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SEP 17, 2020 1:30 PM EDT

Decoding the Variance in Intracellular Organization in Human Stem Cells

Speaker
  • Cell Science at Allen Institute
    Biography
      Susanne Rafelski is a quantitative cell biologist and the Director of Assay Development at the Allen Institute for Cell Science. The mission of the institute is to conjoin genome- editing, live cell imaging, genomics, deep learning and computation to create a human stem cell state space and elucidate the mechanisms of state transitions. Prior to joining the Institute in 2016, Susanne was an Assistant Professor in the Department of Developmental and Cell Biology, the Department of Biomedical Engineering, and the Center for Complex Biological Systems at UC Irvine. Susanne began imaging live cells and visualizing intracellular dynamics in 3D when she was 17 and hasn't been able to stop since. Her life-long scientific goal is to decipher the patterns and rules that transform the overwhelming complexity found inside cells into functioning units of life. She believes that to do this we must understand the organization of the structures within the cell in space and time. Susanne takes an interdisciplinary, quantitative approach to cell biology, combining live-cell image-based assays, molecular genetics, and computational methods.

    Abstract

    The Allen Institute for Cell Science is generating a state space of stem cell signatures. The goal is to understand cell organization, identify cell states, and elucidate how cells transition from state to state. We are doing this by conjoining high replicate 3D live cell imaging of cell lines gene-edited with GFP tagged proteins, single cell RNAseq, computational analyses, and visualization. Here we are investigating biological sources of cellular variation to identify the basis functions of a dimensionally-reduced, interpretable parameter space that represents integrated intracellular organization. We used the Allen Cell Structure Segmenter to create accurate 3D segmentations of cells and nuclei in a large, >100k single cell dataset and in multi- hour 3D timelapse movies. We fit extracted cell/nuclear shapes using spherical harmonic functions perform a PCA analysis. We analyzed the contributions of the first 5 primary axes of variation to describing this dimensionally-reduced cell and nuclear shape space. Each shape mode represented a different source of biological variation in hiPS cell colonies and occurred on a distinct timescale. The first two shape modes represented cell growth during the cell cycle and cell colony packing density occurring over several days. The next three axes of variation represent distinct aspects of cell/nuclear shape, such as how elongated these are in the XY plane, which occur over minute timescales due to constant interactions between neighboring cells. We are now applying these analyses to develop biophysical models of cell/nuclear shape and colony dynamics. This general analysis framework will be extended to each of the key intracellular structures in an integrative fashion.


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