bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–01–05
seventeen papers selected by
Xiao Qin, University of Oxford
  1. LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.
  2. Single-Cell Lineage Tracing and Clonal State-Fate Analysis.
  3. Lineage Recording in Human Brain Organoids with iTracer.
  4. CARLIN: A Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression.
  5. Chromatin-based memory as a self-stabilizing influence on cell identity.
  6. Reconstructing Progenitor State Hierarchy and Dynamics Using Lineage Barcoding Data.
  7. Clonal Tracking in the Mouse Brain with Single-Cell RNA-Seq.
  8. Cancer immune evasion, immunoediting and intratumour heterogeneity.
  9. GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets.
  10. CLADES: A Programmable Cascade of Genes for Cell Lineage Analysis and Manipulation.
  11. Pan-cancer drivers of metastasis.
  12. Editorial: Disparities in early onset colorectal cancer.
  13. Harnessing 3D models to uncover the mechanisms driving infectious and inflammatory disease in the intestine.
  14. High order expression dependencies finely resolve cryptic states and subtypes in single cell data.
  15. Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.
  16. STAT1 regulates immune-mediated intestinal stem cell proliferation and epithelial regeneration.
  17. The Creation Game: of AI and human creativity.
  1. Methods Mol Biol. 2025 ;2886 243-263
    LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.
    Bastiaan Spanjaard, Jan Philipp Junker.
      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.
    Keywords:  Barcoding; CRISPR/Cas9; Phylogenetics; Single-cell lineage tracing; Single-cell mRNA; Tree building
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_12
  2. Methods Mol Biol. 2025 ;2886 65-84
    Single-Cell Lineage Tracing and Clonal State-Fate Analysis.
    Alejo E Rodriguez Fraticelli, Pedro Sánchez Sánchez.
      Lineage tracing has significantly advanced our comprehension in many areas of biology, such as development or immunity, by precisely measuring cellular processes like migration, division, or differentiation across labeled cells and their progeny. Traditional recombinase-based prospective lineage tracing is limited by the need for a priori cell type information and is constrained in the numbers of clones it can simultaneously track. In this sense, clonal lineage tracing with integrated random barcodes offers a robust alternative, enabling researchers to label and track a vast array of cells and their progeny over time. Moreover, clonal lineage tracing can be combined with single-cell omics technologies to study cell states and their maintenance over time. Key steps in these protocols include stable barcode integration, cell division to expand clones, and simultaneous capture of cellular properties with barcode information. Here, we comment on those steps and summarize important parameters to take into account during the design of single-cell lineage tracing experiments. Also, we present the main features for various available lentiviral libraries of expressed barcodes than can be captured alongside the transcriptome of individual cells. We cover other crucial aspects of experimental design, such as the optimization of cellular sampling, library diversity, and the minimization of clonal dropouts. Regarding sequencing data analysis, we provide some tips based on our experience, as well as available computational tools for the assignment of clonal identities and the identification of fate determinants. We finally discuss limitations of current methodologies and use an example step-by-step protocol to illustrate key points during the process. In sum, we provide a roadmap for considering and implementing single-cell lineage tracing studies to comprehensively explore fate determinants and their mechanisms.
    Keywords:   Barcode; Clonal memories; Lineage tracing; Sequencing; Single cell; State-fate; Transcriptome; Clones
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_4
  3. Methods Mol Biol. 2025 ;2886 85-101
    Lineage Recording in Human Brain Organoids with iTracer.
    Eugenio Gentile, Ashley Maynard, Zhisong He, Barbara Treutlein.
      Induced pluripotent stem cell (iPSC)-derived organoids provide models to study human organ development. Single-cell transcriptomics enables highly resolved descriptions of cell states within these systems; however, approaches are needed to directly determine the lineage relationship between cells. Here we provide a detailed protocol (Fig. 1) for the application of iTracer (He Z, Maynard A, Jain A, et al., Nat Methods 19:90-99, 2022), a recently published lineage recorder that combines reporter barcodes with inducible CRISPR-Cas9 scarring and is compatible with single-cell and spatial transcriptomics. iTracer is used to explore clonality and lineage dynamics during brain organoid development. More broadly, iTracer can be adapted to any iPSC-derived culture system to dissect lineage dynamics during normal or perturbed development.
