Connecting past and present: single-cell lineage tracing

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

Limitations and optimizations of cellular lineages tracking

Tracking cellular lineages using genetic barcodes provides insights across biology and has become an important tool. However, barcoding strategies remain ad hoc. We show that elevating barcode insertion probability and thus increasing the average number of barcodes within the cells, adds to the number of traceable lineages but may decrease the accuracy of lineages inference due to reading errors. We establish the trade-off between accuracy in tracing lineages and the total number of traceable lineages, and find optimal experimental parameters under limited resources concerning the populations size of tracked cells and barcode pool complexity.

The landscape of cell lineage tracing

Cell fate changes play a crucial role in the processes of natural development, disease progression, and the efficacy of therapeutic interventions. The definition of the various types of cell fate changes, including cell expansion, differentiation, transdifferentiation, dedifferentiation, reprogramming, and state transitions, represents a complex and evolving field of research known as cell lineage tracing. This review will systematically introduce the research history and progress in this field, which can be broadly divided into two parts: prospective tracing and retrospective tracing. The initial section encompasses an array of methodologies pertaining to isotope labeling, transient fluorescent tracers, non-fluorescent transient tracers, non-fluorescent genetic markers, fluorescent protein, genetic marker delivery, genetic recombination, exogenous DNA barcodes, CRISPR-Cas9 mediated DNA barcodes, and base editor-mediated DNA barcodes. The second part of the review covers genetic mosaicism, genomic DNA alteration, TCR/BCR, DNA methylation, and mitochondrial DNA mutation. In the final section, we will address the principal challenges and prospective avenues of enquiry in the field of cell lineage tracing, with a particular focus on the sequencing techniques and mathematical models pertinent to single-cell genetic lineage tracing, and the value of pursuing a more comprehensive investigation at both the spatial and temporal levels in the study of cell lineage tracing.

scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics

Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. Interestingly, scCamAge, trained solely on yeast images, without additional learning, surpasses generic models in predicting chemical and replication-induced senescence in human fibroblasts, indicating evolutionary conservation of aging-related morphometrics. Finally, we enhanced the generalizability of scCamAge by retraining it on human fibroblast senescence datasets, which improved its ability to predict senescent cells.

Dual-nuclease single-cell lineage tracing by Cas9 and Cas12a

Single-cell lineage tracing based on CRISPR-Cas9 gene editing enables the simultaneous linkage of cell states and lineage history at a high resolution. Despite its immense potential in resolving the cell fate determination and genealogy within an organism, existing implementations of this technology suffer from limitations in recording capabilities and considerable barcode dropout. Here, we introduce DuTracer, a versatile tool that utilizes two orthogonal gene editing systems to record cell lineage history at single-cell resolution in an inducible manner. DuTracer shows the ability to enhance lineage recording with minimized target dropouts and potentially deeper tree depths. Applying DuTracer in mouse embryoid bodies and neuromesodermal organoids illustrates the lineage relationship of different cell types and proposes potential lineage-biased molecular drivers, showcased by identifying transcription factor Foxb1 as a modulator in the cell fate determination of neuromesodermal progenitors. Collectively, DuTracer facilitates the precise and regulatory interrogation of cellular lineages of complex biological processes.

Deciphering the Complexities of Adult Human Steady State and Stress-Induced Hematopoiesis: Progress and Challenges

