Figure 1.
Tabbed-pane for defining parameters.
One of the five tabbed sections related to the ‘mask-generation’ is visible.
Figure 2.
Major steps of the spheroid detection algorithm. Variables with sample values are placed within the angle brackets (i.e. < … >).
Figure 3.
The major steps involved in spheroid detection are illustrated.
Figure 4.
The major steps required for the nucleus detection algorithm. Variables with sample values are placed within the angle brackets (i.e. < …>).
Figure 5.
The critical steps involved in nucleus-detection, in addition to performing the task of superimposing previously generated corresponding spheroid boundaries.
Figure 6.
The major steps in detecting spheroid-membership of a nucleus are shown.
Figure 7.
To perform the candidate-check in Algorithm 3 to primarily detect whether object Y is possibly inside object X or not, using the bounder-box approach. In the case of (A), object Y is inside object X, therefore, at least one corner of the bounder-box of Y must be inside of the bounder-box of X. However, even if any of the corners of the bounder-box of Y is inside X's, Y may not actually be inside of X, such as, case (B).
Figure 8.
Measuring intensities and classification of the intensity distribution.
Figure 9.
Major steps involved in generating the classified read-map.
Figure 10.
The major steps involved in generating the classified read-map via boundary-mask and threshold-mask. Images depict a DU145 spheroid grown in a 3D matrix following immuno-labelling for the α6 integrin subunit. Panels in this figure refer to the intensity of the antibody and distribution of the α6 integrin subunit. Labelling was present primarily in the peripheral region of the spheroid structure.
Figure 11.
Dialog to set the threshold values visually.
Figure 12.
Peripheral versus non-peripheral and clump-breaking defined functions.
A) Steps involved in processing read-map classifications with options set for proportional width and boundary-mask. Images depict a DU145 spheroid in a 3D matrix following immuno-labelling for β1 integrin subunit. Panels in this figure refer to the intensity of the antibody and distribution of the β1 integrin subunit. The distribution of β1 remained primarily around the outer membrane/peripheral region of the spheroid. B) Original image of DU145 spheroid B') Application of PCaAnalyser utilising the clump-breaking functions, after magnifying the signal the software found non-zero signals in between the two spheroids like masses and detected it as a single spheroid.
Figure 13.
The main interface of PCaAnalyser.
PCaAnalyser is offering both single mode and batch mode of operations and offering at least 3 different levels of reporting.
Figure 14.
Central functions of PCaAnalyser, showing the major steps: calling algorithms 1–3 and integrating the database.
Figure 15.
Operational overview of PCaAnalyser software.
Paradigm and flow-diagram of PCaAnalyser, depicting the integration and operation sequences of inputs, central-operations, data-processing, output-generation and output-formats.
Figure 16.
Overall outcome from the application of PCaAnalyser software.
Panels show DU145 and MDA-MB-231 spheroids grown in a 3D matrix following immuno-staining for dapi (nucleus; A, E), Beta 1 integrin (C) and doxorubicin (G). (A) Original image of DU145 spheroid and nucleus. (B) Detected spheroid and detected nucleus. The spheroids interacting window-boundary has been optionally chosen to be excluded, and in such a case the corresponding detected nucleus is shown in a different colour (yellow). (C) Original cytoplasm and membrane area image, having noise of higher intensities as well as quantities. (D) Classified read-areas for studying intensities are highlighted and the distinct spheroids are optionally labelled. The generated mask from (B) helps avoid noise effectively and a fixed width from peripheral-boundary has been selected in this case to generate the classification. (E–H) MDA-MB-231 spheroids treated with doxorubicin and imaged with a ×10 objective. (E) Original image of MDA-MB-231 spheroid and nucleus. (F) Detected spheroid and detected nucleus (G) Original image of doxorubicin staining. (H) Classified read-areas for studying intensities.
Figure 17.
Nucleus and beta 1 integrin detection in morphometrically diverse PC3 cells.
Panels show PC3 spheroids grown in a 3D matrix following immuno-staining for dapi (nucleus; A) and Beta 1 integrin (C). (A) Original image of PC3 spheroid and nucleus. (B) Detected spheroid and detected nucleus. (C) Original cytoplasm and membrane area image. (D) Classified read-areas for studying intensities. (E) Quantitation of Beta 1 intensities in PC3 cells treated with DHT.
Figure 18.
Sub-layer-wise summary ∼.csv report.