bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–04–21
twelve papers selected by
Xiao Qin, University of Oxford
  1. Somatic epimutations enable single-cell lineage tracing in native hematopoiesis across the murine and human lifespan.
  2. Prime editing.
  3. Emerging paradigms in cancer cell plasticity.
  4. Modeling single cell trajectory using forward-backward stochastic differential equations.
  5. The impact of package selection and versioning on single-cell RNA-seq analysis.
  6. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
  7. Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.
  8. Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.
  9. Single-cell immune repertoire analysis.
  10. Probabilistic association of differentially expressed genes with cis-regulatory elements.
  11. Single-Cell Analysis Reveals Histone Deacetylation Factor Guide Intercellular Communication of Tumor Microenvironment that Contribute to Colorectal Cancer Progression and Immunotherapy.
  12. Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation.
  1. bioRxiv. 2024 Apr 01. pii: 2024.04.01.587514. [Epub ahead of print]
    Somatic epimutations enable single-cell lineage tracing in native hematopoiesis across the murine and human lifespan.
    Michael Scherer, Indranil Singh, Martina Braun, Chelsea Szu-Tu, Michael Kardorff, Julia Rühle, Robert Frömel, Sergi Beneyto-Calabuig, Simon Raffel, Alejo Rodriguez-Fraticelli, Lars Velten.
      Current approaches to lineage tracing of stem cell clones require genetic engineering or rely on sparse somatic DNA variants, which are difficult to capture at single-cell resolution. Here, we show that targeted single-cell measurements of DNA methylation at single-CpG resolution deliver joint information about cellular differentiation state and clonal identities. We develop EPI-clone, a droplet-based method for transgene-free lineage tracing, and apply it to study hematopoiesis, capturing hundreds of clonal trajectories across almost 100,000 single-cells. Using ground-truth genetic barcodes, we demonstrate that EPI-clone accurately identifies clonal lineages throughout hematopoietic differentiation. Applied to unperturbed hematopoiesis, we describe an overall decline of clonal complexity during murine ageing and the expansion of rare low-output stem cell clones. In aged human donors, we identified expanded hematopoietic clones with and without genetic lesions, and various degrees of clonal complexity. Taken together, EPI-clone enables accurate and transgene-free single-cell lineage tracing at scale.
    DOI:  https://doi.org/10.1101/2024.04.01.587514
  2. Nat Biotechnol. 2024 Apr;42(4): 570
    Prime editing.
      
    DOI:  https://doi.org/10.1038/s41587-024-02197-0
  3. BMB Rep. 2024 Apr 17. pii: 6190. [Epub ahead of print]
    Emerging paradigms in cancer cell plasticity.
    Hyunbin D Huh, Hyun Woo Park.
      Cancer cells metastasize to distant organs by altering their characteristics within the tumor microenvironment (TME) to effectively overcome challenges during the multistep tumorigenesis. Plasticity endows cancer cell with the capacity to shift between different states to invade, disseminate, and seed metastasis. The epithelial-to-mesenchymal transition (EMT) is a cellular program that abrogates cell-cell adhesions by EMT transcription factors (TF) and acquires mesenchymal features during cancer progression. On the other hand, adherent-to-suspension transition (AST) is an emerging theory that describes the acquisition of hematopoietic features by AST-TFs that can induce the reprogramming of anchorage dependency and promote cancer cell dissemination. The induction and plasticity of EMT and AST dynamically reprogram cell-cell and cell-matrix interaction during cancer dissemination and colonization. Here, we review the mechanisms governing cellular plasticity of AST and EMT during the metastatic cascade and discuss therapeutic challenges posed by these two morphological adaptations to provide insights for establishing new therapeutic interventions.
  4. PLoS Comput Biol. 2024 Apr;20(4): e1012015
    Modeling single cell trajectory using forward-backward stochastic differential equations.
    Kevin Zhang, Junhao Zhu, Dehan Kong, Zhaolei Zhang.
      Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.
    DOI:  https://doi.org/10.1371/journal.pcbi.1012015
  5. bioRxiv. 2024 Apr 11. pii: 2024.04.04.588111. [Epub ahead of print]
    The impact of package selection and versioning on single-cell RNA-seq analysis.
    Joseph Matthew Rich, Lambda Moses, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert, A Sina Booeshaghi, Sindri Antonsson, Delaney K Sullivan, Nicolas Bray, Páll Melsted, Lior Pachter.
      Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.
    DOI:  https://doi.org/10.1101/2024.04.04.588111
  6. Cell Rep Methods. 2024 Apr 10. pii: S2667-2375(24)00089-4. [Epub ahead of print] 100758
    Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
    Hratch M Baghdassarian, Daniel Dimitrov, Erick Armingol, Julio Saez-Rodriguez, Nathan E Lewis.
      In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
    Keywords:  CP: Cell biology; CP: Systems biology; cell-cell communication; context dependent; ligand-receptor interactions; multiple conditions; single-cell RNA sequencing; tensor decomposition
    DOI:  https://doi.org/10.1016/j.crmeth.2024.100758
  7. Genome Biol. 2024 Apr 15. 25(1): 96
    Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.
