Subcellular spatially resolved gene neighborhood networks in single cells

Zhou, Fang; Ford, A. J.; Hu, Thomas; Zhang, Nicholas; Mantalaris, Athanasios; Coşkun, Ahmet F.

doi:10.1016/j.crmeth.2023.100476

Cited by 9 publications

(3 citation statements)

References 77 publications

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“…Let v i denote the cell under consideration. The CNE associated with v i , z i is comprised of information coming from neighboring cells up to three hops away, N k ( v i ) for k = [1, 2, 3], as well as the nodes intrinsic information, x i . The core problem of computing CCA is to pair information in the input to potentially impacted information in the embedding.…”

Section: Methodsmentioning

confidence: 99%

“…In parallel, multiple consortia have been initiated to comprehensively profile the spatial organization underlying physiologic and pathological processes. Computational methods are being developed to query different facets of these data including cell type annotation [1], cell niche clustering [2, 3], and cell-cell interaction [4]. However, few and limited techniques exist to classify tissues profiled with spatial ‘omics technologies according to observed phenotypes, such as response to treatment.…”

Section: Introductionmentioning

confidence: 99%

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SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Shimonov,

Cunningham,

Talmon

et al. 2024

Preprint

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Spatially resolved transcriptomics or proteomics data have the potential to contribute fundamental insights into the mechanisms underlying physiologic and pathological processes. However, analysis of these data capable of relating spatial information, multiplexed markers, and their observed phenotypes remains technically challenging. To analyze these relationships, we developed SORBET, a deep learning framework that leverages recent advances in graph neural networks (GNN). We apply SORBET to predict tissue phenotypes, such as response to immunotherapy, across different disease processes and different technologies including both spatial proteomics and transcriptomics methods. Our results show that SORBET accurately learns biologically meaningful relationships across distinct tissue structures and data acquisition methods. Furthermore, we demonstrate that SORBET facilitates understanding of the spatially-resolved biological mechanisms underlying the inferred phenotypes. In sum, our method facilitates mapping between the rich spatial and marker information acquired from spatial ‘omics technologies to emergent biological phenotypes. Moreover, we provide novel techniques for identifying the biological processes that comprise the predicted phenotypes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Shimonov,

Cunningham,

Talmon

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, SRT allows for not only the inspection of gene co-expression pattern in specific spatial locations but also the identification of extracellular signaling that drives cellular responses from their respective niches. Current approaches to predict gene regulatory interactions using SRT data can be mainly categorized into four types: (a) subcellular proximity: extracting gene proximity relationships from subcellular patches to infer interaction networks using fluorescence in situ hybridization (FISH) imaging-based SRT data 22 ; (b) direct application: directly using algorithms originally designed for scRNA-seq data to infer GRNs from SRT data 23 ; (c) spatial co-expression: deriving spatially specific gene co-expression modules including graph-based Hotspot 24 , spatially weighted CellTrek toolkit 25 , and Bayesian-based SpaceX 26 ; and (d) extracellular to intracellular model: exploiting the effect of cell-cell communications (CCC) to refine GRNs 27–29 . However, these methods pose new challenges owing to SRT data characteristics.…”

Section: Introductionmentioning

confidence: 99%

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Li,

Liu,

Guo

et al. 2023

Preprint

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SUMMARYCells expressing similar transcriptional regulatory circuits spatially aggregate into distinct cell types or states. However, most existing methods for inferring gene regulatory networks from spatially resolved transcriptomics are devoted to spatial co-expression modules or interactions between transcription factors and target genes, neglecting mediated effects from extracellular signals. Here we introduce SpaGRN, a statistical framework for predicting the comprehensive intracellular regulatory network underlying spatial patterns by integrating spatial expression profiles with prior knowledge on regulatory relationships and signaling paths. We validate and assess SpaGRN using simulated and real datasets, demonstrating its efficiency, performance, and robustness. When applied to 3D datasets of developingDrosophilaembryos and larvae, SpaGRN identifies spatiotemporal variations in specific regulatory patterns, delineating the cascade of events from receptor stimulation to downstream transcription factors and targets, revealing synergetic regulation mechanism during organogenesis. Moreover, SpaGRN provides flexible visualization functions. We construct an online 3D regulatory network atlas database for interactive exploration and sharing.

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A Spatial Transcriptomics Based Label‐Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

Park,

Lee,

Lee

et al. 2024

Advanced Therapeutics

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Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. This study introduces a label‐free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. The hMSCs were introduced into a mouse model with lung fibrosis, followed by the manipulation of ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between humans and mice. Furthermore, the method allowed for the examination of molecular changes associated with the spatial distribution of hMSCs. Therefore, our method has the potential to serve as an effective tool for various cell‐based therapeutic agents.This article is protected by copyright. All rights reserved

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Subcellular spatially resolved gene neighborhood networks in single cells

Cited by 9 publications

References 77 publications

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

SpaGRN: investigating spatially informed regulatory paths for spatially resolved transcriptomics data

A Spatial Transcriptomics Based Label‐Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

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