bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–09–08
twelve papers selected by
Xiao Qin, University of Oxford
  1. Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors.
  2. Drug resistant pancreatic cancer cells exhibit altered biophysical interactions with stromal fibroblasts in imaging studies of 3D co-culture models.
  3. Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.
  4. Answering open questions in biology using spatial genomics and structured methods.
  5. A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence.
  6. Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution.
  7. Modeling glioblastoma tumor progression via CRISPR-engineered brain organoids.
  8. Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.
  9. Cancer drug-tolerant persister cells: from biological questions to clinical opportunities.
  10. A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.
  11. Mapping cellular interactions from spatially resolved transcriptomics data.
  12. Inflammation-induced epigenetic imprinting regulates intestinal stem cells.
  1. Cancer Cell. 2024 Aug 28. pii: S1535-6108(24)00304-0. [Epub ahead of print]
    Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors.
    Itay Tirosh, Mario L Suva.
      Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.
    DOI:  https://doi.org/10.1016/j.ccell.2024.08.005
  2. Sci Rep. 2024 Sep 05. 14(1): 20698
    Drug resistant pancreatic cancer cells exhibit altered biophysical interactions with stromal fibroblasts in imaging studies of 3D co-culture models.
    Eric Struth, Maryam Labaf, Vida Karimnia, Yiran Liu, Gwendolyn Cramer, Joanna B Dahl, Frank J Slack, Kourosh Zarringhalam, Jonathan P Celli.
      Interactions between tumor and stromal cells are well known to play prominent roles in progression of pancreatic ductal adenocarcinoma (PDAC). As knowledge of stromal crosstalk in PDAC has evolved, it has become clear that cancer associated fibroblasts can play both tumor promoting and tumor suppressive roles through a combination of paracrine crosstalk and juxtacrine interactions involving direct physical contact. Another major contributor to dismal survival statistics for PDAC is development of resistance to chemotherapy drugs, though less is known about how the acquisition of chemoresistance impacts upon tumor-stromal crosstalk. Here, we use time lapse imaging and image analysis to study how co-culture geometry impacts interactions between epithelial and stromal cells. We show that extracellular matrix (ECM) overlay cultures in which stromal cells (pancreatic stellate cells, or normal human fibroblasts) are placed adjacent to PDAC cells (PANC1) result in direct heterotypic cell adhesions accompanied by dramatic fibroblast contractility. We analyze these interactions in co-cultures using particle image velocimetry (PIV) analysis to quantify cell velocities over the course of time lapse movie sequences. We further contrast co-cultures of PANC1 with those containing a drug resistant subline (PANC1-OR) previously established in our lab and find that heterotypic cell-cell interactions are suppressed in the latter relative to the parental line. We use RNA-seq and bioinformatics analysis to identify differential gene expression in PANC1 and PANC1-OR, which shows that negative regulation of cell adhesion molecules, consistent with increased epithelial mesenchymal transition (EMT), is also correlated with reduction in the hetrotypic cell-cell contact necessary for the contractile behavior observed in drug naïve cultures. Overall these findings elucidate the role of drug-resistance in inhibiting an avenue of stromal crosstalk which is associated with tumor suppression and also help to establish cell culture conditions useful for further mechanistic investigation.
    DOI:  https://doi.org/10.1038/s41598-024-71372-9
  3. Cell Syst. 2024 Aug 30. pii: S2405-4712(24)00212-6. [Epub ahead of print]
    Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.
    Andre Neil Forbes, Duo Xu, Sandra Cohen, Priya Pancholi, Ekta Khurana.
      Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.
    Keywords:  ATAC-seq; cancer; drug repurposing; functional genomics; networks; regulatory networks; therapy
    DOI:  https://doi.org/10.1016/j.cels.2024.08.004
  4. BMC Bioinformatics. 2024 Sep 04. 25(1): 291
    Answering open questions in biology using spatial genomics and structured methods.
    Siddhartha G Jena, Archit Verma, Barbara E Engelhardt.
      Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.
    Keywords:  Biophysics; Cell biology; Machine learning; Spatial genomics; Statistical models
    DOI:  https://doi.org/10.1186/s12859-024-05912-5
  5. NPJ Syst Biol Appl. 2024 Sep 02. 10(1): 99
    A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence.
    Alexis Hernández-Magaña, Antonio Bensussen, Juan Carlos Martínez-García, Elena R Álvarez-Buylla.
      In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition.
    DOI:  https://doi.org/10.1038/s41540-024-00422-9
  6. Nat Commun. 2024 Sep 01. 15(1): 7609
    Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution.
    F Nadalin, M J Marzi, M Pirra Piscazzi, P Fuentes-Bravo, S Procaccia, M Climent, P Bonetti, C Rubolino, B Giuliani, I Papatheodorou, J C Marioni, F Nicassio.
      Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and can be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.
    DOI:  https://doi.org/10.1038/s41467-024-51424-4
  7. bioRxiv. 2024 Aug 03. pii: 2024.08.02.606387. [Epub ahead of print]
    Modeling glioblastoma tumor progression via CRISPR-engineered brain organoids.
    Matthew Ishahak, Rowland H Han, Devi Annamalai, Timothy Woodiwiss, Colin McCornack, Ryan T Cleary, Patrick A DeSouza, Xuan Qu, Sonika Dahiya, Albert H Kim, Jeffrey R Millman.
