Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
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.
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.
ClampFISH 2.0 enables highly specific multiplexed signal amplification in RNA FISH. The approach was used to detect 10 RNA species that ranged in abundance in more than 1 million cells and is also applicable to tissue sections.
This paper presents an iterative procedure where AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions.
Omnipose is a deep neural network algorithm for image segmentation that improves upon existing approaches by solving the challenging problem of accurately segmenting morphologically diverse cells from images acquired with any modality.
Light-Seq uses light-directed DNA barcoding in fixed cells and tissues for multiplexed spatial indexing and subsequent next generation sequencing. This approach blends spatial and omics information to enable analysis of rare cell types in complex tissues.
The EternaBench dataset of synthetic RNA constructs was used to directly compare RNA secondary structure prediction software packages on ensemble-oriented prediction tasks and used to train the EternaFold model for improved performance.
TracX improves the accuracy of single-cell tracking by using a fingerprinting approach to measure the similarity between cells in two consecutive images. The approach is applicable across modalities and enables biological discovery.
A fluorescent sensor for oxytocin called MTRIAOT has been developed. The sensor’s capabilities are demonstrated in fiber photometry measurements in freely behaving mice.