Synthetic recording and in situ readout of lineage information in single cells

Advances in CRISPR-Cas9 in lineage tracing of model animals

Cell lineage tracing is a key technology for describing the developmental history of individual progenitor cells and assembling them to form a lineage development tree. However, traditional methods have limitations of poor stability and insufficient resolution. As an efficient and flexible gene editing tool, CRISPR-Cas9 system has been widely used in biological research. Furthermore, CRISPR-Cas9 gene editing-based tracing methods can introduce fluorescent proteins, reporter genes, or DNA barcodes for high-throughput sequencing, enabling precise lineage analysis, significantly improving precision and resolution, and expanding its application range. In this review, we summarize applications of CRISPR-Cas9 system in cell lineage tracing, with special emphasis on its successful applications in traditional model animals (e.g., zebrafish and mice), large animal models (pigs), and human cells or organoids. We also discussed its potential prospects and challenges in xenotransplantation and regenerative medicine.

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

Single-cell phylodynamic inference of stem cell differentiation and tumor evolution

Phylodynamic inference (PI) quantifies population dynamics and evolutionary trajectories using phylogenetic trees. Single-cell lineage tracing enables phylogenetic tree reconstruction for thousands of cells in multicellular organisms, facilitating PI at the cellular level. However, cell differentiation and somatic evolution challenge the direct application of existing PI frameworks to somatic tissues. We introduce scPhyloX, a computational framework modeling structured cell populations by leveraging single-cell phylogenetic trees to infer tissue development and tumor evolution dynamics. A key advancement is its ability to infer time-varying parameters, capturing dynamic biological processes. Simulations demonstrate scPhyloX's accuracy in scenarios including tissue development, disease treatment, and tumor growth. Application to three real datasets reveals insights into somatic dynamics: cycling stem cell overshoot in fly organ development, clonal expansion of multipotent hematopoietic progenitors during human aging, and pronounced subclonal selection in early colorectal tumorigenesis. scPhyloX thus provides a computational approach for investigating somatic tissue development and evolution.

LoxCode in vivo barcoding reveals epiblast clonal fate bias to fetal organs

Much remains to be learned about the clonal fate of mammalian epiblast cells. Here, we develop high-diversity Cre recombinase-driven LoxCode barcoding for in vivo clonal lineage tracing for bulk tissue and single-cell readout. Embryonic day (E) 5.5 pre-gastrulation embryos were barcoded in utero, and epiblast clones were assessed for their contribution to a wide range of tissues in E12.5 embryos. Some epiblast clones contributed broadly across germ layers, while many were biased toward either blood, ectoderm, mesenchyme, or limbs, across tissue compartments and body axes. Using a stochastic agent-based model of embryogenesis and LoxCode barcoding, we inferred and experimentally validated cell fate biases across tissues in line with shared and segregating differentiation trajectories. Single-cell readout revealed numerous instances of asymmetry in epiblast contribution, including left-versus-right and kidney-versus-gonad fate. LoxCode barcoding enables clonal fate analysis for the study of development and broader questions of clonality in murine biology.

Molecular circuits for genomic recording of cellular events

Advances in precise genome editing are enabling genomic recordings of cellular events. Since the initial demonstration of CRISPR-based genome editing, the field of genomic recording has witnessed key strides in lineage recording, where clonal lineage relationships among cells are indirectly recorded as synthetic mutations. However, methods for directly recording and reconstructing past cellular events are still limited, and their potential for revealing new insights into cell fate decisions has yet to be realized. The field needs new sensing modules and genetic circuit architectures that faithfully encode past cellular states into genomic DNA recordings to achieve such goals. Here we review recently developed strategies to construct diverse sensors and explore how emerging synthetic biology tools may help to build molecular circuits for genomic recording of diverse cellular events.

Modelling human brain development and disease with organoids

Organoids are systems derived from pluripotent stem cells at the interface between traditional monolayer cultures and in vivo animal models. The structural and functional characteristics of organoids enable the modelling of early stages of brain development in a physiologically relevant 3D environment. Moreover, organoids constitute a tool with which to analyse how individual genetic variation contributes to the susceptibility and progression of neurodevelopmental disorders. This Roadmap article describes the features of brain organoids, focusing on the neocortex, and their advantages and limitations - in comparison with other model systems - for the study of brain development, evolution and disease. We highlight avenues for enhancing the physiological relevance of brain organoids by integrating bioengineering techniques and unbiased high-throughput analyses, and discuss future applications. As organoids advance in mimicking human brain functions, we address the ethical and societal implications of this technology.

