Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Jin, Suoqin; Ramos, Raúl

doi:10.1042/bst20210863

Cited by 17 publications

(9 citation statements)

References 80 publications

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“…The spatial information in our MERFISH data offers unique advantages in predicting cell-cell interactions or communications. Previous large-scale predictions of cell-cell interactions or communications have been based on co-expression of ligand-receptor pairs derived from sequencing data 98 , which are prone to false positives 99, 100 . Indeed, to mitigate this problem, such predictions have often relied on validations by imaging experiment to probe whether the cells co-expressing the ligand-receptor pairs are indeed in contact or proximity.…”

Section: Discussionmentioning

confidence: 99%

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

Zhang

Pan

Halpern

et al. 2023

Preprint

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In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues. However, these approaches have only been applied to a few brain regions and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in ~8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH) and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ~300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.

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Section: Discussionmentioning

confidence: 99%

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

Zhang

Pan

Halpern

et al. 2023

Preprint

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“…These include a Bayesian modelling method called DestVI ( 118 ); methods that infer spatial cell composition from scRNAseq data such as CellDART ( 119 ) and Tangram ( 120 ); a graph-based CNN method called DSTG ( 121 ) which was used to uncover cell states of pancreatic tumor tissues. On the other hand, the ability of ST to localize gene expression to specific cell phenotypes in the TME allows effective characterization of cellular communication, which is either through cell-cell direct contact or cell signaling of neighboring cells ( 122 ). Analytic tools developed for cellular communication include a scalable random forest-based method called MISTy ( 123 ), tested on human BC Visium data; a graph NN method called NCEM ( 124 ), tested on MERFISH, PhenoCylcer, and MIBI; a graph CNN model based on a curated list of interacting ligands and receptors, called GCNG ( 125 ), tested on SeqFISH and MERFISH.…”

Section: Ai-enabled Tme Analysismentioning

confidence: 99%

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Lee

Rajapakse

et al. 2023

Front. Oncol.

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Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

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“…Advance multiplex mRNA in-situ hybridization and imaging techniques can be applied to perform targeted observations to quantify accurate counts of ERV gene expressions. Complementary techniques combining hybridization-based single-cell sequencing and spatially resolved transcriptomics linked to computational tools would be better to understand cell-to-cell interactions in complex tissue biopsies [118][119][120] . A promising recent approach shows prefeasibility results from a noninvasive high-resolution technique that captures RNA profiling of full-thickness lesional and nonlesional biopsies from the same psoriatic patient 121 .…”

Section: Future Directionsmentioning

confidence: 99%

Causes of Autoimmune Psoriasis and Associated Cardiovascular Disease: Roles of Human Endogenous Retroviruses and Antihypertensive Drugs—A Systematic Review and Meta-analysis

Rezabakhsh,

Manjili,

Hosseinifard

et al. 2023

Preprint

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Current treatments are ineffective to cure or prevent occurrences of autoimmune psoriasis and psoriatic cardiovascular disease/CVD. Psoriasis is associated with deregulated expressions of human endogenous retroviruses (ERVs) variants. ERV transcripts and proteins are detected in lesioned biopsies—without assembled viral particles—in addition to antibody and T-cell responses against ERV-K dUTPase. In persons living with HIV-1, manifestations of psoriasis are exacerbated variably. These may depend on multiple factors, differences in ERVs expressions, subtypes of HIV-1, and/or epigenetics. This article represents a quantitative risk assessment and meta-analysis approach with an attempt to assess causality. We surmise that mutated ERVs trigger aberrant proliferation and differentiation of keratinocytes, which in turn induce proinflammatory polarization. Independent risk factors and/or covariates with a range of relative risk/RR ratios appear to significantly impact the development of autoimmune psoriasis or immune intolerance, plausibly through ERVs genes activity. Given the antihypertensive drug’s potential in psoriasis development, a probable role in promising either ERVs activation or perturbations in epigenetic factors is questionable. Although the correlational nature of the data based on RR ratios prevents making robust conclusions, we reckon that the likelihood of attributable risk factors for certain antihypertensive drugs may stem from their pleiotropic effects or potentials for inducing ERV-mediated dysregulation of keratinocytes and/or endothelial cells. These findings expand our knowledge regarding ERV activations and HIV-1, antihypertensive drugs use, and incidents of psoriatic disease, and call for exploring cell-specific therapies aimed at blocking or reversing mutated ERVs gene activity toward attaining stable remissions in psoriasis and associated CVD.

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Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Cited by 17 publications

References 80 publications

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Causes of Autoimmune Psoriasis and Associated Cardiovascular Disease: Roles of Human Endogenous Retroviruses and Antihypertensive Drugs—A Systematic Review and Meta-analysis

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