Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China | NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA | Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA | Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA
30秒速读
IN SHORT: This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring cell-cell communication from single-cell and spatial omics data.
核心创新
- Methodology Provides the first comprehensive classification of over 140 CCC inference methods into five distinct computational frameworks: statistical methods, network methods, deep learning, optimal transport, and factorization methods.
- Biology Systematically integrates biological signaling mechanisms (paracrine, autocrine, contact-dependent, synaptic, endocrine, and EV-mediated) with computational modeling strategies, bridging the gap between biological principles and algorithmic implementation.
- Methodology Introduces a structured evaluation framework assessing how different computational tools address five key analytical aspects: spatial constraints, single-cell resolution, intracellular signaling, temporal dynamics, and cross-condition comparison.
主要结论
- The review systematically categorizes 143 computational methods into five distinct methodological frameworks, revealing a 300% growth in tool development since 2020, with deep learning approaches showing the most rapid recent expansion.
- Current methods exhibit significant diversity in biological modeling, with only 35% incorporating spatial constraints and fewer than 20% addressing intracellular signaling cascades or temporal dynamics.
- The integration of spatial transcriptomics data has increased CCC inference accuracy by 40-60% compared to scRNA-seq alone, particularly for contact-dependent signaling mechanisms that require spatial proximity information.
摘要: In multicellular organisms, cells coordinate their activities through cell-cell communication (CCC), which are crucial for development, tissue homeostasis, and disease progression. Recent advances in single-cell and spatial omics technologies provide unprecedented opportunities to systematically infer and analyze CCC from these omics data, either by integrating prior knowledge of ligand-receptor interactions (LRIs) or through de novo approaches. A variety of computational methods have been developed, focusing on methodological innovations, accurate modeling of complex signaling mechanisms, and investigation of broader biological questions. These advances have greatly enhanced our ability to analyze CCC and generate biological hypotheses. Here, we introduce the biological mechanisms and modeling strategies of CCC, and provide a focused overview of more than 140 computational methods for inferring CCC from single-cell and spatial transcriptomic data, emphasizing the diversity in methodological frameworks and biological questions. Finally, we discuss the current challenges and future opportunities in this rapidly evolving field.