Open-ended molecular recording of sequential cellular events into DNA

Genetically encoded DNA recorders noninvasively convert transient biological events into durable mutations in a cell's genome, allowing for the later reconstruction of cellular experiences by DNA sequencing. We present a DNA recorder, peCHYRON, that achieves high-information, durable, and temporally resolved multiplexed recording of multiple cellular signals in mammalian cells. In each step of recording, prime editor, a Cas9-reverse transcriptase fusion protein, inserts a variable triplet DNA sequence alongside a constant propagator sequence that deactivates the previous and activates the next step of insertion. Insertions accumulate sequentially in a unidirectional order, editing can continue indefinitely, and high information is achieved by coexpressing a variety of prime editing guide RNAs (pegRNAs), each harboring unique triplet DNA sequences. We demonstrate that the constitutive expression of pegRNA collections generates insertion patterns for the straightforward reconstruction of cell lineage relationships and that the inducible expression of specific pegRNAs results in the accurate recording of exposures to biological stimuli.

Reconstructing signaling histories of single cells via perturbation screens and transfer learning

Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications, from engineering tissues to treating diseases. Such manipulation requires knowing the signaling states and histories of the cells in situ , for which high-throughput discovery methods are lacking. Here, we present an integrated experimental-computational framework that learns signaling response signatures from a high-throughput in vitro perturbation atlas and infers combinatorial signaling activities in in vivo cell types with high accuracy and temporal resolution. Specifically, we generated signaling perturbation atlas across diverse cell types/states through multiplexed sequential combinatorial screens on human pluripotent stem cells. Using the atlas to train IRIS, a neural network-based model, and predicting on mouse embryo scRNAseq atlas, we discovered global features of combinatorial signaling code usage over time, identified biologically meaningful heterogeneity of signaling states within each cell type, and reconstructed signaling histories along diverse cell lineages. We further demonstrated that IRIS greatly accelerates the optimization of stem cell differentiation protocols by drastically reducing the combinatorial space that needs to be tested. This framework leads to the revelation that different cell types share robust signal response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions.

Gene Editing: An Effective Tool for the Future Treatment of Kidney Disease

Gene editing technology involves modifying target genes to alter genetic traits and generate new phenotypes. Beginning with zinc-finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN), the field has evolved through the advent of clustered regularly interspaced short palindromic repeats and CRISPR-associated protein (CRISPR-Cas) systems, and more recently to base editors (BE) and prime editors (PE). These innovations have provided deep insights into the molecular mechanisms of complex biological processes and have paved the way for novel therapeutic strategies for a range of diseases. Gene editing is now being applied in the treatment of both genetic and acquired kidney diseases, as well as in kidney transplantation and the correction of genetic mutations. This review explores the current applications of mainstream gene editing technologies in biology, with a particular emphasis on their roles in kidney disease research and treatment of. It also addresses the limitations and challenges associated with these technologies, while offering perspectives on their future potential in this field.

Single-cell lineage tracing techniques in hematology: unraveling the cellular narrative

Lineage tracing is a valuable technique that has greatly facilitated the exploration of cell origins and behavior. With the continuous development of single-cell sequencing technology, lineage tracing technology based on the single-cell level has become an important method to study biological development. Single-cell Lineage tracing technology plays an important role in the hematological system. It can help to answer many important questions, such as the heterogeneity of hematopoietic stem cell function and structure, and the heterogeneity of malignant tumor cells in the hematological system. Many studies have been conducted to explore the field of hematology by applying this technology. This review focuses on the superiority of the emerging single-cell lineage tracing technologies of Integration barcodes, CRISPR barcoding, and base editors, and summarizes their applications in the hematology system. These studies have suggested the vast potential in unraveling complex cellular behaviors and lineage dynamics in both normal and pathological contexts.

