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VarID is a computational method that quantifies the dynamics of transcriptional variability with the goal of identifying the role of highly variable genes, such as weakly expressed transcription factors, in cell differentiation or state transitions.
Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Micropatterning of cryo-EM grids enables controlled adhesion of mammalian cells for cryo-ET-based structural studies. This approach leads to reproducible cellular morphology and improves focused ion beam thinning of cells for in-cell structural analyses.
Directly sequencing RNA strands through a nanopore retains the full length of the transcript and allows for analysis of polyA tail length, transcript haplotypes and base modifications.
DuMPLING (dynamic μ-fluidic microscopy phenotyping of a library before in situ genotyping) enables screening of dynamic phenotypes in strain libraries and was used here to study genes that coordinate replication and cell division in Escherichia coli.
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.