FTIR data analysis tutorial

1
FTIR data analysis tutorial

• Bryan Penning

Supported by the NSF Plant Genome Research and
REU Programs
2
Overview

• We are establishing infrared spectrotypes in
  cell wall mutants. Spectrotypes are
  spectroscopic phenotypes, i.e. the spectral
  differences between mutant and wild type
  populations.
• This tutorial will
• Give some background information on how we
  prepare and collect data on mutants (See
  Techniques VII at http//cellwall.genomics.purdue
  .edu/techniques/7.html for more details)
• Show how we analyze the data collected by
  Principal Components Analysis (PCA) and digital
  subtraction
• Give some example spectral peaks that indicate
  differences in cell wall composition between
  mutants and wildtype

3
Plant preparation

• All plants are grown and cell walls isolated
  under the same conditions and processed
  simultaneously for internal consistency of mutant
  and wild type comparisons (see Techniques VII for
  greater detail http//cellwall.genomics.purdue.ed
  u/techniques/7.html)
• Wild type plants are always prepared with mutant
  plants
• Plants are all grown on one-half strength MS
  salts with 1 sucrose and 0.8 agar in the light
  for 12 days
• Plants are all transferred to the dark for 2 days
  to lower starch content (a contaminating feature
  in IR spectrum)
• Plant tissue is crushed in liquid nitrogen, and
  non cell wall components, such as proteins, are
  extracted using an SDS-Tris buffer
• Cell walls are isolated by homogenization in a
  Geno-Grinder (SPEX Certi-Prep), collected on a
  nylon mesh and washed with water and ethanol
• Cell walls are resuspended in distilled water and
  spotted on a gold-plated slide (EZ-Spot,
  Spectra-Tech) for spectral acquisition in an FTIR
  spectrometer (Thermo-Electron, Madison, WI )

4
FTIR spectral acquisition

• Spectra are acquired in a range of 4000 to 650
  cm1 1 spectrum consists of 128 co-added scans
  with an eight- wavenumber resolution
• The following spectral characteristics are used
  to ensure that readings can be compared
• 0.4 to 0.8 max peak reading _at_ 1050-1000 cm-1
• Peak _at_ 1050-1000 cm-1 greater than peak _at_ 1600
  cm-1
• The values _at_ 1800 cm-1 and 800 cm-1 are fairly
  equal with a value below 0.3 absorbance (to
  ensure a good baseline correction)
• Noisy spectra are discarded

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FTIR data analysis
A
B

• Fig. A We collect many spectra (about 40) with a
  computer driven stage and Omnic software (Thermo
  Electron), saving them as grouped data (.spa)
• Fig. B We convert group data to individual
  .jdx files in Omnic

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FTIR data analysis

• We use Win-Das (Kemsley, 1998) software to
  analyze our data instead of Omnic because it is
  capable of area averaging the spectra, an
  essential feature of spectral analysis of plant
  cell walls because the samples vary in thickness

• With the DOS command, RENAME, we convert .jdx
  files into .dx files that Win-Das software can
  recognize

Kemsley. 1998. Discriminant Analysis of
Spectroscopic Data. Chichester, UK John Wiley
and Sons
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FTIR data analysis

• In Win-Das we first construct a matrix
• We add all of our spectra (observations)
• We view the spectra

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FTIR data analysis

• In Win-Das we
• Truncate the spectra from 1801.2 to 798.4 cm-1
  (useful wavenumber range for cell wall molecules)
• Baseline correct the spectra
• Normalize the spectra and save as a .txt
  (spectra) or .wdd (analysis) file

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FTIR data analysis

• Saving data for the web
• After normalizing we save the file as CF-Text
  (column-wise)
• This generates a .txt file we copy and paste
  into Excel

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FTIR data analysis

• You can download these spectra from our website
  (Families Tables)
• You can average the spectra using the average
  command in Excel

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FTIR data analysis

• Digital subtraction
• To perform a digital subtraction, average the
  mutant and wild-type spectra (previous slide) and
  copy over the spectra values (left column of
  spectra files)
• Subtract mutant from wild type and plot versus
  wavenumber (cm-1)
• Look for peaks (differences in cell wall
  components)

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FTIR data analysis

• Absorbances of specific peaks in the IR spectrum
  can be correlated with particular cell wall
  molecules (Kacuráková et al, 2000)
• Cellulose 1162, 1120, 1059, 1033, 930, and 898
  cm-1
• Pectin 1144, 1100, 1047, 1017, 953, 896 cm-1
• Rhamnogalacturonan 1150, 1122, 1070, 1043, 989,
  951, 916, 902 cm-1
• Xyloglucan 1153, 1118, 1078, 1041, 945, 897 cm-1
• However, these peak assignments are based on
  isolated polysaccharides and peaks may shift
  depending on molecular interactions and
  environment within the cell wall
• The more peaks that can be assigned to a
  particular polymer, the more likely that
  component differs between mutant and wild type
  cell walls

Kacuráková, Capek, Sasinková, Wellner, and
Ebringerová. 2000. FT-IR study of plant cell
wall model compounds pectic polysaccharides and
hemicelluloses. Carbohydrate Polymers 43195-
203
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FTIR data analysis

• To develop Discriminant Analysis for our
  classifications (PCAs) in Win-Das
• We create two groups (one wild type and one
  mutant)
• We compress the data using the covariance method

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FTIR data analysis

• PCA analysis
• Cluster plot of separation by PCAs
• Loading plots (difference in groups)
• Discriminate
• We use Squared Mahalanobis distance to see number
  of correct classifications

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FTIR data analysis

• PCA from Variance scores are shown in the gene
  family table
• Number of Principal Components (PCs)
• Percent classified (75/80 93)