Literature DB >> 31501548

Robust and automated detection of subcellular morphological motifs in 3D microscopy images.

Meghan K Driscoll1,2, Erik S Welf1,2, Andrew R Jamieson1,2, Kevin M Dean1,2, Tadamoto Isogai1,2, Reto Fiolka1,2, Gaudenz Danuser3,4.   

Abstract

Rapid developments in live-cell three-dimensional (3D) microscopy enable imaging of cell morphology and signaling with unprecedented detail. However, tools to systematically measure and visualize the intricate relationships between intracellular signaling, cytoskeletal organization and downstream cell morphological outputs do not exist. Here, we introduce u-shape3D, a computer graphics and machine-learning pipeline to probe molecular mechanisms underlying 3D cell morphogenesis and to test the intriguing possibility that morphogenesis itself affects intracellular signaling. We demonstrate a generic morphological motif detector that automatically finds lamellipodia, filopodia, blebs and other motifs. Combining motif detection with molecular localization, we measure the differential association of PIP2 and KrasV12 with blebs. Both signals associate with bleb edges, as expected for membrane-localized proteins, but only PIP2 is enhanced on blebs. This indicates that subcellular signaling processes are differentially modulated by local morphological motifs. Overall, our computational workflow enables the objective, 3D analysis of the coupling of cell shape and signaling.

Entities:  

Mesh:

Year:  2019        PMID: 31501548      PMCID: PMC7238333          DOI: 10.1038/s41592-019-0539-z

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  16 in total

Review 1.  A guide to machine learning for biologists.

Authors:  Joe G Greener; Shaun M Kandathil; Lewis Moffat; David T Jones
Journal:  Nat Rev Mol Cell Biol       Date:  2021-09-13       Impact factor: 94.444

2.  Sensing the shape of a cell with reaction diffusion and energy minimization.

Authors:  Amit R Singh; Travis Leadbetter; Brian A Camley
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-29       Impact factor: 12.779

3.  In vivo 3D profiling of site-specific human cancer cell morphotypes in zebrafish.

Authors:  Dagan Segal; Hanieh Mazloom-Farsibaf; Bo-Jui Chang; Philippe Roudot; Divya Rajendran; Stephan Daetwyler; Reto Fiolka; Mikako Warren; James F Amatruda; Gaudenz Danuser
Journal:  J Cell Biol       Date:  2022-09-26       Impact factor: 8.077

4.  Tailored approach to study Legionella infection using a lattice light sheet microscope (LLSM).

Authors:  Xiyu Yi; Haichao Miao; Jacky Kai-Yin Lo; Maher M Elsheikh; Tek-Hyung Lee; Chenfanfu Jiang; Yuliang Zhang; Brent W Segelke; K Wesley Overton; Peer-Timo Bremer; Ted A Laurence
Journal:  Biomed Opt Express       Date:  2022-07-07       Impact factor: 3.562

5.  Imaging of Human Cancer Cells in 3D Collagen Matrices.

Authors:  Karin Pfisterer; Brooke Lumicisi; Maddy Parsons
Journal:  Bio Protoc       Date:  2021-01-20

6.  Actin-Membrane Release Initiates Cell Protrusions.

Authors:  Erik S Welf; Christopher E Miles; Jaewon Huh; Etai Sapoznik; Joseph Chi; Meghan K Driscoll; Tadamoto Isogai; Jungsik Noh; Andrew D Weems; Theresa Pohlkamp; Kevin Dean; Reto Fiolka; Alex Mogilner; Gaudenz Danuser
Journal:  Dev Cell       Date:  2020-12-11       Impact factor: 12.270

Review 7.  Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.

Authors:  Hee June Choi; Chuangqi Wang; Xiang Pan; Junbong Jang; Mengzhi Cao; Joseph A Brazzo; Yongho Bae; Kwonmoo Lee
Journal:  Phys Biol       Date:  2021-06-17       Impact factor: 2.959

8.  DynaMorph: self-supervised learning of morphodynamic states of live cells.

Authors:  Zhenqin Wu; Bryant B Chhun; Galina Popova; Syuan-Ming Guo; Chang N Kim; Li-Hao Yeh; Tomasz Nowakowski; James Zou; Shalin B Mehta
Journal:  Mol Biol Cell       Date:  2022-02-09       Impact factor: 3.612

9.  On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.

Authors:  Dennis Segebarth; Matthias Griebel; Christoph M Flath; Robert Blum; Nikolai Stein; Cora R von Collenberg; Corinna Martin; Dominik Fiedler; Lucas B Comeras; Anupam Sah; Victoria Schoeffler; Teresa Lüffe; Alexander Dürr; Rohini Gupta; Manju Sasi; Christina Lillesaar; Maren D Lange; Ramon O Tasan; Nicolas Singewald; Hans-Christian Pape
Journal:  Elife       Date:  2020-10-19       Impact factor: 8.140

Review 10.  Data science in cell imaging.

Authors:  Meghan K Driscoll; Assaf Zaritsky
Journal:  J Cell Sci       Date:  2021-04-01       Impact factor: 5.285

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