語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods./
作者:
Jones, Matthew.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:
379 p.
附註:
Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:
Dissertations Abstracts International83-12B.
標題:
Bioinformatics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29211490
ISBN:
9798819370728
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods.
Jones, Matthew.
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods.
- Ann Arbor : ProQuest Dissertations & Theses, 2022 - 379 p.
Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Thesis (Ph.D.)--University of California, San Francisco, 2022.
This item must not be sold to any third party vendors.
Cellular lineages underlie several important biological phenomena, from embyrogenesis to tumor development. Traditional approaches for studying for these lineages have been limited in their throughput or resolution, and have thus have been largely incapable of profiling lineage dynamics in complex organisms. Recently, advances in microfluidic devices, sequencing technologies, and molecular biology have facilitated a genomics revolution enabling researchers to profile molecular species at single-cell resolution. Simultaneously, progress in precise genome editing with CRISPR/Cas9 technologies have been coupled with the revolution in single-cell genomics to provide single-cell-resolution lineage tracing technologies.In this thesis, I first describe computational methodology for inferring models of cell lineages, or phylogenies, from the CRISPR/Cas9-based lineage tracing technology. Using both simulated and real data, I demonstrate that our methodology is both scalable and accurate in comparison to other algorithms. I additionally detail the functionality of our end-to-end software suite, Cassiopeia, and speculate on lineage tracing data analysis best practices.Next, I describe a series of applications of a CRISPR/Cas9-based lineage tracing technology and our computational tools to in vivo cancer models. In one application, I describe the first report of using such technologies to investigate the transcriptional drivers of metastatic dynamics in a xenograft model of non-small-cell lung cancer. Next, I describe work in a genetically engineered mouse model of non-small-cell lung cancer in which we characterize the phylodynamics and evolutionary trajectories that govern a primary tumor as it evolves from a single, transformed cell to a complex, metastatic tumor.Finally, I conclude by contextualizing how the work presented in this thesis fits into the larger picture of lineage tracing technologies and in vivo tumor studies and by speculating on how this informs future work.
ISBN: 9798819370728Subjects--Topical Terms:
553671
Bioinformatics.
Subjects--Index Terms:
Cancer
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods.
LDR
:03290nmm a2200397 4500
001
2349026
005
20220920134648.5
008
241004s2022 ||||||||||||||||| ||eng d
020
$a
9798819370728
035
$a
(MiAaPQ)AAI29211490
035
$a
AAI29211490
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Jones, Matthew.
$0
(orcid)0000-0002-0363-4493
$3
3688410
245
1 0
$a
Following Tumor Progression Step-By-Step with CRISPR/Cas9-Based Single-Cell Lineage Tracing Technologies and Improved Computational Methods.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2022
300
$a
379 p.
500
$a
Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500
$a
Advisor: Weissman, Jonathan S.
502
$a
Thesis (Ph.D.)--University of California, San Francisco, 2022.
506
$a
This item must not be sold to any third party vendors.
520
$a
Cellular lineages underlie several important biological phenomena, from embyrogenesis to tumor development. Traditional approaches for studying for these lineages have been limited in their throughput or resolution, and have thus have been largely incapable of profiling lineage dynamics in complex organisms. Recently, advances in microfluidic devices, sequencing technologies, and molecular biology have facilitated a genomics revolution enabling researchers to profile molecular species at single-cell resolution. Simultaneously, progress in precise genome editing with CRISPR/Cas9 technologies have been coupled with the revolution in single-cell genomics to provide single-cell-resolution lineage tracing technologies.In this thesis, I first describe computational methodology for inferring models of cell lineages, or phylogenies, from the CRISPR/Cas9-based lineage tracing technology. Using both simulated and real data, I demonstrate that our methodology is both scalable and accurate in comparison to other algorithms. I additionally detail the functionality of our end-to-end software suite, Cassiopeia, and speculate on lineage tracing data analysis best practices.Next, I describe a series of applications of a CRISPR/Cas9-based lineage tracing technology and our computational tools to in vivo cancer models. In one application, I describe the first report of using such technologies to investigate the transcriptional drivers of metastatic dynamics in a xenograft model of non-small-cell lung cancer. Next, I describe work in a genetically engineered mouse model of non-small-cell lung cancer in which we characterize the phylodynamics and evolutionary trajectories that govern a primary tumor as it evolves from a single, transformed cell to a complex, metastatic tumor.Finally, I conclude by contextualizing how the work presented in this thesis fits into the larger picture of lineage tracing technologies and in vivo tumor studies and by speculating on how this informs future work.
590
$a
School code: 0034.
650
4
$a
Bioinformatics.
$3
553671
650
4
$a
Genetics.
$3
530508
650
4
$a
Oncology.
$3
751006
650
4
$a
Cellular biology.
$3
3172791
653
$a
Cancer
653
$a
CRISPR
653
$a
Non-small-cell lung cancer
653
$a
Phylogenetics
653
$a
Tumor evolution
653
$a
Cellular lineages
690
$a
0715
690
$a
0369
690
$a
0992
690
$a
0379
710
2
$a
University of California, San Francisco.
$b
Biological and Medical Informatics.
$3
1018680
773
0
$t
Dissertations Abstracts International
$g
83-12B.
790
$a
0034
791
$a
Ph.D.
792
$a
2022
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29211490
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9471464
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入
(1)帳號:一般為「身分證號」;外籍生或交換生則為「學號」。 (2)密碼:預設為帳號末四碼。
帳號
.
密碼
.
請在此電腦上記得個人資料
取消
忘記密碼? (請注意!您必須已在系統登記E-mail信箱方能使用。)