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At least 19 recordsLinked to original sources
PubMed2026

Generation and validation of a Myh11Dre-Spp1Cre intersectional mouse model for lineage tracing of disease-associated smooth muscle cell states.

BACKGROUND: Phenotypic modulation of vascular smooth muscle cells (VSMCs) is a hallmark of vascular remodeling and cardiovascular disease. Recent lineage-tracing and single-cell transcriptomic studies have identified secreted phosphoprotein 1 (SPP1) as a prominent marker associated with disease-associated VSMC states, particularly those linked to fibrotic remodeling and vascular calcification. However, the cellular origins and fate of SPP1-associated VSMC populations remain incompletely understood. METHODS AND RESULTS: We generated a novel Spp1-rSTOPr-Cre (Spp1Cre) knock-in mouse line in which Cre recombinase is expressed from the endogenous Spp1 locus following Dre-mediated excision of a rox-flanked transcriptional STOP cassette. Correct targeting of the knock-in allele was validated by internal, 5' junction, 3' junction, and long-range PCR analyses, as well as Sanger sequencing. To establish an intersectional lineage-tracing strategy, Spp1Cre mice were crossed with Myh11DreERT2 and Rosa26-RSR-LSL-tdTomato-LSL-eGFP reporter mice, enabling permanent labeling of VSMC-derived populations following activation of the endogenous Spp1 locus. Under physiological conditions, eGFP-positive cells were detected at low frequency within the vascular wall and were predominantly negative for the contractile markers ACTA2 and MYH11. As a proof-of-principle application, eGFP-positive cells markedly expanded within atherosclerotic lesions induced by AAV-PCSK9D377Y and high-fat diet feeding. These lineage-traced cells remained largely ACTA2- and MYH11-negative, consistent with a modulated phenotype. Notably, only a minority of eGFP-positive cells expressed SPP1 or fibronectin at the time of analysis, demonstrating the utility of permanent lineage tracing for tracking cells with a history of endogenous Spp1 activation during vascular remodeling. CONCLUSION: We report the generation and validation of a novel Myh11Dre-Spp1Cre intersectional mouse model for lineage tracing of VSMC-derived populations that have activated the endogenous Spp1 locus. This genetic resource provides a valuable platform for investigating the origin, fate, and phenotypic evolution of Spp1-associated VSMC populations during vascular remodeling and cardiovascular disease.

Animals↗
PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗
PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗
PubMed2025

Multicolor Cell Lineage Tracing Using MAGIC Markers Strategies.

Multicolor MAGIC Markers strategies are useful lineage tracing tools to study brain development at a multicellular scale. In this chapter, we describe an in utero electroporation method to simultaneously label multiple neighboring progenitors and their respective progeny using these multicolor reporters. In utero electroporation enables the introduction of any gene of interest into embryonic neural progenitors lining the brain ventricles through a simple pipeline consisting of a micro-injection followed by the application of electrical pulses. Successful in utero electroporation requires a concise yet complete understanding of each step of the surgical protocol, spanning from the preoperative preparation to the postoperative care, as well as the MAGIC Markers tool outlined in this study. Besides a detailed protocol, we present non-integrative and integrative approaches to demonstrate the range of cell and lineage tracking possibilities of multicolored progenitors and their descent over time.

Cell Lineage↗
PubMed2025

Germline stem cell isolation, lineage tracing, and aging in a protochordate.

Germline stem cells (GSCs), the source of gametes, are the only stem cells capable of passing genes to future generations and are therefore considered units of natural selection. Yet, the factors that influence GSC fitness, and thus govern GSC competition, which exist in both protochordates and mammals, remain poorly understood. We studied how aging affects GSC fitness in the protochordate Botryllus schlosseri, an evolutionary crosspoint between invertebrates and vertebrates. GSCs were isolated and distinguished from developing and mature gametes using flow cytometry and scRNA-Seq, facilitated by a new PacBio genome assembly. Moreover, their function was validated through a novel lineage tracing approach that combines membrane-labeled GSC transplantation with scRNA-Seq. Leveraging our method to isolate them, single-cell transcriptomics showed significant age-related changes between young and old GSCs. Spermatids and sperm, however, showed minimal changes, suggesting that reproductive aging is governed by GSCs rather than by gametes. Reduced expressions of markers like DDX4 and PIWIL1 in aged GSCs mirrored trends in mammalian datasets, pointing to a conserved GSC-driven aging mechanism across chordate evolution. This study provides new techniques that lay the foundation to investigate further drivers of GSC fitness and highlights fertility-related genes as promising targets for therapies to preserve reproductive health.

