The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.

The capacity of milli and micro litre bioreactors to accelerate process development has been successfully demonstrated in traditional biotechnology. However, for regenerative medicine present smaller scale culture methods cannot cope with the wide range of processing variables that need to be evaluated. Existing microfabricated culture devices, which could test different culture variables with a minimum amount of resources (e.g. expensive culture medium), are typically not designed with process development in mind. We present a novel, autoclavable, and microfabricated scale-down device designed for regenerative medicine process development. The microfabricated device contains a re-sealable culture chamber that facilitates use of standard culture protocols, creating a link with traditional small-scale culture devices for validation and scale-up studies. Further, the modular design can easily accommodate investigation of different culture substrate/extra-cellular matrix combinations. Inactivated mouse embryonic fibroblasts (iMEF) and human embryonic stem cell (hESC) colonies were successfully seeded on gelatine-coated tissue culture polystyrene (TC-PS) using standard static seeding protocols. The microfluidic chip included in the device offers precise and accurate control over the culture medium flow rate and resulting shear stresses in the device. Cells were cultured for two days with media perfused at 300 µl.h−1 resulting in a modelled shear stress of 1.1×10−4 Pa. Following perfusion, hESC colonies stained positively for different pluripotency markers and retained an undifferentiated morphology. An image processing algorithm was developed which permits quantification of co-cultured colony-forming cells from phase contrast microscope images. hESC colony sizes were quantified against the background of the feeder cells (iMEF) in less than 45 seconds for high-resolution images, which will permit real-time monitoring of culture progress in future experiments. The presented device is a first step to harness the advantages of microfluidics for regenerative medicine process development.

Oxygen plays a key role in stem cell biology as a signaling molecule and as an indicator of cell energy metabolism. Quantification of cellular oxygen kinetics, i.e. the determination of specific oxygen uptake rates (sOURs), is routinely used to understand metabolic shifts. However current methods to determine sOUR in adherent cell cultures rely on cell sampling, which impacts on cellular phenotype. We present real‐time monitoring of cell growth from phase contrast microscopy images, and of respiration using optical sensors for dissolved oxygen. Time‐course data for bulk and peri‐cellular oxygen concentrations obtained for Chinese hamster ovary (CHO) and mouse embryonic stem cell (mESCs) cultures successfully demonstrated this non‐invasive and label‐free approach. Additionally, we confirmed non‐invasive detection of cellular responses to rapidly changing culture conditions by exposing the cells to mitochondrial inhibiting and uncoupling agents. For the CHO and mESCs, sOUR values between 8 and 60 amol cell−1 s−1, and 5 and 35 amol cell−1 s−1 were obtained, respectively. These values compare favorably with literature data. The capability to monitor oxygen tensions, cell growth, and sOUR, of adherent stem cell cultures, non‐invasively and in real time, will be of significant benefit for future studies in stem cell biology and stem cell‐based therapies.

Adherent cell lines are widely used across all fields of biology, including drug discovery, toxicity studies, and regenerative medicine. However, adherent cell processes are often limited by a lack of advances in cell culture systems. While suspension culture processes benefit from decades of development of instrumented bioreactors, adherent cultures are typically performed in static, noninstrumented flasks and well-plates. We previously described a microfabricated bioreactor that enables a high degree of control on the microenvironment of the cells while remaining compatible with standard cell culture protocols. In this report, we describe its integration with automated image-processing capabilities, allowing the continuous monitoring of key cell culture characteristics. A machine learning–based algorithm enabled the specific detection of one cell type within a co-culture setting, such as human embryonic stem cells against the background of fibroblast cells. In addition, the algorithm did not confuse image artifacts resulting from microfabrication, such as scratches on surfaces, or dust particles, with cellular features. We demonstrate how the automation of flow control, environmental control, and image acquisition can be employed to image the whole culture area and obtain time-course data of mouse embryonic stem cell cultures, for example, for confluency.

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