Powers Wiki - User contributions [en]

Safety training and inspection

2013-10-31T16:30:01Z

Bozhang:

==Safety training==
There are two levels of safety training for new students in the lab:
First is the university requirement:

1. Please go through all the materials on this website and get familiar with all the general information about safety.
http://chem.unl.edu/safety/

2. Follow the web-based workshop, and pass all the required examines for specific topics.
http://chem.unl.edu/safety/required-safety-training.pdf
3. After finishing all the tests, send all the certification pages to Dodie.

Note:
You will need your Ncard to log in for these tests. It is not a "have-to", but it will make things much easier.

Second is the departmental level.

Martha Morton is the safety chair and Alexander Sinisskii is the vice chair. Either of them can give you the safety training that you need. The presentation material is online in the same webpage titled:"Chemistry Department Safety Training Materials".

Another written test will be given to assure that you fully comprehend the safety request.

Note:
1. You are required to pass the safety training for NMR facilities separately.

2. The booklet of safety manuals need to be read through and signed for ANYONE who work in the lab.

==Safety inspection==

There are three levels of safety inspections: departmental (daily), university (EHS) (yearly) and federal government (not fixed).
[[Category:Protocols]]

Safety training and inspection

2013-10-31T16:29:28Z

Bozhang:

==Safety training==
There are two levels of safety training for new students in the lab:
First is the university requirement:

1. Please go through all the materials on this website and get familiar with all the general information about safety.
http://chem.unl.edu/safety/

2. Follow the web-based workshop, and pass all the required examines for specific topics.
http://chem.unl.edu/safety/required-safety-training.pdf
3. After finishing all the tests, send all the certification pages to Dodie.

Note: You will need your Ncard to log in for these tests. It is not a "have-to", but it will make things much easier.

Second is the departmental level.

Martha Morton is the safety chair and Alexander Sinisskii is the vice chair. Either of them can give you the safety training that you need. The presentation material is online in the same webpage titled:"Chemistry Department Safety Training Materials".

Another written test will be given to assure that you fully comprehend the safety request.

Note: 1. You are required to pass the safety training for NMR facilities separately.
2. The booklet of safety manuals need to be read through and signed for ANYONE who work in the lab.

==Safety inspection==

There are three levels of safety inspections: departmental (daily), university (EHS) (yearly) and federal government (not fixed).
[[Category:Protocols]]

Safety training and inspection

2013-10-31T16:26:59Z

Bozhang: Created page with "==Safety training== There are two levels of safety training for new students in the lab: First is the university requirement: 1. Please go through all the materials on this w..."

==Safety training==
There are two levels of safety training for new students in the lab:
First is the university requirement:

1. Please go through all the materials on this website and get familiar with all the general information about safety.
http://chem.unl.edu/safety/

2. Follow the web-based workshop, and pass all the required examines for specific topics.
http://chem.unl.edu/safety/required-safety-training.pdf
3. After finishing all the tests, send all the certification pages to Dodie.

Note: You will need your Ncard to log in for these tests. It is not a "have-to", but it will make things much easier.

Second is the departmental level.

Martha Morton is the safety chair and Alexander Sinisskii is the vice chair. Either of them can give you the safety training that you need. The presentation material is online in the same webpage titled:"Chemistry Department Safety Training Materials".

Another written test will be given to assure that you fully comprehend the safety request.

==Safety inspection==

There are three levels of safety inspections: departmental (daily), university (EHS) (yearly) and federal government (not fixed).
[[Category:Protocols]]

Category:Protein expression

2013-09-03T20:48:07Z

Bozhang: Created page with "This is a category for protein over-expression protocols."

This is a category for protein over-expression protocols.

Media preparation

2013-09-03T20:45:52Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]
===1. General Protocols For Minimal Media Protein Expression===

Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source for 1L 5xM9 salts:

64 g Na2HPO4-7H2O
15 g KH2PO4
2.5 g NaCl

H2O to final 1L volume and autoclave
To prepare 500 mL M9 minimal media:

100mL of 5xM9 salts:

1 mL 1 M MgSO4

50 uL 1 M CaCl2

5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)

2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5 g dry 10 mL 20% d-glucose or 2 g dry

Glass distilled & autoclaved H2O to final volume of 500 mL pH solution to 7.3 and filter sterilize (0.2 um filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2 L flask and add ampicillin to a final concentration of 70-100 ug/mL (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37 oC until an OD600 of 0.7
± 0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

===2. General Protocols For LB Media===

For 1 L media preparation

10 g Bacto-tryptone

5 g yeast extract

10 g NaCl

Sterilized by autoclave

Add any antibiotics used to select the strains properly after the medium temperature is blew 37 oC.

===3. General Protocols For High-Cell-Density Media===

For 1 L media preparation

50 mM Na2HPO4.7H2O

25 mM KH2PO4 (pH 8.0-8.2)

10 mM NaCl

5 mM MgSO4

0.2 mM CaCl2

0.1% NH4Cl or 15NH4Cl

1.0% Glucose or 13C-Glucose

To prepare the media, glucose and metal solution are filtered to sterilize. The salt solution is autoclaved and the cooled solution is added by metal solution and glucose.

Main Page

2013-09-03T20:44:57Z

Bozhang:

'''Welcome to the BioNMR Wiki Page.'''

This wiki exists to provide better access to lab protocols for the Powers lab. It is not a replacement for lab notebooks. Feel free to begin documenting new lab protocols or procedures here, or even moving old protocols onto this wiki.

== Getting started ==
For those who need a quick and dirty introduction to MediaWiki formatting, try this reference card:
* [http://bionmr.unl.edu/w/MediaWikiRefCard.pdf MediaWiki Reference Card]

Other general MediaWiki information pages:
* [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]

=== Wiki Categories ===

[[Special:Categories|View All Categories]]

Commonly used Categories:

* [[:Category:Protocols|Protocols]]
* [[:Category:Bioscreen|Bioscreen]]
* [[:Category:FAST-NMR|FAST-NMR]]
* [[:Category:Molecular Docking|Molecular Docking]]
* [[:Category:Metabolomics|Metabolomics]]
* [[:Category:Maxey Demos|Maxey Demos]]
* [[:Category:Protein expression|Protein expression]]
* [[:Category:SMACMS|SMACMS]]

Main Page

2013-09-03T20:38:50Z

Bozhang: /* Wiki Categories */

'''Welcome to the BioNMR Wiki Page.'''

This wiki exists to provide better access to lab protocols for the Powers lab. It is not a replacement for lab notebooks. Feel free to begin documenting new lab protocols or procedures here, or even moving old protocols onto this wiki.

== Getting started ==
For those who need a quick and dirty introduction to MediaWiki formatting, try this reference card:
* [http://bionmr.unl.edu/w/MediaWikiRefCard.pdf MediaWiki Reference Card]

Other general MediaWiki information pages:
* [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]

=== Wiki Categories ===

[[Special:Categories|View All Categories]]

Commonly used Categories:

* [[:Category:Protocols|Protocols]]
* [[:Category:Bioscreen|Bioscreen]]
* [[:Category:FAST-NMR|FAST-NMR]]
* [[:Category:SMACMS|SMACMS]]
* [[:Category:Molecular Docking|Molecular Docking]]
* [[:Category:Metabolomics|Metabolomics]]
* [[:Category:Maxey Demos|Maxey Demos]]
* [[:Category:Protein expression|Protein expression]]

Media preparation

2013-09-03T19:43:52Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]

===1. General Protocols For Minimal Media Protein Expression===

Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source for 1L 5xM9 salts:

64 g Na2HPO4-7H2O
15 g KH2PO4
2.5 g NaCl

H2O to final 1L volume and autoclave
To prepare 500 mL M9 minimal media:

100mL of 5xM9 salts:

1 mL 1 M MgSO4

50 uL 1 M CaCl2

5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)

2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5 g dry 10 mL 20% d-glucose or 2 g dry

Glass distilled & autoclaved H2O to final volume of 500 mL pH solution to 7.3 and filter sterilize (0.2 um filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2 L flask and add ampicillin to a final concentration of 70-100 ug/mL (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37 oC until an OD600 of 0.7
± 0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

===2. General Protocols For LB Media===

For 1 L media preparation

10 g Bacto-tryptone

5 g yeast extract

10 g NaCl

Sterilized by autoclave

Add any antibiotics used to select the strains properly after the medium temperature is blew 37 oC.

===3. General Protocols For High-Cell-Density Media===

For 1 L media preparation

50 mM Na2HPO4.7H2O

25 mM KH2PO4 (pH 8.0-8.2)

10 mM NaCl

5 mM MgSO4

0.2 mM CaCl2

0.1% NH4Cl or 15NH4Cl

1.0% Glucose or 13C-Glucose

To prepare the media, glucose and metal solution are filtered to sterilize. The salt solution is autoclaved and the cooled solution is added by metal solution and glucose.

Noise removal for PCA

2013-05-17T02:44:01Z

Bozhang: /* Noise cutoff calculation */

[[Category:Protocols]]
[[Category:Metabolomics]]

==Prepare the data set==
1. The data can be prepared in txt file from ACDLab 1D processor.

2. After the spectra are "Autophased" and "Referenced" to TMSP correctly, click the "Integration" icon in the tool bar.

3. Click the "Series" from the menu and choose "Table of common integrals".

4. Then export table to the targeted file folder.

5. Open the file in Office Excel. Delete the first row and insert a new row below the row of sample numbers.

6. Fill the row with sample class names.

==Z score transformation==
Z score is used for normalizing the individual spectrum. The scaling of the data set across all the spectra is performed in SIMCA-P+. (UV scaling is by default)

To enter into excel:
1) Click the first row, first column of data
2) Add in minus sign
3) Click first average data point
4) Put () around first 2 terms in equation
5) Add in division sign
6) Click first standard deviation data point
7) Add dollar signs after letter in standard deviation equation point and average equation point (Ex: C$480)
8) Hit enter, click and drag columns.

