Dissecting Mammalian Spermatogenesis Using Spatial Transcriptomics

Chen, Haiqi; Murray, Evan; Laumas, Anisha; Li, Jilong; Nie, Xiaolu; Hotaling, Jim; Guo, Jingtao; Cairns, Bradley R.; Macosko, Evan Z.; Cheng, Chao‐Tzuen; Chen, Fei

doi:10.1101/2020.10.17.343335

Cited by 7 publications

(8 citation statements)

References 67 publications

(80 reference statements)

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“…In developmental biology, time-resolved spatial transcriptomics atlases have been useful to elucidate the spatial dynamics of heart development 51 , spermatogenesis 91 and intestinal development 92 . Similarly, a comprehensive study of the human endometrium during the proliferative and secretory phases of the menstrual cycle identified a role for WNT and Notch signaling in regulating differentiation towards ciliated or secretory epithelial cells 93 .…”

Section: Spatial Transcriptomics Insights Into Development Physiology and Diseasementioning

confidence: 99%

Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

814

610

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Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advancements in next-generation sequencing-based and imaging based approaches have established the potential of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insight in neuroscience, development, plant biology, and a range of diseases including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here, we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs comparing timepoints or conditions -including genetic or environmental perturbations. Finally, spatial transcriptomic data is naturally amenable to integration with other data modalities providing an expandable framework for insight into tissue organization.Many of the notable discoveries in the life sciences followed from the recognition that cellular organization within tissues is intimately linked to biological function. In developmental biology, central topics such as symmetry-breaking between daughter cells and cell fate decisions are based on spatial relationships between cells 1 . In clinical settings, histopathology is often used as a conclusive diagnostic, precisely because diseases are characterized by abnormal spatial organization within tissues 2 . Infectious and inflammatory processes can drastically change the cellular organization of tissues 3 . These discoveries were supported by methods in molecular biology -including in situ hybridization 4 (ISH) and immunohistochemistry 5 -that provided the ability to visualize biological processes more directly by mapping DNA, RNA and protein within tissues. However, these methods limit analysis to at most a handful of genes or proteins at a time.

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Section: Spatial Transcriptomics Insights Into Development Physiology and Diseasementioning

confidence: 99%

Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

814

610

View full text Add to dashboard Cite

show abstract

“…Recently published ST data reveal that the structure of tissues is in potential accordance with this model. For example, the structure of elongating/elongated spermatids is in consistency with the definition of the patch, while their progenitors such as spermatocytes and spermatogonium are located surrounding elongated spermatids which can be described as the matrix 139 . In the context of oncology, the structure of TME and their adjacent tissues is in line with patch–matrix patterns.…”

Section: Spatial Tumour Ecology: Toward a New Research Paradigmmentioning

confidence: 71%

Spatial omics: Navigating to the golden era of cancer research

Cheng

Wang

et al. 2022

Clinical & Translational Med

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The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high‐throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi‐modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.

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“…Spatial transcriptomics has revolutionized the field of single cell genomics and provided new insights into many biological systems such as the brain, heart, liver, and testes [82][83][84][85][86][87][88] . However, the potential of spatial transcriptomics to identify therapeutically actionable pathways has not yet been explored.…”

Section: Discussionmentioning

confidence: 99%

High Resolution Slide-seqV2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways

Marshall

Noel

Wang

et al. 2021

Preprint

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High resolution spatial transcriptomics is a transformative technology that enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remain largely unexplored. Here we applied Slide-seqV2 to mouse and human kidneys, in healthy and in distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in human kidney by analyzing tissue from 9 distinct donors, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially-restricted kidney filter and blood flow regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially-restricted subpopulation of epithelial cells, we also found perturbations in 77 genes associated with the unfolded protein response (UPR). Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.

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Dissecting Mammalian Spermatogenesis Using Spatial Transcriptomics

Cited by 7 publications

References 67 publications

Exploring tissue architecture using spatial transcriptomics

Exploring tissue architecture using spatial transcriptomics

Spatial omics: Navigating to the golden era of cancer research

High Resolution Slide-seqV2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways

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