bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–03–24
nine papers selected by
Xiao Qin, University of Oxford
  1. Representing and extracting knowledge from single-cell data.
  2. Single-cell omics identifies inflammatory signaling as a trans-differentiation trigger in mouse embryos.
  3. Self-organization of embryonic stem cells into a reproducible embryo model through epigenome editing.
  4. Cancer cells hijack physiological metabolic signals to seed liver metastasis.
  5. DANCE: a deep learning library and benchmark platform for single-cell analysis.
  6. Exploiting Transcription Factors to Target EMT and Cancer Stem Cells for Tumor Modulation and Therapy.
  7. Tipping points in epithelial-mesenchymal lineages from single cell transcriptomics data.
  8. Insights into the cell fate decision-making processes from chromosome structural reorganizations.
  9. Studying temporal dynamics of single cells: expression, lineage and regulatory networks.
  1. Biophys Rev. 2024 Feb;16(1): 29-56
    Representing and extracting knowledge from single-cell data.
    Ionut Sebastian Mihai, Sarang Chafle, Johan Henriksson.
      Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
    Keywords:  Generating processes; Graphs; Markov chains; NLP; Neural networks; Single-cell; Statistics; Topology; VAE
    DOI:  https://doi.org/10.1007/s12551-023-01091-4
  2. Dev Cell. 2024 Mar 18. pii: S1534-5807(24)00109-6. [Epub ahead of print]
    Single-cell omics identifies inflammatory signaling as a trans-differentiation trigger in mouse embryos.
    Yifan Zhang, Zhixin Kang, Mengyao Liu, Lu Wang, Feng Liu.
      Trans-differentiation represents a direct lineage conversion; however, insufficient characterization of this process hinders its potential applications. Here, to explore a potential universal principal for trans-differentiation, we performed single-cell transcriptomic analysis of endothelial-to-hematopoietic transition (EHT), endothelial-to-mesenchymal transition, and epithelial-to-mesenchymal transition in mouse embryos. We applied three scoring indexes of entropies, cell-type signature transcription factor expression, and critical transition signals to show common features underpinning the fate plasticity of transition states. Cross-model comparison identified inflammatory-featured transition states and a common trigger role of interleukin-33 in promoting fate conversions. Multimodal profiling (integrative transcriptomic and chromatin accessibility analysis) demonstrated the inflammatory regulation of hematopoietic specification. Furthermore, multimodal omics and fate-mapping analyses showed that endothelium-specific Spi1, as an inflammatory effector, governs appropriate chromatin accessibility and transcriptional programs to safeguard EHT. Overall, our study employs single-cell omics to identify critical transition states/signals and the common trigger role of inflammatory signaling in developmental-stress-induced fate conversions.
    Keywords:  endothelial-to-hematopoietic transition; endothelial-to-mesenchymal transition; epithelial-to-mesenchymal transition; inflammatory signaling; single-cell omics; spi1; trans-differentiation
    DOI:  https://doi.org/10.1016/j.devcel.2024.02.010
  3. bioRxiv. 2024 Mar 10. pii: 2024.03.05.583597. [Epub ahead of print]
    Self-organization of embryonic stem cells into a reproducible embryo model through epigenome editing.
    Gerrald A Lodewijk, Sayaka Kozuki, Clara Han, Benjamin R Topacio, Abolfazl Zargari, Seungho Lee, Gavin Knight, Randolph Ashton, Lei S Qi, S Ali Shariati.
