bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024‒05‒19
thirteen papers selected by
Xiao Qin, University of Oxford
  1. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection.
  2. Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics.
  3. Nanopore sequencing.
  4. GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.
  5. Bidirectional epigenetic editing reveals hierarchies in gene regulation.
  6. Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.
  7. Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations.
  8. A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.
  9. Spatial transition tensor of single cells.
  10. Fast and multiplexed DNA-PAINT.
  11. EmptyDropsMultiome discriminates real cells from background in single-cell multiomics assays.
  12. SCIPAC: quantitative estimation of cell-phenotype associations.
  13. A computational tool suite to facilitate single-cell lineage tracing analyses.
  1. Genome Biol. 2024 May 17. 25(1): 124
    Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection.
    Timothy Barry, Kaishu Mason, Kathryn Roeder, Eugene Katsevich.
      Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
    DOI:  https://doi.org/10.1186/s13059-024-03254-2
  2. Cell Syst. 2024 May 15. pii: S2405-4712(24)00119-4. [Epub ahead of print]15(5): 411-424.e9
    Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics.
    Rory J Maizels, Daniel M Snell, James Briscoe.
      The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.
    Keywords:  RNA velocity; deep learning; gene regulatory network; single-cell transcriptomics; variational autoencoder
    DOI:  https://doi.org/10.1016/j.cels.2024.04.004
  3. Nat Biotechnol. 2024 May;42(5): 698
    Nanopore sequencing.
      
    DOI:  https://doi.org/10.1038/s41587-024-02231-1
  4. Nat Commun. 2024 May 14. 15(1): 4055
    GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.
    Yazdan Zinati, Abdulrahman Takiddeen, Amin Emad.
      We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.
    DOI:  https://doi.org/10.1038/s41467-024-48516-6
  5. Nat Biotechnol. 2024 May 17.
    Bidirectional epigenetic editing reveals hierarchies in gene regulation.
    Naomi M Pacalin, Zachary Steinhart, Quanming Shi, Julia A Belk, Dmytro Dorovskyi, Katerina Kraft, Kevin R Parker, Brian R Shy, Alexander Marson, Howard Y Chang.
      CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.
    DOI:  https://doi.org/10.1038/s41587-024-02213-3
  6. Cell Syst. 2024 May 15. pii: S2405-4712(24)00120-0. [Epub ahead of print]15(5): 462-474.e5
    Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.
    Rohit Singh, Alexander P Wu, Anish Mudide, Bonnie Berger.
      Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.
    Keywords:  Granger causality; RNA velocity; corticogenesis; gene regulatory network; graph neural network; regulatory dynamics; transcription factors
    DOI:  https://doi.org/10.1016/j.cels.2024.04.005
  7. Nat Biotechnol. 2024 May 17.
    Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations.
    C C-S Hsiung, C M Wilson, N A Sambold, R Dai, Q Chen, N Teyssier, S Misiukiewicz, A Arab, T O'Loughlin, J C Cofsky, J Shi, L A Gilbert.
      Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.
    DOI:  https://doi.org/10.1038/s41587-024-02224-0
  8. STAR Protoc. 2024 May 14. pii: S2666-1667(24)00232-6. [Epub ahead of print]5(2): 103067
    A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.
    Kyung Dae Ko, Vittorio Sartorelli.
      Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we present a deep learning framework, called the dynamic batching adversarial autoencoder (DB-AAE), for denoising scRNA-seq datasets. First, we describe steps to set up the computing environment, training, and tuning. Then, we depict the visualization of the denoising results. For complete details on the use and execution of this protocol, please refer to Ko et al.1.
    Keywords:  Bioinformatics; Genomics
    DOI:  https://doi.org/10.1016/j.xpro.2024.103067
  9. Nat Methods. 2024 May 16.
    Spatial transition tensor of single cells.
    Peijie Zhou, Federico Bocci, Tiejun Li, Qing Nie.
      Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.
    DOI:  https://doi.org/10.1038/s41592-024-02266-x
  10. Nat Methods. 2024 May;21(5): 748
    Fast and multiplexed DNA-PAINT.
    Nina Vogt.
      
    DOI:  https://doi.org/10.1038/s41592-024-02287-6
  11. Genome Biol. 2024 May 13. 25(1): 121
    EmptyDropsMultiome discriminates real cells from background in single-cell multiomics assays.
    Stathis Megas, Valentina Lorenzi, John C Marioni.
      Multiomic droplet-based technologies allow different molecular modalities, such as chromatin accessibility and gene expression (scATAC-seq and scRNA-seq), to be probed in the same nucleus. We develop EmptyDropsMultiome, an approach that distinguishes true nuclei-containing droplets from background. Using simulations, we show that EmptyDropsMultiome has higher statistical power and accuracy than existing approaches, including CellRanger-arc and EmptyDrops. On real datasets, we observe that CellRanger-arc misses more than half of the nuclei identified by EmptyDropsMultiome and, moreover, is biased against certain cell types, some of which have a retrieval rate lower than 20%.
    Keywords:  Method; Multiomics; Single-cell
    DOI:  https://doi.org/10.1186/s13059-024-03259-x
  12. Genome Biol. 2024 May 13. 25(1): 119
    SCIPAC: quantitative estimation of cell-phenotype associations.
    Dailin Gan, Yini Zhu, Xin Lu, Jun Li.
      Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.
    Keywords:  Cancer research; Phenotype association; RNA sequencing; Single cell
    DOI:  https://doi.org/10.1186/s13059-024-03263-1
  13. Cell Rep Methods. 2024 May 04. pii: S2667-2375(24)00124-3. [Epub ahead of print] 100780
    A computational tool suite to facilitate single-cell lineage tracing analyses.
    Joshua J Waterfall, Adil Midoun, Leïla Perié.
      Tracking the lineage relationships of cell populations is of increasing interest in diverse biological contexts. In this issue of Cell Reports Methods, Holze et al. present a suite of computational tools to facilitate such analyses and encourage their broader application.
    DOI:  https://doi.org/10.1016/j.crmeth.2024.100780
This page is being maintained by Thomas Krichel. It was last updated on 2024‒06‒05 at 7:56.