Artificial intelligence-based tissue segmentation and cell identification in multiplex-stained histological endometriosis sections.

Study Question: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?

Summary Answer: A combination of a machine learning-based tissue analysis software for tissue segmentation and a deep learning-based algorithm for segmentation-independent cell identification shows strong performance on the automated histological analysis of endometriosis sections.

What Is Known Already: Endometriosis is characterized by the complex interplay of various cell types and exhibits great variation between patients and endometriosis subtypes.

Study Design, Size, Duration: Endometriosis tissue samples of eight patients of different subtypes were obtained during surgery.

RNA-based logic for selective protein expression in senescent cells.

Cellular senescence is a cellular state characterized by irreversible growth arrest, resistance to apoptosis and secretion of inflammatory molecules, which is causally linked to the pathogenesis of many age-related diseases. Besides, there is accumulating evidence that selective removal of senescent cells can benefit therapies for cancer and fibrosis by modulating the inflammatory microenvironment. While the field of so-called senolytics has spawned promising small molecules and peptides for the selective removal of senescent cells, there is still no effective means to detect senescent cells in vivo, a prerequisite for understanding the role of senescence in pathophysiology and to assess the effectiveness of treatments aimed at removing senescent cells.

The role of fibrosis in endometriosis: a systematic review.

Background: Fibrosis is an important pathological feature of endometriotic lesions of all subtypes. Fibrosis is present in and around endometriotic lesions, and a central role in its development is played by myofibroblasts, which are cells derived mainly after epithelial-to-mesenchymal transition (EMT) and fibroblast-to-myofibroblast transdifferentiation (FMT). Transforming growth factor-β (TGF-β) has a key role in this myofibroblastic differentiation.

Endometriotic tissue fragments are viable after cryopreservation in an ex vivo tissue model recapitulating the fibrotic microenvironment.

Study Question: Is it possible to establish an ex vivo endometriosis model using cryopreserved endometriotic tissue fragments?

Summary Answer: Cryopreserved endometriotic tissue fragments remain viable after thawing and during at least 3 days of culture and can therefore be used to establish an ex vivo endometriosis model to efficiently test potential therapeutic agents.

What Is Known Already: Endometriosis is the most prevalent benign gynecologic disease with an enormous societal burden; however, curative therapies are still lacking. To efficiently test potential new therapies, an ex vivo model based on previously cryopreserved endometriotic tissue that recapitulates the different endometriosis subtypes and their microenvironment is highly desirable.

Selective Targeting of Tumor Cells in a Microfluidic Tumor Model with Multiple Cell Types.

Organ-on-a-chip technology allows researchers to precisely monitor drug efficacy in 3D tissue culture systems that are physiologically more relevant to humans compared to 2D cultures and that allow better control over experimental conditions as compared to animal models. Specifically, the high control over microenvironmental conditions combined with the broad range of direct measurements that can be performed in these systems makes organ-on-a-chip devices a versatile tool to investigate tumor targeting and drug delivery. Here, we describe a detailed protocol for studying the cell-selective targeting of protein drugs to tumor cells on an organ-on-a-chip system using a co-culture consisting of BT-474 cancer cells and C5120 human fibroblasts as an example.