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Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.
This paper proposes two new anisotropy metrics—the Fourier shell occupancy and the Bingham test—that can be used to understand the quality of cryogenic electron microscopy maps.
CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis) learns representations of molecular pixel profiles from multiplexed images that can be clustered to quantify subcellular landmarks and capture interpretable cellular phenotypes.
SIMBA learns a co-embedding space of single cells and multiple features such as genes, chromatin-accessible regions and transcription-factor-binding sequences, boosting the performances of various analyses of cellular diversity and regulation.
MISAR-seq combines spatial-ATAC-seq and RNA-seq for spatial profiling of both chromatin accessibility and gene expression, as demonstrated in the developing mouse brain.
The miniature RNA-guided endonuclease IscB and its ωRNA were engineered for efficient gene editing in mammalian cells. Fusions of ‘enIscB’ to T5 exonuclease and cytosine or adenosine deaminase yield versatile tools for genome engineering.
Nano-DMS-MaP focuses in on the structures of individual RNA isoforms, enabling direct examination of the structural diversity of different RNAs inside cells.
This updated analysis of the Cell Tracking Challenge explores how algorithms for cell segmentation and tracking in both 2D and 3D have advanced in recent years, pointing users to high-performing tools and developers to open challenges.
TomoTwin is a deep metric learning-based particle picking method for cryo-electron tomograms. TomoTwin obviates the need for annotating training data and retraining a picking model for each protein.
The light-sensitive LOV domain was engineered into the TurboID enzyme, creating ‘LOV-Turbo’. LOV-Turbo enables optogenetic control over proximity labeling, increasing the spatiotemporal precision of this technique.
A deep learning algorithm maps out the continuous conformational changes of flexible protein molecules from single-particle cryo-electron microscopy images, allowing the visualization of the conformational landscape of a protein with improved resolution of its moving parts.
A microscope objective inspired by the Schmidt telescope offers a large field of view, high numerical aperture, long working distance and compatibility with all homogeneous immersion media for versatile bioimaging.
Nature Methods welcomes manuscript submissions that describe new technology, tool and methodology developments across the spectrum of basic biology research. Here, we clarify our scope and highlight some areas of interest.
