Browse Articles

biVI models the biophysical processes generating nascent and mature single-cell transcriptomes using variational autoencoders.

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.

PINNACLE is a context-specific geometric deep learning model for generating protein representations. Leveraging single-cell transcriptomics combined with networks of protein–protein interactions, cell type-to-cell type interactions and a tissue hierarchy, PINNACLE generates high-resolution protein representations tailored to each cell type.

Bayesian nonparametric Track (BNP-Track) simultaneously determines emitter numbers and their tracks alongside uncertainty, extending the superresolution paradigm from static samples to single-particle tracking even in dense environments.

EpiChem enables the co-profiling of small molecule–DNA interactions and multiple epigenomic features in single cells.

cryoDRGN-ET is a generative neural network method for heterogeneous reconstruction of cryo-ET subtomograms. Using subtomogram tilt-series images, it can capture states diverse in both composition and conformation.

Onavg is a surface template of the human cortex. In contrast to existing templates, the cortical surface is uniformly sampled, which has advantages in numerous applications.

SwitchSeeker combines computational and experimental techniques to identify functional RNA structural switches. Applied to the human transcriptome, it identified a novel RNA switch in the 3ʹUTR of RORC, linked to nonsense-mediated decay.

Being first in a family to aim for a science career takes resilience and resourcefulness.

Bats, the only flying mammals, comprise almost 25% of mammalian species. They are excellent navigators, highly social, and extremely long-lived. Their sense of echolocation has been studied for many years — but many species possess also excellent vision and olfaction. In recent years, bats have emerged as new models for neurobiology of navigation, social neuroscience, aging, and immunity.

Tissues and organs are inherently three-dimensional. Studies to understand their function and dysfunction should therefore aim to maintain the 3D spatial context.

Machine learning approaches can distinguish six different classes of presynapses from electron micrographs across the Drosophila brain.

Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.

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.

This work presents ORFtag that enables proteome-wide functional screens by tagging and overexpression of endogenously encoded proteins via randomly integrated cassettes.