Volume 17 Issue 2, February 2020

A love of math and biology comes together with a SPARK.

“With one eye you are looking at the outside world, while with the other you are looking within yourself.” —Amedeo Modigliani

A 3D culture system recapitulates human development prior to gastrulation and enables transcriptomic studies.

Aerolysin nanopores offer a way to identify natural amino acids and their chemical modifications.

A toolkit facilitating FAIRness assessment.

Searching mass spectra against publicly available tandem MS databases just got easier.

Researchers have developed a strategy to collect and profile specific neuronal cell types on the basis of their physiological activity.

Microbiologists stake their claim: assessing climate change involves new ways of studying the ocean’s microbes.

This Perspective highlights open-source software for single-cell analysis released as part of the Bioconductor project, providing an overview for users and developers.

Comprehensive evaluation of algorithms for inferring gene regulatory networks using synthetic and experimental single-cell RNA-seq datasets finds heterogeneous performance and suggests recommendations to users.

Wtdbg2 assembles genomes with comparable contiguity and accuracy to existing tools using long-read sequencing data, and is several times faster, especially for large genomes.

NicheNet uses expression data, in combination with a previous model built on known signaling and gene regulatory networks, to predict ligand–target links in cell-to-cell communications.

An adaptive excitation source enables two- and three-photon imaging of the awake mouse brain with high spatial and temporal resolution at 30-fold-reduced laser power relative to conventional approaches.

An optogenetic strategy enables selection of proteases with improved catalytic rates. The developed TEV protease variants are well suited for biotechnology applications, including FLARE assays with substantially improved temporal resolution.

Protein–peptide interactions that underpin cell signaling are accurately predicted by wedding the strengths of machine learning with the interpretability of biophysical theory, facilitating detailed mechanistic analyses at the proteome scale.

MaSIF, a deep learning-based method, finds common patterns of chemical and geometric features on biomolecular surfaces for predicting protein–ligand and protein–protein interactions.

A statistical method called SPARK for analyzing spatially resolved transcriptomic data can efficiently identify spatially expressed genes with effective control of type I errors and high statistical power.

The unique advantages of single-particle cryo-electron microscopy and cryo-electron tomography are combined in a method called TYGRESS, here applied to determine the structure of the intact ciliary axoneme at a resolution of 12 Å.

A template-free image processing approach automatically detects and classifies membrane-bound protein complexes in cryo-electron tomograms of isolated endoplasmic reticulum and in intact cells.

Advances in MINFLUX nanoscopy enable multicolor imaging over large fields of view, bringing true nanometer-scale fluorescence imaging to labeled structures in fixed and living cells.

4Pi single-molecule switching microscopy combined with ‘salvaged fluorescence’ enables improved ratiometric imaging that bypasses chromatic aberrations and allows for multicolor whole-cell imaging with sub-10-nm localization precision.

A new method of autophagy measurement is based on the detection of phospho-ATG16L1, a conserved early marker of autophagy. Sensitive detection can be achieved in multiple biological systems and assays with advantages over standard methods.