Human hematopoietic stem cells (HSCs) have traditionally been viewed as self-renewing, multipotent cells with enormous potential in sustaining essential steady state blood and immune cell production throughout life. Indeed, around 86% (1011-1012) of new cells generated daily in a healthy young human adult are of hematopoietic origin. Therapeutically, human HSCs have contributed to over 1.5 million hematopoietic cell transplants (HCTs) globally, making this the most successful regenerative therapy to date. We will commence this review by briefly highlighting selected key achievements (from 1868 to the end of the 20th century) that have contributed to this accomplishment. Much of our knowledge of hematopoiesis is based on small animal models that, despite their enormous importance, do not always recapitulate human hematopoiesis. Given this, we will critically review the progress and challenges faced in identifying adult human HSCs and tracing their lineage differentiation trajectories, referring to murine studies as needed. Moving forward and given that human hematopoiesis is dynamic and can readily adjust to a variety of stressors, we will then discuss recent research advances contributing to understanding (i) which HSPCs maintain daily steady state human hematopoiesis, (ii) where these are located, and (iii) which mechanisms come into play when homeostatic hematopoiesis switches to stress-induced or emergency hematopoiesis.

scLTdb: a comprehensive single-cell lineage tracing database

Single-cell lineage tracing (scLT) is a powerful technique that integrates cellular barcoding with single-cell sequencing technologies. This new approach enables the simultaneous measurement of cell fate and molecular profiles at single-cell resolution, uncovering the gene regulatory program of cell fate determination. However, a comprehensive scLT database is not yet available. Here, we present the single-cell lineage tracing database (scLTdb, https://scltdb.com) containing 109 datasets that are manually curated and analyzed through a standard pipeline. The scLTdb provides interactive analysis modules for visualizing and re-analyzing scLT datasets, especially the comprehensive cell fate analysis and lineage relationship analysis. Importantly, scLTdb also allows users to identify fate-related gene signatures. In conclusion, scLTdb provides an interactive interface of scLT data exploration and analysis, and will facilitate the understanding of cell fate decision and lineage commitment in development and diseases.

Advancements in prospective single-cell lineage barcoding and their applications in research

Single-cell lineage tracing (scLT) has emerged as a powerful tool, providing unparalleled resolution to investigate cellular dynamics, fate determination, and the underlying molecular mechanisms. This review thoroughly examines the latest prospective lineage DNA barcode tracing technologies. It further highlights pivotal studies that leverage single-cell lentiviral integration barcoding technology to unravel the dynamic nature of cell lineages in both developmental biology and cancer research. Additionally, the review navigates through critical considerations for successful experimental design in lineage tracing and addresses challenges inherent in this field, including technical limitations, complexities in data analysis, and the imperative for standardization. It also outlines current gaps in knowledge and suggests future research directions, contributing to the ongoing advancement of scLT studies.

Mesenchymal stem cells lineage and their role in disease development

BACKGROUND

Mesenchymal stem cells (MSCs) are widely dispersed in vivo and are isolated from several tissues, including bone marrow, heart, body fluids, skin, and perinatal tissues. Bone marrow MSCs have a multidirectional differentiation potential, which can be induced to differentiate the medium in a specific direction or by adding specific regulatory factors. MSCs repair damaged tissues through lineage differentiation, and the ex vivo transplantation of bone marrow MSCs can heal injured sites. MSCs have different propensities for lineage differentiation and pathological evolution for different diseases, which are crucial in disease progression. In this study, we describe various lineage analysis methods to explore lineage ontology in vitro and in vivo, elucidate the impact of MSC lineage differentiation on diseases, advance our understanding of the role of MSC differentiation in physiological and pathological states, and explore new targets and ideas associated with disease diagnosis and treatment.

Current advances in understanding endometrial epithelial cell biology and therapeutic applications for intrauterine adhesion

