Herein, a microfluidic device with cistern design for cultivation of adherent eukaryotic cells for the production of recombinant proteins is presented. The geometric configuration of the microchannels in the device provided laminar flow with reduced velocity profiles in the cisterns, resulting in an adequate microenvironment for long-term adherent cell growth with passive pumping flow cycles of 24 hours. CHO-ahIFNα2b and HEK-ahIFNα2b adherent cell lines expressing a novel anti-hIFN-α2b recombinant monoclonal antibody (MAb) for the treatment of systemic lupus erythematosus were cultured on the surface of PDMS/glass microchannels coated with poly-d-lysine. A 24 day culture of CHO-ahIFNα2b cells resulted in MAb concentrations up to 166.4 μg mL per day. The productivity of CHO-ahIFNα2b and HEK-ahIFNα2b cell lines was higher in the microdevice compared to that obtained using the adherent cell culture method (T-flask), with a 5.89- and 7.31-fold increase, respectively. Moreover, biological analysis of the MAbs produced in the microdevice showed no significant differences in the neutralizing antiproliferative activity of the hIFN-α2b or the cytokine cell signaling compared to the MAbs produced with cell adherent methods. These results suggest that this microfluidic device is suitable for long-term culture of mammalian cells and can improve the productivity of cells expressing recombinant MAbs with potential for therapeutic use without affecting the quality attributes of the product.

A key challenge in the analysis of longitudinal microbiomes data is to go beyond computing their compositional profiles and infer the complex web of interactions between the various microbial taxa, their genes, and the metabolites they consume and produce. To address this challenge, we developed a computational pipeline that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to integrate them into a unified model. We discuss how our approach handles the different sampling and progression rates between individuals, how we reduce the large number of different entities and parameters in the DBNs, and the construction and use of a validation set to model edges. Applying our method to data collected from Inflammatory Bowel Disease (IBD) patients, we show that it can accurately identify known and novel interactions between various entities and can improve on current methods for learning such interactions. Experimental validations support several predictions about novel metabolite-taxa interactions. The source code is freely available under the MIT Open Source license agreement and can be downloaded from https://github.com/DaniRuizPerez/longitudinal multiomic analysis public.

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