bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–04–06
twelve papers selected by
Xiao Qin, University of Oxford
  1. Causal machine learning for single-cell genomics.
  2. Single-cell phylodynamic inference of stem cell differentiation and tumor evolution.
  3. Reconstructing signaling histories of single cells via perturbation screens and transfer learning.
  4. SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data.
  5. Epigenome dynamics in early mammalian embryogenesis.
  6. Quantitative characterization of tissue states using multiomics and ecological spatial analysis.
  7. Connections across the Cancer Continuum.
  8. Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue.
  9. Leveraging real-world data to advance biomarker discovery and precision oncology.
  10. Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics.
  11. Colorectal cancer screening: FIT for purpose?
  12. Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer.
  1. Nat Genet. 2025 Mar 31.
    Causal machine learning for single-cell genomics.
    Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer, Hananeh Aliee, Yoshua Bengio, Fabian J Theis.
      Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes. In this Perspective, we delineate the application of causal machine learning to single-cell genomics and its associated challenges. We first present the causal model that is most commonly applied to single-cell biology and then identify and discuss potential approaches to three open problems: the lack of generalization of models to novel experimental conditions, the complexity of interpreting learned models, and the difficulty of learning cell dynamics.
    DOI:  https://doi.org/10.1038/s41588-025-02124-2
  2. Cell Syst. 2025 Mar 28. pii: S2405-4712(25)00077-8. [Epub ahead of print] 101244
    Single-cell phylodynamic inference of stem cell differentiation and tumor evolution.
    Kun Wang, Zhaolian Lu, Zeqi Yao, Xionglei He, Zheng Hu, Da Zhou.
      Phylodynamic inference (PI) quantifies population dynamics and evolutionary trajectories using phylogenetic trees. Single-cell lineage tracing enables phylogenetic tree reconstruction for thousands of cells in multicellular organisms, facilitating PI at the cellular level. However, cell differentiation and somatic evolution challenge the direct application of existing PI frameworks to somatic tissues. We introduce scPhyloX, a computational framework modeling structured cell populations by leveraging single-cell phylogenetic trees to infer tissue development and tumor evolution dynamics. A key advancement is its ability to infer time-varying parameters, capturing dynamic biological processes. Simulations demonstrate scPhyloX's accuracy in scenarios including tissue development, disease treatment, and tumor growth. Application to three real datasets reveals insights into somatic dynamics: cycling stem cell overshoot in fly organ development, clonal expansion of multipotent hematopoietic progenitors during human aging, and pronounced subclonal selection in early colorectal tumorigenesis. scPhyloX thus provides a computational approach for investigating somatic tissue development and evolution.
    Keywords:  lineage tracing; phylodynamic inference; phylogenetics; population dynamics; somatic evolution; stem cells
    DOI:  https://doi.org/10.1016/j.cels.2025.101244
  3. bioRxiv. 2025 Mar 17. pii: 2025.03.16.643448. [Epub ahead of print]
    Reconstructing signaling histories of single cells via perturbation screens and transfer learning.
    Nicholas T Hutchins, Miram Meziane, Claire Lu, Maisam Mitalipova, David Fischer, Pulin Li.
      Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications, from engineering tissues to treating diseases. Such manipulation requires knowing the signaling states and histories of the cells in situ , for which high-throughput discovery methods are lacking. Here, we present an integrated experimental-computational framework that learns signaling response signatures from a high-throughput in vitro perturbation atlas and infers combinatorial signaling activities in in vivo cell types with high accuracy and temporal resolution. Specifically, we generated signaling perturbation atlas across diverse cell types/states through multiplexed sequential combinatorial screens on human pluripotent stem cells. Using the atlas to train IRIS, a neural network-based model, and predicting on mouse embryo scRNAseq atlas, we discovered global features of combinatorial signaling code usage over time, identified biologically meaningful heterogeneity of signaling states within each cell type, and reconstructed signaling histories along diverse cell lineages. We further demonstrated that IRIS greatly accelerates the optimization of stem cell differentiation protocols by drastically reducing the combinatorial space that needs to be tested. This framework leads to the revelation that different cell types share robust signal response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions.
    DOI:  https://doi.org/10.1101/2025.03.16.643448
  4. Cell Syst. 2025 Mar 27. pii: S2405-4712(25)00076-6. [Epub ahead of print] 101243
    SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data.
    Yao Li, Xiaobin Liu, Lidong Guo, Kai Han, Shuangsang Fang, Xinjiang Wan, Dantong Wang, Xun Xu, Ling Jiang, Guangyi Fan, Mengyang Xu.
      Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.
    Keywords:  3D regulatory atlas; cellular interaction mapping; gene regulatory network; receptor; receptor-TF-target cascades; spatial autocorrelation analysis; spatially resolved transcriptomics; spatiotemporal dynamics; transcription factor
    DOI:  https://doi.org/10.1016/j.cels.2025.101243
  5. Nat Rev Genet. 2025 Apr 03.
    Epigenome dynamics in early mammalian embryogenesis.
    Adam Burton, Maria-Elena Torres-Padilla.
      During early embryonic development in mammals, the totipotency of the zygote - which is reprogrammed from the differentiated gametes - transitions to pluripotency by the blastocyst stage, coincident with the first cell fate decision. These changes in cellular potency are accompanied by large-scale alterations in the nucleus, including major transcriptional, epigenetic and architectural remodelling, and the establishment of the DNA replication programme. Advances in low-input genomics and loss-of-function methodologies tailored to the pre-implantation embryo now enable these processes to be studied at an unprecedented level of molecular detail in vivo. Such studies have provided new insights into the genome-wide landscape of epigenetic reprogramming and chromatin dynamics during this fundamental period of pre-implantation development.
