An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells

Growth and differentiation factor-7 immobilized, mechanically strong quadrol-hexamethylene diisocyanate-methacrylic anhydride polyurethane polymer for tendon repair and regeneration

Biological and mechanical cues are both vital for biomaterial aided tendon repair and regeneration. Here, we fabricated mechanically tendon-like (0 s UV) QHM polyurethane scaffolds (Q: Quadrol, H: Hexamethylene diisocyanate; M: Methacrylic anhydride) and immobilized them with Growth and differentiation factor-7 (GDF-7) to produce mechanically strong and tenogenic scaffolds. In this study, we assessed QHM polymer cytocompatibility, amenability to fibrin-coating, immobilization and persistence of GDF-7, and capability to support GDF-7-mediated tendon differentiation in vitro as well as in vivo in mouse subcutaneous and acute rat rotator cuff tendon resection models. Cytocompatibility studies showed that QHM facilitated cell attachment, proliferation, and viability. Fibrin-coating and GDF-7 retention studies showed that mechanically tendon-like 0 s UV QHM polymer could be immobilized with GDF-7 and retained the growth factor (GF) for at least 1-week ex vivo. In vitro differentiation studies showed that GDF-7 mediated bone marrow-derived human mesenchymal stem cell (hMSC) tendon-like differentiation on 0 s UV QHM. Subcutaneous implantation of GDF-7-immobilized, fibrin-coated, QHM polymer in mice for 2 weeks demonstrated de novo formation of tendon-like tissue while implantation of GDF-7-immobilized, fibrin-coated, QHM polymer in a rat acute rotator cuff resection injury model indicated tendon-like tissue formation in situ and the absence of heterotopic ossification. Together, our work demonstrates a promising synthetic scaffold with human tendon-like biomechanical attributes as well as immobilized tenogenic GDF-7 for tendon repair and regeneration. STATEMENT OF SIGNIFICANCE: Biological activity and mechanical robustness are key features required for tendon-promoting biomaterials. While synthetic biomaterials can be mechanically robust, they often lack bioactivity. To biologically augment synthetic biomaterials, numerous drug and GF delivery strategies exist but the large tissue space within the shoulder is constantly flushed with saline during arthroscopic surgery, hindering efficacious controlled release of therapeutic molecules. Here, we coated QHM polymer (which exhibits human tendon-to-bone-like biomechanical attributes) with fibrin for GF binding. Unlike conventional drug delivery strategies, our approach utilizes immobilized GFs as opposed to released GFs for sustained, localized tissue regeneration. Our data demonstrated that GF immobilization can be broadly applied to synthetic biomaterials for enhancing bioactivity, and GDF-7-immobilized QHM exhibit high clinical translational potential for tendon repair.

The importance of cell culture parameter standardization: an assessment of the robustness of the 2102Ep reference cell line

Work undertaken using the embryonic carcinoma 2102Ep line, highlighted the requirement for robust, well-characterized and standardized protocols. A systematic approach utilizing 'quick hit' experiments demonstrated variability introduced into culture systems resulting from slight changes to culture conditions (route A). This formed the basis for longitudinal experiments investigating long-term effects of culture parameters including seeding density and feeding regime (route B).Results demonstrated that specific growth rates (SGR) of passage 59 (P59) cells seeded at 20,000 cells/cm2 and subjected to medium exchange after 48h prior to reseeding at 72h (route B2) on average was marginally higher than, P55 cells cultured under equivalent conditions (route A1); whereby SGR values were (0.021±0.004) and (0.019±0.004). Viability was higher in route B2 over 10 passages with average viability reported as (86.3%±8.1) compared to route A1 (83.3±8.8). The metabolite data demonstrated both culture route B1 (P57 cells seeded at 66,667 cells/cm2) and B2 had consistent-specific metabolite rates (SMR) for glucose, but SMR values of route B1 was consistently lower than route B2 (0.00001 mmol, cell-1.d-1 and 0.000025).Results revealed interactions between phenotype, SMR and feeding regime that may not be accurately reflected by growth rate or observed morphology. This implies that current schemes of protocol control do not adequately account for variability, since key cell characteristics, including phenotype and SMR, change regardless of standardized seeding densities. This highlights the need to control culture parameters through defined protocols, for processes that involve culture for therapeutic use, biologics production, and reference lines.

