Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars

Single-cell lineage tracing approaches to track kidney cell development and maintenance

The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.

Multiscale engineering of brain organoids for disease modeling

Brain organoids hold great potential for modeling human brain development and pathogenesis. They recapitulate certain aspects of the transcriptional trajectory, cellular diversity, tissue architecture and functions of the developing brain. In this review, we explore the engineering strategies to control the molecular-, cellular- and tissue-level inputs to achieve high-fidelity brain organoids. We review the application of brain organoids in neural disorder modeling and emerging bioengineering methods to improve data collection and feature extraction at multiscale. The integration of multiscale engineering strategies and analytical methods has significant potential to advance insight into neurological disorders and accelerate drug development.

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.

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.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

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.

Achieving single-cell-resolution lineage tracing in zebrafish by continuous barcoding mutations during embryogenesis

Unraveling the lineage relationships of all descendants from a zygote is fundamental to advancing our understanding of developmental and stem cell biology. However, existing cell barcoding technologies in zebrafish lack the resolution to capture the majority of cell divisions during embryogenesis. A recently developed method, SMALT, successfully reconstructed high-resolution cell phylogenetic trees for Drosophila melanogaster. Here, we implement the SMALT system in zebrafish, recording a median of 14 substitution mutations on a one-kilobase-pair barcoding sequence for one-day post-fertilization embryos. Leveraging this system, we reconstruct four cell lineage trees for zebrafish fin cells, encompassing both original and regenerated fin. Each tree consists of hundreds of internal nodes with a median bootstrap support of 99%. Analysis of the obtained cell lineage trees reveals that regenerated fin cells mainly originate from cells in the same part of the fins. Through multiple times sampling germ cells from the same individual, we confirm the stability of the germ cell pool and the early separation of germ cell and somatic cell progenitors. Our system offers the potential for reconstructing high-quality cell phylogenies across diverse tissues, providing valuable insights into development and disease in zebrafish.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Dissecting the spatiotemporal diversity of adult neural stem cells

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

Modeling single cell trajectory using forward-backward stochastic differential equations

Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.

Neutrophils facilitate the epicardial regenerative response after zebrafish heart injury

Despite extensive studies on endogenous heart regeneration within the past 20 years, the players involved in initiating early regeneration events are far from clear. Here, we assessed the function of neutrophils, the first-responder cells to tissue damage, during zebrafish heart regeneration. We detected rapid neutrophil mobilization to the injury site after ventricular amputation, peaking at 1-day post-amputation (dpa) and resolving by 3 dpa. Further analyses indicated neutrophil mobilization coincides with peak epicardial cell proliferation, and recruited neutrophils associated with activated, expanding epicardial cells at 1 dpa. Neutrophil depletion inhibited myocardial regeneration and significantly reduced epicardial cell expansion, proliferation, and activation. To explore the molecular mechanism of neutrophils on the epicardial regenerative response, we performed scRNA-seq analysis of 1 dpa neutrophils and identified enrichment of the FGF and MAPK/ERK signaling pathways. Pharmacological inhibition of FGF signaling indicated its' requirement for epicardial expansion, while neutrophil depletion blocked MAPK/ERK signaling activation in epicardial cells. Ligand-receptor analysis indicated the EGF ligand, hbegfa, is released from neutrophils and synergizes with other FGF and MAPK/ERK factors for induction of epicardial regeneration. Altogether, our studies revealed that neutrophils quickly motivate epicardial cells, which later accumulate at the injury site and contribute to heart regeneration.

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 ).

Developmental progression of DNA double-strand break repair deciphered by a single-allele resolution mutation classifier

DNA double-strand breaks (DSBs) are repaired by a hierarchically regulated network of pathways. Factors influencing the choice of particular repair pathways, however remain poorly characterized. Here we develop an Integrated Classification Pipeline (ICP) to decompose and categorize CRISPR/Cas9 generated mutations on genomic target sites in complex multicellular insects. The ICP outputs graphic rank ordered classifications of mutant alleles to visualize discriminating DSB repair fingerprints generated from different target sites and alternative inheritance patterns of CRISPR components. We uncover highly reproducible lineage-specific mutation fingerprints in individual organisms and a developmental progression wherein Microhomology-Mediated End-Joining (MMEJ) or Insertion events predominate during early rapid mitotic cell cycles, switching to distinct subsets of Non-Homologous End-Joining (NHEJ) alleles, and then to Homology-Directed Repair (HDR)-based gene conversion. These repair signatures enable marker-free tracking of specific mutations in dynamic populations, including NHEJ and HDR events within the same samples, for in-depth analysis of diverse gene editing events.

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.