<math>Z=(x_i-\overline x)/\sigma</math>

==Noise cutoff calculation==

This is based on the Excel template that is exported directly from the ACDLabs.

0. The calculation is based on the z-score data set.

1. Cross the board, calculate the standard deviation and average values for each row.

2. Calculate the absolute value for relative standard deviation by dividing the standard deviation by the absolute average values.

3. Find out the average value and standard deviation for the pre-assigned noise region of bins for each sample (chemical shift<0ppm or >10ppm). Calculate the cutoff equals to the average plus 3 times standard deviation.

4. Only when the z score is smaller than 0, AND the value of relative standard deviation is smaller than the cutoff of the noise, then that bin can be considered as a noise bin. All noise region-defined bins should be set to 0 and remove from the analysis data sets.

==Noise cutoff application==
If the data set is prepared for PCA, only the noise region across the whole data set can be removed. For data set for OPLS-DA, the noise region determined for each class can be removed separately.

Noise removal for PCA

2013-03-29T01:21:46Z

Bozhang: /* Z score transformation */

[[Category:Protocols]]
[[Category:Metabolomics]]

==Prepare the data set==
1. The data can be prepared in txt file from ACDLab 1D processor.

2. After the spectra are "Autophased" and "Referenced" to TMSP correctly, click the "Integration" icon in the tool bar.

3. Click the "Series" from the menu and choose "Table of common integrals".

4. Then export table to the targeted file folder.

5. Open the file in Office Excel. Delete the first row and insert a new row below the row of sample numbers.

6. Fill the row with sample class names.

==Z score transformation==
Z score is used for normalizing the individual spectrum. The scaling of the data set across all the spectra is performed in SIMCA-P+. (UV scaling is by default)

To enter into excel:
1) Click the first row, first column of data
2) Add in minus sign
3) Click first average data point
4) Put () around first 2 terms in equation
5) Add in division sign
6) Click first standard deviation data point
7) Add dollar signs after letter in standard deviation equation point and average equation point (Ex: C$480)
8) Hit enter, click and drag columns.

<math>Z=(x_i-\overline x)/\sigma</math>

==Noise cutoff calculation==
0. The calculation is based on the z-score data set.

1. For each class, calculate the standard deviation and average values.

2. Calculate the absolute value for relative standard deviation by dividing the standard deviation by the absolute average values.

3. Find out the maximum for each row. If the maximum is smaller than 0. It indicates all the z score values are smaller than 0.

4. Find out the region that no peak exists, find out the maximum of the relative standard deviation for each class.

5. Only when the z score is smaller than 0, AND the value of relative standard deviation is smaller than the maximum of the noise region, then that bin can be considered as a noise region. All noise region-defined bins should be set to 0.

==Noise cutoff application==
If the data set is prepared for PCA, only the noise region across the whole data set can be removed. For data set for OPLS-DA, the noise region determined for each class can be removed separately.

Making Heatmaps

2012-12-23T04:08:43Z

Bozhang: /* Name the rows */

==Load the data==
The data can be prepared in csv or text file to load the data in to R. For example, to load ''list.csv'':

heatmapname <- read.csv (“list.csv”)

The "file directory" can be easily found by dragging the file to the active window of R, then it will be shown with an error report. Ignore the error, but copy and paste the file directory.

==Name the rows==
You can then name the rows of the heatmap like so:

row.names(heatmapname) <- data$Name

To exclude the first column from the heat map, use a command of similar form to the following:

heatmapname <- heatmapname [,2: n]

In the above command, '''n''' is the number of columns to be included in the heat map.

Another simpler way is to combine this step with the file importation by adding two command after the import:
row.names=1, header=TRUE

==Build a data matrix==
Use the following command to build a data matrix for making the heat map:

heatmapname_matrix <- data.matrix (heatmapname)

==Plot the heat map==
'''Note: Gnuplot package must be installed in R before heat maps may be displayed!''' Run the following command to load in the ''gplots'' library.

library("gplots")

To make heat map, run the following command:

heatmap.2 (heatmapname_matrix, dendrogram="row", col= redgreen (75), scale="none",
key = TRUE, keysize = 1.0, margins = c(4,30),
density.info="none", trace="none")

'''Note: To rescale the color key, add break function to the heatmap.2. '''

'''breaks=c(seq(-1,0.8,length=10),seq(0.8,1.2,length=10),seq(1.2,3,length=10),'''

This will define the color range:

"red=[-1,0.8]

black=[0.8,1.2]

green=[1.2,3]"

[[Category:Protocols]]
[[Category:Metabolomics]]

==Run from text file==
'''Note: It is also possible to write all the steps in txt file as a single script.'''
===Open the script ===
* In the file menu click open script
** Choose the proper directory and script file name
** The script file will be opened in the R Editor window
** If necessary changes can be made on the R Editor window

===Run the script ===
* Click Run all or Run line or selection form the Edit menu

Making Heatmaps

2012-12-23T04:07:04Z

Bozhang: /* Load the data */

==Load the data==
The data can be prepared in csv or text file to load the data in to R. For example, to load ''list.csv'':

heatmapname <- read.csv (“list.csv”)

The "file directory" can be easily found by dragging the file to the active window of R, then it will be shown with an error report. Ignore the error, but copy and paste the file directory.

==Name the rows==
You can then name the rows of the heatmap like so:

row.names(heatmapname) <- data$Name

To exclude the first column from the heat map, use a command of similar form to the following:

heatmapname <- heatmapname [,2: n]

In the above command, '''n''' is the number of columns to be included in the heat map.

==Build a data matrix==
Use the following command to build a data matrix for making the heat map:

heatmapname_matrix <- data.matrix (heatmapname)

==Plot the heat map==
'''Note: Gnuplot package must be installed in R before heat maps may be displayed!''' Run the following command to load in the ''gplots'' library.

library("gplots")

To make heat map, run the following command:

heatmap.2 (heatmapname_matrix, dendrogram="row", col= redgreen (75), scale="none",
key = TRUE, keysize = 1.0, margins = c(4,30),
density.info="none", trace="none")

'''Note: To rescale the color key, add break function to the heatmap.2. '''

'''breaks=c(seq(-1,0.8,length=10),seq(0.8,1.2,length=10),seq(1.2,3,length=10),'''

This will define the color range:

"red=[-1,0.8]

black=[0.8,1.2]

green=[1.2,3]"

[[Category:Protocols]]
[[Category:Metabolomics]]

==Run from text file==
'''Note: It is also possible to write all the steps in txt file as a single script.'''
===Open the script ===
* In the file menu click open script
** Choose the proper directory and script file name
** The script file will be opened in the R Editor window
** If necessary changes can be made on the R Editor window

===Run the script ===
* Click Run all or Run line or selection form the Edit menu

Media preparation

2012-12-23T04:03:27Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]
[[Category:Metabolomics]]

1. General Protocols For Minimal Media Protein Expression

Expression Protocol in M9 Minimal Media via T7 Promoter: The following protocol has been used successfully to 15N or 13C/15N label our proteins using our pET1120/BL21(DE3) expression system: Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source For 1L 5xM9 salts:

64 g Na2HPO4-7H2O
15 g KH2PO4
2.5 g NaCl

H2O to final 1L volume and autoclave
To prepare 500 mL M9 minimal media:

100mL of 5xM9 salts
1 mL 1 M MgSO4
50 uL 1 M CaCl2
5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)
2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5 g dry
10 mL 20% d-glucose or 2 g dry

Glass distilled & autoclaved H2O to final volume of 500 mL pH solution to 7.3 and filter sterilize (0.2 um filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2 L flask and add ampicillin to a final concentration of 70-100 ug/ml (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37 oC until an OD600 of 0.7
± 0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

2. General Protocols For LB Media

For 1 L media preparation,
10 g Bacto-tryptone
5 g yeast extract
10 g NaCl

Sterilized by autoclave

Media preparation

2012-12-23T04:03:06Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]
[[Category:Metabolomics]]

1. General Protocols For Minimal Media Protein Expression

Expression Protocol in M9 Minimal Media via T7 Promoter: The following protocol has been used successfully to 15N or 13C/15N label our proteins using our pET1120/BL21(DE3) expression system: Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source For 1L 5xM9 salts:

64 g Na2HPO4-7H2O
15 g KH2PO4
2.5 g NaCl

H2O to final 1L volume and autoclave
To prepare 500 mL M9 minimal media:

100mL of 5xM9 salts
1 mL 1 M MgSO4
50 uL 1 M CaCl2
5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)
2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5 g dry
10 mL 20% d-glucose or 2 g dry

Glass distilled & autoclaved H2O to final volume of 500 mL pH solution to 7.3 and filter sterilize (0.2 um filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2 L flask and add ampicillin to a final concentration of 70-100 ug/ml (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37 oC until an OD600 of 0.7
± 0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

2. General Protocols For LS Media

For 1 L media preparation,
10 g Bacto-tryptone
5 g yeast extract
10 g NaCl

Sterilized by autoclave

Media preparation

2012-12-23T03:40:54Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]
[[Category:Metabolomics]]

1. General Protocols For Minimal Media Protein Expression
Expression Protocol in M9 Minimal Media via T7 Promoter:The following protocol has been used successfully to {{SimpleNuclide2|N| 15}} or 13C/15N label our proteins using our pET1120/BL21(DE3) expression system: Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source For 1L 5xM9 salts:

64 g Na2HPO4-7H2O
15 g KH2PO4
2.5 g NaCl

H2O to final 1L volume and autoclave
To prepare 500mL M9 minimal media:

100mL of 5xM9 salts
1 mL 1 M MgSO4
50 uL 1 M CaCl2
5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)
2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5 g dry
10 mL 20% d-glucose or 2 g dry

Glass distilled & autoclaved H2O to final volume of 500 mL pH solution to 7.3 and filter sterilize (0.2 um filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2 L flask and add ampicillin to a final concentration of 70-100 ug/ml (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37 oC until an OD600 of 07+/-0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

2. General Protocols For LS Media

For 1 L media preparation,
10 g Bacto-tryptone
5 g yeast extract
10 g NaCl

Sterilized by autoclave

Noise removal for PCA

2012-12-23T03:08:09Z

Bozhang: /* Noise cutoff calculation */

[[Category:Protocols]]
[[Category:Metabolomics]]

==Prepare the data set==
1. The data can be prepared in txt file from ACDLab 1D processor.

