Methods in Brief

Here is a selection of recently published methods papers curated by Nature Methods editors.
Published in Protocols & Methods

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Every month, Nature Methods editors meet and discuss recently published papers that interest a broad audience. We discuss papers published beyond Nature titles. Unfortunately, we cannot highlight all the exciting methods papers in our journal page. Here are our top picks that we have discussed in our recent Research Highlight meeting.

Oikonomou, P., et al. In vivo mRNA display enables large-scale proteomics by next generation sequencing. PNAS
The researchers employ high-throughput sequencing as a readout to identify in vivo expressed proteins in yeast. They fused the MS2 coat protein to the target protein and introduced the MS RNA stem-loop downstream of the target gene. Upon translation, the fusion protein binds to its encoding mRNA. Cells can then be assayed using for example immunoprecipitation of a bait that preserve the RNA–protein interaction. The researchers generated an in vivo mRNA display library of the yeast proteome and characterized the interaction patterns of a protein of interest in the native cellular environment. 

Pilsl, S. et al. Optoribogenetic control of regulatory RNA molecules. Nat. Commun. 11, 4825 (2020). 
Researchers have developed an optogenetic approach for controlling the function of short regulatory RNAs, such as miRNA and shRNA. Target RNAs are fused to an RNA sequence recognized in a light-dependent manner by the light-oxygen-voltage photoreceptor PAL, allowing for precise spatiotemporal control of target RNA sequestration.

Chen, R. et al. Deep brain optogenetics without intracranial surgery. Nat. Biotechnol. (2020).
Chen et al. demonstrate that the channelrhodopsin ChRmine can achieve transcranial optogenetic control over neuronal activity up to a depth of 7 mm. This allowed behavioral manipulations in mice without the need for cranial surgery.

Tremblay, S. et al. An Open Resource for Non-human Primate Optogenetics. Neuron (2020).
The NHP Optogenetics Open Database has been created to document successful and unsuccessful attempts at optogenetic manipulation in non-human primates to support researchers in their decision-making. The database contains more than 1000 entries from 45 labs and is accessible at

Brunner, C. et al. A Platform for Brain-wide Volumetric Functional Ultrasound Imaging and Analysis of Circuit Dynamics in Awake Mice. Neuron (2020).
Brunner et al. perform volumetric functional ultrasound imaging (vfUSI) in behaving mice. In response to whisker or visual stimulation, changes in cerebral blood volume can be measured in the barrel cortex or visual cortex, respectively, as well as in other brain regions at 6 Hz with the help of a 2D-array transducer.

Aksel, T. et al. “Molecular goniometers for single-particle cryo-electron microscopy of DNA-binding proteins.” Nat Biotechnol (2020)
DNA origami has been used as a scaffold to present macromolecules such as proteins. Aksel et al. designed DNA origami-based molecular goniometers to dock a DNA-binding protein on a stage, which can help researchers to manipulate the protein orientation and thus reconstruct single-particle cryoEM images. As a proof-of-concept, they resolved the structure of BurrH (6.5 Å), an 82-kDa DNA-binding protein with a known crystal structure.

Liebel, M. et al. Surface-enhanced Raman scattering holography. Nat. Nanotechnol. (2020)
Holographic imaging of spontaneous Raman signals was achieved by coupling a Michelson interferometer with a holographic microscope based on a shearing interferometer. Imaging was enabled by the development of superclusters of nanoparticles encoded with SERS-active molecules. This combination allowed for 3D localization of the SERS nanoparticles from a single image and for single-particle tracking of the nanoparticles in cells.

Segebarth, D. et al. On the objectivity, reliability, and validity of deep learning enabled bioimage analyses. eLife 9: e59780 (2020). DOI: 10.7554/eLife.59780.
Deep learning can be a valuable approach for segmenting structures in fluorescence microscopy images. This work explores the question of how manual annotation of data affects the performance of deep-learning-based models, and finds that ground truth annotations from multiple human annotators combined into consensus ground truth estimations can establish objectivity. The work also offers guidelines for improving reproducibility in deep-learning-based image analysis.

Bepler, T. et al. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11: 5208 (2020).
Topaz-Denoise is a deep learning based model for denoising cryoEM images for improved downstream analysis. The researchers present a general models that are able to denoise new datasets without additional training that can be used to improve cryoEM and cryoET datasets and reduce data collection time. 

Leitner, A. et al. "Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry." Structure (2020).
This white paper comes from 30 academic labs and companies involved in the development and application of cross-linking mass spectrometry (XL-MS). It identifies areas where the field lacks standards, and presents recommendation to increase reliability, transparency, and access of XL-MS experiments and results. The guidelines were developed through numerous discussions within the XL-MS community since 2015.

Lukonin, I. et al. Phenotypic landscape of intestinal organoid regeneration. Nature 586, 275–280 (2020). 
Using an image-based screening assay, Lukonin et al characterized the development of intestinal organoids from single cells in the presence of chemical perturbations. By screening 450,000 organoids, this paper presents a functional map of the interactions that govern intestinal organoid development. 

Liu, Z. et al. Detecting Tumor Antigen-Specific T Cells via Interaction-Dependent Fucosyl-Biotinylation. Cell
Liu et al present FucoID, a method for the detection of endogenous antigen-specific T cells. Tumor antigen-presenting dendritic cells tag interacting T cells with fucosylated biotin which allows the antigen-specific T cell population to be specifically isolated and analyzed.

Weidmann, C.A. et al. Analysis of RNA–protein networks with RNP-MaP defines functional hubs on RNA. Nat Biotechnol. (2020)
Weidmann et al develop a mutational profiling (RNP-MaP) approach to map RNA-protein interactions in live cells. They identified a small molecule that can crosslink protein residues with RNA nucleotides. The crosslinked proteins are digested to short peptide adducts that can introduce non-template nucleotides during the MaP reverse transcription. The researchers are able to map multi-protein interactions within one RNA molecule. 

Vantourout, J.C. et al. Serine-selective bioconjugation. J. Am. Chem. Soc. 142: 17236−17242 (2020). 
Site-specific labeling of proteins has proven invaluable for numerous biochemical assays. Typically, this involves labeling cysteine residues or engineering non-natural amino acids into proteins. This work describes a general method for functionalization of serine residues in native polypeptides and proteins. The approach uses a reagent platform based on the P(V) oxidation state and can be used to append a variety of cargo onto serine generating a phosphorothioate linkage.

Gao, Y. et al. Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nat Commun. 11, 5131 (2020).
Biased representation of different ethnic groups in biomedical data can cause disparity in basic and clinical areas. By analyzing cancer omics data, Gao et al find that the performance of Artificial Intelligence (AI) models shows substantial ethnic-level variation caused by data inequality and data distribution discrepancies between ethnic groups. The authors develop a deep transfer learning strategy to mitigate this effect.

Zhou, D. et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet 52, 1239–1246 (2020).
Transcriptome-wide association studies (TWASs) are powerful strategies for prioritizing candidate genes underlying complex traits and diseases. Zhou et al present a statistical framework that integrates joint-tissue imputation (JTI) and Mendelian randomization (MR) for TWAS and causal inference.

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