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Tracking cells is a time-consuming part of biological image analysis, and traditional manual annotation methods are prohibitively laborious for tracking neurons in the deforming and moving Caenorhabditis elegans brain. By leveraging machine learning to develop a ‘targeted augmentation’ method, we substantially reduced the number of labeled images required for tracking.

Targettrack is a deep-learning-based pipeline for automatic tracking of neurons within freely moving C. elegans. Using targeted augmentation, the pipeline has a reduced need for manually annotated training data.

MISCOs are spatially arranged ventral midbrain–striatum–cortical organoids that enable investigations into the human dopaminergic system.

We developed MAbID, a method for combined genomic profiling of histone modifications and chromatin-binding proteins in single cells, enabling researchers to study the interconnectivity between gene-regulatory mechanisms. We demonstrated MAbID’s implementation in profiling multifactorial changes in chromatin signatures during in vitro neural differentiation and in primary mouse bone marrow tissue.

Although single-cell RNA-sequencing has revolutionized biomedical research, exploring cell states from an extracellular vesicle viewpoint has remained elusive. We present an algorithm, SEVtras, that accurately captures signals from small extracellular vesicles and determines source cell-type secretion activity. SEVtras unlocks an extracellular dimension for single-cell analysis with diagnostic potential.

SEVtras is an algorithm that uses single-cell RNA sequencing data to assess small extracellular vesicle activity at droplet resolution.

This paper proposes quantitative modeling of single-cell ATAC-seq data, which improves various downstream analyses.

This study demonstrates the need and advantage of uniformly quantifying single-nucleus ATAC-seq data using Paired-Insertion Counting.

MAbID offers a multiplexing approach to uncover the genomic distributions of various epigenetic markers, enabling the study of how these markers jointly direct gene expression.

Next-generation red and green G-protein-coupled receptor-based dopamine sensors with improved properties have been developed. Their performance is demonstrated in cell culture, in brain slices and in vivo in the mouse.

An analysis of AlphaFold protein structure predictions shows that while in many cases the predictions are highly accurate, there are also many instances where the predicted structures or parts of predicted structures do not agree with experimentally resolved data. Therefore, care must be taken when using these predictions for informing structural hypotheses.

An integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution that combines two methods, localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT).

Monomeric and tandem dimer derivatives of the bright and photostable green fluorescent protein StayGold offer versatile tools for tagging target proteins and membranes in extended live-cell imaging.

By combining fast lift-over and selective re-mapping, levioSAM2 enables efficient and accurate read mapping and variant calling leveraging complete reference genomes.

Labs range in size, and no one size is likely to fit all. Here some researchers share their lab size experiences and preferences.

A combination of hardware developments has increased the achievable spatial resolution in 7 Tesla human neuroimaging to about 0.4 mm.

We developed a machine learning model, RoseTTAFoldNA, that can predict the structures of protein–DNA and protein–RNA complexes. Our model is capable of predicting accurate structures of protein families for which structural information is unknown.

Fluorescent actinometers enable the measurement of light intensity even in the depths of samples and over wide ranges of wavelengths and intensities. We introduce two protocols to quantitatively characterize the spatial distribution of light of various fluorescence imaging systems and to calibrate the illumination of commercially available instruments and light sources.

Two methods for fluorescence-based actinometry using organic dyes and photoconvertible fluorescent proteins enable rapid and precise measurement of light intensity at the sample in fluorescence microscopes.

Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments, and includes data processing features such as mass recalibration, statistical analyses such as single-ion segmentation and rough annotation-based classification, and analyses of large-scale multitissue experiments.