A standardized and automated method of perineuronal net analysis using Wisteria floribunda agglutinin staining intensity. (December 2016)

• Record Type: Journal Article
• Authors: Slaker, Megan L.; Harkness, John H.; Sorg, Barbara A.
• Is Part Of: IBRO reports. Volume 1(2016)
• Page Start: 54
• Page End: 60
• Publication Date: 2016-12
• Subjects: Extracellular matrix -- Perineuronal net -- Wisteria floribunda agglutinin (WFA) -- Automated image analysis; au arbitrary unit -- CPu caudate/putamen -- ECM extracellular matrix -- Hip dorsal hippocampus -- NaN not a number -- OF orbitofrontal cortex -- PL prelimbic -- PFC prefrontal cortex -- PNN perineuronal net -- ROI region of interest -- SD standard deviation -- SEM standard error of the mean -- WFA Wisteria floribunda agglutinin; Nervous system -- Periodicals; Brain -- Periodicals; Brain -- Research -- Periodicals; Nervous system; Brain -- Research; Brain; Neurology; Nervous System Physiological Phenomena; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodical
• DOI: 10.1016/j.ibror.2016.10.001
• Languages: English
• ISSNs: 2451-8301

Abstract

Abstract: Perineuronal nets (PNNs) are aggregations of extracellular matrix molecules that are critical for plasticity. Their altered development or changes during adulthood appear to contribute to a wide range of diseases/disorders of the brain. An increasing number of studies examining the contribution of PNN to various behaviors and types of plasticity have analyzed the fluorescence intensity of Wisteria floribunda agglutinin (WFA) as an indirect measure of the maturity of PNNs, with brighter WFA staining corresponding to a more mature PNN and dim WFA staining corresponding to an immature PNN. However, a clearly-defined and unified method for assessing the intensity of PNNs is critical to allow us to make comparisons across studies and to advance our understanding of how PNN plasticity contributes to normal brain function and brain disease states. Here we examined methods of PNN intensity quantification and demonstrate that creating a region of interest around each PNN and subtracting appropriate background is a viable method for PNN intensity quantification that can be automated. This method produces less variability and bias across experiments compared to other published analyses, and this method increases reproducibility and reliability of PNN intensity measures, which is critical for comparisons across studies in this emerging field. Highlights: PNN intensity quantification varies across the field. The "ROI" method decreases variability among researchers. The "ROI" method can be automated.