Immunohistochemical toolkit for tracking and quantifying xenotransplanted human stem cells.

AIM: Biomarker-based tracking of human stem cells xenotransplanted into animal models is crucial for studying their fate in the field of cell therapy or tumor xenografting. MATERIALS & METHODS: Using immunohistochemistry and in situ hybridization, we analyzed the expression of three human-specific biomarkers: Ku80, human mitochondria (hMito) and Alu. RESULTS: We showed that Ku80, hMito and Alu biomarkers are broadly expressed in human tissues with no or low cross-reactivity toward rat, mouse or pig tissues. In vitro, we demonstrated that their expression is stable over time and does not change along the differentiation of human-derived induced pluripotent stem cells or human glial-restricted precursors. We tracked in vivo these cell populations after transplantation in rodent spinal cords using aforementioned biomarkers and human-specific antibodies detecting apoptotic, proliferating or neural-committed cells. CONCLUSION: This study assesses the human-species specificity of Ku80, hMito and Alu, and proposes useful biomarkers for characterizing human stem cells in xenotransplantation paradigms.

Clustering methods applied in the detection of Ki67 hot-spots in whole tumor slide images: an efficient way to characterize heterogeneous tissue-based biomarkers.

Whole-slide scanners allow the digitization of an entire histological slide at very high resolution. This new acquisition technique opens a wide range of possibilities for addressing challenging image analysis problems, including the identification of tissue-based biomarkers. In this study, we use whole-slide scanner technology for imaging the proliferating activity patterns in tumor slides based on Ki67 immunohistochemistry. Faced with large images, pathologists require tools that can help them identify tumor regions that exhibit high proliferating activity, called "hot-spots" (HSs). Pathologists need tools that can quantitatively characterize these HS patterns. To respond to this clinical need, the present study investigates various clustering methods with the aim of identifying Ki67 HSs in whole tumor slide images. This task requires a method capable of identifying an unknown number of clusters, which may be highly variable in terms of shape, size, and density. We developed a hybrid clustering method, referred to as Seedlink. Compared to manual HS selections by three pathologists, we show that Seedlink provides an efficient way of detecting Ki67 HSs and improves the agreement among pathologists when identifying HSs.

TIMP-4 and CD63: new prognostic biomarkers in human astrocytomas.

Based on the molecular profiling of astrocytomas, we previously identified a series of genes involved in astrocytoma invasion. Of these, tissue inhibitor of metalloproteinase-4 (TIMP-4) was found to be overexpressed in pilocytic astrocytomas relative to diffuse astrocytomas of any histological grade. Although some data suggest that TIMP-4 may be an anti-tumoral actor in astrocytomas, recent findings challenge this concept. The present study aims to investigate the diagnostic and prognostic values of TIMP-4 and its putative partner CD63 in human astrocytomas. Tissue microarray and image analysis were first carried out to quantitatively analyze the immunohistochemical expression of these proteins in 471 gliomas including 354 astrocytomas. Pathological semi-quantitative scores of both markers' expression were then established and correlated to astrocytoma diagnosis and patient prognosis. TIMP-4 and CD63 expressions were both overexpressed in astrocytomas compared with oligodendrogliomas (P<0.001) and in pilocytic astrocytomas compared with grade II diffuse astrocytomas (P<0.001). In glioblastomas, high TIMP-4/CD63 co-expression scores were identified as independent prognostic factors associated with progression and shorter survival. In conclusion, this work provides the first evidence of a TIMP-4/CD63 association in astrocytoma tumor cells. It identifies TIMP-4 and CD63 as markers of the astrocytic phenotype in patients with gliomas. In addition, this work highlights the contribution of high TIMP-4/CD63 co-expression to the adverse outcomes of patients with glioblastomas.

Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis.

Antibody-based proteomics applied to tissue microarray (TMA) technology provides a very efficient means of visualizing and locating antigen expression in large collections of normal and pathological tissue samples. To characterize antigen expression on TMAs, the use of image analysis methods avoids the effects of human subjectivity evidenced in manual microscopical analysis. Thus, these methods have the potential to significantly enhance both precision and reproducibility. Although some commercial systems include tools for the quantitative evaluation of immunohistochemistry-stained images, there exists no clear agreement on best practices to allow for correct and reproducible quantification results. Our study focuses on practical aspects regarding (i) image acquisition (ii) segmentation of staining and counterstaining areas and (iii) extraction of quantitative features. We illustrate our findings using a commercial system to quantify different immunohistochemistry markers targeting proteins with different expression patterns (cytoplasmic, nuclear or membranous) in colon cancer or brain tumor TMAs. Our investigations led us to identify several steps that we consider essential for standardizing computer-assisted immunostaining quantification experiments. In addition, we propose a data normalization process based on reference materials to be able to compare measurements between studies involving different TMAs. In conclusion, we recommend certain critical prerequisites that commercial or in-house systems should satisfy in order to permit valid immunostaining quantification.