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.
We developed LIONESS, a technology that leverages improvements to optical super-resolution microscopy and prior information on sample structure via machine learning to overcome the limitations (in 3D-resolution, signal-to-noise ratio and light exposure) of optical microscopy of living biological specimens. LIONESS enables dense reconstruction of living brain tissue and morphodynamics visualization at the nanoscale.
Genome architecture mapping (GAM) enables understanding of 3D genome structure in the nucleus. We directly compared multiplex-GAM and Hi-C data and found that local chromatin interactions were generally detected by both methods, but active genomic regions rich in enhancers that established higher-order contacts were preferentially detected by GAM.
Cell type-specific transgene expression in mice has broad utility in biomedical research. We developed a versatile system for in vivo transgene delivery using adeno-associated virus (AAV). Efficient and tissue-specific transgene expression is achieved by regulating the expression of the gene encoding the AAV receptor, thereby precisely targeting AAV to the cell type of interest.
Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.
A deep learning algorithm maps out the continuous conformational changes of flexible protein molecules from single-particle cryo-electron microscopy images, allowing the visualization of the conformational landscape of a protein with improved resolution of its moving parts.
We developed EmbryoNet, a deep learning tool that can automatically identify and classify developmental defects caused by perturbations of signaling pathways in vertebrate embryos. The tool could help to elucidate the mechanisms of action of pharmaceuticals, potentially transforming the drug discovery process.
Prime editing systems hold tremendous promise for the precise correction of pathogenic mutations. We developed a method to tag sequences modified by a prime editor to evaluate its genome-wide precision for therapeutic applications.
A new mutagenesis platform enables the fast, cost-efficient and automatable production of defined multi-site sequence variants for a wide range of applications. Demonstrations of this method included the generation of SARS-CoV-2 spike gene variants, DNA fragments for large-scale genome engineering, and adeno-associated virus 2 (AAV2) cap genes with improved packaging capacity.
Photoselective sequencing is a new method for genomic and epigenomic profiling within specific regions of a biological specimen that are chosen using light microscopy. This combination of spatial and sequencing information preserves the connections between genomic and environmental properties and deepens our understanding of structure–function relationships in cells and tissues.
We highlight the BUDDY software, which was developed to accurately determine the molecular formulae of unknown chemicals in mass spectrometry data. BUDDY is a bottom-up approach that shows superior annotation performance on reference spectra and experimental datasets. Incorporation of global peak annotation could enable BUDDY to refine formula annotations and reveal feature interrelationships.
Unlike cell surface proteins, secreted proteins are difficult to quantify and trace back to individual cells. We show that the capture of secreted proteins onto their source cell surfaces using an affinity matrix enables simultaneous measurement of protein secretion, cell surface proteins and transcriptomics in thousands of cells at single-cell resolution.
Cells exchange information with one another using secreted chemicals as data carriers. We developed an all-optogenetic synaptic transmission system that replaced a chemical neurotransmitter with emitted photons. This system enabled synthetic signaling between unconnected neurons and the generation of prosthetic synaptic circuits.
Simultaneous maximization of sensitivity, data completeness and throughput in mass-spectrometry proteomics often necessitates trade-offs. To mitigate these trade-offs, we introduce a prioritization algorithm that achieves high sensitivity and data completeness while maximizing throughput. With prioritized single-cell proteomics (pSCoPE), we consistently and accurately quantify proteins and their post-translational modifications in single macrophages and link them to endocytic activity.
We evolved the brilliant monomeric red fluorescent protein mScarlet3 using a multiparameter screening approach. Owing to a newly engineered hydrophobic patch inside its β-barrel structure, mScarlet3 combines a high quantum yield and high fluorescence lifetime with fast and complete maturation. Consequently, mScarlet3 performs well as a fusion tag in live-cell imaging.
Light-activated drugs and signaling molecules have therapeutic potential and are valuable experimental tools. Photoactivation of a mu opioid receptor agonist in the mouse brain rapidly triggered pain relief and locomotion, demonstrating that in vivo photopharmacology can drive dynamic studies into animal behavior.
Stimulated Raman scattering (SRS) microscopy has the capability to simultaneously visualize the spatial distribution of different biomolecules, but it remains challenging to reach super-resolution. To achieve this goal, a deconvolution algorithm, A-PoD, was developed and combined with SRS microscopy, enabling examination of nanoscopic biomolecular distribution and subcellular metabolic activity in cells and tissues.
Communication between cells is crucial for coordinated cellular functions in multicellular organisms. We present an optimal transport theory-based tool to infer cell–cell communication networks, spatial signaling directions and downstream targets in multicellular systems from spatial gene expression data.
Alignment of single-cell proteomics data across platforms is difficult when different data sets contain limited shared features, as is typical in single-cell assays with antibody readouts. Therefore, we developed matching with partial overlap (MARIO) to enable confident and accurate matching for multimodal data integration and cross-species analysis.
Dimension reduction is a cornerstone of exploratory data analysis; however, traditional methods fail to preserve the spatial context of spatial genomics data. In this work, we develop a nonnegative spatial factorization (NSF) model that allows interpretable, parts-based decomposition of spatial single-cell count data. NSF allows label-free annotation of regions of interest in spatial genomics data and identifies genes and cells that can be used to define those regions.
