Volume 20 Issue 6, June 2023

Job-hunting is never easy, and more tasks get added on for members of the LGBTQ+ community as they search for welcoming environments.

Volume electron microscopy (vEM) is a group of techniques that reveal the 3D ultrastructure of cells and tissues through continuous depths of at least 1 micrometer. A burgeoning grassroots community effort is fast building the profile and revealing the impact of vEM technology in the life sciences and clinical research.

A single-shot hyperspectral phasor camera (SHy-Cam) enables fast, multiplexed volumetric imaging.

As they pursue their puzzles of interest, scientists are inventive caretakers of the organisms in their keep, especially the young.

Nano-DMS-MaP focuses in on the structures of individual RNA isoforms, enabling direct examination of the structural diversity of different RNAs inside cells.

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.

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.

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 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.

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.

This Perspective discusses how machine learning can help in studying rare diseases using various emerging approaches.

EmbryoNet is an automated approach to the phenotyping of developing embryos that surpasses experts in terms of speed, accuracy and sensitivity. A large annotated image dataset of zebrafish, medaka and stickleback development rounds out this resource.

This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

For EM-based connectomics applications, a staining protocol for large tissue samples in the range of a centimeter has been developed, which avoids artifacts common with established protocols.

MEGAA, an efficient template-guided method to synthesize high-fidelity DNA variants

Nano-DMS-MaP combines the power of DMS mutational profiling and long-read nanopore sequencing to resolve structural differences among RNA isoforms, revealing the structural landscape of HIV-1 transcripts in cells.

3D Flexible Refinement (3DFlex) is a generative neural network model for continuous molecular heterogeneity for cryo-EM data that can be used to determine the structure and motion of flexible biomolecules. It enables visualization of nonrigid motion and improves 3D structure resolution by aggregating information from particle images spanning the conformational landscape of the target molecule.

TomoTwin is a deep metric learning-based particle picking method for cryo-electron tomograms. TomoTwin obviates the need for annotating training data and retraining a picking model for each protein.

BUDDY is a bottom-up tandem MS (MS/MS) interrogation method for de novo molecular formula annotation with significance estimation.

Editor summary: A native-mass-spectrometry-based approach analyzes integral membrane protein–lipid complexes directly from near-physiological membrane conditions, providing information about protein oligomeric states, lipid identities, and membrane properties.

PE-tag is a genome-wide approach to map the off-target effects of in vitro and in vivo prime editing.

The light-sensitive LOV domain was engineered into the TurboID enzyme, creating ‘LOV-Turbo’. LOV-Turbo enables optogenetic control over proximity labeling, increasing the spatiotemporal precision of this technique.

The NEMO series of genetically encoded calcium indicators report calcium activity in neuronal and non-neuronal cells with high signal-to-baseline ratio, which is shown in neuronal culture, slice preparations, in vivo and in planta.

iGluSnFR variants with improved signal-to-noise ratios and targeting to postsynaptic sites have been developed, enabling the analysis of glutamatergic neurotransmission in vivo as illustrated in the mouse visual and somatosensory cortex.

XTC is a supervised deep-learning-based image-restoration approach that is trained with images from different modalities and applied to an in vivo modality with no ground truth. XTC’s capabilities are demonstrated in synapse tracking in the mouse brain.