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Cell Reports Methods
All content is freely available to readers and supported through open access

Nov 22, 2021

Volume 1Issue 7
Open Access
On the cover: Tissue clearing methods have revolutionized neuroscience research. In this issue, Kosmidis et al. present Fast 3D Clear, a technique that renders tissues rapidly and reversibly transparent while preserving a variety of fluorophores. The cover image is inspired by Rene Magritte's artwork Decalcomanie, which parallels transparency to solidity to reveal “a hidden meaning in everything we see.” Similarly, Fast 3D Clear reveals the shape and connection between cells that are otherwise hidden when viewing an opaque brain. Artwork and cover concept by Matteo Farinella....
On the cover: Tissue clearing methods have revolutionized neuroscience research. In this issue, Kosmidis et al. present Fast 3D Clear, a technique that renders tissues rapidly and reversibly transparent while preserving a variety of fluorophores. The cover image is inspired by Rene Magritte's artwork Decalcomanie, which parallels transparency to solidity to reveal “a hidden meaning in everything we see.” Similarly, Fast 3D Clear reveals the shape and connection between cells that are otherwise hidden when viewing an opaque brain. Artwork and cover concept by Matteo Farinella.

Q&A

Reports

Articles

  • Using soft X-ray tomography for rapid whole-cell quantitative imaging of SARS-CoV-2-infected cells

    • Valentina Loconte,
    • Jian-Hua Chen,
    • Mirko Cortese,
    • Axel Ekman,
    • Mark A. Le Gros,
    • Carolyn Larabell,
    • Ralf Bartenschlager,
    • Venera Weinhardt
    High-resolution, rapid imaging techniques are needed to analyze 3D cell architecture for understanding viral disease mechanisms. Here, Loconte et al. use soft X-ray tomography to rapidly image SARS-CoV-2-infected whole cells, opening avenues to analyze virus-cell interactions and efficacy of antiviral drugs in statistically significant numbers.
  • GEM-IL: A highly responsive fluorescent lactate indicator

    • Ramsey Bekdash,
    • Jose R. Quejada,
    • Shunnosuke Ueno,
    • Fuun Kawano,
    • Kumi Morikawa,
    • Alison D. Klein,
    • Kenji Matsumoto,
    • Tetz C. Lee,
    • Koki Nakanishi,
    • Amy Chalan,
    • Teresa M. Lee,
    • Rui Liu,
    • Shunichi Homma,
    • Chyuan-Sheng Lin,
    • Maria V. Yelshanskaya,
    • Alexander I. Sobolevsky,
    • Keisuke Goda,
    • Masayuki Yazawa
    Bekdash et al. report the development of a genetically encoded metabolic indicator for probing lactate (GEM-IL) based on superfolder fluorescent proteins and mutagenesis. With improvements in the design, specificity, and sensitivity, the authors demonstrate GEM-IL's usefulness and functionality compared with previous lactate indicators, Laconic and Green Lindoblum.
  • Genetically encoded biosensors for evaluating NAD+/NADH ratio in cytosolic and mitochondrial compartments

    • Qingxun Hu,
    • Dan Wu,
    • Matthew Walker,
    • Pei Wang,
    • Rong Tian,
    • Wang Wang
    Hu et al. expressed a fluorescent biosensor for NAD+/NADH ratio inside mitochondria. They show that mitochondrial NAD+/NADH redox ratio is separately maintained though connected with cytosolic NAD pools and responds differently to pathophysiological perturbations. The study presents an approach to evaluate NAD+/NADH ratios in subcellular compartments of live cells.
  • A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy

    • Junbong Jang,
    • Chuangqi Wang,
    • Xitong Zhang,
    • Hee June Choi,
    • Xiang Pan,
    • Bolun Lin,
    • Yudong Yu,
    • Carly Whittle,
    • Madison Ryan,
    • Yenyu Chen,
    • Kwonmoo Lee
    Jang et al. develop a deep learning pipeline for segmentation of live cell imaging data. The pipeline includes MARS-Net, a neural network that achieves higher accuracy through training on multiple types of microscopy data. Accurate cell edge identification in each movie frame allows quantification of edge velocities.
  • A deep-learning toolkit for visualization and interpretation of segmented medical images

    • Sambuddha Ghosal,
    • Pratik Shah
    Ghosal and Shah present a deep-learning toolkit for high-accuracy segmentation of tumors and organs. The toolkit includes a range of analytical techniques for statistical evaluation and visual interpretation of pixel-level segmentation of medical images for reproducibility and performance improvement of deep-learning models.
  • Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry

    • Sebastian Jarosch,
    • Jan Köhlen,
    • Rim S.J. Sarker,
    • Katja Steiger,
    • Klaus-Peter Janssen,
    • Arne Christians,
    • Christian Hennig,
    • Ernst Holler,
    • Elvira D'Ippolito,
    • Dirk H. Busch
    Understanding of tissue biology requires in situ analyses of cell phenotypes. Jarosch et al. describe the application of the multiplexed imaging method ChipCytometry to formalin-fixed and paraffin-embedded tissue sections. The high staining quality allowed them to develop a pipeline for automated signal quantifications facilitating reliable and reproducible analyses of high-parameter imaging.
  • Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA

    • James C. Willey,
    • Tom B. Morrison,
    • Bradley Austermiller,
    • Erin L. Crawford,
    • Daniel J. Craig,
    • Thomas M. Blomquist,
    • Wendell D. Jones,
    • Aminah Wali,
    • Jennifer S. Lococo,
    • Nathan Haseley,
    • Todd A. Richmond,
    • Natalia Novoradovskaya,
    • Rebecca Kusko,
    • Guangchun Chen,
    • Quan-Zhen Li,
    • Donald J. Johann Jr.,
    • Ira W. Deveson,
    • Timothy R. Mercer,
    • Leihong Wu,
    • Joshua Xu
    Willey et al. spiked synthetic internal standards (IS) into contrived circulating tumor DNA samples to control for technical error in NGS. IS enabled calculation of technical error rate and limit of detection for each actionable mutation in each sample, increasing the number of measurable true positives without loss of specificity.
  • Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

    • Liang Cao,
    • Viktor Varga,
    • Zhe S. Chen
    Cao et al. develop supervised and unsupervised methods to extract amplitude and phase information from high-density rodent hippocampal electrophysiological recordings and demonstrate their use in position decoding, replay analysis, and decision prediction.

Correction

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