Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing

• Published in: International journal of neural systems 2015-12, Vol.25 (8), p.1550033
• Main Authors: Mari, João Fernando, Saito, José Hiroki, Neves, Amanda Ferreira, Lotufo, Celina Monteiro da Cruz, Destro-Filho, João-Batista, Nicoletti, Maria do Carmo
• Format: Article
• Language: English
• Subjects: Animals; Cell Culture Techniques - instrumentation; Cell Culture Techniques - methods; Cell Size; Cells, Cultured; Classification; Computer simulation; Culture; Fluorescence; Ganglia, Spinal - cytology; Ganglia, Spinal - physiology; Hough transformation; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Image transmission; Information retrieval; Male; Mathematical morphology; Medical imaging; Microelectrodes; Microscopy; Microscopy, Fluorescence - methods; Morphology; Neurons; Neurons - cytology; Neurons - physiology; Parameter estimation; Pattern Recognition, Automated - methods; Process parameters; Qualitative analysis; Quantitative analysis; Rats, Wistar; Recording; Signal processing
• ISSN: 0129-0657; 1793-6462
• DOI: 10.1142/S0129065715500331

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.