Whole-organism lineage tracing by combinatorial and cumulative genome editing

An Optimized Zebrafish Nursery Feeding Regimen Improves Growth Rates and Labor Costs

Setting nutritional standards for larval zebrafish (Danio rerio) that maximize growth, survival, and reproductive success is challenging. We evaluated the effects of different feeding regimens on larval zebrafish by comparing Gemma Micro 75 pelleted diet and live-type L rotifers (Brachionus plicatilis) in 3 feeding regimens starting at 9 days postfertilization (dpf): bolus feeding of live diet (BL), continuous feeding of live diet (CL), and pelleted diet (PD). Animals in the PD and CL groups were longer than the BL group at 4-5 weeks postfertilization. The PD group was also greater in body depth than both live diet groups. There was no significant difference in weight between the groups. There were also no significant differences in fecundity or sex ratios indicating that all feeding methods successfully promote growth of a useful breeding stock of fish. In addition, we quantified the equipment, consumable, and labor costs associated with these methods, and found that the PD regimen was superior to both live diet regimens. These data suggest that providing a high nutrient-density pelleted diet to larval and juvenile zebrafish is an effective means to increase early growth and to decrease cost and labor associated with nursery care.

Applications of high-resolution clone tracking technologies in cancer

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and non-genetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of non-genetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

Lrp5 Mutant and Crispant Zebrafish Faithfully Model Human Osteoporosis, Establishing the Zebrafish as a Platform for CRISPR-Based Functional Screening of Osteoporosis Candidate Genes

Genomewide association studies (GWAS) have improved our understanding of the genetic architecture of common complex diseases such as osteoporosis. Nevertheless, to attribute functional skeletal contributions of candidate genes to osteoporosis-related traits, there is a need for efficient and cost-effective in vivo functional testing. This can be achieved through CRISPR-based reverse genetic screens, where phenotyping is traditionally performed in stable germline knockout (KO) mutants. Recently it was shown that first-generation (F0) mosaic mutant zebrafish (so-called crispants) recapitulate the phenotype of germline KOs. To demonstrate feasibility of functional validation of osteoporosis candidate genes through crispant screening, we compared a crispant to a stable KO zebrafish model for the lrp5 gene. In humans, recessive loss-of-function mutations in LRP5, a co-receptor in the Wnt signaling pathway, cause osteoporosis-pseudoglioma syndrome. In addition, several GWAS studies identified LRP5 as a major risk locus for osteoporosis-related phenotypes. In this study, we showed that early stage lrp5 KO larvae display decreased notochord mineralization and malformations of the head cartilage. Quantitative micro-computed tomography (micro-CT) scanning and mass-spectrometry element analysis of the adult skeleton revealed decreased vertebral bone volume and bone mineralization, hallmark features of osteoporosis. Furthermore, regenerating fin tissue displayed reduced Wnt signaling activity in lrp5 KO adults. We next compared lrp5 mutants with crispants. Next-generation sequencing analysis of adult crispant tissue revealed a mean out-of-frame mutation rate of 76%, resulting in strongly reduced levels of Lrp5 protein. These crispants generally showed a milder but nonetheless highly comparable skeletal phenotype and a similarly reduced Wnt pathway response compared with lrp5 KO mutants. In conclusion, we show through faithful modeling of LRP5-related primary osteoporosis that crispant screening in zebrafish is a promising approach for rapid functional screening of osteoporosis candidate genes. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution

Barcoding technology has greatly improved the throughput of cells and genes detected in single-cell RNA sequencing (scRNA-seq) studies. Recently, increasing studies have paid more attention to the use of this technology to increase the throughput of samples, as it has greatly reduced the processing time, technical batch effects, and library preparation costs, and lowered the per-sample cost. In this review, the various DNA-based barcoding methods for sample multiplexing are focused on, specifically, on the four major barcoding strategies. A detailed comparison of the barcoding methods is also presented, focusing on aspects such as sample/cell throughput and gene detection, and guidelines for choosing the most appropriate barcoding technique according to the personalized requirements are developed. Finally, the critical applications of sample multiplexing and technical challenges in combinatorial labeling, barcoding in vivo, and multimodal tagging at the spatially resolved resolution, as well as, the future prospects of multiplexed scRNA-seq, for example, prioritizing and predicting the severity of coronavirus disease 2019 (COVID-19) in patients of different gender and age are highlighted.