    Yue Fan, Lei Li, Shiquan Sun.
      We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
    Keywords:  Non-parametric models; Temporal expression patterns; Time-course scRNA-seq data
    DOI:  https://doi.org/10.1186/s13059-024-03237-3
  8. Genome Biol. 2024 Apr 19. 25(1): 104
    Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.
    SCA Consortium
      Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.
    Keywords:  Flow cytometry; Human database; Mass cytometry; Multi-omics; Single Cell Atlas; Single-cell ATAC-sequencing; Single-cell RNA-sequencing; Single-cell immune profiling; Single-cell omics; Spatial transcriptomics
    DOI:  https://doi.org/10.1186/s13059-024-03246-2
  9. Nat Methods. 2024 Apr 18.
    Single-cell immune repertoire analysis.
    Sergio E Irac, Megan Sioe Fei Soon, Nicholas Borcherding, Zewen Kelvin Tuong.
      Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
    DOI:  https://doi.org/10.1038/s41592-024-02243-4
  10. Genome Res. 2024 Apr 17. pii: gr.278598.123. [Epub ahead of print]
    Probabilistic association of differentially expressed genes with cis-regulatory elements.
    Brian S Roberts, Ashlyn G Anderson, Chris Partridge, Gregory M Cooper, Richard M Myers.
      Differential gene expression in response to perturbations is mediated at least in part by changes in binding of transcription factors (TFs) and other proteins at specific genomic regions. Association of these cis-regulatory elements (CREs) with their target genes is a challenging task that is essential to address many biological and mechanistic questions. Many current approaches rely on chromatin conformation capture techniques or single-cell correlational methods to establish CRE-to-gene associations. These methods can be effective but have limitations, including resolution, gaps in detectable association distances, and cost. As an alternative, we have developed DegCre, a nonparametric method that evaluates correlations between measurements of perturbation-induced differential gene expression and differential regulatory signal at CREs to score possible CRE-to-gene associations. It has several unique features, including the ability to: use any type of CRE activity measurement; yield probabilistic scores for CRE-to-gene pairs; and assess CRE-to-gene pairings across a wide range of sequence distances. We apply DegCre to six data sets, each employing different perturbations and containing a variety of regulatory signal measurements, including chromatin openness, histone modifications, and TF occupancy. To test their efficacy, we compare DegCre associations to Hi-C loop calls and CRISPR-validated CRE-to-gene associations, establishing good performance by DegCre that is comparable or superior to competing methods. DegCre is a novel approach to the association of CREs to genes from a perturbation-differential perspective, with strengths that are complementary to existing approaches and allow for new insights into gene regulation.
    DOI:  https://doi.org/10.1101/gr.278598.123
  11. Biochem Genet. 2024 Apr 18.
    Single-Cell Analysis Reveals Histone Deacetylation Factor Guide Intercellular Communication of Tumor Microenvironment that Contribute to Colorectal Cancer Progression and Immunotherapy.
    Zihan Zhao, Yarui Wu, Xuhua Geng, Congrui Yuan, Guibin Yang.
      In this study, single-cell RNA-seq data were collected to analyze the characteristics of Histone deacetylation factor (HDF). The tumor microenvironment (TME) cell clusters related to prognosis and immune response were identified by using CRC tissue transcriptome and immunotherapy cohorts from public repository. We explored the expression characteristics of HDF in stromal cells, macrophages, T lymphocytes, and B lymphocytes of the CRC single-cell dataset TME and further identified 4 to 6 cell subclusters using the expression profiles of HDF-associated genes, respectively. The regulatory role of HDF-associated genes on the CRC tumor microenvironment was explored by using single-cell trajectory analysis, and the cellular subtypes identified by biologically characterized genes were compared with those identified by HDF-associated genes. The interaction of HDF-associated gene-mediated microenvironmental cell subtypes and tumor epithelial cells were explored by using intercellular communication analysis, revealing the molecular regulatory mechanism of tumor epithelial cell heterogeneity. Based on the expression of feature genes mediated by HDF-related genes in the microenvironment T-cell subtypes, enrichment scoring was performed on the feature gene expression in the CRC tumor tissue transcriptome dataset. It was found that the feature gene scoring of microenvironment T-cell subtypes (HDF-TME score) has a certain predictive ability for the prognosis and immunotherapy benefits of CRC tumor patients, providing data support for precise immunotherapy in CRC tumors.
    Keywords:  Colorectal cancer; Histone deacetylases; Histone deacetylation factor; Tumor microenvironment
    DOI:  https://doi.org/10.1007/s10528-024-10730-8
  12. bioRxiv. 2024 Apr 06. pii: 2024.04.05.588333. [Epub ahead of print]
    Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation.
    M M Inge, R Miller, H Hook, D Bray, J L Keenan, R Zhao, T D Gilmore, T Siggers.
      Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here we describe the Co factor Rec ruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced H3K27ac. Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data supports clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.
    DOI:  https://doi.org/10.1101/2024.04.05.588333
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