BackgroundDigital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools.MethodsConsecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue.ResultsSlide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores.ConclusionsOur solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools.
Introduction: Many theories have been proposed to explain pathogenesis of COPD; however, remains unclear why the majority of smokers (~80%) do not develop COPD, or only develop a mild disease. To explore if COPD has an autoimmune component, the role of T regulatory lymphocytes (Tregs) in the lung tissue of COPD patients is of crucial importance. Material and methods: Bronchial tissue biopsy samples were prospectively collected from 64 patients (39 COPD and 25 controls-15 smokers and 10 non-smokers). The patients with COPD were subdivided into mild/moderate (GOLD stage I−II) and severe/very severe (GOLD stage III−IV) groups. Digital image analysis was performed to estimate densities of CD4+ CD25+ cell infiltrates in double immunohistochemistry slides of the biopsy samples. Blood samples were collected from 42 patients (23 COPD and 19 controls) and tested for CD3+ CD4+ CD25+ bright lymphocytes by flow cytometry. Results: The number of intraepithelial CD4+ CD25+ lymphocytes mm-2 epithelium was significantly lower in the severe/very severe COPD (GOLD III-IV) group as well as in the control non-smokers (NS) group (p < 0,0001). Likewise, the absolute number of Treg (CD3+ CD4+ CD25+ bright) cells in the peripheral blood samples was significantly different between the four groups (p = 0.032). The lowest quantity of Treg cells was detected in the severe/very severe COPD and healthy non-smokers groups. Conclusion: Our findings suggest that severe COPD is associated with lower levels of Tregs in the blood and bronchial mucosa, while higher Tregs levels in the smokers without COPD indicate potential protective effect of Tregs against developing COPD.
Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions.
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