Extensive phylogenies of human development inferred from somatic mutations

Starting from the zygote, all cells in the human body continuously acquire mutations. Mutations shared between different cells imply a common progenitor and are thus naturally occurring markers for lineage tracing1,2. Here we reconstruct extensive phylogenies of normal tissues from three adult individuals using whole-genome sequencing of 511 laser capture microdissections. Reconstructed embryonic progenitors in the same generation of a phylogeny often contribute to different extents to the adult body. The degree of this asymmetry varies between individuals, with ratios between the two reconstructed daughter cells of the zygote ranging from 60:40 to 93:7. Asymmetries pervade subsequent generations and can differ between tissues in the same individual. The phylogenies resolve the spatial embryonic patterning of tissues, revealing contiguous patches of, on average, 301 crypts in the adult colonic epithelium derived from a most recent embryonic cell and also a spatial effect in brain development. Using data from ten additional men, we investigated the developmental split between soma and germline, with results suggesting an extraembryonic contribution to primordial germ cells. This research demonstrates that, despite reaching the same ultimate tissue patterns, early bottlenecks and lineage commitments lead to substantial variation in embryonic patterns both within and between individuals.

Clonal dynamics in early human embryogenesis inferred from somatic mutation

Cellular dynamics and fate decision in early human embryogenesis remain largely unknown owing to the challenges of performing studies in human embryos¹. Here, we explored whole-genomes of 334 single-cell colonies and targeted deep sequences of 379 bulk tissues obtained from various anatomical locations of seven recently deceased adult human donors. Using somatic mutations as an intrinsic barcode, we reconstructed early cellular phylogenies that demonstrate (1) an endogenous mutational rate that is higher in the first cell division but decreases to approximately one per cell per cell division later in life; (2) universal unequal contribution of early cells to embryo proper, resulting from early cellular bottlenecks that stochastically set aside epiblast cells within the embryo; (3) examples of varying degrees of early clonal imbalances between tissues on the left and right sides of the body, different germ layers and specific anatomical parts and organs; (4) emergence of a few ancestral cells that will substantially contribute to adult cell pools in blood and liver; and (5) presence of mitochondrial DNA heteroplasmy in the fertilized egg. Our approach also provides insights into the age-related mutational processes and loss of sex chromosomes in normal somatic cells. In sum, this study provides a foundation for future studies to complete cellular phylogenies in human embryogenesis.

A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing

Lineage reconstruction is central to understanding tissue development and maintenance. To overcome the limitations of current techniques that typically reconstruct clonal trees using genetically encoded reporters, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single-cell-division resolution by using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell pre-implantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. In addition, we developed scH&G-seq to sequence both 5hmC and genomic DNA from the same cell. Given that genomic DNA sequencing yields information on both copy number variations and single-nucleotide polymorphisms, when combined with scPECLR it enables more accurate lineage reconstruction of larger trees. Finally, we show that scPECLR can also be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the "immortal strand" hypothesis. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual-cell-division resolution.

Unraveling Tumor Heterogeneity by Using DNA Barcoding Technologies to Develop Personalized Treatment Strategies in Advanced-Stage PDAC

Tumor heterogeneity is a hallmark of many solid tumors, including pancreatic ductal adenocarcinoma (PDAC), and an inherent consequence of the clonal evolution of cancers. As such, it is considered the underlying concept of many characteristics of the disease, including the ability to metastasize, adapt to different microenvironments, and to develop therapy resistance. Undoubtedly, the high mortality of PDAC can be attributed to a high extent to these properties. Despite its apparent importance, studying tumor heterogeneity has been a challenging task, mainly due to its complexity and lack of appropriate methods. However, in recent years molecular DNA barcoding has emerged as a sophisticated tool that allows mapping of individual cells or subpopulations in a cell pool to study heterogeneity and thus devise new personalized treatment strategies. In this review, we provide an overview of genetic and non-genetic inter- and intra-tumor heterogeneity and its impact on (personalized) treatment strategies in PDAC and address how DNA barcoding technologies work and can be applied to study this clinically highly relevant question.

Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.

LineageOT is a unified framework for lineage tracing and trajectory inference

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. Measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. Here, we present LineageOT, a unified framework for lineage tracing and trajectory inference. Specifically, we leverage mathematical tools from graphical models and optimal transport to reconstruct developmental trajectories from time courses with snapshots of both cell states and lineages. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way could enable accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.