      Glioblastoma (GBM) is an aggressive form of brain cancer that is highly resistant to therapy due to significant intra-tumoral heterogeneity. The lack of robust in vitro models to study early tumor progression has hindered the development of effective therapies. Here, we develop engineered GBM organoids (eGBOs) harboring GBM subtype-specific oncogenic mutations to investigate the underlying transcriptional regulation of tumor progression. Single-cell and spatial transcriptomic analyses revealed that these mutations disrupt normal neurodevelopment gene regulatory networks resulting in changes in cellular composition and spatial organization. Upon xenotransplantation into immunodeficient mice, eGBOs form tumors that recapitulate the transcriptional and spatial landscape of human GBM samples. Integrative single-cell trajectory analysis of both eGBO-derived tumor cells and patient GBM samples revealed the dynamic gene expression changes in developmental cell states underlying tumor progression. This analysis of eGBOs provides an important validation of engineered cancer organoid models and demonstrates their utility as a model of GBM tumorigenesis for future preclinical development of therapeutics.
    DOI:  https://doi.org/10.1101/2024.08.02.606387
  8. Science. 2024 Sep 06. 385(6713): eadk9217
    Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.
    Cancer Genome Atlas Analysis Network‡
      To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.
    DOI:  https://doi.org/10.1126/science.adk9217
  9. Nat Rev Cancer. 2024 Sep 02.
    Cancer drug-tolerant persister cells: from biological questions to clinical opportunities.
    Mariangela Russo, Mengnuo Chen, Elisa Mariella, Haoning Peng, Sumaiyah K Rehman, Elena Sancho, Alberto Sogari, Tzen S Toh, Nathalie Q Balaban, Eduard Batlle, Rene Bernards, Mathew J Garnett, Matthew Hangauer, Eleonora Leucci, Jean-Christophe Marine, Catherine A O'Brien, Yaara Oren, E Elizabeth Patton, Caroline Robert, Susan M Rosenberg, Shensi Shen, Alberto Bardelli.
      The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.
    DOI:  https://doi.org/10.1038/s41568-024-00737-z
  10. Genome Res. 2024 Sep 05. pii: gr.279126.124. [Epub ahead of print]
    A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.
    Cameron Y Park, Shouvik Mani, Nicolas Beltran-Velez, Katie Maurer, Teddy Huang, Shuqiang Li, Satyen Gohil, Kenneth J Livak, David A Knowles, Catherine J Wu, Elham Azizi.
      Characterizing cell-cell communication and tracking its variability over time are crucial for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies. Existing tools fail to capture time-dependent intercellular interactions, and primarily rely on existing databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA sequencing data from multiple time points. Our method utilizes structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their coevolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.
    DOI:  https://doi.org/10.1101/gr.279126.124
  11. Nat Methods. 2024 Sep 03.
    Mapping cellular interactions from spatially resolved transcriptomics data.
    James Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C Gahan, Nisha Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Zhuo Zhao, Danny Z Chen, Raquibul Hannan, Siyuan Zhang, Guanghua Xiao, Ping Mu, Ariella B Hanker, Douglas Strand, Carlos L Arteaga, Neil Desai, Xinlei Wang, Yang Xie, Tao Wang.
      Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
    DOI:  https://doi.org/10.1038/s41592-024-02408-1
  12. Cell Stem Cell. 2024 Aug 24. pii: S1934-5909(24)00292-3. [Epub ahead of print]
    Inflammation-induced epigenetic imprinting regulates intestinal stem cells.
    Dongchang Zhao, Visweswaran Ravikumar, Tyler J Leach, Daniel Kraushaar, Emma Lauder, Lu Li, Yaping Sun, Katherine Oravecz-Wilson, Evan T Keller, Fengju Chen, Laure Maneix, Robert R Jenq, Robert Britton, Katherine Y King, Ana E Santibanez, Chad J Creighton, Arvind Rao, Pavan Reddy.
      It remains unknown whether and how intestinal stem cells (ISCs) adapt to inflammatory exposure and whether the adaptation leaves scars that will affect their subsequent regeneration. We investigated the consequences of inflammation on Lgr5+ ISCs in well-defined clinically relevant models of acute gastrointestinal graft-versus-host disease (GI GVHD). Utilizing single-cell transcriptomics, as well as organoid, metabolic, epigenomic, and in vivo models, we found that Lgr5+ ISCs undergo metabolic changes that lead to the accumulation of succinate, which reprograms their epigenome. These changes reduced the ability of ISCs to differentiate and regenerate ex vivo in serial organoid cultures and also in vivo following serial transplantation. Furthermore, ISCs demonstrated a reduced capacity for in vivo regeneration despite resolution of the initial inflammatory exposure, demonstrating the persistence of the maladaptive impact induced by the inflammatory encounter. Thus, inflammation imprints the epigenome of ISCs in a manner that persists and affects their sensitivity to adapt to future stress or challenges.
    Keywords:  allogeneic hematopoietic stem cell transplantation; epigenetics; epithelial cell memory; graft-versus-host disease; intestinal stem cells; intestine organoids; metabolism; oxidative phosphorylation
    DOI:  https://doi.org/10.1016/j.stem.2024.08.006
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