Ludovico Fusco

A versatile toolkit to produce sensitive FRET biosensors to visualize signaling in time and space

Next-generation biosensors enable in vivo monitoring of ERK activity and detection of RhoA activity in small cellular extensions. Seeing Signaling in Action Biosensors consist of two fluorophores that produce a light signal when in close proximity and a “sensing module,” which is a protein (or protein fragment) that detects a signaling event, such as the activated state of a guanosine triphosphatase (GTPase) or the activity of a kinase. Producing optimal biosensors to monitor specific signaling events is challenging and time-consuming. Fritz et al. constructed a library of FRET vectors that enabled the rapid generation of highly effective biosensors and created an improved biosensor for RhoA GTPase activity that was used to detect spatial regulation of RhoA activity in filopodia and neuronal growth cones and another that monitors activity of the mitogen-activated protein kinase ERK and was used to detect the activity of this enzyme in living zebrafish. Genetically encoded, ratiometric biosensors based on fluorescence resonance energy transfer (FRET) are powerful tools to study the spatiotemporal dynamics of cell signaling. However, many biosensors lack sensitivity. We present a biosensor library that contains circularly permutated mutants for both the donor and acceptor fluorophores, which alter the orientation of the dipoles and thus better accommodate structural constraints imposed by different signaling molecules while maintaining FRET efficiency. Our strategy improved the brightness and dynamic range of preexisting RhoA and extracellular signal–regulated protein kinase (ERK) biosensors. Using the improved RhoA biosensor, we found micrometer-sized zones of RhoA activity at the tip of F-actin bundles in growth cone filopodia during neurite extension, whereas RhoA was globally activated throughout collapsing growth cones. RhoA was also activated in filopodia and protruding membranes at the leading edge of motile fibroblasts. Using the improved ERK biosensor, we simultaneously measured ERK activation dynamics in multiple cells using low-magnification microscopy and performed in vivo FRET imaging in zebrafish. Thus, we provide a construction toolkit consisting of a vector set, which enables facile generation of sensitive biosensors.

Automated quantification of morphodynamics for high-throughput live cell time-lapse datasets

We present a fully automatic method to track and quantify the morphodynamics of differentiating neurons in fluorescence time-lapse datasets. Previous high-throughput studies have been limited to static analysis or simple behavior. Our approach opens the door to rich dynamic analysis of complex cellular behavior in high-throughput time-lapse data. It is capable of robustly detecting, tracking, and segmenting all the components of the neuron including the nucleus, soma, neurites, and filopodia. It was designed to be efficient enough to handle the massive amount of data from a high-throughput screen. Each image is processed in approximately two seconds on a notebook computer. To validate the approach, we applied our method to over 500 neuronal differentiation videos from a small-scale RNAi screen. Our fully automated analysis of over 7,000 neurons quantifies and confirms with strong statistical significance static and dynamic behaviors that had been previously observed by biologists, but never measured.

Machine learning-based tools to model and to remove the off-target effect

A RNA interference, also called a gene knockdown, is a biological technique which consists of inhibiting a targeted gene in a cell. By doing so, one can identify statistical dependencies between a gene and a cell phenotype. However, during such a gene inhibition process, additional genes may also be modified. This is called the “off-target effect”. The consequence is that there are some additional phenotype perturbations which are “off-target”. In this paper, we study new machine learning tools that both model the cell phenotypes and remove the “off-target effect”. We propose two new automatic methods to remove the “off-target” components from a data sample. The first method is based on vector quantization (VQ). The second method we propose relies on a classification forest. Both methods rely on analyzing the homogeneity of several repetitions of a gene knockdown. The baseline we consider is a Gaussian mixture model whose parameters are learned under constraints with a standard Expectation–Maximization algorithm. We evaluate these methods on a real data set, a semi-synthetic data set, and a synthetic toy data set. The real data set and the semi-synthetic data set are composed of cell growth dynamic quantities measured in time laps movies. The main result is that we obtain the best recognition performance with the probabilistic version of the VQ-based method.

Automated quantification of morphodynamics for high-throughput live cell time-lapse dataset

We present a fully automatic method to track and quantify the morphodynamics of differentiating neurons in uorescence time-lapse microscopy datasets. While previous efforts have successfully quantified the dynamics of organelles such as the cell body, nucleus, or chromosomes of cultured cells, neurons have proved to be uniquely challenging due to their highly deformable neurites which expand, branch, and collapse. Our approach is capable of robustly detecting, tracking, and segmenting all the components of each neuron present in the sequence including the nucleus, soma, neurites, and filopodia. To meet the demands required for high-throughput processing, our framework is designed tobe extremely effcient, capable of processing a single image in approximately two seconds on a conventional notebook computer. For validation of our approach, we analyzed neuronal differentiation datasets in which a set of genes was perturbed using RNA interference. Our analysis confirms previous quantitative findings measured from static images, as well as previous qualitative observations of morphodynamic phenotypes that could not be measured on a large scale. Finally, we present new observations about the behavior of neurons made possible by our quantitative analysis, which are not immediately obvious to a human observer.