Lineage tracing reveals metastatic dynamics

Combining single-cell lineage tracing with RNA sequencing has provided unprecedented opportunities to prospectively explore metastatic dynamics in vivo. In this issue of Cancer Cell, Simeonov et al. developed the macsGESTALT lineage recording system to reveal that hybrid EMT states and S100 expression are associated with elevated metastatic abilities in a pancreatic cancer model.

Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states

The underpinnings of cancer metastasis remain poorly understood, in part due to a lack of tools for probing their emergence at high resolution. Here we present macsGESTALT, an inducible CRISPR-Cas9-based lineage recorder with highly efficient single-cell capture of both transcriptional and phylogenetic information. Applying macsGESTALT to a mouse model of metastatic pancreatic cancer, we recover ∼380,000 CRISPR target sites and reconstruct dissemination of ∼28,000 single cells across multiple metastatic sites. We find that cells occupy a continuum of epithelial-to-mesenchymal transition (EMT) states. Metastatic potential peaks in rare, late-hybrid EMT states, which are aggressively selected from a predominately epithelial ancestral pool. The gene signatures of these late-hybrid EMT states are predictive of reduced survival in both human pancreatic and lung cancer patients, highlighting their relevance to clinical disease progression. Finally, we observe evidence for in vivo propagation of S100 family gene expression across clonally distinct metastatic subpopulations.

John Sulston (1942-2018): a personal perspective

John Sulston changed the way we do science, not once, but three times - initially with the complete cell lineage of the nematode Caenorhabditis elegans, next with completion of the genome sequences of the worm and human genomes and finally with his strong and active advocacy for open data sharing. His contributions were widely recognized and in 2002 he received the Nobel Prize in Physiology and Medicine.

Retrospective cell lineage reconstruction in humans by using short tandem repeats

Cell lineage analysis aims to uncover the developmental history of an organism back to its cell of origin. Recently, novel in vivo methods utilizing genome editing enabled important insights into the cell lineages of animals. In contrast, human cell lineage remains restricted to retrospective approaches, which still lack resolution and cost-efficient solutions. Here, we demonstrate a scalable platform based on short tandem repeats targeted by duplex molecular inversion probes. With this human cell lineage tracing method, we accurately reproduced a known lineage of DU145 cells and reconstructed lineages of healthy and metastatic single cells from a melanoma patient who matched the anatomical reference while adding further refinements. This platform allowed us to faithfully recapitulate lineages of developmental tissue formation in healthy cells. In summary, our lineage discovery platform can profile informative somatic mutations efficiently and provides solid lineage reconstructions even in challenging low-mutation-rate healthy single cells.

CeLaVi: an interactive cell lineage visualization tool

Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualizing, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualization tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualizing the spatial distribution, identities and properties of cells. CeLaVi’s principal functions include the ability to explore and manipulate the cell lineage tree; to visualise the spatial distribution of cell clones at different depths of the tree; to colour cells in the 3D viewer based on lineage relationships; to visualise various cell qualities on the 3D viewer (e.g. gene expression, cell type) and to annotate selected cells/clones. All these capabilities are demonstrated with four different example data sets. CeLaVi is available at http://www.celavi.pro.

Scalable control of developmental timetables by epigenetic switching networks

During development, progenitor cells follow timetables for differentiation that span many cell generations. These developmental timetables are robustly encoded by the embryo, yet scalably adjustable by evolution, facilitating variation in organism size and form. Epigenetic switches, involving rate-limiting activation steps at regulatory gene loci, control gene activation timing in diverse contexts, and could profoundly impact the dynamics of gene regulatory networks controlling developmental lineage specification. Here, we develop a mathematical framework to model regulatory networks with genes controlled by epigenetic switches. Using this framework, we show that such epigenetic switching networks uphold developmental timetables that robustly span many cell generations, and enable the generation of differentiated cells in precisely defined numbers and fractions. Changes to epigenetic switching networks can readily alter the timing of developmental events within a timetable, or alter the overall speed at which timetables unfold, enabling scalable control over differentiated population sizes. With their robust, yet flexibly adjustable nature, epigenetic switching networks could represent central targets on which evolution acts to manufacture diversity in organism size and form.

Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.

From development toward therapeutics, a collaborative effort on blood progenitors

The National Heart, Lung, and Blood Institute Progenitor Cell Translational Consortium Blood Progenitor Meeting was hosted virtually on November 5, 2020, with 93 attendees across 20 research groups. The purpose of this meeting was to exchange recent findings, discuss current efforts, and identify prospective opportunities in the field of hematopoietic stem and progenitor cell research and therapeutic discovery.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Variability within rare cell states enables multiple paths toward drug resistance

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA fluorescence in situ hybridization to directly capture rare cells that give rise to cellular behaviors of interest. Applying Rewind to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1,000-1:10,000 cells) and relative persistence of MAP kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences among several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones after drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.

A single-cell-resolution fate map of endoderm reveals demarcation of pancreatic progenitors by cell cycle

A progenitor cell could generate a certain type or multiple types of descendant cells during embryonic development. To make all the descendant cell types and developmental trajectories of every single progenitor cell clear remains an ultimate goal in developmental biology. Characterizations of descendant cells produced by each uncommitted progenitor for a full germ layer represent a big step toward the goal. Here, we focus on early foregut endoderm, which generates foregut digestive organs, including the pancreas, liver, foregut, and ductal system, through distinct lineages. Using unbiased single-cell labeling techniques, we label every individual zebrafish foregut endodermal progenitor cell out of 216 cells to visibly trace the distribution and number of their descendant cells. Hence, single-cell-resolution fate and proliferation maps of early foregut endoderm are established, in which progenitor regions of each foregut digestive organ are precisely demarcated. The maps indicate that the pancreatic endocrine progenitors are featured by a cell cycle state with a long G1 phase. Manipulating durations of the G1 phase modulates pancreatic progenitor populations. This study illustrates foregut endodermal progenitor cell fate at single-cell resolution, precisely demarcates different progenitor populations, and sheds light on mechanistic insights into pancreatic fate determination.

BAR-Seq clonal tracking of gene-edited cells

Gene editing by engineered nucleases has revolutionized the field of gene therapy by enabling targeted and precise modification of the genome. However, the limited availability of methods for clonal tracking of edited cells has resulted in a paucity of information on the diversity, abundance and behavior of engineered clones. Here we detail the wet laboratory and bioinformatic BAR-Seq pipeline, a strategy for clonal tracking of cells harboring homology-directed targeted integration of a barcoding cassette. We present the BAR-Seq web application, an online, freely available and easy-to-use software that allows performing clonal tracking analyses on raw sequencing data without any computational resources or advanced bioinformatic skills. BAR-Seq can be applied to most editing strategies, and we describe its use to investigate the clonal dynamics of human edited hematopoietic stem/progenitor cells in xenotransplanted hosts. Notably, BAR-Seq may be applied in both basic and translational research contexts to investigate the biology of edited cells and stringently compare editing protocols at a clonal level. Our BAR-Seq pipeline allows library preparation and validation in a few days and clonal analyses of edited cell populations in 1 week.

Lineage tracing and analog recording in mammalian cells by single-site DNA writing

Studying cellular and developmental processes in complex multicellular organisms can require the non-destructive observation of thousands to billions of cells deep within an animal. DNA recorders address the staggering difficulty of this task by converting transient cellular experiences into mutations at defined genomic sites that can be sequenced later in high throughput. However, existing recorders act primarily by erasing DNA. This is problematic because, in the limit of progressive erasure, no record remains. We present a DNA recorder called CHYRON (Cell History Recording by Ordered Insertion) that acts primarily by writing new DNA through the repeated insertion of random nucleotides at a single locus in temporal order. To achieve in vivo DNA writing, CHYRON combines Cas9, a homing guide RNA and the template-independent DNA polymerase terminal deoxynucleotidyl transferase. We successfully applied CHYRON as an evolving lineage tracer and as a recorder of user-selected cellular stimuli.

