Material Type  

 학위논문

 

0017163824

Date and Time of Latest Transaction  

20250211152758

ISBN  

9798383650899

DDC  

574

Author  

Butka, Emily Grace.

Title/Author  

Predicting Cell Fate Using Single-Cell State and Lineage Information.

Publish Info  

[S.l.] : Washington University in St. Louis., 2024

Publish Info  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Material Info  

190 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Morris, Samantha A.

학위논문주기  

Thesis (Ph.D.)--Washington University in St. Louis, 2024.

Abstracts/Etc  

요약Humans begin as a single cell, with successive cell divisions ultimately giving rise to a complex organism comprised of innumerable highly specialized cells. In this process, cells begin as pluripotent stem cells and slowly shift towards these highly specialized identities. Several technological innovations have enabled scientists to measure and track many aspects of a single cell's identity as these divisions occur, at increasingly large scale and rapidly declining cost. As a result, our fundamental understanding of cell identity has grown to encompass several layers of cellular dynamics, including aspects that are both intrinsic and extrinsic, transient and permanent, active and repressed. One genomic assay paved the way for this single-cell revolution and today remains arguably the most prominent metric for defining cell identity: single-cell RNA sequencing (scRNA-seq). Adjustments to previously-developed RNA-sequencing technologies have enabled scientists to capture a portion of mRNA present in a cell, painting a holistic picture of the transcriptomic activity ongoing in an individual cell at the time of capture. In addition to the transcriptome, another aspect of cell identity has fascinated developmental biologists in particular: lineage. Lineage tracing was historically accomplished by a variety of visual or microscopy-based methods. The age of genomics and sequencing created a vacuum for sequencing-based lineage tracing technologies that is today filled by a myriad of methods coupling single-cell assays with lineage information captured from the same cells.Here, we perform an exploration of single-cell RNA and lineage tracing analysis methods and tools, offering a few contributions to the field. First, we present ideas inspired by hidden Markov models that use lineage information to link clonally-related cells across developmental time in an effort to understand patterns of cell identity changes based on scRNA-seq information. With this framework, we aim to use ground truth lineage data to predict cell fate from cell state in ways that have not previously been substantially powered. An approach such as this one is still limited by the resolution of both single-cell transcriptomic information and the lineage information we are able to capture. Second, we develop single-cell analysis tools focused on the design and interpretation of lineage information. The CellTag Simulator is designed to accompany the CellTagging technology for better experimental design, with adjustable parameters for three simulations to ensure robust, identifiable clones in each unique system where CellTagging is applied. Third, Megatron (Mega-Trajectories of Clones) is developed to aid interpretation of single-cell-omics data coupled with lineage information that has been embedded in a low-dimensional manifold. We discuss the principles of clustering clones and implement methods to produce the novel concept of the 'metaclone,' a group of clones with a shared trajectory.We ultimately leverage these methods and knowledge of current challenges to better understand the process of development of mature adipocytes in skin adipose tissue. Specifically, we employ CellTagging to measure the developmental outcomes of two populations of adipocyte precursor cells, progenitors and preadipocytes. We identify a novel cell identity in this lineage that we term 'immature preadipocytes;' this population exhibits differential abundances at distinct developmental time points and tissues, and thus occupies a unique niche in distinct adipose tissues including the inguinal and skin adipose. Further, we computationally isolate the transcription factor Sox9 as a candidate whose expression may promote the differentiation of precursors toward a committed and mature adipose identity; our validation efforts suggest that Sox9 is uniquely able to affect this differentiation in progenitors alone. Finally, we investigate through cross-depot transplantation the dynamics of adipose environment on progenitors. We find that skin progenitors retain a skin progenitor identity in the inguinal adipose depot, but they differentiate to committed preadipocytes at a lower rate more that is more characteristic of inguinal adipose. In summary, this work proposes and implements several methods for design and analysis of single-cell lineage tracing experiments, as well as employs a suite of experimental and computational technologies to showcase the value of these experiments through characterization of the lineage and developmental hierarchy of skin adipogenesis.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Developmental biology.

Subject Added Entry-Topical Term  

Genetics.

Subject Added Entry-Topical Term  

Dermatology.

Subject Added Entry-Topical Term  

Cellular biology.

Index Term-Uncontrolled  

Adipogenesis

Index Term-Uncontrolled  

Lineage tracing

Index Term-Uncontrolled  

Single-cell RNA-sequencing

Index Term-Uncontrolled  

Cell identity

Index Term-Uncontrolled  

Skin progenitors

Added Entry-Corporate Name  

Washington University in St. Louis Biology & Biomedical Sciences (Computational & Systems Biology)

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:657144