Volume 21 Issue 7, July 2024

Amid the brutality of war around them, some Ukrainian scientists find calm in bioinformatics.

Bats, the only flying mammals, comprise almost 25% of mammalian species. They are excellent navigators, highly social, and extremely long-lived. Their sense of echolocation has been studied for many years — but many species possess also excellent vision and olfaction. In recent years, bats have emerged as new models for neurobiology of navigation, social neuroscience, aging, and immunity.

Tissues, organs and organ systems are composed of interacting cells (the cellome). We discuss the emergence of an omics approach that we refer to as cellomics. It enables cellome-wide analysis in whole-organ or whole-body specimens, based on advanced three-dimensional imaging and image analysis technology. We think that cellomics will pave the way for the incorporation of cellular, intercellular and spatial information across millions of cells in our body.

Machine learning approaches can distinguish six different classes of presynapses from electron micrographs across the Drosophila brain.

Assessing forest health and resilience is an urgent, many-methods task that takes global collaboration, data collected at multiple scales and a willingness to be surprised.

The annotation of structural glycomics data is a bottleneck in gaining insight into complex carbohydrates. An artificial intelligence model, CandyCrunch, has now been developed that accelerates the annotation process by orders of magnitude and achieves high accuracy in the prediction of glycan structures from tandem mass spectrometry data.

We created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.

This Perspective discusses the methods and tools required for three-dimensional histology in large samples, an approach that promises insights into tissue and organ physiology as well as disease.

tidyomics offers a software ecosystem for omic data manipulation and analysis that bridges Bioconductor with the tidyverse framework.

Photoconversion of popular organic dyes results in blue-shifted emission and altered fluorescence lifetimes that can cause artifacts in quantitative microscopy. These can be avoided through proper labeling strategies such as using exchangeable dyes.

This work describes scEdU-seq for studying replication fork speed in single cells, which enables researchers to investigate variability in replication speed along S phase and its associations with transcription and DNA damage.

Mellon accurately estimates cell-state densities using high-dimensional single-cell data, unveiling regulatory dynamics and mechanisms in cell differentiation.

CellRank 2 is a comprehensive framework of cell fate analysis using large-scale multiview single-cell data.

CandyCrunch is a deep learning-based tool for predicting glycan structures from tandem mass spectrometry data. The paper also introduces CandyCrumbs that automatically annotates fragment ions in higher-order tandem mass spectrometry spectra.

Blush regularization makes use of a neural network pre-trained on a diverse set of high-resolution cryo-EM half-maps to improve image alignment, effectively lowering the size barrier, during cryo-EM structure determination.

Although not widely appreciated, measured smFRET efficiencies can vary depending on illumination intensity. Self-healing fluorophores offer robust triplet state suppression to enable accurate smFRET measurements under broad illumination regimes.

Integrative and reference-informed tissue segmentation (IRIS) harnesses single-cell RNA sequencing data for the accurate identification of spatial domains in spatially resolved transcriptomics. IRIS is computationally efficient and uniquely suited for analyzing large datasets.

Deconwolf is a computationally efficient and user-friendly software tool for fluorescence microscopy image deconvolution that improves the analysis of diverse fluorescence in situ hybridization methods and can handle large datasets.

Permittivity tensor imaging is a label-free computational microscopy approach that enables the three-dimensional measurement of molecular permittivity tensors, revealing information about a biomolecule’s dry mass and orientation in cells and tissues.

PdCO is a switchable optogenetic tool for inhibiting synaptic transmission in neuronal terminals in vivo, as demonstrated in a variety of contexts mainly in the mouse.

DART (drug acutely restricted by tethering) enables the manipulation of native receptors on genetically defined neurons. This work describes second-generation DART reagents for manipulating GABA_A and AMPA receptors with higher cellular specificity than previously achieved.

Bessel-droplet two-photon fluorescence microscopy offers high-contrast and high-resolution volumetric imaging in vivo and can be used for high-throughput mapping of functional synaptic organization in the mouse brain.

Generating training data for training deep-learning-based tools is time consuming. The DELiVR pipeline facilitates this process as demonstrated in this study on detecting c-Fos⁺ cells or microglia in the brain, following tissue clearing and imaging with light-sheet microscopy.

Lightning Pose is an efficient pose estimation approach that requires few labeled training data owing to its semi-supervised learning strategy and ensembling.

Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.

The EMDataResource Ligand Model Challenge aimed at assessing the reliability and reproducibility of modeling ligands bound to protein and protein–nucleic acid complexes in cryo-EM maps determined at near-atomic resolution. This analysis presents the results and recommends best practices for assessing cryo-EM structures of liganded macromolecules.

This Registered Report presents the results of the Long-read RNA-Seq Genome Annotation Assessment Project, which is a community effort for benchmarking long-read methods for transcriptome analyses, including transcript isoform detection, quantification and de novo transcript detection.