The lives of cells, recorded

A paradigm for biology is emerging in which cells can be genetically programmed to write their histories into their own genomes. These records can subsequently be read, and the cellular histories reconstructed, which for each cell could include a record of its lineage relationships, extrinsic influences, internal states and physical locations, over time. DNA recording has the potential to transform the way that we study developmental and disease processes. Recent advances in genome engineering are driving the development of systems for DNA recording, and meanwhile single-cell and spatial omics technologies increasingly enable the recovery of the recorded information. Combined with advances in computational and phylogenetic inference algorithms, the DNA recording paradigm is beginning to bear fruit. In this Perspective, we explore the rationale and technical basis of DNA recording, what aspects of cellular biology might be recorded and how, and the types of discovery that we anticipate this paradigm will enable.

SpaceBar enables clone tracing in spatial transcriptomic data

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Development of an inducible DNA barcoding system to understand lineage changes in Arabidopsis regeneration

Plants demonstrate a high degree of developmental plasticity, capable of regenerating entire individuals from detached somatic tissues-a regenerative phenomenon rarely observed in metazoa. Consequently, elucidating the lineage relationship between somatic founder cells and descendant cells in regenerated plant organs has long been a pursuit. In this study, we developed and optimized both DNA barcode- and multi-fluorescence-based cell-lineage tracing toolsets, employing an inducible method to mark individual cells in Arabidopsis donor somatic tissues at the onset of regeneration. Utilizing these complementary methods, we scrutinized cell identities at the single-cell level and presented compelling evidence that all cells in the regenerated Arabidopsis plants, irrespective of their organ types, originated from a single progenitor cell in the donor somatic tissue. Our discovery suggests a single-cell passage directing the transition from multicellular donor tissue to regenerated plants, thereby creating opportunities for cell-cell competition during plant regeneration-a strategy for maximizing survival.

Transformer-based modeling of Clonal Selection and Expression Dynamics reveals resistance mechanisms in breast cancer

Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection. Using single-cell gene expression and the fitness of barcoded clones, TraCSED identifies interpretable gene programs and the time points at which they are associated with clonal selection. When applied to cells treated with either giredestrant, a selective estrogen receptor (ER) antagonist and degrader, or palbociclib, a CDK4/6 inhibitor, pathways dynamically associated with resistance are revealed. For example, ER activity is associated with positive selection around day four under palbociclib treatment and this adaptive response can be suppressed by combining the drugs. Yet, in the combination treatment, one clone still emerged. Clustering based on partial least squares regression found that high baseline expression of both SNHG25 and SNCG genes was the primary marker of positive selection to co-treatment and thus potentially associated with innate resistance - an aspect that traditional differential analysis methods missed. In conclusion, TraCSED enables associating features with phenotypes in a time-dependent manner from scRNA-seq data.

Barcoding Notch signaling in the developing brain

Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control, while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER-seq with an expanded juvenile brain atlas to identify cell types derived from Notch-active founders. Our data reveal rare examples where differential Notch activities in ancestral progenitors are detected in terminally differentiated neuronal subtypes. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.

Post-translational digital data encoding into the genomes of mammalian cell populations

High resolution cellular signal encoding is critical for better understanding of complex biological phenomena. DNA-based biosignal encoders alter genomic or plasmid DNA in a signal dependent manner. Current approaches involve the signal of interest affecting a DNA edit by interacting with a signal specific promoter which then results in expression of the effector molecule (DNA altering enzyme). Here, we present the proof of concept of a biosignal encoding system where the enzyme terminal deoxynucleotidyl transferase (TdT) acts as the effector molecule upon directly interacting with the signal of interest. A template independent DNA polymerase (DNAp), TdT incorporates nucleotides at the 3' OH ends of DNA substrate in a signal dependent manner. By employing CRISPR-Cas9 to create double stranded breaks in genomic DNA, we make 3'OH ends available to act as substrate for TdT. We show that this system can successfully resolve and encode different concentrations of various biosignals into the genomic DNA of HEK-293T cells. Finally, we develop a simple encoding scheme associated with the tested biosignals and encode the message "HELLO WORLD" into the genomic DNA of HEK-293T cells at a population level with 91% accuracy. This work demonstrates a simple and engineerable system that can reliably store local biosignal information into the genomes of mammalian cell populations.

Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system

Single-cell and single-nucleus genomic approaches can provide unbiased and multimodal insights. Here, we discuss what constitutes a molecular cell atlas and how to leverage single-cell omics data to generate hypotheses and gain insights into cell transitions in development and disease of the nervous system. We share points of reflection on what to consider during study design and implementation as well as limitations and pitfalls.

