Slide-to-slide IHC staining variation of the 10 multi-tissue control samples, represented by selected IA output variables, is presented in the Table 1 and Figure 2 (A, B, C, D and E, F, G, H plots represent data obtained by the Genie/Nuclear and Colocalization algorithms, respectively).
Firstly, rather significant intra-core variation can be noted in the variables reflecting the sample size of the spots (Total nuclei, Total stained area, Figure 2A, E): while continuous drift of these variables is likely to reflect tissue variability in the consecutive sections, the irregularities, often parallel in majority of the spots, may reflect tissue artefacts and/or staining variation. Indeed, inspection of the spot images with major abnormalities revealed presence of tissue artefacts.
Secondly, the variation of Ki67-positive nuclei detected in the consecutive sections was rather significant (Figure 2B, C); it was relatively more notable at the low end of scale (where main clinical interest is), also represented by higher relative error values in the cores with less Ki67 positivity (Table 1). To avoid potential impact of misdetection of negative tumour nuclei on the Ki67 positivity estimation, we calculated the "Positive Density" variable as the ratio of Ki67-positive nuclei to the Area of Analysis to be used in further analyses. Remarkably, the variation of the Positive Density appeared less aberrant at the low end of scale (Figure 2D), although this was not necessarily reflected by the Relative Error values compared to the Ki67-positive percent (Table 1).
Thirdly, the variation of the Brown and Blue Intensity as well as their ratio (Figure 2F, G, H) reflected inter-core variation dependent on the Ki67 positivity of the tumours sampled; however, the range of Blue Intensity inter-core variation was lower than that of the Brown Intensity. Intra-core variation of both Brown and Blue Intensity was rather low, while aberrant spot images revealed mostly tissue artefacts affecting the IA results.
Finally, since the multi-controls represent tissue samples from tumours with different Ki67 positivity, it is expected that the IA results on individual spots would reflect this; however, slide-to-slide variation of the same core would reveal continuous change due to some unavoidable tissue variation in the serial sections. Importantly, one can note the pattern that in some slides this variation appears parallel in most spots, while on other occasions it appears unrelated (Figure 1).
To further investigate potential sources of this variation, we have aggregated the IA results from the 10 cores as appropriate to represent them as one sample. Since the tissue-related variation in all of the 10 cores is expected to be random (except possible variation of the tissue section thickness and the slide scanning regime), aggregation of the data would represent a "super-sample" were tissue-related impact on the IA variance would be reduced. Therefore, variables like Median Blue Intensity, Total Stained area, Total Nuclei, would summarize parallel but disregard random variation of the individual core IA data. Factor analysis of the aggregated variables (Figure 3) revealed that the major source of variation (Factor 1) was characterized by positive loadings of the variables reflecting "sample size" detected by the IA algorithms: Blue Area and Brown Area by the Colocalization, and Area of Analysis, Positive Nuclei, Negative Nuclei by the Genie/Nuclear. Remarkably, the Factor 1 also revealed strong negative loading of Blue Intensity values (more intense blue correlated with more tissue detected by both algorithms). Meanwhile, the Factor 2 was represented by positive loadings of the Percent of Positive Nuclei and negative loadings of Brown Intensity (more intense brown correlated with higher Percent of Positive Nuclei). The factor pattern implies possible impact of tissue staining intensity variation on IA performance in terms of tissue detection, however, the percentage of positive nuclei is relatively independent of this effect (by definition, Factors 1 and 2 are linearly independent). To further demonstrate the relationships, the plots of the Factor 1 and 2 scores in the consecutive sections are presented in the Figure 4: while the Factor 2 scores reveal aberrant variation, the Factor 1 scores present notable drift with several peaks, potentially pointing to the IHC counterstain intensity changes, although impact of tissue-related factors cannot be ruled out. The peculiar relationship between the variables is also illustrated by the plot of Area of Analysis (detected by the Genie) and Blue Intensity (Figure 5).
Since the main feature to be extracted from the IHC tissue controls is Brown and Blue staining intensity (the variation is expected to be parallel to that of a test sample), we further concentrated on exploring the variation sources of the intensity variables in the individual cores, as presented in Figure 2F, G. The data were transformed to enable factor analysis on Brown and Blue intensity for each spot; furthermore, MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation. Indeed, factor analysis (Figure 6A) extracted Factor-1 characterized by positive loadings of DiffBrownBlue Intensity and Factor-2 characterized by positive loadings of MeanBrownBlue Intensity of the majority of the 10 cores. Since by definition these factors are independent, Factor-1 is expected to reflect Brown-Blue Intensity variation in opposite directions but parallel in the majority of the spots and represents the colour balance per se, mostly independent of the tissue-related variation. Factor-2 characterizes absolute intensity variation of both colours in the same direction, parallel in most spots, and therefore is likely to be dependent on tissue and/or scanning variations (section thickness, scanning regime, etc.). The pattern of the Factor-3 (Figure 6B) is somewhat peculiar: it is characterized by parallel variance of the MeanBrownBlue and DiffBrownBlue for the Core#9 and opposite variance of these variables for the Core#7. In other words, when Core#9 becomes darker it is because of deeper Brown, and vice versa, when Core#7 becomes darker it is because of deeper Blue. Importantly, the Factor-3 does reveal variable loading pattern for other cores, therefore, it is likely to express core-specific behaviour of the colour balance (with 2 extreme examples Core#7 and Core#9), thus can be interpreted as tissue-related variation which has been extracted as "noise" from the Factors 1 and 2. We therefore suggest that the Factor-1 scores provide a quantitative measure of Brown and Blue Intensity balance "purified" from the impact of tissue-related variation removed into the Factors 2 and 3. Consequently, slide-to-slide variation of the Factor scores can be monitored as depicted on Figure 7 and 7 further explored for quality assurance of digital IHC.
Interdependencies between the Genie/Nuclear and Colocalization variables were further investigated by factor analyses performed for each individual tissue core. Although the factor patterns revealed some peculiarities for individual tissue cores, some common variance patterns could be generalized from the majority of the cores. As an example, a rather representative factor pattern of the Core#2 is plotted in the Figure 8A. Factor 1 was mainly represented by positive loadings of the variables expressing the epithelial cancer compartment size (analysis area, counts of positive and negative nuclei). Factor 2 reflected variation of the Percent of Positive Nuclei with opposite loadings of the Negative Density and Brown Intensity (less intense brown colour). Of note, more intense blue correlated with the Factor 1 in the Core#2, however, the loadings of Intensity variables were rather variable in different tissue cores. For comparison, similar factor pattern of the Core#9 is presented in the Figure 8B.