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4D cell biology: Big data image analytics and lattice light‐sheet imaging reveal dynamics of clathrin‐mediated endocytosis in stem cell‐derived intestinal organoids
Author(s) -
Schöneberg Johannes,
Dambournet Daphné,
Liu TsungLi,
Forster Ryan,
Hockemeyer Dirk,
Betzig Eric,
Drubin David
Publication year - 2019
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2019.33.1_supplement.659.3
Subject(s) - organoid , stem cell , embryonic stem cell , big data , clathrin , live cell imaging , endocytosis , biology , computational biology , microbiology and biotechnology , computer science , cell , genetics , data mining , gene
New methods in stem cell 3D organoid tissue culture, advanced imaging, and big data image analytics now allow tissue‐scale 4D cell biology, but currently available analytical pipelines are inadequate for handing and analyzing the resulting gigabytes and terabytes of high‐content imaging data. We expressed fluorescent protein fusions of clathrin and dyna‐ min2 at endogenous levels in genome‐edited human embryonic stem cells, which were differentiated into hESC‐derived intestinal epithelial organoids. Lattice light‐sheet imaging with adaptive optics (AO‐LLSM) allowed us to image large volumes of these organoids (70 × 60 × 40 μm xyz) at 5.7 s/frame. We developed an open‐source data analysis package termed pyLattice to process the resulting large (□60 GB) movie data sets and to track clathrin‐mediated endocytosis (CME) events. CME tracks could be recorded from □35 cells at a time, resulting in □4000 processed tracks per movie. On the basis of their localization in the organoid, we classified CME tracks into apical, lateral, and basal events and found that CME dynamics is similar for all three classes, despite reported differences in membrane tension. pyLattice coupled with AO‐LLSM makes possible quantitative high temporal and spatial resolution analysis of subcellular events within tissues. Support or Funding Information J.S. acknowledges a Moore/Sloan Data Science Fellowship from the Berkeley Institute for Data Science and a Siebel Fellowship from the Siebel Stem Cell Institute–UC Berkeley. This work was supported by National Institutes of Health (NIH) Grant R35GM118149 to D.G.D. D.H. is a Pew–Stewart Scholar for Cancer Research supported by the Pew Charitable Trusts and the Alexander and Margaret Stewart Trust. D.H. is supported by the Siebel Stem Cell Institute and NIH R01‐CA196884. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .