Volume 15 Issue 12, December 2018

It’s hard to say goodbye.

YETI emerges from a dance between genomics and computation.

Inbred mice are preferred over outbred mice because it is assumed that they display less trait variability. We compared coefficients of variation and did not find evidence of greater trait stability in inbred mice. We conclude that contrary to conventional wisdom, outbred mice might be better subjects for most biomedical research.

The concept of massively parallel single-molecule protein sequencing emerges.

Adaptive light-sheet microscopy enables imaging of mouse development through early organogenesis with single-cell resolution.

Acquisition of RNA into CRISPR arrays allows the recording of transcriptional dynamics in cells.

A new computational method integrates RNA single-cell sequencing and spatial data.

Flipping opsins within the membrane diversifies the array of available optogenetics tools.

Users of super-resolution imaging describe how they match probes to imaging modalities

A recent approach for single-cell RNA-sequencing data uses Bayesian deep learning to correct technical artifacts and enable accurate and multifaceted downstream analyses.

A Perspective on super-resolution structured illumination microscopy reviews advances in these methods and focuses on matching user needs in terms of imaging speed, sample depth, and desired resolution with the appropriate instrumentation.

This review discusses and contrasts different acoustic-tweezer technologies and their applications in biology.

NATIVE is a correlative light and electron microscopy approach that is based on nanobody-mediated immunohistochemistry. The approach does not require harsh permeabilization and preserves ultrastructure well.

An AAV-based platform achieves sparse yet bright labeling of neurons with cell-type specificity. This technology will facilitate the reconstruction of neurons in the mouse brain.

A closed-loop all-optical strategy allows manipulation of neurons on the basis of their ongoing activity and can be used to clamp neuronal activity to a preset level, boost sensory-evoked activity or yoke together the activity of trigger and target neurons.

DEMIC uses contigs and coverage data to compare growth rates between bacterial samples.

The combination of positive and negative selection strategies, paired with the use of shRNAs to avoid random integration, allows efficient and scarless CRISPR-based homologous recombination.

YETI puts individual -omics experiments in the context of public genomics data by creating an integrated dataset-specific functional network, thus allowing more thorough interpretation of the data.

scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.

CellDMC finds cell type–specific differential methylation in mixtures of cells, including epithelial tissues, and scenarios in which methylation changes in opposite directions in different cell types.

The machine learning approach FIT leverages public mouse and human expression data to improve the translation of mouse model results to analogous human disease.

Proximity-CLIP, a method that combines proximity-based protein biotinylation and UV crosslinking, profiles RNAs and ribonucleoproteins in any cellular compartment.

A particle-filter algorithm for single-particle cryo-electron microscopy, implemented in a tool called THUNDER, provides high-dimensional parameter estimation, improving the obtainable resolution for several protein structures.

Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.

An ex vivo 3D culture model of mycobacterial granulomas recapitulates the in vivo physiology of these structures and enables longitudinal imaging studies. The platform allows genetic and pharmacological manipulation of this key structure.

VARNAM is a red-shifted genetically encoded voltage sensor based on the Ace opsin. It is applied in Drosophila, mouse brain slices and behaving mice. It can be readily combined with blue-light-sensitive tools for dual-color applications.

Imaging of neuronal activity across the whole zebrafish brain in combination with online analysis allows for manipulating neuronal activity according to function. This approach is used to ablate or activate neurons in fictively swimming zebrafish larvae.