Romanowsky-Giemsa as a counterstain for immunohistochemistry: optimizing a traditional reagent

Romanowsky staining: history, recent advances and future prospects from a chemistry perspective

Romanowsky staining was an important methodological breakthrough in diagnostic hematology and cytopathology during the late 19th and early 20th centuries; it has facilitated for decades the work of biologists, hematologists and pathologists working with blood cells. Despite more than a century of studying Romanowsky staining, no systematic review has been published that explains the chemical processes that produce the "Romanowsky effect" or "Romanowsky-Giemsa effect" (RGE), i.e., a purple coloration arising from the interaction of an azure dye with eosin and not due merely to their simultaneous presence. Our review is an attempt to build a bridge between chemists and biomedical scientists and to summarize the available data on methylene blue (MB) demethylation as well as the related reduction and decomposition of MB to simpler compounds by both light and enzyme systems and microorganisms. To do this, we analyze modern data on the mechanisms of MB demethylation both in the presence of acids and bases and by disproportionation due to the action of light. We also offer an explanation for why the RGE occurs only when azure B, or to a lesser extent, azure A is present by applying experimental and calculated physicochemical parameters including dye-DNA binding constants and electron density distributions in the molecules of these ligands. Finally, we discuss modern techniques for obtaining new varieties of Romanowsky dyes by modifying previously known ones. We hope that our critical literature study will help scientists understand better the chemical and physicochemical processes and mechanisms of cell staining with such dyes.

Generation of human midbrain organoids from induced pluripotent stem cells

The development of brain organoids represents a major technological advance in the stem cell field, a novel bridge between traditional 2D cultures and in vivo animal models. In particular, the development of midbrain organoids containing functional dopaminergic neurons producing neuromelanin granules, a by-product of dopamine synthesis, represents a potential new model for Parkinson’s disease. To generate human midbrain organoids, we introduce specific inductive cues, at defined timepoints, during the 3D culture process to drive the stem cells towards a midbrain fate. In this method paper, we describe a standardized protocol to generate human midbrain organoids (hMOs) from induced pluripotent stem cells (iPSCs). This protocol was developed to demonstrate how human iPSCs can be successfully differentiated into numerous, high quality midbrain organoids in one batch. We also describe adaptations for cryosectioning of fixed organoids for subsequent histological analysis.

Hematoxylin and Eosin Counterstaining Protocol for Immunohistochemistry Interpretation and Diagnosis

Hematoxylin and eosin (H&E) staining is a well-established technique in histopathology. However, immunohistochemistry (IHC) interpretation is done exclusively with hematoxylin counterstaining. Our goal was to investigate the potential of H&E as counterstaining (H&E-IHC) to allow for visualization of a marker while confirming the diagnosis on the same slide. The quality of immunostaining and the fast-technical performance were the main criteria to select the final protocol. We stained multiple diagnostic tissues with class I IHC tests with different subcellular localization markers (anti-CK7, CK20, synaptophysin, CD20, HMB45, and Ki-67) and with double-staining on prostate tissues with anti-high molecular weight keratins/p63 (DAB detection) and p504s (alkaline phosphatase detection). To validate the efficacy of the counterstaining, we stained tissue microarrays from the Canadian Immunohistochemistry Quality Control (cIQc) with class II IHC tests (ER, PR, HER2, and p53 markers). Interobserver and intraobserver concordance was assessed by κ statistics. Excellent agreement of H&E-IHC interpretation was observed in comparison with standard IHC from our laboratory (κ, 0.87 to 1.00), and with the cIQc reference values (κ, 0.81 to 1.00). Interobserver and intraobserver agreement was excellent (κ, 0.89 to 1.00 and 0.87 to 1.00, respectively). We therefore show for the first time the potential of using H&E counterstaining for IHC interpretation. We recommend the H&E-IHC protocol to enhance diagnostic precision for the clinical workflow and research studies.

Immunohistochemistry for Pathologists: Protocols, Pitfalls, and Tips

Immunohistochemistry (IHC) is an important auxiliary method for pathologists in routine diagnostic work as well as in basic and clinical research including exploration of biomarkers, as IHC allows confirmation of target molecule expressions in the context of microenvironment. Although there has been a considerable progress in automation and standardization of IHC, there are still many things to be considered in proper optimization and appropriate interpretation. In this review, we aim to provide possible pitfalls and useful tips for practicing pathologists and residents in pathology training. First, general procedure of IHC is summarized, followed by pitfalls and tips in each step and a summary of troubleshooting. Second, ways to an accurate interpretation of IHC are discussed, with introduction to general quantification and analysis methods. This review is not intended to provide complete information on IHC, but to be used as a basic reference for practice and publication.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system

Background

Digital 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.

Methods

Consecutive 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.

Results

Slide-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.

Conclusions

Our 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.