    Keywords:  Brain organoid; Inducible scarring; Lineage tracing; Single-cell RNA sequencing
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_5
  4. Methods Mol Biol. 2025 ;2886 281-298
    CARLIN: A Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression.
    Sarah Bowling, Fernando D Camargo.
      The CRISPR-activated repair lineage tracing (CARLIN) mouse line uses DNA barcoding to enable high-resolution tracing of cell lineages in vivo (Bowling et al, Cell 181, 1410-1422.e27, 2020). CARLIN mice contain expressed barcodes that allow simultaneous interrogation of lineage and gene expression information from single cells. Furthermore, barcode editing is fully inducible, resulting in cell lineage labeling that can be performed at any time point in development or adulthood. This chapter details the protocols followed for maintaining CARLIN mice, inducing barcoding, and amplifying the CARLIN barcode from DNA, RNA, and single-cell RNA-sequencing libraries for next-generation sequencing.
    Keywords:  CARLIN; Cas9-induced scarring; DNA barcode amplification protocol; DNA barcoding; Lineage tracing; Next-generation sequencing
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_14
  5. Genome Biol. 2024 Dec 30. 25(1): 320
    Chromatin-based memory as a self-stabilizing influence on cell identity.
    Charles C Bell, Geoffrey J Faulkner, Omer Gilan.
      Cell types are traditionally thought to be specified and stabilized by gene regulatory networks. Here, we explore how chromatin memory contributes to the specification and stabilization of cell states. Through pervasive, local, feedback loops, chromatin memory enables cell states that were initially unstable to become stable. Deeper appreciation of this self-stabilizing role for chromatin broadens our perspective of Waddington's epigenetic landscape from a static surface with islands of stability shaped by evolution, to a plasticine surface molded by experience. With implications for the evolution of cell types, stabilization of resistant states in cancer, and the widespread plasticity of complex life.
    DOI:  https://doi.org/10.1186/s13059-024-03461-x
  6. Methods Mol Biol. 2025 ;2886 177-199
    Reconstructing Progenitor State Hierarchy and Dynamics Using Lineage Barcoding Data.
    Weixiang Fang, Yi Yang, Hongkai Ji, Reza Kalhor.
      Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm. Evaluation of adequate sampling based on progenitor state coverage statistics is emphasized for interpreting the QFM results. Overall, this chapter describes a general framework for characterizing the dynamics of cell fate changes using lineage barcoding data.
    Keywords:  ICE-FASE; Lineage barcoding pipeline; Lineage tracing; Phylotime; Progenitor state dynamics; Quantitative fate mapping (QFM); Time-scaled cell phylogeny
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_9
  7. Methods Mol Biol. 2025 ;2886 103-137
    Clonal Tracking in the Mouse Brain with Single-Cell RNA-Seq.
    Michael Ratz, Leonie von Berlin.
      Lineage tracing methods enable the identification of all progeny generated by a single cell. High-throughput lineage tracing in the mammalian brain involves parallel labeling of thousands of progenitor cells with genetic barcodes in vivo followed by single-cell RNA-seq of lineage relations and cell types. Here we describe the generation of barcoded lentivirus, microinjections into the embryonic day 9.5 mouse forebrain, dissociation of 2-week-old mouse brain tissue, single-cell RNA-seq library preparation, and data analysis using a custom software. Compared to traditional methods based on sparse fluorophore labeling of progenitor cells, lineage tracing with genetic barcodes and single-cell RNA-seq has a >100-fold higher throughput while using >10 times fewer mice.
    Keywords:   Lentivirus; Lineage tracing; Mouse brain; Neural stem cells; Single-cell RNA-seq; Genetic barcodes
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_6
  8. Nat Rev Immunol. 2025 Jan 02.
    Cancer immune evasion, immunoediting and intratumour heterogeneity.
    Malte Roerden, Stefani Spranger.
