Published January 4, 2016 JCB: ArticleTools Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling Ludovico Fusco,1* Riwal Lefort,2* Kevin Smith,3* Fethallah Benmansour,3 German Gonzalez,3 Caterina Barillari,4 Bernd Rinn,4 Francois Fleuret,2 Pascal Fua,3 and Olivier Pertz1 1Department of Biomedicine, University of Basel, 4058 Basel, Switzerland 2Institut Dalla Molle d'Intelligence Artificielle Perceptive (IDI AP Research Institute), 1920 Martigny, Switzerland 3Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland 4Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule, 4058 Basel, Switzerland Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by Downloaded from specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present Neu- riteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturba- tion of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to jcb.rupress.org opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate differ- ent spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotempo- ral Rho GTPase signaling networks that regulate neurite outgrowth. on January 6, 2016 Introduction Neurite outgrowth is a prerequisite step to produce the axons (Nadarajah and Parnavelas, 2002). Specific spatiotemporal and dendrites that wire the adult brain. Neuronal cell migra- signaling networks control the cytoskeletal, trafficking, and tion is crucial for brain morphogenesis. In cell culture, neurite adhesion dynamics required for each MP to occur. Because outgrowth is highly dynamic and involves a series of dynamic neurite outgrowth and cell migration use overlapping cellular morphogenetic processes (MPs) such as neurite initiation, machineries, it is conceivable that some molecular components elongation, branching, growth cone motility, and collapse (da might regulate both processes. These MPs fluctuate on length Silva and Dotti, 2002). Cultured neuronal cells are also highly and time scales of tens of microns and minutes to hours, which THE JOURNAL OF CELL BIOLOGY motile, which might reflect the process of neuronal migration have been missed in steady-state neurite outgrowth measure- ments, most often at a late differentiation stage. Identification of MP-specific signaling networks thus requires the quantifica- *L. Fusco, R. Lefort, and K. Smith contributed equally to this paper. tion their morphodynamics. Correspondence to Olivier Pertz: [email protected] Rho GTPases are key regulators of the cytoskeletal dy- R. Lefort’s present address is Lab-STI CC (UMR CNRS 6285), ENS TA Bretagne (Universite Europeenne de Bretagne), 29806 Brest Cedex 9, France. namics that regulate neuronal cell morphogenesis (da Silva and K. Smith’s present address is SciLifeLab/KTH Royal Institute of Technology Dotti, 2002). Their activity is tightly controlled in time and School of Computer Science and Communication, 114 28 Stockholm, Sweden. space by guanine nucleotide-exchange factors (GEFs; Rossman F. Benmansour’s present address is F. Hoffmann-La Roche AG, 4070 Basel, et al., 2005) and GTPase-activating proteins (GAPs; Moon and Switzerland. Zheng, 2003) that activate and deactivate GTPases, respectively. G. Gonzalez’s present address is Dept. of Radiology, Brigham and Women’s This regulation, as well as coupling of Rho GTPases to specific Hospital, Boston, MA 02115. downstream effectors, dictates their cytoskeletal output at any O. Pertz’s present address is Institute of Cell Biology, University of Bern, 3012 Bern, Switzerland. given subcellular localization (Pertz, 2010). Current models Abbreviations used in this paper: CV, computer vision; FRET, Förster resonance energy transfer; GAP, GTPase-activating protein; GEF, guanine nucleotide-ex- © 2016 Fusco et al. This article is distributed under the terms of an Attribution–Noncommercial– change factor; HDS, hierarchical data structure; HS, high stringency; KD, knock- Share Alike–No Mirror Sites license for the first six months after the publication date (see down; LS, low stringency; MDS, morphodynamic signature; MLCK, myosin light http ://www .rupress .org /terms). After six months it is available under a Creative Commons chain kinase; MP, morphogenetic process; MRCK, myotonin-related dystrophyn License (Attribution–Noncommercial–Share Alike 3.0 Unported license, as described at kinase myosin light chain kinase; pMLC, myosin