Supporting file legends:
Supporting file: iRoCS_SupportingMethods.pdf
Contents:
Section S1: Cell/Nucleus Detection
S1.1: Features for Nucleus Detection and Classification
S1.2: Evaluation of the Nucleus Detection
S1.3: Cell Segmentation Based on Cell Wall/Membrane Markers
S1.4: Evaluation of the Epidermis Nucleus Labelling
Section S2: iRoCS Fitting
S2.1: Nucleus-based Initialisation
S2.2: Cell Boundary-Based Initialisation
S2.3: Simultaneous Axis Fitting and Thickness Estimation
S2.4: Transforming Euclidean Points into iRoCS
Section S3: Evaluation of the Cell Layer Assignment
Section S4: Mitosis Distribution Analysis (Automatic)
Section S5: Evaluation of the Layer Assignment for shr
Section S6: RAM-Length Estimation
Section S7: The iRoCS Toolbox: Short User’s Guide
S7.1: Introduction
S7.2: Installation
S7.3: Getting Started
S7.4: How to Convert lsm/Imaris/… to HDF5?
Supporting file: iRoCS_SupportingFigures.pdf
Figure S1: PIN protein localisation within the Arabidopsis RAM. The protein patterns are
shown in red. As reference the white channel shows the nuclei stained with DAPI. (a) PIN2.
(b) PIN4. Scale bars: 100μm.
Figure S2: Nucleus detection evaluation. (a) Orthographic view through the per voxel
probabilities of being the centre of a nucleus. The probability is output by the nucleus detector
after training with two sample roots; (b) Precision-Recall-curves for the detection of nuclei of
different tissue layers, with decreasing detector response between 1 and 0.5. Scale bar: 50μm..
Figure S3: Automatic cell membrane-based segmentation. (a) Raw data; (b) enhanced image
after anisotropic diffusion; (c) Segmented cells with random colour coding. (d) Surface
rendering of cells of half of the root virtually cut along an axial plane. Coloured lines indicate
orthoview cut planes. Scale bars: 100μm.
Figure S4: Qualitative evaluation of root axis fitting. The solid yellow lines indicate the root
axis estimate. The dotted lines near the root boundary indicate the estimated root thickness.
Top: The epidermis nucleus candidate positions are indicated by cyan circles and crosses.
Circle size indicates proximity to cut position. Bottom: Cell segmentation is indicated by cyan
polygons. Scale bars: 500μm.
Figure S5: Fully automatic mitosis distribution analysis. RC: root cap; Epid: epidermis; Cor:
cortex; Endo: endodermis; Peri: pericycle; Vasc: vasculature. (a) wild type (n=17); (b) pin2
(n=11); (c) pin4 (n=26); (d) Axial mitosis distributions for the different cell layers. Median
values (bar), interquartile range (IQR) (box), lowest and highest data within 1.5 IQR of lower
and upper quartiles (lines) and outliers (open circles).
Figure S6: The bi-sigmoid axial cell density model. (a) The blue line indicates the left
sigmoid modelling the transition between background and root cap (columella). The green
line models the transition between proliferation and elongation zone. The thick stippled black
line is the final cell density model, incorporating both transitions. We define the RAM length
as the distance between QC and the position of the inflection point of pr , which is marked
with a green circle. (b) The integral of the model which is actually fitted to the data.
Figure S7: Cumulative cell counts for the individual roots of the four analysed populations.
(a) wild type, (b) pin4, (c) pin2, (d) shr.
Figure S8: Summary of the cell densities and the cumulative cell count for the four analysed
populations. Population sizes were: wild type - 16, pin4 - 25, pin2 - 9, shr - 4. The shr roots
were recorded up to a length of 300μm and have therefore been analysed up to 250μm only.
(a) Solid lines indicate the per-population mean cell density, shaded areas outlined by dashed
lines indicate the standard error of the mean. Circles on the mean lines mark the inflection
point of pr and are repeated on the x-axis for direct comparison. (b) Solid lines indicate the
mean cell count, shaded areas outlined by dashed lines indicate the standard error of the mean.
Figure S9: The iRoCS Toolbox labelling GUI. The orthoview of a cellular segmentation is
shown with corresponding 3D rendering.
Supporting file: iRoCS_SupportingTables.pdf
Table S1: Classification accuracy of the epidermis labelling. The confusion matrix shows the
number of nucleus candidates classified as either epidermis, other nucleus or background. The
rows indicate the real label (manually annotated), whereas the columns show the label
predicted by a soft-margin SVM with RBF kernel. The overall accuracy excluding the
background class is given in the lower right corner.
Table S2: Classification accuracy of the layer assignment. The confusion matrix shows the
number of nucleus candidates classified as either root cap, epidermis, cortex, endodermis,
pericycle, vasculature (either in interphase or in mitosis) or background. The rows indicate the
real label (manually annotated), the columns show the label predicted by a soft-margin SVM
with RBF kernel. The overall accuracy excluding the background class is given in the lower
right corner.
Table S3: Classification accuracy of the shr layer assignment after retraining. The confusion
matrix shows the number of nucleus candidates classified as either root cap, epidermis,
cortex, pericycle, vasculature or background. The rows indicate the real label (manually
annotated), whereas the columns show the label predicted by a soft-margin SVM with RBF
kernel. The overall accuracy excluding the background class is given in the lower right
corner.
Table S4: Running times and memory consumption of the iRoCS pipeline on reference
workstation setups.