STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

Sun, Dongqing; Li, Taiwen; Wu, Qiu Liang; Wang, Chenfei

doi:10.1093/nar/gkac150

Cited by 65 publications

(53 citation statements)

References 42 publications

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“…Several machine learning algorithms have been proposed to integrate spatial transcriptomics data and other data (39). Dongqing Sun et al (40) presented STRIDE to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics based on the machine learning method. Not only do this algorithm map rare cell types to spatial locations, but it also improves gene and domain localization.…”

Section: Discussionmentioning

confidence: 99%

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

et al. 2022

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ObjectiveThe decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy.MethodUsing the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene–drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted.ResultsA significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8+ T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques.ConclusionM1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.

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Section: Discussionmentioning

confidence: 99%

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

et al. 2022

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“…These datasets are from 3 different biological systems, the human pancreatic ductal adenocarcinoma dataset [ 28 ] and the human squamous cell carcinoma dataset [ 29 ] are from the tumor microenvironment system; the mouse cortex dataset [ 30 ] is from the nervous system; and the human heart dataset [ 31 ] and the human intestine dataset [ 32 ] are from the developmental system. The scRNA-seq and ST data preprocessing were done by Seurat V3 [ 59 ], and the cell type deconvolution of ST data was done by STRIDE [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

“…We used STRIDE [ 60 ] to perform the spatial cell type deconvolution for each ST dataset with their matched annotated scRNA-seq data. STRIDE is a topic modeling-based method for accurately decomposing and integrating ST slides.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Sun

Wang

2022

Genome Biol

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Background Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. Results We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. Conclusions Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub (https://github.com/wanglabtongji/CCI).

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“…As described above, deconvolution algorithms have been developed in recent years to support the development of high-throughput single-cell resolution spatial transcriptome techniques. According to our survey, there are 15 tools that can be used for ST data deconvolution, including AdRoit [17] , DSTG [23] , Cell2location [19] , RCTD [25] , Stereoscope [26] , DestVI [22] , STRIDE [30] , CARD [28] , NMFreg [13] , SpatialDecon [18] , SpatialDWLS [27] , SPOTlight [29] , Seurat V3 [20] , Tangram [21] , and STdeconvolve [24] . These tools can be broadly classified as machine learning-based, statistical modeling-based, regression-based, data mapping-based, and reference-free according to the main strategies they use.…”

Section: Inferring the Proportions Of Cellular Subtypes For Spotsmentioning

confidence: 99%

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

Zhang

Lin

Yao

et al. 2023

Computational and Structural Biotechnology Journal

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STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

Cited by 65 publications

References 42 publications

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

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