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.

Automatic detection of hypoxia in renal tissue stained with HIF-1alpha.

OBJECTIVE: The objective of this study was the identification of the stain HIF-alpha using the Image Cytometry, and to help to count the positive cells (with HIF-alpha) and the negative cells (without HIF-alpha) from the same sample. METHOD: 17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis. The acquired images were then analyzed through the free softwares CellProfiler (version 2.1.1) and CellProfiler Analyst (version 2.0). In the software CellProfiler Anlyst, there was a separation with the classes of the object, using a classifier, and the classes were: 1) class with HIF-alpha and 2) class without HIF-alpha. RESULTS: With the data obtained through Score All, it was possible to calculate the percentage of cells that had HIF-alpha; out of 12.587 objects of the sample, 6.773 (54%) had HIF-alpha and 5.814 (46%) did not have HIF-alpha. Data of sensibility 0.90, specificity 0.84 and standard deviation 0.10 and 0.12. CONCLUSION: The research shows that the free software CellProfiler, through the light microscope, was able to identify the stains, perform the machine's learning, and subsequently count and separate cells from distinct classes (with and without the stain of HIF-alpha).

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.

OBJECTIVE: The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation. METHODS: A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software. RESULTS: Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively. CONCLUSION: Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.