2013 — 2014 |
Lee, Ly James Otero, Jose Javier |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Large Scale Nanochannel Electroporation (Nep) For Cell Reprogramming
DESCRIPTION (provided by applicant): Cell reprogramming holds great promise for a number of medical and biological applications, including regenerative/reparative medicine and cellular disease models. Significant progress has been made in this field since the introduction of induced pluripotent stem cells (iPSCs) and the subsequent development of directed nuclear reprogramming (i.e., transdifferentiation) approaches. However, nuclear reprogramming technologies have not been used to date to treat patients due to several obstacles, among them, the high heterogeneity and sometimes inherent unpredictability of the reprogrammed cell population, which is largely due in part to the inability to control the quantity and combination o the transfected reprogramming factors (DNA- or mRNA- based). A number of transfection methods, biological (e.g., viral vectors), chemical (e.g., lipoplexes, polycations) and physical (e.g., microinjection, electroporation), have been developed; however, the great majority of these techniques, with the exception of microinjection, are based on stochastic processes that lead to random cell transfection with significant cell-to-cell variations. Microinjection on the oter hand is only compatible with relatively large cells, and has low yields. New technologies capable of delivering reprogramming factors in a controlled (i.e., timing and dosage), safe, and efficient manner, at the single cell level, are clearly needed in this field for successful transition from te lab bench to the clinic. Our recently developed nanochannel-based electroporation (NEP) technology meets these criteria, thus potentially making it a powerful tool for this purpose. Here we propose to build upon our unique expertise on NEP to develop a more robust and versatile 3D system that could be implemented in a wide range of cell reprogramming applications. We will first implement modeling and micro/nanoscale technologies to develop an optimum 3D NEP platform that can support sequential transfection of large cell numbers (¿106), and then we will test this platform using induced pluripotency and direct neuronal transdifferentiation as nuclear reprogramming models. Finally, we will use our NEP technology to methodically study a number of aspects of the cell reprogramming process that cannot be addressed using conventional transfection technologies.
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0.956 |
2016 — 2020 |
Czeisler, Catherine (co-PI) [⬀] Otero, Jose Javier |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Mechanisms of Congenital Hypoventilation
? DESCRIPTION (provided by applicant): Incomplete respiratory neuron maturation causes significant morbidity during the perinatal period, yet the mechanisms by which respiratory neuron maturation occurs during this vulnerable time window is not understood. Thus, there is a critical need to identify these basic neural mechanisms of perinatal respiratory control. The objectives of the proposed research are to elucidate developmental processes of respiratory neuron network maturation and to identify brainstem respiratory centers/circuits necessary for perinatal breathing. The central hypothesis is that hindbrain respiratory neuron networks undergo critical developmental maturation during the late embryonic, perinatal, and post-natal periods in mammals, and that developmental abnormalities in neuronal and glial maturation contribute to the pathophysiology of autonomic respiratory neuron dysfunction. The proposed research is inspired by our group's findings of Central Congenital Hypoventilation Syndrome (CCHS), a rare human disorder characterized by an inability to sense CO2 and which is linked to PHOX2B poly-alanine repeat and non-polyalanine repeat (NPARM) mutations. The rationale for the proposed research is that the lack of a basic fundamental understanding of which autonomic neural circuits are required for perinatal breathing represents a barrier to the ultimate implementation of interventions aimed at improving morbidity for premature infants. Guided by strong preliminary data, this hypothesis will be tested by pursuing three specific aims: 1) Determine the extent to which selective expression of a dominant negative NPARM-PHOX2B mutation regulates perinatal chemosensation-induced respiratory drive, 2) Determine which brainstem circuits are lost in NPARM-CCHS, and 3) Determine the extent to which selected ablation of brainstem astrocyte population promote congenital hypoventilation. Under the first aim, we will test the effects on ventilation control and brainstem anatomy after targeted brainstem expression of a dominant negative NPARM PHOX2B mutation using an already proven conditional transgenic mouse approach. In the second aim, we will combine an innovative transgenic approach to identify which brainstem circuits are lost in congenital hypoventilation. In the third aim, we will determine the extent to which neuronal-glial interaction are necessary for appropriate autonomic respiratory control in the perinatal and post-natal period. The approach is innovative because it uses novel and validated tools, techniques, and reagents from distinct disciplines that allow us to address previously unanswerable questions. The proposed research is significant, because it is expected to vertically advance and expand understanding of which neuronal-glial circuits are required for proper control of autonomic regulation of breathing at birth. The tools and basic knowledge gained from these studies will form the foundation of future studies where interventions to improve autonomic respiratory neuron function in premature babies are designed and validated.
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0.956 |
2020 — 2021 |
Otero, Jose Javier Thomas, Diana L (co-PI) [⬀] |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Implementation of Machine Learning Workflows in Primary Brain Tumor Diagnostics
Abstract The diagnosis of primary brain tumors requires a layered approach of histologic, anatomic and molecular features to generate an integrated diagnosis with clinical and prognostic significance. The diagnostic workup of diffuse gliomas in particular requires a panel of immunohistochemical stains with a subset of tumors requiring additional molecular testing to reach a diagnostic category recognized by the World Health Organization. In the United States and worldwide, scarce resources are available to perform these tests, so methods that improve pre-test probabilities and decrease false positive results have significant clinical and financial impact. Our long-term goal is to improve and standardize testing and diagnoses for brain tumor patients worldwide by validating new diagnostic workflows using digital imaging, immunohistochemical tests, open source computing platforms and machine learning algorithms to improve diagnostic capabilities. We will achieve these goals by completing three specific aims in this R03 Pilot/Feasibility project. First, we will determine the extent to which predictive diagnostic models developed from public domain data show generalizability to cases evaluated at a tertiary brain cancer care center. We have already generated a prototype statistical predictive model, which we will expand to all CNS tumor types and test with data from patients at James Cancer Hospital. Second, we will generate and validate models that predict the probability of false-positive 1p/19q FISH testing using histological features from OLIG2-immunostained brain tumor slides obtained from whole slide imaging. Lastly, we will consolidate data containing whole slide digital images, immunohistochemical features, clinical data and molecular features of diffuse gliomas. Consolidating these data will allow us to begin data analysis correlating histological images to immunohistochemical and molecular features. This dataset will represent the core dataset that upon which we will base our next R01-level proposal. To achieve these goals, we have assembled a multidisciplinary team composed of an image analysis expert and neuropathologist (JO), a molecular neuropathologist (DT), and a high dimensionality bioinformaticist (JZ). The Ohio State University is the first US cancer center to transition to complete whole slide imaging, and therefore we are in a unique position to generate a significant, vertical advance in improving diagnostic accuracy in neuropathology with modern Pathology Informatics approaches.
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0.956 |