An Engineered Approach to Stem Cell Culture: Automating the Decision Process for Real-Time Adaptive Subculture of Stem Cells

Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation

Conference of Advance Research and Innovation (ICARI-2014) 118 ICARI

Abstract With the advent of highly advanced optics and imaging system, currently biological research has reached a stage where scientists can study biological entities and processes at molecular and cellular-level in real time. However, a single experiment consists of hundreds and thousands of parameters to be recorded and a large population of microscopic objects to be tracked. Thus, making manual inspection of such events practically impossible. This calls for an approach to computer-vision based automated tracking and monitoring of cells in biological experiments. This technology promises to revolutionize the research in cellular biology and medical science which includes discovery of diseases by tracking the process in cells, development of therapy and drugs and the study of microscopic biological elements. This article surveys the recent literature in the area of computer vision based automated cell tracking. It discusses the latest trends and successes in the development and introduction of automated cell tracking techniques and systems

Cancer Drug Screening Scale-up: Combining Biomimetic Microfluidic Platforms and Deep Learning Image Analysis

The development of cancer drugs is usually costly and time-consuming, mainly due to growing complexity in screening large number of candidate compounds and high failure rates in translation from preclinical trials to clinical approval. Despite the great efforts, the preclinical screening platforms combing good clinical relevance and high throughput for large-scale drug testing is still lacking. In addition, accumulating evidence suggests that cancer drug response can be altered by tumor microenvironment (TME), which includes not only cancer cells but also physical, and biochemical cues in niches. To improve the current cancer drug screening assays, it is important to mimic local TME to achieve better physiological relevance. In the first part of this dissertation, three TME-mimicking microfluidic platforms were introduced for three different in-vitro TME-mimicking tumor sphere models: spheres in matrix, self-aggregated spheres, and single-cell clonal spheres. First, a 3D gel-island chip investigated the heterogeneity of single-cell drug responses in biomimetic extracellular matrix (ECM). With 1,500 isolated single cell chambers containing ECM, it was demonstrated that ECM support was favorable for some population of cancer cells to maintain stemness and develop drug resistance. This result suggested the importance of drug screening at single-cell resolution in TME-mimicking platforms. Secondly, a drug combination screening chip enabling high-throughput and scalable combinatorial drug screening was demonstrated for the aggregated sphere model. Instead of screening a single drug on each of the tumors, this chip allows the screening of all pairwise drug combinations from eight different cancer drugs, in total 172 different treatment conditions, and 1,032 tested samples in a single microfluidic chip. The presented design approach was easily scalable to incorporate arbitrary number of drugs for large-scale drug screening. Finally, single-cell Hi-Sphere chip enabled high-throughput clonal sphere culture and selective retrieval. Combining fluorescent dye on-situ staining techniques, we identified rare cancer stem-like cell population and confirms its location at the leading edge of spheres. Advance in experimental throughput generates massive data, which demands the corresponding automatic analysis and intelligent interpretation capabilities. The second part of this dissertation focuses on the applications of computer vision and machine learning algorithms to automated biomedical data processing. Image analysis with convolutional neural network was applied for drug efficacy evaluation in a fast and label-free manner. The estimated drug efficacy is highly correlated with the experimental ground truth (R-value > 0.93), while the predicted half-maximal inhibitory concentration is within 8% error range. In addition, metastatic fast-moving cells could be identified after extracting morphological features from the microscope images and applying deep learning algorithm for image analysis, achieving over 99% accuracy for cell movement direction prediction and 91% for speed prediction. In summary, this dissertation presents high-throughput TME-mimicking microfluidics and deep learning image analysis for large-scale drug screening solutions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163039/1/zhangzx_1.pd

Detecting cells and analyzing their behaviors in microscopy images using deep neural networks

The computer-aided analysis in the medical imaging field has attracted a lot of attention for the past decade. The goal of computer-vision based medical image analysis is to provide automated tools to relieve the burden of human experts such as radiologists and physicians. More specifically, these computer-aided methods are to help identify, classify and quantify patterns in medical images. Recent advances in machine learning, more specifically, in the way of deep learning, have made a big leap to boost the performance of various medical applications. The fundamental core of these advances is exploiting hierarchical feature representations by various deep learning models, instead of handcrafted features based on domain-specific knowledge. In the work presented in this dissertation, we are particularly interested in exploring the power of deep neural network in the Circulating Tumor Cells detection and mitosis event detection. We will introduce the Convolutional Neural Networks and the designed training methodology for Circulating Tumor Cells detection, a Hierarchical Convolutional Neural Networks model and a Two-Stream Bidirectional Long Short-Term Memory model for mitosis event detection and its stage localization in phase-contrast microscopy images”--Abstract, page iii

Finding a targetable super-hub within the network of cancer cell persistency and adaptiveness: a clinician-scientist quantitative perspective for melanoma

In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-gamma-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy.Comment: 12 pages, 3 figure