Journal Article↗
PubMed2025

Paired Single-Cell Transcriptome and DNA Barcode Detection in Zebrafish Using ScarTrace.

ScarTrace is a CRISPR/Cas9-based genetic lineage tracing method that allows for uniquely barcoding the DNA of single cells at a target GFP sequence during developing zebrafish embryos. Single cells from barcoded adult zebrafish can be isolated from various tissues (e.g., marrow, brain, eyes, fins), and their transcriptome and barcode sequences are captured by single-cell cDNA amplification and genomic DNA nested PCR, respectively. Computationally, cell type and barcode identification permit clone tracing and lineage tree reconstruction of tissues to unravel fate decisions during embryogenesis.

Animals↗
PubMed2025

CLADES: A Programmable Cascade of Genes for Cell Lineage Analysis and Manipulation.

In the Drosophila brain, neuronal diversity originates from approximately 100 neural stem cells, each dividing asymmetrically. Precise mapping of cell lineages at the single-cell resolution is crucial for understanding the mechanisms that direct neuronal specification. However, existing methods for high-resolution lineage tracing are notably time-consuming and labor-intensive. Here, we outline the best practices for lineage tracing using CLADES (cell lineage access driven by an edition sequence), a revolutionary approach to neuronal lineage tracing that addresses the limitations of previous methods. CLADES effectively traces the birth order of neurons using approximately 100 samples. The technique relies on a genetic cascade of reporter activations and deactivations that delineate lineage progression through color-coded markers. This system not only facilitates the detailed mapping of neuronal lineages but also holds the potential to be applied to tracking biological events and producing cell types for therapeutic purposes.

Animals↗
PubMed2025

Cloning and validating systems for high throughput molecular recording.

Molecular recording technologies record and store information about cellular history. Lineage tracing is one form of molecular recording and produces information describing cellular trajectories during mammalian development, differentiation and maintenance of adult stem cell niches, and tumor evolution. Our molecular recorder technology utilizes CRISPR-Cas9 barcode editing to generate mutations in genomically integrated, engineered DNA cassettes, which are read out by single-cell RNA sequencing and used to produce high-resolution lineage trees. Here, we describe optimized cloning and validation procedures to construct the molecular recorder lineage tracing system. We include information on considerations of technology design, cloning procedures, the generation of lineage tracing cell lines, and time course experiments to assess their performance.

Cloning, Molecular↗
PubMed2026

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (i) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (ii) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈25%-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is implemented in C++ and is available as both a command-line interface and as a Python library at: github.com/raphael-group/LAML-Pro.

Cell Lineage↗
PubMed2026

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈ 25-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

Journal Article↗
PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗
PubMed2023

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Journal Article↗
PubMed2025

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Humans↗
PubMed2026

Transient YAP activation uncovers the neurogenic potential of proliferative mammalian Müller glia.

The Hippo pathway effector YAP promotes spontaneous proliferation of Müller glia (MG), suggesting that bypassing Hippo signaling and activating YAP could enhance retinal regeneration. However, whether proliferative adult MGs retain meaningful neurogenic competence remains unclear. Here, using viral delivery of a Hippo-resistant YAP variant to wild-type adult MGs, we achieved transient YAP activation in adult MGs, inducing proliferation followed by cell-cycle withdrawal and differentiation. Intersectional genetic lineage tracing and EdU labeling, combined with transcriptomic analyses, revealed that YAP-activated MGs predominantly regenerate MGs, whereas only a subset gives rise to bipolar cell-like neurons. These results indicate that proliferative MGs acquire a state resembling that of late-stage retinal progenitors, with limited neurogenic lineage potential. We conclude that YAP-activated cell-cycle reentry inefficiently reprograms adult MGs toward photoreceptor or ganglion cell fates. These findings define the limited competence of proliferative adult MGs to contribute to neurogenic fates and provide a rigorous framework for assessing in vivo glial reprogramming strategies.