2. After the spectra are "Autophased" and "Referenced" to TMSP correctly, click the "Integration" icon in the tool bar.

3. Click the "Series" from the menu and choose "Table of common integrals".

4. Then export table to the targeted file folder.

5. Open the file in Office Excel. Delete the first row and insert a new row below the row of sample numbers.

6. Fill the row with sample class names.

==Z score transformation==
Z score is used for normalizing the individual spectrum. The scaling of the data set across all the spectra is performed in SIMCA-P+. (UV scaling is by default)
<math>Z=(x_i-\overline x)/\sigma</math>

==Noise cutoff calculation==
0. The calculation is based on the z-score data set.

1. For each class, calculate the standard deviation and average values.

2. Calculate the absolute value for relative standard deviation by dividing the standard deviation by the absolute average values.

3. Find out the maximum for each row. If the maximum is smaller than 0. It indicates all the z score values are smaller than 0.

4. Find out the region that no peak exists, find out the maximum of the relative standard deviation for each class.

5. Only when the z score is smaller than 0, AND the value of relative standard deviation is smaller than the maximum of the noise region, then that bin can be considered as a noise region. All noise region-defined bins should be set to 0.

==Noise cutoff application==
If the data set is prepared for PCA, only the noise region across the whole data set can be removed. For data set for OPLS-DA, the noise region determined for each class can be removed separately.

Noise removal for PCA

2012-12-23T03:07:21Z

Bozhang: /* Prepare the data set */

[[Category:Protocols]]
[[Category:Metabolomics]]

==Prepare the data set==
1. The data can be prepared in txt file from ACDLab 1D processor.

2. After the spectra are "Autophased" and "Referenced" to TMSP correctly, click the "Integration" icon in the tool bar.

3. Click the "Series" from the menu and choose "Table of common integrals".

4. Then export table to the targeted file folder.

5. Open the file in Office Excel. Delete the first row and insert a new row below the row of sample numbers.

6. Fill the row with sample class names.

==Z score transformation==
Z score is used for normalizing the individual spectrum. The scaling of the data set across all the spectra is performed in SIMCA-P+. (UV scaling is by default)
<math>Z=(x_i-\overline x)/\sigma</math>

==Noise cutoff calculation==
0. The calculation is based on the z-score data set.

1. For each class, calculate the standard deviation and average values.

2. Calculate the absolute value for relative standard deviation by dividing the standard deviation by the absolute average values.

3. Find out the maximum for each row. If the maximum is smaller than 0. It indicates all the z score values are smaller than 0.

4. Find out the region that no peak exists, find out the maximum of the relative standard deviation for each class.

5. Only when the z score is smaller than 0, AND the value of relative standard deviation is smaller than the maximum of the noise region, then that bin can be considered as a noise region.

==Noise cutoff application==
If the data set is prepared for PCA, only the noise region across the whole data set can be removed. For data set for OPLS-DA, the noise region determined for each class can be removed separately.

Vitamin C in Juices

2012-12-23T03:02:23Z

Bozhang: /* Materials */

[[Category:Maxey Demos]]

=Experimental outline=

Determine the Vitamin C content in various freshly squeezed fruit juices. Compare kiwis and oranges (high concentration of Vitamin C) to peaches or apples (low concentration of Vitamin C). Or, blend a vegetable in water to create a solution that can be tested. Use cheesecloth or fiber to remove the fiber from the solution. Bell peppers are particularly high in Vitamin C but the green ones are supposed to have higher content than the red or orange ones. The Natural Hub has we page that lists the Vitamin C content of many fruits at: http://www.naturalhub.com/natural_food_guide_fruit_vitamin_c.htm

=Materials=

#Fruit juices and fruit drinks containing vitamin C (use 1.00 mL per experiment)
#Vitamin C Standard solution (1 mg/mL)(use 1.00 mL per experiment)
#Starch solution (sue 1.00 mL per experiment and control for 2.00 mL total)
#Iodine solution (we actually make I3-; need up to 1.00 mL per experiment, so need 2.00 L)

=Procedure for fruit juice or drink=

#Carefully squirt 1.0 mL of fruit juice or drink into a 10 mL graduate cylinder
#Carefully squirt 1.0 mL of starch solution in to the same 10 mL graduated cylinder
#Empty the contents of the cylinder into a small Erlenmeyer flask.
#Use a pipet bulb to add the iodine solution.
;Count the number of drops it takes to form a purple color.
Swirl the flask to make sure the solution stays purple. If it doesn’t, add one more drop.
#Report your number of drops to the assistant.
#Repeat the procedure with the vitamin C standard

=Procedure for vitamin C standard=

#Carefully squirt 1.0 mL of vitamin C standard into a 10 mL graduated cylinder
#Carefully squirt 1.0 mL of starch solution into the same 10 mL graduated cylinder
#Empty the contents of the cylinder into a small Erlenmeyer flask.
#Use a pipet bulb to add the iodine solution.
;Count the number of drops it takes to form a purple color.
Swirl the flask to make sure the solution stays purple. If it doesn’t, add one more drop.
#Report your number of drops to the assistant.
#You can calculate the mg vitamin C from the following equation:
;Amount of Vitamin C (mg)=(number of drops to fruit juice) / (number of drops to Vitamin C standard)

The equation above has been simplified from this equation
Estimated Vitamin C content = (1 mL) x (1mg/mL) x (number of drops to fruit juice) / (number of drops to Vitamin C standard)

Vitamin C in Juices

2012-12-22T06:35:12Z

Bozhang:

[[Category:Maxey Demos]]

=Experimental outline=

Determine the Vitamin C content in various freshly squeezed fruit juices. Compare kiwis and oranges (high concentration of Vitamin C) to peaches or apples (low concentration of Vitamin C). Or, blend a vegetable in water to create a solution that can be tested. Use cheesecloth or fiber to remove the fiber from the solution. Bell peppers are particularly high in Vitamin C but the green ones are supposed to have higher content than the red or orange ones. The Natural Hub has we page that lists the Vitamin C content of many fruits at: http://www.naturalhub.com/natural_food_guide_fruit_vitamin_c.htm

=Materials=

#Fruit juices and fruit drinks containing vitamin C (use 1.00 mL per experiment)
#Vitamin C Standard solution (1 mg/mL)(use 1.00 mL per experiment)
#Starch solution (sue 1.00 mL per experiment and control for 2.00 mL total)
#Iodine solution (we actually make I3 - ; need up to 1.00 mL per experiment, so need 2.00 L)

=Procedure for fruit juice or drink=

#Carefully squirt 1.0 mL of fruit juice or drink into a 10 mL graduate cylinder
#Carefully squirt 1.0 mL of starch solution in to the same 10 mL graduated cylinder
#Empty the contents of the cylinder into a small Erlenmeyer flask.
#Use a pipet bulb to add the iodine solution.
;Count the number of drops it takes to form a purple color.
Swirl the flask to make sure the solution stays purple. If it doesn’t, add one more drop.
#Report your number of drops to the assistant.
#Repeat the procedure with the vitamin C standard

=Procedure for vitamin C standard=

#Carefully squirt 1.0 mL of vitamin C standard into a 10 mL graduated cylinder
#Carefully squirt 1.0 mL of starch solution into the same 10 mL graduated cylinder
#Empty the contents of the cylinder into a small Erlenmeyer flask.
#Use a pipet bulb to add the iodine solution.
;Count the number of drops it takes to form a purple color.
Swirl the flask to make sure the solution stays purple. If it doesn’t, add one more drop.
#Report your number of drops to the assistant.
#You can calculate the mg vitamin C from the following equation:
;Amount of Vitamin C (mg)=(number of drops to fruit juice) / (number of drops to Vitamin C standard)

The equation above has been simplified from this equation
Estimated Vitamin C content = (1 mL) x (1mg/mL) x (number of drops to fruit juice) / (number of drops to Vitamin C standard)

Spec20

2012-12-22T01:10:43Z

Bozhang: /* Experiment */

==Purpose==

To find an unknown concentration of a solution by plotting known concentration of the same solution vs absorbance.

==Experiment==

0. Preparation: Make sure the spectrometer wavelength is set to 590 nm. The instructions to calibrate the spectrometer is labeled on the front of each Spec20. Prepare six test tubes with percents ranging from 0% to 100% of blue dyes. Test tube 1 is water only it is to be used as the blank. Also every student should have one test tube labeled unknown.

