Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays

By Ao Chen, Sha Liao, Mengnan Cheng, Kailong Ma, Liang Wu, Yiwei Lai, Xiaojie Qiu, Jin Yang, Wenjia Li, Jiangshan Xu and 49 more. bioRxiv, 2021

1,485 citations recorded by the source corpus.

Spatially resolved transcriptomic technologies are promising tools to study cell fate decisions in a physical microenvironment, which is fundamental for enhancing our knowledge of mammalian development. However, the imbalance between resolution, transcript capture and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation mammalian embryos. Here, we combined DNA nanoball (DNB) patterned arrays and tissue RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution and sensitivity. We have applied Stereo-seq to study the kinetics and directionality of transcriptional variation in a time course of mouse organogenesis. We used this information to gain insight into the molecular basis of regional specification, neuronal migration and differentiation in the developing brain. Furthermore, we mapped the expression of a panel of developmental disease-related loci on our global transcriptomic maps to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes an essential resource to investigate longstanding questions concerning normal and abnormal mammalian development.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a5dcc6b0d51668115a3382468382e074d053801a

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Citations in the corpus

Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “Newly emerging methods such as Stereo-seq [5], PIXEL-Seq [6], and Seq-Scope [9] can achieve sub-micrometer and thus subcellular resolution.”
Methods and applications for single-cell and spatial multi-omics (2023, background): “These approaches allow for transcriptome-wide profiling through capturing of polyadenylated (poly(A)) RNA transcripts released from fixed and permeabilized tissue sections, making use of thousands of arrayed and spatially barcoded oligo-dT spots (for example, Spatial Transcriptomics 170,176 and 10x ”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Finally, we focus on a handful of techniques that are commercially available or nearing availability at the time of writing, representing the four classes of spatial transcriptomics techniques identified above: MERSCOPE (based on MERFISH ISH technology [45]), Esper (ISHbased) [46], Xenium (based on ”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “Finally, we integrated mouse olfactory bulb datasets from two ST technologies (10 × Genomics Visium and Stereoseq).”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “We extensively tested GraphST for the three analysis tasks on different 10x Visium, Stereo-seq, and Slide-seqV2 datasets of human and mice tissues, including the human brain, human breast cancer tissue, human lymph node, mouse breast cancer, mouse olfactory bulb, mouse brain, and mouse embryo.”