Protocol to study the inheritance and propagation of non-genetically encoded states using barcode decay lineage tracing

Here, we present a protocol to perform barcode decay lineage tracing followed by single-cell transcriptome analysis (BdLT-Seq). We describe steps for BdLT-Seq experimental design, building barcoded episome reporters, performing episome transfection, and barcode retrieval. We then describe procedures for sequencing library construction while providing options for sample multiplexing and data analysis. This BdLT-Seq technique enables the assessment of clonal evolution in a directional manner while preserving isogeneity, thus allowing the comparison of non-genetic molecular features between isogenic cell lineages. For complete details on the use and execution of this protocol, please refer to Shlyakhtina et al. (2023).1.

Past, present, and future of CRISPR genome editing technologies

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.

Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.

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.

Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

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.

Astrocyte growth is driven by the Tre1/S1pr1 phospholipid-binding G protein-coupled receptor

Astrocytes play crucial roles in regulating neural circuit function by forming a dense network of synapse-associated membrane specializations, but signaling pathways regulating astrocyte morphogenesis remain poorly defined. Here, we show the Drosophila lipid-binding G protein-coupled receptor (GPCR) Tre1 is required for astrocytes to establish their intricate morphology in vivo. The lipid phosphate phosphatases Wunen/Wunen2 also regulate astrocyte morphology and, via Tre1, mediate astrocyte-astrocyte competition for growth-promoting lipids. Loss of s1pr1, the functional analog of Tre1 in zebrafish, disrupts astrocyte process elaboration, and live imaging and pharmacology demonstrate that S1pr1 balances proper astrocyte process extension/retraction dynamics during growth. Loss of Tre1 in flies or S1pr1 in zebrafish results in defects in simple assays of motor behavior. Tre1 and S1pr1 are thus potent evolutionarily conserved regulators of the elaboration of astrocyte morphological complexity and, ultimately, astrocyte control of behavior.

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.

The zebrafish as a potential model for vaccine and adjuvant development

INTRODUCTION

Zebrafishes represent a proven model for human diseases and systems biology, exhibiting physiological and genetic similarities and having innate and adaptive immune systems. However, they are underexplored for human vaccinology, vaccine development, and testing. Here we summarize gaps and challenges.

AREAS COVERED

Zebrafish models have four potential applications: 1) Vaccine safety: The past successes in using zebrafishes to test xenobiotics could extend to vaccine and adjuvant formulations for general safety or target organs due to the zebrafish embryos' optical transparency. 2) Innate immunity: The zebrafish offers refined ways to examine vaccine effects through signaling via Toll-like or NOD-like receptors in zebrafish myeloid cells. 3) Adaptive immunity: Zebrafishes produce IgM, IgD,and two IgZ immunoglobulins, but these are understudied, due to a lack of immunological reagents for challenge studies. 4) Systems vaccinology: Due to the availability of a well-referenced zebrafish genome, transcriptome, proteome, and epigenome, this model offers potential here.

EXPERT OPINION

It remains unproven whether zebrafishes can be employed for testing and developing human vaccines. We are still at the hypothesis-generating stage, although it is possible to begin outlining experiments for this purpose. Through transgenic manipulation, zebrafish models could offer new paths for shaping animal models and systems vaccinology.

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.

Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing

CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.

Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development

During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 h post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.

LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data

Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.

An oncogenic phenoscape of colonic stem cell polarization

Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell fate, we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by (1) colorectal cancer (CRC) oncogenic mutations, (2) microenvironmental fibroblasts and macrophages, (3) stromal ligands, and (4) signaling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation phenoscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)+ revival colonic stem cells (revCSCs) to oncogene-driven LRIG1+ hyper-proliferative CSCs (proCSCs). The transition from revCSCs to proCSCs is regulated by decreasing WNT3A and TGF-β-driven YAP signaling and increasing KRASG12D or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find that APC loss and KRASG12D collaboratively limit access to revCSCs and disrupt stromal-epithelial communication-trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.

ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data

CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.

A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells

Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insufficient barcode diversity and single-cell lineage coverage for profiling tissues composed of millions of cells. Here, we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive lineage barcodes across tissues, and enables the detection of edited barcodes in ∼70% of profiled single cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we established a protocol for joint transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellular clonal memory is associated with genome-wide DNA methylation rather than gene expression or chromatin accessibility. DARLIN will enable the high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.

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 sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Ex utero embryogenesis of non-human primate embryos and beyond

Understanding cellular and molecular processes underlying the human early post-implantation development represents one of the most fundamental questions in development and stem cell biology. As embryos implant into the uterus a week after fertilization, human development beyond the blastocyst stage is extremely difficult to study due to the inaccessibility of embryos and ethical concerns. The advents in the human embryo in vitro culture system provide an easily accessible, tractable, and perturbable platform to dissect key developmental events of human early embryonic development. However, these studies stopped around gastrulation to technical and ethical limitations, and our understanding of human gastrulation and early organogenesis remains poor. As closely related species to humans, non-human primates (NHPs) are suitable surrogate species to interrogate mechanisms underpinning human embryonic development. Here, we review the most recent advances in embryo in vitro culture systems of NHP and discuss their potential optimization strategies and applications.

