scholarly journals A Simple, Flexible, Nonfluorescent System for the Automated Screening of Neurite Outgrowth

2005 ◽  
Vol 11 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Raymond D. Price ◽  
Tomoya Oe ◽  
Takayuki Yamaji ◽  
Nobuya Matsuoka
Keyword(s):  
Neurite Outgrowth ◽  
Kinase Inhibitor ◽  
Rho Kinase ◽  
Automated Analysis ◽  
Automated System ◽  
Neurite Length ◽  
Multiple Parameters ◽  
Neurotrophic Activity ◽  
Automated Screening ◽  
Analysis System

Measurement of neurite outgrowth is a common assay of neurotrophic activity. However, currently available techniques for measuring neurite outgrowth are either time or resource intensive. The authors established a system in which chronic treatment of a subcloned SH-SY5Y cell line with aphidicolin and various concentrations of nerve growth factor (NGF) induced discernable alterations in proliferation and differentiation. Cells were fixed, labeled with a nonfluorescent dye, and evaluated both manually and with an automated analysis system. NGF increased multiple parameters of differentiation, including neurite length, the proportion of cells extending neurites, and branching, as well as promoting cellular survival/proliferation. Interestingly, although NGF treatment increased the total number of branches, it actually decreased the proportion of branches per neurite length. The authors observed no differences in results obtained using the manual and automated systems, but the automated system was orders of magnitude faster. To demonstrate the flexibility of the system, the authors also show that they could measure changes in differentiation induced by a small-molecule Rho kinase inhibitor, as well as by retinoic acid cotreatment with brain-derived neurotrophic factor. In addition to this flexibility, this system does not require specialized equipment or fluorescent antibodies for analysis and therefore provides a less resource-intensive alternative to fluorescence-based systems.

Automated Screening of Neurite Outgrowth

2003 ◽  
Vol 8 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Peter Ramm ◽  
Yuriy Alexandrov ◽  
Andrzej Cholewinski ◽  
Yuriy Cybuch ◽  
Robert Nadon ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Automated System ◽  
Neurite Length ◽  
Neurotrophic Activity ◽  
Manual Methods ◽  
Automated Screening ◽  
Analysis System ◽  
Automated Algorithms ◽  
Immunofluorescence Labeling

Outgrowth of neurites in culture is used for assessing neurotrophic activity. Neurite measurements have been performed very slowly using manual methods or more efficiently with interactive image analysis systems. In contrast, medium-throughput and noninteractive image analysis of neurite screens has not been well described. The authors report the performance of an automated image acquisition and analysis system (IN Cell Analyzer 1000) in the neurite assay. Neuro-2a (N2a) cells were plated in 96-well plates and were exposed to 6 conditions of retinoic acid. Immunofluorescence labeling of the cytoskeleton was used to detect neurites and cell bodies. Acquisition of the images was automatic. The image set was then analyzed by both manual tracing and automated algorithms. On 5 relevant parameters (number of neurites, neurite length, total cell area, number of cells, neurite length per cell), the authors did not observe a difference between the automated analysis and the manual analysis done by tracing. These data suggest that the automated system addresses the same biology as human scorers and with the same measurement precision for treatment effects. However, throughput of the automated system is orders of magnitude higher than with manual methods. ( Journal of Biomolecular Screening 2003:7-18)

scholarly journals A Combined In Vitro Assay for Evaluation of Neurotrophic Activity and Cytotoxicity

2017 ◽  
Vol 22 (6) ◽  
pp. 667-675 ◽  
Author(s):  
Narayan D. Chaurasiya ◽  
Surabhi Shukla ◽  
Babu L. Tekwani
Keyword(s):  
Neurite Outgrowth ◽  
Kinase Inhibitor ◽  
Neuronal Cells ◽  
Great Promise ◽  
Response Analysis ◽  
Neurite Length ◽  
Vitro Assay ◽  
Neurotrophic Activity ◽  
Compound Libraries

