computationally inferred via pseudo-temporal ordering of single cell transcriptomes. Temporal information can also come from analysis of intronic reads or from RNA metabolic labeling, which can provide additional evidence for pseudo-time trajectories and enable more fine-grained analysis of gene regulatory interactions. These approaches measure dynamics on short timescales of hours. Emerging methods for high-throughput lineage tracing now enable information storage over long timescales by using CRISPR/Cas9 to record information in the genome, which can later be read out by sequencing.

Genetically Engineering the Nervous System with CRISPR-Cas

The multitude of neuronal subtypes and extensive interconnectivity of the mammalian brain presents a substantial challenge to those seeking to decipher its functions. While the molecular mechanisms of several neuronal functions remain poorly characterized, advances in next-generation sequencing (NGS) and gene-editing technology have begun to close this gap. The clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein (CRISPR-Cas) system has emerged as a powerful genetic tool capable of manipulating the genome of essentially any organism and cell type. This technology has advanced our understanding of complex neurologic diseases by enabling the rapid generation of novel, disease-relevant in vitro and transgenic animal models. In this review, we discuss recent developments in the rapidly accelerating field of CRISPR-mediated genome engineering. We begin with an overview of the canonical function of the CRISPR platform, followed by a functional review of its many adaptations, with an emphasis on its applications for genetic interrogation of the normal and diseased nervous system. Additionally, we discuss limitations of the CRISPR editing system and suggest how future modifications to existing platforms may advance our understanding of the brain.

Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering

As DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biological engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnology and digital technology, a deficit in software and laboratory techniques diminishes the ability to make biotechnology more agile, reproducible, and transparent while, at the same time, limiting the security and safety of synthetic biology constructs. To partially address some of these problems, this paper presents an approach for physically linking engineered cells to their digital footprint-we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialized version control system for collaborative strain engineering via simple barcoding protocols.

Phenomics-Based Quantification of CRISPR-Induced Mosaicism in Zebrafish

Genetic mosaicism can manifest as spatially variable phenotypes that vary from site to site within an organism. Here, we use imaging-based phenomics to quantitate phenotypes at many sites within the axial skeleton of CRISPR-edited G0 zebrafish. Through characterization of loss-of-function cell clusters in the developing skeleton, we identify a distinctive size distribution shown to arise from clonal fragmentation and merger events. We quantitate the phenotypic mosaicism produced by somatic mutations of two genes, plod2 and bmp1a, implicated in human osteogenesis imperfecta. Comparison of somatic, CRISPR-generated G0 mutants to homozygous germline mutants reveals phenotypic convergence, suggesting that CRISPR screens of G0 animals can faithfully recapitulate the biology of inbred disease models. We describe statistical frameworks for phenomic analysis of spatial phenotypic variation present in somatic G0 mutants. In sum, this study defines an approach for decoding spatially variable phenotypes generated during CRISPR-based screens.

Glial cell ecology in zebrafish development and regeneration

Zebrafish have been found to be the premier model organism in biological and biomedical research, specifically offering many advantages in developmental biology and genetics. The zebrafish (Danio rerio) has the ability to regenerate its spinal cord after injury. However, the complete molecular and cellular mechanisms behind glial bridge formation in zebrafish remains unclear. In our review paper, we examine the extracellular and intracellular molecular signaling factors that control zebrafish glial cell bridging and glial cell development in the forebrain. The interplay between initiating and terminating molecular feedback cycles deserve future investigations during glial cell growth, movement, and differentiation.

CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution

Single-cell technologies are offering unparalleled insight into complex biology, revealing the behavior of rare cell populations that are masked in bulk population analyses. One current limitation of single-cell approaches is that lineage relationships are typically lost as a result of cell processing. We recently established a method, CellTagging, permitting the parallel capture of lineage information and cell identity via a combinatorial cell indexing approach. CellTagging integrates with high-throughput single-cell RNA sequencing, where sequential rounds of cell labeling enable the construction of multi-level lineage trees. Here, we provide a detailed protocol to (i) generate complex plasmid and lentivirus CellTag libraries for labeling of cells; (ii) sequentially CellTag cells over the course of a biological process; (iii) profile single-cell transcriptomes via high-throughput droplet-based platforms; and (iv) generate a CellTag expression matrix, followed by clone calling and lineage reconstruction. This lentiviral-labeling approach can be deployed in any organism or in vitro culture system that is amenable to viral transduction to simultaneously profile lineage and identity at single-cell resolution.

Viral approaches to study the mammalian brain: Lineage tracing, circuit dissection and therapeutic applications

Viral vectors are widely used to study the development, function and pathology of neural circuits in the mammalian brain. Their flexible payloads with customizable choices of tool genes allow versatile applications ranging from lineage tracing, circuit mapping and functional interrogation, to translational and therapeutic applications. Different applications have distinct technological requirements, therefore, often utilize different types of virus. This review introduces the most commonly used viruses for these applications and some recent advances in improving the resolution and throughput of lineage tracing, the efficacy and selectivity of circuit tracing and the specificity of cell type targeting.

Defining endogenous barcoding sites for CRISPR/Cas9-based cell lineage tracing in zebrafish

There is a growing interest in developing experimental methods for tracking the developmental cell lineages of a complex organism. The recently developed CRISPR/Cas9-based barcoding method is, although highly promising, difficult to scale up because it relies on exogenous barcoding sequences that are engineered into the genome. In this study, we characterized 78 high-quality endogenous sites in the zebrafish genome that can be used as CRISPR/Cas9-based barcoding sites. The 78 sites are all highly expressed in most of the cell types according to single-cell RNA sequencing (scRNA-seq) data. Hence, the barcoding information of the 78 endogenous sites is recovered by the available scRNA-seq platforms, enabling simultaneous characterization of cell type and cell lineage information.

Quantifying Hematopoietic Stem Cell Clonal Diversity by Selecting Informative Amplicon Barcodes

Hematopoietic stem cells (HSCs) are functionally and genetically diverse and this diversity decreases with age and disease. Numerous systems have been developed to quantify HSC diversity by genetic barcoding, but no framework has been established to empirically validate barcode sequences. Here we have developed an analytical framework, Selection of informative Amplicon Barcodes from Experimental Replicates (SABER), that identifies barcodes that are unique among a large set of experimental replicates. Amplicon barcodes were sequenced from the blood of 56 adult zebrafish divided into training and validation sets. Informative barcodes were identified and samples with a high fraction of informative barcodes were chosen by bootstrapping. There were 4.2 ± 1.8 barcoded HSC clones per sample in the training set and 3.5 ± 2.1 in the validation set (p = 0.3). SABER reproducibly quantifies functional HSCs and can accommodate a wide range of experimental group sizes. Future large-scale studies aiming to understand the mechanisms of HSC clonal evolution will benefit from this new approach to identifying informative amplicon barcodes.

A network approach to analyze neuronal lineage and layer innervation in the Drosophila optic lobes

The optic lobes of the fruit fly Drosophila melanogaster form a highly wired neural network composed of roughly 130.000 neurons of more than 80 different types. How neuronal diversity arises from very few cell progenitors is a central question in developmental neurobiology. We use the optic lobe of the fruit fly as a paradigm to understand how neuroblasts, the neural stem cells, generate multiple neuron types. Although the development of the fly brain has been the subject of extensive research, very little is known about the lineage relationships of the cell types forming the adult optic lobes. Here we perform a large-scale lineage bioinformatics analysis using the graph theory. We generated a large collection of cell clones that genetically label the progeny of neuroblasts and built a database to draw graphs showing the lineage relationships between cell types. By establishing biological criteria that measures the strength of the neuronal relationships and applying community detection tools we have identified eight clusters of neurons. Each cluster contains different cell types that we pose are the product of eight distinct classes of neuroblasts. Three of these clusters match the available lineage data, supporting the predictive value of the analysis. Finally, we show that the neuronal progeny of a neuroblast do not have preferential innervation patterns, but instead become part of different layers and neuropils. Here we establish a new methodology that helps understanding the logic of Drosophila brain development and can be applied to the more complex vertebrate brains.