The human endometrium is a highly regenerative tissue capable of undergoing scarless repair during the menstruation and postpartum phases. This process is mediated by endometrial adult stem/progenitor cells. During the healing of endometrial injuries, swift reepithelization results in the rapid covering of the wound surface and facilitates subsequent endometrial restoration. The involvement of endogenous endometrial epithelial stem cells, stromal cells, and bone marrow-derived cells in the regeneration of the endometrial epithelium has been a subject of prolonged debate. Increasing evidence suggests that the regeneration of the endometrial epithelium mainly relies on epithelial stem cells rather than stromal cells and bone marrow-derived cells. Currently, no consensus has been established on the identity of epithelial stem cells in the epithelial compartment. Several markers, including stage-specific embryonic antigen-1 (SSEA-1), sex-determining region Y-box 9 (SOX9), neural-cadherin (N-cadherin), leucine-rich-repeat-containing G-protein-coupled receptor 5 (LGR5), CD44, axis inhibition protein 2 (Axin2), and aldehyde dehydrogenase 1A1 (ALDH1A1), have been suggested as potential candidate markers for endometrial epithelial stem cells. The identification of endometrial epithelial stem cells contributes to our understanding of endometrial regeneration and offers new therapeutic insights into diseases characterized by regenerative defects in the endometrium, such as intrauterine adhesion. This review explores different perspectives on the origins of human and mouse endometrial epithelial cells. It summarizes the potential markers, locations, and hierarchies of epithelial stem cells in both human and mouse endometrium. It also discusses epithelial cell-based treatments for intrauterine adhesion, hoping to inspire further research and clinical application of endometrial epithelial stem cells.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Beyond the bulk: overview and novel insights into the dynamics of muscle satellite cells during muscle regeneration

Skeletal muscle possesses remarkable regenerative capabilities, fully recovering within a month following severe acute damage. Central to this process are muscle satellite cells (MuSCs), a resident population of somatic stem cells capable of self-renewal and differentiation. Despite the highly predictable course of muscle regeneration, evaluating this process has been challenging due to the heterogeneous nature of myogenic precursors and the limited insight provided by traditional markers with overlapping expression patterns. Notably, recent advancements in single-cell technologies, such as single-cell (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), have revolutionized muscle research. These approaches allow for comprehensive profiling of individual cells, unveiling dynamic heterogeneity among myogenic precursors and their contributions to regeneration. Through single-cell transcriptome analyses, researchers gain valuable insights into cellular diversity and functional dynamics of MuSCs post-injury. This review aims to consolidate classical and new insights into the heterogeneity of myogenic precursors, including the latest discoveries from novel single-cell technologies.

Regulatory mechanisms orchestrating cellular diversity of Cd36+ olfactory sensory neurons revealed by scRNA-seq and scATAC-seq analysis

Recent discoveries have revealed remarkable complexity within olfactory sensory neurons (OSNs), including the existence of two OSN populations based on the expression of Cd36. However, the regulatory mechanisms governing this cellular diversity in the same cell type remain elusive. Here, we show the preferential expression of 79 olfactory receptors in Cd36+ OSNs and the anterior projection characteristics of Cd36+ OSNs, indicating the non-randomness of Cd36 expression. The integrated analysis of single-cell RNA sequencing (scRNA-seq) and scATAC-seq reveals that the differences in Cd36+/- OSNs occur at the immature OSN stage, with Mef2a and Hdac9 being important regulators of developmental divergence. We hypothesize that the absence of Hdac9 may affect the activation of Mef2a, leading to the up-regulation of Mef2a target genes, including teashirt zinc finger family member 1 (Tshz1), in the Cd36+ OSN lineage. We validate that Tshz1 directly promotes Cd36 expression through enhancer bindings. Our study unravels the intricate regulatory landscape and principles governing cellular diversity in the olfactory system.

Identification of hypoxic macrophages in glioblastoma: Unveiling therapeutic insights from tumour microenvironment analysis

Tumor-associatedmacrophages (TAMs) exhibit remarkable heterogeneity in glioblastoma. Spatially resolved single-cell transcriptomic studies identified a monocyte-derived TAM subset localized in the peri-necrotic niche, driven by hypoxic cues to acquire ahypoxia response signature. These hypoxia-TAMs destabilize endothelial adherens junctions through adrenomedullin paracrine signaling, promoting the formation of hyperpermeable neovasculature that impedes drug delivery. Blocking adrenomedullin produced by hypoxia-TAMs restores vascular integrity, increases drug deliveryinto tumors, and provides combinatorial therapeutic benefits. Here we discuss the heterogeneity of TAMs regarding functional states and locations in glioblastomas, and propose future directions for studying the temporospatial dynamics of multifaceted TAM. HIGHLIGHTS: Single-cell omics reveal a functionally and spatially distinct hypoxia-TAM subset in glioblastoma. Adrenomedullin secreted by hypoxia-TAM destabilizes tumor vasculature and its blockade enhances vessel integrity and drug delivery.

