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LILAC is a photoactivatable version of Lifeact, a tool for labeling F-actin. LILAC can help avoid cytotoxicity, which is sometimes associated with the use of Lifeact.
Communication between cells is crucial for coordinated cellular functions in multicellular organisms. We present an optimal transport theory-based tool to infer cell–cell communication networks, spatial signaling directions and downstream targets in multicellular systems from spatial gene expression data.
A deep learning approach called DeepPiCt facilitates segmentation and macromolecular identification in the cellular jungle of electron cryotomography data.
DeePiCt (deep picker in context) is a versatile, open-source deep-learning framework for supervised segmentation and localization of subcellular organelles and biomolecular complexes in cryo-electron tomography.
This work presents a computational framework, COMMOT, to spatially infer cell–cell communication from transcriptomics data based on a variant of optimal transport (OT).
An optimized pipeline for improved inference and analysis of structural variants (SVs) has been developed, which uses Iris for refining breakpoints and sequences, and Jasmine for comparing SV calls at population scale.
Alignment of single-cell proteomics data across platforms is difficult when different data sets contain limited shared features, as is typical in single-cell assays with antibody readouts. Therefore, we developed matching with partial overlap (MARIO) to enable confident and accurate matching for multimodal data integration and cross-species analysis.
A computational pipeline for haplotype-aware pantranscriptome analysis has been developed, which enables spliced pangenome graph construction, RNA sequencing data alignment, and estimation of haplotype-specific transcript expression levels.
Cryogenic correlated light, ion and electron microscopy (cryo-CLIEM) integrates three-dimensional confocal microscopy with focused ion beam–scanning electron microscopy for efficient preparation of lamellae containing target structures for in situ structural biology with cryo-electron tomography.
The ELI-TriScope advances cryo-CLEM by focusing light, electron and ion beams on cryopreserved samples for markedly improved preparation of cryo-lamellae containing target structures.
Dramatic advances in protein structure prediction have sparked debate as to whether the problem of predicting structure from sequence is solved or not. Here, I argue that AlphaFold2 and its peers are currently limited by the fact that they predict only a single structure, instead of a structural distribution, and that this realization is crucial for the next generation of structure prediction algorithms.