Single-Nucleotide-Resolution Computing and Memory in Living Cells

The ability to process and store information in living cells is essential for developing next-generation therapeutics and studying biology in situ. However, existing strategies have limited recording capacity and are challenging to scale. To overcome these limitations, we developed DOMINO, a robust and scalable platform for encoding logic and memory in bacterial and eukaryotic cells. Using an efficient single-nucleotide-resolution Read-Write head for DNA manipulation, DOMINO converts the living cells' DNA into an addressable, readable, and writable medium for computation and storage. DOMINO operators enable analog and digital molecular recording for long-term monitoring of signaling dynamics and cellular events. Furthermore, multiple operators can be layered and interconnected to encode order-independent, sequential, and temporal logic, allowing recording and control over the combination, order, and timing of molecular events in cells. We envision that DOMINO will lay the foundation for building robust and sophisticated computation-and-memory gene circuits for numerous biotechnological and biomedical applications.

Using single-cell RNA sequencing to unravel cell lineage relationships in the respiratory tract

The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, ‘hybrid’ mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.

CRISPR-mediated modeling and functional validation of candidate tumor suppressor genes in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer that remains among the most lethal of solid tumor malignancies. Recent genomic sequencing studies have identified many recurrently mutated genes in human SCLC tumors. However, the functional roles of most of these genes remain to be validated. Here, we have adapted the CRISPR-Cas9 system to a well-established murine model of SCLC to rapidly model loss-of-function mutations in candidate genes identified from SCLC sequencing studies. We show that loss of the gene p107 significantly accelerates tumor progression. Notably, compared with loss of the closely related gene p130, loss of p107 results in fewer but larger tumors as well as earlier metastatic spread. In addition, we observe differences in proliferation and apoptosis as well as altered distribution of initiated tumors in the lung, resulting from loss of p107 or p130 Collectively, these data demonstrate the feasibility of using the CRISPR-Cas9 system to model loss of candidate tumor suppressor genes in SCLC, and we anticipate that this approach will facilitate efforts to investigate mechanisms driving tumor progression in this deadly disease.

In vivo single-cell lineage tracing in zebrafish using high-resolution infrared laser-mediated gene induction microscopy

Heterogeneity broadly exists in various cell types both during development and at homeostasis. Investigating heterogeneity is crucial for comprehensively understanding the complexity of ontogeny, dynamics, and function of specific cell types. Traditional bulk-labeling techniques are incompetent to dissect heterogeneity within cell population, while the new single-cell lineage tracing methodologies invented in the last decade can hardly achieve high-fidelity single-cell labeling and long-term in-vivo observation simultaneously. In this work, we developed a high-precision infrared laser-evoked gene operator heat-shock system, which uses laser-induced CreERT2 combined with loxP-DsRedx-loxP-GFP reporter to achieve precise single-cell labeling and tracing. In vivo study indicated that this system can precisely label single cell in brain, muscle and hematopoietic system in zebrafish embryo. Using this system, we traced the hematopoietic potential of hemogenic endothelium (HE) in the posterior blood island (PBI) of zebrafish embryo and found that HEs in the PBI are heterogeneous, which contains at least myeloid unipotent and myeloid-lymphoid bipotent subtypes.

In situ readout of DNA barcodes and single base edits facilitated by in vitro transcription

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-base-pair) DNA barcodes. In this system, called Zombie, phage RNA polymerases transcribe engineered barcodes in fixed cells. The resulting 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 dense combinatorial barcode libraries and single-base mutations produced by CRISPR base editors without requiring barcode expression in 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.

