The single-cell and spatial transcriptional landscape of human gastrulation and early brain development

Zeng, Bo; Liu, Zeyuan; Lü, Yongtao; Zhong, Shaobo; Qin, Shenyue; Huang, Lan; Zeng, Yang; Li, Zixiao; Dong, Hao; Shi, Yingchao; Yang, Jialei; Dai, Yalun; Ma, Qiang; Sun, Le; Bian, Lihong; Han, Demin; Chen, Youqiao; Qiu, Xin; Wang, Wei; Marı́n, Oscar; Wu, Qian; Wang, Yongjun; Wang, Xiaoqun

doi:10.1016/j.stem.2023.04.016

Cited by 28 publications

(15 citation statements)

References 79 publications

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“…Importantly, our findings suggested that these functional associations were mechanistically linked to extracellular matrix organization and the TGF-beta signaling pathway (Figure 3H). Additionally, Zic1 (+) was predicted to be regulated by a set of known co-expressed receptors including Fgfr2 53 , Gjb2 54 , Gpr182 55 , Thbd 56 , Cdh1 57 , Ramp3 58 , Cd44 59 , and Anxa2 60 , and we also identified previously unreported receptors Il31ra , Mpzl2 , and Asgr1 . Another regulon, Hivep2 (+), exhibited a distribution primarily enriched in the frontal cortex and hippocampus 61 (Figure S3E).…”

Section: Resultssupporting

confidence: 55%

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Li,

Liu,

Guo

et al. 2023

Preprint

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SUMMARYCells expressing similar transcriptional regulatory circuits spatially aggregate into distinct cell types or states. However, most existing methods for inferring gene regulatory networks from spatially resolved transcriptomics are devoted to spatial co-expression modules or interactions between transcription factors and target genes, neglecting mediated effects from extracellular signals. Here we introduce SpaGRN, a statistical framework for predicting the comprehensive intracellular regulatory network underlying spatial patterns by integrating spatial expression profiles with prior knowledge on regulatory relationships and signaling paths. We validate and assess SpaGRN using simulated and real datasets, demonstrating its efficiency, performance, and robustness. When applied to 3D datasets of developingDrosophilaembryos and larvae, SpaGRN identifies spatiotemporal variations in specific regulatory patterns, delineating the cascade of events from receptor stimulation to downstream transcription factors and targets, revealing synergetic regulation mechanism during organogenesis. Moreover, SpaGRN provides flexible visualization functions. We construct an online 3D regulatory network atlas database for interactive exploration and sharing.

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Section: Resultssupporting

confidence: 55%

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Li,

Liu,

Guo

et al. 2023

Preprint

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“…While dissociation-based approaches struggle to preserve tissue structure, thereby restricting expression analysis within the natural context, spatial transcriptomic technologies aim to overcome this limitation. Whereas some studies have employed spatial transcriptomics technologies to study mouse ( Peng et al, 2019 ; Chen et al, 2022 ; Sampath Kumar et al, 2023 ; Srivatsan et al, 2021 ) and human (Xu et al, 2023; Zeng et al, 2023 ) embryogenesis, the employed techniques currently still face too many technical and computational challenges (reviewed by ( Zhang et al, 2023 ; Zhou et al, 2023 )) to be routinely used for comparative analyses.…”

Section: Comparative Analysis Methods Of Stem Cell-based Embryo Model...mentioning

confidence: 99%

A comprehensive review: synergizing stem cell and embryonic development knowledge in mouse and human integrated stem cell-based embryo models

Dupont

2024

Front. Cell Dev. Biol.

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Mammalian stem cell-based embryo models have emerged as innovative tools for investigating early embryogenesis in both mice and primates. They not only reduce the need for sacrificing mice but also overcome ethical limitations associated with human embryo research. Furthermore, they provide a platform to address scientific questions that are otherwise challenging to explore in vivo. The usefulness of a stem cell-based embryo model depends on its fidelity in replicating development, efficiency and reproducibility; all essential for addressing biological queries in a quantitative manner, enabling statistical analysis. Achieving such fidelity and efficiency requires robust systems that demand extensive optimization efforts. A profound understanding of pre- and post-implantation development, cellular plasticity, lineage specification, and existing models is imperative for making informed decisions in constructing these models. This review aims to highlight essential differences in embryo development and stem cell biology between mice and humans, assess how these variances influence the formation of partially and fully integrated stem cell models, and identify critical challenges in the field.

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“…A comprehensive understanding of tissue architecture is rooted in the exploration of anatomical and histological regions, complemented by the study of cell-type spatial organizations. This pursuit has been profoundly advanced by the emergence of spatially resolved transcriptomics (SRT), 1 , 2 , 3 , 4 , 5 which has illuminated recent studies in neuroscience, 6 , 7 developmental biology, 8 , 9 and a range of diseases. 10 , 11 To visualize and explore these datasets, cells are in situ annotated and labeled, creating a cellular cartography where each cell type is distinguished by a unique color.…”

Section: Introductionmentioning

confidence: 99%

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution

Jing,

Zhu,

et al. 2024

Patterns

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The single-cell and spatial transcriptional landscape of human gastrulation and early brain development

Cited by 28 publications

References 79 publications

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

A comprehensive review: synergizing stem cell and embryonic development knowledge in mouse and human integrated stem cell-based embryo models

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution

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