1. Ask the students to draw two columns on a sheet of paper with PERCENT on top of column one and DATA on top of column two.

2. Ask the students to put test tube number 1(BLANK) into spec20. Check each station to make sure the absorbance reading is approximately is zero.

3. Ask the students to put test tube from number 2 to number 5 while reading off the reading corresponding percents 20%, 50%, 80% and 100% and tell them to record it in the PERCENT column as well as record the absorbance in the DATA column.

4. Ask the students to put the last test tube labeled "ev"(UNKNOWN) into the Spec20. Write down the absorbance reading in the data column and mark it as "x" in the percent column.

5. Inform the students to take the data sheets to the computer station.

[[Category:Maxey Demos]]

Computer station

2012-12-22T01:08:59Z

Bozhang: /* Computer Station */

== Computer Station ==

'''Preparation''' ''(should be ready the day before)'':

0.0 Six to eight volunteering laptops from group members should be provided and they are all set in a classroom in the Maxey elementary school.

0.1 Set up an account for Maxey demo for each laptop.

0.2 Install ChemBioDraw and KaleidaGraph software on each laptop.

0.3 Set up wireless printer/router for each laptop.

'''Experiment A:'''

KaleidaGraph is used for processing the Spec20 Data. ''(Make sure the students finish the Spec20 experiment before they come to this station.)''

1. Click the first white box in the toolbar and you can open an empty spreadsheet with three columns.

2. Input the data of concentration and absorbance in column A and B. (Do not input the percentage symbol or the unknown sample absorbance.)

3. Go to “Gallery" from the menu, click “linear” then “scattering”, then there is a window popping up. Choose the A column to be the X-axis, B column to be the y-axis and click “new plot” icon down below.

4. After you have the plot, change the title to be "Absorbance vs. concentration"

5. Go to “file” and click “print graphics”.

'''Experiment B:'''

Three molecules will be drawn by using ChemBioDraw: Acetic acid is the main component of vinegar. Acetylsalicylic acid is the active reagent of aspirin. Glucose is the major energy source in our body.

On the left side of the working window, a toolbar contains all the tools that are handy for drawing those molecules, such as single bonds, double bonds, benzene, texting and eraser.

1. Find an empty place and draw the first bond by dragging the icon of a bond from the toolbar to that place and loose your mouse.

2. Move the mouse to the end of that bond where you want to start another bond or a word (for example, "H" is for proton or hydrogen. "C" is for carbon and "O" is for oxygen).

3. After drawing the molecule, use the “lasso” from the toolbar to select the whole molecule, and then go to the menu and select “convert structure to name”. Find out if you do the right thing by comparing the name that the computer tells you with the name you are supposed to draw.

[[Category:Maxey Demos]]

Tube deep cleaning

2012-11-30T07:39:51Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

1. Empty all contents from dirty NMR tubes into appropriate residue container. Remove the labels on the NMR tube by rinsing with ethanol.

2. Fill the container outside the protect bottles with liquid nitrogen and cool it down.

3. Load the tubes into the tube cleaning apparatus (tube washer) with proper solvents as noted below.

4. The operation of the tube washer:

a)With the left valve closed, slowly and briefly opening the T-valve to the vacuum position evacuates the air from the Labconco®
vacuum bottle and places it under a slight vacuum without removing the solvent.
b)Turn the T-valve to the vent position opens the vacuum bottle to atmosphere breaking the vacuum and forcing solvent into the NMR tube.
c)Switch the T-valve back to the vacuum position places the NMR tubes back under vacuum and remove the solvent from the NMR tube.
d)Repeat the process two or three times effectively washes the NMR tubes with solvent.
e)Switch the T-valve to the vent position and opening the left valve rapidly removes the solvent from the NMR tube cleaner into the
filter flask. The process can be repeated with other solvents.

5. Rinse NMR tubes with nano pure water by filling with water then empty all contents from dirty NMR tubes into appropriate residue container.

6. Rinse NMR tubes with ethanol by filling with ethanol then empty all contents from dirty NMR tubes into appropriate residue container.

7. Rinse NMR tubes with acetone by filling with acetone then empty all contents from dirty NMR tubes into appropriate residue container. The amount of organic solvents should be well-controlled.

8. Fill tubes with nano pure water.

9. Place NMR tubes in a concentrated nitric acid bath and soak 24hrs.

10. Empty contents of NMR tubes into the acid bath.

11. Rinse the NMR tubes under the tap water to remove most of the acid.

12. Load the tubes back to the tube washer.

13. Rinse NMR tubes in baking soda solution to neutralize acid.

14. Rinse NMR tube thoroughly with nano pure water until the solvent waste is neutral.

15. Dry NMR tubes: lay tubes horizontally in the oven.

16. Once dry inspect tubes for any crack, divots or other defect. The tubes with crack on the opening should be collected for further modification and reuse. Other problematic tubes have to be disposed.

[[File:Tubewasher1.jpg]][[File:Tubewasher2.jpg]]

A demonstration video can be found at here [http://bionmr.unl.edu/files/misc/tubewasher-instructions.wmv]

1H NMR Analysis (ACDLab)

2012-11-20T10:48:31Z

Bozhang: /* Import spectral data */

[[Category:Protocols]]
[[Category:Metabolomics]]

=ACDLab/SpecManager=

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:

1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “OK”.

3. When all of the data files are correctly listed, click “OK” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “OK” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved table can be imported directly into SIMCA or opened in Excel.

1H NMR Analysis (ACDLab)

2012-11-20T10:47:52Z

Bozhang: /* Import spectral data */

[[Category:Protocols]]
[[Category:Metabolomics]]

=ACDLab/SpecManager=

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:

1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “Ok”.

3. When all of the data files are correctly listed, click “Ok” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “Ok” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved table can be imported directly into SIMCA or opened in Excel.

Noise removal for PCA

2012-10-13T05:44:55Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

==Prepare the data set==
1. The data can be prepared in txt file from ACDLab 1D processor.

2. After the spectra are "Autophased" and "Referenced" to TMSP correctly, click the "Integration" icon in the tool bar.

3. Click the "Series" from the menu and choose "Table of common integrals".

4. Then export table to the targeted file folder.

5. Open the file in Office Excel. Delete the first row and insert a new row below the first row of sample numbers.

6. Fill the row with sample class names.

==Z score transformation==
Z score is used for normalizing the individual spectrum. The scaling of the data set across all the spectra is performed in SIMCA-P+. (UV scaling is by default)
<math>Z=(x_i-\overline x)/\sigma</math>

==Noise cutoff calculation==
0. The calculation is based on the z-score data set.

1. For each class, calculate the standard deviation and average values.

2. Calculate the absolute value for relative standard deviation by dividing the standard deviation by the absolute average values.

3. Find out the maximum for each row. If the maximum is smaller than 0. It indicates all the z score values are smaller than 0.

4. Find out the region that no peak exists, find out the maximum of the relative standard deviation for each class.

5. Only when the z score is smaller than 0, AND the value of relative standard deviation is smaller than the maximum of the noise region, then that bin can be considered as a noise region.

==Noise cutoff application==
If the data set is prepared for PCA, only the noise region across the whole data set can be removed. For data set for OPLS-DA, the noise region determined for each class can be removed separately.

Tube deep cleaning

2012-10-13T05:30:04Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

1. Empty all contents from dirty NMR tubes into appropriate residue container. Remove the labels on the NMR tube by rinsing with ethanol.

2. Fill the container outside the protect bottles with liquid nitrogen and cool it down.

3. Load the tubes into the tube cleaning apparatus (tube washer) with proper solvents as noted below.

4. The operation of the tube washer:

a)With the left valve closed, slowly and briefly opening the T-valve to the vacuum position evacuates the air from the Labconco®
vacuum bottle and places it under a slight vacuum without removing the solvent.
b)Turn the T-valve to the vent position opens the vacuum bottle to atmosphere breaking the vacuum and forcing solvent into the NMR tube.
c)Switch the T-valve back to the vacuum position places the NMR tubes back under vacuum and remove the solvent from the NMR tube.
d)Repeat the process two or three times effectively washes the NMR tubes with solvent.
e)Switch the T-valve to the vent position and opening the left valve rapidly removes the solvent from the NMR tube cleaner into the
filter flask. The process can be repeated with other solvents.

5. Rinse NMR tubes with nano pure water by filling with water then empty all contents from dirty NMR tubes into appropriate residue container.

6. Rinse NMR tubes with ethanol by filling with ethanol then empty all contents from dirty NMR tubes into appropriate residue container.

7. Rinse NMR tubes with acetone by filling with acetone then empty all contents from dirty NMR tubes into appropriate residue container. The amount of organic solvents should be well-controlled.

8. Fill tubes with nano pure water.

9. Place NMR tubes in a concentrated nitric acid bath and soak 24hrs.

10. Empty contents of NMR tubes into the acid bath.

11. Rinse the NMR tubes under the tap water to remove most of the acid.

12. Load the tubes back to the tube washer.

13. Rinse NMR tubes in baking soda solution to neutralize acid.

14. Rinse NMR tube thoroughly with nano pure water until the solvent waste is neutral.

15. Dry NMR tubes: lay tubes horizontally in the oven.

16. Once dry inspect tubes for any crack, divots or other defect. The tubes with crack on the opening should be collected for further modification and reuse. Other problematic tubes have to be disposed.