A community effort to create standards for evaluating tumor subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Advancing CRISPR-Based Programmable Platforms beyond Genome Editing in Mammalian Cells

Human diseases are caused by dysregulation of cellular biological programs that are encoded in DNA. Unveiling the endogenous programs and encoding new programs into the genome are key to creating novel diagnostic and therapeutic strategies. CRISPR/Cas9, originally identified in bacteria, has revolutionized genome editing in mammalian cells. Recent advances in CRISPR technologies have provided new programmable platforms for modifying cell function and behavior. CRISPR-based transcriptional regulators and modified gRNAs have enabled multiplexed regulation and visualization of genome dynamics with spatiotemporal precision. Using these toolkits, genome-scale screening platforms can identify key genetic elements or combinations thereof that modulate phenotypes in mammalian cells. In addition, imaging platforms for multiplexed genomic labeling have been created to study the conformation and dynamics of chromatin in living cells, which are essential for genome function. Furthermore, CRISPR-based computation and memory platforms have been built in living mammalian cells by using DNA as a data processing and storage medium to regulate and monitor cellular behaviors. The conditional regulation of CRISPR-based parts has enabled the design of complex multilayered biological programs. CRISPR-based memory platforms can continuously record biological events as mutations in defined DNA loci. By making use of base editors, CRISPR-based computation and memory platforms have been interconnected to perform logic operations based on past events. These technologies open up new avenues for understanding biological phenomena and designing mammalian cells as living machines for biomedical applications.

Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair

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.

Clonal dynamics in chronic lymphocytic leukemia

Chronic lymphocytic leukemia has a highly variable disease course across patients, thought to be driven by the vast inter- and intrapatient molecular heterogeneity described in several large-scale DNA-sequencing studies conducted over the past decade. Although the last 5 years have seen a dramatic shift in the therapeutic landscape for chronic lymphocytic leukemia, including the regulatory approval of several potent targeted agents (ie, idelalisib, ibrutinib, venetoclax), the vast majority of patients still inevitably experience disease recurrence or persistence. Recent genome-wide sequencing approaches have helped to identify subclonal populations within tumors that demonstrate a broad spectrum of somatic mutations, diverse levels of response to therapy, patterns of repopulation, and growth kinetics. Understanding the impact of genetic, epigenetic, and transcriptomic features on clonal growth dynamics and drug response will be an important step toward the selection and timing of therapy.

Macrophages in Zebrafish Models of Liver Diseases

Hepatic macrophages are key components of the liver immunity and consist of two main populations. Liver resident macrophages, known as Kupffer cells in mammals, are crucial for maintaining normal liver homeostasis. Upon injury, they become activated to release proinflammatory cytokines and chemokines and recruit a large population of inflammatory monocyte-derived macrophages to the liver. During the progression of liver diseases, macrophages are highly plastic and have opposing functions depending on the signaling cues that they receive from the microenvironment. A comprehensive understanding of liver macrophages is essential for developing therapeutic interventions that target these cells in acute and chronic liver diseases. Mouse studies have provided the bulk of our current knowledge of liver macrophages. The emergence of various liver disease models and availability of transgenic tools to visualize and manipulate macrophages have made the teleost zebrafish (Danio rerio) an attractive new vertebrate model to study liver macrophages. In this review, we summarize the origin and behaviors of macrophages in healthy and injured livers in zebrafish. We highlight the roles of macrophages in zebrafish models of alcoholic and non-alcoholic liver diseases, hepatocellular carcinoma, and liver regeneration, and how they compare with the roles that have been described in mammals. We also discuss the advantages and challenges of using zebrafish to study liver macrophages.

High-throughput dense reconstruction of cell lineages

The first meeting exclusively dedicated to the 'High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.