Piecemeal regulation of convergent neuronal lineages by bHLH transcription factors in Caenorhabditis elegans

Cells of the same type can be generated by distinct cellular lineages that originate in different parts of the developing embryo ('lineage convergence'). Several Caenorhabditis elegans neuron classes composed of left/right or radially symmetric class members display such lineage convergence. We show here that the C. elegans Atonal homolog lin-32 is differentially expressed in neuronal lineages that give rise to left/right or radially symmetric class members. Loss of lin-32 results in the selective loss of the expression of pan-neuronal markers and terminal selector-type transcription factors that confer neuron class-specific features. Another basic helix-loop-helix (bHLH) gene, the Achaete-Scute homolog hlh-14, is expressed in a mirror image pattern relative to lin-32 and is required to induce neuronal identity and terminal selector expression on the contralateral side of the animal. These findings demonstrate that distinct lineage histories converge via different bHLH factors at the level of induction of terminal selector identity determinants, which thus serve as integrators of distinct lineage histories. We also describe neuron-to-neuron identity transformations in lin-32 mutants, which we propose to also be the result of misregulation of terminal selector gene expression.

Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges

Computational and mathematical models are key to obtain a system-level understanding of biological processes, but their limitations have to be clearly defined to allow their proper application and interpretation. Crowdsourced benchmarks in the form of challenges provide an unbiased assessment of methods, and for the past decade, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) organized more than 15 systems biology challenges. From transcription factor binding to dynamical network models, from signaling networks to gene regulation, from whole-cell models to cell-lineage reconstruction, and from single-cell positioning in a tissue to drug combinations and cell survival, the breadth is broad. To celebrate the 5-year anniversary of Cell Systems, we review the genesis of these systems biology challenges and discuss how interlocking the forward- and reverse-modeling paradigms allows to push the rim of systems biology. This approach will persist for systems levels approaches in biology and medicine.

The Intestinal Epithelium - Fluid Fate and Rigid Structure From Crypt Bottom to Villus Tip

The single-layered, simple epithelium of the gastro-intestinal tract controls nutrient uptake, coordinates our metabolism and shields us from pathogens. Despite its seemingly simple architecture, the intestinal lining consists of highly distinct cell populations that are continuously renewed by the same stem cell population. The need to maintain balanced diversity of cell types in an unceasingly regenerating tissue demands intricate mechanisms of spatial or temporal cell fate control. Recent advances in single-cell sequencing, spatio-temporal profiling and organoid technology have shed new light on the intricate micro-structure of the intestinal epithelium and on the mechanisms that maintain it. This led to the discovery of unexpected plasticity, zonation along the crypt-villus axis and new mechanism of self-organization. However, not only the epithelium, but also the underlying mesenchyme is distinctly structured. Several new studies have explored the intestinal stroma with single cell resolution and unveiled important interactions with the epithelium that are crucial for intestinal function and regeneration. In this review, we will discuss these recent findings and highlight the technologies that lead to their discovery. We will examine strengths and limitations of each approach and consider the wider impact of these results on our understanding of the intestine in health and disease.

Melanoma models for the next generation of therapies

There is a lack of appropriate melanoma models that can be used to evaluate the efficacy of novel therapeutic modalities. Here, we discuss the current state of the art of melanoma models including genetically engineered mouse, patient-derived xenograft, zebrafish, and ex vivo and in vitro models. We also identify five major challenges that can be addressed using such models, including metastasis and tumor dormancy, drug resistance, the melanoma immune response, and the impact of aging and environmental exposures on melanoma progression and drug resistance. Additionally, we discuss the opportunity for building models for rare subtypes of melanomas, which represent an unmet critical need. Finally, we identify key recommendations for melanoma models that may improve accuracy of preclinical testing and predict efficacy in clinical trials, to help usher in the next generation of melanoma therapies.

Drivers of dynamic intratumor heterogeneity and phenotypic plasticity

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

New insights on CRISPR/Cas9-based therapy for breast Cancer

CRISPR/Cas9 has revolutionized genome-editing techniques in various biological fields including human cancer research. Cancer is a multi-step process that encompasses the accumulation of mutations that result in the hallmark of the malignant state. The goal of cancer research is to identify these mutations and correlate them with the underlying tumorigenic process. Using CRISPR/Cas9 tool, specific mutations responsible for cancer initiation and/or progression could be corrected at least in animal models as a first step towards translational applications. In the present article, we review various novel strategies that employed CRISPR/Cas9 to treat breast cancer in both in vitro and in vivo systems.

Bayesian inference of gene expression states from single-cell RNA-seq data

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

How should we define mammary stem cells?