      Embryonic stem cells (ESCs) can self-organize in vitro into developmental patterns with spatial organization and molecular similarity to that of early embryonic stages. This self-organization of ESCs requires transmission of signaling cues, via addition of small molecule chemicals or recombinant proteins, to induce distinct embryonic cellular fates and subsequent assembly into structures that can mimic aspects of early embryonic development. During natural embryonic development, different embryonic cell types co-develop together, where each cell type expresses specific fate-inducing transcription factors through activation of non-coding regulatory elements and interactions with neighboring cells. However, previous studies have not fully explored the possibility of engineering endogenous regulatory elements to shape self-organization of ESCs into spatially-ordered embryo models. Here, we hypothesized that cell-intrinsic activation of a minimum number of such endogenous regulatory elements is sufficient to self-organize ESCs into early embryonic models. Our results show that CRISPR-based activation (CRISPRa) of only two endogenous regulatory elements in the genome of pluripotent stem cells is sufficient to generate embryonic patterns that show spatial and molecular resemblance to that of pre-gastrulation mouse embryonic development. Quantitative single-cell live fluorescent imaging showed that the emergence of spatially-ordered embryonic patterns happens through the intrinsic induction of cell fate that leads to an orchestrated collective cellular motion. Based on these results, we propose a straightforward approach to efficiently form 3D embryo models through intrinsic CRISPRa-based epigenome editing and independent of external signaling cues. CRISPRa-Programmed Embryo Models (CPEMs) show highly consistent composition of major embryonic cell types that are spatially-organized, with nearly 80% of the structures forming an embryonic cavity. Single cell transcriptomics confirmed the presence of main embryonic cell types in CPEMs with transcriptional similarity to pre-gastrulation mouse embryos and revealed novel signaling communication links between different embryonic cell types. Our findings offer a programmable embryo model and demonstrate that minimum intrinsic epigenome editing is sufficient to self-organize ESCs into highly consistent pre-gastrulation embryo models.
    DOI:  https://doi.org/10.1101/2024.03.05.583597
  4. Cancer Res. 2024 Mar 19.
    Cancer cells hijack physiological metabolic signals to seed liver metastasis.
    Andres Rettig, Karuna Ganesh.
      Metastasis arises from cancer-cell intrinsic adaptations and permissive tumor microenvironments (TME) that are distinct across different organs. Deciphering the mechanisms underpinning organotropism could provide novel preventive and therapeutic strategies for cancer patients. Rogava and colleagues identified Pip4kc as a driver of liver metastasis, acting by sensitizing cancer cells to insulin-dependent PI3K/AKT signaling, which could be reversed by dual pharmacological inhibition of PI3K and SGLT2 or a ketogenic diet. The study highlights the importance of tumor: microenvironment communication in the context of systemic physiology and points towards potential combination therapies.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-24-0835
  5. Genome Biol. 2024 Mar 19. 25(1): 72
    DANCE: a deep learning library and benchmark platform for single-cell analysis.
    Jiayuan Ding, Renming Liu, Hongzhi Wen, Wenzhuo Tang, Zhaoheng Li, Julian Venegas, Runze Su, Dylan Molho, Wei Jin, Yixin Wang, Qiaolin Lu, Lingxiao Li, Wangyang Zuo, Yi Chang, Yuying Xie, Jiliang Tang.
      DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
    Keywords:  Benchmarking; Cell type annotation; Cell type deconvolution; Clustering; Deep learning; Gene imputation; Multimodality integration; Single-cell multimodal analysis; Single-cell spatial analysis; Spatial domain identification
    DOI:  https://doi.org/10.1186/s13059-024-03211-z
  6. Semin Cancer Biol. 2024 Mar 17. pii: S1044-579X(24)00021-X. [Epub ahead of print]
    Exploiting Transcription Factors to Target EMT and Cancer Stem Cells for Tumor Modulation and Therapy.
    Abdul Q Khan, Adria Hasan, Snober S Mir, Khalid Rashid, Shahab Uddin, Martin Stienhoff.