Genetic and epigenetic control of retinal development in zebrafish

The vertebrate retina is a complex structure composed of seven cell types (six neuron and one glia), and all of which originate from a seemingly homogeneous population of proliferative multipotent retinal progenitor cells (RPCs) that exit the cell cycle and differentiate in a spatio-temporally regulated and stereotyped fashion. This neurogenesis process requires intricate genetic regulation involving a combination of cell intrinsic transcription factors and extrinsic signaling molecules, and many critical factors have been identified that influence the timing and composition of the developing retina. Adding complexity to the process, over the past decade, a variety of epigenetic regulatory mechanisms have been shown to influence neurogenesis, and these include changes in histone modifications and the chromatin landscape and changes in DNA methylation and hydroxymethylation patterns. This review summarizes recent findings in the genetic and epigenetic regulation of retinal development, with an emphasis on the zebrafish model system, and it outlines future areas of investigation that will continue to push the field forward into the epigenomics era.

A single-cell transcriptome atlas for zebrafish development

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

Cell lineage inference from SNP and scRNA-Seq data

Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here, we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types.

Somatic mutations - Evolution within the individual

With the rapid advancement of sequencing technologies over the last two decades, it is becoming feasible to detect rare variants from somatic tissue samples. Studying such somatic mutations can provide deep insights into various senescence-related diseases, including cancer, inflammation, and sporadic psychiatric disorders. While it is still a difficult task to identify true somatic mutations, relentless efforts to combine experimental and computational methods have made it possible to obtain reliable data. Furthermore, state-of-the-art machine learning approaches have drastically improved the efficiency and sensitivity of these methods. Meanwhile, we can regard somatic mutations as a counterpart of germline mutations, and it is possible to apply well-formulated mathematical frameworks developed for population genetics and molecular evolution to analyze this 'somatic evolution'. For example, retrospective cell lineage tracing is a promising technique to elucidate the mechanism of pre-diseases using single-cell RNA-sequencing (scRNA-seq) data.

Investigating Tumor Heterogeneity in Mouse Models

Cancer arises from a single cell through a series of acquired mutations and epigenetic alterations. Tumors gradually develop into a complex tissue comprised of phenotypically heterogeneous cancer cell populations, as well as noncancer cells that make up the tumor microenvironment. The phenotype, or state, of each cancer and stromal cell is influenced by a plethora of cell-intrinsic and cell-extrinsic factors. The diversity of these cellular states promotes tumor progression, enables metastasis, and poses a challenge for effective cancer treatments. Thus, the identification of strategies for the therapeutic manipulation of tumor heterogeneity would have significant clinical implications. A major barrier in the field is the difficulty in functionally investigating heterogeneity in tumors in cancer patients. Here we review how mouse models of human cancer can be leveraged to interrogate tumor heterogeneity and to help design better therapeutic strategies.

Unravelling cellular relationships during development and regeneration using genetic lineage tracing

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate processes such as embryonic development and stem cell differentiation. In classical lineage tracing experiments, cells are fluorescently labelled to allow identification by microscopy of a limited number of cell clones. To track a larger number of clones in complex tissues, fluorescent proteins are now replaced by heritable DNA barcodes that are read using next-generation sequencing. In prospective lineage tracing, unique DNA barcodes are introduced into single cells through genetic manipulation (using, for example, Cre-mediated recombination or CRISPR-Cas9-mediated editing) and tracked over time. Alternatively, in retrospective lineage tracing, naturally occurring somatic mutations can be used as endogenous DNA barcodes. Finally, single-cell mRNA-sequencing datasets that capture different cell states within a developmental or differentiation trajectory can be used to recapitulate lineages. In this Review, we discuss methods for prospective or retrospective lineage tracing and demonstrate how trajectory reconstruction algorithms can be applied to single-cell mRNA-sequencing datasets to infer developmental or differentiation tracks. We discuss how these approaches are used to understand cell fate during embryogenesis, cell differentiation and tissue regeneration.