    DOI:  https://doi.org/10.1038/s41576-025-00831-4
  6. Nat Genet. 2025 Apr 01.
    Quantitative characterization of tissue states using multiomics and ecological spatial analysis.
    Daisy Yi Ding, Zeyu Tang, Bokai Zhu, Hongyu Ren, Alex K Shalek, Robert Tibshirani, Garry P Nolan.
      The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.
    DOI:  https://doi.org/10.1038/s41588-025-02119-z
  7. Cancer Discov. 2025 Apr 02. 15(4): 663
    Connections across the Cancer Continuum.
    Luis A Diaz, Lewis C Cantley.
      
    DOI:  https://doi.org/10.1158/2159-8290.CD-25-0210
  8. bioRxiv. 2025 Mar 29. pii: 2025.03.06.641951. [Epub ahead of print]
    Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue.
    Yubao Cheng, Shengyuan Dang, Yuan Zhang, Yanbo Chen, Ruihuan Yu, Miao Liu, Shengyan Jin, Ailin Han, Samuel Katz, Siyuan Wang.
      Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, compositions, interactions, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution. Leading spatial-capture or spatial-tagging transcriptomics techniques that rely on in-vitro sequencing offer whole-transcriptome coverage, in principle, but at the cost of lower spatial resolution compared to image-based techniques. In contrast, high-performance image-based spatial transcriptomics techniques, which rely on in situ hybridization or in situ sequencing, achieve single-molecule spatial resolution and retain sub-cellular morphologies, but are limited by probe libraries that target only a subset of the transcriptome, typically covering several hundred to a few thousand transcript species. Together, these limitations hinder unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop a new image-based spatial transcriptomics technology termed Reverse-padlock Amplicon Encoding FISH (RAEFISH) with whole-genome level coverage while retaining single-molecule spatial resolution in intact tissues. We demonstrate image-based spatial transcriptomics targeting 23,000 human transcript species or 22,000 mouse transcript species, including nearly the entire protein-coding transcriptome and several thousand long-noncoding RNAs, in single cells in cultures and in tissue sections. Our analyses reveal differential subcellular localizations of diverse transcripts, cell-type-specific and cell-type-invariant tissue zonation dependent transcriptome, and gene expression programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.
    DOI:  https://doi.org/10.1101/2025.03.06.641951
  9. Cancer Cell. 2025 Mar 25. pii: S1535-6108(25)00113-8. [Epub ahead of print]
    Leveraging real-world data to advance biomarker discovery and precision oncology.
    Jiaqian Luo, David B Solit.
      Precision oncology is predicated on the availability of robust biomarkers deployed at scale at the point of care. Although simple in conception, precision oncology often fails in practice because of the limitations of current diagnostic platforms, the emergence of drug resistance, and an incomplete understanding of cancer pathogenesis and the host immune response. Here, we discuss using real-world data, including exceptional responder analyses, to identify biomarkers of therapy response and strategies for overcoming barriers to the broader adoption of precision oncology paradigms.
    DOI:  https://doi.org/10.1016/j.ccell.2025.03.012
  10. Cancer Cell. 2025 Mar 21. pii: S1535-6108(25)00083-2. [Epub ahead of print]
    Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics.
    Yunhe Liu, Ansam Sinjab, Jimin Min, Guangchun Han, Francesca Paradiso, Yuanyuan Zhang, Ruiping Wang, Guangsheng Pei, Yibo Dai, Yang Liu, Kyung Serk Cho, Enyu Dai, Akshay Basi, Jared K Burks, Kimal I Rajapakshe, Yanshuo Chu, Jiahui Jiang, Daiwei Zhang, Xinmiao Yan, Paola A Guerrero, Alejandra Serrano, Mingyao Li, Tae Hyun Hwang, Andrew Futreal, Jaffer A Ajani, Luisa M Solis Soto, Amir A Jazaeri, Humam Kadara, Anirban Maitra, Linghua Wang.
      Cancer-associated fibroblasts (CAFs) are a multifaceted cell population essential for shaping the tumor microenvironment (TME) and influencing therapy responses. Characterizing the spatial organization and interactions of CAFs within complex tissue environments provides critical insights into tumor biology and immunobiology. In this study, through integrative analyses of over 14 million cells from 10 cancer types across 7 spatial transcriptomics and proteomics platforms, we discover, validate, and characterize four distinct spatial CAF subtypes. These subtypes are conserved across cancer types and independent of spatial omics platforms. Notably, they exhibit distinct spatial organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic profiles. Their abundance and composition vary across tissues, shaping TME characteristics, such as levels, distribution, and state composition of tumor-infiltrating immune cells, tumor immune phenotypes, and patient survival. This study enriches our understanding of CAF spatial heterogeneity in cancer and paves the way for novel approaches to target and modulate CAFs.
    Keywords:  cancer-associated fibroblast; cell-cell communication; cellular neighborhood; lymphoid aggregate; pan-cancer; spatial multi-omics; spatial transcriptomics; tertiary lymphoid structure; tumor associated macrophage; tumor microenvironment
    DOI:  https://doi.org/10.1016/j.ccell.2025.03.004
  11. Lancet. 2025 Mar 29. pii: S0140-6736(25)00619-1. [Epub ahead of print]405(10484): 1027
    Colorectal cancer screening: FIT for purpose?
    The Lancet.
      
    DOI:  https://doi.org/10.1016/S0140-6736(25)00619-1
  12. bioRxiv. 2025 Mar 19. pii: 2025.03.18.643980. [Epub ahead of print]
    Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer.
    Daylin Morgan, Andrea L Gardner, Amy Brock.
      Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.
    DOI:  https://doi.org/10.1101/2025.03.18.643980
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