Overcoming bioprocess bottlenecks in the large-scale expansion of high-quality hiPSC aggregates in vertical-wheel stirred suspension bioreactors

BACKGROUND

Human induced pluripotent stem cells (hiPSCs) hold enormous promise in accelerating breakthroughs in understanding human development, drug screening, disease modeling, and cell and gene therapies. Their potential, however, has been bottlenecked in a mostly laboratory setting due to bioprocess challenges in the scale-up of large quantities of high-quality cells for clinical and manufacturing purposes. While several studies have investigated the production of hiPSCs in bioreactors, the use of conventional horizontal-impeller, paddle, and rocking-wave mixing mechanisms have demonstrated unfavorable hydrodynamic environments for hiPSC growth and quality maintenance. This study focused on using computational fluid dynamics (CFD) modeling to aid in characterizing and optimizing the use of vertical-wheel bioreactors for hiPSC production.

METHODS

The vertical-wheel bioreactor was modeled with CFD simulation software Fluent at agitation rates between 20 and 100 rpm. These models produced fluid flow patterns that mapped out a hydrodynamic environment to guide in the development of hiPSC inoculation and in-vessel aggregate dissociation protocols. The effect of single-cell inoculation on aggregate formation and growth was tested at select CFD-modeled agitation rates and feeding regimes in the vertical-wheel bioreactor. An in-vessel dissociation protocol was developed through the testing of various proteolytic enzymes and agitation exposure times.

RESULTS

CFD modeling demonstrated the unique flow pattern and homogeneous distribution of hydrodynamic forces produced in the vertical-wheel bioreactor, making it the opportune environment for systematic bioprocess optimization of hiPSC expansion. We developed a scalable, single-cell inoculation protocol for the culture of hiPSCs as aggregates in vertical-wheel bioreactors, achieving over 30-fold expansion in 6 days without sacrificing cell quality. We have also provided the first published protocol for in-vessel hiPSC aggregate dissociation, permitting the entire bioreactor volume to be harvested into single cells for serial passaging into larger scale reactors. Importantly, the cells harvested and re-inoculated into scaled-up vertical-wheel bioreactors not only maintained consistent growth kinetics, they maintained a normal karyotype and pluripotent characterization and function.

CONCLUSIONS

Taken together, these protocols provide a feasible solution for the culture of high-quality hiPSCs at a clinical and manufacturing scale by overcoming some of the major documented bioprocess bottlenecks.

A novel method of cell culture based on the microfluidic chip for regulation of cell density

The regulation of cell density is an important segment in microfluidic cell culture, particularly in the repeated assays. Traditionally, consistent cell density is difficult to achieve, owing to the inaccurate regulation of cell density with manual feedback. A novel cell culture method with automatic feedback is proposed for real-time regulation of cell density based on microfluidic chip in this paper. Here, an integrated microfluidic system combining cell culture, density detection, and control of proliferation rate was developed. Interdigital electrode structures were sputtered on the microchannel automatically to provide the real-time feedback information of impedance. The most sensitive frequency was studied to improve the detection resolution of the sensing chip. Cells were cultured on the chip surface and cell density was detected by monitoring the alternation of the impedance. The feedback controller was established by the least squares support vector machines. Then, the cell proliferation rate was automatically controlled using the feedback controller to achieve the desired cell density in the repeated assays. The results show that the standard error of this method is 2.8% indicating that the method can keep a consistency of cell density in the repeated assays. This study provides a basis for improving the accuracy and repeatability in the further assays of finding the optimal drug concentration.

Predicting in vitro human mesenchymal stromal cell expansion based on individual donor characteristics using machine learning

BACKGROUND

Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics.

METHODS

In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics.

RESULTS

Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups.

DISCUSSION

In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.

