Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Qian, Jingyang; Liao, Jie; Liu, Ziqi; Chi, Ying; Yin, Fang; Zheng, Yanrong; Shao, Xin; Liu, Bingqi; Cui, Yongjin; Guo, Wenbo; Hu, Yining; Bao, Hudong; Yang, Penghui; Chen, Qian; Li, Mingxiao; Zhang, Bing; Fan, Xiaohui

doi:10.1038/s41467-023-38121-4

Cited by 13 publications

(8 citation statements)

References 92 publications

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“…For the reference-free simulations, scCube aims to generate random or customized spatial patterns for cell populations and combine them with the simulated gene expression profiles to de novo construct complete benchmarking datasets from scRNA-seq data. Compared with simulations starting from real SRT data such as implemented in SRTsim, this strategy can generate simulated SRT data that feeds both single-cell resolution and whole transcriptomes, which is suitable as the ground truth for evaluating the performance of those integration methods, such as spot deconvolution, gene imputation, and spatial reconstruction 59 , 65 , 66 . In this paper, we have demonstrated in detail the flexibility of the reference-free spatial pattern simulation of scCube by setting specified parameter values: including the simulations of multiple variability in SRT data based on a random manner, as well as the customized generations of more biologically interpretable spatial patterns.…”

Section: Discussionmentioning

confidence: 99%

Simulating multiple variability in spatially resolved transcriptomics with scCube

Qian,

Bao,

Shao

et al. 2024

Nat Commun

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A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube (https://github.com/ZJUFanLab/scCube), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

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Section: Discussionmentioning

confidence: 99%

Simulating multiple variability in spatially resolved transcriptomics with scCube

Qian,

Bao,

Shao

et al. 2024

Nat Commun

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“…These technologies have been applied to create comprehensive atlases of cell types and cell states in the kidney and other organ systems. Advanced computational approaches and orthogonal validation methods can be used to infer spatial mapping information from scRNA-seq data [ 3 – 6 ]. However, in their essence, both bulk and single-cell RNA-seq are dissociative approaches, meaning that they disrupt normal tissue architecture during the process of data generation.…”

Section: Strategies To Quantify Gene Expression In Tissuementioning

confidence: 99%

Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Smith,

Prince,

MacDonald

et al. 2024

Glomerular Dis

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Background: The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future. Summary: In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy. Key Message: The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.

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“…Tangram mapped single cells to pre-fixed spatial voxels by designing a probability mapping. scSpace 36 extracted a feature subspace with transfer component analysis and predicted positions using a multi-layer perceptron. CeLEry 37 predicted positions using deep learning and enhanced the spatial transcriptome with data augmentation.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning of multicellular organization via single-cell and spatial transcriptomics

Tan,

Wang,

Wang

et al. 2024

Preprint

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Understanding the cellular spatial organization is a paramountly important direction of exploring the intricate functionalities of tissues and organs. However, conventional single-cell RNA sequencing (scRNA-seq) technologies often lose cells’ spatial information. Recent spatial transcriptomics (ST) techniques aim to preserve locations but easily come with either low resolution or high costs. Here, we introduce iSORT (anintegrative method for reconstructingSpatialORganization of cells viaTransfer learning), which can recover the spatial organization of large-scale multi-source scRNA-seq dataset with only one ST reference. iSORT employs a transfer learning approach to assign coordinates for each single cell with scRNA-seq. iSORT also integrates multiple ST tissue slices as references to enhance accuracy and capture multi-slice biological heterogeneity. Furthermore, iSORT conduces to the precise identification of spatially variable genes (SVGs). We demonstrated the effectiveness of iSORT by applying it to four benchmark datasets, including those fromDrosophilaembryo, mouse embryo, mouse brain, and human brain tissues, and by a comparative analysis with five other algorithms as well. Additionally, we collected scRNA-seq data from the patients with atherosclerosis to reconstruct the structure of arteries for the first time, using ST data from other individuals as references. Atherosclerosis-related SVGs were identified and verified by the gene set enrichment analysis. As a promising tool, iSORT provides deep insights into the single-cell omics data analysis and biomedical research.

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Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Cited by 13 publications

References 92 publications

Simulating multiple variability in spatially resolved transcriptomics with scCube

Simulating multiple variability in spatially resolved transcriptomics with scCube

Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Transfer learning of multicellular organization via single-cell and spatial transcriptomics

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