Integrated Colony Imaging, Analysis, and Selection Device for Regenerative Medicine

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellXTM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellXTM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care

The integration of artificial intelligence (AI) and machine learning (ML) into regenerative orthopedics heralds a paradigm shift in clinical methodologies and patient management. This review article scrutinizes AI's role in augmenting diagnostic accuracy, refining predictive models, and customizing patient care in orthopedic medicine. Focusing on innovations such as KeyGene and CellNet, we illustrate AI's adeptness in navigating complex genomic datasets, cellular differentiation, and scaffold biodegradation, which are critical components of tissue engineering. Despite its transformative potential, AI's clinical adoption remains in its infancy, contending with challenges in validation, ethical oversight, and model training for clinical relevance. This review posits AI as a vital complement to human intelligence (HI), advocating for an interdisciplinary approach that merges AI's computational prowess with medical expertise to fulfill precision medicine's promise. By analyzing historical and contemporary developments in AI, from the foundational theories of McCullough and Pitts to sophisticated neural networks, the paper emphasizes the need for a synergistic alliance between AI and HI. This collaboration is imperative for improving surgical outcomes, streamlining therapeutic modalities, and enhancing the quality of patient care. Our article calls for robust interdisciplinary strategies to overcome current obstacles and harness AI's full potential in revolutionizing patient outcomes, thereby significantly contributing to the advancement of regenerative orthopedics and the broader field of scientific research.

Automated in-process characterization and selection of cell-clones for quality and efficient cell manufacturing

Delivery of safe, effective and reliable cellular therapies, whether based on mesenchymal stromal cells (MSCs) or induced pluripotent stem cells (iPSCs), demand standardization of cell culture protocols. There is a need to develop automation platform that enables the users to generate culture expanded human cell populations that improves the quality and reduces batch-to-batch variation with respect to biological potential. Cell X™ robot was designed to address these current challenges in the cell fabrication industry. It utilizes non-invasive large field of view quantitative image analysis to guide an automated process of targeted "biopsy" (cells or media), "picking" (selection) of desired cells or colonies, or "weeding" (removal) of undesired cells, thus providing an unprecedented ability to acquire quantitative measurement in a complex heterogeneous cell environment "in process" and then to act on those measurements to define highly reproducible methods for cell and colony "management" based on application specific critical quality attributes to improve the quality of the manufactured cell lines and cell products.

Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

Progenitor cells from different zones of human cartilage and their correlation with histopathological osteoarthritis progression

Cell-based therapies development for the treatment of osteoarthritis (OA) requires an understanding of the disease progression and attributes of the cells resident in cartilage. This study focused on quantitative assessment of the concentration and biological potential of stem and progenitor cells resident in different zones of cartilage displaying macroscopic Outerbridge grade 1-2 OA, and their correlation with OA progression based on established histologic scoring system. Lateral femoral condyles were collected from 15 patients with idiopathic OA and varus knees undergoing total knee arthroplasty. Superficial(C_sp , top ∼ 500 µm) and deep cartilage(C_dp ) was separated. Chondrogenic Connective Tissue Progenitors (CTP-C) were assayed by standardized Colony-Forming-Unit assay using automated image analysis (Colonyze^TM ) based on ASTM standard F-2944-12. Cell concentration (cells/mg) was significantly greater in C_sp (median: 7,000; range: 3,440-17,600) than C_dp (median: 5,340; range: 3,393-9,660), p = 0.039. Prevalence (CTPs/million cells) was not different between C_sp (median: 1,274; range: 0-3,898) and C_dp (median:1,365; range:0-6,330), p = 0.42. In vitro performance of CTP-C progeny varied widely within and between patients, manifest by variation in colony size and morphology. Mean histopathological Mankin score was 4.7 (SD = 1.2), representing mild to moderate OA. Tidemark breach by blood vessels was associated with lower C_sp cell concentration (p = 0.02). Matrix degradation was associated with lower C_dp cell and CTP-C concentration (p = 0.015 and p = 0.095, respectively), independent of articular surface changes. These findings suggest that the initiation of OA may occur in either superficial or deep zones. The pathological changes affect CTP-Cs in C_sp and C_dp cartilage zones differently. The heterogeneity among the available CTP-Cs in C_sp and C_dp suggests performance-based selection to optimize cell-sourcing strategies for therapy. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1728-1738, 2018.

