東華大學圖書館 |

Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics./
Author:	Zheng, Shiwei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	218 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Systematic biology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22622090
ISBN:	9798607311315

Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics.
Zheng, Shiwei.

Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 218 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--New York University, 2020.

This item must not be sold to any third party vendors.

The human immune system is well-known for the diversity of cell types and associated functions. Previous studies with ex vivo and transplantation techniques have constructed a solid foundation for understanding the developmental hierarchy of immune system, where immune cells originate from the hematopoietic stem cell (HSC), which give rise to terminally differentiated cells through progenitors that become sequentially restricted in lineage potentials. While the molecular dynamics of fate transitions are not fully understood, the characterization of immune cell state complexity has remained debatable as well. The emergence of high-throughput single cell RNA sequencing (scRNA-seq) technology enables a finer view of cell state complexity within the immune system based on data-driven analysis of gene expression profiles on the level of individual cells, and therefore provides a new angle in the dissection of complex systems.In this dissertation, I will describe work that investigate the immune cell type composition with scRNA-seq. We first focused on the early developmental hierarchy of human cord blood stem and progenitor cells (HSPCs), whose cell state composition and developmental hierarchy were not fully resolved by traditional characterization methods. We sequenced the transcriptomes of over 20,000 HSPCs sampled uniformly from human cord blood with Drop-seq, a microfluidic system that captures single cell transcriptomes using oil-based droplets and primer-conjugated beads. With unsupervised clustering we were able to identify extensive heterogeneity of cord blood HSPCs, including two distinct subsets of myeloid progenitors which previously were believed to originate from the same path. We demonstrated that the two myeloid progenitor subsets were transcriptomically distinct, and using computational reconstruction of developmental trajectories, we further showed that different intermediate progenitors generated these myeloid progenitors, which suggests a refined model of early hematopoiesis. While we identified gene expression programs associated with the priming and commitment of early fate transitions, the dynamics on mRNA correlated with chromatin dynamics from ATAC-seq data as well. Finally, by combining surface protein expression data with scRNA-seq, we identified CD52 as a marker for early lymphoid-primed progenitors that associate with functional output.The identification of CD52 from integrated single cell transcriptomic and surface protein data inspired us to expand the scale of multi-modal profiling for immune cells. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) was developed as a high-throughput profiling method for the simultaneous measurements of surface protein and mRNA from the same cell. In order to tackle the problem of doublets and batch effects when performing large-scale scRNA-seq experiments, we modified CITE-seq to multiplex single cells with distinct sample origins for super-loading the microfluidic system, using antibodies that target universal surface proteins expressed on all cells. We also designed a computational method that demultiplex data from this modified CITE-seq technology (named Cell Hashing), assigning single cells back to their sample origins based on antibody data. We then combined CITE-seq and Cell Hashing to jointly profile the transcriptomes and surface protein expression from human mononuclear cells including diverse immune cell types. While mRNA and surface protein data were in general coherent, we argued that they each had their unique strengths and weaknesses for every cell. Therefore, we developed a computational pipeline that performed cell-specific weighted integration of single cell multi-modal data, in order to generate a joint representation for both transcriptional and protein expression. We showed that compared with uni-modal characterization of mononuclear cells, our joint representation achieved a more comprehensive view of the immune cell complexity, by recovering immune cell subsets which were not identified by mRNA or protein data alone.

ISBN: 9798607311315Subjects--Topical Terms:

3173492
Systematic biology.
Subjects--Index Terms:

Hematopoiesis

Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics.
LDR:05296nmm a2200373 4500 001 2269301
005 20200908090437.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9798607311315
035 $a (MiAaPQ)AAI22622090
035 $a AAI22622090
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zheng, Shiwei. $3 3546617
245 1 0 $a Characterizing the Heterogeneity of Human Immune Cells with Single Cell Genomics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 218 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Satija, Rahul.
502 $a Thesis (Ph.D.)--New York University, 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a The human immune system is well-known for the diversity of cell types and associated functions. Previous studies with ex vivo and transplantation techniques have constructed a solid foundation for understanding the developmental hierarchy of immune system, where immune cells originate from the hematopoietic stem cell (HSC), which give rise to terminally differentiated cells through progenitors that become sequentially restricted in lineage potentials. While the molecular dynamics of fate transitions are not fully understood, the characterization of immune cell state complexity has remained debatable as well. The emergence of high-throughput single cell RNA sequencing (scRNA-seq) technology enables a finer view of cell state complexity within the immune system based on data-driven analysis of gene expression profiles on the level of individual cells, and therefore provides a new angle in the dissection of complex systems.In this dissertation, I will describe work that investigate the immune cell type composition with scRNA-seq. We first focused on the early developmental hierarchy of human cord blood stem and progenitor cells (HSPCs), whose cell state composition and developmental hierarchy were not fully resolved by traditional characterization methods. We sequenced the transcriptomes of over 20,000 HSPCs sampled uniformly from human cord blood with Drop-seq, a microfluidic system that captures single cell transcriptomes using oil-based droplets and primer-conjugated beads. With unsupervised clustering we were able to identify extensive heterogeneity of cord blood HSPCs, including two distinct subsets of myeloid progenitors which previously were believed to originate from the same path. We demonstrated that the two myeloid progenitor subsets were transcriptomically distinct, and using computational reconstruction of developmental trajectories, we further showed that different intermediate progenitors generated these myeloid progenitors, which suggests a refined model of early hematopoiesis. While we identified gene expression programs associated with the priming and commitment of early fate transitions, the dynamics on mRNA correlated with chromatin dynamics from ATAC-seq data as well. Finally, by combining surface protein expression data with scRNA-seq, we identified CD52 as a marker for early lymphoid-primed progenitors that associate with functional output.The identification of CD52 from integrated single cell transcriptomic and surface protein data inspired us to expand the scale of multi-modal profiling for immune cells. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) was developed as a high-throughput profiling method for the simultaneous measurements of surface protein and mRNA from the same cell. In order to tackle the problem of doublets and batch effects when performing large-scale scRNA-seq experiments, we modified CITE-seq to multiplex single cells with distinct sample origins for super-loading the microfluidic system, using antibodies that target universal surface proteins expressed on all cells. We also designed a computational method that demultiplex data from this modified CITE-seq technology (named Cell Hashing), assigning single cells back to their sample origins based on antibody data. We then combined CITE-seq and Cell Hashing to jointly profile the transcriptomes and surface protein expression from human mononuclear cells including diverse immune cell types. While mRNA and surface protein data were in general coherent, we argued that they each had their unique strengths and weaknesses for every cell. Therefore, we developed a computational pipeline that performed cell-specific weighted integration of single cell multi-modal data, in order to generate a joint representation for both transcriptional and protein expression. We showed that compared with uni-modal characterization of mononuclear cells, our joint representation achieved a more comprehensive view of the immune cell complexity, by recovering immune cell subsets which were not identified by mRNA or protein data alone.
590 $a School code: 0146.
650 4 $a Systematic biology. $3 3173492
650 4 $a Bioinformatics. $3 553671
653 $a Hematopoiesis
653 $a Immune cells
653 $a Multi-modal
653 $a Multi-omics
653 $a Single cell
690 $a 0423
690 $a 0715
710 2 $a New York University. $b Biology. $3 1029435
773 0 $t Dissertations Abstracts International $g 81-10B.
790 $a 0146
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22622090