Reimagining Cortical Connectivity by Deconstructing Its Molecular Logic into Building Blocks

Comprehensive maps of neuronal connectivity provide a foundation for understanding the structure of neural circuits. In a circuit, neurons are diverse in morphology, electrophysiology, gene expression, activity, and other neuronal properties. Thus, constructing a comprehensive connectivity map requires associating various properties of neurons, including their connectivity, at cellular resolution. A commonly used approach is to use the gene expression profiles as an anchor to which all other neuronal properties are associated. Recent advances in genomics and anatomical techniques dramatically improved the ability to determine and associate the long-range projections of neurons with their gene expression profiles. These studies revealed unprecedented details of the gene-projection relationship, but also highlighted conceptual challenges in understanding this relationship. In this article, I delve into the findings and the challenges revealed by recent studies using state-of-the-art neuroanatomical and transcriptomic techniques. Building upon these insights, I propose an approach that focuses on understanding the gene-projection relationship through basic features in gene expression profiles and projections, respectively, that associate with underlying cellular processes. I then discuss how the developmental trajectories of projections and gene expression profiles create additional challenges and necessitate interrogating the gene-projection relationship across time. Finally, I explore complementary strategies that, together, can provide a comprehensive view of the gene-projection relationship.

Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis

Tumor progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumors can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumor progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumor expansion, plasticity, and metastasis co-evolve with microenvironmental remodeling in a Kras;p53-driven mouse model of lung adenocarcinoma. We find that rapid tumor expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with the emergence of pro-metastatic cancer cell states. Furthermore, metastases arise from spatially-confined subclones of primary tumors and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, we present a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumor progression.

Mapping lineage-traced cells across time points with moslin

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.

Reconstructing signaling history of single cells with imaging-based molecular recording

The intensity and duration of biological signals encode information that allows a few pathways to regulate a wide array of cellular behaviors. Despite the central importance of signaling in biomedical research, our ability to quantify it in individual cells over time remains limited. Here, we introduce INSCRIBE, an approach for reconstructing signaling history in single cells using endpoint fluorescence images. By regulating a CRISPR base editor, INSCRIBE generates mutations in genomic target sequences, at a rate proportional to signaling activity. The number of edits is then recovered through a novel ratiometric readout strategy, from images of two fluorescence channels. We engineered human cell lines for recording WNT and BMP pathway activity, and demonstrated that INSCRIBE faithfully recovers both the intensity and duration of signaling. Further, we used INSCRIBE to study the variability of cellular response to WNT and BMP stimulation, and test whether the magnitude of response is a stable, heritable trait. We found a persistent memory in the BMP pathway. Progeny of cells with higher BMP response levels are likely to respond more strongly to a second BMP stimulation, up to 3 weeks later. Together, our results establish a scalable platform for genetic recording and in situ readout of signaling history in single cells, advancing quantitative analysis of cell-cell communication during development and disease.

Multi-layered computational gene networks by engineered tristate logics

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Toward DNA-Based Recording of Biological Processes

Exploiting the inherent compatibility of DNA-based data storage with living cells, various cellular recording approaches have been developed for recording and retrieving biologically relevant signals in otherwise inaccessible locations, such as inside the body. This review provides an overview of the current state of engineered cellular memory systems, highlighting their design principles, advantages, and limitations. We examine various technologies, including CRISPR-Cas systems, recombinases, retrons, and DNA methylation, that enable these recording systems. Additionally, we discuss potential strategies for improving recording accuracy, scalability, and durability to address current limitations in the field. This emerging modality of biological measurement will be key to gaining novel insights into diverse biological processes and fostering the development of various biotechnological applications, from environmental sensing to disease monitoring and beyond.

Symbolic recording of signalling and cis-regulatory element activity to DNA

Measurements of gene expression or signal transduction activity are conventionally performed using methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm in which such biological activities are stably recorded to the genome. Enhancer-driven genomic recording of transcriptional activity in multiplex (ENGRAM) is based on the signal-dependent production of prime editing guide RNAs that mediate the insertion of signal-specific barcodes (symbols) into a genomically encoded recording unit. We show how this strategy can be used for multiplex recording of the cell-type-specific activities of dozens to hundreds of cis-regulatory elements with high fidelity, sensitivity and reproducibility. Leveraging signal transduction pathway-responsive cis-regulatory elements, we also demonstrate time- and concentration-dependent genomic recording of WNT, NF-κB and Tet-On activities. By coupling ENGRAM to sequential genome editing via DNA Typewriter1, we stably record information about the temporal dynamics of two orthogonal signalling pathways to genomic DNA. Finally we apply ENGRAM to integratively record the transient activity of nearly 100 transcription factor consensus motifs across daily windows spanning the differentiation of mouse embryonic stem cells into gastruloids, an in vitro model of early mammalian development. Although these are proof-of-concept experiments and much work remains to fully realize the possibilities, the symbolic recording of biological signals or states within cells, to the genome and over time, has broad potential to complement contemporary paradigms for how we make measurements in biological systems.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