Variability in the Preparation, Reporting, and Use of Bone Marrow Aspirate Concentrate in Musculoskeletal Disorders: A Systematic Review of the Clinical Orthopaedic Literature

BACKGROUND

Interest in the therapeutic potential of bone marrow aspirate concentrate (BMAC) has grown exponentially. However, comparisons among studies and their processing methods are challenging because of inconsistent reporting of protocols, as well as poor characterization of the composition of the initial bone marrow aspirate and of the final products delivered. The purpose of this study was to perform a systematic review of the literature to evaluate the level of reporting related to the protocols used for BMAC preparation and the composition of BMAC utilized in the treatment of musculoskeletal diseases in published clinical studies.

METHODS

A systematic review of the literature was performed by searching PubMed, MEDLINE, the Cochrane Database of Systematic Reviews, and the Cochrane Central Register of Controlled Trials from 1980 to 2016. Inclusion criteria were human clinical trials, English language, and manuscripts that reported on the use of BMAC in musculoskeletal conditions.

RESULTS

After a comprehensive review of the 986 identified articles, 46 articles met the inclusion criteria for analysis. No study provided comprehensive reporting that included a clear description of the preparation protocol that could be used by subsequent investigators to repeat the method. Only 14 (30%) of the studies provided quantitative metrics of the composition of the BMAC final product.

CONCLUSIONS

The reporting of BMAC preparation protocols in clinical studies was highly inconsistent and studies did not provide sufficient information to allow the protocol to be reproduced. Moreover, comparison of the efficacy and yield of BMAC products is precluded by deficiencies in the reporting of preparation methods and composition. Future studies should contain standardized and stepwise descriptions of the BMAC preparation protocol, and the composition of the BMAC delivered, to permit validating and rationally optimizing the role of BMAC in musculoskeletal care.

Variation in primary and culture-expanded cells derived from connective tissue progenitors in human bone marrow space, bone trabecular surface and adipose tissue

BACKGROUND AIMS

Connective tissue progenitors (CTPs) embody the heterogeneous stem and progenitor cell populations present in native tissue. CTPs are essential to the formation and remodeling of connective tissue and represent key targets for tissue-engineering and cell-based therapies. To better understand and characterize CTPs, we aimed to compare the (i) concentration and prevalence, (ii) early in vitro biological behavior and (iii) expression of surface-markers and transcription factors among cells derived from marrow space (MS), trabecular surface (TS), and adipose tissues (AT).

METHODS

Cancellous-bone and subcutaneous-adipose tissues were collected from 8 patients. Cells were isolated and cultured. Colony formation was assayed using Colonyze software based on ASTM standards. Cell concentration ([Cell]), CTP concentration ([CTP]) and CTP prevalence (P_CTP) were determined. Attributes of culture-expanded cells were compared based on (i) effective proliferation rate and (ii) expression of surface-markers CD73, CD90, CD105, SSEA-4, SSEA-3, SSEA-1/CD15, Cripto-1, E-Cadherin/CD324, Ep-CAM/CD326, CD146, hyaluronan and transcription factors Oct3/4, Sox-2 and Nanog using flow cytometry.

RESULTS

Mean [Cell], [CTP] and P_CTP were significantly different between MS and TS samples (P = 0.03, P = 0.008 and P= 0.0003), respectively. AT-derived cells generated the highest mean total cell yield at day 6 of culture-4-fold greater than TS and more than 40-fold greater than MS per million cells plated. TS colonies grew with higher mean density than MS colonies (290 ± 11 versus 150 ± 11 cell per mm²; P = 0.0002). Expression of classical-mesenchymal stromal cell (MSC) markers was consistently recorded (>95%) from all tissue sources, whereas all the other markers were highly variable.

CONCLUSIONS

The prevalence and biological potential of CTPs are different between patients and tissue sources and lack variation in classical MSC markers. Other markers are more likely to discriminate differences between cell populations in biological performance. Understanding the underlying reasons for variation in the concentration, prevalence, marker expression and biological potential of CTPs between patients and source tissues and determining the means of managing this variation will contribute to the rational development of cell-based clinical diagnostics and targeted cell-based therapies.