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Now is the time to enter Nikon's Small World Photo and Video Competitions. April 30th is the deadline to submit your images and movies taken through the light microscope. In celebration of Nikon's 100th anniversary, the grand prizewinners will receive $3000 and a trip to Japan. The lucky winners will also receive a private tour of Nikon's glass and lens manufacturing facilities.
One of the goals of biological imaging is to watch biological processes where they occur – within living tissue or even within a living animal. But in vivo imaging presents a set of challenges that are not encountered when imaging relatively small, flat samples like cells. In this Focus issue, we bring together papers on methods for optical imaging within living tissue. Two Reviews discuss: microscopy methods for functional brain imaging and the principles and practicalities of the different flavours of light sheet microscopy. A Perspective describes the use of adaptive optics to correct aberrations in scattering samples. This issue also includes primary research papers on multiphoton microscopy, an editorial that reminds our readers of the many areas of this large and exciting field that we do not cover, and a collection of recent papers on deep imaging from Nature Research journals.
Automatic tracing of ultra-volumes of neuronal images pp332 - 333 Hanchuan Peng, Zhi Zhou, Erik Meijering, Ting Zhao, Giorgio A Ascoli et al. doi:10.1038/nmeth.4233 Automated tracing algorithms can extract neuronal morphology from fluorescent or brightfield images. UltraTracer scales up the capability of existing tracing algorithms to handle datasets of ever-increasing size.
Focus on deep imaging of live tissue In vivo imaging of neural activity pp349 - 359 Weijian Yang and Rafael Yuste doi:10.1038/nmeth.4230 Yang and Yuste review currently available technologies for optical imaging of neural circuits, comparing them to help researchers choose optimal ones for their applications.
Power analysis of single-cell RNA-sequencing experiments pp381 - 387 Valentine Svensson, Kedar Nath Natarajan, Lam-Ha Ly, Ricardo J Miragaia, Charlotte Labalette et al. doi:10.1038/nmeth.4220 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.
Optogenetic control with a photocleavable protein, PhoCl pp391 - 394 Wei Zhang, Alexander W Lohman, Yevgeniya Zhuravlova, Xiaocen Lu, Matthew D Wiens et al. doi:10.1038/nmeth.4222 A photocleavable protein (PhoCl) that spontaneously dissociates into two fragments after illumination with violet light expands the toolkit for cellular optogenetics.
Detecting DNA cytosine methylation using nanopore sequencing pp407 - 410 Jared T Simpson, Rachael E Workman, P C Zuzarte, Matei David, L J Dursi et al. doi:10.1038/nmeth.4184 A hidden Markov model (HMM)-based tool enables detection of 5-methylcytosine (5-mC) from single-molecule nanopore-sequencing data generated directly from human genomic DNA without chemical treatment.
Mapping DNA methylation with high-throughput nanopore sequencing pp411 - 413 Arthur C Rand, Miten Jain, Jordan M Eizenga, Audrey Musselman-Brown, Hugh E Olsen et al. doi:10.1038/nmeth.4189 A tool based on hidden Markov model and hierarchical Dirichlet process (HMM-HDP) can call two methylated cytosine variants and a methylated adenine variant directly from genomic DNA using nanopore sequencing data.
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Focus on deep imaging of live tissue Volumetric two-photon imaging of neurons using stereoscopy (vTwINS) pp420 - 426 Alexander Song, Adam S Charles, Sue Ann Koay, Jeff L Gauthier, Stephan Y Thiberge et al. doi:10.1038/nmeth.4226 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.
Automated synaptic connectivity inference for volume electron microscopy pp435 - 442 Sven Dorkenwald, Philipp J Schubert, Marius F Killinger, Gregor Urban, Shawn Mikula et al. doi:10.1038/nmeth.4206 SyConn is a computational framework that infers the synaptic wiring of neurons in volume electron microscopy data sets with machine learning. It has been applied to zebra finch, mouse and zebrafish neuronal tissue samples.
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