Figure S1. Growth profiles of plants used in this study, depicting growth rates
(% change / hr) relative to plant position (segment #) for specific segments
over time.
Surface plots (left): Growth rates (% change per hour, vertical axis) are plotted
against the number of segments (defined by optical marker tags) from the apex
downwards, over the duration of the imaging period in 10 minutes intervals. The
red-shaded, nearest profile denotes the last 10 minute interval before harvest,
depicted in greater detail in the right-hand scatterplot.
Scatter plots (right):
Growth rates (% change in length per hour) are plotted against distance from the
stem base for specific segments. Segments are numbered from the top of the plant
downwards in the right-hand margin, where sampled segments are indicated as
white on black. The LOWESS regression curve follows the best fit through the
growth rate data for this plant over a given 10' interval.
Green dotted lines
represented 65% confidence intervals for the LOWESS regression curve. Asterix
indicates the stem position approximating the maximum growth rate of the
regression curve, while the closed triangle indicates the first position below the top
of the stem where the growth rate falls to zero (segment 10). See Hall and Ellis
(2012) for further explanation (i.e. GKP for study of cell expansion in Arabidopsis
inflorescences).
Figure S2. Absolute fluorescence intensity (In.abs) of selected pectin-specific
antibodies. Bar plots are given for each antibody:tissue-type combination, where
values represent the mean of the normalized absolute fluorescence intensity (In.abs)
for 3 biological replicates for each stage (YNG, MGR, and CSS). Plots are arranged by
tissue type (column) and antibody (row). The final column presents the mean of
sum of all normalized absolute fluorescence intensities for each biological replicate
(denoted ‘Sum’). Antibodies are arranged row-wise according to their specificity to
the degree of methyl-esterified pectins (‘High’, ‘Mod’/moderate, and ‘Low’; top to
bottom, respectively). Values (Table S3) were derived from Ibn values (Table S2) via
the method described in Figure S3. Error bars represent 95% confidence intervals
computed from standard deviations (alpha=0.05).
Figure S3. Relative representation of glycan-specificities in various assemblages of
available antibodies.
Values (ordinate) are expressed as a percentage of the total number of antibodies in those
respective assemblages. The abscissa depicts the cell wall glycan categories appearing as
‘superclades’ in Pattathil et al., 2010, or otherwise categorized on the basis of published
descriptions. Collections of antibodies are listed in the figure key with their total number
in brackets. From left to right (top to bottom in key); those listed in the CCRC database
as of 2012-10-26 (‘CCRC.listed’), those studied in Pattathil et al., 2010 (‘Pattat..study’),
those provided in the CCRC-SKCMA-AR1 kit (‘SKCMA.AR1’), those provided in the
CCRC-SKCMA-AM1 kit (‘SKCMA.AM1’),
and those examined in this study
(‘Hall.study’).
Figure S4. Methodology for computation of absolute and relative intensity scores
Flow chart depicts matrices or vectors (single columns) of various values within the R
workspace, arrow connectors indicate computational processes used to generate
matrices/vectors. Equations within gray boxes indicate the corresponding functions for
computing those matrices/vectors. Definitions of entities and variables are presented in
the following description of the method of calculation of Īn and Īrel.adj from Ībc.
A relative fluorescence intensity value for the mean of three biological replicates was
computed for each antibody according to:
Īrel=Ībc (an,tn) / ∑Ībc(n)
(Eq. 1)
where:

Ībc=absolute, background normalized (bc) intensity of a specific antibody::tissuetype combination (mean of biological replicates)

Īrel=relative fluorescence intensity for each antibody::tissue-type combination

an= the antibody (ranging from 0 to 55)

tn= the tissue number (ranging from 1 to 7)
However, a subset of antibodies exhibited low fluorescence intensity in all tissue
types. Though signals were close to background, some values were many times greater
than others leading to artificially elevated Īrel scores among tissue types. Such noise
disrupts clustering similarities among the remainder of antibodies at a given stage. Rather
than eliminate these dim antibodies from analysis, an approach was taken to scale the Īrel
scores where sums of Ībc scores fell below a certain threshold. In order to do so, a
proportional sum of intensities (PSI) was first derived to determine the relative, overall
brightness of the tissue with respect to the brightest overall antibody at that stage using
the relation:
PSIn=∑Ībc(n) / max({∑Ībc,max})
(Eq. 2)
where:

PSIn = the proportional sum of intensities for a given antibody (n)

Ībc = mean fluorescent intensity for each tissue (Ibc) for a given antibody at a
specific stage

max({∑Ībc})= maximum of all sums of mean fluorescence intensities (i.e. 55 sums
for 55 antibodies).
The vector 'PSI' was then used to calculate a correction factor (CF) according to the
logistic function:
CFn=1 / [1+e(-Wt*(PSIn-Th)]
(Eq. 3)
where:

CFn= correction factor computed for each antibody, outputted to a vector
containing values for all antibodies.

Wt=weighting factor controlling the steepness of the logistic transformation of
PSIn score

Th=thresholding factor regulating the CF score below which the thresholding
correction is applied

e= natural logarithm (~2.718281828)
The CF values were are also used to compute scores (Īrel.adj) according to the formula:
Īrel.adj= Īrel + (Īrel - Tn-1)*CFn
(Eq. 4)
where:

Tn = number of tissues (seven in this experiment)
Īrel.adj=the relative intensity score for each antibody::tissue-type combination, adjusted by
thresholding.
For examination of absolute intensities across multiple antibodies (see Figure S4 on
pectin-related antibodies), ‘normalized absolute intensity’ (Īn.abs) wer computed according
through the formula:
Īn.abs = Ībc * CFn
(Eq.5)
In this case, CF parameters (‘Wt’ and ‘Th’) were adopted from hierarchical clustering
optimizations.