Recordings were obtained using Multiclamp 700B (Molecular Devices), sampled at 50 kHz and filtered at 4 kHz, and collected in Igor Pro (WaveMetrics). Dynamic clamp recordings were performed with an ITC-18 computer interface controlled by mafPC in Igor Pro (WaveMetrics). Data were analyzed using custom-written scripts in MATLAB (MathWorks) and Igor Pro (WaveMetrics). Autocorrelation and cross-correlation analyses were performed by generating accumulative histograms of spikes distribution within a 20 ms time window centering all spikes from the reference file (self-reference for autocorrelation, and PCs spike times for cross-correlation), and normalized by the total spikes number of the reference file and the bin size of the histograms (i.e., the Δt). All summary data are shown as the mean ± SEM unless otherwise indicated. The distributions of unitary PC input sizes in young and juvenile animals in Figure 1f were compared with a Kolmogorov–Smirnov test. The unpaired t-test was performed with Welch’s correction.
Igor pro
Manufactured by Wavemetrics
Sourced in United States, Germany, Jamaica, United Kingdom, Australia
Igor Pro is a graphical data analysis and visualization software developed by WaveMetrics. It is a powerful tool for scientific data processing, analysis, and presentation. Igor Pro provides a wide range of features for data acquisition, processing, and visualization, catering to the needs of researchers, scientists, and engineers across various fields.
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Lab products found in correlation
Matlab, by MathWorks (73 mentions)
Multiclamp 700b amplifier, by Molecular Devices (22 mentions)
Fitmaster, by HEKA Elektronik (20 mentions)
Clampfit, by Molecular Devices (19 mentions)
Prism 8, by GraphPad (14 mentions)
Labview, by National Instruments (12 mentions)
Axopatch 200b amplifier, by Molecular Devices (12 mentions)
Statview, by Abacus (12 mentions)
Originpro, by OriginLab (10 mentions)
Multiclamp 700b, by Molecular Devices (10 mentions)
Prism 7, by GraphPad (9 mentions)
Fitmaster software, by HEKA Elektronik (8 mentions)
Prism 5, by GraphPad (8 mentions)
Prism 6, by GraphPad (8 mentions)
Matlab, by Wavemetrics (7 mentions)
Prism 9, by GraphPad (7 mentions)
Pclamp 10, by Molecular Devices (7 mentions)
Powerlab, by ADInstruments (6 mentions)
Neuromatic, by Wavemetrics (6 mentions)
Prism software, by GraphPad (6 mentions)
1 089 protocols using igor pro
1
Neuronal spike analysis protocol
2
Electrophysiological Recordings and Analysis
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Recordings were obtained using Multiclamp 700B (Molecular Devices), sampled at 50 kHz and filtered at 4 kHz, and collected in Igor Pro (WaveMetrics). Dynamic-clamp recordings were performed with an ITC-18 computer interface controlled by mafPC in Igor Pro (WaveMetrics). Data were analyzed using custom-written scripts in MATLAB (MathWorks) and Igor Pro (WaveMetrics). Auto-correlation and cross-correlation analysis were performed by generating accumulative histograms of spikes distribution within a 20 ms time window centering all spikes from the reference file (self-reference for auto-correlation, and PCs spike times for cross-correlation), and normalized by the total spikes number of the reference file and the bin size of the histograms (that is, the Δt). All summary data are shown as the mean ± SEM unless otherwise indicated. The distributions of unitary PC input sizes in young and juvenile animals in Fig. 1f were compared with a Kolmogorov–Smirnov test. The unpaired t test was performed with Welch’s correction.
3
Spectroscopic Analysis of Protein Structure
4
Channel Conductance Analysis Workflow
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Data were analyzed with patchmaster (HEKA) and IgorPro (Wave-Metrics). All results, where appropriate, are presented as mean ± SEM. Curve fitting was performed using Origin software and singe channel conductance analysis using IgorPro (Wave-Metrics). Statistical comparisons were made by using one-way ANOVA with Bonferroni correction. P < 0.05 indicated statistical significance.
5
Symbolic and Numerical Analysis Protocol
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Symbolic calculation and numerical evaluation of expressions was done with Maple (version 2020.2, Maplesoft, Waterloo Maple Inc., Waterloo, Ontario, Canada). Curve fitting was done with Maple, Igor Pro (Igor Pro 8, version 8.0.4.2, Wavemetrics, Lake Oswego, Oregon, USA) or Origin (2019 pro, version 9.60; Originlab Corp. Northampton, MA).