Heterostructures Based on 2D Materials: A Versatile Platform for Efficient Catalysis

The unique structural and electronic properties of 2D materials, including the metal and metal-free ones, have prompted intense exploration in the search for new catalysts. The construction of different heterostructures based on 2D materials offers great opportunities for boosting the catalytic activity in electo(photo)chemical reactions. Particularly, the merits resulting from the synergism of the constituent components and the fascinating properties at the interface are tremendously interesting. This scenario has now become the state-of-the-art point in the development of active catalysts for assisting energy conversion reactions including water splitting and CO₂ reduction. Here, starting from the theoretical background of the fundamental concepts, the progressive developments in the design and applications of heterostructures based on 2D materials are traced. Furthermore, a personal perspective on the exploration of 2D heterostructures for further potential application in catalysis is offered.

Heterogeneous beta-catenin activation is sufficient to cause hepatocellular carcinoma in zebrafish

Up to 41% of hepatocellular carcinomas (HCCs) result from activating mutations in the CTNNB1 gene encoding β-catenin. HCC-associated CTNNB1 mutations stabilize the β-catenin protein, leading to nuclear and/or cytoplasmic localization of β-catenin and downstream activation of Wnt target genes. In patient HCC samples, β-catenin nuclear and cytoplasmic localization are typically patchy, even among HCC with highly active CTNNB1 mutations. The functional and clinical relevance of this heterogeneity in β-catenin activation are not well understood. To define mechanisms of β-catenin-driven HCC initiation, we generated a Cre-lox system that enabled switching on activated β-catenin in (1) a small number of hepatocytes in early development; or (2) the majority of hepatocytes in later development or adulthood. We discovered that switching on activated β-catenin in a subset of larval hepatocytes was sufficient to drive HCC initiation. To determine the role of Wnt/β-catenin signaling heterogeneity later in hepatocarcinogenesis, we performed RNA-seq analysis of zebrafish β-catenin-driven HCC. At the single-cell level, 2.9% to 15.2% of hepatocytes from zebrafish β-catenin-driven HCC expressed two or more of the Wnt target genes axin2, mtor, glula, myca and wif1, indicating focal activation of Wnt signaling in established tumors. Thus, heterogeneous β-catenin activation drives HCC initiation and persists throughout hepatocarcinogenesis.

Advances in quantitative biology methods for studying replicative aging in Saccharomyces cerevisiae

Aging is a complex, yet pervasive phenomenon in biology. As human cells steadily succumb to the deteriorating effects of aging, so too comes a host of age-related ailments such as neurodegenerative disorders, cardiovascular disease and cancer. Therefore, elucidation of the molecular networks that drive aging is of paramount importance to human health. Progress toward this goal has been aided by studies from simple model organisms such as Saccharomyces cerevisiae. While work in budding yeast has already revealed much about the basic biology of aging as well as a number of evolutionarily conserved pathways involved in this process, recent technological advances are poised to greatly expand our knowledge of aging in this simple eukaryote. Here, we review the latest developments in microfluidics, single-cell analysis and high-throughput technologies for studying single-cell replicative aging in S. cerevisiae. We detail the challenges each of these methods addresses as well as the unique insights into aging that each has provided. We conclude with a discussion of potential future applications of these techniques as well as the importance of single-cell dynamics and quantitative biology approaches for understanding cell aging.

Donor-to-Donor Heterogeneity in the Clonal Dynamics of Transplanted Human Cord Blood Stem Cells in Murine Xenografts