AAV↗
PubMed2025

Tracking HIV persistence across T cell lineages during early ART-treated HIV-1-infection using a reservoir-marking humanized mouse model.

Human immunodeficiency virus (HIV) infection depletes CD4 T-cells, and long-term persistence of latent virus prevents full clearance of HIV even in the presence of effective antiretroviral therapy (ART), Here we present the HIV-1-induced lineage tracing (HILT) system, a model that irreversibly marks infected cells within a humanized mouse model, which detects rare latently infected cells. Immunodeficient mice transplanted with genetically modified hematopoietic stem cells develop a human immune system, in which CD4 T-cells contain a genetic switch that permanently labels cells infected by HIV-1 expressing cre-recombinase. Through single-cell RNA sequencing of HILT-marked cells during acute infection and post-ART treatment, we identify distinct CD4+ T-cell transcriptional lineages enriched in either active or latent infections. Comparative gene expression analysis highlights common pathways modulated in both states, including EIF2, Sirtuin, and protein ubiquitination. Critical regulators of these pathways, including JUN, BCL2, and MDM2, change to opposite directions in the two states, highlighting gene expression programs that may support HIV persistence across T-cell lineages and states.

Animals↗
PubMed2025

Zonal endothelial cell heterogeneity underlies murine renal vascular development.

The renal vasculature consists of highly specialized blood vessels with distinct physiological functions. Defining their transcriptional signatures and tracing their developmental ontogeny has thus far been challenging due to a lack of regionally specific endothelial biomarkers. Here, we performed single nuclear RNA sequencing (snucRNA-Seq) to interrogate the transcriptional heterogeneity of embryonic renal endothelial cells (ECs). We identified ten endothelial subtypes, and validated regionally restricted expression of novel marker genes of glomeruli, arteries, vasa recta, and immature capillary subtypes using multiplex RNAscope. We also define previously uncharacterized and heterogeneous molecular signatures of the immature renal vasculature, including putative endothelial progenitors. We interrogate biological characteristics of immature EC types using a variety of in vivo tools. Lineage tracing of Esm1-expressing cells reveals the previously unrecognized multi-origin and multi-clonal endothelial tip cell contribution to the glomerular vasculature. Together, this study provides a validated, tool-focused developmental atlas of the murine renal vasculature and elucidates novel cellular mechanisms of nephron vascularization.

Animals↗
PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗
PubMed2026

Athero-oncology: Vascular smooth muscle cell tumor-like transformation in atherosclerosis and therapeutic opportunities.

Atherosclerosis (AS) is the main pathological basis of cardiovascular diseases, and its pathogenesis and treatment strategies remain major challenges. Recent advances in single-cell RNA sequencing and lineage tracing have revealed that vascular smooth muscle cells (VSMCs) are not merely passive structural components of atherosclerotic plaques, but highly plastic participants that undergo clonal expansion, phenotypic modulation, and transdifferentiation into functionally diverse cell states. These findings have prompted the emergence of an "athero-oncology" framework, which explores selected tumor-like cellular programs in VSMCs during AS without equating atherosclerosis with cancer. In this review, we summarize the evidence supporting VSMC-derived clonal expansion and phenotypic diversification in atherosclerotic lesions and discuss key mechanisms involved in this process, including proliferative expansion and survival programs, metabolic reprogramming, epigenetic regulation, DNA damage and genomic stress, VSMC senescence, pathological angiogenesis, and remodeling of the inflammatory and immune microenvironment. We further highlight shared signaling pathways between VSMC-driven plaque remodeling and tumor biology, while emphasizing fundamental differences between AS and malignant disease in growth limitation, mutational burden, metastatic potential, and clinical behavior. Finally, we discuss oncology-inspired therapeutic opportunities and boundaries, including pathway-level targeting of proliferative, metabolic, epigenetic, and inflammatory programs, as well as the risks of directly repurposing anticancer therapies for chronic vascular disease. This framework may provide new insights into vascular biology and therapeutic development.

atherosclerosis↗