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From Single Cells to Human Disease: High-Resolution Phenotyping of Male Infertility Models Using Single-Cell RNA Sequencing
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | From Single Cells to Human Disease: High-Resolution Phenotyping of Male Infertility Models Using Single-Cell RNA Sequencing. |
| 개인저자 | Jung, Min. |
| 단체저자명 | Washington University in St. Louis. Biology & Biomedical Sciences (Human & Statistical Genetics). |
| 발행사항 | [S.l.]: Washington University in St. Louis., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 148 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781088346051 |
| 학위논문주기 | Thesis (Ph.D.)--Washington University in St. Louis, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Includes supplementary digital materials. Advisor: Conrad, Donald F. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Male infertility is a complex disease that can result in significant emotional distress and treatment costs. Globally, male infertility affects 7% of males, and while its incidence is rising, its etiology remains elusive. In order to improve patient care, it is critical to identify the nature of spermatogenic failure in as many men as possible. The extensive cellular heterogeneity of testis has limited the application of bulk expression measurements to capture crucial information to dissect molecular mechanisms of defects in the infertile patients. Thus, the application of single-cell RNA-sequencing on male germ cells provides an amazing new set of scientific opportunities for research in male reproductive biology and translational medicine. We developed a single-cell framework that utilizes high-throughput single-cell RNA-sequencing from normal and disease models to elucidate normal spermatogenesis, and to dissect spermatogenic defects in male infertility models. As part of our single-cell framework, we first a developed fast, efficient yet high-throughput single-cell isolation method that can be easily applied to different mammalian species. Using Drop-seq, we generated a 57,600-cell dataset from testes of wild-type mice and mice with gonadal defects due to disruption of the genes, Mlh3, Hormad1, Cul4a or CNP. For analyzing this novel data, we introduce a model-based factor analysis method, Sparse Decomposition of Arrays (SDA), to jointly analyze mutant and wildtype cells and decompose our data into latent factors ("components") that represent genes that co-vary across subsets of cells. Our single-cell framework identified novel cell-type specific markers, co-regulated gene modules and mutant-specific pathological processes. It also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/-and Hormad1-/-models, an area typically associated with immune privilege. These results demonstrate the potential of our single-cell framework for expanding the ability to dissect pathophysiology in tissues with extensive cellular heterogeneity and decrypt the spermatogenic failure in more patients. |
| 일반주제명 | Genetics. Bioinformatics. Developmental biology. |
| 언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