Skeletal stem and progenitor cells in bone development and repair

Bone development, growth, and repair are complex processes involving various cell types and interactions, with central roles played by skeletal stem and progenitor cells. Recent research brought new insights into the skeletal precursor populations that mediate intramembranous and endochondral bone development. Later in life, many of the cellular and molecular mechanisms determining development are reactivated upon fracture, with powerful trauma-induced signaling cues triggering a variety of postnatal skeletal stem/progenitor cells (SSPCs) residing near the bone defect. Interestingly, in this injury context, the current evidence suggests that the fates of both SSPCs and differentiated skeletal cells can be considerably flexible and dynamic, and that multiple cell sources can be activated to operate as functional progenitors generating chondrocytes and/or osteoblasts. The combined implementation of in vivo lineage tracing, cell surface marker-based cell selection, single-cell molecular analyses, and high-resolution in situ imaging has strongly improved our insights into the diversity and roles of developmental and reparative stem/progenitor subsets, while also unveiling the complexity of their dynamics, hierarchies, and relationships. Albeit incompletely understood at present, findings supporting lineage flexibility and possibly plasticity among sources of osteogenic cells challenge the classical dogma of a single primitive, self-renewing, multipotent stem cell driving bone tissue formation and regeneration from the apex of a hierarchical and strictly unidirectional differentiation tree. We here review the state of the field and the newest discoveries in the origin, identity, and fates of skeletal progenitor cells during bone development and growth, discuss the contributions of adult SSPC populations to fracture repair, and reflect on the dynamism and relationships among skeletal precursors and differentiated cell lineages. Further research directed at unraveling the heterogeneity and capacities of SSPCs, as well as the regulatory cues determining their fate and functioning, will offer vital new options for clinical translation toward compromised fracture healing and bone regenerative medicine.

Application of Lineage Tracing in Central Nervous System Development and Regeneration

The central nervous system (CNS) is a complicated neural network. The origin and evolution of functional neurons and glia cells remain unclear, as do the cellular alterations that occur during the course of cerebral disease rehabilitation. Lineage tracing is a valuable method for tracing specific cells and achieving a better understanding of the CNS. Recently, various technological breakthroughs have been made in lineage tracing, such as the application of various combinations of fluorescent reporters and advances in barcode technology. The development of lineage tracing has given us a deeper understanding of the normal physiology of the CNS, especially the pathological processes. In this review, we summarize these advances of lineage tracing and their applications in CNS. We focus on the use of lineage tracing techniques to elucidate the process CNS development and especially the mechanism of injury repair. Deep understanding of the central nervous system will help us to use existing technologies to diagnose and treat diseases.

Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data

MOTIVATION

Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division.

RESULTS

We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems.

AVAILABILITY AND IMPLEMENTATION

The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

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.

A statistical method for quantifying progenitor cells reveals incipient cell fate commitments

Quantifying the number of progenitor cells that found an organ, tissue or cell population is of fundamental importance for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. This strategy is, however, often hindered by the lack of ideal markers. Here we propose a general statistical method to quantify the progenitors of any tissues or cell populations in an organism, even in the absence of progenitor-specific markers, by exploring the cell phylogenetic tree that records the cell division history during development. The method, termed targeting coalescent analysis (TarCA), computes the probability that two randomly sampled cells of a tissue coalesce within the tissue-specific monophyletic clades. The inverse of this probability then serves as a measure of the progenitor number of the tissue. Both mathematic modeling and computer simulations demonstrated the high accuracy of TarCA, which was then validated using real data from nematode, fruit fly and mouse, all with related cell phylogenetic trees. We further showed that TarCA can be used to identify lineage-specific upregulated genes during embryogenesis, revealing incipient cell fate commitments in mouse embryos.