Automated collective motion analysis validates human keratinocyte stem cell cultures

Identification and quality assurance of stem cells cultured in heterogeneous cell populations are indispensable for successful stem cell therapy. Here we present an image-processing pipeline for automated identification and quality assessment of human keratinocyte stem cells. When cultivated under appropriate conditions, human epidermal keratinocyte stem cells give rise to colonies and exhibit higher locomotive capacity as well as significant proliferative potential. Image processing and kernel density estimation were used to automatically extract the area of keratinocyte colonies from phase-contrast images of cultures containing feeder cells. The DeepFlow algorithm was then used to calculate locomotion speed of the colony area by analyzing serial images. This image-processing pipeline successfully identified keratinocyte stem cell colonies by measuring cell locomotion speed, and also assessed the effect of oligotrophic culture conditions and chemical inhibitors on keratinocyte behavior. Therefore, this study provides automated procedures for image-based quality control of stem cell cultures and high-throughput screening of small molecules targeting stem cells.

A new method of SC image processing for confluence estimation

Stem cells images are a strong instrument in the estimation of confluency during their culturing for therapeutic processes. Various laboratory conditions, such as lighting, cell container support and image acquisition equipment, effect on the image quality, subsequently on the estimation efficiency. This paper describes an efficient image processing method for cell pattern recognition and morphological analysis of images that were affected by uneven background. The proposed algorithm for enhancing the image is based on coupling a novel image denoising method through BM3D filter with an adaptive thresholding technique for improving the uneven background. This algorithm works well to provide a faster, easier, and more reliable method than manual measurement for the confluency assessment of stem cell cultures. The present scheme proves to be valid for the prediction of the confluency and growth of stem cells at early stages for tissue engineering in reparatory clinical surgery. The method used in this paper is capable of processing the image of the cells, which have already contained various defects due to either personnel mishandling or microscope limitations. Therefore, it provides proper information even out of the worst original images available.

Cell motion predicts human epidermal stemness

Keratinocyte stem cell colonies can be identified by analyzing cell motion, an emergent stem cell property.

Image-based identification of cultured stem cells and noninvasive evaluation of their proliferative capacity advance cell therapy and stem cell research. Here we demonstrate that human keratinocyte stem cells can be identified in situ by analyzing cell motion during their cultivation. Modeling experiments suggested that the clonal type of cultured human clonogenic keratinocytes can be efficiently determined by analysis of early cell movement. Image analysis experiments demonstrated that keratinocyte stem cells indeed display a unique rotational movement that can be identified as early as the two-cell stage colony. We also demonstrate that α6 integrin is required for both rotational and collective cell motion. Our experiments provide, for the first time, strong evidence that cell motion and epidermal stemness are linked. We conclude that early identification of human keratinocyte stem cells by image analysis of cell movement is a valid parameter for quality control of cultured keratinocytes for transplantation.

Automated and online characterization of adherent cell culture growth in a microfabricated bioreactor

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.

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

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.

Selection and tuning of a fast and simple phase-contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

Morphology-based features for adaptive mitosis detection of in vitro stem cell tracking data

OBJECTIVES

The cultivation of adherently growing cell populations is a major task in the field of adult stem cell production used for drug discovery and in the field of regenerative medicine. To assessthe quality of a cell population, a crucial event is the mitotic cell division: the precise knowledge of these events enables the reconstruction of lineages and accurate proliferation curves as well as a detailed analysis of cell cycles. To serve in an autonomous cell farming framework, such a detector requires to work reliably and unsupervised.

METHODS

We introduce a mitosis detector that is using a maximum likelihood (ML) estimator based on morphological cell features (cell area, brightness, length, compactness). It adapts to the 3 phases of cell growth (lag, log and stationary phase). As a concurrent model, we compared ML with kernel SVMs using linear, quadratic and Gaussian kernel functions. All approaches are evaluated for their ability to distinguish between mitotic and non-mitotic events. The large, publicly available benchmark data CeTReS (reference data set A with >240,000 segmented cells, >2,000 mitotic events) is used for this evaluation.

RESULTS

The adaptive (unsupervised) ML approach clearly outperforms previously published non-adaptive approaches and the linear SVM. Furthermore, it robustly reaches a performance comparable to quadratic and Gaussian SVM.

CONCLUSIONS

The proposed simple and label free adaptive variant might be the method of choice when it comes to autonomous cell farming. Hereby, it is essential to have reliable and unsupervised mitosis detection that covers all phases of cell growth.