[[File:Tubewasher1.jpg]][[File:Tubewasher2.jpg]]

A demonstration video can be found at here [http:\\bionmr-c1.unl.edu\DATA\bozhang\Standards\Movie.wma]

Tube deep cleaning

2012-10-13T05:27:55Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

1. Empty all contents from dirty NMR tubes into appropriate residue container. Remove the labels on the NMR tube by rinsing with ethanol.

2. Fill the container outside the protect bottles with liquid nitrogen and cool it down.

3. Load the tubes into the tube cleaning apparatus (tube washer) with proper solvents as noted below.

4. The operation of the tube washer:

a)With the left valve closed, slowly and briefly opening the T-valve to the vacuum position evacuates the air from the Labconco®
vacuum bottle and places it under a slight vacuum without removing the solvent.
b)Turn the T-valve to the vent position opens the vacuum bottle to atmosphere breaking the vacuum and forcing solvent into the NMR tube.
c)Switch the T-valve back to the vacuum position places the NMR tubes back under vacuum and remove the solvent from the NMR tube.
d)Repeat the process two or three times effectively washes the NMR tubes with solvent.
e)Switch the T-valve to the vent position and opening the left valve rapidly removes the solvent from the NMR tube cleaner into the
filter flask. The process can be repeated with other solvents.

5. Rinse NMR tubes with nano pure water by filling with water then empty all contents from dirty NMR tubes into appropriate residue container.

6. Rinse NMR tubes with ethanol by filling with ethanol then empty all contents from dirty NMR tubes into appropriate residue container.

7. Rinse NMR tubes with acetone by filling with acetone then empty all contents from dirty NMR tubes into appropriate residue container. The amount of organic solvents should be well-controlled.

8. Fill tubes with nano pure water.

9. Place NMR tubes in a concentrated nitric acid bath and soak 24hrs.

10. Empty contents of NMR tubes into the acid bath.

11. Rinse the NMR tubes under the tap water to remove most of the acid.

12. Load the tubes back to the tube washer.

13. Rinse NMR tubes in baking soda solution to neutralize acid.

14. Rinse NMR tube thoroughly with nano pure water until the solvent waste is neutral.

15. Dry NMR tubes: lay tubes horizontally in the oven.

16. Once dry inspect tubes for any crack, divots or other defect. The tubes with crack on the opening should be collected for further modification and reuse. Other problematic tubes have to be disposed.

[[File:Tubewasher1.jpg]][[File:Tubewasher2.jpg]]

A demonstration video can be found at here[http:\\bionmr-c1.unl.edu\DATA\bozhang\Standards\Movie.wma]

Main Page

2012-10-13T05:05:11Z

Bozhang: /* Wiki Categories */

'''Welcome to the BioNMR Wiki Page.'''

This wiki exists to provide better access to lab protocols for the Powers lab. It is not a replacement for lab notebooks. Feel free to begin documenting new lab protocols or procedures here, or even moving old protocols onto this wiki.

== Getting started ==
For those who need a quick and dirty introduction to MediaWiki formatting, try this reference card:
* [http://bionmr.unl.edu/w/MediaWikiRefCard.pdf MediaWiki Reference Card]

Other general MediaWiki information pages:
* [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]

=== Wiki Categories ===

[[Special:Categories|View All Categories]]

Commonly used Categories:

* [[:Category:Protocols|Protocols]]
* [[:Category:Bioscreen|Bioscreen]]
* [[:Category:FAST-NMR|FAST-NMR]]
* [[:Category:SMACMS|SMACMS]]
* [[:Category:Molecular Docking|Molecular Docking]]
* [[:Category:Metabolomics|Metabolomics]]
* [[:Category:Maxey Demos|Maxey Demos]]

Tube deep cleaning

2012-10-13T05:04:50Z

Bozhang:

500 MHz NMR checklist

2012-10-13T05:03:09Z

Bozhang:

===Check in===
Make sure the system is accessible, e.g. not being used or under maintenance

Check the status of the cryoprobe shown on the laptop screen

Check your belongings on your body and remove anything with ferromagnetic properties such as electronic devices and keep them away from the probe

Login

Load your sample(s)

Start topspin software

Type in “rsp” to save the last dataset

Type in “rsh” (use the newest cryoprobe shim set of your specific solvent)

Type in “lock” and when finished, push the button standby Shim Z1, Z2 manually

Type in “gradshim” (use the proper gradshim settings for a heavily protonated or deuterated solvent)

Wait for it to finish and evaluate the results, if they are not good repeat gradshim

Close gradshim

Make sure the lock light is on Type in “atma” to tune and match the probe Find the proton 90 time (“p1”)

Be sure to use zg and not zg30 Update p1

Note: The command “edprosol” opens the field values 1H pulsewidth then power to change proton 90 degree pulse (for example getprosol 1H 8.3 2 will set the proton 90 time to 8.3 µsec at a power level of 2 dB for your current TOPSPIN session only. Each value is separated by a blank space).

Choose the solvent

Type in “rgacryo” Start running

If you use the BACS automation:

a) Store the necessary gradshim file for automation, understand that ICON willlook for the shim file under a specific name

b) Use administrative password to change default p1

c) Type in “getprosol” to update the parameters

d) Set up the parameters for your first sample in “icon” program

e) Change au_zg to zgonly if you need constant rg value

f) Make sure the sample is not rotating: Go to configuration and click master switches, check the box next to never rotate sample note the selections on the other switches that are important like the lock selections and the standard shim file loading selection. You must be logged in as “nmrsu” (nmr superuser) to change this.

===During the running===

Pay attention to the weather conditions especially any electric outage. Follow the “cryo platform outage procedure” as being described by Joe.

===Check out/end of run===

If any samples are reloaded into the belt, verify that the sample tube is not set to the wrong depth (spinner tightness)

Load the reference CDCl3 sample

Type in “rfshim” and choose the newest CDCl3 cryoprobe shim set

Type in “lock CDCl3” Type in “atma” Change p1 back to the pulse length listed in the back of the log book by the computer

Eject the sample and remove the NMR tube from the spinner Leave the spinner in the black holder at the workstation

Sign up the log book and clear up the area

If you use the BACS automation: First thing is to: Stop the running (when all samples are finished)

Type in “edprosol” and change p1 value back (same procedure above)

===Note===

1. Make sure your tube is clean, the spinner is clean, the sample is 600 µL and the tube is in the right depth of the spinner, holding tightly. If you have multiple samples, they need to have the same volume and fit into the spinners at the same height.

2. Use a container or a rack to carry the NMR tubes from your own lab to the NMR lab. Make sure the sample is not reactive with the solvents and uniformly dissolved in your solvent.

[[Category:Protocols]]
[[Category:Metabolomics]]

500 MHz NMR checklist

2012-10-13T03:46:27Z

Bozhang:

1H NMR Analysis (SIMCA)

2012-10-03T05:59:43Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]
==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.

For PLS-DA analysis, the y-variable values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option The data formatting should now look correct.

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

10. Use the “Commands” button (bottom left) to “transpose” the data set
The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
It may already be labeled as primary.

12. Click on the “Observation IDs primary” button
The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row
It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button.
This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button.
This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”

25. Click “OK”

==PLS Analysis==

1. The data are prepared in an Excel file as described above.

2. The Excel file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the discriminator values).

6. The region containing the bucketed intensities is highlighted.
These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

7. Next, the discriminator values should be set as 1 or 0.

8. Selected data may still be excluded prior to the statistical analysis

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. This manual is for SIMCA-P+ 12.0 or higher version.

2. Start from an Excel file that has NOT been autoscaled and any outliers should be removed prior to OPLS analysis.

3. The data file should be first analyzed using either PCA or PLS as described above to find two unsupervised, separated groups for comparison. Wild-type or control groups should be assigned as "0", mutant or treatment group should be assigned as "1". This column of "0" and "1" should be taken as Y values.

4. Follow the same procedure as PCA to import the data.

5. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance. A new Workset window will appear.

6. Click "Yes to All" and then another workset window will pop-up with "model number", "type of model", etc.

7. Double click it and click "Workset" icon on that pop-up window.

8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

9. Click Autofit button on the toolbar

1H NMR Analysis (SIMCA)

2012-10-03T05:58:28Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]
==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.

For PLS-DA analysis, the y-variable values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

10. Use the “Commands” button (bottom left) to “transpose” the data set.
The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
It may already be labeled as primary.

12. Click on the “Observation IDs primary” button
The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row
It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button.
This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button.
This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”

25. Click “OK”

==PLS Analysis==

1. The data are prepared in an Excel file as described above.

2. The Excel file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the discriminator values).

6. The region containing the bucketed intensities is highlighted.
These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

7. Next, the discriminator values should be set as 1 or 0.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. This manual is for SIMCA-P+ 12.0 or higher version.

2. Start from an Excel file that has NOT been autoscaled and any outliers should be removed prior to OPLS analysis.

3. The data file should be first analyzed using either PCA or PLS as described above to find two unsupervised, separated groups for comparison. Wild-type or control groups should be assigned as "0", mutant or treatment group should be assigned as "1". This column of "0" and "1" should be taken as Y values.

4. Follow the same procedure as PCA to import the data.

5. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance. A new Workset window will appear.

6. Click "Yes to All" and then another workset window will pop-up with "model number", "type of model", etc.

7. Double click it and click "Workset" icon on that pop-up window.

8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

9. Click Autofit button on the toolbar

1H NMR Analysis (SIMCA)

2012-10-03T05:58:09Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

=1H NMR Analysis (SIMCA)=

==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.

For PLS-DA analysis, the y-variable values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

10. Use the “Commands” button (bottom left) to “transpose” the data set.
The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
It may already be labeled as primary.