light chain phosphorylation. http ://creativecommons .org /licenses /by -nc -sa /3 .0 /). Supplemental Material can be found at: http://jcb.rupress.org/content/suppl/2015/12/31/jcb.201506018.DC1.html The Rockefeller University Press $30.00 J. Cell Biol. Vol. 212 No. 1 91–111 www.jcb.org/cgi/doi/10.1083/jcb.201506018 JCB 91 Published January 4, 2016 state that Rac1 and Cdc42 regulate neurite outgrowth, whereas exhibiting a specific range in fluorescence intensities and cell RhoA controls neurite collapse (da Silva and Dotti, 2002). areas (Fig. S1, h–j). We then time-lapsed neurite outgrowth dy- However, multiple GEFs, GAPs, and effectors are ubiquitously namics in 10 fields of view per well across a 24-well plate with expressed by cells and outnumber their cognate Rho GTPases 12-min time resolution for a total of 19.6 h. (Moon and Zheng, 2003; Rossman et al., 2005). This raises the question of the significance of this signaling complexity. NeuriteTracker pipeline to quantify We present NeuriteTracker, a computer vision (CV) plat- neuronal dynamics form to track neuronal morphodynamics from high-content To analyze the time-lapse datasets, we engineered Neur- time-lapse imaging datasets. Automatic extraction of a large set iteTracker, an automated CV pipeline to segment and track of morphological and morphodynamic features, coupled with soma and neurite morphodynamics described in Materials and adequate statistical analysis, can then quantify the dynamics of methods (Fig. 1, b and c; and Video 2). First, mCherry-NLS– neuronal morphogenesis. Our pipeline identifies distinct, ste- labeled nuclei and their associated somata were identified reotyped morphodynamic phases during neuronal cell morpho- using maximally stable extremal regions and a fast marching genesis and quantifies a set of morphodynamic phenotypes in a region growing step (Fig. 1 c, steps 1 and 2). Second, a graph- siRNA screen targeting a candidate Rho GTPase interactome. association tracking method was applied over the time-lapse This provides insight into the spatiotemporal Rho GTPase sig- data to provide a unique label for each tracked cell over time naling networks regulating distinct MPs. As proof of concept for (Fig. 1 c, steps 3 and 4). Neurites were extracted for each cell our screen, we show that two RhoA-specific GAPs regulate two by applying a calibrated Hessian-based filter, computing a geo- distinct spatiotemporal RhoA signaling networks controlling desic distance combining intensity and geometric distance, different cytoskeletal outputs. Our data provide an initial re- and applying a threshold (Fig. 1 c, steps 5–7). Candidate ter- Downloaded from source to study the complex spatiotemporal Rho GTPase sig- minals were identified, and neurite filaments were detected by naling networks that regulate neuronal cell morphogenesis. backtracing and reconstruction with a minimal spanning tree (Fig. 1 c, step 8). Again applying the graph association to the neurites yielded automatic segmentation and tracking of indi- Results vidual cell nuclei, soma, and neurites (Fig. 2 a and Video 2). Our pipeline provided robust results across a wide variety of fluorescence intensities that result from transient transfection High-content live-cell imaging pipeline jcb.rupress.org To study neuronal dynamics, we used neuronal-like mouse and was evaluated against human annotated ground-truth data- N1E-115 neuroblastoma cells. To visualize cell morphology, sets (Fig. S2 and Videos 3 and 4). we used a bicistronic vector that expresses Lifeact-GFP, a fu- To describe the segmented cells, we defined a series of pa- sion of GFP with the F-actin binding peptide Lifeact (Riedl rameters that describe nucleus, soma, and neurite morphology, et al., 2008), and a nuclear localization NLS-mCherry fusion, the neurite being modeled as a tree arborescence (Figs. 2 b and which labels the nucleus for cell detection (Fig. 1 a). This con- S3; also see Materials and methods, Definition of nuclei and on January 6, 2016 struct can be expressed at a high level without affecting neurite soma parameters and Definition of neurite parameters). These outgrowth (Fig. S1, a and b) and provides homogeneous high data were then organized in a hierarchical data structure (HDS) contrast on neurites and somata for imaging with air objectives