Organ-on-Chips (OOCs) are micro-fabricated devices which are used to culture cells in order to mimic functional units of human organs. The devices are designed to simulate the physiological environment of tissues in vivo. Cells in some types of OOCs can be stimulated in situ by electrical and/or mechanical actuators. These actuations can mimic physiological conditions in real tissue and may include fluid or air flow, or cyclic stretch and strain as they occur in the lung and heart. These conditions similarly affect cultured cells and may influence their ability to respond appropriately to physiological or pathological stimuli. To date, most focus has been on devices specifically designed to culture just one functional unit of a specific organ: lung alveoli, kidney nephrons or blood vessels, for example. In contrast, the modular Cytostretch membrane platform described here allows OOCs to be customized to different OOC applications. The platform utilizes silicon-based micro-fabrication techniques that allow low-cost, high-volume manufacturing. We describe the platform concept and its modules developed to date. Membrane variants include membranes with (i) through-membrane pores that allow biological signaling molecules to pass between two different tissue compartments; (ii) a stretchable micro-electrode array for electrical monitoring and stimulation; (iii) micro-patterning to promote cell alignment; and (iv) strain gauges to measure changes in substrate stress. This paper presents the fabrication and the proof of functionality for each module of the Cytostretch membrane. The assessment of each additional module demonstrate that a wide range of OOCs can be achieved.
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The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.@en