Figure 1.
Development of a 3D functional genomic screen to examine ARF GTPase contribution to collective cancer cell behavior. (A) Cartoon, ARF GTPase cycle. (B) Cartoon, pLKO.4-mem:Venus shRNA lentiviral vector. PGK, phosphoglycerate kinase. WPRE, Woodchuck Hepatitis Virus post-transcriptional regulatory element. (C) Schema, distribution of ARFome components in shRNA library. (D) Schema, 96-well based lentiviral infection of ARFome shRNA into PC3 cells that were then cultured as heterogeneous 3D acini in ECM. (E) Images of PC3 acini expressing pLKO.4-mem:Venus Scr shRNA. Yellow outlines, mem:Venus-positive acini. Scale bars, 300 μm. Magnified images of boxed regions (a–c) show acini at various times. Scale bars, 100 μm. White or red arrowheads, presence or absence of mem:Venus, respectively. (F) Schema, heterogeneous PC3 acini imaged over time vary in size, shape, and movement characteristics. Properties measured and information extracted for thousands of mem:Venus-positive acini. (G) Schema, phase images of three acini (yellow outlines) exhibiting morphological heterogeneity (Round, Spindle, Spread). Machine learning used to classify phenotypic states, train for accuracy, and generate user-defined rules. Rules then applied to all datasets; Round, Spindle, or Spread (red, green, blue outlines, respectively) and changes in global state frequency tracked over time. Scale bars, 100 μm. (H) PC3 acini (yellow outlines) and their user-defined classifications, Round, Spindle, or Spread (red, green, blue outlines, respectively). Scale bars, 300 μm. Magnified images of boxed regions (a–c) show classification of heterogeneous acini at various times. Scale bars, 100 μm. Cartoon, changes in user-defined classification over time.

Development of a 3D functional genomic screen to examine ARF GTPase contribution to collective cancer cell behavior. (A) Cartoon, ARF GTPase cycle. (B) Cartoon, pLKO.4-mem:Venus shRNA lentiviral vector. PGK, phosphoglycerate kinase. WPRE, Woodchuck Hepatitis Virus post-transcriptional regulatory element. (C) Schema, distribution of ARFome components in shRNA library. (D) Schema, 96-well based lentiviral infection of ARFome shRNA into PC3 cells that were then cultured as heterogeneous 3D acini in ECM. (E) Images of PC3 acini expressing pLKO.4-mem:Venus Scr shRNA. Yellow outlines, mem:Venus-positive acini. Scale bars, 300 μm. Magnified images of boxed regions (a–c) show acini at various times. Scale bars, 100 μm. White or red arrowheads, presence or absence of mem:Venus, respectively. (F) Schema, heterogeneous PC3 acini imaged over time vary in size, shape, and movement characteristics. Properties measured and information extracted for thousands of mem:Venus-positive acini. (G) Schema, phase images of three acini (yellow outlines) exhibiting morphological heterogeneity (Round, Spindle, Spread). Machine learning used to classify phenotypic states, train for accuracy, and generate user-defined rules. Rules then applied to all datasets; Round, Spindle, or Spread (red, green, blue outlines, respectively) and changes in global state frequency tracked over time. Scale bars, 100 μm. (H) PC3 acini (yellow outlines) and their user-defined classifications, Round, Spindle, or Spread (red, green, blue outlines, respectively). Scale bars, 300 μm. Magnified images of boxed regions (a–c) show classification of heterogeneous acini at various times. Scale bars, 100 μm. Cartoon, changes in user-defined classification over time.

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