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Sulzer lab downloads, tutorials, and supplemental material


1. Cell cultures

This booklet details our substantia nigra and VTA neuronal culture protocols used for neurodegeneration and neurotransmission studies (current version is August 2010, updated by Ellen Kanter).

This short set of instructions details our methods for chromaffin cell culture.

2. Tutorial on random walks and Schmitz/Sulzer modified random walks for simulations of electrochemical detection of neurotransmitter release and reuptake

For a tutorial that explains random walks and how to design a random walk spreadsheet, read

Chapter 1 introduction to using spreadsheets to simulate random walks

To learn how to integrate Michealis-Menten kinetics (Km and Vmax) for uptake transporters in a random walk (Schmitz/Sulzer model) read

Chapter 2.Schmitz/Sulzer random walk

To use the model, we have prepared the following instructions and Excel spreadsheets. First, download Chapter 3 and follow the advice.

Chapter 3. Using the spreadsheets

To download the Microsoft Excel spreadsheets designed to simulate amperometry or cyclic votammetry in the acute slice, download

Excel files

Random walk model for amperometry (.xls file)

Random walk model for cyclic voltammetry (.xls file)

(new as of January 2006) amperometry spread sheet with flux through a pore model (.xls file)

binhex files

Random walk model for amperometry (binhex)

Random walk model for cyclic voltammetry (binhex)

the binhex files require "unstuffing". The program "Expander" can be downloaded from


3. Information on neuromelanin isolation, identification, and breakdown

Details on EPR for neuromelanin detection

the following Quicktime videos by Kester Phillips show microglia phagocytosing and degrading neuromelanin in culture in timelapse video

Here is a differential interference contrast video of ventral midbrain / astrocyte / microglial coculture which shows a microglial filopodium grabbing a neuromelanin particle for phagocytosis. The frames advance by one image per two minutes.

Here is another DIC video in which a small microglial cell attaches to a neuromelanin particle, appears to activate nearby microglia, and then migrates with the particle towards a group of other microglial cells that proceed to destroy the particle. Frame rate as above.

A bright field video (.mov prepared in QuickTime) or here in mpeg (mp4) which shows the degradation, i.e., loss of pigment better than DIC optics, although without DIC's morphological detail.

4. Analysis of quantal release events in an Igor XOP
(written by Eugene Mosharov: e-mail Eugene)

Requires Igor Pro version 4.07 or later.

This analysis program can be used to detect and characterize amperometric spikes recorded from chromaffin cells, PC12 cells, neurons, etc. The following parameters can be calculated:

Peak parameters:
Amplitude (Imax, pA)
Duration (t1/2, ms)
Charge (Q, pC or # molecules)
Interspike Interval (ms)

Peak rising phase parameters:
Slope (pA/s)
Time to Peak (tP, ms)

Peak falling phase parameters:
(Can be fit by linear, exponential or double-exponential regressions)
Duration (ms)
Decay time constants (tau1 and tau2, ms)

Peak foot parameters:
Amplitude (Ifoot, pA)
Duration (tfoot, ms)
Charge (Qfoot, pC)

Click here to download the file in "ipf" (Igor Pro) format. The current version of the program is 8.20.

This is an ipf file (Igor Procedure File) for Igor Pro (has to be version 4.07 or later). To install the routine, unzip the archive and put the file Quanta_Analysis_ver.ipf into the Igor Pro/Igor Procedures folder. After starting Igor, you should see a bookmark "Prepare for quanta analysis" under the Macros menu. Clicking it creates windows and controls for the program. (As the program was written on a PC, the sizes of panels and windows may look odd on a Mac.) Many tune-ups in the program (on the topmost Menu panel) change how the data are analyzed and presented.

If you have questions about the program, or can suggest improvements, please e-mail Eugene Mosharov (

5. Normal probability distributions in Excel

Many statistics programs ignore normal probability distributions, but they are important in our work: William Van der Kloot has written articles on their merits for detecting multiple populations, as does our review here.

Normal probability plotting provides a graphical method for determining whether sample data conform to a normal distribution based on visual examination of the plot. The data are plotted against a theoretical normal distribution for which the points form a straight line. Departures from this line indicate departures from normality, and if the plot is fit with more than one line, multiple populations are present. We find it useful for analyzing quantal release events and for examining whether the activity of large numbers of synaptic terminals measured optically can develop multiple populations (see the papers by Nigel Bamford and Niko Gubernator from the publicatons page)- which we suspect is how the brain selects particular synapses for learning and behavior.

To make a normal probability plot:

a. All of the data points (for example, times to half destaining of a synaptic terminal or quantal size) are ranked from smallest to largest (xi): this can be done in Excel with a "sort" function.

Then, the theoretical normal distribution of your sample (that is, what the distribution of each point would be if it were genuinely normal) is determined (yi):

b. For each observation xi, the cumulative frequency (pi) is calculated as i/n,where n is the total number of data points.

c. For each yi, the inverse of a cumulative standard normal distribution with a mean of zero and a standard deviation of one is calculated using yi =NORMSINV(pi) function in Excel.

d. The dependence of xi (your genuine data) on yi (where the data would fall if it were normally distributed) produces a normal probability plot, which presents each data point in terms of its deviation from the predicted mean of the population.

Here is a sample Excel spread sheet you can adapt. There is also a means to get a regression statistic that reports how good the fit of the real data is to the normal distribution. However, I think the real value of this plot is that it allows smaller populations that would otherwise be hidden in the tails of a histogram to be noticed. While biology tends to be interested in mean values and not to be interested in these outliers, they presumably play an important role in plasticity of the nervous system.

6. video of Glyt-1 effects on neuronal branching (see Schmitz et al 2009 J Neurosci)

7. Image analysis program for destaining of presynaptic punctain the brain slice

download the full routines and instructions from

this routine was written by Jozsef Meszaros and Daniela Pereira and introduced in

Pereira et al. 2016 Nature Neuroscience

please see the paper for a thorough additional explanation of the approach

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