12. Click on the “Observation IDs primary” button
The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row
It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button.
This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button.
This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”

25. Click “OK”

==PLS Analysis==

1. The data are prepared in an Excel file as described above.

2. The Excel file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the discriminator values).

6. The region containing the bucketed intensities is highlighted.
These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

7. Next, the discriminator values should be set as 1 or 0.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. This manual is for SIMCA-P+ 12.0 or higher version.

2. Start from an Excel file that has NOT been autoscaled and any outliers should be removed prior to OPLS analysis.

3. The data file should be first analyzed using either PCA or PLS as described above to find two unsupervised, separated groups for comparison. Wild-type or control groups should be assigned as "0", mutant or treatment group should be assigned as "1". This column of "0" and "1" should be taken as Y values.

4. Follow the same procedure as PCA to import the data.

5. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance. A new Workset window will appear.

6. Click "Yes to All" and then another workset window will pop-up with "model number", "type of model", etc.

7. Double click it and click "Workset" icon on that pop-up window.

8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

9. Click Autofit button on the toolbar

1H NMR Analysis (SIMCA)

2012-10-03T05:54:09Z

Bozhang: /* OPLS-DA Analysis */

[[Category:Protocols]]
[[Category:Metabolomics]]

=1H NMR Analysis (SIMCA)=

==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.

For PLS-DA analysis, the y-variable values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

10. Use the “Commands” button (bottom left) to “transpose” the data set.
The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
It may already be labeled as primary

12. Click on the “Observation IDs primary” button
The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row
It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button.
This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button.
This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing.

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”.

25. Click “OK”.

==PLS Analysis==

1. The data are prepared in an Excel file as described above.

2. The Excel file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the discriminator values).

6. The region containing the bucketed intensities is highlighted.
These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

7. Next, the discriminator values should be set as 1 or 0.
Normally, "0" is assigned to wild-type or control group while "1" is assigned to mutant or treatment group.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. This manual is for SIMCA-P+ 12.0 or higher version.

2. Start from an Excel file that has NOT been autoscaled and any outliers should be removed prior to OPLS analysis.

3. The data file should be first analyzed using either PCA or PLS as described above to find two unsupervised, separated groups for comparison. Wild-type or control groups should be assigned as "0", mutant or treatment group should be assigned as "1". This column of "0" and "1" should be taken as Y values.

4. Follow the same procedure as PCA to import the data.

5. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance. A new Workset window will appear.

6. Click "Yes to All" and then another workset window will pop-up with "model number", "type of model", etc.

7. Double click it and click "Workset" icon on that pop-up window.

8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

9. Choose the Analysis pull-down menu and select Autofit (…or just use the Autofit button on the toolbar).

1H NMR Analysis (SIMCA)

2012-10-03T05:23:31Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

=1H NMR Analysis (SIMCA)=

==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.

For PLS-DA analysis, the y-variable values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

10. Use the “Commands” button (bottom left) to “transpose” the data set.
The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
It may already be labeled as primary

12. Click on the “Observation IDs primary” button
The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row
It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button.
This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button.
This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing.

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”.

25. Click “OK”.

==PLS Analysis==

1. The data are prepared in an Excel file as described above.

2. The Excel file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the discriminator values).

6. The region containing the bucketed intensities is highlighted.
These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

7. Next, the discriminator values should be set as 1 or 0.
Normally, "0" is assigned to wild-type or control group while "1" is assigned to mutant or treatment group.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. I need to do more reading… But, I believe that I have basic command sequence needed to explore the approach using SIMCA-P+ Version 12.0.

2. Start from an Excel file that has NOT been noise corrected and NOT been autoscaled.

3. The data file should be first analyzed using either PCA or PLS as described above.

4. The analysis is reportedly hindered by extreme outliers found in PCA 2D scores plot (where did I read this…?...the Umetrics manual maybe…). So, any outliers should be removed prior to OPLS analysis.

5. Open the Excel file in SIMCA, go to the Dataset pull-down menu. Choose Spectral Filters. In the available column, scroll down and select OCS. Press the button labeled as => to move OCS into the selected column.

6. Click OK.

7. The OSC panel should appear. Refer to your NMR spectral data to identify bins that contain strong peaks for metabolites (e.g., citrate peaks in mouse urine spectra).

8. Highlight several of these bins (maybe 5-10).

9. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance.

10. The result, both in terms of the scores plot and the plots showing the difference between PC score points, depends which peaks are assigned as the Y values.

11. A new OSC panel will appear.
There should be a table with columns labeled No, Angle in Degreees, Remaining SS in % and Eigenvalue.

12. Click on the “next component” button. Generally, two components are recommended by Umetrics.

13. Click on the Next button.

14. Check the destination folder and file name. Click on the “Finish” button.

15. Read and close the OSC message box.

16. The current model will probably say “PLS <unfitted>” in the Type column. Fit the data using the usual commands for autofit and plot visualization

17. Go the Analysis pull-down menu and select Change Model Type.

18. From the list, choose OPLS/O2PLS.

19. Choose the Analysis pull-down menu and select Autofit (…or just use the Autofit button on the toolbar).

1H NMR Analysis (SIMCA)

2012-10-03T04:34:02Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

=1H NMR Analysis (SIMCA)=

==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise.

For PLS-DA analysis, the y-variable (e.g., the paralysis score for mouse urine metabolomics) values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. By default, SIMCA-P will use "UV" autoscale the imported data set.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct

6. Click “OK”

7. Project type should be SIMCA-P Project

8. Click “next” button

9. Respond “No” to the question regarding the one row that is “empty or contains only text”
It is probably the row of labels that was inserted using Excel.

10. Use the “Commands” button (bottom left) to “transpose” the data set. The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

11. After transposing, make sure that first column has been identified as the “primary observation ID’s”. To do this, click on the button at the top of the column
(Note: it may already be labeled as primary) and then click on the “Observation IDs primary” button. The observation IDs should now be color coded with the observation ID primary color (dark yellow or mustard color). If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

12. Click on the butoon for the first row (note: it may already be labeled as primary) and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

13. Click the “Finish” button

14. Exclude the solvent region and “ends” of the spectra

15. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge)

16. Repeat for each desired region

17. Click “Next” then click “Finish”

18. Using the menu bar, click on the “autofit” button. This should calculate the first and second primary components. Additional components can be calculated using the “Calculate next component” button.

19. View the results, click on the “Create four overview plots” button. This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

20. Expand the score scatter plot for better viewing.

21. Click on data point

22. Right-click and choose “Properties”

23. Choose the color tab and choose coloring type by “identifiers”
The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

24. Click “Apply”.

25. Click “OK”.

==PLS Analysis==

Note:
1. The data are prepared in an Excel file as described above.

2. The EXCEL file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the
discriminator values).

6. The region containing the bucketed intensities is highlighted. These values are labeled as “x-variables” by clicking on the VARIABLE button
in the left hand frame and choosing x-variables.

7. Next, the discriminator values (e.g., the single column of paralysis scores in mouse urine metabolomics) are highlighted. These values are labeled as “y-variables” by clicking on the VARIABLES button and choosing “y-variables”. The column of y-variables will not have a variable ID number in Row 1 because that row was not created in the ACD processing. Incrementing the variable number and typing the value into the cell will save SIMCA from asking about that issue. Also, SIMCA does not like a mix of text and numerical values in the “y-variables” column. I discovered this when I used simply “0, 1, 2, 3, 4” instead of “EAE-0, EAE-1,…,EAE-4”. One response is to write in the text value using the missing value box in the left hand panel (it is below the “exclude data button”). This approach preserves the numerical content for PLS. Another answer is to use all text or all numerical values, but not a mix.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

==OPLS-DA Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. I need to do more reading… But, I believe that I have basic command sequence needed to explore the approach using SIMCA-P+ Version 12.0.

2. Start from an Excel file that has NOT been noise corrected and NOT been autoscaled.

3. The data file should be first analyzed using either PCA or PLS as described above.

4. The analysis is reportedly hindered by extreme outliers found in PCA 2D scores plot (where did I read this…?...the Umetrics manual maybe…). So, any outliers should be removed prior to OPLS analysis.

5. Open the Excel file in SIMCA, go to the Dataset pull-down menu. Choose Spectral Filters. In the available column, scroll down and select OCS. Press the button labeled as => to move OCS into the selected column. Click OK.

6. The OSC panel should appear. Refer to your NMR spectral data to identify bins that contain strong peaks for metabolites (e.g., citrate peaks in mouse urine spectra). Highlight several of these bins (maybe 5-10). Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance.

7. The result, both in terms of the scores plot and the plots showing the difference between PC score points, depends which peaks are assigned as the Y values.

8. A new OSC panel will appear. There should be a table with columns labeled No, Angle in Degreees, Remaining SS in % and Eigenvalue. Click on the “next component” button. Generally, two components are recommended by Umetrics. Click on the Next button.

9. Check the destination folder and file name. Click on the “Finish” button.

10. Read and close the OSC message box.

11. The current model will probably say “PLS <unfitted>” in the Type column. Fit the data using the usual commands for autofit and plot visualization

12. Go the Analysis pull-down menu and select Change Model Type. From the list, choose OPLS/O2PLS. Choose the Analysis pull-down menu and select Autofit (…or just use the Autofit button on the toolbar).

1H NMR Analysis (ACDLab)

2012-10-03T04:23:56Z

Bozhang: /* Integrate the spectra and export the peak list */

[[Category:Protocols]]
[[Category:Metabolomics]]

=ACDLab/SpecManager=

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:
1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “Ok”.

