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Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single‐cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time‐dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time‐dependent transcriptional variation of patient‐derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient‐specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time‐dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition. Synopsis A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents. A general method is developed for de novo construction of quantitative network models of cancer cell State Transitions and Growth (STAG) from single‐cell measurements. Patient‐derived glioblastoma cells transit between transcriptional states, recapitulating normal neural cell types, in a hierarchical fashion. The STAG model can identify patient differences in cell state dynamics and define how therapeutic agents can alter the transition network. The long‐term cell population growth and cell state composition can be predicted by a mathematical eigendecomposition of the STAG network. Graphical Abstract A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents.

Cell fate determination is a fundamental process in multicellular development. In multicellular organisms, cells display plasticity in their fate, allowing them to revert to prior states or adopt alternative differentiation pathways, thereby altering their identity and functional specialization in response to specific stimuli. Investigating cell fate determination and its plasticity enhances our understanding of organ development, tissue homeostasis, and disease pathogenesis and progression, providing novel insights into regenerative medicine strategies. Lineage tracing technologies have fundamentally revolutionized this understanding of cell fate dynamics by enabling the identification and tracking of cells and their progeny in vivo. These technologies have progressed significantly, from the direct observation and manual annotation of cell lineage trees to complex recombinase‐mediated genetic labeling techniques. With the advent of sequencing technologies, the resolution and scale of lineage tracing have also developed toward the single‐cell level in individual organisms. Furthermore, lineage tracing is increasingly expanding to investigate how the tissue microenvironment influences cell fate decisions. Here, the evolution of lineage tracing technologies is introduced and their applications in cell fate determinations across development, regeneration, and diseases contexts. This figure is a candidate for the cover image. It shows a whole‐mount fluorescent image of lymphatic vessels (red) in the heart of an adult Cdh5‐Dre; Prox1‐RSR‐CreER; Rosa26‐tdT mouse. Image courtesy of Ximeng Han.

Elucidating the chromatin dynamics that orchestrate embryogenesis is a fundamental question in developmental biology. Here, we exploit position effects on expression as an indicator of chromatin activity and infer the chromatin activity landscape in every lineaged cell during Caenorhabditis elegans early embryogenesis. Systems‐level analyses reveal that chromatin activity distinguishes cellular states and correlates with fate patterning in the early embryos. As cell lineage unfolds, chromatin activity diversifies in a lineage‐dependent manner, with switch‐like changes accompanying anterior–posterior fate asymmetry and characteristic landscapes being established in different cell lineages. Upon tissue differentiation, cellular chromatin from distinct lineages converges according to tissue types but retains stable memories of lineage history, contributing to intra‐tissue cell heterogeneity. However, the chromatin landscapes of cells organized in a left–right symmetric pattern are predetermined to be analogous in early progenitors so as to pre‐set equivalent states. Finally, genome‐wide analysis identifies many regions exhibiting concordant chromatin activity changes that mediate the co‐regulation of functionally related genes during differentiation. Collectively, our study reveals the developmental and genomic dynamics of chromatin activity at the single‐cell level. SYNOPSIS This study investigates the influence of local chromatin environment on reporter gene expression during Caenorhabditis elegans embryogenesis, based on single‐cell lineage tracing and live‐cell imaging. The chromatin activity landscape is inferred in lineage‐resolved single cells during C. elegans early embryogenesis. Chromatin activity diversifies in a lineage‐dependent manner, accompanying lineage fate commitment and anterior‐posterior fate asymmetry. Chromatin activity converges on tissue‐specific states but retains memories of lineage origins that contribute to cell heterogeneity within tissues. Predetermination of analogous chromatin activity occurs in early progenitor cells during left‐right symmetry establishment. Graphical Abstract This study investigates the influence of local chromatin environment on reporter gene expression during Caenorhabditis elegans embryogenesis, based on single‐cell lineage tracing and live‐cell imaging.

Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.

