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SN2N, a Self-inspired Noise2Noise module, offers a versatile solution for volumetric time-lapse super-resolution imaging of live cells. SN2N uses self-supervised data generation and self-constrained learning for training with a single noisy frame.

This work introduces a k-mer-based approach to customizing a pangenome reference, making it more relevant to a new sample of interest. This method enhances the accuracy of genotyping small variants and large structural variants.

DNA origami tension sensors can provide insights into mechanotransduction in a physiological environment.

The global imaging community is pursuing innovative approaches to achieve more equitable access to instruments and expertise.

Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE) is a robust feature extraction and classification pipeline for diverse and heterogeneous structures in both 2D and 3D single-molecule localization microscopy data.

MiLoPYP is a two-step, dataset-specific contrastive learning-based method for fast and accurate detection and localization of a diverse range of target structures in cryo-electron tomography data, enabling improved in situ structural biology.

Point spread function (PSF) splitting with the ‘Circulator’, which encodes the fluorophore emission band into the PSF, improves the information content of fluorescence microscopy and enables improved super-resolution imaging and single-particle tracking.

Collaborative augmented reconstruction (CAR) is a platform for large-scale reconstruction of neurons and other cells from multi-dimensional imaging datasets. It can be accessed from a variety of devices simultaneously for efficient and accurate reconstruction.

Spacia is a multiple-instance learning model for cell–cell communication (CCC) interference in single-cell resolution spatially resolved transcriptomics data. Spacia can map complex CCCs by modeling cell proximity and CCC-driven gene perturbation.

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.