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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.
The authors describe stem cell-derived bone marrow organoids that accurately model the structural and functional properties of the human bone marrow niche.
Vibrational painting (VIBRANT) is a high-content single-cell phenotypic profiling method using mid-infrared imaging with vibrational probes for metabolic activity, which offers high accuracy with minimal batch effects to capture cellular responses to perturbation.
SATURN performs cross-species integration and analysis using both single-cell gene expression and protein representations generated by protein language models.
MEISTER is an integrative experimental and computational framework for mass spectrometry that integrates three-dimensional, organ-wide biomolecular mapping with single-cell analysis for multiscale profiling of spatial–biochemical organization.
We developed Tapioca, an integrative ensemble machine learning-based framework, to accurately predict global protein–protein interaction network dynamics. Tapioca enabled the characterization of host regulation during reactivation from latency of an oncogenic virus. Introducing an interactome homology analysis method, we identified a proviral host factor with broad relevance for herpesviruses.
Tapioca is an ensemble machine learning framework for studying protein–protein interactions (PPIs) that facilitates integration of curve-based dynamic PPI data from thermal proximity coaggregation, ion-based proteome-integrated solubility alteration or cofractionation mass spectrometry data with static interaction data to predict PPIs in dynamic contexts.