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3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testing. However, a common limitation of immunostainings is the number of dyes that can be imaged simultaneously, as overlaps in the spectral profiles of the different dyes may result in cross talk. We therefore present a 3D deep learning method, able to predict fluorescent stainings of specific antigens on the basis of a nuclei staining. Using the proliferation marker Ki67, we showed that the presented model was able to predict the Ki67 staining with a strong correlation to the real signal. Additional analysis showed, that the model was not relying on signal cross talk. This approach, based on staining of the cell nuclei and subsequent prediction of the target antigen, could reduce the number of parallel antigen stains to a minimum and incompatible staining panels could be circumvented in the future.

Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a generative adversarial network (GAN) training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality. Integration of biophysical simulation into cell data synthesis enhances cell arrangement and achieves superior segmentation performance compared to previous methods and manual annotation. [ABSTRACT FROM AUTHOR]

Three-dimensional cell cultures, such as spheroids or organoids, serve as important models for drug screening purposes. Optical tissue clearing (OTC) enhances the visualization of fluorescence stainings and enables in toto microscopy of 3D cell culture models. Furthermore, subsequent automated image analysis tools convert qualitative confocal image sets into quantitative data. In this chapter, we describe a detailed protocol for preparation of HT29 cancer spheroids, 3D in toto immunostaining, glycerol-based OTC, whole-mount imaging, and semi-automated downstream image processing and segmentation for nuclear image analysis using open-source software., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

There is evidence that cell-associated IL-1 alpha supports immune response induction. Here we explored the impact of malignant cell-derived IL-1 on immunogenicity, immune response induction and tumor-induced immunosuppression using 3-methylcholanthrene-induced fibrosarcoma lines derived from wild-type (wt), IL-1 alpha-, IL-1 beta- or IL-1a beta-knockout (IL-1 alpha(-/-), IL-1 beta(-/-), IL-1 alphabeta(-/-)) C57BL6 mice. The wt, IL-1 alpha(-/-), IL-1 beta(-/-) and IL-1 alphabeta(-/-) fibrosarcoma lines express MHC class I molecules at a high level. The lines do not differ in their susceptibility toward NK cells, macrophages, and allogeneic CTL, or in their capacity as stimulators of an allogeneic response. However, IL-1 beta(-/-) tumors rarely grow in the syngeneic host, which is the consequence of a strong T helper and CTL response induction by IL-1 alpha-competent, IL-1 beta(-/-) tumors. On the other hand, IL-1 beta-competent, IL-1 alpha(-/-) tumors strongly assist CD11b(+)Gr-1(+) myeloid-derived suppressor cell and regulatory T cell expansion, which both suppress with high efficacy activated T helper cell proliferation and CTL lysis. In IL-1 alphabeta(-/-) tumors, the absence of IL-1 alpha becomes decisive, i.e. despite reduced suppressor cell recruitment, tumor growth was unimpaired due to inefficient immune response induction. Thus, sarcoma cell-derived IL-1 alpha and IL-1 beta do not act in concert. Induction of a strong immune response by IL-1 alpha demands therapeutic exploitation, which may become more efficient if systemic induction of immunosuppression by IL-1 beta can also be circumvented., ((c) 2008 Wiley-Liss, Inc.)