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Yang and Yuste review currently available technologies for optical imaging of neural circuits, comparing them to help researchers choose optimal ones for their applications.
Single-cell consensus clustering (SC3) provides user-friendly, robust and accurate cell clustering as well as downstream analysis for single-cell RNA-seq data.
A mass spectrometry–based method to pinpoint UV-induced crosslinks in ribonucleoprotein complexes at protein residue and RNA nucleotide resolution provides key structural information for integrative modeling.
vTwINS enables high-speed volumetric calcium imaging via a V-shaped point spread function and a dedicated data-processing algorithm. Song et al. apply this strategy to image population activity in the mouse visual cortex and hippocampus.
A lysine-less, internally affinity-tagged ubiquitin construct is deployed to discover linear polyubiquitinated substrates via a mass-spectrometry-based proteomics approach.
A library of plasmids expressing two gRNAs allows for the mapping of combinatorial genetic interactions with the CRISPR system. Results in cancer cells suggest that cellular context is an important factor for the interaction network.
Single-cell multiple displacement amplification (SCMDA) and a tool for single-nucleotide-variant calling (SCcaller) dramatically decrease artifacts in genome-wide variant calling from single cells.
New fluorescent biosensors enable the first super-resolution imaging of enzyme activity in live cells via fluorescence fluctuation increase by contact (FLINC).
Selection-linked integration in the Plasmodium genome allows for rapid selection of genes inserted at a genomic locus or induction of the inactivation of gene products.
A photocleavable protein (PhoCl) that spontaneously dissociates into two fragments after illumination with violet light expands the toolkit for cellular optogenetics.
The SIMLR software identifies similarities between cells across a range of single-cell RNA-seq data, enabling effective dimension reduction, clustering and visualization.
Salmon is a computational tool that uses sample-specific models and a dual-phase inference procedure to correct biases in RNA-seq data and rapidly quantify transcript abundances.
A comparison framework applied to 15 single-cell RNA-seq protocols reveals differences in accuracy and sensitivity and discusses the utility of RNA spike-in standards.