The evolution of developmental biology through conceptual and technological revolutions

Developmental biology-the study of the processes by which cells, tissues, and organisms develop and change over time-has entered a new golden age. After the molecular genetics revolution in the 80s and 90s and the diversification of the field in the early 21st century, we have entered a phase when powerful technologies provide new approaches and open unexplored avenues. Progress in the field has been accelerated by advances in genomics, imaging, engineering, and computational biology and by emerging model systems ranging from tardigrades to organoids. We summarize how revolutionary technologies have led to remarkable progress in understanding animal development. We describe how classic questions in gene regulation, pattern formation, morphogenesis, organogenesis, and stem cell biology are being revisited. We discuss the connections of development with evolution, self-organization, metabolism, time, and ecology. We speculate how developmental biology might evolve in an era of synthetic biology, artificial intelligence, and human engineering.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Use of CRISPRoff and synthetic Notch to modulate and relay endogenous gene expression programs in engineered cells

Uncovering the stimulus-response histories that give rise to cell fates and behaviors is an area of great interest in developmental biology, tissue engineering, and regenerative medicine. A comprehensive accounting of cell experiences that lead to the development of organs and tissues can help us to understand developmental anomalies that may underly disease. Perhaps more provocatively, such a record can also reveal clues as to how to drive cell collective decision-making processes, which may yield predictable cell-based therapies or facilitate production of tissue substitutes for transplantation or in vitro screening of prospective therapies to mitigate disease. Toward this end, various methods have been applied to molecularly trace developmental trajectories and record interaction histories of cells. Typical methods involve artificial gene circuits based on recombinases that activate a suite of fluorescent reporters or CRISPR-Cas9 genome writing technologies whose nucleic acid-based record keeping serves to chronicle cell-cell interactions or past exposure to stimuli of interests. Exciting expansions of the synthetic biology toolkit with artificial receptors that permit establishment of defined input-to-output linkages of cell decision-making processes opens the door to not only record cell-cell interactions, but to also potentiate directed manipulation of the outcomes of such interactions via regulation of carefully selected transgenes. Here, we combine CRISPR-based strategies to genetically and epigenetically manipulate cells to express components of the synthetic Notch receptor platform, a widely used artificial cell signaling module. Our approach gives rise to the ability to conditionally record interactions between human cells, where the record of engagement depends on expression of a state-specific marker of a subset of cells in a population. Further, such signal-competent interactions can be used to direct differentiation of human embryonic stem cells toward pre-selected fates based on assigned synNotch outputs. We also implemented CRISPR-based manipulation of native gene expression profiles to bias outcomes of cell engagement histories in a targeted manner. Thus, we present a useful strategy that gives rise to both state-specific recording of cell-cell interactions as well as methods to intentionally influence products of such cell-cell exchanges.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

Barcoding Notch signaling in the developing brain

Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control while the recorder accumulates mutations that represent Notch activity in founder cells. We combine SABER-seq with an expanded juvenile brain atlas to define cell types whose fates are determined downstream of Notch signaling. We identified examples wherein Notch signaling may have differential impact on terminal cell fates. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin

The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.

