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Organoids are three-dimensional (3D) structures that can be derived from stem cells or adult tissue progenitor cells and exhibit an extraordinary ability to autonomously organize and resemble the cellular composition and architectural integrity of specific tissue segments. This feature makes them a useful tool for analyzing therapeutical relevant aspects, including organ development, wound healing, immune disorders and drug discovery. Most organoid models do not contain cells that mimic the neighboring tissue’s microenvironment, which could potentially hinder deeper mechanistic studies. However, to use organoid models in mechanistic studies, which would enable us to better understand pathophysiological processes, it is necessary to emulate the in situ microenvironment. This can be accomplished by incorporating selected cells of interest from neighboring tissues into the organoid culture. Nevertheless, the detection and quantification of organoids in such co-cultures remains a major technical challenge. These imaging analysis approaches would require an accurate separation of organoids from the other cell types in the co-culture. To efficiently detect and analyze 3D organoids in co-cultures, we developed a high-throughput imaging analysis platform. This method integrates automated imaging techniques and advanced image processing tools such as grayscale conversion, contrast enhancement, membrane detection and structure separation. Based on machine learning algorithms, we were able to identify and classify 3D organoids within dense co-cultures of immune cells. This procedure allows a high-throughput analysis of organoid-associated parameters such as quantity, size, and shape. Therefore, the technology has significant potential to advance contextualized research using organoid co-cultures and their potential applications in translational medicine.

Abstract Background Risks for living-liver donors are lower in case of a left liver donation, however, due to lower graft volume, the risk for small-for-size situations in the recipients increases. This study aims to prevent small-for-size situations in recipients using an auxiliary two-staged partial resection liver transplantation (LTX) of living-donated left liver lobes. Case presentation Two patients received a two-stage auxiliary LTX using living-donated left liver lobes after left lateral liver resection. The native extended right liver was removed in a second operation after sufficient hypertrophy of the left liver graft had occurred. Neither donor developed postoperative complications. In both recipients, the graft volume increased by an average of 105% (329 ml to 641 ml), from a graft-to-body-weight ratio of 0.54 to 1.08 within 11 days after LTX, so that the remnant native right liver could be removed. No recipient developed small-for-size syndrome; graft function and overall condition is good in both recipients after a follow-up time of 25 months. Conclusions Auxiliary two-staged partial resection LTX using living-donor left lobes is technically feasible and can prevent small-for-size situation. This new technique can expand the potential living-donor pool and contributes to increase donor safety.

Abstract Background Anaplastic thyroid carcinomas are associated with rapid tumor growth, short survival time and without any promising therapy to improve the poor prognosis. In this study, expression of immunoregulative receptor CD1d and lymphocyte infiltration in different thyroid tumors as well as in healthy tissue were analyzed in order to find new targets for an immunotherapeutic approach. Methods CD1d immunohistochemistry was performed in samples of 18 anaplastic, 17 follicular, 27 papillary, and 4 medullary thyroid carcinomas as well as in 19 specimens from normal thyroid tissue and additionally in 10 samples of sarcoma, seven malignant melanoma and three spindle‐cell lung carcinoma. Furthermore, thyroid samples were stained with antibodies against CD3, CD20, CD56, CD68, and LCA in order to analyze lymphocyte infiltration. Results For the first time CD1d receptor expression on normal thyroid tissue could be demonstrated. Moreover, anaplastic thyroid carcinomas showed significantly higher expression levels compared to other thyroid samples. Most astonishingly, CD1d expression disappeared from the cellular surface and was detected rather in the cytoplasm of anaplastic thyroid carcinoma cells. In addition, histologically similar tumors to anaplastic carcinoma like sarcoma and malignant melanoma revealed distinct CD1d staining patterns. Furthermore, infiltration of T cells, B cells, and macrophages in anaplastic thyroid carcinomas was different when compared to normal thyroid tissue and all other thyroid carcinomas. Conclusions Anaplastic thyroid carcinomas show significantly higher expression of CD1d, a receptor for NKT cells, which are subject of several anticancer therapy studies. These results may offer a novel approach to explore immunotherapeutic treatment options.

Background: There is an urgent need to develop and implement low cost, high-throughput standardized methods for routine molecular assessment of transplant biopsies. Given the vast archive of formalin-fixed and paraffin-embedded (FFPE) tissue blocks in transplant centers, a reliable protocol for utilizing this tissue bank for clinical validation of target molecules as predictors of graft outcome over time, would be of great value.Methods: We designed and optimized assays to quantify 19 target genes, including previously reported set of tissue common rejection module (tCRM) genes. We interrogated their performance for their clinical utility for detection of graft rejection and inflammation by analyzing gene expression microarrays analysis of 163 renal allograft biopsies, and subsequently validated in 40 independent FFPE archived kidney transplant biopsies at a single center.Results: A QPCR (Fluidigm) and a barcoded oligo-based (NanoString) gene expression platform were compared for evaluation of amplification of gene expression signal for 19 genes from degraded RNA extracted from FFPE biopsy sections by a set protocol. Increased expression of the selected 19 genes, that reflect a combination of specific cellular infiltrates (8/19 genes) and a graft inflammation score (11/19 genes which computes the tCRM score allowed for segregation of kidney transplant biopsies with stable allograft function and normal histology from those with histologically confirmed acute rejection (AR; p = 0.0022, QPCR; p = 0.0036, barcoded assay) and many cases of histological borderline inflammation (BL). Serial biopsy shaves used for gene expression were also processed for in-situ hybridization (ISH) for a subset of genes. ISH confirmed a high degree of correlation of signal amplification and tissue localization.Conclusions: Target gene expression amplification across a custom set of genes can identify AR independent of histology, and quantify inflammation from archival kidney transplant biopsy tissue, providing a new tool for clinical correlation and outcome analysis of kidney allografts, without the need for prospective kidney biopsy biobanking efforts.