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An optogenetic system enables the controlled release of soluble and transmembrane proteins for precise exploration of cellular protein function at the single-molecule level and streamlined single-molecule imaging.
Implementation of ultralong transients on an Orbitrap mass spectrometer improves mass resolution, sensitivity and accuracy of charge determination in the analysis of large macromolecular ions.
Deng et al. expand the toolbox of neurotransmitter sensors with high-sensitivity green and red genetically encoded serotonin sensors. These are suitable for in vivo applications, as demonstrated in a variety of applications in mice.
A copper(II)-functionalized Mycobacteriumsmegmatis porin A nanopore enables direct identification of all 20 proteinogenic amino acids, one unnatural amino acid and two post-translational modifications, and shows potential for peptide discrimination and sequencing.
Effortless landmark detection is an unsupervised deep learning-based approach that addresses key challenges in landmark detection and image registration for accurate performance across diverse tissue imaging datasets.
Collaborations between researchers and companies can progress swimmingly and teams quickly validate findings and mature methods. All too often, things can’t advance and the ‘Valley of Death’ looms. New ways to collaborate, underpinned by computational muscle, can help.
mBaoJin is a monomeric derivative of the bright and photostable green fluorescent protein StayGold. mBaoJin offers favorable photophysical properties for use in diverse protein tagging and subcellular labeling applications.
Pretrained using over 33 million single-cell RNA-sequencing profiles, scGPT is a foundation model facilitating a broad spectrum of downstream single-cell analysis tasks by transfer learning.
Multi-sheet RESOLFT combines the speed and optical sectioning of light-sheet fluorescence microscopy with reversibly photoswitchable fluorescent proteins to enable fast, volumetric super-resolution imaging in live cells.
A-SOiD is a computational platform for behavioral annotation whose training includes elements of supervised and unsupervised learning. The approach is demonstrated on mouse, macaque and human datasets.
Targeting coalescent analysis (TarCA) is a statistical method that quantifies the number of progenitor cells of a given population using single-cell phylogenetic data.
We developed a high-content profiling method named vibrational painting (VIBRANT) for single-cell drug response measurements, combining vibrational imaging, multiplexed vibrational probes and machine learning. VIBRANT showed high performance in predicting drug mechanisms of action, discovering novel compounds and assessing drug combinations, demonstrating great promise for phenotypic drug discovery.
We introduce a biomimetic antigen-presenting system that uses hexapod heterostructures for specific T cell recognition at the single-molecule and single-cell levels. The system enables high-resolution T cell activation, uses magnetic forces to increase immune responses, and offers flexible and precise identification of antigen-specific T cell receptors, aiding the study of T cell recognition and immune cell mechanics.
Interactions between RNA and RNA-binding proteins (RBPs) define the fate and function of every RNA molecule. We present TREX, or targeted RNase H-mediated extraction of crosslinked RBPs, an efficient and accurate method to unbiasedly reveal the protein interactors of specific regions of RNAs isolated from living cells.
We established a method to generate complex self-organizing bone marrow-like organoids (BMOs) via concomitant differentiation of human induced pluripotent stem cells. These BMOs consist of hematopoietic cells, stromal niche cells and de novo vascular networks. In addition, they contain multipotent hematopoietic stem and progenitor cells, as well as mesenchymal stem and progenitor cells; they model aspects of the three-dimensional bone marrow architecture and can be used to study developmental and aberrant hematopoiesis.
We developed Significant Latent Factor Interaction Discovery and Exploration (SLIDE), an interpretable machine learning approach that can infer hidden states (latent factors) underlying biological outcomes. These states capture the complex interplay between factors derived from multiscale, multiomic datasets across biological contexts and scales of resolution.