Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays

Chen, Ao; Liao, Sha; Cheng, Mengnan; Ma, Kailong; Wu, Liang; Lai, Yiwei; Qiu, Xiaojie; Yang, Jin Min; Li, Wenjiao; Xu, Jiangshan; Hao, Shijie; Wang, Xin; Lü, Huifang; Chen, Xi; Xing, Liu; Huang, Xin; Lin, Feng; Zhao, Li; Hong, Yan; Fu, Defeng; Jiang, Yujia; Peng, Jian; Liu, Shuai; Shen, Mengzhe; Liu, Chuanyu; Li, Quanshui; Yuan, Yue; Zheng, Huiwen; Wang, Zhifeng; Wang, Zhaohui; Xiang, Haitao; Han, Lei; Qin, Baoming; Guo, Pengcheng; Canoves, Pura Munoz; Thiéry, Jean Paul; Wu, Qing-Feng; Zhao, Fuxiang; Li, Mei; Kuang, Haoyan; Hui, Junhou; Wang, Ou; Lu, Haorong; Wang, Bo; Liu, Shiping; Ni, Ming; Zhang, Wenwei; Mu, Feng; Yin, Ye; Yang, Huanming; Lisby, Michael; Cornall, Richard J.; Mulder, Jan; Uhlén, Mathias; Esteban, Miguel A.; Li, Yuxiang; Liu, Longqi; Xu, Xun; Wang, Jian

doi:10.1101/2021.01.17.427004

Cited by 55 publications

(51 citation statements)

References 85 publications

(133 reference statements)

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“…Such spatially resolved transcriptomes of histological tissues enable the reconstruction of tissue architecture and cell-cell interactions. 1,2,3,4,5,6,7,8,9 This approach has proven valuable in many applications including studies on brain disorders, 2,10 tumour microenvironments, 3,11 and embryonic development. 12…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics

Chong

et al. 2021

Preprint

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108

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Spatial transcriptomics enable us to dissect tissue heterogeneity and map out inter-cellular communications. Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting the data. We present SEDR, an unsupervised spatially embedded deep representation of both transcript and spatial information. The SEDR pipeline uses a deep autoencoder to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. We applied SEDR on human dorsolateral prefrontal cortex data and achieved better clustering accuracy, and correctly retraced the prenatal cortex development order with trajectory analysis. We also found the SEDR representation to be eminently suited for batch integration. Applying SEDR to human breast cancer data, we discerned heterogeneous sub-regions within a visually homogenous tumor region, identifying a tumor core with pro-inflammatory microenvironment and an outer ring region enriched with tumor associated macrophages which drives an immune suppressive microenvironment.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics

Chong

et al. 2021

Preprint

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108

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“…While the current manuscript is in preparation, another technology, Stereo-Seq, claimed a nominal transcriptome resolution of 0.5-0.7 μm (Chen et al, 2021), which is similar to Seq-Scope. However, unlike Seq-Scope, Stereo-Seq suffered a low transcriptome capture efficiency, which is ~170 unique transcripts per 100 μm 2 (less than 20% of current Seq-Scope output).…”

Section: Discussionmentioning

confidence: 93%

“…However, unlike Seq-Scope, Stereo-Seq suffered a low transcriptome capture efficiency, which is ~170 unique transcripts per 100 μm 2 (less than 20% of current Seq-Scope output). Due to the low efficiency, most of the transcriptome studies in the Stereo-Seq work (Chen et al, 2021) were performed in 36 μm-sided square grids, which can contain more than 10 different single cells. Therefore, although Stereo-Seq was able to identify gross anatomical structures of embryonic organs and adult brain compartments (Chen et al, 2021), it did not reveal microscopic details such as single cell- or subcellular-level transcriptome information.…”

Section: Discussionmentioning

confidence: 99%

Seq-Scope: Submicrometer-resolution spatial transcriptomics for single cell and subcellular studies

Cho

Park

et al. 2021

Preprint

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Spatial barcoding technologies have the potential to reveal histological details of transcriptomic profiles; however, they are currently limited by their low resolution. Here we report Seq-Scope, a spatial barcoding technology with a resolution almost comparable to an optical microscope. Seq-Scope is based on a solid-phase amplification of randomly barcoded single-molecule oligonucleotides using an Illumina sequencing-by-synthesis platform. The resulting clusters annotated with spatial coordinates are processed to expose RNA-capture moiety. These RNA-capturing barcoded clusters define the pixels of Seq-Scope that are approximately 0.5-1 μm apart from each other. From tissue sections, Seq-Scope visualizes spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation-fibrosis (injured liver) axes, cellular components including single cell types and subtypes, and subcellular architectures of nucleus, cytoplasm and mitochondria. Seq-scope is quick, straightforward and easy-to-implement, and makes spatial single cell analysis accessible to a wide group of biomedical researchers.

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“…Except that our method captures two-modality information in the same neuron at the expense of downstream targets spatial information, while Connectid captures transcriptome at source area and connectome at downstream targets area at the expense of mapping rate and recovery rate. We expect that combining with commercial or self-made spatial transcriptomics technology [35][36][37][38][39] , MERGE-seq can add spatial information as a new modality.…”

Section: Discussionmentioning

confidence: 99%

High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling

Peng

Yuan

et al. 2021

Preprint

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Deciphering mesoscopic connectivity of the mammalian brain is a pivotal step in neuroscience. Most imaging-based conventional neuroanatomical tracing methods identify area-to-area or sparse single neuronal labeling information. Although recently developed barcode-based connectomics has been able to map a large number of single-neuron projections efficiently, there is a missing link in single-cell connectome and transcriptome. Here, combining single-cell RNA sequencing technology, we established a retro-AAV barcode-based multiplexed tracing method called MEGRE-seq (Multiplexed projEction neuRons retroGrade barcodE), which can resolve projectome and transcriptome of source neurons simultaneously. Using the ventromedial prefrontal cortex (vmPFC) as a proof-of-concept neocortical region, we investigated projection patterns of its excitatory neurons targeting five canonical brain regions, as well as corresponding transcriptional profiles. Dedicated, bifurcated or collateral projection patterns were inferred by digital projectome. In combination with simultaneously recovered transcriptome, we find that certain projection pattern has a preferential layer or neuron subtype bias. Further, we fitted single-neuron two-modal data into a machine learning-based model and delineated gene importance by each projection target. In summary, we anticipate that the new multiplexed digital connectome technique is potential to understand the organizing principle of the neural circuit by linking projectome and transcriptome.

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Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays

Cited by 55 publications

References 85 publications

Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics

Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics

Seq-Scope: Submicrometer-resolution spatial transcriptomics for single cell and subcellular studies

High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling

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