Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

Abstract

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which is the first strategy to decipher context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand receptor pairs) linked to COVID-19 severities. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

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

“…Integrating scRNA-seq data with spatial transcriptomics as well as gene regulatory networks offers a unique opportunity to study the spatial patterns of cell–cell communication and gene regulatory networks as well as to investigate how cell–cell communication affects cell-specific signaling networks within the context of a tissue. Second, sophisticated methods like Tensor-cell2cell [ 42 ] are highly needed to discern context-shared and -specific signaling patterns across conditions. Particularly, how to extract the biologically relevant signaling while removing the batch effects remains challenging [ 76 ].…”

“…Differential expression-based methods have advantages in detecting context-specific signaling, but likely fail to identify shared interactions across distinct contexts. More recently, different from scTensor for a single condition [ 41 ], a sophisticated approach called Tensor-cell2cell has been presented to decipher complex cell–cell communication patterns across diverse conditions [ 42 ]. This method is attractive because it deciphers context-driven intercellular communication by simultaneously accounting for multiple conditions by utilizing a tensor decomposition framework.…”

“…Moreover, these issues also pose an urgent need to comprehensively benchmark the accuracy and robustness of each method using datasets with different biological variabilities, tissues, protocols, and platforms. While few studies have attempted to generate simulated datasets with ground truth against which various methods could be benchmarked [ 41 , 42 , 51 ], simulation of cell–cell communication with spatial information remains challenging due to the inherent complexity and redundancy of signaling mechanisms across multicellular systems. Existing case studies usually validate and compare inference results based on findings from biological experiments [ 15 ], bulk tissue data [ 20 ] or spatial transcriptomics data [ 34 ].…”

Modeling and inference of spatial intercellular communications and multilayer signaling regulations using stMLnet

“…Although in vitro data are not the perfect gold standard, to the best of our knowledge, cell line perturbation data have been the best choice for benchmarking ligand-target predictions until now, as differential responses of target genes to ligand or receptor perturbations characterize the potential regulations between them. Among the existing methods (Armingol et al , 2022a; Armingol et al , 2022b; Arnol et al , 2019; Baccin et al , 2020; Baruzzo et al , 2022; Browaeys et al , 2020; Cabello-Aguilar et al , 2020; Cang & Nie, 2020; Dries et al , 2021b; Efremova et al , 2020; Hou et al , 2020b; Jin et al , 2021; Noël et al , 2021; Pham et al , 2020; Tanevski et al , 2021; Wang et al , 2019a; Wang et al , 2019b; Yuan & Bar-Joseph, 2020; Zhang et al , 2021) ( Table S1 ), we chose NicheNet, CytoTalk, and MISTy as competitors for benchmarking stMLnet, as they can output prediction scores of ligand-target regulations that can be compared to the ground truth (differential expression of targets in response to ligand/receptor perturbations). The results show that stMLnet outperformed the other methods on multiple datasets.…”

Context-Aware Diagnostic Specificity (CADS)

“…In this special issue of Biosensors, we build on the concept of context-awareness through establishment of CADS: Context-Aware Diagnostic Specificity, an application specifically focused on diagnostics. The notion of context awareness is a computational discovery framework [ 11 ], and has recently been extended for applications in cell-cell communication [ 12 ], biological inspired design [ 13 ], and synthetic biology [ 14 ]. For application in diagnostics, context awareness is extended to include molecular systems applied for specific detection of a target (or multiple targets).…”