Recent advances in endothelial colony-forming cells: from the transcriptomic perspective

Endothelial colony-forming cells (ECFCs) are progenitors of endothelial cells with significant proliferative and angiogenic ability. ECFCs are a promising treatment option for various diseases, such as ischemic heart disease and peripheral artery disease. However, some barriers hinder the clinical application of ECFC therapeutics. One of the current obstacles is that ECFCs are dysfunctional due to the underlying disease states. ECFCs exhibit dysfunctional phenotypes in pathologic states, which include but are not limited to the following: premature neonates and pregnancy-related diseases, diabetes mellitus, cancers, haematological system diseases, hypoxia, pulmonary arterial hypertension, coronary artery diseases, and other vascular diseases. Besides, ECFCs are heterogeneous among donors, tissue sources, and within cell subpopulations. Therefore, it is important to elucidate the underlying mechanisms of ECFC dysfunction and characterize their heterogeneity to enable clinical application. In this review, we summarize the current and potential application of transcriptomic analysis in the field of ECFC biology. Transcriptomic analysis is a powerful tool for exploring the key molecules and pathways involved in health and disease and can be used to characterize ECFC heterogeneity.

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.

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.

Lineage Tracing by Single-Cell Transcriptomics Decoding Dynamics of Lineage Commitment

Tracing the fate of individual cells and their progeny is necessary and significant for stem cell research and cancer research. Changes in lineage-specific transcription factor levels during lineage commitment are gradual and continuous. Development of single-cell sequencing technology allows many different states of cells to be sequenced at an unprecedented resolution, and it has been proved that single-cell transcriptomics meets lineage tracing. Here, we introduce a detailed protocol for the lineage tracing by single-cell transcriptomics to clarify the dynamics of lineage commitment.

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.

How colonization bottlenecks, tissue niches, and transmission strategies shape protozoan infections

Protozoan pathogens such as Plasmodium spp., Leishmania spp., Toxoplasma gondii, and Trypanosoma spp. are often associated with high-mortality, acute and chronic diseases of global health concern. For transmission and immune evasion, protozoans have evolved diverse strategies to interact with a range of host tissue environments. These interactions are linked to disease pathology, yet our understanding of the association between parasite colonization and host homeostatic disruption is limited. Recently developed techniques for cellular barcoding have the potential to uncover the biology regulating parasite transmission, dissemination, and the stability of infection. Understanding bottlenecks to infection and the in vivo tissue niches that facilitate chronic infection and spread has the potential to reveal new aspects of parasite biology.

Stem cell heterogeneity, plasticity, and regulation

As a population of homogeneous cells with both self-renewal and differentiation potential, stem cell pools are highly compartmentalized and contain distinct subsets that exhibit stable but limited heterogeneity during homeostasis. However, their striking plasticity is showcased under natural or artificial stress, such as injury, transplantation, cancer, and aging, leading to changes in their phenotype, constitution, metabolism, and function. The complex and diverse network of cell-extrinsic niches and signaling pathways, together with cell-intrinsic genetic and epigenetic regulators, tightly regulate both the heterogeneity during homeostasis and the plasticity under perturbation. Manipulating these factors offers better control of stem cell behavior and a potential revolution in the current state of regenerative medicine. However, disruptions of normal regulation by genetic mutation or excessive plasticity acquisition may contribute to the formation of tumors. By harnessing innovative techniques that enhance our understanding of stem cell heterogeneity and employing novel approaches to maximize the utilization of stem cell plasticity, stem cell therapy holds immense promise for revolutionizing the future of medicine.

