Rutgers

Firestein Laboratory

  

 

Tools

Development of novel tools to describe dendrite branching patterns

We have developed a novel method and analysis program to identify changes in the dendrite arbor not easily captured by existing methodologies. Dendrite morphology is frequently analyzed by Sholl analysis or by counting the total number of dendrites and branch tips. However, the time and resources required to perform such analysis by hand is prohibitive for the processing of large data sets and introduces problems with data auditing and reproducibility. Furthermore, analyses performed by hand or using course-grained morphometric data extraction tools can obscure subtle differences in data sets because they do not store the data in a form that facilitates the application of multiple analytical tools. To address these shortcomings, my laboratory has developed a program (titled “Bonfire”) to facilitate digitization of dendrite morphology and subsequent Sholl analysis. Our program builds upon other available open-source morphological analysis tools by performing Sholl analysis on subregions of the dendritic arbor, enabling the detection of local level changes in dendrite and axon branching behavior, which would not otherwise be uncovered by conventional analyses (Langhammer et al., 2010; Kutzing et al., 2010).

Development of novel tools for quantifying muscle cell and neuron behavior

We have developed novel analysis programs to identify and quantitate muscle contractions and neuronal action potentials in pure and co-culture.  Although most of my laboratory focuses on the interaction between neurons, we have an interest in how neurons communicate with other cell types. One model of interest is the neuromuscular junction. We are currently developing noninvasive methods to analyze muscle and NMJ function in vitro. We have developed a numerical procedure, using image processing and a pattern recognition algorithm that makes it possible to quantify contractile behavior of multiple myotubes simultaneously, based on video data (Langhammer et al., 2010). Our program quantifies contractility on a population level, can be adapted for use in laboratories capable of digital video capture from a microscope, and may be coupled with other experimental techniques to supplement existing research tools. Similar to our analysis of muscle cells, we developed a method to record the activity of individual cell types grown on multielectrode arrays (MEAs). We have composed a program which successfully allows a user to identify the activity of individual cells in MEA data (Langhammer et al., 2011).  Our program is flexible enough to identify activity from multiple cell types (myotubes and neurons) and returns accurate information on the morphology, as well as the timing, of their action potentials.  Furthermore, the program reduces the time required to analyze the data and the user- introduced bias by partially automating the process. This program will now allow us to measure the activity of neurons and muscle cells in networks under different pharmacological and physiological conditions.

 

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