      Transcription factors (TFs) are essential in controlling gene regulatory networks that determine cellular fate during embryogenesis and tumor development. TFs are the major players in promoting cancer stemness by regulating the function of cancer stem cells (CSCs). Understanding how TFs interact with their downstream targets for determining cell fate during embryogenesis and tumor development is a critical area of research. CSCs are increasingly recognized for their significance in tumorigenesis and patient prognosis, as they play a significant role in cancer initiation, progression, metastasis, and treatment resistance. However, traditional therapies have limited effectiveness in eliminating this subset of cells, allowing CSCs to persist and potentially form secondary tumors. Recent studies have revealed that cancer cells and tumors with CSC-like features also exhibit genes related to the epithelial-to-mesenchymal transition (EMT). EMT-associated transcription factors (EMT-TFs) like TWIST and Snail/Slug can upregulate EMT-related genes and reprogram cancer cells into a stem-like phenotype. Importantly, the regulation of EMT-TFs, particularly through post-translational modifications (PTMs), plays a significant role in cancer metastasis and the acquisition of stem cell-like features. PTMs, including phosphorylation, ubiquitination, and SUMOylation, can alter the stability, localization, and activity of EMT-TFs, thereby modulating their ability to drive EMT and stemness properties in cancer cells. Although targeting EMT-TFs holds potential in tackling CSCs, current pharmacological approaches to do so directly are unavailable. Therefore, this review aims to explore the role of EMT- and CSC-TFs, their connection and impact in cellular development and cancer, emphasizing the potential of TF networks as targets for therapeutic intervention.
    Keywords:  Cancer stem cells; Drug resistance; EMT; Transcription factors
    DOI:  https://doi.org/10.1016/j.semcancer.2024.03.002
  7. Biophys J. 2024 Mar 18. pii: S0006-3495(24)00201-7. [Epub ahead of print]
    Tipping points in epithelial-mesenchymal lineages from single cell transcriptomics data.
    Manuel Barcenas, Federico Bocci, Qing Nie.
      Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high resolution single cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single cell transcriptomics data to infer the stability and gene regulatory networks (GRN) along cell lineages. Our method uses the unspliced and spliced counts from single cell RNA sequencing (scRNA-seq) data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in gene regulatory networks (GRN) and their variation along the cell lineage. When applied to epithelial-mesenchymal transition (EMT) in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling EMT rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single cell transcriptomics data.
    Keywords:  bifurcation; epithelial-mesenchymal transition; lineage; single cell transcriptomics; tipping point
    DOI:  https://doi.org/10.1016/j.bpj.2024.03.021
  8. Biophys Rev (Melville). 2022 Dec;3(4): 041402
    Insights into the cell fate decision-making processes from chromosome structural reorganizations.
    Xiakun Chu, Jin Wang.
      The cell fate decision-making process, which provides the capability of a cell transition to a new cell type, involves the reorganizations of 3D genome structures. Currently, the high temporal resolution picture of how the chromosome structural rearrangements occur and further influence the gene activities during the cell-state transition is still challenging to acquire. Here, we study the chromosome structural reorganizations during the cell-state transitions among the pluripotent embryonic stem cell, the terminally differentiated normal cell, and the cancer cell using a nonequilibrium landscape-switching model implemented in the molecular dynamics simulation. We quantify the chromosome (de)compaction pathways during the cell-state transitions and find that the two pathways having the same destinations can merge prior to reaching the final states. The chromosomes at the merging states have similar structural geometries but can differ in long-range compartment segregation and spatial distribution of the chromosomal loci and genes, leading to cell-type-specific transition mechanisms. We identify the irreversible pathways of chromosome structural rearrangements during the forward and reverse transitions connecting the same pair of cell states, underscoring the critical roles of nonequilibrium dynamics in the cell-state transitions. Our results contribute to the understanding of the cell fate decision-making processes from the chromosome structural perspective.
    DOI:  https://doi.org/10.1063/5.0107663
  9. Biophys Rev. 2024 Feb;16(1): 57-67
    Studying temporal dynamics of single cells: expression, lineage and regulatory networks.
    Xinhai Pan, Xiuwei Zhang.
      Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.
    Supplementary Information: The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
    Keywords:  Gene regulatory network inference; Lineage tracing; Single-cell RNA sequencing; Trajectory inference
    DOI:  https://doi.org/10.1007/s12551-023-01090-5
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