Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type–specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.

Genomic alteration can reshape tumor microenvironment to drive tumor malignancy. However, how PTEN deficiency influences microenvironment-mediated cell-cell interactions in glioblastoma (GBM) remains unclear. Here, we show that PTEN deficiency induces a symbiotic glioma-M2 macrophage interaction to support glioma progression. Mechanistically, PTEN -deficient GBM cells secrete high levels of galectin-9 (Gal-9) via the AKT-GSK3β-IRF1 pathway. The secreted Gal-9 drives macrophage M2 polarization by activating its receptor Tim-3 and downstream pathways in macrophages. These macrophages, in turn, secrete VEGFA to stimulate angiogenesis and support glioma growth. Furthermore, enhanced Gal-9/Tim-3 expression predicts poor outcome in glioma patients. In GBM models, blockade of Gal-9/Tim-3 signaling inhibits macrophage M2 polarization and suppresses tumor growth. Moreover, α-lactose attenuates glioma angiogenesis by down-regulating macrophage-derived VEGFA, providing a novel antivascularization strategy. Therefore, our study suggests that blockade of Gal-9/Tim-3 signaling is effective to impair glioma progression by inhibiting macrophage M2 polarization, specifically for PTEN -null GBM.

Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.

Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. Supplementary data are available at Bioinformatics online.