Development of image analysis software for quantification of viable cells in microchips

GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Open-source method of image cytometry in dorsal root ganglia tissue with immunofluorescence

Immunohistochemistry (IHC) is a valuable tool in clinical and biological research for evaluating proteins and other antigens in spatially bound tissue. In neuroinflammatory pain research, primary afferent neurons of the dorsal root ganglion (DRG) are studied to understand molecular signaling mechanisms involved in nociception (pain) and inflammation. Measuring IHC (immunofluorescence) in DRG neurons requires manual hand tracing of nuclear and somatic boundaries, which is laborious, error-prone, and may require several weeks to collect the appropriate sample size with a mouse or pen-input display monitor. To overcome these limitations and increase standardization of sampling and measurement, we employed a reliable neuronal cytoplasmic reporter, exclusive to DRG neuronal soma, in a semi-automated algorithm-based approach of Image Cytometry in rat DRG (IC-DRG). The resulting output images are binary nuclear and somatic masks of DRG neurons, defining boundaries of measurement for CellProfiler and manually scored at 94% accurate. Herein, we successfully show a novel approach of automated image analysis for DRG neurons using a robust ImageJ/FIJI script, overcoming morphological variability and imaging artifacts native to imaging frozen tissue sections processed with immunofluorescence.

Long-term single-cell passaging of human iPSC fully supports pluripotency and high-efficient trilineage differentiation capacity

Objectives:

To establish a straightforward single-cell passaging cultivation method that enables high-quality maintenance of human induced pluripotent stem cells without the appearance of karyotypic abnormalities or loss of pluripotency.

Methods:

Cells were kept in culture for over 50 passages, following a structured chronogram of passage and monitoring cell growth by population doubling time calculation and cell confluence. Standard procedures for human induced pluripotent stem cells monitoring as embryonic body formation, karyotyping and pluripotency markers expression were evaluated in order to assess the cellular state in long-term culture. Cells that underwent these tests were then subjected to differentiation into keratinocytes, cardiomyocytes and definitive endoderm to evaluate its differentiation capacity.

Results:

Human induced pluripotent stem cells clones maintained its pluripotent capability as well as chromosomal integrity and were able to generate derivatives from the three germ layers at high passages by embryoid body formation and high-efficient direct differentiation into keratinocytes, cardiomyocytes and definitive endoderm.

Conclusions:

Our findings support the routine of human induced pluripotent stem cells single-cell passaging as a reliable procedure even after long-term cultivation, providing healthy human induced pluripotent stem cells to be used in drug discovery, toxicity, and disease modeling as well as for therapeutic approaches.

Estimating Bacterial Concentrations in Fibrous Substrates Through a Combination of Scanning Electron Microscopy and ImageJ

Conventional signal-based microanalytical techniques for estimating bacterial concentrations are often susceptible to false signals. A visual quantification, therefore, may compliment such techniques by providing additional information and support better management decisions in the event of outbreaks. Herein, we explore a method that combines electron microscopy (EM) and image-analysis techniques and allows both visualization and quantification of pathogenic bacteria adherent even to complex nonuniform substrates. Both the estimation and imaging parameters were optimized to reduce the estimation error ( E, %) to close to ±5%. The method was validated against conventional microbiological techniques such as the use of optical density, flow cytometry, and quantitative real-time PCR (qPCR). It could easily be tailored to estimate different species of pathogens, such as Escherichia coli O157, Listeria innocua, Staphylococcus aureus, Enterococcus faecalis, and Bacillus anthracis, on samples similar to those in real-time contamination scenarios. The present method is sensitive enough to detect ∼100 bacterial CFU/mL but has the potential to estimate even lower concentrations with increased imaging and computation times. Overall, this imaging-based method may greatly complement any signal-based pathogen-detection technique, especially in negating false signals, and therefore may significantly contribute to the field of analytical microbiology and biochemistry.