Mammary stem cells (MaSCs) have been defined by cell surface marker expression and their ability to repopulate a cleared fat pad, a capacity now known to result from reprogramming upon transplantation. Furthermore, lineage-tracing studies have provoked controversy as to whether MaSCs are unipotent or bi/multipotent. Various innovative experimental approaches, including single-cell RNA sequencing (scRNA-Seq), epigenetic analyses, deep tissue and live imaging, and advanced mouse models, have provided new and unexpected insights into stem and progenitor cells; thus, it is now timely to reappraise our concept of the MaSC hierarchy. Here, I highlight misconceptions, suggest definitions of stem and progenitor cells, and propose a way forward in our search for an understanding of MaSCs.

Imaging cell lineage with a synthetic digital recording system

During multicellular development, spatial position and lineage history play powerful roles in controlling cell fate decisions. Using a serine integrase-based recording system, we engineered cells to record lineage information in a format that can be read out in situ. The system, termed integrase-editable memory by engineered mutagenesis with optical in situ readout (intMEMOIR), allowed in situ reconstruction of lineage relationships in cultured mouse cells and flies. intMEMOIR uses an array of independent three-state genetic memory elements that can recombine stochastically and irreversibly, allowing up to 59,049 distinct digital states. It reconstructed lineage trees in stem cells and enabled simultaneous analysis of single-cell clonal history, spatial position, and gene expression in Drosophila brain sections. These results establish a foundation for microscopy-readable lineage recording and analysis in diverse systems.

Lineage barcoding in mice with homing CRISPR

Classic approaches to mapping the developmental history of cells in vivo have relied on techniques that require complex interventions and often capture only a single trajectory or moment in time. We have previously described a developmental barcoding system to address these issues using synthetically induced mutations to record information about each cell's lineage in its genome. This system uses MARC1 mouse lines, which have multiple homing guide RNAs that each generate hundreds of mutant alleles and combine to produce an exponential diversity of barcodes. Here, we detail two MARC1 lines that are available from a public repository. We describe strategies for using MARC1 mice and experimental design considerations. We provide a protocol for barcode retrieval and sequencing as well as the analysis of the sequencing data. This protocol generates barcodes based on synthetically induced mutations in mice to enable lineage analysis.

Single-Cell Toolkits Opening a New Era for Cell Engineering

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.

Single-cell genomics to study developmental cell fate decisions in zebrafish

New developments in single-cell genomics have transformed developmental biology in recent years by enabling systematic analysis of embryonic cell types and differentiation trajectories. Ongoing efforts in experimental and computational method development aim to reveal gene-regulatory mechanisms and to provide additional spatio-temporal information about developmental cell fate decisions. Here, we discuss recent technological developments as well as biological applications of single-cell genomics, with a particular focus on analysis of developmental cell fate decisions. Although the approaches described here are generally applicable to a broad range of model systems, we focus our discussion on applications in zebrafish, which has proven to be a particularly powerful model organism for establishing novel methods in single-cell genomics.

Efficient CRISPR/Cas9 mutagenesis for neurobehavioral screening in adult zebrafish

Adult zebrafish are widely used to interrogate mechanisms of disease development and tissue regeneration. Yet, the prospect of large-scale genetics in adult zebrafish has traditionally faced a host of biological and technical challenges, including inaccessibility of adult tissues to high-throughput phenotyping and the spatial and technical demands of adult husbandry. Here, we describe an experimental pipeline that combines high-efficiency CRISPR/Cas9 mutagenesis with functional phenotypic screening to identify genes required for spinal cord repair in adult zebrafish. Using CRISPR/Cas9 dual-guide ribonucleic proteins, we show selective and combinatorial mutagenesis of 17 genes at 28 target sites with efficiencies exceeding 85% in adult F₀ “crispants”. We find that capillary electrophoresis is a reliable method to measure indel frequencies. Using a quantifiable behavioral assay, we identify seven single- or duplicate-gene crispants with reduced functional recovery after spinal cord injury. To rule out off-target effects, we generate germline mutations that recapitulate the crispant regeneration phenotypes. This study provides a platform that combines high-efficiency somatic mutagenesis with a functional phenotypic readout to perform medium- to large-scale genetic studies in adult zebrafish.