3. When all of the data files are correctly listed, click “Ok” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “Ok” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved table can be imported directly into SIMCA or opened in Excel.

1H NMR Analysis (ACDLab)

2012-10-03T04:23:34Z

Bozhang:

[[Category:Protocols]]
[[Category:Metabolomics]]

=ACDLab/SpecManager=

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:
1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “Ok”.

3. When all of the data files are correctly listed, click “Ok” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “Ok” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved table can be imported directly into SIMCA or opened in Excel.

Spec20

2012-10-03T04:15:22Z

Bozhang:

Coin battery

2012-10-03T04:11:29Z

Bozhang:

[[Category:Maxey Demos]]

==Materials:==

A small box of coins (quarters, dimes and nickels)

Saturated potassium chloride (200mL)

Filter paper strips (>100)

Voltage meters (5-10)

Paper towels

Goggles and gloves

==Intro for the students:==

Ask the students about batteries in daily life

Explain the function and usage of voltage meter

Explain why the stack of coins can produce electronic current

==Procedures (for each group):==

a. Soak the filter paper strips in saturated potassium chloride.

b. Stack two pile of coins with various combination.

c. Put the two piles together with the filter paper in between.

d. Measure the voltage of the whole stack using a voltage meter.

e. Repeat to get the highest voltage value by changing the order and combination of coins.

==Notes:==

a. Make sure all the voltage meters are working before use. If not, change the battery.

b. Make sure the alligator clip from the voltage meter has a clean metal surface. If not, use sand paper to remove the rust.

Coin battery

2012-10-03T04:09:55Z

Bozhang:

[[Category:Maxey Demos]]

==Materials:==

A small box of coins (quarters, dimes and nickels)

Saturated potassium chloride (200mL)

Filter paper strips (>100)

Voltage meters (5-10)

Paper towels

Goggles and gloves

==Intro for the students:==

Ask the students about batteries in daily life

Explain the function and usage of voltage meter

Explain why the stack of coins can produce electronic current

==Procedures (for each group):==

a. Soak the filter paper strips in saturated potassium chloride.

b. Stack two pile of coins with various combination

c. Put the two piles together with the filter paper in between.

d. Measure the voltage of the whole stack using a voltage meter.

e. Repeat to get the highest voltage value by changing the order and combination of coins.

==Notes:==

a. Make sure all the voltage meters are working before use. If not, change the battery.

b. Make sure the alligator clip from the voltage meter has a clean metal surface. If not, use sand paper to remove the rust. σ

Checklist electronic shop

2012-10-03T04:06:20Z

Bozhang:

Laptop X 1

Laptop charger X 1

Laptop lock X 1

LCD projector X 1

Router X 1

Ethernet cable X 1

Voltage meter X 1

Speaker X 1 pair

Electric extension cable and board X 8

[[Category:Maxey Demos]]

1H NMR Analysis (SIMCA)

2012-09-25T06:19:31Z

Bozhang: Created page with "Category:Protocols Category:Metabolomics ===1H NMR Analysis (SIMCA)=== ==Excel Processing== Secondary observation ID labels can be added by inserting a blank second..."

[[Category:Protocols]]
[[Category:Metabolomics]]

===1H NMR Analysis (SIMCA)===

==Excel Processing==
Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Note:

Use the standard method to remove instrument noise.

For PLS-DA analysis, the y-variable (e.g., the paralysis score for mouse urine metabolomics) values are placed in a row below the row containing the last NMR integral bucket value. When the spreadsheet is transposed in SIMCA, this last row becomes the last column.

After noise removal, the data should be autoscaled. However, the application of this step is still being explored. According to van den Berg and coauthors (see BMC Genomics 2006, 7, 142), the scaling is applied to the individual bins.

==PCA Analysis==

1. Start a “new” project by opening the desired integral table spreadsheet.

2. Click on new project icon

3. Select the desired Excel file

4. Click “Open”

5. Click on the comma option. The data formatting should now look correct. Click “OK”

6. Project type should be SIMCA-P Project.

7. Click “next” button.

8. Respond “No” to the question regarding the one row that is “empty or contains only text”. It is probably the row of labels that was inserted
using Excel.

9. Use the “Commands” button (bottom left) to “transpose” the data set. The rows should now correspond to a given NMR spectrum. Each row is an
“observation”. The integral values contained in each row are referred to as “variables”.

10. After transposing, make sure that first column has been identified as the “primary observation ID’s”. To do this, click on the button at the
top of the column (note: it may already be labeled as primary) and then click on the “Observation IDs primary” button. The observation IDs should now
be color coded with the observation ID primary color (dark yellow or mustard color). If secondary observation IDs were inserted using Excel, then
click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light
yellow).

11. Click on the butoon for the first row (note: it may already be labeled as primary) and then click on the “Variable IDs primary”
button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right. Click the “Finish” button.

12. Exclude the solvent region and “ends” of the spectra.

13. Highlight the desired rows by dragging the cursor along the top set of buttons and then click the “exclude” button (along the left edge).

14. Repeat for each desired region.

15. Click “Next” then click “Finish”

16. Using the menu bar, click on the “autofit” button. This should calculate the first and second primary components. Additional components can
be calculated using the “Calculate next component” button.

17. View the results, click on the “Create four overview plots” button. This produces the “Score Scatter” plot in the upper-left hand corner.
The lower-left hand corner contains the “Loading Scatter” plot.

18. Expand the score scatter plot for better viewing.

19. Click on data point

20. Right-click and choose “Properties”

21. Choose the color tab and choose coloring type by “identifiers”. The default then is to color by secondary observation IDs. This uses the
labels inserted using Excel. If desired, change the default colors. Click “Apply”. Click “Ok”.

==PLS Analysis==

Note:
1. The data are prepared in an Excel file as described above.

2. The EXCEL file is opened in SIMCA as described above for PCA analysis.

3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

4. The label information for observations and variables should be processed as described for PCA analysis above.

5. The difference between the PCA approach and the PLS approach occurs with labeling of the variables (i.e., the bucketed intensities and the
discriminator values).

6. The region containing the bucketed intensities is highlighted. These values are labeled as “x-variables” by clicking on the VARIABLE button
in the left hand frame and choosing x-variables.

7. Next, the discriminator values (e.g., the single column of paralysis scores in mouse urine metabolomics) are highlighted. These values are
labeled as “y-variables” by clicking on the VARIABLES button and choosing “y-variables”. The column of y-variables will not have a variable ID number
in Row 1 because that row was not created in the ACD processing. Incrementing the variable number and typing the value into the cell will save SIMCA
from asking about that issue. Also, SIMCA does not like a mix of text and numerical values in the “y-variables” column. I discovered this when I
used simply “0, 1, 2, 3, 4” instead of “EAE-0, EAE-1,…,EAE-4”. One response is to write in the text value using the missing value box in the left
hand panel (it is below the “exclude data button”). This approach preserves the numerical content for PLS. Another answer is to use all text or all
numerical values, but not a mix.

8. Selected data may still be excluded prior to the statistical analysis.

9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA
analysis described above.

==OPLS Analysis==

1. This approach applies orthogonal signal correction prior to PLS analysis. I need to do more reading… But, I believe that I have basic command sequence needed to explore the approach using SIMCA-P+ Version 12.0.

2. Start from an Excel file that has NOT been noise corrected and NOT been autoscaled.

3. The data file should be first analyzed using either PCA or PLS as described above.

4. The analysis is reportedly hindered by extreme outliers found in PCA 2D scores plot (where did I read this…?...the Umetrics manual maybe…).
So, any outliers should be removed prior to OPLS analysis.

5. Open the Excel file in SIMCA, go to the Dataset pull-down menu. Choose Spectral Filters. In the available column, scroll down and select
OCS. Press the button labeled as => to move OCS into the selected column. Click OK.

6. The OSC panel should appear. Refer to your NMR spectral data to identify bins that contain strong peaks for metabolites (e.g., citrate peaks
in mouse urine spectra). Highlight several of these bins (maybe 5-10). Click on Y to change the state of these to Y values. Click on the “Next >”
button. There may be a message regarding exclusion of variables with no variance.

7. The result, both in terms of the scores plot and the plots showing the difference between PC score points, depends which peaks are assigned as the Y values.

8. A new OSC panel will appear. There should be a table with columns labeled No, Angle in Degreees, Remaining SS in % and Eigenvalue. Click on
the “next component” button. Generally, two components are recommended by Umetrics. Click on the Next button.

9. Check the destination folder and file name. Click on the “Finish” button.

10. Read and close the OSC message box.

11. The current model will probably say “PLS <unfitted>” in the Type column. Fit the data using the usual commands for autofit and plot
visualization

12. Go the Analysis pull-down menu and select Change Model Type. From the list, choose OPLS/O2PLS. Choose the Analysis pull-down menu and select
Autofit (…or just use the Autofit button on the toolbar).

1H NMR Analysis (ACDLab)

2012-09-25T05:40:16Z

Bozhang: Created page with "Category:Protocols Category:Metabolomics ===ACDLab/SpecManager=== ==Import spectral data== Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. W..."

[[Category:Protocols]]
[[Category:Metabolomics]]

===ACDLab/SpecManager===

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:
1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “Ok”.

3. When all of the data files are correctly listed, click “Ok” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “Ok” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved
table can be imported directly into SIMCA or opened in Excel.

1H NMR Analysis

2012-09-25T05:36:46Z

Bozhang: Created page with "===ACDLab/SpecManager=== ==Import spectral data== Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatme..."

===ACDLab/SpecManager===

==Import spectral data==

Use 1D Macro to load the desired series of 1D 1H NMR ".fid" data files. When this is done, by default, the “group treatment” is on and ACD operations will be applied to the entire group.

