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The authors present a workflow integrating imaging mass cytometry and imaging mass spectrometry to deconvolute metabolic heterogeneity at the single-cell level.
Single-cell RNA-sequencing and spatial transcriptomics data enable the inference of how information is transmitted from one cell to another and how it modulates gene expression within cells. Now, a learning method infers networks describing how the inflow of one signal, mediated by intracellular gene modules, drives the outflow of other signals for intercellular communication.
Using single-cell and spatial transcriptomics data, FlowSig provides a unified signaling modeling framework by connecting intercellular communication mediated by ligand–receptor interactions and intracellular gene expression modules.
Biomaterials are revolutionizing organoid development by offering tunable platforms that provide instructive cues, which enhance cell fate transitions, tissue-level functions and reproducibility. These advances are crucial for harnessing the translational potential of organoids.
This Perspective discusses the integration of small-scale datasets with each other or with larger reference atlases, particularly in the context of single-cell approaches.
Early-career scientists shared some of their plans, hopes and dreams about being a principal investigator at the 2024 annual meeting of the International Society for Stem Cell Research.
Inspired by the success of large-scale machine learning models in natural language, several groups are adapting these models for cellular data using massive single-cell datasets.
Risks from AI in basic biology research can be addressed with a dual mitigation strategy that comprises basic education in AI ethics and community governance measures that are tailored to the needs of individual research communities.
Artificial intelligence-enabled computational tools not only help us to elucidate biological processes but also facilitate the programming of biology through molecular and cellular engineering.
New approaches in artificial intelligence (AI), such as foundation models and synthetic data, are having a substantial impact on many areas of applied computer science. Here we discuss the potential to apply these developments to the computational challenges associated with producing synapse-resolution maps of nervous systems, an area in which major ambitions are currently bottlenecked by AI performance.
Mass spectrometry-based proteomics provides broad and quantitative detection of the proteome, but its results are mostly presented as protein lists. Artificial intelligence approaches will exploit prior knowledge from literature and harmonize fragmented datasets to enable mechanistic and functional interpretation of proteomics experiments.
Methods for predicting bimolecular interactions are seeing tremendous growth, but challenges remain in capturing the full physical complexity of these interactions.
The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.