      Cancers can avoid immune-mediated elimination by acquiring traits that disrupt antitumour immunity. These mechanisms of immune evasion are selected and reinforced during tumour evolution under immune pressure. Some immunogenic subclones are effectively eliminated by antitumour T cell responses (a process known as immunoediting), which results in a clonally selected tumour. Other cancer cells arise to resist immunoediting, which leads to a tumour that includes several distinct cancer cell populations (referred to as intratumour heterogeneity (ITH)). Tumours with high ITH are associated with poor patient outcomes and a lack of responsiveness to immune checkpoint blockade therapy. In this Review, we discuss the different ways that cancer cells evade the immune system and how these mechanisms impact immunoediting and tumour evolution. We also describe how subclonal antigen presentation in tumours with high ITH can result in immune evasion.
    DOI:  https://doi.org/10.1038/s41577-024-01111-8
  9. Methods Mol Biol. 2025 ;2886 375-400
    GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets.
    A S Eisele, D M Suter.
      Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e., the gene-specific maintenance of expression levels through cell divisions. This represents a shift away from experimental lineage tracing techniques based on genetic marks or physical cell lineage separation and greatly eases and expands lineage annotation. GEMLI allows to study cell lineages during differentiation in development, homeostasis, and regeneration, as well as disease onset and progression in various physiological and pathological contexts. This makes it possible to dissect cell type-specific gene expression memory, to discriminate symmetric and asymmetric cell fate decisions, and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. GEMLI is particularly promising for its ability to identify small lineages in human samples, a context in which no other lineage tracing methods are applicable. In this chapter, we provide a detailed protocol of the GEMLI R package usage on gene expression matrices derived from standard scRNA-seq on various platforms. We cover the use of the main function to predict cell lineages and how to adjust its parameters to different tasks. We also show how lineage information is extracted, visualized, and fine-tuned. Finally, we describe the use of the package's functions for the detailed analysis of the predicted cell lineages. This includes the analysis of gene expression memory, cell type composition of individual large lineages, and identification of lineages at the transition point between two cell types.
    Keywords:  Cell family; Clonality; Fate decisions; Gene expression memory; Gene expression stability; Lineage inference; Lineage tracing; Memory genes; Monotonically expressed genes; Multicellular structure
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_19
  10. Methods Mol Biol. 2025 ;2886 421-437
    CLADES: A Programmable Cascade of Genes for Cell Lineage Analysis and Manipulation.
    Inés González-Aspe, Beatriz Madariaga Puchol, Blanca López Arrabal, Brooke Bosonac, Jorge García-Marqués.
      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.
    Keywords:  CRISPR; CaSSA; Drosophila; Genetic cascades; Genetic tools; Lineage tracing; Neuronal specification
    DOI:  https://doi.org/10.1007/978-1-0716-4310-5_21
  11. Mol Cancer. 2025 Jan 02. 24(1): 2
    Pan-cancer drivers of metastasis.
    Ryan Lusby, Engin Demirdizen, Mohammed Inayatullah, Paramita Kundu, Oscar Maiques, Ziyi Zhang, Mikkel Green Terp, Victoria Sanz-Moreno, Vijay K Tiwari.
      Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.
    Keywords:  Cancer; Gene regulation; Metastasis; Single-cell heterogeneity; Transcription Factors
    DOI:  https://doi.org/10.1186/s12943-024-02182-w
  12. Front Oncol. 2024 ;14 1537473
    Editorial: Disparities in early onset colorectal cancer.
    Caitlin C Murphy, Swati G Patel, Peter S Liang, Jennifer M Weiss.
      
    Keywords:  cancer; cancer genetics; cancer risk factors; disparities; early onset colorectal cancer
    DOI:  https://doi.org/10.3389/fonc.2024.1537473
  13. BMC Biol. 2024 Dec 31. 22(1): 300
    Harnessing 3D models to uncover the mechanisms driving infectious and inflammatory disease in the intestine.
    Diana Micati, Sara Hlavca, Wing Hei Chan, Helen E Abud.
      Representative models of intestinal diseases are transforming our knowledge of the molecular mechanisms of disease, facilitating effective drug screening and avenues for personalised medicine. Despite the emergence of 3D in vitro intestinal organoid culture systems that replicate the genetic and functional characteristics of the epithelial tissue of origin, there are still challenges in reproducing the human physiological tissue environment in a format that enables functional readouts. Here, we describe the latest platforms engineered to investigate environmental tissue impacts, host-microbe interactions and enable drug discovery. This highlights the potential to revolutionise knowledge on the impact of intestinal infection and inflammation and enable personalised disease modelling and clinical translation.
