Biomedical Engineering Reference
In-Depth Information
specified time window centered on the expected chromatographic
retention time. Since only a subset of the total peptides will be
eluting within any given time window, the duty cycle of the instru-
ment is maximized. Thus, while the number of transitions can
be significantly increased, the dwell time for each transition is
maintained to uphold the peak definitions. Using the example dis-
each measured with 25 ms dwell times, will have a duty cycle of
2.5 s. If, for example, only four peptides are detected between
12 and 16 min in the LC gradient, scheduled MRM would
only need to measure 12 transitions for this 4 min time window
(2500 ms/12 transitions
208 ms). In its simplest form, a sched-
uled MRM method can be used to increase the dwell times for all
transitions while maintaining a fixed duty cycle. Overall, transition
dwell times are increased, but chromatographic peak definition
is constant. Sensitivity and signal-to-noise are improved, without
compromising the number of points measured across the chro-
matographic eluting peak. Finally, it should be noted that chro-
matographic reproducibility is very important in order to obtain a
useful sMRM.
=
Data processing of MRM experiments typically requires that
the extracted ion current (XIC) from the detected transitions
are integrated to determine peak area. Depending on the soft-
ware used, processed results are summarized in tables that list
each transition with its corresponding peak area. Typical anal-
ysis involves comparison of the relative abundance of transi-
tions for specific peptides across different sample conditions.
However, this can quickly become a daunting task when pro-
teins, each with at least three peptides, need to be analyzed and
compared.
When performing comparative analysis several factors should
(a) Due to inherent differences in ionization efficiency the rel-
ative abundance can only be compared between peptides of
identical sequence and modifications.
(b) Calculate technical and biological variability to ensure that
the proper numbers of experiment are conducted.
(c) Use calibration curves to ensure that the response of the
samples is linear. These analyses can be conducted by com-
paring the response/XIC of each transition correlates with
injection of an increasing amount of sample.
(d) Data normalization can be extremely valuable. By including
an internal or external control for normalization, variabil-
ity in sample processing can better be accounted for. This
is very important when comparing samples prepared from
multiple different conditions.
3.5.DataProcessing
andQuantification
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