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Common cellular segmentation models based on machine learning perform suboptimally for test images that differ greatly from training images. Cellpose 2.0 allows biologists to quickly train state-of-the-art segmentation models on their own imaging data. This was previously only possible using large, annotated datasets and required expert machine learning knowledge.
Detecting rare-variant associations in the noncoding genome is challenging. We present a scalable, flexible and streamlined rare-variant association analysis framework for biobank-scale whole-genome sequencing data, including gene-centric and non-gene-centric analyses by incorporating multiple variant functional annotations using various coding and noncoding units, conditional analysis, result summary and visualization.
A combination of light-sheet fluorescence microscopy (LSFM) with structured illumination doubles resolving power over LSFM alone. We show a practical implementation using a single objective for illumination and fluorescence detection and demonstrate its use for rapid volumetric imaging.
The generation of a whole larval zebrafish brain electron microscopy volume in tandem with automated tools lays the groundwork for producing the first vertebrate brain connectome.
This Perspective describes common pitfalls that can occur when using light sheet microscopy and offers guidance for improved quantitative imaging with these instruments.
Light-Seq combines high resolution imaging with next generation sequencing of selected cell populations in fixed biological samples. Specifically, microscopically analyzed cells can be subjected to RNA expression profiling while keeping the sample intact for further assays, enabling cellular phenotypes and states to be assessed in the context of the original tissue.
An approach for integrating the wealth of heterogeneous brain data — from gene expression and neurotransmitter receptor density to structure and function — allows neuroscientists to easily place their data within the broader neuroscientific context.
This Review summarizes recent methodological advances in experimental and computational tools developed in studying RNA structures, which provides a bridge for communication between both experimentalists and computational experts.
RNA molecules designed by citizen scientists and probed in high-throughput experiments highlighted discrepancies among RNA folding algorithms in their ability to predict RNA structure ensembles. These datasets were used to train a new algorithm that demonstrated improved performance in a collection of independent datasets, including viral genomic RNAs and mRNAs probed in cells.
RNA comprises a substantial fraction of eukaryotic chromatin, but techniques to identify and map RNAs are cumbersome. We adapted existing tagmentation-based profiling techniques to enable chromatin-associated RNAs to be profiled in a simple workflow, enhancing the capability to identify regulatory RNAs.
BIONIC (Biological Network Integration using Convolutions) is a scalable deep learning network integration approach that learns and combines diverse data representations across a range of biological network types to consolidate knowledge of gene function. BIONIC outperforms existing integration approaches by capturing biological information more comprehensively and with greater accuracy than previously possible.
A genetically encoded green fluorescent sensor for oxytocin, MTRIAOT, offers an opportunity to perform real-time recording of brain oxytocin dynamics in living animals.
Joint profiling of multiple modalities in the same cell is challenging. We developed a method with a modular design to enable the simultaneous detection of chromatin accessibility and the transcriptome within single cells with flexible throughput.
A tissue engineering method using a 3D scaffolding enables the generation of an artificial human thymus from inducible pluripotent stem cells (iPSCs). The artificial thymus can be used to study human T cell development in hematopoietic humanized mice.
Scanning transmission electron microscopy (STEM) techniques reveal atomic-resolution details of organic and inorganic materials. The application of STEM to biological vitrified specimens under low-dose cryogenic imaging conditions demonstrates that STEM also achieves near-atomic-resolution 3D structures of biological macromolecules.
In vivo, forces applied to molecular interactions between T cells and antigen-presenting cells are essential for specific foreign antigen recognition. A new technology, BATTLES, applies force to thousands of T cells interacting with tens of candidate antigens to identify antigens capable of efficient T cell activation. The method improves throughput over current methods that profile force-dependent interactions.
In this Perspective, technologies and challenges in the cardiac tissue engineering field are discussed and strategies to overcome these challenges are proposed.