Neurotrophic assays are phenotypic methods to identify molecules that stimulate differentiation of neuronal cells. Bioactive small molecules with neurotrophic actions hold great promise as therapeutic agents for the treatment of neurodegenerative diseases and neuronal injuries by virtue of their ability to stimulate neuritic outgrowth. A combined in vitro method, which measures neurotrophic activity and cytotoxicity in a single assay, has been described. This assay, performed in 96-well microplates with PC12 and Neuroscreen-1 (NS-1; a subclone of PC12) cells, is a simple tool for identification of new neurotrophic agents. Stimulation of neurite outgrowth was measured with NIS software by analysis of digital cell images as multiple parameters, namely, mean neurite length, neurite length/cell, nodes/cell, and number of neurites/cell. The assay has been standardized and validated with dose-response analysis for nerve growth factor (NGF) and mechanism-based inhibitors of NGF-induced neurite outgrowth, namely, SU6656 (an Src family kinase inhibitor) and PD98059 (a MEK inhibitor). The assay has been successfully applied for screening natural and synthetic compound libraries for cytotoxicity and neurotrophic activity. Screening of a set of harmala alkaloids identified harmine as a potential neurotrophic molecule that significantly stimulated NGF-induced neurite outgrowth in the NS-1 cells. Important advantages of this method are its simplicity and determination of cytotoxicity and neurotrophic activity in a single assay. This assay may be suitable for primary and cultured neuronal cells.

scholarly journals A comparison of the Mouse Song Analyzer and DeepSqueak ultrasonic vocalization analysis systems in C57BL/6J, FVB.129, and FVB mice

2021 ◽  
Author(s):  
Matthew S Binder ◽  
Zachary P Pranske ◽  
Joaquin N Lugo
Keyword(s):  
Fundamental Frequency ◽  
Vocal Communication ◽  
Automated Analysis ◽  
Ultrasonic Vocalization ◽  
Ultrasonic Vocalizations ◽  
Automated System ◽  
Two Systems ◽  
Similar Quantity ◽  
Analysis System

Vocal communication is an essential behavior in mammals and is relevant to human neurodevelopmental conditions. Mice produce communicative vocalizations, known as ultrasonic vocalizations (USVs), that can be recorded with various programs. The Mouse Song Analyzer is an automated analysis system, while DeepSqueak is a semi-automated system. We used data from C57BL/6J, FVB.129, and FVB mice to compare whether the DeepSqueak and Mouse Song Analyzer systems measure a similar total number, duration, and fundamental frequency of USVs. We found that the two systems detected a similar quantity of USVs for FVB.129 mice (r= .90, p< .001), but displayed lower correlations for C57BL/6J (r= .76, p< .001) and FVB mice (r= .60, p< .001). We also found that DeepSqueak detected significantly more USVs for C57BL/6J mice than the Mouse Song Analyzer. The two systems detected a similar duration of USVs for C57BL/6J (r= .82, p< .001), but lower correlations for FVB.129 (r= .13, p< .001) and FVB mice (r= .51, p< .01) were found, with DeepSqueak detecting significantly more USVs per each strain. We found lower than acceptable correlations for fundamental frequency in C57BL/6J (r= .54, p< .01), FVB.129 (r= .76, p< .001), and FVB mice (r= .07, p= .76), with the Mouse Song Analyzer detecting a significantly higher fundamental frequency for FVB.129 mice. These findings demonstrate that the strain of mouse used significantly affects the number, duration, and fundamental frequency of USVs that are detected between programs. Overall, we found that DeepSqueak is more accurate than the Mouse Song Analyzer.

scholarly journals Hydrogel Tissue Construct-Based High-Content Compound Screening

2010 ◽  
Vol 16 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Vy Lam ◽  
Tetsuro Wakatsuki
Keyword(s):  
Kinase Inhibitor ◽  
Physiological Mechanism ◽  
Rho Kinase ◽  
Assay System ◽  
Physiological Status ◽  
Multiple Parameters ◽  
Compound Screening ◽  
Tissue Construct

