Single-cell approaches for understanding morphogenesis using Computational Morphodynamics

In multicellular organisms cells grow, divide and adopt different fates, resulting in tissues and organs with specific functions. In recent years, a number of studies have brought quantitative knowledge about how these processes are orchestrated, shed-ding new light on cells as active and central players in morphogenesis. We explore recent advances in understanding plant morphogenesis from a quantitative perspective, defining the re-search field of Computational Morphodynamics. The focus is on studies combining theoretical and experimental approaches integrating hypotheses of how molecular and mechanical regulation at the cellular level lead to tissue behaviour. Finally, we dis-cuss some of the main challenges for future work

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

Computing Interpretable Representations of Cell Morphodynamics

Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces

Quantitative automated analysis of host-pathogen interactions

This work aims to broaden knowledge about neutrophil biology in their interaction with fungi species that most frequently cause invasive fungal diseases (IFD). The questions addressed include the alteration of neutrophil morphology after interaction with Candida albicans or C. glabrata, revealing factors that modulate the production and composition of neutrophil-derived extracellular vesicles (EVs) obtained in confrontation assay with conidia of Aspergillus fumigatus and analysing EVs activity against this fungus. Alongside fundamental interests, those questions have important applied aspects in the medicine of IFD. In particular, for diagnostic purposes and infection process monitoring. The results of this work include: 1 a novel segmentation and tracking algorithm which is capable of working with low-contrast cell images, producing accurate cell contours and providing data about positions of clusters, which would improve further analysis; 2 a novel workflow algorithm for analysis of neutrophil continuous morphological spectrum without consensus-based manual annotation; 3 quantitative evidence that morphodynamics of isolated neutrophils depends on the infectious agent (C. albicans or C. glabrata) used in whole blood infection assay; 4 quantitative evidence that neutrophil-derived extracellular vesicles, obtained in confrontation assays with conidia of A. fumigatus could inhibit hyphae development and damage hyphae cell wall; 5 quantitative evidence that EVs inhibition activity is strain-specific

Computer vision profiling of neurite outgrowth mordphodynamics reveals spatio-temporal modularity of Rho GTPase signaling

Neurite outgrowth is essential to build the neuronal processes that produce axons and dendrites that connect the adult brain. In cultured cells, the neurite outgrowth process is highly dynamic, and consists of a series of repetitive morphogenetic sub-processes (MSPs), such as neurite initiation, elongation, branching, growth cone motility and collapse (da Silva and Dotti 2002). Neurons also actively migrate, which might in part reflect neuronal migration during brain development. Each of the different MSPs inherent to neurite outgrowth and cell migration is likely to be regulated by precise spatio-temporal signaling networks that control cytoskeletal dynamics, trafficking and adhesion events. These MSPs can occur on a range of time and length scales. For example, microtubule bundling in the neurite shaft can be maintained during hours, while growth cone filopodia dynamically explore their surrounding on time scales of seconds and length scales of single microns. This implies that a correct understanding of these processes will require analysis with an adequate spatio-temporal resolution. The Rho family of GTPases are signaling switches that regulate a wide variety of cellular processes, such as actin and adhesion dynamics, gene transcription, and neuronal differentiation (Boguski and McCormick 1993). Rho GTPases are activated by guanine nucleotide exchange factors (GEFs), and are switched off by GTPase activating proteins (GAPs). Upon activation, Rho GTPases can associate with effectors to initiate a downstream response. Current models propose that Rac1 and Cdc42 regulate neurite extension, while RhoA controls growth cone collapse and neurite retraction (da Silva and Dotti 2002). However, until now the effects of Rho GTPases on neurite outgrowth have mostly been assessed using protein mutants in steady-state experiments, most often at late differentiation stages, which do not provide any insight about the different MSPs during neurite outgrowth. However, our proteomic analysis of biochemically-purified neurites from N1E-115 neuronal-like cells (Pertz et al. 2008), has suggested the existence of an unexpectedly complex 220 proteins signaling network consisting of multiple GEFs, GAPs, Rho GTPases, effectors and additional interactors. This is inconsistent with the simplistic view that classical experiments have provided before. In order to gain insight into the complexity of this Rho GTPase signaling network, we performed a siRNA screen that targets each of these 220 proteins individually. We hypothesized that specific spatio-temporal Rho GTPase signaling networks control different MSPs occurring during neurite outgrowth, and therefore designed an integrated approach to capture the whole morphodynamic continuum of this process. Perturbations of candidates that lead to a similar phenotype might be part of a given spatio-temporal signaling network. This approach consisted of: 1) A high content microscopy platform that allowed us to produce 8000 timelapse movies of 660 siRNA perturbations; 2) A custom built, computer vision approach that allowed us to automatically segment and track neurite and soma morphodynamics in the timelapse movies (collaboration with the group of Pascal Fua, EPFL, Lausanne); 3) A sophisticated statistical analysis pipeline that allowed the extraction of morphological and morphodynamic signatures (MDSs) relevant to each siRNA perturbation (collaboration with the group of Francois Fleuret, IDIAP). Analysis of our dataset revealed that each siRNA perturbation led to a quantifiable phenotype, emphasizing the quality of our proteomic dataset. Hierarchical clustering of the MDSs revealed the existence of 24 phenoclusters that provide information about neurite length, branching, number of neurites, soma migration speed, and a panel of additional morphological and morphodynamic features that are more difficult to grasp using visual inspection. This complex phenotypic space can more easily be understood when classified according to the first 4 features. Our screen then suggests the existence of 4 major morphodynamic phenotypes that define distinct stages of the neurite outgrowth process. These consist of phenotypes with short neurites, multiple short neurites, long neurites, and long and branched neurites. Further subdivision using the other features provides more information, with cell migration features being very often affected. This implies a high overlap between the signaling machinery that regulates the neurite outgrowth and cell migration processes. The high phenotypical redundancy (24 clusters for 220 candidate genes) provides only limited information to deduce unambiguous signaling networks regulating distinct MSPs. Further knowledge acquired from other approaches we used to study Rho GTPase signaling (FRET biosensors, and other live cell imaging techniques), made us realize that some morphodynamic phenotypes can only be understood when growth cone dynamics are inspected at a much higher resolution. For this purpose, we decided to further investigate a defined subset of genes using high resolution live cell imaging and a custom built growth cone segmentation and tracking pipeline for accurate quantification (collaboration with the group of Gaudenz Danuser, Harvard Medical School, Boston). These results shed light into how distinct cytoskeletal networks enabling growth cone advance can globally impact the neurite outgrowth process. A clear understanding of spatio-temporal Rho GTPase signaling will therefore require multi-scale approaches. Our results provide the first insight into the complexity of spatio-temporal Rho GTPase signaling during neurite outgrowth. The technologies we devised and our initial results, pave the way for a systems biology understanding of these complex signaling systems

