Protein Lysate Microarrays - PowerPoint PPT Presentation

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Protein Lysate Microarrays

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Title: Protein Lysate Microarrays


1
Protein Lysate Microarrays
  • Clay Scott
  • Ryan McConnell
  • Shannon Neeley

2
Biological and Technical Background
3
Motivation
  • DNA/RNA microarrays are used to determine gene
    expression
  • Proteomic profiling can help yield more direct
    answers to biological questions
  • Those molecules that can answer our questions are
    proteins, not mRNA
  • Biological effector molecules
  • Diagnostic markers
  • Pharmaceutical targets

4
History of Proteomic Profiling
  • 2D-PAGE (two-dimensional polyacrylamide gel
    electrophoresis)
  • Introduced in 1975
  • Semiquantitation of most abundant 1000 spots
  • Problems with identifying spots with a particular
    protein
  • Microarray formats for proteomic profiling
  • More recent development (papers published in
    2001)
  • Robotic spotting of antibodies that capture the
    protein molecule to be assessed

5
Reverse-phase protein lysate microarrays
  • Opposite configuration from previous microarray
  • Samples assessed are robotically spotted
  • An antibody is used to measure amount of a
    particular protein present in the sample
  • Limitation Measure one protein per slide
  • Advantage All samples analyzed side-by-side in a
    single array
  • Can compare protein levels across samples rather
    than samples across protein type

6
Protein lysate preparation
  • Lysis The dissolution or destruction of cells
    by the action of a specific lysin that disrupts
    the cell membrane
  • Lysate The cellular debris and fluid produced by
    lysis

7
Protein Lysate Array Design
  • Each microarray is a glass slide containing
    caspase protein samples from different patients
  • 18 spots for every sample
  • 3 replicates
  • 6 dilution levels (with dilution factor of 2)

8
Western Blottingused to screen specificity of
antibodies
  • Choose Antibody that will bind to caspase

9
Detection of protein on microarray
  • The slide is exposed to the antibody
  • Antibody binds to the protein, depending on how
    much protein is present
  • Microarray is scanned to form an image with
    darker spots reflecting higher levels of protein
  • Use two antibody detection system

10
Statistical Applications
11
From Image to Number
  • Software draws a circle around each spot
  • Darker spots have greater quantities of protein
  • sVOL reflects the total amount of protein

Two-fold serial dilutions
sVOL (pr2)(average intensity inside
circle)-(average background intensity)
12
The Data Set
  • 80 x 6 x 3 matrix of sVOL values for the caspase
    protein
  • 80 patients on one microarray chip
  • 6 two-fold serial dilutions for each patient
  • 3 replicates for each patient
  • Control BSA data set for calibration and
    error-assessment purposes

Dilutions
P A T I E N T S
1
6
80
13
Assessing Data Distribution
  • log(sVol) is roughly linear with respect to
    dilution level

14
Assessing Data Distribution, Cont.
  • Log transformation of sVOL values decays linearly
    with dilution number
  • Lower right graph plots the mean log(sVOL) for
    each dilution

15
Assessing Normality
  • Caspase distribution is not normal
  • How should we model this distribution?
    Transformations?
  • BSA control data has sections that are normal
    see next slide

16
Assessing Normality, Continued
  • Caspase distribution is non-normal
  • BSA distribution is normal within one standard
    deviation of the mean

17
Potential Tasks to Undertake
  • From the 18 sVOL values for each sample, extract
    a single robust number that is representative of
    that sample
  • for example, the mean is not robust
  • Provide an error estimate of that number

18
Saturation DetectionDevise procedure for
extracting linear portion of log(sVOL)
For highly concentrated samples, circles drawn by
computer may be too small (sVOL smaller than
expected) For large dilutions, sVOL is dominated
by background noise (sVOL tails off)
19
Fine Tuning the Segmentation Program
  • Outlying sVOL values may occur even when
    saturation is not a problem
  • Detecting outliers may help improve the
    segmentation software
  • Control BSA data may be useful here
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