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

Sun, Dongqing; Xiao, Yihan; Liu, Zhaoyang; Li, Taiwen; Wu, Qiu Liang; Wang, Chenfei

doi:10.1101/2021.09.08.459458

Cited by 8 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parallels can also be drawn between cell-type deconvolution and topic modeling in text mining; cell types are analogous to topics, and genes are analogous to words. Latent Dirichlet allocation (LDA) from topic modeling has been adapted to cell-type deconvolution, such as in spatial transcriptomics deconvolution by topic modeling (STRIDE) 115 and STdeconvolve 116 ; the latter is unsupervised and does not require a scRNA-seq reference. Downstream.…”

Section: Discussionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

View full text Add to dashboard Cite

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

show abstract

Section: Discussionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

View full text Add to dashboard Cite

show abstract

“…Additionally, combining SPRITE with new methods for estimating spatial gene expression prediction uncertainty [26] may provide an ecosystem for interpreting conclusions drawn from predicted spatial gene expression and performing useful scientific inference.…”

Section: Discussionmentioning

confidence: 99%

SPRITE: improving spatial gene expression imputation with gene and cell networks

Sun,

Ma,

Zou

2024

Preprint

View full text Add to dashboard Cite

Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expressionin situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce SPRITE (Spatial Propagation and Reinforcement of Imputed Transcript Expression) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs. SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE is available as a software package and can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression.

show abstract

“…However, resolution remains a main hurdle to the potential applications of this technology, as each spot ideally contains one to ten cells. Several in silico methods have been developed to deconvolute or infer single cell information from a single spot (Andersson et al, 2020; Cable et al, 2021; Dong and Yuan, 2021; Elosua-Bayes et al, 2021; Hao et al, 2021b; Sun et al, 2022; Wang et al, 2019; Zhao et al, 2021) nonetheless these approaches often require scRNA-seq or bulk RNA-seq data as initial references. Eventually, the advent of more dense spot arrays will be overcome this technical bottleneck.…”

Section: Discussionmentioning

confidence: 99%

Spatially resolved transcriptomics reveals innervation-responsive functional clusters in skeletal muscle

D’Ercole

D'Angelo

Ruggieri

et al. 2022

Preprint

View full text Add to dashboard Cite

Striated muscle is a highly organized structure composed by well-defined anatomical domains with integrated but distinct assignments. So far, the lack of a direct correlation between tissue architecture and gene expression has limited our understanding of how each unit responds to physio-pathologic contexts. Here, we show how the combined use of spatially resolved transcriptomics and immunofluorescence can bridge this gap by enabling the unbiased identification of such domains and the characterization of their response to external perturbations. Using a spatiotemporal analysis, we followed the changes in the transcriptomics profile of specific domains in muscle in a model of denervation. Furthermore, our approach allowed us to identify the spatial distribution and nerve dependence of atrophic signalling pathway and polyamine metabolism to glycolytic fibers. Indeed, we demonstrate a pronounced alteration of polyamine homeostasis upon denervation. Our dataset will serve as a resource for future studies of the mechanisms underlying skeletal muscle homeostasis and innervation.

show abstract

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

Cited by 8 publications

References 45 publications

Museum of spatial transcriptomics

Museum of spatial transcriptomics

SPRITE: improving spatial gene expression imputation with gene and cell networks

Spatially resolved transcriptomics reveals innervation-responsive functional clusters in skeletal muscle

Contact Info

Product

Resources

About