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空间scERA:一种在多细胞生物中重建空间单细胞增强子活性的方法。

Spatial-scERA: a method for reconstructing spatial single-cell enhancer activity in multicellular organisms.

作者信息

Alberti Baptiste, Vincent Séverine, Stévant Isabelle, Lajoignie Damien, Tarayre Hélène, Mendes Juliette, Sarnataro Sergio, Villoutreix Paul, Ghavi-Helm Yad

机构信息

Institut de Génomique Fonctionnelle de Lyon, UMR5242, Ecole Normale Supérieure de Lyon, Centre National de la Recherche Scientifique, Université Claude Bernard-Lyon 1, 46 allée d'Italie, 69007 Lyon, France.

Aix-Marseille Université, MMG, Inserm U1251, Turing Centre for Living systems, 27 Bd Jean Moulin, 13005 Marseille, France.

出版信息

Nucleic Acids Res. 2025 Jul 19;53(14). doi: 10.1093/nar/gkaf684.


DOI:10.1093/nar/gkaf684
PMID:40694852
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12282952/
Abstract

Enhancers play an essential role in developmental processes by regulating the spatiotemporal expression of genes. Characterizing their spatiotemporal activity remains however an important challenge. Here we introduce a novel in vivo/in silico method for spatial single-cell enhancer-reporter assays (spatial-scERA) designed to reconstruct the spatial activity of candidate enhancer regions in parallel in multicellular organisms. Spatial-scERA integrates parallel reporter assays with single-cell RNA sequencing and spatial reconstruction using optimal transport, to map cell-type-specific enhancer activity at the single-cell level on a 3D virtual sample. We evaluated spatial-scERA in Drosophila embryos using 25 candidate enhancers, and validated the robustness of our reconstructions by comparing them to in situ hybridization. Remarkably, spatial-scERA faithfully reconstructed the spatial activity of these enhancers, even when the reporter construct was expressed in as few as 10 cells. Our results demonstrate the importance of integrating transcriptomic and spatial data for accurately predicting enhancer activity patterns in complex multicellular samples and linking enhancers to their potential target genes. Overall, spatial-scERA provides a scalable approach to map the spatial activity of enhancers at single-cell resolution without the need for imaging or a priori knowledge of embryology and can be applied to any multicellular organism amenable to transgenesis.

摘要

增强子通过调控基因的时空表达在发育过程中发挥着至关重要的作用。然而,表征它们的时空活性仍然是一项重大挑战。在此,我们介绍一种用于空间单细胞增强子 - 报告基因检测(spatial - scERA)的新型体内/计算机模拟方法,该方法旨在在多细胞生物体中并行重建候选增强子区域的空间活性。Spatial - scERA将并行报告基因检测与单细胞RNA测序以及使用最优传输的空间重建相结合,以在三维虚拟样本上的单细胞水平绘制细胞类型特异性增强子活性图谱。我们使用25个候选增强子在果蝇胚胎中评估了spatial - scERA,并通过与原位杂交进行比较验证了我们重建结果的稳健性。值得注意的是,即使报告基因构建体仅在10个细胞中表达,spatial - scERA也能忠实地重建这些增强子的空间活性。我们的结果证明了整合转录组学和空间数据对于准确预测复杂多细胞样本中增强子活性模式以及将增强子与其潜在靶基因联系起来的重要性。总体而言,spatial - scERA提供了一种可扩展的方法,能够在单细胞分辨率下绘制增强子的空间活性图谱,而无需成像或胚胎学的先验知识,并且可以应用于任何适合转基因的多细胞生物体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/b4553e6cff56/gkaf684fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/d3cc881a77db/gkaf684figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/8ee003c180e5/gkaf684fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/9e28f5dc184f/gkaf684fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/231b6051488d/gkaf684fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/eea9691f73bb/gkaf684fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/b4553e6cff56/gkaf684fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/d3cc881a77db/gkaf684figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/8ee003c180e5/gkaf684fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/9e28f5dc184f/gkaf684fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/231b6051488d/gkaf684fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/eea9691f73bb/gkaf684fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc46/12282952/b4553e6cff56/gkaf684fig5.jpg

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本文引用的文献

[1]
Massively parallel reporter assays and mouse transgenic assays provide correlated and complementary information about neuronal enhancer activity.

Nat Commun. 2025-5-23

[2]
COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics. 2025-3-4

[3]
CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.

Patterns (N Y). 2024-7-9

[4]
Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters.

Nat Methods. 2024-6

[5]
FlyBase: updates to the Drosophila genes and genomes database.

Genetics. 2024-5-7

[6]
Enhancer-promoter interactions can form independently of genomic distance and be functional across TAD boundaries.

Nucleic Acids Res. 2024-2-28

[7]
Spatiotemporal, optogenetic control of gene expression in organoids.

Nat Methods. 2023-10

[8]
Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks.

Adv Sci (Weinh). 2023-7

[9]
An unbiased AAV-STARR-seq screen revealing the enhancer activity map of genomic regions in the mouse brain in vivo.

Sci Rep. 2023-4-25

[10]
MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features.

Comput Biol Med. 2023-6

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