Particle tracking is a ubiquitous task in the study of dynamic molecular and cellular processes by live microscopy. Light-sheet microscopy has recently opened a path to acquiring complete cell volumes for investigation in 3-dimensions (3D). However, hypothesis formulation and quantitative analysis have remained difficult due to fundamental challenges in the visualization and the verification of large sets of 3D particle trajectories. Here we describe u-track 3D, a software package that addresses these two challenges with three algorithmic innovations. Building on the established framework of globally optimal particle association in space and time implemented in the u-track package and recent advances in gaining association robustness in the case of erratic motion, we first report a complete and versatile pipeline for particle tracking. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability, which provides for every trajectory a confidence score, thereby overcoming the challenges of visual validation of trajectories in dense particle fields. With these combined strategies, u-track 3D provides a framework for the unbiased study of molecular processes in complex volumetric sequences.

For most human cells, anchorage is a key necessity for survival. Cell-substrate adhesion activates diverse signaling pathways, without which cells undergo anoikis – a form of programmed cell death1. Acquisition of anoikis resistance is a pivotal step in cancer disease progression, as metastasizing cancer cells often lose firm attachment to surrounding tissue2–5. In these poorly attached states, cells often adopt rounded morphologies and form small hemispherical plasma membrane protrusions called blebs6–13. Bleb function has long been investigated in the context of amoeboid migration but is far less deeply examined in other scenarios14–19. Here we show by quantitative subcellular 3D imaging and manipulation of cell morphological states that blebbing triggers the formation of membrane-proximal signaling hubs that initiate signaling cascades leading to anoikis resistance. Specifically, in melanoma cells we discovered that blebbing generates plasma membrane contours that recruit curvature sensing septin proteins, which scaffold constitutively active mutant NRAS and effectors, driving the upregulation of ERK and PI3K signaling. Inhibition of blebs or septins has little effect on the survival of well-adhered cells, but in detached cells causes NRAS mislocalization, reduced MAPK and PI3K signaling, and ultimately, death. These data unveil an unanticipated morphological requirement for mutant NRAS to operate as an effective oncoprotein, suggesting novel clinical targets for the treatment of NRAS-driven melanoma. Furthermore, they define an unforeseen role for blebs as potent signaling organelles capable of integrating myriad cellular information flows into concerted signaling responses, in this case granting robust anoikis resistance.Abstract Figure

The heterogeneity of cancer necessitates developing a multitude of targeted therapies. We propose the view that cancer drug discovery is a low rank tensor completion problem. We implement this vision by using heterogeneous public data to construct a tensor of drug-target-disease associations. We show the validity of this approach computationally by simulations, and experimentally by testing drug candidates. Specifically, we show that a novel drug candidate, SU11652, controls melanoma tumor growth, including BRAF WT melanoma. Independently, we show that another molecule, TC-E 5008, controls tumor proliferation on ex vivo ER+ human breast cancer. Most importantly, we identify these chemicals with only a few computationally selected experiments as opposed to brute-force screens. The efficiency of our approach enables use of ex vivo human tumor assays as a primary screening tool. We provide a web server, the Cancer Vulnerability Explorer (accessible at https://cavu.biohpc.swmed.edu), to facilitate the use of our methodology.