Inferring chromatin accessibility during murine hematopoiesis through phylogenetic analysis

OBJECTIVE

Diversification of cell types and changes in epigenetic states during cell differentiation processes are important for understanding development. Recently, phylogenetic analysis using DNA methylation and histone modification information has been shown useful for inferring these processes. The purpose of this study was to examine whether chromatin accessibility data can help infer these processes in murine hematopoiesis.

RESULTS

Chromatin accessibility data could partially infer the hematopoietic differentiation hierarchy. Furthermore, based on the ancestral state estimation of internal nodes, the open/closed chromatin states of differentiating progenitor cells could be predicted with a specificity of 0.86-0.99 and sensitivity of 0.29-0.72. These results suggest that the phylogenetic analysis of chromatin accessibility could offer important information on cell differentiation, particularly for organisms from which progenitor cells are difficult to obtain.

Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes

Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

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.

Tuberous Sclerosis Complex Kidney Lesion Pathogenesis: A Developmental Perspective

The phenotypic diversity of tuberous sclerosis complex (TSC) kidney pathology is enigmatic. Despite a well-established monogenic etiology, an incomplete understanding of lesion pathogenesis persists. In this review, we explore the question: How do TSC kidney lesions arise? We appraise literature findings in the context of mutational timing and cell-of-origin. Through a developmental lens, we integrate the critical results from clinical studies, human specimens, and genetic animal models. We also review novel insights gleaned from emerging organoid and single-cell sequencing technologies. We present a new model of pathogenesis which posits a phenotypic continuum, whereby lesions arise by mutagenesis during development from variably timed second-hit events. This model can serve as a conceptual framework for testing hypotheses of TSC lesion pathogenesis, both in the kidney and in other affected tissues.

Base editors: development and applications in biomedicine

Base editor (BE) is a gene-editing tool developed by combining the CRISPR/Cas system with an individual deaminase, enabling precise single-base substitution in DNA or RNA without generating a DNA double-strand break (DSB) or requiring donor DNA templates in living cells. Base editors offer more precise and secure genome-editing effects than other conventional artificial nuclease systems, such as CRISPR/Cas9, as the DSB induced by Cas9 will cause severe damage to the genome. Thus, base editors have important applications in the field of biomedicine, including gene function investigation, directed protein evolution, genetic lineage tracing, disease modeling, and gene therapy. Since the development of the two main base editors, cytosine base editors (CBEs) and adenine base editors (ABEs), scientists have developed more than 100 optimized base editors with improved editing efficiency, precision, specificity, targeting scope, and capacity to be delivered in vivo, greatly enhancing their application potential in biomedicine. Here, we review the recent development of base editors, summarize their applications in the biomedical field, and discuss future perspectives and challenges for therapeutic applications.

Vascular smooth muscle cells in intracranial aneurysms

Intracranial aneurysm (IA) is a severe cerebrovascular disease characterized by abnormal bulging of cerebral vessels that may rupture and cause a stroke. The expansion of the aneurysm accompanies by the remodeling of vascular matrix. It is well-known that vascular remodeling is a process of synthesis and degradation of extracellular matrix (ECM), which is highly dependent on the phenotype of vascular smooth muscle cells (VSMCs). The phenotypic switching of VSMC is considered to be bidirectional, including the physiological contractile phenotype and alternative synthetic phenotype in response to injury. There is increasing evidence indicating that VSMCs have the ability to switch to various phenotypes, including pro-inflammatory, macrophagic, osteogenic, foamy and mesenchymal phenotypes. Although the mechanisms of VSMC phenotype switching are still being explored, it is becoming clear that phenotype switching of VSMCs plays an essential role in IA formation, progression, and rupture. This review summarized the various phenotypes and functions of VSMCs associated with IA pathology. The possible influencing factors and potential molecular mechanisms of the VSMC phenotype switching were further discussed. Understanding how phenotype switching of VSMC contributed to the pathogenesis of unruptured IAs can bring new preventative and therapeutic strategies for IA.