Umbilical cord blood (UCB) provides an alternative source of hematopoietic stem cells (HSCs) for allogeneic transplantation. Administration of sufficient donor HSCs is critical to restore recipient hematopoiesis and to maintain long-term polyclonal blood formation. However, due to lack of unique markers, the frequency of HSCs among UCB CD34⁺ cells is the subject of ongoing debate, urging for reproducible strategies for their counting. Here, we used cellular barcoding to determine the frequency and clonal dynamics of human UCB HSCs and to determine how data analysis methods affect these parameters. We transplanted lentivirally barcoded CD34⁺ cells from 20 UCB donors into Nod/Scid/IL2Ry^-/- (NSG) mice (n = 30). Twelve recipients (of 8 UCB donors) engrafted with >1% GFP⁺ cells, allowing for clonal analysis by multiplexed barcode deep sequencing. Using multiple definitions of clonal diversity and strategies for data filtering, we demonstrate that differences in data analysis can change clonal counts by several orders of magnitude and propose methods to improve their consistency. Using these methods, we show that the frequency of NSG-repopulating cells was low (median ∼1 HSC/10⁴ CD34⁺ UCB cells) and could vary up to 10-fold between donors. Clonal patterns in blood became increasingly consistent over time, likely reflecting initial output of transient progenitors, followed by long-term HSCs with stable hierarchies. The majority of long-term clones displayed multilineage output, yet clones with lymphoid- or myeloid-biased output were also observed. Altogether, this study uncovers substantial interdonor and analysis-induced variability in the frequency of UCB CD34⁺ clones that contribute to post-transplant hematopoiesis. As clone tracing is increasingly relevant, we urge for universal and transparent methods to count HSC clones during normal aging and upon transplantation.

A tale of two tracts: history, current advances, and future directions of research on sexual differentiation of reproductive tracts†

Alfred Jost's work in the 1940s laid the foundation of the current paradigm of sexual differentiation of reproductive tracts, which contends that testicular hormones drive the male patterning of reproductive tract system whereas the female phenotype arises by default. Once established, the sex-specific reproductive tracts undergo morphogenesis, giving rise to anatomically and functionally distinct tubular organs along the rostral-caudal axis. Impairment of sexual differentiation of reproductive tracts by genetic alteration and environmental exposure are the main causes of disorders of sex development, and infertility at adulthood. This review covers past and present work on sexual differentiation and morphogenesis of reproductive tracts, associated human disorders, and emerging technologies that have made impacts or could radically expand our knowledge in this field.

Exploration of Plant-Microbe Interactions for Sustainable Agriculture in CRISPR Era

Plants and microbes are co-evolved and interact with each other in nature. Plant-associated microbes, often referred to as plant microbiota, are an integral part of plant life. Depending on the health effects on hosts, plant-microbe (PM) interactions are either beneficial or harmful. The role of microbiota in plant growth promotion (PGP) and protection against various stresses is well known. Recently, our knowledge of community composition of plant microbiome and significant driving factors have significantly improved. So, the use of plant microbiome is a reliable approach for a next green revolution and to meet the global food demand in sustainable and eco-friendly agriculture. An application of the multifaceted PM interactions needs the use of novel tools to know critical genetic and molecular aspects. Recently discovered clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-mediated genome editing (GE) tools are of great interest to explore PM interactions. A systematic understanding of the PM interactions will enable the application of GE tools to enhance the capacity of microbes or plants for agronomic trait improvement. This review focuses on applying GE techniques in plants or associated microbiota for discovering the fundamentals of the PM interactions, disease resistance, PGP activity, and future implications in agriculture.

The next generation of CRISPR-Cas technologies and applications

The prokaryote-derived CRISPR-Cas genome editing systems have transformed our ability to manipulate, detect, image and annotate specific DNA and RNA sequences in living cells of diverse species. The ease of use and robustness of this technology have revolutionized genome editing for research ranging from fundamental science to translational medicine. Initial successes have inspired efforts to discover new systems for targeting and manipulating nucleic acids, including those from Cas9, Cas12, Cascade and Cas13 orthologues. Genome editing by CRISPR-Cas can utilize non-homologous end joining and homology-directed repair for DNA repair, as well as single-base editing enzymes. In addition to targeting DNA, CRISPR-Cas-based RNA-targeting tools are being developed for research, medicine and diagnostics. Nuclease-inactive and RNA-targeting Cas proteins have been fused to a plethora of effector proteins to regulate gene expression, epigenetic modifications and chromatin interactions. Collectively, the new advances are considerably improving our understanding of biological processes and are propelling CRISPR-Cas-based tools towards clinical use in gene and cell therapies.