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.

Biallelic variants in FLII cause pediatric cardiomyopathy by disrupting cardiomyocyte cell adhesion and myofibril organization

Pediatric cardiomyopathy (CM) represents a group of rare, severe disorders that affect the myocardium. To date, the etiology and mechanisms underlying pediatric CM are incompletely understood, hampering accurate diagnosis and individualized therapy development. Here, we identified biallelic variants in the highly conserved flightless-I (FLII) gene in 3 families with idiopathic, early-onset dilated CM. We demonstrated that patient-specific FLII variants, when brought into the zebrafish genome using CRISPR/Cas9 genome editing, resulted in the manifestation of key aspects of morphological and functional abnormalities of the heart, as observed in our patients. Importantly, using these genetic animal models, complemented with in-depth loss-of-function studies, we provided insights into the function of Flii during ventricular chamber morphogenesis in vivo, including myofibril organization and cardiomyocyte cell adhesion, as well as trabeculation. In addition, we identified Flii function to be important for the regulation of Notch and Hippo signaling, crucial pathways associated with cardiac morphogenesis and function. Taken together, our data provide experimental evidence for a role for FLII in the pathogenesis of pediatric CM and report biallelic variants as a genetic cause of pediatric CM.

Decoding pancreatic endocrine cell differentiation and β cell regeneration in zebrafish

In contrast to mice, zebrafish have an exceptional yet elusive ability to replenish lost β cells in adulthood. Understanding this framework would provide mechanistic insights for β cell regeneration, which may be extrapolated to humans. Here, we characterize a krt4-expressing ductal cell type, which is distinct from the putative Notch-responsive cells, showing neogenic competence and giving rise to the majority of endocrine cells during postembryonic development. Furthermore, we demonstrate a marked ductal remodeling process featuring a Notch-responsive to krt4+ luminal duct transformation during late development, indicating several origins of krt4+ ductal cells displaying similar transcriptional patterns. Single-cell transcriptomics upon a series of time points during β cell regeneration unveil a previously unrecognized dlb+ transitional endocrine precursor cell, distinct regulons, and a differentiation trajectory involving cellular shuffling through differentiation and dedifferentiation dynamics. These results establish a model of zebrafish pancreatic endocrinogenesis and highlight key values of zebrafish for translational studies of β cell regeneration.

Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding

A fundamental interest in developmental neuroscience lies in the ability to map the complete single-cell lineages within the brain. To this end, we developed a CRISPR editing-based lineage-specific tracing (CREST) method for clonal tracing in Cre mice. We then used two complementary strategies based on CREST to map single-cell lineages in developing mouse ventral midbrain (vMB). By applying snapshotting CREST (snapCREST), we constructed a spatiotemporal lineage landscape of developing vMB and identified six progenitor archetypes that could represent the principal clonal fates of individual vMB progenitors and three distinct clonal lineages in the floor plate that specified glutamatergic, dopaminergic or both neurons. We further created pandaCREST (progenitor and derivative associating CREST) to associate the transcriptomes of progenitor cells in vivo with their differentiation potentials. We identified multiple origins of dopaminergic neurons and demonstrated that a transcriptome-defined progenitor type comprises heterogeneous progenitors, each with distinct clonal fates and molecular signatures. Therefore, the CREST method and strategies allow comprehensive single-cell lineage analysis that could offer new insights into the molecular programs underlying neural specification.

High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS)

High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.

Cellular barcoding: From developmental tracing to anti-tumor drug discovery

Clonal evolution has gained immense attention in explaining cancer cell status, history, and fate during cancer progression. Current single-cell or spatial transcriptome technologies have broadened our understanding of various mechanisms underlying cancer initiation, relapse, and drug resistance. However, technical challenges still hinder a better understanding of the dynamics of distinctive phenotypic states and abnormal trajectories from normal physiological transition to malignant stages. Cellular barcoding enabled lineage tracing on parallelly massive cells at single-cell resolution through different mechanisms lately, enabling new insights into exploring developmental trajectories, cancer progression, and targeted therapies. This review summarizes the latest noteworthy and robust strategies for different types of cellular barcodes. To introduce the major characteristics, advantages and limitations of these different strategies, this review will further guide in choosing or improving cellular barcoding technologies and their applications in cancer research.

