ANDA: an open-source tool for automated image analysis of in vitro neuronal cells
ANDA: an open-source tool for automated image analysis of in vitro neuronal cells
Abstract
Background Imaging of in vitro neuronal differentiation and measurements of cell morphologies have led to novel insights into neuronal development. Live-cell imaging techniques and large datasets of images have increased the demand for automated pipelines for quantitative analysis of neuronal morphological metrics.
Results ANDA is an analysis workflow that quantifies various aspects of neuronal morphology from high-throughput live-cell imaging screens of in vitro neuronal cell types. This tool automates the analysis of neuronal cell numbers, neurite lengths and neurite attachment points. We used chicken, rat, mouse, and human in vitro models for neuronal differentiation and have demonstrated the accuracy, versatility, and efficiency of the tool.
Conclusions ANDA is an open-source tool that is easy to use and capable of automated processing from time-course measurements of neuronal cells. The strength of this pipeline is the capability to analyse high-throughput imaging screens.
Background
Background
One of the defining characteristics of the central nervous system is the neuronal interconnectivity which facilitates the cell-to-cell communication required for normal brain function. Establishment of neuronal networks in the developing brain are constituted by neuronal connections and can be influenced by the surrounding glia. Neural development is a spatiotemporally fine-tuned biological process that spans the genesis of neurons to the maturation of functional neural tissues. The differentiation of neural cells is composed of steps such as cellular proliferation, neurite extension, neurite branching, synaptogenesis, and refinement of connections. Modelling neuronal differentiation in a dish can provide new insights into how these connections are formed and altered. Many in vitro neuronal models are in use for genetic and pharmacologic screens such as neuronal differentiation cultures of mouse and human embryonic stem cells, induced pluripotent cells, neuronal stem cells, immortalized tumour cells (human neuroblastoma cells SH-SY5Y), NT2 human embryonal carcinoma cells, PC twelve rat pheochromocytoma cells, and chicken and rodent primary neuronal cultures. When microscopy is applied in these studies, the focus has been on changes to different morphologic parameters of neuronal cells, often in a high-throughput manner. The use of label-free time-course phase contrast microscopy has increased our understanding on the rise, development, and maturation of neuronal networks without being confounded by factors such as phototoxicity. Moreover, live-cell imaging with high spatial and temporal resolution has resulted in a massive increase in data volume and complexity. Image processing of large datasets from high throughput imaging platforms generally involve many steps of image pre-processing, segmentation, phenotype quantification and subsequent analysis. This stresses the need for software applications for large image data sets and automated approaches for reproducibility.
We have developed ANDA, an open-source tool for automated high-throughput image analysis of in-vitro neuronal cell cultures. ANDA is a desktop application built with TAURI that uses Python three scripts for data handling and function-call execution, summoning ImageJ functions from Fiji. ANDA's main advantage is that it offers a graphical user interface that makes it easier to analyze phase contrast images with ImageJ, a process which would otherwise require extensive serial batch macro scripting that would have to be modified on a case-by-case basis.
We show that ANDA can quantify various metrics of three neuronal cell models with distinct differences in morphologies: chicken cerebellar granule neurons, mouse primary neurons, neuronally differentiating rat PC twelve cells, human neuroblastoma SH-SY5Y cells and pre-terminally neuronal differentiated human-derived embryonal carcinoma NTERA two cells. Decisive metrics in neuronal morphology, particularly cell bodies, neurites and neurite attachment points are retrieved and reproducibly quantified, either at single time points or in time series. ANDA is open source under the MIT license and is available on GitHub.