Microfluidics as an Emerging Precision Tool in Developmental Biology

Microfluidics has become a precision tool in modern biology. It enables omics data to be obtained from individual cells, as compared to averaged signals from cell populations, and it allows manipulation of biological specimens in entirely new ways. Cells and organisms can be perturbed at extraordinary spatiotemporal resolution, revealing mechanistic insights that would otherwise remain hidden. In this perspective article, we discuss the current and future impact of microfluidic technology in the field of developmental biology. In addition, we provide detailed information on how to start using this technology even without prior experience.

Integrative single-cell analysis

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

Brown Adipose Tissue Development and Metabolism

Brown adipose tissue is well known to be a thermoregulatory organ particularly important in small rodents and human infants, but it was only recently that its existence and significance to metabolic fitness in adult humans have been widely realized. The ability of active brown fat to expend high amounts of energy has raised interest in stimulating thermogenesis therapeutically to treat metabolic diseases related to obesity and type 2 diabetes. In parallel, there has been a surge of research aimed at understanding the biology of rodent and human brown fat development, its remarkable metabolic properties, and the phenomenon of white fat browning, in which white adipocytes can be converted into brown like adipocytes with similar thermogenic properties. Here, we review the current understanding of the developmental and metabolic pathways involved in forming thermogenic adipocytes, and highlight some of the many unknown functions of brown fat that make its study a rich and exciting area for future research.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Towards quantitative and multiplexed in vivo functional cancer genomics

Large-scale sequencing of human tumours has uncovered a vast array of genomic alterations. Genetically engineered mouse models recapitulate many features of human cancer and have been instrumental in assigning biological meaning to specific cancer-associated alterations. However, their time, cost and labour-intensive nature limits their broad utility; thus, the functional importance of the majority of genomic aberrations in cancer remains unknown. Recent advances have accelerated the functional interrogation of cancer-associated alterations within in vivo models. Specifically, the past few years have seen the emergence of CRISPR-Cas9-based strategies to rapidly generate increasingly complex somatic alterations and the development of multiplexed and quantitative approaches to ascertain gene function in vivo.

Efficient in situ barcode sequencing using padlock probe-based BaristaSeq

Cellular DNA/RNA tags (barcodes) allow for multiplexed cell lineage tracing and neuronal projection mapping with cellular resolution. Conventional approaches to reading out cellular barcodes trade off spatial resolution with throughput. Bulk sequencing achieves high throughput but sacrifices spatial resolution, whereas manual cell picking has low throughput. In situ sequencing could potentially achieve both high spatial resolution and high throughput, but current in situ sequencing techniques are inefficient at reading out cellular barcodes. Here we describe BaristaSeq, an optimization of a targeted, padlock probe-based technique for in situ barcode sequencing compatible with Illumina sequencing chemistry. BaristaSeq results in a five-fold increase in amplification efficiency, with a sequencing accuracy of at least 97%. BaristaSeq could be used for barcode-assisted lineage tracing, and to map long-range neuronal projections.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing

The CRISPR-Cas system is a powerful genome editing tool that functions in a diverse array of organisms and cell types. The technology was initially developed to induce targeted mutations in DNA, but CRISPR-Cas has now been adapted to target nucleic acids for a range of purposes. CHOPCHOP is a web tool for identifying CRISPR-Cas single guide RNA (sgRNA) targets. In this major update of CHOPCHOP, we expand our toolbox beyond knockouts. We introduce functionality for targeting RNA with Cas13, which includes support for alternative transcript isoforms and RNA accessibility predictions. We incorporate new DNA targeting modes, including CRISPR activation/repression, targeted enrichment of loci for long-read sequencing, and prediction of Cas9 repair outcomes. Finally, we expand our results page visualization to reveal alternative isoforms and downstream ATG sites, which will aid users in avoiding the expression of truncated proteins. The CHOPCHOP web tool now supports over 200 genomes and we have released a command-line script for running larger jobs and handling unsupported genomes. CHOPCHOP v3 can be found at https://chopchop.cbu.uib.no.

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.