Transcriptional and epigenetic control of hematopoietic stem cell fate decisions in vertebrates

Hematopoietic stem cells (HSCs) are the foundation of adult hematopoiesis that produce all types of mature blood lineages. In vertebrates, HSC development is a stepwise process, coordinately regulated by chromatin architectures and a group of transcriptional and epigenetic regulators. A deeper understanding of the molecular mechanisms governing the generation, expansion, and function of HSCs holds great promise in the generation and expansion of engraftable HSCs in vitro for clinical applications. This study reviewed recent advances in transcriptional and epigenetic control of hematopoietic stem cell fate decisions in vertebrates.

Loss of Znt8 function in diabetes mellitus: risk or benefit?

The zinc transporter 8 (ZnT8) plays an essential role in zinc homeostasis inside pancreatic β cells, its function is related to the stabilization of insulin hexameric form. Genome-wide association studies (GWAS) have established a positive and negative relationship of ZnT8 variants with type 2 diabetes mellitus (T2DM), exposing a dual and controversial role. The first hypotheses about its role in T2DM indicated a higher risk of developing T2DM for loss of function; nevertheless, recent GWAS of ZnT8 loss-of-function mutations in humans have shown protection against T2DM. With regard to the ZnT8 role in T2DM, most studies have focused on rodent models and common high-risk variants; however, considerable differences between human and rodent models have been found and the new approaches have included lower-frequency variants as a tool to clarify gene functions, allowing a better understanding of the disease and offering possible therapeutic targets. Therefore, this review will discuss the physiological effects of the ZnT8 variants associated with a major and lower risk of T2DM, emphasizing the low- and rare-frequency variants.

Robust direct digital-to-biological data storage in living cells

DNA has been the predominant information storage medium for biology and holds great promise as a next-generation high-density data medium in the digital era. Currently, the vast majority of DNA-based data storage approaches rely on in vitro DNA synthesis. As such, there are limited methods to encode digital data into the chromosomes of living cells in a single step. Here, we describe a new electrogenetic framework for direct storage of digital data in living cells. Using an engineered redox-responsive CRISPR adaptation system, we encoded binary data in 3-bit units into CRISPR arrays of bacterial cells by electrical stimulation. We demonstrate multiplex data encoding into barcoded cell populations to yield meaningful information storage and capacity up to 72 bits, which can be maintained over many generations in natural open environments. This work establishes a direct digital-to-biological data storage framework and advances our capacity for information exchange between silicon- and carbon-based entities.

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Germ cell differentiation requires Tdrd7-dependent chromatin and transcriptome reprogramming marked by germ plasm relocalization

In many animal models, primordial germ cell (PGC) development depends on maternally deposited germ plasm, which prevents somatic cell fate. Here, we show that PGCs respond to regulatory information from the germ plasm in two distinct phases using two distinct mechanisms in zebrafish. We demonstrate that PGCs commence zygotic genome activation together with the somatic blastocysts with no demonstrable differences in transcriptional and chromatin opening. Unexpectedly, both PGC and somatic blastocysts activate germ-cell-specific genes, which are only stabilized in PGCs by cytoplasmic germ plasm determinants. Disaggregated perinuclear relocalization of germ plasm during PGC migration is regulated by the germ plasm determinant Tdrd7 and is coupled to dramatic divergence between PGC and somatic transcriptomes. This transcriptional divergence relies on PGC-specific cis-regulatory elements characterized by promoter-proximal distribution. We show that Tdrd7-dependent reconfiguration of chromatin accessibility is required for elaboration of PGC fate but not for PGC migration.

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No evidence for transcriptional activation delay in zebrafish PGCs

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Germ-plasm-associated post-transcriptional divergence during ZGA

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Epigenetic reprogramming marks onset of PGC migration

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Epigenetic reprogramming in PGCs relies on Tdrd7, coupled to germ plasm relocalization

Clonal tracking of haematopoietic cells: insights and clinical implications

Recent advances in high-throughput genomics have enabled the direct tracking of outputs from many cell types, greatly accelerating the study of developmental processes and tissue regeneration. The capacity for long-term self-renewal with multilineage differentiation potential characterises the cellular dynamics of a special set of developmental states that are critical for maintaining homeostasis. In haematopoiesis, the archetypal model for development, lineage-tracing experiments have elucidated the roles of haematopoietic stem cells to ongoing blood production and the importance of long-lived immune cells to immunological memory. An understanding of the biology and clonal dynamics of these cellular fates and states can provide clues to the response of haematopoiesis to ageing, the process of malignant transformation, and are key to designing more efficacious and durable clinical gene and cellular therapies.

