factors based on total ion current (TIC) for individual samples
or through normalized spectral abundance factor (NSAF). The
main features, strengths, and weaknesses of each method have
been thoughtfully discussed elsewhere [13, 38]. There are
different software packages and applications available that
allow running straightforward these and further statistical
data analyses (https://ms-utils.org). Note that for peptide/
protein quantification following either a label-free or isobariclabeling approach usually only unique peptides (those that are
present only in one protein) are included in the analysis to
avoid protein quantification inaccuracies due to the use of
shared (nonunique) peptides between different proteins.
2. Quantification and normalization methods for isobaric labeling
analysis
For quantitative proteomic data obtained after isobaric
labeling of multiple samples analyzed in the same multiplex
experiment (i.e., the simultaneous analysis by LC–MS/MS of
the combined isobaric labeled samples), corrections are based
on the total signal (sum of intensities of all peptides) for each
label through reported ion intensities. This intra-experiment
normalization is easily done taking one channel (one of the
isobaric labels) as reference and dividing the intensities of all
other channels by a correction factor, CF ¼ total signal for each
channel/total signal for the reference channel.
Despite multiplexing approach using isobaric labels allows
the analysis of multiple samples (up to 16 for new TMTpro™plex products) at the same time, there are complex experiments
(e.g., analyzing a high number of treatments) that require to
include a number of biological replicates that exceed the multiplexing capacity of a single experiment. In such cases, the
results of several single multiplex experiments need to be analyzed jointly, then a necessary inter-experiment normalization
step to allow comparing data across experiments becomes a
nontrivial task. The inclusion of a reference sample, often consisting in a pool of all other samples (known as master pool), in
every experimental run is usually recommended [14] although
other strategies without including this reference sample have
also been proposed (e.g., [39, 40]). We provide here two interexperimental normalization methods that have worked quite
well in our proteomic data; an example of how to easily
perform these normalization strategies is given in Fig. 2 (see
Note 20).
The first method makes use of a reference sample or master
pool included in every single multiplexing experiment. In this
case, the intensity of each protein is normalized by the intensity
of the same protein in the reference sample (Fig. 2A) (see Note
21). The second one is inspired by the CONSTANd method
Shotgun Proteomics in Non-model Organisms
93
or through normalized spectral abundance factor (NSAF). The
main features, strengths, and weaknesses of each method have
been thoughtfully discussed elsewhere [13, 38]. There are
different software packages and applications available that
allow running straightforward these and further statistical
data analyses (https://ms-utils.org). Note that for peptide/
protein quantification following either a label-free or isobariclabeling approach usually only unique peptides (those that are
present only in one protein) are included in the analysis to
avoid protein quantification inaccuracies due to the use of
shared (nonunique) peptides between different proteins.
2. Quantification and normalization methods for isobaric labeling
analysis
For quantitative proteomic data obtained after isobaric
labeling of multiple samples analyzed in the same multiplex
experiment (i.e., the simultaneous analysis by LC–MS/MS of
the combined isobaric labeled samples), corrections are based
on the total signal (sum of intensities of all peptides) for each
label through reported ion intensities. This intra-experiment
normalization is easily done taking one channel (one of the
isobaric labels) as reference and dividing the intensities of all
other channels by a correction factor, CF ¼ total signal for each
channel/total signal for the reference channel.
Despite multiplexing approach using isobaric labels allows
the analysis of multiple samples (up to 16 for new TMTpro™plex products) at the same time, there are complex experiments
(e.g., analyzing a high number of treatments) that require to
include a number of biological replicates that exceed the multiplexing capacity of a single experiment. In such cases, the
results of several single multiplex experiments need to be analyzed jointly, then a necessary inter-experiment normalization
step to allow comparing data across experiments becomes a
nontrivial task. The inclusion of a reference sample, often consisting in a pool of all other samples (known as master pool), in
every experimental run is usually recommended [14] although
other strategies without including this reference sample have
also been proposed (e.g., [39, 40]). We provide here two interexperimental normalization methods that have worked quite
well in our proteomic data; an example of how to easily
perform these normalization strategies is given in Fig. 2 (see
Note 20).
The first method makes use of a reference sample or master
pool included in every single multiplexing experiment. In this
case, the intensity of each protein is normalized by the intensity
of the same protein in the reference sample (Fig. 2A) (see Note
21). The second one is inspired by the CONSTANd method
Shotgun Proteomics in Non-model Organisms
93