Browse Articles

The exception proves the rule.

DeepInterpolation is a self-supervised deep learning-based denoising approach for calcium imaging, electrophysiology and fMRI data. The approach increases the signal-to-noise ratio and allows extraction of more information from the processed data than from the raw data.

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.

Technological innovations in optical object recognition and high-throughput ultrasensitive mass spectrometry are enabling subcellular metabolomics and peptidomics, providing unprecedented opportunities to study small-molecule mediators of cellular function with important implications in health and disease.

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

Dynamic mass photometry, a method based on optical imaging of unlabeled proteins, enables direct observation and tracking of single-protein interactions on lipid membranes.

A gene sequence-to-expression machine learning model achieves improved accuracy by incorporating information about potential long-range interactions.

A compact adaptive optics module corrects aberrations in two-photon and three-photon microscopy, enabling structural and functional imaging deep in the mouse brain, the mouse spinal cord and the zebrafish larva.

An iSCAT image processing and analysis strategy enables mass-sensitive particle tracking (MSPT) of single unlabeled biomolecules on a supported lipid bilayer. MSPT was used to observe the (dis-)assembly of membrane complexes in real-time.

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.

This work describes inCITE-seq that jointly measures intranuclear protein levels and the transcriptome in single nuclei, which is applied to mouse brain tissue to relate quantitative protein levels of TFs to gene expression programs.

Dynamic mass photometry allows label-free tracking and mass measurement of individual membrane-associated proteins diffusing on supported lipid bilayers. The approach can be used to monitor dynamic (dis)assembly of protein complexes.

By using a new deep learning architecture, Enformer leverages long-range information to improve prediction of gene expression on the basis of DNA sequence.

SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.