6
Spectroscopic Analysis of Protein Structure
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Band decomposition analysis was performed using a multi-peak fitting analysis program (IGOR Pro, WaveMetrics, Inc.). IR s-SNOM spectra were evaluated with Lorentzian line shapes19 (link) and bulk FTIR spectra were evaluated with Gaussian line shapes20 (link). For IR s-SNOM of nanowires, bR, and lysozyme, data are represented with red markers. Fitting results (blue curves) were obtained by applying band-decomposition analysis to the data, which yielded good agreement (χ2 <0.02). Areas under the curve were integrated to estimate the percentages of α-helix, β-sheet, and coil components (Supplementary Table 1a for lysozyme and Supplementary Table 1b for OmcS and OmcZ nanowires). In amide I mode, α-helices give rise to main absorption band located at 1660 cm−1. β-sheets show a stronger band at 1625 cm−1 and a weaker band near 1685 cm−1, ~60 cm−1 away from the stronger band17 (link). This observed splitting in the β-sheet formation arises due to the transition dipole coupling mechanism17 (link). The formation of β-sheets in IR s-SNOM spectra for OmcS and OmcZ nanowires was evident from the presence of these two β-sheet bands17 (link) (Fig. 5d ,e ). All analyses were performed using IGOR Pro software (WaveMetrics, Inc.).
7
Structural Characterization of Repeat Proteins
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For a detailed
description, see theSupporting Information Methods Section . In brief, repeat arrays were constructed
in the background of a pRSET vector and expressed in E. coli. Equilibrium denaturation experiments were performed using guanidine
hydrochloride in sodium phosphate buffer, pH 6.8, 150 mM NaCl in a
96-well plate format.71 (link) CTPRrv4 with a
C-terminal solvating helix was crystallized in a solution containing
0.2 M MgCl2, 0.1 M sodium cacodylate, pH 6.5, and 50% v/v
PEG200. Further details on data collection and processing can be found
in theSupporting Information Methods Section . Angles between repeat planes were calculated essentially as published
previously.13 (link) Constructs were prepared
for force spectroscopy using site-specific modification of either
terminal ybbR-tags or cysteine residues.72 (link),73 (link) All single-molecule force spectroscopy data was collected on a custom-built
instrument,74 (link) processed using custom scripts
developed in Igor Pro (WaveMetrics), and further analyzed using Igor
Pro or Python.75 (link)−81 (link) Theoretical FDCs were calculated using custom C++/CUDA software.
Structural representations were generated using PyMol82 or VMD.83 (link)
description, see the
in the background of a pRSET vector and expressed in E. coli. Equilibrium denaturation experiments were performed using guanidine
hydrochloride in sodium phosphate buffer, pH 6.8, 150 mM NaCl in a
96-well plate format.71 (link) CTPRrv4 with a
C-terminal solvating helix was crystallized in a solution containing
0.2 M MgCl2, 0.1 M sodium cacodylate, pH 6.5, and 50% v/v
PEG200. Further details on data collection and processing can be found
in the
previously.13 (link) Constructs were prepared
for force spectroscopy using site-specific modification of either
terminal ybbR-tags or cysteine residues.72 (link),73 (link) All single-molecule force spectroscopy data was collected on a custom-built
instrument,74 (link) processed using custom scripts
developed in Igor Pro (WaveMetrics), and further analyzed using Igor
Pro or Python.75 (link)−81 (link) Theoretical FDCs were calculated using custom C++/CUDA software.
Structural representations were generated using PyMol82 or VMD.83 (link)
8
Optical and Electrophysiological Analysis
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Optical and electrophysiological signals were analyzed using custom macro programs on Igor Pro (WaveMetrics Inc., OR, USA). All data are expressed as the mean ± SEM; and n represents the number of slices. Statistical significance was tested with Tukey’s mutiple comparisons after one-way analysis of variance (ANOVA) using Igor Pro (WaveMetrics Inc., OR, USA).
9
Characterization of TRPV1 Mutants
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Mutation selection was performed with Excel (Microsoft). Graphs were made using Prism (GraphPad Software, Inc.) and Igor Pro (WaveMetrics). Illustrations were made in Illustrator (Adobe Systems). Histograms of gap lengths between mutations were generated by counting gap lengths (for temperature ‘functional’ or capsaicin ‘functional’ mutations), or by generating 10 random distributions of 287 (temperature) or 248 (capsaicin) mutations over 838 positions with Excel (Microsoft) and then counting gap lengths. Electrophysiological data were analyzed with Igor Pro (WaveMetrics).
Statistical analysis of hydropathy histograms was performed on R using a custom made script. We used a likelihood ratio (LRT) to test whether the frequency of temperature-characterized mutations with ∆hydropathy > −2 is the same between each of the two functional categories. Vhalf statistical analysis was done using an unpaired Student’s t-test by comparing wild-type TRPV1 to each mutant.
Statistical analysis of hydropathy histograms was performed on R using a custom made script. We used a likelihood ratio (LRT) to test whether the frequency of temperature-characterized mutations with ∆hydropathy > −2 is the same between each of the two functional categories. Vhalf statistical analysis was done using an unpaired Student’s t-test by comparing wild-type TRPV1 to each mutant.
10
Imaging and Electrophysiology Data Analysis
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Imaging data were analyzed using ImageJ (http://rsbweb.nih.gov/ij/ ). Extracted fluorescence traces, linescans and electrophysiological data were analyzed using in house routines programmed in Igor Pro versions 5 or 6.2 (Wavemetrics) and in pClamp 10 (Molecular Devices). Statistical analysis was performed in Matlab (MathWorks, Natick, MA, USA) or Igor Pro (Wavemetrics, Portland, OR, USA). Experimental groups were compared using a t-test and were assumed to be significantly different if the found p-values were <0.05.
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