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Image-seq isolates cells from specific tissue locations under image guidance for analysis by single-cell RNA sequencing. The technique can be combined with in vivo imaging to document the temporal and dynamic history of the cells prior to sequencing.
The AlphaFill algorithm transplants missing small molecules and ions from experimentally determined structures to predicted protein models in the AlphaFold protein structure database. All AlphaFill entries are available for visual inspection and download through the AlphaFill website.
RS-FISH is a user-friendly software for accurate spot detection that is applicable to smFISH experiments, spatial transcriptomics, and spatial genomics. The approach enables fast spot detection in even very large volumetric datasets.
This work presents m6Anet, which implements a neural-network-based multiple instance learning model to detect m6A modifications from direct RNA sequencing data.
An improved version of the MS2-MCP system for imaging RNA dynamics involves tethering translation termination factors to tagged mRNAs to bypass destabilization caused by NMD machinery.
Cellpose 2.0 improves cell segmentation by offering pretrained models that can be fine-tuned using a human-in-the-loop training pipeline and fewer than 1,000 user-annotated regions of interest.
This Resource presents and analyzes four datasets containing both gene expression and morphological profile data for cells subjected to hundreds to thousands of chemical or genetic perturbations and highlights their complementary nature.
The engineered hyperfolder YFP (hfYFP) and variants offer unprecedented chemical and thermal stability, making them versatile probes for microscopy as well as for challenging applications like correlative light and electron microscopy and expansion microscopy.
Unsupervised discovery of tissue architecture with graphs (UTAG) combines information on cellular morphology and protein expression with the physical proximity of cells to identify architectural domains from highly multiplexed imaging data.
Richardson–Lucy Network (RLN) combines the traditional Richardson–Lucy iteration with deep learning for improved deconvolution. RLN is more generalizable, offers fewer artifacts and requires less computing time than alternative approaches.
STAARpipeline is a comprehensive framework for flexible and scalable rare-variant association analysis using whole-genome sequencing data and annotation information.
cAMPFIREs are genetically encoded cAMP sensors that are suitable for in vivo imaging of cAMP signaling, as demonstrated in Drosophila larvae and behaving mice.
STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference dataset and discover new cell types.
This Resource presents a serial block-face EM dataset of the whole larval zebrafish brain, including automated segmentation of neurons, detection of synapses and reconstruction of circuitry for visual motion processing.
The longstanding goal of combining the optical sectioning of light-sheet illumination and the resolving power of multidirectional structured illumination microscopy is realized using an oblique plane microscope for improved fast 3D subcellular imaging.
Iterative Synthetically Phosphorylated Isomers (iSPI) is a proteome-scale library of human-derived phosphoserine-containing phosphopeptides with precisely known positions of phosphorylation. This multi-purpose resource is available for optimization, standardization, and benchmarking of key steps in phosphoproteomics workflows.