Deciphering neural heterogeneity through cell lineage tracing

Understanding how an adult brain reaches an appropriate size and cell composition from a pool of progenitors that proliferates and differentiates is a key question in Developmental Neurobiology. Not only the control of final size but also, the proper arrangement of cells of different embryonic origins is fundamental in this process. Each neural progenitor has to produce a precise number of sibling cells that establish clones, and all these clones will come together to form the functional adult nervous system. Lineage cell tracing is a complex and challenging process that aims to reconstruct the offspring that arise from a single progenitor cell. This tracing can be achieved through strategies based on genetically modified organisms, using either genetic tracers, transfected viral vectors or DNA constructs, and even single-cell sequencing. Combining different reporter proteins and the use of transgenic mice revolutionized clonal analysis more than a decade ago and now, the availability of novel genome editing tools and single-cell sequencing techniques has vastly improved the capacity of lineage tracing to decipher progenitor potential. This review brings together the strategies used to study cell lineages in the brain and the role they have played in our understanding of the functional clonal relationships among neural cells. In addition, future perspectives regarding the study of cell heterogeneity and the ontogeny of different cell lineages will also be addressed.

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

Sequential Activation of Guide RNAs to Enable Successive CRISPR-Cas9 Activities

Currently, either highly multiplexed genetic manipulations can be delivered to mammalian cells all at once or extensive engineering of gene regulatory sequences can be used to conditionally activate a few manipulations. Here, we provide proof of principle for a new system enabling multiple genetic manipulations to be executed as a preprogrammed cascade of events. The system leverages the programmability of the S. pyogenes Cas9 and is based on flexible arrangements of individual modules of activity. The basic module consists of an inactive single-guide RNA (sgRNA)-like component that is converted to an active state through the effects of another sgRNA. Modules can be arranged to bring about an algorithmic program of sequential genetic manipulations without the need for engineering cell-type-specific promoters or gene regulatory sequences. With the expanding diversity of available tools that use spCas9, this sgRNA-based system provides multiple levels of interfacing with mammalian cell biology.

Zebrafish: A Model Organism for Studying Enteric Nervous System Development and Disease

The Enteric Nervous System (ENS) is a large network of enteric neurons and glia that regulates various processes in the gastrointestinal tract including motility, local blood flow, mucosal transport and secretion. The ENS is derived from stem cells coming from the neural crest that migrate into and along the primitive gut. Defects in ENS establishment cause enteric neuropathies, including Hirschsprung disease (HSCR), which is characterized by an absence of enteric neural crest cells in the distal part of the colon. In this review, we discuss the use of zebrafish as a model organism to study the development of the ENS. The accessibility of the rapidly developing gut in zebrafish embryos and larvae, enables in vivo visualization of ENS development, peristalsis and gut transit. These properties make the zebrafish a highly suitable model to bring new insights into ENS development, as well as in HSCR pathogenesis. Zebrafish have already proven fruitful in studying ENS functionality and in the validation of novel HSCR risk genes. With the rapid advancements in gene editing techniques and their unique properties, research using zebrafish as a disease model, will further increase our understanding on the genetics underlying HSCR, as well as possible treatment options for this disease.

Full-Spectrum Targeted Mutagenesis in Plant and Animal Cells

Directed evolution is a powerful approach for protein engineering and functional studies. However, directed evolution outputs from bacterial and yeast systems do not always translate to higher organisms. In situ directed evolution in plant and animal cells has previously been limited by an inability to introduce targeted DNA sequence diversity. New hypermutation tools have emerged that can generate targeted mutations in plant and animal cells, by recruiting mutagenic proteins to defined DNA loci. Progress in this field, such as the development of CRISPR-derived hypermutators, now allows for all DNA nucleotides within user-defined regions to be altered through the recruitment of error-prone DNA polymerases or highly active DNA deaminases. The further engineering of these mutagenesis systems will potentially allow for all transition and transversion substitutions to be generated within user-defined genomic windows. Such targeted full-spectrum mutagenesis tools would provide a powerful platform for evolving antibodies, enzymes, structural proteins and RNAs with specific desired properties in relevant cellular contexts. These tools are expected to benefit many aspects of biological research and, ultimately, clinical applications.