The IROATM methodology uses isotopically labeled media to automatically simplify metabolomics analysis. It requires isotopically paired media and software to extract data from mass spectrometry files. The method involves growing control and experimental cell populations in different isotopic media to label molecules. The populations are then mixed and analyzed together as one sample by mass spectrometry. Software then identifies control and experimental molecules based on isotopic signatures and detects outliers affected by experimental stressors.

1. The IROATM
methodology was developed to
automate and simplify metabolomics so that
investigators can achieve clear and reliable results
easily. It only requires suitable isotopically-paired
media and software to extract and process the
mass spectrometer raw data files. The following
slides describe an IROA experiment in its simplest
form. This basic methodology may be just as
easily applied to simple experimental systems or
much more complex experimental systems, only a
change in cell medium is required.
IROA™: Isotopic Ratio Outlier Analysis

2. media-free
cells
Wash
Culture
Biological
A. The use of a pooled biological population at the outset of the experiment
will reduce biological variance. The pooled biological population is divided
into two and each of these is placed in separate isotopically defined media.
B. The control and experimental cells are each allowed to grow through
enough divisions to dilute out the natural abundance carbon and replace it
with the characteristic isotopic distributions of their particular media. This
gives each and every molecule a unique “label”, definitive to source. The
experimental conditions are applied to the experimental population, and
vehicle to the control population.
The IROA™ Analysis
95% 12
C
and 5% 13
C
media
control
Experimental
TreatIncubate
Experimental
95% 13
C
and 5% 12
C
media
drug etc.
Add toxin,

3. Composite
media-free
12
cells
+
media-free
13
cells
Total Ion
Data
Analytical
The IROA™ Analysis
C. After the experimentally defined period the
experimental and control cells are mixed.
D. The mixed sample, containing both control and
experimental populations in one sample, is
prepared and analyzed by mass spectrometry as a
single sample. The number of samples to be
analyzed is reduced by half.
As all of the molecules from each population are
distinguishable from one another AND from artifactual
molecules, it is possible to look at any peak and
understand its origin. In addition, since the control and
experimental populations share the same handling-
induced variances compound–by-compound (i.e. error or
losses), there is no variance between or within them. This
“noise-free” environment is a unique feature of IROA
TM
, and
is the most significant reason for its success.

4. E. Artifacts are discarded; the respective areas for control-derived
and experimentally-derived compounds are identified (by isotopic
signature) and compared (by ratio).
F. The distribution of ratios is analyzed for outliers. Compounds with
abnormal ratios are molecules affected by the experimental stressor,
(toxin, drug etc.) allowing for clear interpretation.
Normalized
Ratio
0
-5.0
5.0
The IROA™ Analysis

5. IROA Software data flow
Normalized
Ratio
0
-5.0
5.0
108
datapoints (mostly noise)
B.
C.
A.
5X102
datapoints (pure data)
Variance control,
Data reduction,
Noise removal,
Data definition
Fully automated data reduction of complex raw data to concise, high value information.
The IROATM
software is capable of interpreting these datasets, and
will allow for a very dramatic reduction in data size as it easily
sorts through the dataset removing irrelevant data. No other
system can do this as efficiently or consistently.

6. IROA Software data flow
Normalized
Ratio
0
-5.0
5.0
108
datapoints (mostly noise)
B.
C.
A.
5X102
datapoints (pure data)
Variance control,
Data reduction,
Noise removal,
Data definition
Fully automated data reduction of complex raw data to concise, high value information.
The IROATM
software is capable of interpreting these datasets, and
will allow for a very dramatic reduction in data size as it easily
sorts through the dataset removing irrelevant data. No other
system can do this as efficiently or consistently.