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We developed PINNACLE, a graph-based AI model for learning protein representations across cell-type contexts. These contextualized protein representations enable the integration of 3D protein structure with single-cell genomic-based representations to enhance protein–protein interaction prediction, analysis of drug effects across cell-type contexts, and prediction of therapeutic targets in a cell type-specific manner.
This Perspective discusses the methods and tools required for three-dimensional histology in large samples, an approach that promises insights into tissue and organ physiology as well as disease.
A systematic comparison of 11 sequencing-based spatial transcriptomics methods reveals molecular diffusion as a critical variable that influences the effective resolution and data interpretation across platforms. Our benchmarking study should aid biologists in selecting the most appropriate method for their specific tissue.
SpatialGlue is a tool designed to decipher spatial domains from spatial multi-omics data acquired from a single tissue section. It employes graph neural networks with a dual-attention mechanism to accomplish within-omics integration of measured features and spatial information, followed by cross-omics integration.
We demonstrate CRISPRdelight, a robust CRISPR–Cas12a-based method for imaging non-repetitive genomic DNAs in a highly efficient way. This system is a powerful tool for studying functional links between gene dynamics, localization and regulation, and reveals heterogeneity in the expression of differently localized alleles in the same cells.
The annotation of structural glycomics data is a bottleneck in gaining insight into complex carbohydrates. An artificial intelligence model, CandyCrunch, has now been developed that accelerates the annotation process by orders of magnitude and achieves high accuracy in the prediction of glycan structures from tandem mass spectrometry data.
A fundamental mechanism for information processing in the brain is electrical activity. However, observing such activity at the single-cell level is challenging. We have developed an optical microscope that combines the advantages of targeted illumination and confocal gating to enable kilohertz-rate voltage imaging across large fields of view in thick tissue.
Comparing brain connectivity between chimpanzees and humans is a means of understanding human cognition and evolution. To address the scarcity of chimpanzee neuroimaging data, we introduce a high-quality MRI resource that reveals previously unseen anatomical details, offering valuable insights into human brain evolution.
uiPSF is a toolbox to measure point spread functions based on inverse modeling that improves single-molecule localization microscopy (SMLM) localization and microscope characterization, and that works for many microscopy technologies.
An experimental method to study how cells sense and react to external mechanical forces combines controlled mechanical stimulation using nanopipettes with fluorescence imaging of membrane tension. This approach facilitates the study of mechanosensitive ion channels and the propagation of cell membrane tension.
Spatial transcriptomics and mRNA splicing measurements encode rich spatiotemporal information for cell states and their transitions. We present a multiscale dynamical system method for reconstructing cell-state-specific dynamics and spatial state transitions. This theory-based approach reconciles short-timescale local tensor streamlines between cells with long-timescale transition paths that connect cell attractors.
Pebblescout navigates vast, rapidly growing nucleotide content in resources by providing indexing and search capabilities. We used Pebblescout to index a metagenomic subset of Sequence Read Archive and seven other resources into databases spanning over 3.7 petabases and searchable interactively at a pilot website using queries as short as 42 bases.
We developed a two-pronged strategy to functionally probe the enormous repertoire of noncoding DNA within genomes. Our approach markedly improved signal-to-noise ratio and successfully intersected single-cell genomics with reporter assays. The result delivers a multiplex and highly quantitative readout of regulatory sequences’ activity in dynamic and multicellular systems.
Combining post-translational modification site-centric base editing with phenotypic screens uncovers the function of phosphorylation sites in high throughput, enabling the study of expansive signaling networks at a speed comparable to that of functional genomics.
This Perspective discusses the potential of protein structure-prediction models for exploring the structural landscape and specificity of TCR–pMHC interactions.
We created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.
Several research groups are making it easier for other neuroscientists to analyze large datasets by providing tools that can be accessed and used from anywhere in the world.
