Microfluidic Bioreactors for Cellular Microarrays

Downscaling screening cultures in a multifunctional bioreactor array-on-a-chip for speeding up optimization of yeast-based lactic acid bioproduction

A key challenge for bioprocess engineering is the identification of the optimum process conditions for the production of biochemical and biopharmaceutical compounds using prokaryotic as well as eukaryotic cell factories. Shake flasks and bench-scale bioreactor systems are still the golden standard in the early stage of bioprocess development, though they are known to be expensive, time-consuming, and labor-intensive as well as lacking the throughput for efficient production optimizations. To bridge the technological gap between bioprocess optimization and upscaling, we have developed a microfluidic bioreactor array to reduce time and costs, and to increase throughput compared with traditional lab-scale culture strategies. We present a multifunctional microfluidic device containing 12 individual bioreactors (V_t  = 15 µl) in a 26 mm × 76 mm area with in-line biosensing of dissolved oxygen and biomass concentration. Following initial device characterization, the bioreactor lab-on-a-chip was used in a proof-of-principle study to identify the most productive cell line for lactic acid production out of two engineered yeast strains, evaluating whether it could reduce the time needed for collecting meaningful data compared with shake flasks cultures. Results of the study showed significant difference in the strains' productivity within 3 hr of operation exhibiting a 4- to 6-fold higher lactic acid production, thus pointing at the potential of microfluidic technology as effective screening tool for fast and parallelizable industrial bioprocess development.

Robotic Cell Printing for Constructing Living Yeast Cell Microarrays in Microfluidic Chips

Living cell microarrays in microfluidic chips allow the non-invasive multiplexed molecular analysis of single cells. Here, we developed a simple and affordable perfusion microfluidic chip containing a living yeast cell array composed of a population of cell variants (green fluorescent protein (GFP)-tagged Saccharomyces cerevisiae clones). We combined mechanical patterning in 102 microwells and robotic piezoelectric cell dispensing in the microwells to construct the cell arrays. Robotic yeast cell dispensing of a yeast collection from a multiwell plate to the microfluidic chip microwells was optimized. The developed microfluidic chip and procedure were validated by observing the growth of GFP-tagged yeast clones that are linked to the cell cycle by time-lapse fluorescence microscopy over a few generations. The developed microfluidic technology has the potential to be easily upscaled to a high-density cell array allowing us to perform dynamic proteomics and localizomics experiments.

Binary polymer brush patterns from facile initiator stickiness for cell culturing

We report a new initiator stickiness method to fabricate micropatterned binary polymer brush surfaces, which are ideal platforms for studying cell adhesion behavior. The atom transfer radical polymerization (ATRP) initiator, ω-mercaptoundecyl bromoisobutyrate (MUDBr), is found to adsorb on several hosting polymer brushes, including poly[oligo(ethylene glycol)methyl ether methacrylate] (POEGMA), poly(2-hydroxyethyl methacrylate) (PHEMA), and poly(glycidyl methacrylate) (PGMA) brushes. Based on the initiator stickiness, micropatterned initiator molecules are printed onto a layer of homogenous hosting polymer brushes via microcontact printing (μCP), and then, vertically, a patterned second layer of polymer brushes is grown from the initiator areas. With this simple, fast, and additive method, we demonstrate the fabrication of various binary polymer brushes, and show their applications for patterning cell microarrays and controlling cell orientation. This new approach to generating binary polymer brushes shows great potential for the manipulation of interfacial phenomena, facilitating a range of applications from semiconductors and lubrication to fundamental cell biology studies.

3d Tissue Engineered In Vitro Models Of Cancer In Bone

Biological models are necessary tools for gaining insight into underlying mechanisms governing complex pathologies such as cancer in the bone. Models range from in vitro tissue culture systems to in vivo models and can be used with corresponding epidemiological and clinical data to understand disease etiology, progression, driver mutations, and signaling pathways. In bone cancer, as with many other cancers, in vivo models are often too complex to study specific cell-cell interactions or protein roles, and 2D models are often too simple to accurately represent disease processes. Consequently, researchers have increasingly turned to 3D in vitro tissue engineered models as a useful compromise. In this review, tissue engineered 3D models of bone and cancer are described in depth and compared to 2D models. Biomaterials and cell types used are described, and future directions in the field of tissue engineered bone cancer models are proposed.

Simple agarose micro-confinement array and machine-learning-based classification for analyzing the patterned differentiation of mesenchymal stem cells

The geometrical confinement of small cell colonies gives differential cues to cells sitting at the periphery versus the core. To utilize this effect, for example to create spatially graded differentiation patterns of human mesenchymal stem cells (hMSCs) in vitro or to investigate underpinning mechanisms, the confinement needs to be robust for extended time periods. To create highly repeatable micro-fabricated structures for cellular patterning and high-throughput data mining, we employed here a simple casting method to fabricate more than 800 adhesive patches confined by agarose micro-walls. In addition, a machine learning based image processing software was developed (open code) to detect the differentiation patterns of the population of hMSCs automatically. Utilizing the agarose walls, the circular patterns of hMSCs were successfully maintained throughout 15 days of cell culture. After staining lipid droplets and alkaline phosphatase as the markers of adipogenic and osteogenic differentiation, respectively, the mega-pixels of RGB color images of hMSCs were processed by the software on a laptop PC within several minutes. The image analysis successfully showed that hMSCs sitting on the more central versus peripheral sections of the adhesive circles showed adipogenic versus osteogenic differentiation as reported previously, indicating the compatibility of patterned agarose walls to conventional microcontact printing. In addition, we found a considerable fraction of undifferentiated cells which are preferentially located at the peripheral part of the adhesive circles, even in differentiation-inducing culture media. In this study, we thus successfully demonstrated a simple framework for analyzing the patterned differentiation of hMSCs in confined microenvironments, which has a range of applications in biology, including stem cell biology.