Lineage tracing reveals fate bias and transcriptional memory in human B cells

We combined single-cell transcriptomics and lineage tracing to understand fate choice in human B cells. Using the antibody sequences of B cells, we tracked clones during in vitro differentiation. Clonal analysis revealed a subset of IgM+ B cells which were more proliferative than other B-cell types. Whereas the population of B cells adopted diverse states during differentiation, clones had a restricted set of fates available to them; there were two times more single-fate clones than expected given population-level cell-type diversity. This implicated a molecular memory of initial cell states that was propagated through differentiation. We then identified the genes which had strongest coherence within clones. These genes significantly overlapped known B-cell fate determination programs, suggesting the genes which determine cell identity are most robustly controlled on a clonal level. Persistent clonal identities were also observed in human plasma cells from bone marrow, indicating that these transcriptional programs maintain long-term cell identities in vivo. Thus, we show how cell-intrinsic fate bias influences differentiation outcomes in vitro and in vivo.

Skeletal stem cells: origins, definitions, and functions in bone development and disease

Skeletal stem cells (SSCs) are tissue-specific stem cells that can self-renew and sit at the apex of their differentiation hierarchy, giving rise to mature skeletal cell types required for bone growth, maintenance, and repair. Dysfunction in SSCs is caused by stress conditions like ageing and inflammation and is emerging as a contributor to skeletal pathology, such as the pathogenesis of fracture nonunion. Recent lineage tracing experiments have shown that SSCs exist in the bone marrow, periosteum, and resting zone of the growth plate. Unraveling their regulatory networks is crucial for understanding skeletal diseases and developing therapeutic strategies. In this review, we systematically introduce the definition, location, stem cell niches, regulatory signaling pathways, and clinical applications of SSCs.

Spatiotemporal Omics-Refining the landscape of precision medicine

Current streamline of precision medicine uses histomorphological and molecular information to indicate individual phenotypes and genotypes to achieve optimal outcome of treatment. The knowledge of detected mutations and alteration can hardly describe molecular interaction and biological process which can finally be manifested as a disease. With molecular diagnosis revising the modalities of disease, there is a trend in precision medicine to apply multiomic and multidimensional information to decode tumors, regarding heterogeneity, pathogenesis, prognosis, etc. Emerging state-of-art spatiotemporal omics provides a novel vision for in discovering clinicopathogenesis associated findings, some of which show a promising potential to be translated to facilitate clinical practice. Here, we summarize the available spatiotemporal omic technologies and algorithms, highlight the novel scientific findings and explore potential applications in the clinical scenario. Spatiotemporal omics present the ability to provide impetus to rewrite clinical pathology and to answer outstanding clinical questions. This review emphasizes the novel vision of spatiotemporal omics to refine the landscape of precision medicine in the clinic.

Recent advances in the use of CRISPR/Cas for understanding the early development of molecular gaps in glial cells

Glial cells are non-neuronal elements of the nervous system (NS) and play a central role in its development, maturation, and homeostasis. Glial cell interest has increased, leading to the discovery of novel study fields. The CRISPR/Cas system has been widely employed for NS understanding. Its use to study glial cells gives crucial information about their mechanisms and role in the central nervous system (CNS) and neurodegenerative disorders. Furthermore, the increasingly accelerated discovery of genes associated with the multiple implications of glial cells could be studied and complemented with the novel screening methods of high-content and single-cell screens at the genome-scale as Perturb-Seq, CRISP-seq, and CROPseq. Besides, the emerging methods, GESTALT, and LINNAEUS, employed to generate large-scale cell lineage maps have yielded invaluable information about processes involved in neurogenesis. These advances offer new therapeutic approaches to finding critical unanswered questions about glial cells and their fundamental role in the nervous system. Furthermore, they help to better understanding the significance of glial cells and their role in developmental biology.