Re-track: Software to analyze the retraction and protrusion velocities of neurites, filopodia and other structures

A convex-hull based method with manifold projections for detecting cell protrusions

Cell protrusions play an important role in a variety of cell physiological processes. In this paper, we propose a convex-hull based method, combined with manifold projections, to detect cell protrusions. A convex hull is generated based on the cell surface. We consider the cell surface and the boundary of its convex hull as two manifolds, which are diffeomorphic, and define a depth function based on the distance between the cell surface and its convex hull boundary. The extreme points of the depth function represent the positions of cell protrusions. To find the extreme points easily, we project the points on the cell surface onto the boundary of the convex hull and expand them in spherical polar coordinates. We conducted experiments on three types of cell protrusions. The proposed method achieved the average precision of 98.9%, 95.6%, and 94.7% on blebs, filopodia, and lamellipodia, respectively. Experiments on three datasets show that the proposed method has a robust performance.

filoVision - using deep learning and tip markers to automate filopodia analysis

Filopodia are slender, actin-filled membrane projections used by various cell types for environment exploration. Analyzing filopodia often involves visualizing them using actin, filopodia tip or membrane markers. Due to the diversity of cell types that extend filopodia, from amoeboid to mammalian, it can be challenging for some to find a reliable filopodia analysis workflow suited for their cell type and preferred visualization method. The lack of an automated workflow capable of analyzing amoeboid filopodia with only a filopodia tip label prompted the development of filoVision. filoVision is an adaptable deep learning platform featuring the tools filoTips and filoSkeleton. filoTips labels filopodia tips and the cytosol using a single tip marker, allowing information extraction without actin or membrane markers. In contrast, filoSkeleton combines tip marker signals with actin labeling for a more comprehensive analysis of filopodia shafts in addition to tip protein analysis. The ZeroCostDL4Mic deep learning framework facilitates accessibility and customization for different datasets and cell types, making filoVision a flexible tool for automated analysis of tip-marked filopodia across various cell types and user data.

Neuronal filopodia: From stochastic dynamics to robustness of brain morphogenesis

Brain development relies on dynamic morphogenesis and interactions of neurons. Filopodia are thin and highly dynamic membrane protrusions that are critically required for neuronal development and neuronal interactions with the environment. Filopodial interactions are typically characterized by non-deterministic dynamics, yet their involvement in developmental processes leads to stereotypic and robust outcomes. Here, we discuss recent advances in our understanding of how filopodial dynamics contribute to neuronal differentiation, migration, axonal and dendritic growth and synapse formation. Many of these advances are brought about by improved methods of live observation in intact developing brains. Recent findings integrate known and novel roles ranging from exploratory sensors and decision-making agents to pools for selection and mechanical functions. Different types of filopodial dynamics thereby reveal non-deterministic subcellular decision-making processes as part of genetically encoded brain development.

Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images

When studying physical cellular response observed by light microscopy, variations in cell behavior are difficult to quantitatively measure and are often only discussed on a subjective level. Hence, cell properties are described qualitatively based on a researcher’s impressions. In this study, we aim to define a comprehensive approach to estimate the physical cell activity based on migration and morphology based on statistical analysis of a cell population within a predefined field of view and timespan. We present quantitative measurements of the influence of drugs such as cytochalasin D and taxol on human neuroblastoma, SH-SY5Y cell populations. Both chemicals are well known to interact with the cytoskeleton and affect the cell morphology and motility. Being able to compute the physical properties of each cell for a given observation time, requires precise localization of each cell even when in an adhesive state, where cells are not visually differentiable. Also, the risk of confusion through contaminants is desired to be minimized. In relation to the cell detection process, we have developed a customized encoder-decoder based deep learning cell detection and tracking procedure. Further, we discuss the accuracy of our approach to quantify cell activity and its viability in regard to the cell detection accuracy.

Stimulation of the Gαq/phospholipase Cβ1 signaling pathway returns differentiated cells to a stem-like state

Phospholipase Cβ1 is activated by Gαq to generate calcium signals in response to hormones and neurotransmitters. Besides carrying out this plasma membrane function, PLCβ1 has a cytosolic population that helps to drive the differentiation of PC12 cells by inhibiting a nuclease that promotes RNA-induced silencing (C3PO). Here, we show that down-regulating PLCβ1 or reducing its cytosolic population by activating Gαq to localize it to the plasma membrane returns differentiated PC12 and SK-N-SH cells to an undifferentiated state. In this state, PC12 cells have a spherical morphology, resume proliferation, and express the stem cell transcription factors nanog and Oct4. Similar changes are seen when C3PO is down-regulated. This return to a stem-like state is accompanied by shifts in multiple miR populations. Surprisingly, de-differentiation can be induced by extended stimulation of Gαq where cells return to a spherical morphology and levels of specific miRs return to their undifferentiated values. In complementary studies, we followed the real-time hydrolysis of a fluorescent-tagged miR in cells where PLCβ1 or C3PO were down-regulated in PC12 cells and find substantial differences in miR processing in the undifferentiated and differentiated states. Taken together, our studies suggest that PLCβ1, through its ability to regulate C3PO and endogenous miR populations, mediates the differentiation of two types of cultured neuronal cells.