Using positional information to provide context for biological image analysis with MorphoGraphX 2.0

Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially-coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here we present recent advances in the MorphoGraphX software (Barbier de Reuille et al., 2015)⁠ that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them

Correlative light and electron microscopy: new strategies for improved throughput and targeting precision

The need for quantitative analysis is crucial when studying fundamental mechanisms in cell biology. Common assays consist of interfering with a system via protein knockdowns or drug treatments. These very often lead to important response variability that is generally addressed by analyzing large populations. Whilst the imaging throughput in light microscopy (LM) is high enough for such large screens, electron microscopy (EM) still lags behind and is not adapted to collect large amounts of data from highly heterogeneous cell populations. Nevertheless, EM is the only technique that offers high-resolution imaging of the entire subcellular context. Correlative light and electron microscopy (CLEM) has made it possible to look at rare events or addressing heterogeneous populations. Our goal is to develop new strategies in CLEM. More specifically, we aim at automatizing the processes of screening large cell populations (living cells or pre-fixed), identifying the sub-populations of interest by LM, targeting these by EM and measuring the key components of the subcellular organization. New 3D-EM techniques like focused ion beam - scanning electron microscopy (FIB-SEM) enable a high degree of automation for the acquisition of high-resolution, full cell datasets. So far, this has only been applied to individual target volumes, often isotropic and has not been designed to acquire multiple regions of interest. The ability to acquire full cells with up to 5 nm x 5 nm x 5 nm voxel size (x, y referring to pixel size, z referring to slice thickness), leads to the accumulation of large datasets. Their analysis involves tedious manual segmentation or so far not well established automated segmentation algorithms. To enable the analysis and quantification of an extensive amount of data, we decided to explore the potential of stereology protocols in combination with automated acquisition in the FIB-SEM. Instead of isotropic datasets, a few evenly spaced sections are used to quantify subcellular structures. Our strategy therefore combines CLEM, 3D-EM and stereology to collect and analyze large amounts of cells selected based on their phenotype as visible by fluorescence microscopy. We demonstrate the power of the approach in a systematic screen of the Golgi apparatus morphology upon alteration of the expression of 10 proteins, plus negative and positive control. In parallel to this core project, we demonstrate the power of combining correlative approaches with 3D-EM for the detailed structural analysis of fundamental cell biology events during cell division and also for the understanding on complex physiological transitions in a multicellular model organism

Mechano-chemical regulation of complex cell shape formation: Epidermal pavement cells—A case study

All plant cells are encased by walls, which provide structural support and control their morphology. How plant cells regulate the deposition of the wall to generate complex shapes is a topic of ongoing research. Scientists have identified several model systems, the epidermal pavement cells of cotyledons and leaves being an ideal platform to study the formation of complex cell shapes. These cells indeed grow alternating protrusions and indentations resulting in jigsaw puzzle cell shapes. How and why these cells adopt such shapes has shown to be a challenging problem to solve, notably because it involves the integration of molecular and mechanical regulation together with cytoskeletal dynamics and cell wall modifications. In this review, we highlight some recent progress focusing on how these processes may be integrated at the cellular level along with recent quantitative morphometric approaches