bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024‒09‒22
eight papers selected by
Xiao Qin, University of Oxford
  1. Cancer-Associated Fibroblasts: From Spectators to Protagonists in Pancreatic Cancer Progression.
  2. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.
  3. Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements.
  4. Gene-level alignment of single-cell trajectories.
  5. EnhancerNet: A predictive model of cell identity dynamics through enhancer selection.
  6. From Inception to Malignancy: the Co-evolution of Pancreatic Cancer and Its Immunosuppressive Microenvironment.
  7. Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk.
  8. Interpreting cis-regulatory interactions from large-scale deep neural networks.
  1. Cancer Res. 2024 Sep 16. 84(18): 2938-2940
    Cancer-Associated Fibroblasts: From Spectators to Protagonists in Pancreatic Cancer Progression.
    Gianluca Mucciolo, Wenlong Li, Giulia Biffi.
      Our knowledge of the origins, heterogeneity, and functions of cancer-associated fibroblasts (CAF) in pancreatic ductal adenocarcinoma (PDAC) has exponentially increased over the last two decades. This has been facilitated by the implementation of new models and single-cell technologies. However, a few key studies preceded the current exciting times in CAF research and were fundamental in initiating the investigation of CAFs and of their roles in PDAC. With their study published in Cancer Research in 2008, Hwang and colleagues have been first to successfully isolate and immortalize human pancreatic stellate cells (HPSC) from PDAC tissues. This new tool allowed them to probe the roles of CAFs in PDAC as never done before. By performing complementary in vitro and in vivo analyses, the authors demonstrated the involvement of HPSCs in PDAC malignant cell proliferation, invasion, and therapy resistance. Here, we leverage that seminal study as a framework to discuss the advances made over the last 16 years in understanding the complexity and central roles of CAFs in PDAC progression. See related article by Hwang and colleagues, Cancer Res 2008;68:918-26.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-24-2448
  2. Nat Protoc. 2024 Sep 16.
    CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.
    Suoqin Jin, Maksim V Plikus, Qing Nie.
      Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.
    DOI:  https://doi.org/10.1038/s41596-024-01045-4
  3. Nat Commun. 2024 Sep 18. 15(1): 8209
    Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements.
    Florence M Chardon, Troy A McDiarmid, Nicholas F Page, Riza M Daza, Beth K Martin, Silvia Domcke, Samuel G Regalado, Jean-Benoît Lalanne, Diego Calderon, Xiaoyi Li, Lea M Starita, Stephan J Sanders, Nadav Ahituv, Jay Shendure.
      CRISPR-based gene activation (CRISPRa) is a strategy for upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis-regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate genes in a cell type-specific manner.
    DOI:  https://doi.org/10.1038/s41467-024-52490-4
  4. Nat Methods. 2024 Sep 19.
    Gene-level alignment of single-cell trajectories.
    Dinithi Sumanaweera, Chenqu Suo, Ana-Maria Cujba, Daniele Muraro, Emma Dann, Krzysztof Polanski, Alexander S Steemers, Woochan Lee, Amanda J Oliver, Jong-Eun Park, Kerstin B Meyer, Bianca Dumitrascu, Sarah A Teichmann.
      Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.
    DOI:  https://doi.org/10.1038/s41592-024-02378-4
  5. Development. 2024 Sep 17. pii: dev.202997. [Epub ahead of print]
    EnhancerNet: A predictive model of cell identity dynamics through enhancer selection.
    Omer Karin.
      Understanding how cell identity is encoded by the genome and acquired during differentiation is a central challenge in cell biology. We have developed a theoretical framework called EnhancerNet, which models the regulation of cell identity through the lens of transcription factor (TF)-enhancer interactions. We demonstrate that autoregulation in these interactions imposes a constraint on the model, resulting in simplified dynamics that can be parameterized from observed cell identities. Despite its simplicity, EnhancerNet recapitulates a broad range of experimental observations on cell identity dynamics, including enhancer selection, cell fate induction, hierarchical differentiation through multipotent progenitor states, and direct reprogramming by TF overexpression. The model makes specific quantitative predictions, reproducing known reprogramming recipes and the complex hematopoietic differentiation hierarchy without fitting unobserved parameters. EnhancerNet provides insights into how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution.
    Keywords:  Cell Fate; Cell Identity; Dynamical Systems; Enhancer Selection; Statistical Physics; Systems Biology
    DOI:  https://doi.org/10.1242/dev.202997
  6. Cancer Res. 2024 Sep 16. 84(18): 2944-2946
    From Inception to Malignancy: the Co-evolution of Pancreatic Cancer and Its Immunosuppressive Microenvironment.
    Filip Bednar, Lee Y Olsen, Marina Pasca di Magliano.
      Published in Cancer Research in 2007, Clark and colleagues first introduced the concept that the immune microenvironment evolves in lockstep with the progression of pancreatic cancer. Leveraging genetically engineered mouse models of the disease that were described a few years earlier, Clark and colleagues used a combination of approaches to describe the dynamics of immune evolution in precursor lesions all the way to overt malignancy. They discovered that immunosuppression is established at the earliest stages of carcinogenesis. Here, we discuss their findings, how they led to a wealth of functional work, and how they have been expanded upon since the advent of -omics technologies. See related article by Clark and colleagues, Cancer Res 2007;67:9518-27.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-24-2732
  7. Nat Genet. 2024 Sep 16.
    Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk.
    Philip J Law, James Studd, James Smith, Jayaram Vijayakrishnan, Bradley T Harris, Maria Mandelia, Charlie Mills, Malcolm G Dunlop, Richard S Houlston.
      Genome-wide association studies of colorectal cancer (CRC) have identified 170 autosomal risk loci. However, for most of these, the functional variants and their target genes are unknown. Here, we perform statistical fine-mapping incorporating tissue-specific epigenetic annotations and massively parallel reporter assays to systematically prioritize functional variants for each CRC risk locus. We identify plausible causal variants for the 170 risk loci, with a single variant for 40. We link these variants to 208 target genes by analyzing colon-specific quantitative trait loci and implementing the activity-by-contact model, which integrates epigenomic features and Micro-C data, to predict enhancer-gene connections. By deciphering CRC risk loci, we identify direct links between risk variants and target genes, providing further insight into the molecular basis of CRC susceptibility and highlighting potential pharmaceutical targets for prevention and treatment.
    DOI:  https://doi.org/10.1038/s41588-024-01900-w
  8. Nat Genet. 2024 Sep 16.
    Interpreting cis-regulatory interactions from large-scale deep neural networks.
    Shushan Toneyan, Peter K Koo.
      The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with orthogonal experimental data, providing insights into generalization but offering limited insights into their decision-making process. Existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we present cis-regulatory element model explanations (CREME), an in silico perturbation toolkit that interprets the rules of gene regulation learned by a genomic DNN. Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions. CREME can provide interpretations across multiple scales of genomic organization, from cis-regulatory elements to fine-mapped functional sequence elements within them, offering high-resolution insights into the regulatory architecture of the genome. CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation.
    DOI:  https://doi.org/10.1038/s41588-024-01923-3
This page is being maintained by Thomas Krichel. It was last updated on 2024‒09‒22 at 8:23.