Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Abstract

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell–cell communication can be inferred through ligand–receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand–receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell–cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell–cell communication estimation tools for tumor-targeted therapy.

Graph neural network and multi-data heterogeneous networks for microbe-disease prediction

“…Due to the relatively late development of microbe-disease association prediction, there are still many deficiencies and challenges at the present stage. Nevertheless, many studies have made preliminary exploration on the design of the prediction model ( Peng et al, 2017 , 2021 , 2022a , b ; Shen et al, 2022 ), which can be summarized as follows:…”

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

“…Moreover, its prediction performance needs further improvement. In the future, we will consider biological features of circRNAs and develop more efficient machine learning, Frontiers in Genetics frontiersin.org especially ensemble learning models (Zhou et al, 2021a;Peng et al, 2022a) and deep learning models (Peng et al, 2021b;Zhou et al, 2021b;Sun et al, 2022;Yang et al, 2022) to discover potential biomarkers for bladder cancer and bladder urothelial cancer.…”

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Obeticholic acid and 5β-cholanic acid 3 exhibit anti-tumor effects on liver cancer through CXCL16/CXCR6 pathway

“…By forming ligand–receptor complexes, cell–cell communication plays a crucial role in coordinating diverse biological processes in the tumor immune microenvironment ( 29 , 30 ). CellphoneDB is an open database where ligands, receptors, and their interactions are stored, enabling users to achieve a systematic and comprehensive analysis of intercellular communication.…”