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Oct 25, 2021

Volume 1Issue 6
Open Access
On the cover: Brain-wide imaging at high resolution to trace axonal arbors of single neurons to their termini presents a huge challenge, as this typically requires 1 or 2 weeks of continuous imaging. In this issue, Chen et al. develop SMART, a platform to rapidly image brain-wide neural projections at synaptic resolution by taking advantage of the sparsity of signals. The cover image shows fluorescence of about three dozen neurons in the primary visual cortex and their brain-wide projections collected using the SMART system in about 10 hours. Cover image by Yuexin Yang....
On the cover: Brain-wide imaging at high resolution to trace axonal arbors of single neurons to their termini presents a huge challenge, as this typically requires 1 or 2 weeks of continuous imaging. In this issue, Chen et al. develop SMART, a platform to rapidly image brain-wide neural projections at synaptic resolution by taking advantage of the sparsity of signals. The cover image shows fluorescence of about three dozen neurons in the primary visual cortex and their brain-wide projections collected using the SMART system in about 10 hours. Cover image by Yuexin Yang.
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  • Practical machine learning for disease diagnosis

    • Huw D. Summers
    Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.

Review

  • Computational tools for analyzing single-cell data in pluripotent cell differentiation studies

    • Jun Ding,
    • Amir Alavi,
    • Mo R. Ebrahimkhani,
    • Ziv Bar-Joseph
    Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.

Report

  • A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

    • Corin F. Otesteanu,
    • Martina Ugrinic,
    • Gregor Holzner,
    • Yun-Tsan Chang,
    • Christina Fassnacht,
    • Emmanuella Guenova,
    • Stavros Stavrakis,
    • Andrew deMello,
    • Manfred Claassen
    The requirement of manually labeled single-cell images for training of machine learning models severely limits their application in clinical diagnosis strategies. Here, Otesteanu et al. present iCellCnn, a weakly supervised deep learning approach for label-free imaging flow cytometry-based blood diagnostics and demonstrates its utility in morphology-based diagnosis of Sézary syndrome.

Articles

  • Probe design for simultaneous, targeted capture of diverse metagenomic targets

    • Zachery W. Dickson,
    • Dirk Hackenberger,
    • Melanie Kuch,
    • Art Marzok,
    • Arinjay Banerjee,
    • Laura Rossi,
    • Jennifer Ann Klowak,
    • Alison Fox-Robichaud,
    • Karen Mossmann,
    • Matthew S. Miller,
    • Michael G. Surette,
    • Geoffrey Brian Golding,
    • Hendrik Poinar
    Dickson et al. present HUBDesign: a pipeline that can be used to design probes for targeted DNA capture in contexts where high levels of background sequences are expected or it is unknown which of a broad set of target organisms of interest will be present.

  • Development of mammalian cell logic gates controlled by unnatural amino acids

    • Emily M. Mills,
    • Victoria L. Barlow,
    • Arwyn T. Jones,
    • Yu-Hsuan Tsai
    Logic gates enable regulation of protein function by small molecules, but these are often drug(-like) molecules with intrinsic biological activities. Mills et al. present an alternative approach through genetic code expansion, demonstrating that unnatural amino acids can act as biologically inert switches for effective mammalian cell logic operations.

  • Timing RNA polymerase pausing with TV-PRO-seq

    • Jie Zhang,
    • Massimo Cavallaro,
    • Daniel Hebenstreit
    Zhang et al. develop a next-generation sequencing method based on PRO-seq that allows genome-wide measurement of pausing times of RNA polymerases at single-base resolution. TV-PRO-seq reveals frequent short pausing events in promoter-proximal regions and uncovers links between pausing times and gene expression as well as chromatin states.

  • An integrated pipeline for mammalian genetic screening

    • Christian Kramme,
    • Alexandru M. Plesa,
    • Helen H. Wang,
    • Bennett Wolf,
    • Merrick Pierson Smela,
    • Xiaoge Guo,
    • Richie E. Kohman,
    • Pranam Chatterjee,
    • George M. Church
    Kramme et al. develop STAMPScreen, an integrated mammalian genetic screening pipeline. The authors develop methods for screening target identification and perturbation tool selection. They develop MegaGate, a toxin-less Gateway cloning tool for ORF library creation. Finally, they demonstrate utilization of STAMPScreen in NGS-coupled readouts for simultaneous transcript and barcode capture.

  • Metatranscriptomics to characterize respiratory virome, microbiome, and host response directly from clinical samples

    • Seesandra V. Rajagopala,
    • Nicole G. Bakhoum,
    • Suman B. Pakala,
    • Meghan H. Shilts,
    • Christian Rosas-Salazar,
    • Annie Mai,
    • Helen H. Boone,
    • Rendie McHenry,
    • Shibu Yooseph,
    • Natasha Halasa,
    • Suman R. Das
    Rajagopala et al. develop a metatranscriptomic approach for simultaneously and directly characterizing the respiratory virome, microbiome, and host response from low biomass clinical samples such as nasal swabs.

  • Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding

    • Veronica F. Busa,
    • Alexander V. Favorov,
    • Elana J. Fertig,
    • Anthony K.L. Leung
    The nearBynding algorithm calculates and visualizes spatial relationships across the transcriptome. Busa et al. demonstrate that nearBynding can recapitulate known protein-binding preferences for structured RNA and RNA modifications as well as known geometries between RNA-binding proteins. nearBynding's spatial correlations provide biological insights into protein binding of G-quadruplexes.

  • Sparse imaging and reconstruction tomography for high-speed high-resolution whole-brain imaging

    • Han Chen,
    • Tianyi Huang,
    • Yuexin Yang,
    • Xiao Yao,
    • Yan Huo,
    • Yu Wang,
    • Wenyu Zhao,
    • Runan Ji,
    • Hongjiang Yang,
    • Zengcai V. Guo
    Brain-wide imaging at synaptic resolution presents a huge challenge in data acquisition and processing. Here, Chen et al. develop sparse imaging and reconstruction tomography (SMART), which enables high-speed high-resolution whole-brain imaging to facilitate single-neuron reconstruction.

  • An automated platform for structural analysis of membrane proteins through serial crystallography

    • Robert D. Healey,
    • Shibom Basu,
    • Anne-Sophie Humm,
    • Cedric Leyrat,
    • Xiaojing Cong,
    • Jérôme Golebiowski,
    • Florine Dupeux,
    • Andrea Pica,
    • Sébastien Granier,
    • José Antonio Márquez
    Membrane proteins control many biological processes and represent attractive targets for drug discovery, but are difficult to study structurally. Healey et al. present an automated approach, combining the CrystalDirect technology and serial crystallography, for rapid structural analysis of membrane proteins and opening new opportunities for high-throughput drug discovery.

  • Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity

    • Ziqi Zhang,
    • Xiuwei Zhang
    Zhang et al. present CellPath, a trajectory inference algorithm that infers cell developmental trajectory by using both single-cell gene expression and RNA velocity information. CellPath finds high-resolution trajectories that can distinguish close lineages and is shown to work with datasets with complex trajectory topology.
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