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The CPJUMP1 Resource comprises Cell Painting images and profiles of 75 million cells treated with hundreds of chemical and genetic perturbations. The dataset enables exploration of their relationships and lays the foundation for the development of advanced methods to match perturbations.
scGHOST offers a computational tool to annotate single-cell subcompartments from scHi-C or imaging data through graph representation learning with constrained random walk sampling.
Kilosort4 is a spike-sorting algorithm with improved performance compared to previous versions, owing to the use of a graph-based clustering approach. The tool extracts the activity of individual neurons from electrophysiological recordings acquired with, for example, Neuropixels electrodes.
The combination of light sheet illumination and reversibly switchable fluorophores enables improved structured illumination microscopy for fast, low-background super-resolution imaging in cells and spheroids.
Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.
RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.
Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.
Deep interactome profiling by mass spectrometry (DIP-MS) combines affinity purification with native BN-PAGE fractionation and mass spectrometry to resolve protein complexes sharing the same target protein. The paper also presents PPIprophet, a data-driven neural network-based protein complex deconvolution approach.
RoboEM enables automated proofreading of electron microscopy datasets using a strategy akin to that of self-steering cars. This decreases the need for manual proofreading of segmented datasets and facilitates connectomic analyses.
Improved green cAMP and red calcium sensors were developed to facilitate dual-color imaging in vivo. These sensors will allow studying the relationship between calcium and cAMP signaling.
SpatialData is a user-friendly computational framework for exploring, analyzing, annotating, aligning and storing spatial omics data that can seamlessly handle large multimodal datasets.
scPROTEIN is a deep graph contrastive learning framework that can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings under a unified framework.