bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–08–11
eleven papers selected by
Xiao Qin, University of Oxford
  1. SIRV: spatial inference of RNA velocity at the single-cell resolution.
  2. A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data.
  3. Studying RNA dynamics from single-cell RNA sequencing snapshots.
  4. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.
  5. The origin of novel traits in cancer.
  6. Redox heterogeneity in mouse embryonic stem cells individualizes cell fate decisions.
  7. From neoadjuvant to organ-sparing immunotherapy for colorectal cancer.
  8. Will computer-aided detection of polyps decrease colorectal cancer incidence and mortality?
  9. Precision Cancer Medicine 2.0-Oncology in the postgenomic era.
  10. Oncogenic transcription factors instruct promoter-enhancer hubs in individual triple negative breast cancer cells.
  11. Bioprinted, spatially defined breast tumor microenvironment models of intratumoral heterogeneity and drug resistance.
  1. NAR Genom Bioinform. 2024 Sep;6(3): lqae100
    SIRV: spatial inference of RNA velocity at the single-cell resolution.
    Tamim Abdelaal, Laurens M Grossouw, R Jeroen Pasterkamp, Boudewijn P F Lelieveldt, Marcel J T Reinders, Ahmed Mahfouz.
      RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.
    DOI:  https://doi.org/10.1093/nargab/lqae100
  2. Nat Commun. 2024 Aug 03. 15(1): 6573
    A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data.
    Ariel Madrigal, Tianyuan Lu, Larisa M Soto, Hamed S Najafabadi.
      Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.
    DOI:  https://doi.org/10.1038/s41467-024-50963-0
  3. Nat Methods. 2024 Aug;21(8): 1418-1419
    Studying RNA dynamics from single-cell RNA sequencing snapshots.
      
    DOI:  https://doi.org/10.1038/s41592-024-02366-8
  4. Cell Syst. 2024 Aug 02. pii: S2405-4712(24)00183-2. [Epub ahead of print]
    PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.
    Alexander T F Bell, Jacob T Mitchell, Ashley L Kiemen, Melissa Lyman, Kohei Fujikura, Jae W Lee, Erin Coyne, Sarah M Shin, Sushma Nagaraj, Atul Deshpande, Pei-Hsun Wu, Dimitrios N Sidiropoulos, Rossin Erbe, Jacob Stern, Rena Chan, Stephen Williams, James M Chell, Lauren Ciotti, Jacquelyn W Zimmerman, Denis Wirtz, Won Jin Ho, Neeha Zaidi, Elizabeth Thompson, Elizabeth M Jaffee, Laura D Wood, Elana J Fertig, Luciane T Kagohara.
      This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
    Keywords:  Visium; Xenium; imaging mass cytometry; machine learning; multi-omics; pancreatic adenocarcinoma; pancreatic intraepithelial neoplasia; spatial transcriptomics; transfer learning
    DOI:  https://doi.org/10.1016/j.cels.2024.07.001
  5. Trends Cancer. 2024 Aug 06. pii: S2405-8033(24)00145-6. [Epub ahead of print]
    The origin of novel traits in cancer.
    Steven A Frank, Itai Yanai.
      The traditional view of cancer emphasizes a genes-first process. Novel cancer traits arise by genetic mutations that spread to drive phenotypic change. However, recent data support a phenotypes-first process in which nonheritable cellular variability creates novel traits that later become heritably stabilized by genetic and epigenetic changes. Single-cell measurements reinforce the idea that phenotypes lead genotypes, showing how cancer evolution follows normal developmental plasticity and creates novel traits by recombining parts of different cellular developmental programs. In parallel, studies in evolutionary biology also support a phenotypes-first process driven by developmental plasticity and developmental recombination. These advances in cancer research and evolutionary biology mutually reinforce a revolution in our understanding of how cells and organisms evolve novel traits in response to environmental challenges.
    Keywords:  cancer progression; cellular plasticity; drug resistance; evolution
    DOI:  https://doi.org/10.1016/j.trecan.2024.07.005
  6. Dev Cell. 2024 Jul 30. pii: S1534-5807(24)00445-3. [Epub ahead of print]
    Redox heterogeneity in mouse embryonic stem cells individualizes cell fate decisions.
    Agnes Ulfig, Ursula Jakob.
      Pluripotent embryonic stem cells (ESCs) can develop into any cell type in the body. Yet, the regulatory mechanisms that govern cell fate decisions during embryogenesis remain largely unknown. We now demonstrate that mouse ESCs (mESCs) display large natural variations in mitochondrial reactive oxygen species (mitoROS) levels that individualize their nuclear redox state, H3K4me3 landscape, and cell fate. While mESCs with high mitoROS levels (mitoROSHIGH) differentiate toward mesendoderm and form the primitive streak during gastrulation, mESCs, which generate less ROS, choose the alternative neuroectodermal fate. Temporal studies demonstrated that mesendodermal (ME) specification of mitoROSHIGH mESCs is mediated by a Nrf2-controlled switch in the nuclear redox state, triggered by the accumulation of redox-sensitive H3K4me3 marks, and executed by a hitherto unknown ROS-dependent activation process of the Wnt signaling pathway. In summary, our study explains how ESC heterogeneity is generated and used by individual cells to decide between distinct cellular fates.
    Keywords:  H3K4me3 epigenome; Nrf2 signaling; Wnt signaling pathway; cell fate decision; embryonic stem cells; mesendoderm; neuroectoderm; reactive oxygen species; redox; stem cell heterogeneity
    DOI:  https://doi.org/10.1016/j.devcel.2024.07.008
  7. Nat Med. 2024 Aug 08.
    From neoadjuvant to organ-sparing immunotherapy for colorectal cancer.
    Benoit Rousseau, Andrea Cercek, Luis A Diaz.
      