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

The roles of cellular orientation during trabecular and ventricular wall morphogenesis are unknown, and so are the underlying mechanisms that regulate cellular orientation. Myocardial-specific Numb and Numblike double-knockout (MDKO) hearts display a variety of defects, including in cellular orientation, patterns of mitotic spindle orientation, trabeculation, and ventricular compaction. Furthermore, Numb- and Numblike-null cardiomyocytes exhibit cellular behaviors distinct from those of control cells during trabecular morphogenesis based on single-cell lineage tracing. We investigated how Numb regulates cellular orientation and behaviors and determined that N-cadherin levels and membrane localization are reduced in MDKO hearts. To determine how Numb regulates N-cadherin membrane localization, we generated an mCherry:Numb knockin line and found that Numb localized to diverse endocytic organelles but mainly to the recycling endosome. Consistent with this localization, cardiomyocytes in MDKO did not display defects in N-cadherin internalization but rather in postendocytic recycling to the plasma membrane. Furthermore, N-cadherin overexpression via a mosaic model partially rescued the defects in cellular orientation and trabeculation of MDKO hearts. Our study unravels a phenomenon that cardiomyocytes display spatiotemporal cellular orientation during ventricular wall morphogenesis, and its disruption leads to abnormal trabecular and ventricular wall morphogenesis. Furthermore, we established a mechanism by which Numb modulates cellular orientation and consequently trabecular and ventricular wall morphogenesis by regulating N-cadherin recycling to the plasma membrane.

Lineage tracing at single‐cell resolution is vital for mapping cell fate decisions, yet synthetic barcoding faces limitations in precision, diversity, and toxicity—especially in human pluripotent stem cells (hPSCs). Here, we repurpose naturally occurring somatic mutations in mitochondrial transcripts from single‐cell RNA sequencing as endogenous genetic barcodes. By enriching mitochondrial reads and applying a robust computational pipeline, we identified clonally informative variants to trace hematopoietic lineage emergence from hPSCs during early embryogenesis. Integrating mitochondrial barcoding with synthetic lineage tracing, we modeled embryonic tissue development and reconstructed the transcriptional logic and regulatory networks driving fate specification using a dynamical systems model. Extending this approach to spatial transcriptomics, we mapped the clonal architecture of human embryonic organoids, revealing spatial zonation orchestrated by NOTCH‐mediated crosstalk between stromal cells and hematopoietic progenitors. This multimodal strategy links clonal dynamics with niche‐driven fate decisions, offering a scalable, non‐invasive method to dissect tissue organization in development and disease. Together, our work establishes a scalable, non‐invasive multimodal framework that leverages endogenous mitochondrial DNA variants to reconstruct high‐resolution spatiotemporal clonal dynamics and decode niche‐driven fate decisions in a human stem cell‐derived model. This approach provides a powerful strategy for dissecting tissue self‐organization in development and disease.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia—a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia .

Cardiac fibrosis is the excess deposition of extracellular matrix (ECM), such as collagen. Myofibroblasts are major players in the production of collagen, and are differentiated primarily from resident fibroblasts. Collagen can compensate for the dead cells produced by injury. The appropriate production of collagen is beneficial for preserving the structural integrity of the heart, and protects the heart from cardiac rupture. However, excessive deposition of collagen causes cardiac dysfunction. Recent studies have demonstrated that myofibroblasts can change their phenotypes. In addition, myofibroblasts are found to have functions other than ECM production. Myofibroblasts have macrophage-like functions, in which they engulf dead cells and secrete anti-inflammatory cytokines. Research into fibroblasts has been delayed due to the lack of selective markers for the identification of fibroblasts. In recent years, it has become possible to genetically label fibroblasts and perform sequencing at single-cell levels. Based on new technologies, the origins of fibroblasts and myofibroblasts, time-dependent changes in fibroblast states after injury, and fibroblast heterogeneity have been demonstrated. In this paper, recent advances in fibroblast and myofibroblast research are reviewed.

Pulmonary alveolar type I (AT1) cells cover more than 95% of alveolar surface and are essential for the air–blood barrier function of lungs. AT1 cells have been shown to retain developmental plasticity during alveolar regeneration. However, the development and heterogeneity of AT1 cells remain largely unknown. Here, we conducted a single-cell RNA-seq analysis to characterize postnatal AT1 cell development and identified insulin-like growth factor-binding protein 2 (Igfbp2) as a genetic marker specifically expressed in postnatal AT1 cells. The portion of AT1 cells expressing Igfbp2 increases during alveologenesis and in post pneumonectomy (PNX) newly formed alveoli. We found that the adult AT1 cell population contains both Hopx⁺Igfbp2⁺ and Hopx⁺Igfbp2⁻ AT1 cells,which have distinct cell fates during alveolar regeneration. Using an Igfbp2-CreER mouse model, we demonstrate that Hopx⁺Igfbp2⁺ AT1 cells represent terminally differentiated AT1 cells that are not able to transdifferentiate into AT2 cells during post-PNX alveolar regeneration. Our study provides tools and insights that will guide future investigations into the molecular and cellular mechanism or mechanisms underlying AT1 cell fate during lung development and regeneration.