hapln1a+ cells guide coronary growth during heart morphogenesis and regeneration

Although several tissues and chemokines orchestrate coronary formation, the guidance cues for coronary growth remain unclear. Here, we profile the juvenile zebrafish epicardium during coronary vascularization and identify hapln1a+ cells enriched with vascular-regulating genes. hapln1a+ cells not only envelop vessels but also form linear structures ahead of coronary sprouts. Live-imaging demonstrates that coronary growth occurs along these pre-formed structures, with depletion of hapln1a+ cells blocking this growth. hapln1a+ cells also pre-lead coronary sprouts during regeneration and hapln1a+ cell loss inhibits revascularization. Further, we identify serpine1 expression in hapln1a+ cells adjacent to coronary sprouts, and serpine1 inhibition blocks vascularization and revascularization. Moreover, we observe the hapln1a substrate, hyaluronan, forming linear structures along and preceding coronary vessels. Depletion of hapln1a+ cells or serpine1 activity inhibition disrupts hyaluronan structure. Our studies reveal that hapln1a+ cells and serpine1 are required for coronary production by establishing a microenvironment to facilitate guided coronary growth.

Resveratrol attenuated fatty acid synthesis through MAPK-PPAR pathway in red tilapia

High-fat (HF) diets have been shown to cause hepatic impairment in fish species, but the mode of action, especially the pathways involved, has not yet been determined. In this study, the effects of resveratrol (RES) supplementation on the hepatic structure and fat metabolism of red tilapia (Oreochromis niloticus) were determined. Based on transcriptome and proteomics results, RES was found to promote fatty acid β-oxidation in the blood, liver, and liver cells associated with apoptosis and the MAPK/PPAR signaling pathway. RES supplementation was found to alter the expression of genes related to apoptosis and fatty acid pathways like blood itga6a and armc5 which were upregulated and downregulated respectively by high-fat feeding while ggh and ensonig00000008711 increased and decreased, respectively, with RES addition. Relative to the PPAR signaling pathway, fabp10a and acbd7 showed a reverse U-shaped tendency, both in different treatments and at different times. Proteomics results demonstrated that MAPK/PPAR, carbon/glyoxylate, dicarboxylate/glycine serine, and threonine/drug-other enzymes/beta-alanine metabolism pathways in the RES group were significantly affected, and Fasn and Acox1 decreased and increased, respectively, with RES addition. Seven subgroups were obtained using scRNA-seq, and enrichment analysis showed that the PPAR signaling pathway was upregulated with RES supplementation. RES significantly increased the expression of the marked genes (pck1) ensonig00000037711, fbp10a, granulin, hbe1, and zgc:136461, which are liver cell-specific genes. In conclusion, RES resulted in significantly enriched DGEs associated with fat metabolism and synthesis via the MAPK-PPAR signaling pathway.

Formation of the Heart: Defining Cardiomyocyte Progenitors at Single-Cell Resolution

PURPOSE OF REVIEW

Formation of the heart requires the coordinated addition of multiple progenitor sources which have undergone different pathways of specification and differentiation. In this review, I aim to put into context how recent studies defining cardiac progenitor heterogeneity build on our understanding of early heart development and also discuss the questions raised by this new insight.

RECENT FINDINGS

With the development of sequencing technologies and imaging approaches, it has been possible to define, at high temporal resolution, the molecular profile and anatomical location of cardiac progenitors at the single-cell level, during the formation of the mammalian heart. Given the recent progress in our understanding of early heart development and technical advances in high-resolution time-lapse imaging and lineage analysis, we are now in a position of great potential, allowing us to resolve heart formation at previously impossible levels of detail. Understanding how this essential organ forms not only addresses questions of fundamental biological significance but also provides a blueprint for strategies to both treat and model heart disease.

Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes

Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Spinal Cord Organoids to Study Motor Neuron Development and Disease

Motor neuron diseases (MNDs) are a heterogeneous group of disorders that affect the cranial and/or spinal motor neurons (spMNs), spinal sensory neurons and the muscular system. Although they have been investigated for decades, we still lack a comprehensive understanding of the underlying molecular mechanisms; and therefore, efficacious therapies are scarce. Model organisms and relatively simple two-dimensional cell culture systems have been instrumental in our current knowledge of neuromuscular disease pathology; however, in the recent years, human 3D in vitro models have transformed the disease-modeling landscape. While cerebral organoids have been pursued the most, interest in spinal cord organoids (SCOs) is now also increasing. Pluripotent stem cell (PSC)-based protocols to generate SpC-like structures, sometimes including the adjacent mesoderm and derived skeletal muscle, are constantly being refined and applied to study early human neuromuscular development and disease. In this review, we outline the evolution of human PSC-derived models for generating spMN and recapitulating SpC development. We also discuss how these models have been applied to exploring the basis of human neurodevelopmental and neurodegenerative diseases. Finally, we provide an overview of the main challenges to overcome in order to generate more physiologically relevant human SpC models and propose some exciting new perspectives.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.