Identifying multicellular spatiotemporal organization of cells with SpaceFlow

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: July 14, 2022

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression information using spatially regularized deep graph networks. Based on embedding, a pseudo-Spatiotemporal Map that integrates pseudotime concept with locations of cells unravel spatiotemporal patterns cells. By comparing multiple existing methods several at spot single-cell resolutions, SpaceFlow shown produce robust domain segmentation identify biologically meaningful patterns. Applications reveal evolving lineage heart developmental data tumor-immune interactions human breast cancer data. Our study provides flexible learning framework

Language: Английский

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Spatial omics: Navigating to the golden era of cancer research

Clinical and Translational Medicine, Journal Year: 2022, Volume and Issue: 12(1)

Published: Jan. 1, 2022

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

Language: Английский

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

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Oct. 17, 2022

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

Language: Английский

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Computational solutions for spatial transcriptomics

Computational and Structural Biotechnology Journal, Journal Year: 2022, Volume and Issue: 20, P. 4870 - 4884

Published: Jan. 1, 2022

Transcriptome level expression data connected to the spatial organization of cells and molecules would allow a comprehensive understanding how gene is structure function in biological systems. The transcriptomics platforms may soon provide such information. However, current still lack resolution, capture only fraction transcriptome heterogeneity, or throughput for large scale studies. strengths weaknesses ST computational solutions need be taken into account when planning basis analysis developed single-cell RNA-sequencing data, with advancements taking connectedness transcriptomes. scRNA-seq tools are modified new like deep learning-based joint expression, spatial, image extract information spatially resolved can reveal remarkable insights patterns cell signaling, type variations connection type-specific signaling complex tissues. This review covers topics that help choosing platform research. We focus on currently available methods their limitations. Of solutions, we an overview steps used analysis. compatibility types provided by frameworks summarized.

Language: Английский

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Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks

Briefings in Bioinformatics, Journal Year: 2022, Volume and Issue: 23(5)

Published: July 17, 2022

Abstract The rapid development of spatial transcriptomics allows the measurement RNA abundance at a high resolution, making it possible to simultaneously profile gene expression, locations cells or spots, and corresponding hematoxylin eosin-stained histology images. It turns promising predict expression from images that are relatively easy cheap obtain. For this purpose, several methods devised, but they have not fully captured internal relations 2D vision features dependency between spots. Here, we developed Hist2ST, deep learning-based model RNA-seq Around each sequenced spot, image is cropped into an patch fed convolutional module extract features. Meanwhile, with whole neighbored patches through Transformer graph neural network modules, respectively. These learned then used by following zero-inflated negative binomial distribution. To alleviate impact small data, self-distillation mechanism employed for efficient learning model. By comprehensive tests on cancer normal datasets, Hist2ST was shown outperform existing in terms both prediction region identification. Further pathway analyses indicated our could reserve biological information. Thus, enables generating data elucidating molecular signatures tissues.

Language: Английский

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