Characterizing Cell–Cell Interactions Induced Spatial Organization of Cell Phenotypes: Application to Density-Dependent Protein Nucleocytoplasmic Distribution

AbstractCell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans’ cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.Graphical abstract

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Counterfactual Hypothesis Testing of Tumor Microenvironment Scenarios Through Semantic Image Synthesis

10.1101/2020.10.27.358101 ◽

2020 ◽

Author(s):

Daniel Li ◽

Qiang Ma ◽

Jennifer Chen ◽

Andrew Liu ◽

Justin Cheung ◽

...

Keyword(s):

Spatial Organization ◽

Immune Cell ◽

Image Synthesis ◽

Cell Interaction ◽

Cell Interactions ◽

Generative Adversarial Network ◽

Imaging Technologies ◽

Multiple Imaging ◽

Cell Cell

AbstractRecent multiplexed protein imaging technologies make it possible to characterize cells, their spatial organization, and interactions within microenvironments at unprecedented resolution. Although observational data can reveal spatial associations, it does not allow users to infer biologically causative relationships and interactions between cells. To address this challenge, we develop a generative model that allows users to test hypotheses about the effect of cell-cell interactions on protein expression through in silico perturbation. Our Cell-Cell Interaction GAN (CCIGAN) model employs a generative adversarial network (GAN) architecture to generate biologically realistic multiplexed cell images from semantic cell segmentations. Our approach is unique in considering all imaging channels simultaneously, and we show that it successfully captures known tumor-immune cell interactions missed by other state-of-the-art GAN models, and yields biological insights without requiring in vivo manipulation. CCIGAN accepts data from multiple imaging technologies and can infer interactions from single images in any health or disease context.

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Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

10.1101/265256 ◽

2018 ◽

Cited By ~ 9

Author(s):

Damien Arnol ◽

Denis Schapiro ◽

Bernd Bodenmiller ◽

Julio Saez-Rodriguez ◽

Oliver Stegle

Keyword(s):

Variance Component ◽

Spatial Organization ◽

Expression Profiles ◽

Component Analysis ◽

Molecular Data ◽

Cell Interactions ◽

Variance Component Analysis ◽

Spatial Variance ◽

Technological Advances ◽

Cell Cell

AbstractTechnological advances allow for assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing physiological tissue contexts of single cell variation. While methods for the high-throughput generation of spatial expression profiles are increasingly accessible, computational methods for studying the relevance of the spatial organization of tissues on cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying the effect of cell-cell interactions, as well as environmental and intrinsic cell features on the expression levels of individual genes or proteins. In application to a breast cancer Imaging Mass Cytometry dataset, our model allows for robustly estimating spatial variance signatures, identifying cell-cell interactions as a major driver of expression heterogeneity. Finally, we apply SVCA to high-dimensional imaging-derived RNA data, where we identify molecular pathways that are linked to cell-cell interactions.

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