Abstract 1305: Classification of cell morphology using machine learning and label-free live-cell imaging

Despite recent remarkable advances in microscopic techniques, it still remains very challenging to directly observe the complex structure of cytoplasmic organelles in live cells without a fluorescent label.

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Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging

10.1101/144238 ◽

2017 ◽

Cited By ~ 1

Author(s):

Chuangqi Wang ◽

Hee June Choi ◽

Sung-Jin Kim ◽

Aesha Desai ◽

Namgyu Lee ◽

...

Keyword(s):

Machine Learning ◽

Live Cell Imaging ◽

Cell Imaging ◽

Leading Edge ◽

Live Cell ◽

Imaging Data ◽

Computational Framework ◽

Cell Protrusion ◽

Learning Analysis ◽

Subcellular Level

AbstractCell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanistic understanding of protrusion activities is usually based on the ensemble average of actin regulator dynamics at the cellular or population levels. Here, we establish a machine learning-based computational framework called HACKS (deconvolution of Heterogeneous Activity Coordination in cytosKeleton at a Subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion in migrating cells. HACKS quantitatively identifies distinct subcellular protrusion phenotypes from highly heterogeneous protrusion activities and reveals their underlying actin regulator dynamics at the leading edge. Furthermore, it can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation. Using our method, we discovered ‘accelerating’ protrusion phenotype in addition to ‘fluctuating’ and ‘periodic’ protrusions. Intriguingly, the accelerating protrusion was driven by the temporally coordinated actions between Arp2/3 and VASP: initiated by Arp2/3-mediated actin nucleation, and then accelerated by VASP-mediated actin elongation. We were able to confirm it by pharmacological perturbations using CK666 and Cytochalasin D, which specifically reduced ‘strong accelerating protrusion’ activities. Taken together, we have demonstrated that HACKS allows us to discover the fine differential coordination of molecular dynamics underlying subcellular protrusion heterogeneity via a machine learning analysis of live cell imaging data.

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Holotomography: refractive index as an intrinsic imaging contrast for 3-D label-free live cell imaging

10.1101/106328 ◽

2017 ◽

Cited By ~ 15

Author(s):

Doyeon Kim ◽

SangYun Lee ◽

Moosung Lee ◽

JunTaek Oh ◽

Su-A Yang ◽

...

Keyword(s):

Refractive Index ◽

Cell Biology ◽

Live Cell Imaging ◽

Cell Imaging ◽

Imaging Techniques ◽

Live Cell ◽

Label Free ◽

Essential Information ◽

Research Fields ◽

Intrinsic Imaging

AbstractLive cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

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