RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer

Pascual-Vargas, Patricia; Cooper, Samuel J.; Sero, Julia E.; Bousgouni, Vicky; Arias-Garcia, Mar; Bakal, Chris

doi:10.1038/sdata.2017.18

Cited by 35 publications

(33 citation statements)

References 39 publications

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“…Our long term goal is to disentangle these signaling cascades in the context of collective cell migration. Although the roles of GEFs and their interactions with Rho GTPases are widely studied for single cell migration (Goicoechea et al, 2014;Pascual-Vargas et al, 2017), less is known how they regulate collective migration (Hidalgo-Carcedo et al, 2011;Omelchenko et al, 2014;Plutoni et al, 2016). Here, we report a comprehensive and validated, image-based GEF screen that identified differential roles of GEFs.…”

Section: Introductionmentioning

confidence: 91%

Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration

Zaritsky

Tseng

Rabadan

et al. 2016

Preprint

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Efficient collective migration depends on a balance between contractility and cytoskeletal rearrangements, adhesion, and mechanical cell-cell communication, all controlled by GTPases of the RHO family. By comprehensive screening of guanine nucleotide exchange factors (GEFs) in human bronchial epithelial cell monolayers, we identified GEFs that are required for collective migration at large, such as SOS1 and β-PIX, and RHOA GEFs that are implicated in intercellular communication. Downregulation of the latter GEFs differentially enhanced front-to-back propagation of guidance cues through the monolayer, and was mirrored by downregulation of RHOA expression and myosin-II activity. Phenotype-based clustering of knock-down behaviors identified RHOA-ARHGEF18 and ARHGEF3-ARHGEF28-ARHGEF11 clusters, indicating that the latter may signal through other RHO-family GTPases. Indeed, knock-down of RHOC produced an intermediate between the two phenotypes. We conclude that for effective collective migration the RHOA-GEFs-→ARHOA/C→ actomyosin pathways must be optimally tuned to compromise between generation of motility forces and restriction of intercellular communication.

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Section: Introductionmentioning

confidence: 91%

Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration

Zaritsky

Tseng

Rabadan

et al. 2016

Preprint

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“…To compare the performance of the deep-learned cell descriptors to conventional, shape-based descriptors of cell states (Bakal et al, 2007;Goodman and Carpenter, 2016;Gordonov et al, 2015;Pascual-Vargas et al, 2017;Scheeder et al, 2018;Sero and Bakal, 2017;Yin et al, 2013) we segmented phase contrast cell images of multiple cell types with diverse appearances. We used LEVER (Winter et al, 2016) for this task (Fig.…”

Section: Incorporating Temporal Information To Distinguish Between Cementioning

confidence: 99%

“…The power of cell appearance for determining cell functional states has been the basis of decades of histopathology (Beck et al, 2011;Yuan et al, 2012) and it has also been explicitly established in predicting the state of signaling pathways that are directly implicated in the regulation of cell morphogenesis (Bakal et al, 2007;Goodman and Carpenter, 2016;Gordonov et al, 2015;Pascual-Vargas et al, 2017;Scheeder et al, 2018;Sero and Bakal, 2017;Yin et al, 2013). Other studies used deep neural networks to classify cell cycle states and diabetic retinopathy from fluorescent-labeled cells (Eulenberg et al, 2017), to predict a differentiation marker prior to the actual expression in the cells from live bright-field microscopy (Buggenthin et al, 2017;Orth et al, 2017), and to reconstruct pseudo-lineages from single cell snapshots (Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Zaritsky

Jamieson

Welf

et al. 2020

Preprint

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Deep convolutional neural networks have emerged as a powerful technique to identify hidden patterns in complex cell imaging data. However, these machine learning techniques are often criticized as uninterpretable "black-boxes" -lacking the ability to provide meaningful explanations for the cell properties that drive the machine's prediction. Here, we demonstrate that the latent features extracted from label-free live cell images by an adversarial auto-encoding deep convolutional neural network capture subtle details of cell appearance that allow classification of melanoma cell states, including the metastatic efficiency of seven patientderived xenograft models that reflect clinical outcome. Although trained exclusively on patientderived xenograft models, the same classifier also predicted the metastatic efficiency of immortalized melanoma cell lines suggesting that the latent features capture properties that are specifically associated with the metastatic potential of a melanoma cell regardless of its origin.We used the autoencoder to generate "in-silico" cell images that amplified the cellular features driving the classifier of metastatic efficiency. These images unveiled pseudopodial extensions and increased light scattering as functional hallmarks of metastatic cells. We validated this interpretation by analyzing experimental image time-lapse sequences in which melanoma cells spontaneously transitioned between states indicative of low and high metastatic efficiency.Together, this data is an example of how the application of Artificial Intelligence supports the identification of processes that are essential for the execution of complex integrated cell functions but are too subtle to be identified by a human expert. -Gurion University of the Negev, Israel (to AZ). We thank Sean Morrison for PDX-derived cell models. We thank Andrew R. Cohen for LEVER. Author ContributionAZ, ESW and GD conceived the study. ESW designed the experimental assay, optimized culture conditions for PDX cells, and trained AN and JC to perform the experiments. AN performed all live imaging and mouse experiments. JC performed additional experiments. UE generated the PDX samples. AZ and ARJ developed analytic tools, analyzed, and interpreted the data. AZ drafted the manuscript. All authors wrote and edited the manuscript and approved its content.GD mentored all authors.

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“…3e, f). Thus as in other cell types [33][34][35]42,60 , in LM2 cells morphology determines YAP/TAZ translocation dynamics.…”

Section: Dock5 Depletion Results In Polarity and Adhesion Defectsmentioning

confidence: 99%

Multiplexed quantitative screens of single cell shape and YAP/TAZ localisation identify DOCK5 as a coincident detector of polarity and adhesion during migration

Pascual-Vargas

Arias-Garcia

Roumeliotis

et al. 2020

Preprint

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YAP and TAZ are transcriptional co-activators that are often constitutively active in triple negative breast cancer (TNBC) cells. However the mechanisms responsible for YAP/TAZ dysregulation in TNBC remain unclear. We hypothesised that RhoGEF activity in TNBC cells underpins YAP/TAZ activation in TNBC cells. Through systematic RNAi screening we found that the Focal Adhesion (FA) associated RhoGEF DOCK5 promotes YAP/TAZ nuclear translocation in proliferating TNBC cells. DOCK5 is required for 2D migration and 3D invasion, acting as a coincident detector that maintains cell polarity by stabilising GSK3β activation downstream of CDC42 at polarised leading edges only where FAs are being assembled. DOCK5 and CDC42 are required for cell survival and drug resistance in TNBC cells. Based on these studies we propose that cancer cell phenotypes such as drug resistance can be driven by cells adopting polarised, migratory shapes.

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RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer

Cited by 35 publications

References 39 publications

Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration

Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Multiplexed quantitative screens of single cell shape and YAP/TAZ localisation identify DOCK5 as a coincident detector of polarity and adhesion during migration

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