Current pharmaceutical compound screening systems rely on cell-based assays to identify therapeutic candidates and potential toxicities. However, cells grown on 2D substrata or in suspension do not exhibit the mechanical or physiological properties of cells in vivo. To address this limitation, the authors developed an in vitro, high-throughput, 3D hydrogel tissue construct (HTC)–based assay system to quantify cell and tissue mechanical properties and multiple parameters of physiology. HTC mechanics was quantified using an automated device, and physiological status was assessed using spectroscopy-based indicators that were read on microplate readers. To demonstrate the application of this system, the authors screened 4 test compounds—rotenone (ROT), cytochalasin D (CD), 2,4-dinitrophenol (DNP), and Rho kinase inhibitor (H-1152)—for their ability to modulate HTC contractility without affecting actin integrity, mitochondrial membrane potential (MMP), or viability. All 4 compounds dose-dependently reduced HTC contractility. However, ROT was toxic, DNP dissipated MMP, and CD reduced both intracellular F-actin and viability. H-1152 was found to be the best candidate compound since it reduced HTC contractility with minimal side effects. The authors propose that their HTC-based assay system can be used to screen for compounds that modulate HTC contractility and assess the underlying physiological mechanism(s) of compound activity and toxicity.

scholarly journals Wnt-3a and Dvl Induce Neurite Retraction by Activating Rho-Associated Kinase

2004 ◽  
Vol 24 (10) ◽  
pp. 4487-4501 ◽  
Author(s):  
Shosei Kishida ◽  
Hideki Yamamoto ◽  
Akira Kikuchi
Keyword(s):  
Neurite Outgrowth ◽  
Pc12 Cells ◽  
Mammalian Cells ◽  
Planar Cell Polarity ◽  
Kinase Inhibitor ◽  
Rho Kinase ◽  
Binding Domain ◽  
Serum Starvation ◽  
Neurite Retraction ◽  
Rho Kinase Inhibitor

ABSTRACT Dvl is a key protein that transmits the Wnt signal to the canonical β-catenin pathway and the noncanonical planar cell polarity (PCP) pathway. We studied the roles of Rho-associated kinase (Rho-kinase), which is activated by Dvl in the PCP pathway of mammalian cells. The expression of Dvl-1, Wnt-1, or Wnt-3a activated Rho-kinase in COS cells, and this activation was inhibited by the Rho-binding domain of Rho-kinase. The expression of Dvl-1 in PC12 cells activated Rho and inhibited nerve growth factor (NGF)-induced neurite outgrowth. This inhibition was reversed by a Rho-kinase inhibitor but not by a c-Jun N-terminal kinase inhibitor. Dvl-1 also inhibited serum starvation-dependent neurite outgrowth of N1E-115 cells, and expression of the Rho-binding domain of Rho-kinase reversed this inhibitory activity of Dvl-1. Dvl-1 mutants that did not activate Rho-kinase did not inhibit the neurite outgrowth of N1E-115 cells. Furthermore, the purified Wnt-3a protein activated Rho-kinase and inhibited the NGF-dependent neurite outgrowth of PC12 cells. Wnt-3a-dependent neurite retraction was also prevented by a Rho-kinase inhibitor and a Dvl-1 mutant that suppresses Wnt-3a-dependent activation of Rho-kinase. These results suggest that Wnt-3a and Dvl regulate neurite formation through Rho-kinase and that PC12 and N1E-115 cells are useful for analyzing the PCP pathway.

scholarly journals Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

2006 ◽  
Vol 2006 ◽  
pp. 1-10
Author(s):  
S. Venkatraman ◽  
M. J. Doktycz ◽  
H. Qi ◽  
J. L. Morrell-Falvey
Keyword(s):  
Fluorescence Microscopy ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Networks ◽  
Automated Analysis ◽  
Automated System ◽  
Positive Interactions ◽  
Protein Interaction Dataset ◽  
Analysis System ◽  
Microscopy Images

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

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