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Next-generation red and green G-protein-coupled receptor-based dopamine sensors with improved properties have been developed. Their performance is demonstrated in cell culture, in brain slices and in vivo in the mouse.
An analysis of AlphaFold protein structure predictions shows that while in many cases the predictions are highly accurate, there are also many instances where the predicted structures or parts of predicted structures do not agree with experimentally resolved data. Therefore, care must be taken when using these predictions for informing structural hypotheses.
An integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution that combines two methods, localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT).
Monomeric and tandem dimer derivatives of the bright and photostable green fluorescent protein StayGold offer versatile tools for tagging target proteins and membranes in extended live-cell imaging.
By combining fast lift-over and selective re-mapping, levioSAM2 enables efficient and accurate read mapping and variant calling leveraging complete reference genomes.
We developed a machine learning model, RoseTTAFoldNA, that can predict the structures of protein–DNA and protein–RNA complexes. Our model is capable of predicting accurate structures of protein families for which structural information is unknown.
Fluorescent actinometers enable the measurement of light intensity even in the depths of samples and over wide ranges of wavelengths and intensities. We introduce two protocols to quantitatively characterize the spatial distribution of light of various fluorescence imaging systems and to calibrate the illumination of commercially available instruments and light sources.
Two methods for fluorescence-based actinometry using organic dyes and photoconvertible fluorescent proteins enable rapid and precise measurement of light intensity at the sample in fluorescence microscopes.
Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments, and includes data processing features such as mass recalibration, statistical analyses such as single-ion segmentation and rough annotation-based classification, and analyses of large-scale multitissue experiments.
SegCLR automatically annotates segmented electron microscopy datasets of the brain with information such as cellular subcompartments and cell types, using a self-supervised contrastive learning approach.
This manuscript describes a refinement protocol that extends the e2gmm method to optimize both the orientation and conformation estimation of particles to improve the alignment for flexible domains of proteins.
DeepSeMi is a self-supervised denoising framework that can enhance SNR over 12 dB across diverse samples and imaging modalities. DeepSeMi enables extended longitudinal imaging of subcellular dynamics with high spatiotemporal resolution.
Enhanced super-resolution radial fluctuations (eSRRF) offers improved image fidelity and resolution compared to the popular SRRF method and further enables volumetric live-cell super-resolution imaging at high speeds.
A phenylalanine-containing peptide probe can be used for discriminating all 20 amino acids via current blockage during translocation through an α-hemolysin (αHL) nanopore. The paper provides proof-of-concept peptide sequencing demonstrations.
Adaptable, turn-on maturation (ATOM) biosensors use monobody or nanobody targeting to control fluorescent protein maturation for fluorescence in the presence of target biomolecules, enabling bright and specific cellular biosensing.
