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Integrated intracellular organization and its variations in human iPS cells

Author

Listed:
  • Matheus P. Viana

    (Allen Institute for Cell Science)

  • Jianxu Chen

    (Allen Institute for Cell Science)

  • Theo A. Knijnenburg

    (Allen Institute for Cell Science)

  • Ritvik Vasan

    (Allen Institute for Cell Science)

  • Calysta Yan

    (Allen Institute for Cell Science)

  • Joy E. Arakaki

    (Allen Institute for Cell Science)

  • Matte Bailey

    (Allen Institute for Cell Science)

  • Ben Berry

    (Allen Institute for Cell Science)

  • Antoine Borensztejn

    (Allen Institute for Cell Science)

  • Eva M. Brown

    (Allen Institute for Cell Science)

  • Sara Carlson

    (Allen Institute for Cell Science)

  • Julie A. Cass

    (Allen Institute for Cell Science)

  • Basudev Chaudhuri

    (Allen Institute for Cell Science)

  • Kimberly R. Cordes Metzler

    (Allen Institute for Cell Science)

  • Mackenzie E. Coston

    (Allen Institute for Cell Science)

  • Zach J. Crabtree

    (Allen Institute for Cell Science)

  • Steve Davidson

    (Allen Institute for Cell Science)

  • Colette M. DeLizo

    (Allen Institute for Cell Science)

  • Shailja Dhaka

    (Allen Institute for Cell Science)

  • Stephanie Q. Dinh

    (Allen Institute for Cell Science)

  • Thao P. Do

    (Allen Institute for Cell Science)

  • Justin Domingus

    (Allen Institute for Cell Science)

  • Rory M. Donovan-Maiye

    (Allen Institute for Cell Science)

  • Alexandra J. Ferrante

    (Allen Institute for Cell Science)

  • Tyler J. Foster

    (Allen Institute for Cell Science)

  • Christopher L. Frick

    (Allen Institute for Cell Science)

  • Griffin Fujioka

    (Allen Institute for Cell Science)

  • Margaret A. Fuqua

    (Allen Institute for Cell Science)

  • Jamie L. Gehring

    (Allen Institute for Cell Science)

  • Kaytlyn A. Gerbin

    (Allen Institute for Cell Science)

  • Tanya Grancharova

    (Allen Institute for Cell Science)

  • Benjamin W. Gregor

    (Allen Institute for Cell Science)

  • Lisa J. Harrylock

    (Allen Institute for Cell Science)

  • Amanda Haupt

    (Allen Institute for Cell Science)

  • Melissa C. Hendershott

    (Allen Institute for Cell Science)

  • Caroline Hookway

    (Allen Institute for Cell Science)

  • Alan R. Horwitz

    (Allen Institute for Cell Science)

  • H. Christopher Hughes

    (Allen Institute for Cell Science)

  • Eric J. Isaac

    (Allen Institute for Cell Science)

  • Gregory R. Johnson

    (Allen Institute for Cell Science)

  • Brian Kim

    (Allen Institute for Cell Science)

  • Andrew N. Leonard

    (Allen Institute for Cell Science)

  • Winnie W. Leung

    (Allen Institute for Cell Science)

  • Jordan J. Lucas

    (Allen Institute for Cell Science)

  • Susan A. Ludmann

    (Allen Institute for Cell Science)

  • Blair M. Lyons

    (Allen Institute for Cell Science)

  • Haseeb Malik

    (Allen Institute for Cell Science)

  • Ryan McGregor

    (Allen Institute for Cell Science)

  • Gabe E. Medrash

    (Allen Institute for Cell Science)

  • Sean L. Meharry

    (Allen Institute for Cell Science)

  • Kevin Mitcham

    (Allen Institute for Cell Science)

  • Irina A. Mueller

    (Allen Institute for Cell Science)

  • Timothy L. Murphy-Stevens

    (Allen Institute for Cell Science)

  • Aditya Nath

    (Allen Institute for Cell Science)

  • Angelique M. Nelson

    (Allen Institute for Cell Science)

  • Sandra A. Oluoch

    (Allen Institute for Cell Science)

  • Luana Paleologu

    (Allen Institute for Cell Science)

  • T. Alexander Popiel

    (Allen Institute for Cell Science)

  • Megan M. Riel-Mehan

    (Allen Institute for Cell Science)

  • Brock Roberts

    (Allen Institute for Cell Science)

  • Lisa M. Schaefbauer

    (Allen Institute for Cell Science)

  • Magdalena Schwarzl

    (Allen Institute for Cell Science)

  • Jamie Sherman

    (Allen Institute for Cell Science)

  • Sylvain Slaton

    (Allen Institute for Cell Science)

  • M. Filip Sluzewski

    (Allen Institute for Cell Science)

  • Jacqueline E. Smith

    (Allen Institute for Cell Science)

  • Youngmee Sul

    (Allen Institute for Cell Science)

  • Madison J. Swain-Bowden

    (Allen Institute for Cell Science)

  • W. Joyce Tang

    (Allen Institute for Cell Science)

  • Derek J. Thirstrup

    (Allen Institute for Cell Science)

  • Daniel M. Toloudis

    (Allen Institute for Cell Science)

  • Andrew P. Tucker

    (Allen Institute for Cell Science)

  • Veronica Valencia

    (Allen Institute for Cell Science)

  • Winfried Wiegraebe

    (Allen Institute for Cell Science)

  • Thushara Wijeratna

    (Allen Institute for Cell Science)

  • Ruian Yang

    (Allen Institute for Cell Science)

  • Rebecca J. Zaunbrecher

    (Allen Institute for Cell Science)

  • Ramon Lorenzo D. Labitigan

    (University of Washington
    Stanford University)

  • Adrian L. Sanborn

    (Stanford University
    Stanford University)

  • Graham T. Johnson

    (Allen Institute for Cell Science)

  • Ruwanthi N. Gunawardane

    (Allen Institute for Cell Science)

  • Nathalie Gaudreault

    (Allen Institute for Cell Science)

  • Julie A. Theriot

    (University of Washington)

  • Susanne M. Rafelski

    (Allen Institute for Cell Science)

Abstract

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour3,4—at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

Suggested Citation

  • Matheus P. Viana & Jianxu Chen & Theo A. Knijnenburg & Ritvik Vasan & Calysta Yan & Joy E. Arakaki & Matte Bailey & Ben Berry & Antoine Borensztejn & Eva M. Brown & Sara Carlson & Julie A. Cass & Basu, 2023. "Integrated intracellular organization and its variations in human iPS cells," Nature, Nature, vol. 613(7943), pages 345-354, January.
  • Handle: RePEc:nat:nature:v:613:y:2023:i:7943:d:10.1038_s41586-022-05563-7
    DOI: 10.1038/s41586-022-05563-7
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    Cited by:

    1. James Burgess & Jeffrey J. Nirschl & Maria-Clara Zanellati & Alejandro Lozano & Sarah Cohen & Serena Yeung-Levy, 2024. "Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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