Steps:
1. Click on the “1D NMR Macro” button.

2. On the “Execute Group Window”, make sure:

a) “Plate” should not be checked.

b) Check “spectra”.

c) Click on “add group”. The wild cards ("*" for numbers, "?" for letters) can be inserted in the directory mask line. The file mask should read “.fid”. Click “Ok”.

3. When all of the data files are correctly listed, click “Ok” to execute the group macro and load the files into ACDLab/SpecManager. Assuming that the files loaded correctly, click “Ok” when the process if finished.

==Pre-process the spectra==

If appropriate, apply a window function. To match what TOPSPIN does automatically when processing data on the 500 MHz console, use a 0.3 Hz exponential window function prior to Fourier transform (FT).

Steps:

1. Click on “WFunctions” in the operations bar.

2. Set LB = 0.3 Hz

3. Choose “Exponential”.

4. Click “Ok”.

5. Fourier transform the FID files

6. Click on “Fourier Tr.” in the operations bar.

7. Phase correct the spectra.

8. Click on the “Phase Corr.” button on the operations bar.

9. Use the “Auto Simple” option.

10. Click on the “checkmark” to accept the result.

11. Set chemical shift reference using the TMSP peak.

12. Click on the “Reference” button on the operations bar.

13. Check the “Options” to verify that TMS is set. Both TMSP and TMS have chemical shift values of 0.000 ppm.

14. Click on “Auto”. Then click “Ok” in response to the “There are no labeled peaks available…” message if it appears.

==Integrate the spectra and export the peak list==

Using the “auto integrate” option:

Steps:

1. Click on the Integration operation.

2. Check the “Options”

a) Set for “Bucket Integration”

b) Set width of buckets to 0.025 ppm

c) Choose intelligent bucketing with width looseness (%) = 50 and method = sum

d) Set reference to “whole spectrum”.

e) Click “Ok”

f) Click on “Auto” to perform the integration

g) Click on the “checkmark” to accept the result.

3. Use the Series pull-down menu to bring up the table of common integrals. A right-click will allow one to save the integral table. The saved
table can be imported directly into SIMCA or opened in Excel.

Making Heatmaps

2012-09-22T00:29:18Z

Bozhang:

==Load the data==
The data can be prepared in csv or text file to load the data in to R. For example, to load ''list.csv'':

heatmapname <- read.csv (“list.csv”)

==Name the rows==
You can then name the rows of the heatmap like so:

row.names(heatmapname) <- data$Name

To exclude the first column from the heat map, use a command of similar form to the following:

heatmapname <- heatmapname [,2: n]

In the above command, '''n''' is the number of columns to be included in the heat map.

==Build a data matrix==
Use the following command to build a data matrix for making the heat map:

heatmapname_matrix <- data.matrix (heatmapname)

==Plot the heat map==
'''Note: Gnuplot package must be installed in R before heat maps may be displayed!''' Run the following command to load in the ''gplots'' library.

library("gplots")

To make heat map, run the following command:

heatmap.2 (heatmapname_matrix, dendrogram="row", col= redgreen (75), scale="none",
key = TRUE, keysize = 1.0, margins = c(4,30),
density.info="none", trace="none")

'''Note: To rescale the color key, add break function to the heatmap.2. '''

'''breaks=c(seq(-1,0.8,length=10),seq(0.8,1.2,length=10),seq(1.2,3,length=10),'''

This will define the color range:

"red=[-1,0.8]

black=[0.8,1.2]

green=[1.2,3]"

[[Category:Protocols]]
[[Category:Metabolomics]]

500 MHz NMR checklist

2012-09-22T00:15:36Z

Bozhang:

==CHECKLIST _500 MHz NMR facility manipulation==

===Check in===
Make sure the system is accessible, e.g. not being used or under maintenance

Check the status of the cryoprobe shown on the laptop screen

Check your belongings on your body and remove anything with ferromagnetic properties such as electronic devices and keep them away from the probe

Login

Load your sample(s)

Start topspin software

Type in “rsp” to save the last dataset

Type in “rsh” (use the newest cryoprobe shim set of your specific solvent)

Type in “lock” and when finished, push the button standby Shim Z1, Z2 manually

Type in “gradshim” (use the proper gradshim settings for a heavily protonated or deuterated solvent)

Wait for it to finish and evaluate the results, if they are not good repeat gradshim

Close gradshim

Make sure the lock light is on Type in “atma” to tune and match the probe Find the proton 90 time (“p1”)

Be sure to use zg and not zg30 Update p1

Note: The command “edprosol” opens the field values 1H pulsewidth then power to change proton 90 degree pulse (for example getprosol 1H 8.3 2 will set the proton 90 time to 8.3 µsec at a power level of 2 dB for your current TOPSPIN session only. Each value is separated by a blank space).

Choose the solvent

Type in “rgacryo” Start running

If you use the BACS automation:

a) Store the necessary gradshim file for automation, understand that ICON willlook for the shim file under a specific name

b) Use administrative password to change default p1

c) Type in “getprosol” to update the parameters

d) Set up the parameters for your first sample in “icon” program

e) Change au_zg to zgonly if you need constant rg value

f) Make sure the sample is not rotating: Go to configuration and click master switches, check the box next to never rotate sample note the selections on the other switches that are important like the lock selections and the standard shim file loading selection. You must be logged in as “nmrsu” (nmr superuser) to change this.

===During the running===

Pay attention to the weather conditions especially any electric outage. Follow the “cryo platform outage procedure” as being described by Joe.

===Check out/end of run===

If any samples are reloaded into the belt, verify that the sample tube is not set to the wrong depth (spinner tightness)

Load the reference CDCl3 sample

Type in “rfshim” and choose the newest CDCl3 cryoprobe shim set

Type in “lock CDCl3” Type in “atma” Change p1 back to the pulse length listed in the back of the log book by the computer

Eject the sample and remove the NMR tube from the spinner Leave the spinner in the black holder at the workstation

Sign up the log book and clear up the area

If you use the BACS automation: First thing is to: Stop the running (when all samples are finished)

Type in “edprosol” and change p1 value back (same procedure above)

===Note===

1. Make sure your tube is clean, the spinner is clean, the sample is 600 µL and the tube is in the right depth of the spinner, holding tightly. If you have multiple samples, they need to have the same volume and fit into the spinners at the same height.

2. Use a container or a rack to carry the NMR tubes from your own lab to the NMR lab. Make sure the sample is not reactive with the solvents and uniformly dissolved in your solvent.

[[Category:Protocols]]
[[Category:Lab safety and management]]
[[Category:Metabolomics]]

Making Heatmaps

2012-09-22T00:13:43Z

Bozhang:

==Load the data==
The data can be prepared in csv or text file to load the data in to R. For example, to load ''list.csv'':

data <- read.csv (“list.csv”)

==Name the rows==
You can then name the rows of the heatmap like so:

row.names(data) <- data$Name

To exclude the first column from the heat map, use a command of similar form to the following:

Data <- data [,2: n]

In the above command, '''n''' is the number of columns to be included in the heat map.

==Build a data matrix==
Use the following command to build a data matrix for making the heat map:

Data_matrix <- data.matrix (data)

==Plot the heat map==
'''Note: Gnuplot package must be installed in R before heat maps may be displayed!''' Run the following command to load in the ''gplots'' library.

library("gplots")

To make heat map, run the following command:

heatmap.2 (Data_matrix, dendrogram="row", col= redgreen (75), scale="none",
key = TRUE, keysize = 1.0, margins = c(4,30),
density.info="none", trace="none")

[[Category:Protocols]]
[[Category:Metabolomics]]

Media preparation

2012-09-22T00:10:47Z

Bozhang:

[[Category:Protocols]]
[[Category:Protein expression]]
[[Category:Metabolomics]]
1. General Protocols For Minimal Media Protein Expression

Expression Protocol in M9 Minimal Media via T7 Promoter:The following protocol has been used successfully to 15N or 13C/15N label our proteins using our pET1120/BL21(DE3) expression system: Preparing M9 minimal media begins with preparing a 5x stock solution of M9 salts. Generally, M9 salts contain a nitrogen source in the form of NH4Cl. Since we want to add a labeled nitrogen source, our 5x salts are prepared minus NH4Cl. Standard 5 X M9 Minimal Media salts minus nitrogen source For 1L 5xM9 salts:

64g Na2HPO4-7H2O
15g KH2PO4
2.5g NaCl

H2O to final 1L volume and autoclave
To prepare 500mL M9 minimal media:

100mL of 5xM9 salts
1 mL 1M MgSO4
50 uL 1M CaCl2
5 mL 100x Basal Medium Eagle Vitamin Solution (Gibco)
2.5 mL filter sterilized NH4Cl (0.2 g/mL) or 0.5g dry
10 mL 20% d-glucose or 2g dry

Glass distilled & autoclaved H2O to final volume of 500mL pH solution to 7.3 and filter sterilize (0.2u filter).

Introduce media to a pre-autoclaved, wide-bottom (baffled) 2L flask and add ampicillin to a final concentration of 70-100 ug/ml (or any antibiotics used to select the strains).

Grow 5mL overnight culture in same media to inoculate 500mL M9.
Shake culture at 37oC until an OD600 of 07+/-0.2 then induce protein expression with the addition of IPTG (0.01-0.1 mM final concentration).

2. General Protocols For LS Media

For 1L media preparation,
10g Bacto-tryptone
5g yeast extract
10g NaCl

Sterilized by autoclave