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Cardelino leverages variant information from single-cell RNA-seq data for inferring clonal tree configuration and mapping cells to their clone of origin.
Single-molecule displacement/diffusivity mapping (SMdM) enables nanoscale mapping of freely diffusing molecules in mammalian cells and reveals the structural basis of variations in local diffusivity in both the cytoplasm and nucleus.
VesSAP is a tissue clearing- and deep learning-based pipeline for comprehensively analyzing mouse vasculature, from large vessels to small capillaries.
The combination of Intel SGX platform with sketching algorithms enables efficient compaction of genomic data and the execution of secure GWAS in an untrusted cloud environment.
Complex behaviors and the underlying neural activity in adult zebrafish can be accessed through a virtual reality system in combination with two-photon microscopy.
The TooManyCells approach to scRNA-seq data facilitates efficient and unbiased identification and visualization of cell clades and rare subpopulations. Application of TooManyCells to drug-resistant leukemia cells identifies a rare resistant-like subpopulation of treatment-naive cells.
Mass cytometry in combination with a thiol-reactive barcoding strategy allows analysis and comparison of cell-type-specific signaling networks in organoids.
SMAC-seq combines long-read sequencing with open chromatin methylation by DNA methyltransferases to enable mapping of nucleosome position and chromatin accessibility.
Ubiquitous mammalian enzymes can scavenge uracil analogs, leading to non-specific background in cell-type-specific RNA labeling. This work reveals the enzymes involved and describes the uridine/cytidine kinase 2 and 2′-azidouridine pair as a highly specific and non-toxic alternative.
A statistical method called SPARK for analyzing spatially resolved transcriptomic data can efficiently identify spatially expressed genes with effective control of type I errors and high statistical power.
Advances in MINFLUX nanoscopy enable multicolor imaging over large fields of view, bringing true nanometer-scale fluorescence imaging to labeled structures in fixed and living cells.
Phenotypic earth mover’s distance (PhEMD) facilitates the comparison of single-cell experimental conditions, each of which is a high-dimensional dataset, and identifies axes of variation among multicellular biospecimens.
A template-free image processing approach automatically detects and classifies membrane-bound protein complexes in cryo-electron tomograms of isolated endoplasmic reticulum and in intact cells.
Protein–peptide interactions that underpin cell signaling are accurately predicted by wedding the strengths of machine learning with the interpretability of biophysical theory, facilitating detailed mechanistic analyses at the proteome scale.