For clarifying KRAS’s role in CA, mutant versions of this gene were introduced into the mammary cell line MCF10A with the help of viruses that were manipulated so that upon infecting the cell, they delivered the mutated gene into the nucleus, integrating it into the DNA (lentiviral transduction). Centrosomal status was analyzed at various time points after transduction by IF microscopy. During IF, proteins of the centrioles and also its surrounding pericentriolar matrix were stained using fluorescent antibodies (CP110, centrin, pericentrin and γ-tubulin) that attach to them and can be visualized under the microscope.
The IF experiments showed that the cells that successfully carried (i.e. expressed) a KRAS mutation, were featured with a modest but statistically significant level of CA (Fig. 1). It was also observed that the rate of CA, in general, increased over the course of two weeks. Our results suggest that point mutations of KRAS are able to induce moderate levels of centrosome amplification, and – in the light of previous findings - might therefore be responsible for the elevated chromosome number seen in HHDpALL. The pilot investigation using patient samples showed signs of CA in about ¼ of the cells analyzed. Further experiments are needed to confirm our findings, investigating more patient-samples and experimenting with further genes and cells more similar to the leukemic cells of HHDpALL, to gain more disease-specific conclusions. (Results had been submitted for conference presentation.)
For creating an automated centrosome detection workflow, cells with different levels of CA were used in order to train the DNN. Centrosomes were stained using IF, whereas the nucleus was counterstained with a DNA stain. After scanning slides from 5 different cell-lines, cells were identified based on counterstain-related appearance. In the following, more than 35 000 cells were categorized (labelled) into seven classes manually, for training the DNN. The 7 classes were (1) interphase cell with CA; (2) CA with (pseudo)bipolar mitosis; (3) CA with multipolar mitosis; (4) non-amplified centrosomes in G1-phase of the cell cycle; (5) non-amplified centrosomes in S/G2-phases; (6) non-amplified centrosomes in mitosis; (7) other (Fig. 2). The above also means that classes 1-3 belong to the bigger group of ‘amplified centrosomes’ whereas classes 4-6 represent cells with ‘non-amplified centrosomes’. Slides were scanned using Metafer Slide-Scanning System.
The data set generated upon manual labelling was divided into two subsets (training set and validation set) to train a custom deep neural net (DNN). To increase robustness and avoid pure memorization of images by the DNN, the training images were continuously changed using various techniques such as image flipping, rotation, zooming and the addition of random noise.
Currently, the DNN’s overall accuracy in predicting all of the classes correctly is 80.5%, whereas its ability to distinguish ‘amplified’ from ‘non-amplified’, is close to 90% (88.3%). Further experiments are needed to fine-tune performance, before integrating the DNN into the automated slide scanning system. This will result in a workflow which fully automatically analyses a sample, predicting the normal to aberrant ratio of centrosomes with the only mandatory user interactions being the initial loading of the slide feeder and the final review of results. (Results had been submitted for conference presentation; journal submission is expected within 3-6 months.)