    DOI:  https://doi.org/10.1038/s41591-024-03182-5
  8. Endoscopy. 2024 Aug 07.
    Will computer-aided detection of polyps decrease colorectal cancer incidence and mortality?
    Uri Ladabaum.
      
    DOI:  https://doi.org/10.1055/a-2371-1556
  9. Mol Oncol. 2024 Aug 07.
    Precision Cancer Medicine 2.0-Oncology in the postgenomic era.
    Riccardo Masina, Carlos Caldas.
      Genomic medicine has transformed the lives of patients with cancer by enabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.
    Keywords:  cancer; data integration; machine learning; precision cancer medicine; translational research; tumour biomarkers
    DOI:  https://doi.org/10.1002/1878-0261.13707
  10. Sci Adv. 2024 Aug 09. 10(32): eadl4043
    Oncogenic transcription factors instruct promoter-enhancer hubs in individual triple negative breast cancer cells.
    Jingru Zhao, Yeqiao Zhou, Ilias Tzelepis, Noah G Burget, Junwei Shi, Robert B Faryabi.
      Sequencing-based mapping of ensemble pairwise interactions among regulatory elements support the existence of topological assemblies known as promoter-enhancer hubs or cliques in cancer. Yet, prevalence, regulators, and functions of promoter-enhancer hubs in individual cancer cells remain unclear. Here, we systematically integrated functional genomics, transcription factor screening, and optical mapping of promoter-enhancer interactions to identify key promoter-enhancer hubs, examine heterogeneity of their assembly, determine their regulators, and elucidate their role in gene expression control in individual triple negative breast cancer (TNBC) cells. Optical mapping of individual SOX9 and MYC alleles revealed the existence of frequent multiway interactions among promoters and enhancers within spatial hubs. Our single-allele studies further demonstrated that lineage-determining SOX9 and signaling-dependent NOTCH1 transcription factors compact MYC and SOX9 hubs. Together, our findings suggest that promoter-enhancer hubs are dynamic and heterogeneous topological assemblies, which are controlled by oncogenic transcription factors and facilitate subtype-restricted gene expression in cancer.
    DOI:  https://doi.org/10.1126/sciadv.adl4043
  11. Trends Biotechnol. 2024 Aug 06. pii: S0167-7799(24)00154-9. [Epub ahead of print]
    Bioprinted, spatially defined breast tumor microenvironment models of intratumoral heterogeneity and drug resistance.
    Tianying Yuan, Xihong Fu, Rongcheng Hu, Xiaochun Zheng, Dong Jiang, Lanyu Jing, Xiaying Kuang, Zhongwei Guo, Xu Luo, Yixin Liu, Xuenong Zou, Gary D Luker, Shengli Mi, Chun Liu, Wei Sun.
      Cellular, extracellular matrix (ECM), and spatial heterogeneity of tumor microenvironments (TMEs) regulate disease progression and treatment efficacy. Developing in vitro models that recapitulate the TME promises to accelerate studies of tumor biology and identify new targets for therapy. Here, we used extrusion-based, multi-nozzle 3D bioprinting to spatially pattern triple-negative MDA-MB-231 breast cancer cells, endothelial cells (ECs), and human mammary cancer-associated fibroblasts (HMCAFs) with biomimetic ECM inks. Bioprinted models captured key features of the spatial architecture of human breast tumors, including varying-sized dense regions of cancer cells and surrounding microvessel-rich stroma. Angiogenesis and ECM stiffening occurred in the stromal area but not the cancer cell-rich (CCR) regions, mimicking pathological changes in patient samples. Transcriptomic analyses revealed upregulation of angiogenesis-related and ECM remodeling-related signatures in the stroma region and identified potential ligand-receptor (LR) mediators of these processes. Breast cancer cells in distinct parts of the bioprinted TME showed differing sensitivities to chemotherapy, highlighting environmentally mediated drug resistance. In summary, our 3D-bioprinted tumor model will act as a platform to discover integrated functions of the TME in cancer biology and therapy.
    Keywords:  3D bioprinting; ECM remodeling; drug resistance; spatial heterogeneity; tumor angiogenesis
    DOI:  https://doi.org/10.1016/j.tibtech.2024.06.007
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