Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

TUNEL Assay: A Powerful Tool for Kidney Injury Evaluation

Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay is a long-established assay used to detect cell death-associated DNA fragmentation (3'-OH DNA termini) by endonucleases. Because these enzymes are particularly active in the kidney, TUNEL is widely used to identify and quantify DNA fragmentation and cell death in cultured kidney cells and animal and human kidneys resulting from toxic or hypoxic injury. The early characterization of TUNEL as an apoptotic assay has led to numerous misinterpretations of the mechanisms of kidney cell injury. Nevertheless, TUNEL is becoming increasingly popular for kidney injury assessment because it can be used universally in cultured and tissue cells and for all mechanisms of cell death. Furthermore, it is sensitive, accurate, quantitative, easily linked to particular cells or tissue compartments, and can be combined with immunohistochemistry to allow reliable identification of cell types or likely mechanisms of cell death. Traditionally, TUNEL analysis has been limited to the presence or absence of a TUNEL signal. However, additional information on the mechanism of cell death can be obtained from the analysis of TUNEL patterns.

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Background

Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia.

Methods

Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF⁺ and VEGF⁻ cells.

Results

VEGF was detected and cells were counted with high sensitivity and specificity.

Conclusion

With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.

Comparison between two programs for image analysis, machine learning and subsequent classification

In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the first programs for image analysis that included morphological parameters was ImageJ, published in 1997. One of the newer programs for image analysis that is not limited to fluorescence images is the free software CellProfiler. In 2008, the same group published a new software, CellProfiler Analyst. One part of CellProfiler Analyst is a supervised machine-learning-based classifier that allows users to conduct imaging-based diagnoses, e.g., cellular diagnosis based on morphology. Another relatively new, free software for image analysis is QuPath. The aim of the present study was to compare two free programs for conducting image analysis, CellProfiler and QuPath, and the subsequent classification based on machine learning. For this study, images of renal tissue were analyzed, and the identified objects were classified. The same images were loaded in both software programs. Advanced statistical analysis was used to compare the two methods. The Bland-Altman assay showed that all of the differences were within the mean ± 1.96 * standard deviation, i.e., the differences are normally distributed, and the software programs are comparable. For the analyzed samples (renal tissue stained with HIF and TUNEL), the use of QuPath was easier because it offers image analysis without a previous processing of the images (e.g., conversion to grayscale, inverted intensities) and an unsupervised machine learning process.

Oxidative Stress Modifies the Levels and Phosphorylation State of Tau Protein in Human Fibroblasts

Since the tau protein is closely involved in the physiopathology of Alzheimer's disease (AD), studying its behavior in cellular models might lead to new insights on understanding this devastating disease at molecular levels. In the present study, primary cultures of human fibroblasts were established and used to determine the expression and localization of the tau protein in distinct phosphorylation states in both untransfected and tau gene-transfected cells subjected to oxidative stress. Higher immunopositivity to phospho-tau was observed in cell nuclei in response to oxidative stress, while the levels of total tau in the cytosol remained unchanged. These findings were observed in both untransfected cells and those transfected with the tau gene. The present work represents a useful model for studying the physiopathology of AD at the cellular level in terms of tau protein implications.