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LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.
A transcriptional analysis of kidney organoids reveals batch effects as the key drivers of variation, mainly through differences in maturity, and provides a list of highly variable genes and a method for estimating differentiation stage for improved disease modeling.
kBET informs attempts at single-cell RNA-seq data integration by quantifying batch effects and determining how well batch regression and normalization approaches remove technical variation while preserving biological variability.
A compressed sensing approach enables the identification of key neurons involved in a particular behavior with few measurements, using genetic tools with limited specificity. The approach is demonstrated in the C. elegans interneuron circuitry.
A protocol adapted to xeno- and feeder-free conditions is shown to generate reliable and consistent cortical brain organoids across differentiations and source stem cell lines, making it suitable for disease modeling and other applications.
DART-seq alters droplet sequencing in a simple and flexible way to simultaneously profile the transcriptome and multiplexed targeted RNAs, such as viral transcripts and immunoglobulin chains, in single cells.
A user-friendly ImageJ plugin enables the application and training of U-Nets for deep-learning-based image segmentation, detection and classification tasks with minimal labeling requirements.
U-ExM enables near-native expansion microscopy of samples in vitro and in cells. The combination of U-ExM with confocal microscopy and HyVolution revealed details of centriole chirality that were previously accessible only by electron microscopy.
Segmental Duplication Assembler (SDA) uses long sequence reads to resolve segmental duplications that are collapsed in current genome assemblies. These assemblies correspond in total to the length of an average human chromosome.
The length of the guide RNA for Cas12a-VPR determines whether a target gene is edited or activated and allows for multiplexed, combinatorial gene modifications.
Deep learning enables cross-modality super-resolution imaging, including confocal-to-STED and TIRF-to-TIRF-SIM image transformation. Imaging of a larger FOV and greater depth of field is possible with higher resolution and SNR at lower light doses.
The DNA-based, ratiometric fluorescent reporter CalipHluor enables quantitative imaging of pH and calcium in acidic organelles with single-organelle resolution. The probe was used to identify a lysosome-specific Ca2+ importer in animals.
fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.
An ex vivo 3D culture model of mycobacterial granulomas recapitulates the in vivo physiology of these structures and enables longitudinal imaging studies. The platform allows genetic and pharmacological manipulation of this key structure.
Imaging of neuronal activity across the whole zebrafish brain in combination with online analysis allows for manipulating neuronal activity according to function. This approach is used to ablate or activate neurons in fictively swimming zebrafish larvae.
The combination of positive and negative selection strategies, paired with the use of shRNAs to avoid random integration, allows efficient and scarless CRISPR-based homologous recombination.
A particle-filter algorithm for single-particle cryo-electron microscopy, implemented in a tool called THUNDER, provides high-dimensional parameter estimation, improving the obtainable resolution for several protein structures.
CellDMC finds cell type–specific differential methylation in mixtures of cells, including epithelial tissues, and scenarios in which methylation changes in opposite directions in different cell types.