Development of a motion-based cell-counting system for Trypanosoma parasite using a pattern recognition approach

• Published in: BioTechniques Vol. 66; no. 4; pp. 179 - 185
• Main Authors: Takagi, Yuko, Nosato, Hirokazu, Doi, Motomichi, Furukawa, Koji, Sakanashi, Hidenori
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis Group 01.04.2019
• Subjects: cell count; Cell Count - methods; Chagas disease; Chagas Disease - parasitology; Humans; image analysis; Image Processing, Computer-Assisted - methods; Machine Learning; microscopy; Microscopy, Fluorescence - methods; Motion; Optical Imaging - methods; Parasitic Sensitivity Tests - methods; pattern recognition; Pattern Recognition, Automated - methods; Software; Trypanosoma cruzi - cytology; Trypanosoma cruzi - isolation & purification; protozoan parasite; Chagas disease; cell count; microscopy; machine learning; image analysis; pattern recognition
• ISSN: 0736-6205; 1940-9818
• DOI: 10.2144/btn-2018-0163

Automated cell counters that utilize still images of sample cells are widely used. However, they are not well suited to counting slender, aggregate-prone microorganisms such as Trypanosoma cruzi. Here, we developed a motion-based cell-counting system, using an image-recognition method based on a cubic higher-order local auto-correlation feature. The software successfully estimated the cell density of dispersed, aggregated, as well as fluorescent parasites by motion pattern recognition. Loss of parasites activeness due to drug treatment could also be detected as a reduction in apparent cell count, which potentially increases the sensitivity of drug screening assays. Moreover, the motion-based approach enabled estimation of the number of parasites in a co-culture with host mammalian cells, by disregarding the presence of the host cells as a static background.