Recent advances in enzyme-free and enzyme-mediated single-nucleotide variation assay in vitro

Single-nucleotide variants (SNVs) are the most common type variation of sequence alterations at a specific location in the genome, thus involving significant clinical and biological information. The assay of SNVs has engaged great awareness, because many genome-wide association studies demonstrated that SNVs are highly associated with serious human diseases. Moreover, the investigation of SNV expression levels in single cells are capable of visualizing genetic information and revealing the complexity and heterogeneity of single-nucleotide mutation-related diseases. Thus, developing SNV assay approaches in vitro, particularly in single cells, is becoming increasingly in demand. In this review, we summarized recent progress in the enzyme-free and enzyme-mediated strategies enabling SNV assay transition from sensing interface to the test tube and single cells, which will potentially delve deeper into the knowledge of SNV functions and disease associations, as well as discovering new pathways to diagnose and treat diseases based on individual genetic profiles. The leap of SNV assay achievements will motivate observation and measurement genetic variations in single cells, even within living organisms, delve into the knowledge of SNV functions and disease associations, as well as open up entirely new avenues in the diagnosis and treatment of diseases based on individual genetic profiles.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

From Hematopoietic Stem Cells to Platelets: Unifying Differentiation Pathways Identified by Lineage Tracing Mouse Models

Platelets are the terminal progeny of megakaryocytes, primarily produced in the bone marrow, and play critical roles in blood homeostasis, clotting, and wound healing. Traditionally, megakaryocytes and platelets are thought to arise from multipotent hematopoietic stem cells (HSCs) via multiple discrete progenitor populations with successive, lineage-restricting differentiation steps. However, this view has recently been challenged by studies suggesting that (1) some HSC clones are biased and/or restricted to the platelet lineage, (2) not all platelet generation follows the "canonical" megakaryocytic differentiation path of hematopoiesis, and (3) platelet output is the default program of steady-state hematopoiesis. Here, we specifically investigate the evidence that in vivo lineage tracing studies provide for the route(s) of platelet generation and investigate the involvement of various intermediate progenitor cell populations. We further identify the challenges that need to be overcome that are required to determine the presence, role, and kinetics of these possible alternate pathways.

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.

Resolving cellular dynamics using single-cell temporal transcriptomics

Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.

Extracting, filtering and simulating cellular barcodes using CellBarcode tools

Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.

Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR

Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.

The Current Situation and Development Prospect of Whole-Genome Screening

High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.

Reconstructing cell lineage trees with genomic barcoding: approaches and applications

In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.

DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination

In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Dissecting mammalian reproduction with spatial transcriptomics

BACKGROUND

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction.

OBJECTIVE AND RATIONALE

We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come.

SEARCH METHODS

The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed.

OUTCOMES

First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations.

WIDER IMPLICATIONS

Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

Retrospective analysis of enhancer activity and transcriptome history

Cell state changes in development and disease are controlled by gene regulatory networks, the dynamics of which are difficult to track in real time. In this study, we used an inducible DCM-RNA polymerase subunit b fusion protein which labels active genes and enhancers with a bacterial methylation mark that does not affect gene transcription and is propagated in S-phase. This DCM-RNA polymerase fusion protein enables transcribed genes and active enhancers to be tagged and then examined at later stages of development or differentiation. We apply this DCM-time machine (DCM-TM) technology to study intestinal homeostasis, revealing rapid and coordinated activation of enhancers and nearby genes during enterocyte differentiation. We provide new insights in absorptive-secretory lineage decision-making in intestinal stem cell (ISC) differentiation and show that ISCs retain a unique chromatin landscape required to maintain ISC identity and delineate future expression of differentiation-associated genes. DCM-TM has wide applicability in tracking cell states, providing new insights in the regulatory networks underlying cell state changes.

Craniofacial developmental biology in the single-cell era

The evolution of a unique craniofacial complex in vertebrates made possible new ways of breathing, eating, communicating and sensing the environment. The head and face develop through interactions of all three germ layers, the endoderm, ectoderm and mesoderm, as well as the so-called fourth germ layer, the cranial neural crest. Over a century of experimental embryology and genetics have revealed an incredible diversity of cell types derived from each germ layer, signaling pathways and genes that coordinate craniofacial development, and how changes to these underlie human disease and vertebrate evolution. Yet for many diseases and congenital anomalies, we have an incomplete picture of the causative genomic changes, in particular how alterations to the non-coding genome might affect craniofacial gene expression. Emerging genomics and single-cell technologies provide an opportunity to obtain a more holistic view of the genes and gene regulatory elements orchestrating craniofacial development across vertebrates. These single-cell studies generate novel hypotheses that can be experimentally validated in vivo. In this Review, we highlight recent advances in single-cell studies of diverse craniofacial structures, as well as potential pitfalls and the need for extensive in vivo validation. We discuss how these studies inform the developmental sources and regulation of head structures, bringing new insights into the etiology of structural birth anomalies that affect the vertebrate head.