DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

Su, Jing; Song, Qisheng

doi:10.1101/2020.10.20.347195

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…Similarly, NMF regression (NMFref) is used in SlideSeq 52 . Probability-based methods such as Stereoscope 135 , Cell2location 136 , and RSTG 137 , as well as graph-based 138 and deep-learning based such as Tangram 139 have been introduced. In Stereoscope 135 , the cell type parameters are assigned by maximum likelihood estimation on the single-cell and use those to estimate each spot composition.…”

Section: Characterizementioning

confidence: 99%

“…Cell2location 136 is similar to Stereoscope, but additionally attempts to infer the absolute number of cells per spot. DSTG 138 uses single-cell data to construct pseudo-spots, and then links real and pseudo spots in a graph of nearest neighbors. The spatialDWLS method borrows from methodologies previously used for bulk RNASeq deconvolution and applies cell type enrichment followed by a dampened weighted least squares method to determine spot composition 140 .…”

Section: Characterizementioning

confidence: 99%

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Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

919

610

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Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advancements in next-generation sequencing-based and imaging based approaches have established the potential of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insight in neuroscience, development, plant biology, and a range of diseases including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here, we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs comparing timepoints or conditions -including genetic or environmental perturbations. Finally, spatial transcriptomic data is naturally amenable to integration with other data modalities providing an expandable framework for insight into tissue organization.Many of the notable discoveries in the life sciences followed from the recognition that cellular organization within tissues is intimately linked to biological function. In developmental biology, central topics such as symmetry-breaking between daughter cells and cell fate decisions are based on spatial relationships between cells 1 . In clinical settings, histopathology is often used as a conclusive diagnostic, precisely because diseases are characterized by abnormal spatial organization within tissues 2 . Infectious and inflammatory processes can drastically change the cellular organization of tissues 3 . These discoveries were supported by methods in molecular biology -including in situ hybridization 4 (ISH) and immunohistochemistry 5 -that provided the ability to visualize biological processes more directly by mapping DNA, RNA and protein within tissues. However, these methods limit analysis to at most a handful of genes or proteins at a time.

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Section: Characterizementioning

confidence: 99%

Section: Characterizementioning

confidence: 99%

Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

919

610

View full text Add to dashboard Cite

show abstract

“…, Visium), deconvolution methods can relate transcriptomes measured in a spot to the cell profiles present in tissue ( Fig. 3b, Table 1 ) [130][131][132][133][134][135][136][137][138][139] . In contrast to deconvolution approaches, that explicitly model the spatial observation as an aggregate of cells, label projection methods have been developed to map the cellular states identified in scRNA-seq experiments to lower resolution spatial data 86,140,141 .…”

Section: Integrating Modalitiesmentioning

confidence: 99%

Spatial components of molecular tissue biology

et al. 2022

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Methods for profiling RNA and protein expression in a spatially-resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and to develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Here we propose to group these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed to facilitate further progress in spatial transcriptomics and proteomics.Computational methods are key to extracting patterns from such data and will be especially powerful if they are designed to take into account the specific biological questions at hand as well as the distinct features and limitations of different measurement methods. Here, we review the computational methods for spatial molecular analysis organized by the biological questions they address and the spatial methods capable of measuring relevant parameters. We will focus specifically on the challenges that different length scales pose for experimental methods, and highlight the types of analysis methods that can be deployed in such studies. We define 'length scales' as the spatial context in which a biological process occurs: short-range length scales include direct cell-cell interactions, whereas long-range length scales include global gradients, such as in oxygen or metabolites ( Fig. 1). In addition, we emphasize conceptual overlaps with and distinctions from computational methods currently used for analyzing single cell, dissociation-based methods to show how studies based on single-cell profiling approaches can be complemented by spatial approaches, and vice versa. We hope this conceptual and methodological roadmap will help drive the development of new computational methods for key biological questions in tissue biology, provide guidance to biologists seeking to apply methods, and help in sharpening concepts in cell and tissue biology. Modeling variation at length scales for cell and tissue biologyA long-standing goal in biology is to understand how tissue organization ('structure') relates to tissue physiology ('function'). In a cell-centric model, tissue organization can be described by the different properties (or variables) that distinguish cells from each other (Fig. 1a) . Some of these components can be seen as dependent variables, with which we are able to measure variation between cells, whereas others can be seen as independent variables, with which we are able to explain the observed variation . In dissociated single cell prof...

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“…ST technologies have the potential to describe cellular organization and functioning in intact multicellular environments and elucidate interactions between gene expression and cellular environment. Several methods have been proposed to integrate scRNA-seq with spatial transcriptomics to study the heterogeneity of intact tissue (Asp et al, 2019;Cable et al, 2020;Hunter et al, 2020;Ji et al, 2020;Moncada et al, 2020;Su and Song, 2020). The common way is to estimate reference cell type/cluster signatures from scRNA-seq profile, and then map the signatures onto spots to decompose ST at single-cell resolution.…”

Section: Integrated Analysis Of Scrna-seq and Spatial Transcriptomementioning

confidence: 99%

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

Fan

Zhao

et al. 2021

Front. Genet.

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The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell–cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

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DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

Cited by 4 publications

References 44 publications

Exploring tissue architecture using spatial transcriptomics

Exploring tissue architecture using spatial transcriptomics

Spatial components of molecular tissue biology

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

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