bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–06–09
seven papers selected by
Xiao Qin, University of Oxford
  1. A developmental constraint model of cancer cell states and tumor heterogeneity.
  2. Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells.
  3. Scanorama: integrating large and diverse single-cell transcriptomic datasets.
  4. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding.
  5. A Hypoxia-Epigenetics Axis Drives EMT in Pancreatic Cancer.
  6. Protocol to generate human liver spheroids to study liver fibrosis induced by metabolic stress.
  7. Microscale measurements of protein complexes from single cells.
  1. Cell. 2024 Jun 06. pii: S0092-8674(24)00458-6. [Epub ahead of print]187(12): 2907-2918
    A developmental constraint model of cancer cell states and tumor heterogeneity.
    Ayushi S Patel, Itai Yanai.
      Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.
    Keywords:  cancer cell states; cellular plasticity; developmental constraints; tumor heterogeneity; tumorigenesis
    DOI:  https://doi.org/10.1016/j.cell.2024.04.032
  2. Sci Adv. 2024 Jun 07. 10(23): eadj7706
    Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells.
    James H Park, Parvinder Hothi, Adrian Lopez Garcia de Lomana, Min Pan, Rachel Calder, Serdar Turkarslan, Wei-Ju Wu, Hwahyung Lee, Anoop P Patel, Charles Cobbs, Sui Huang, Nitin S Baliga.
      Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.
    DOI:  https://doi.org/10.1126/sciadv.adj7706
  3. Nat Protoc. 2024 Jun 06.
    Scanorama: integrating large and diverse single-cell transcriptomic datasets.
    Brian L Hie, Soochi Kim, Thomas A Rando, Bryan Bryson, Bonnie Berger.
      Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.
    DOI:  https://doi.org/10.1038/s41596-024-00991-3
  4. Cell Syst. 2024 May 29. pii: S2405-4712(24)00124-8. [Epub ahead of print]
    Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding.
    Yuzhe Li, Jinsong Zhang, Xin Gao, Qiangfeng Cliff Zhang.
      Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.
    Keywords:  cell community; cell-cell interaction; deep learning; graph attention network; spatial transcriptomics data analysis; tissue module
    DOI:  https://doi.org/10.1016/j.cels.2024.05.001
  5. Cancer Res. 2024 Jun 04. 84(11): 1739-1741
    A Hypoxia-Epigenetics Axis Drives EMT in Pancreatic Cancer.
    Matthias Wirth, Günter Schneider.
      Epithelial-to-mesenchymal transition (EMT) is a classical cellular plasticity process induced by various cell-intrinsic and -extrinsic triggers. Although prominent factors, such as TGFβ, mediate EMT via well-characterized pathways, alternative avenues are less well understood. Transcriptomic subtyping of pancreatic ductal adenocarcinoma (PDAC) has demonstrated that basal-like PDACs enrich a mesenchymal-like expression program, emphasizing the relevance of EMT in the disease. In this issue of Cancer Research, Brown and colleagues demonstrate the tight connection of EMT to hypoxia. Through a detailed mechanistic analysis, the authors deciphered that hypoxia-induced signals are integrated by the histone H3 lysine 36 di-methylation (H3K36me2) mark. On the one hand, hypoxia decreased activity of the H3K36me2 eraser KDM2A, while on the other hand promoting stabilization of the H3K36me2 writer NSD2. Hypoxia diminished the expression of a set of serine-threonine phosphatases, subsequently resulting in SRC kinase family-dependent activation of canonical MEK, ERK, and JNK signaling to impinge on NSD2 expression. In addition, reduced expression of the protein phosphatase PP2Cδ was linked to increased NSD2 protein expression. These discoveries illuminate the close relationship of hypoxia signaling to the epigenetic machinery and cellular plasticity processes. See related article by Brown et al., p. 1764.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-23-3578
  6. STAR Protoc. 2024 Jun 03. pii: S2666-1667(24)00276-4. [Epub ahead of print]5(2): 103111
    Protocol to generate human liver spheroids to study liver fibrosis induced by metabolic stress.
    Hyun Young Kim, Wonseok Lee, Xiao Liu, Haeum Jang, Sadatsugu Sakane, Raquel Carvalho-Gontijo Weber, Karin Diggle, Samuel A Kerk, Christian M Metallo, Tatiana Kisseleva, David A Brenner.
      Currently, there is no effective treatment for obesity and alcohol-associated liver diseases, partially due to the lack of translational human models. Here, we present a protocol to generate 3D human liver spheroids that contain all the liver cell types and mimic "livers in a dish." We describe strategies to induce metabolic and alcohol-associated hepatic steatosis, inflammation, and fibrosis. We outline potential applications, including using human liver spheroids for experimental and translational research and drug screening to identify potential anti-fibrotic therapies.
    Keywords:  cell isolation; metabolism; organoid
    DOI:  https://doi.org/10.1016/j.xpro.2024.103111
  7. Curr Opin Struct Biol. 2024 Jun 06. pii: S0959-440X(24)00087-3. [Epub ahead of print]87 102860
    Microscale measurements of protein complexes from single cells.
    Tanushree Dutta, Julea Vlassakis.
      Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.
    Keywords:  Binding and unbinding kinetics; Protein–protein interaction; Single cell microtechnology
    DOI:  https://doi.org/10.1016/j.sbi.2024.102860
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