News & Comment

Reprogrammed natural killer cells show enhanced functional properties and anti-tumor efficacy.

NanoporeTERs, engineered reporter proteins, can be detected on MinION nanopore sensor arrays.

A deep-learning-based tool and a large ground truth dataset enable spike inference from calcium imaging data acquired in a variety of experimental conditions.

Researchers have registered a gene expression atlas to a whole-body EM volume of a marine bristle worm.

Community-driven initiatives are proposing standards to improve the reporting and reproducibility of machine learning in biology. We support these developments, some of which are described in this month’s special issue.

Researchers develop single-cell SPRITE to detect higher-order 3D genome structures in single cells.

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.

Scuba diving and jazz bring together chemistry, neuroscience and life inland.

To study the neural complexities of animals moving though physical and social space, labs increasingly turn to wireless technology.

A software platform for analyzing data-independent acquisition (DIA) proteomics data.

A single-molecule calorimeter measures the thermodynamic properties of individual proteins.

Sensitive synthetic protein-phosphorylation networks manifest fast bistable dynamics.

Constructing a fair and accurate author list can be one of the most fraught aspects of manuscript publication. We provide some advice and resources for authors at all career levels.

Researchers use an in vitro assay in combination with a polymer model to study protein–DNA condensation and how condensates bring distant DNA elements into close proximity.

A co-culture system to mimic the development of functional mouse oocytes in vitro.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

She connects equity, samurai swords, imaging and an automated way to count and track mitochondria.

Proteins and sugars interact in sweet, complex ways. More tools are emerging, but working in this field still takes endurance.

Applying principles from super-resolution fluorescence microscopy increases the resolution of atomic force microscopy.