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A collection of antigen-stabilizable fluorescent nanobodies becomes fluorescent upon binding to their intracellular targets and enables background-free multicolor imaging and biosensing in living systems, revealing protein dynamics, metabolites and signaling across cellular and in vivo models, including the zebrafish embryo and mouse brain.
This Perspective introduces the concept of multifactor authentication for extracellular vesicle (EV) analysis as an effective tool to resolve the heterogeneity problem that often confounds mechanistic understanding of EV biology.
NirFAP680 is a near-infrared fluorogen-activating protein that has an order of magnitude greater cellular brightness and superior photostability compared to currently available NIR FAPs and fluorescent proteins in both single- and two-photon excitation and allows robust imaging of proteins in live cells and in vivo.
The open-source Orthrus RNA language model maps evolutionary patterns across mammalian species to predict key mRNA properties, advancing RNA biology research.
Researchers often have to move — within their country or across the world. Packing up and moving an established lab is a logistic and emotional challenge.
Orthrus is an RNA foundation model pretrained via contrastive learning based on splicing isoforms and orthologous transcripts. This pretraining strategy leads to its strong performances in various tasks about RNA properties and functions, distinguishing Orthrus from other genomic foundation models.
The genetically encoded voltage indicators JEDI3sub and JEDI3hyp enhance monitoring of subthreshold neuronal activity in the awake mouse brain using a variety of two-photon microscopy techniques.
A series of spontaneously blinking dyes in the far-red range facilitate single-molecule localization microscopy. These dyes vary in their blinking properties and can be matched to the applications and microscopy modalities as needed.
We introduce neural fields for adaptive optical two-photon fluorescence microscopy (NeAT), a framework that reconstructs 3D structure from a volumetric image stack while estimating wavefront aberrations and correcting sample motion and conjugation errors. Application of NeAT on custom and commercial microscopes improved quality of in vivo structural and functional imaging of the mouse brain.
NeAT is a computational framework that facilitates incorporating adaptive optics into custom or commercial two-photon microscopes. This allows correcting aberrations and conjugation errors in real time in the mouse brain during morphological or functional imaging.
Deciphering the genomic regulatory code driving cell type-specific gene regulation has been a research quest for decades. We present CREsted, a software package that provides data-driven insights into the regulatory elements driving enhancer function in cell types from single-cell chromatin accessibility atlases in various tissues and species.
A new sequencing-based method optimizes barcode-based synaptic mapping by capturing paired neuronal barcodes from intact synaptic fragments, achieving the efficiency needed to scale toward comprehensive brain connectivity maps.
Language model-inferred embeddings are replacing structure-derived descriptions of proteins, genes and genomes. We propose a model-agnostic measure to quantify reliability of these new representations.
