Cell Type Assignments for Spatial Transcriptomics Data

Teng, Haotian; Yuan, Ye; Bar‐Joseph, Ziv

doi:10.1101/2021.02.25.432887

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Pci-Seq uses probabilistic cell typing and is able to identify cell types more efficiently in larger tissue areas 23,86 . FICT, another method, integrates expression and neighbourhood information to assign cell types 141 . In the case of imaging-based methods, each DAPI-stained nucleus can be classified as a cell type according to its distance from marker gene RNAs 86 .…”

Section: Characterizementioning

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: Characterizementioning

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

“…Next the initial cluster is split into sub-clusters if its spots are spatially separated. sm shHmrf (30) is another spatial clustering method that starts by the SVM classi er trained using scRNA-seq data as mentioned above. It then updates cell clustering according to the principle that neighbor cells of the same identity have higher score.…”

Section: Introductionmentioning

confidence: 99%

CCST: Cell clustering for spatial transcriptomics data with graph neural network

Chen

Pan

et al. 2021

Preprint

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Spatial transcriptomics data can provide high-throughput gene expression profiling and spatial structure of tissues simultaneously. An essential question of its initial analysis is cell clustering. However, most existing studies rely on only gene expression information and cannot utilize spatial information efficiently. Taking advantages of two recent technical development, spatial transcriptomics and graph neural network, we thus introduce CCST, Cell Clustering for Spatial Transcriptomics data with graph neural network, an unsupervised cell clustering method based on graph convolutional network to improve ab initio cell clustering and discovering of novel sub cell types based on curated cell category annotation. CCST is a general framework for dealing with various kinds of spatially resolved transcriptomics. With application to five in vitro and in vivo spatial datasets, we show that CCST outperforms other spatial cluster approaches on spatial transcriptomics datasets, and can clearly identify all four cell cycle phases from MERFISH data of cultured cells, and find novel functional sub cell types with different micro-environments from seqFISH+ data of brain, which are all validated experimentally, inspiring novel biological hypotheses about the underlying interactions among cell state, cell type and micro-environment.

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