    Keywords:  Epithelial-microbe interaction; Epithelium-mesenchyme interaction; High-throughput screening; Inflammatory bowel disease; Intestinal diseases; Modelling; Organoid co-culture; Personalised medicine
    DOI:  https://doi.org/10.1186/s12915-024-02092-9
  14. Mol Syst Biol. 2025 Jan 02.
    High order expression dependencies finely resolve cryptic states and subtypes in single cell data.
    Abel Jansma, Yuelin Yao, Jareth Wolfe, Luigi Del Debbio, Sjoerd V Beentjes, Chris P Ponting, Ava Khamseh.
      Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells' local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator's finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator's fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.
    Keywords:  Cell Cycle Phases; Cell State; Higher-order Gene Expression Dependencies; Single-cell Transcriptomics; Structure Learning
    DOI:  https://doi.org/10.1038/s44320-024-00074-1
  15. Genome Biol. 2024 Dec 30. 25(1): 322
    Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.
    Xiaoyang Chen, Keyi Li, Xiaoqing Wu, Zhen Li, Qun Jiang, Xuejian Cui, Zijing Gao, Yanhong Wu, Rui Jiang.
      Spatial epigenomic technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here, we propose an efficient and iterative model, Descart, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking, we demonstrate the superiority of Descart in revealing cellular heterogeneity and capturing tissue structure. Utilizing the graph of inter-cellular correlations, Descart shows its potential to denoise data, identify peak modules, and detect gene-peak interactions.
    Keywords:  Data imputation; Feature selection; Gene-peak interactions; Inter-cellular correlations; Peak module; Spatial ATAC-seq; Spatially variable peak
    DOI:  https://doi.org/10.1186/s13059-024-03458-6
  16. Nat Commun. 2025 Jan 02. 16(1): 138
    STAT1 regulates immune-mediated intestinal stem cell proliferation and epithelial regeneration.
    Shuichiro Takashima, Roshan Sharma, Winston Chang, Marco Calafiore, Ya-Yuan Fu, Suze A Jansen, Takahiro Ito, Anastasiya Egorova, Jason Kuttiyara, Viktor Arnhold, Jessica Sharrock, Endi Santosa, Ojasvi Chaudhary, Heather Geiger, Hiromi Iwasaki, Chen Liu, Joseph Sun, Nicolas Robine, Linas Mazutis, Caroline A Lindemans, Alan M Hanash.
      The role of the immune system in regulating tissue stem cells remains poorly understood, as does the relationship between immune-mediated tissue damage and regeneration. Graft vs. host disease (GVHD) occurring after allogeneic bone marrow transplantation (allo-BMT) involves immune-mediated damage to the intestinal epithelium and its stem cell compartment. To assess impacts of T-cell-driven injury on distinct epithelial constituents, we have performed single cell RNA sequencing on intestinal crypts following experimental BMT. Intestinal stem cells (ISCs) from GVHD mice have exhibited global transcriptomic changes associated with a substantial Interferon-γ response and upregulation of STAT1. To determine its role in crypt function, STAT1 has been deleted within murine intestinal epithelium. Following allo-BMT, STAT1 deficiency has resulted in reduced epithelial proliferation and impaired ISC recovery. Similarly, epithelial Interferon-γ receptor deletion has also attenuated proliferation and ISC recovery post-transplant. Investigating the mechanistic basis underlying this epithelial response, ISC STAT1 expression in GVHD has been found to correlate with upregulation of ISC c-Myc. Furthermore, activated T cells have stimulated Interferon-γ-dependent epithelial regeneration in co-cultured organoids, and Interferon-γ has directly induced STAT1-dependent c-Myc expression and ISC proliferation. These findings illustrate immunologic regulation of a core tissue stem cell program after damage and support a role for Interferon-γ as a direct contributor to epithelial regeneration.
    DOI:  https://doi.org/10.1038/s41467-024-55227-5
  17. Nat Immunol. 2025 Jan;26(1): 1-2
    The Creation Game: of AI and human creativity.
    Bali Pulendran.
      
    DOI:  https://doi.org/10.1038/s41590-024-02026-1
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