cell-cell signaling inference

Systematic characterization of cell–cell communication (CCC) in multicellular organisms is essential for understanding development, homeostasis, and disease pathogenesis; however, existing computational methods for inferring CCC from single-cell and spatial omics data suffer from a lack of standardized evaluation and biological interpretability. To address this, we systematically reviewed over 140 computational tools and propose the first unified evaluation framework integrating ligand–receptor prior knowledge with de novo network inference. Our framework quantitatively benchmarks tools across three critical dimensions: modeling signaling pathway complexity, incorporating spatial contextual information, and ensuring biological interpretability. By synergistically integrating single-cell transcriptomics, spatial transcriptomics, curated ligand–receptor databases, and machine learning models, we substantially improve both prediction accuracy and mechanistic traceability of CCC inference. The resulting framework establishes a reproducible analytical paradigm for developmental biology and precision medicine, while identifying key algorithmic challenges—including context-specific signal propagation and cross-modal integration—and charting future directions for methodological advancement.

Calibrating computational models of cellular signaling to match time-varying biological responses remains challenging, particularly due to difficulties in reconciling simulated dynamics with empirical reference behaviors and disentangling the influence of receptor trafficking mechanisms on signal transduction efficacy. Method: We propose a visual analytics framework integrating time-series graphs and parallel coordinates to jointly map model parameter spaces and dynamic outputs (e.g., time-resolved signal intensity), enabling interactive parameter sensitivity analysis, behavioral plausibility validation, and exploration of how receptor transport pathways modulate signaling efficiency. Contribution/Results: Evaluated on real-world case studies, the framework significantly enhances interdisciplinary collaboration between modelers and biologists. It advances model calibration accuracy, facilitates hypothesis generation regarding mechanistic underpinnings, and improves result interpretability—demonstrating clear innovation in integrative computational biology and systems pharmacology.

Quantifying spatiotemporal pattern differences in 5D live-cell microscopy (x, y, z, channel, time) remains challenging due to high dimensionality, noise, and lack of ground-truth annotations. Method: We propose an unsupervised, training-free embedding framework grounded in normalized information distance (NID) and Kolmogorov complexity. It constructs a cell signal structure function (SSF) using only the cell radius as a parameter, integrating lossless compression statistics, 3D spatiotemporal filtering, and centrosome-based registration to map raw volumes into embedding points—where Euclidean distances theoretically optimally approximate true pattern dissimilarity. Contribution/Results: This work introduces NID for the first time to quantify patterns in multidimensional live imaging without prior models or labels. Validated on synthetic data and real biological systems—including ERK/AKT mutation responses, optogenetic perturbations, and organoid differentiation—it achieves zero-shot, high-resolution pattern discrimination with no annotated training data.

This work addresses the challenge of efficiently exploring and interpreting the high-dimensional combinatorial space of gene perturbations generated by AI-based virtual cell models and their complex transcriptional responses across diverse cell types. To this end, we propose a visual analytics system that, for the first time, integrates clustered overviews, compact glyph-based encodings, and coordinated multi-view interactions to enable systematic comparison and interpretable exploration of perturbation strategies. By incorporating AI-generated predictions and validating through real-world case studies and expert interviews, we demonstrate that our approach substantially enhances researchers’ understanding of perturbation effects and improves decision-making efficiency in drug discovery, effectively bridging the cognitive gap between computational models and biomedical experts.

This study addresses the challenges of deciphering cellular heterogeneity, tissue spatial architecture, and dynamic biological processes—including development, neuronal activity, and tumor evolution—from spatial multi-omics data. We propose a systematic analytical framework integrating spatial transcriptomics, spatial proteomics, deep learning, graph neural networks (GNNs), and multimodal fusion algorithms. Our approach overcomes key limitations of conventional methods in modeling cell–cell spatial neighborhood relationships and spatiotemporal regulatory networks. It enables high-resolution characterization of spatial cellular patterning during organogenesis and identification of critical molecular features and regulatory circuits within the tumor microenvironment. The framework significantly advances understanding of spatial–molecular coordination in complex biological systems. By providing a scalable, integrative computational paradigm and open analytical tools, it facilitates mechanistic investigation of human diseases and accelerates discovery of precision medicine targets.

本文研究了空间转录组学可视化实践，通过调查148篇论文和1824个图表面板，评估现有方法并指出未来挑战。

该研究提出了一种基于拓扑信息的逆向设计框架TI$^2$PS，通过结合Betti向量和逆向代理建模方法来估计生成目标多细胞模式所需的参数，解决了细胞水平参数估计和定量评估随机增殖与死亡下的多细胞布局拓扑特征的问题。

Existing methods for modeling protein–protein interaction networks often neglect prior biological knowledge and assume a static network structure across individuals, thereby failing to capture covariate-driven, personalized interaction patterns. This work proposes a conditional Gaussian graphical model that, for the first time, integrates database-derived priors and covariate-dependent relationships within a unified framework. By employing structured weighted L1 regularization, the method simultaneously incorporates population-level priors while preserving context-specific perturbations. It effectively distinguishes between universal interactions and disease-specific alterations. Applied to proteomic data from the UK Biobank (n = 49,129), the approach identified 34 network centrality–based biomarkers and six functionally coherent protein modules, with several biomarkers detectable only through connectivity changes rather than differential expression.

本文针对分子信号网络贝叶斯推断中的模型错误设定问题，提出了一种后贝叶斯方法（PrO posterior），以处理非线性和未观测混杂因素带来的不确定性。

This work addresses the challenge of implementing probabilistic computations such as Bayesian inference in biochemical systems, where conventional chemical reaction networks (CRNs) are often prohibitively large for practical use. The authors introduce, for the first time, factor graph reduction theory into CRN design by uncovering the implicit factor graph structure embedded within the Napp–Adams compilation framework and applying graph reduction algorithms. This approach significantly compresses the network size while preserving the fixed points of belief propagation for key variables. By doing so, it overcomes a critical limitation of existing CRN simplification techniques, which are unable to handle probabilistic models, thereby enabling efficient and exact compression of probabilistic CRNs.