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Probabilistic cell typing by in situ sequencing (pciSeq), leverages previous single-cell RNA sequencing classification and multiplexed in situ RNA detection to spatially map cell types accurately in the mouse hippocampus and isocortex.
Tissue fixation with formaldehyde and a water-soluble carbodiimide crosslinker (EDC) leads to retention of extracellular vesicles within tissues and allows for reliable extracellular vesicle imaging for semiquantitative imaging applications.
FreeHi-C takes Hi-C sequencing data as input and simulates reads with random mutations and indels from the interacting fragment pairs. FreeHi-C-simulated replicates are used for benchmarking Hi-C analysis methods and enable data augmentation for differential chromatin interaction analysis.
Deep-Z uses deep learning to go from a two-dimensional snapshot to three-dimensional fluorescence images. The method improves imaging speed while reducing light dose, and was shown to be useful for accurate structural and functional imaging of neurons in Caenorhabditis elegans.
Simultaneous two-photon microscopic and one-photon mesoscopic imaging of calcium signals enables correlation of local cellular and brain-wide network activity.
mmvec, a neural-network-based algorithm, uses paired multiomics data (microbial sequence counts and metabolite abundances) to compute the conditional probability of observing a metabolite in the presence of a specific microorganism.
One third of verified gene knock outs with CRISPR still show residual protein expression owing to translation reinitiation or exon skipping. Several proteins are still functional. The authors call for a systematic analysis of protein levels after genome editing.
A multi-beam two-photon microscope enables imaging of calcium activity or neurovascular dynamics in the brain with millisecond-scale temporal resolution.
UniRep learns fundamental protein features from millions of amino-acid sequences using a recurrent neural network. This summary of features can then be used for protein engineering.
Seamless integration of single-molecule localization microscopy and STED allows for correlative live imaging of protein position and movement at the nanoscale in the context of fine morphological features.
The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation.
An engineering approach guided by machine learning results in high-performance channelrhodopsin variants that are suitable for systemic viral delivery and illumination through a thinned skull.
Cellular lipids, labeled with a charged reporter, yield characteristic MS1 and MS2 patterns during mass spectrometry. These reporters allow sample multiplexing and sensitive detection of lipid metabolism at single cell resolution.
An integrated pipeline for processing cryo-ET data implemented in EMAN2 streamlines data processing to minimize human bias, and improves the quality and resolution of resulting macromolecular structures, both in vitro and in cells.
Optobodies combine split intracellular antibodies (intrabodies) with light-controlled dimerization tools for spatiotemporal control of intrabody activity. The developed tools demonstrate the versatility and power of this approach for probing protein function.
SAUCIE, a deep learning platform to analyze single-cell data across samples and platforms, allows information to be obtained from the internal layers of the network, which provides additional mechanistic understanding that can be used to further tune data analysis.
Expression of ETV2 in human cortical organoids induces the formation of vascular-like networks, which reduces cell death within organoids and increases their functional maturation.