Nature Methods, Published online: 08 December 2023; doi:10.1038/s41592-023-02099-0

DeepMainmast is a protein structure modeling protocol for cryo-EM that combines the strengths of a deep-learning-based de novo protein main-chain-tracing approach with AlphaFold2-based structure predictions for improved performance.

Nature Methods, Published online: 08 December 2023; doi:10.1038/s41592-023-02123-3

How accurate is the prediction of protein structure by AlphaFold? Terwilliger et al. address this question with a rigorous assessment of the accuracy of AlphaFold-predicted structures by comparing them with experimentally determined X-ray crystallographic data.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02132-2

Better detection for localization microscopy

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02106-4

High-resolution connectomics of the human brain is the next frontier in neuroscience.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02109-1

New twists on established methods and multimodal imaging are poised to bridge gaps between cellular and organismal imaging.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02105-5

Large language models are learning the language of genomics.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02108-2

Developments in nanopore-based peptide detection and sequencing show promise of a breakthrough.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02110-8

Spatially resolved multimodal omics offers a collective way to capture molecular information in complex tissues.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02111-7

Artificial ECMs enable recapitulation of tissue microenvironments.

Nature Methods, Published online: 06 December 2023; doi:10.1038/s41592-023-02104-6

Advances will enable proteome-scale structure determination in cells.

Nature Methods, Published online: 05 December 2023; doi:10.1038/s41592-023-02096-3

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.

Nature Methods, Published online: 05 December 2023; doi:10.1038/s41592-023-02080-x

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

Nature Methods, Published online: 05 December 2023; doi:10.1038/s41592-023-02097-2

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.

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02090-9

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.

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02112-6

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

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02091-8

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.

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02118-0

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.

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02117-1

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

Nature Methods, Published online: 04 December 2023; doi:10.1038/s41592-023-02103-7

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

Nature Methods, Published online: 30 November 2023; doi:10.1038/s41592-023-02087-4

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.

Nature Methods, Published online: 30 November 2023; doi:10.1038/s41592-023-02100-w

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.