Dataset Information

Characterizing phenotypically distinct T cell populations

ABSTRACT: Characterizing phenotypically distinct T cell populations

PROVIDER: PRJNA648405 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	SRR12318462.fastq.gz	Fastqsanger.gz
	SRR12318463.fastq.gz	Fastqsanger.gz
	SRR12318464.fastq.gz	Fastqsanger.gz
	SRR12318465.fastq.gz	Fastqsanger.gz
	SRR12318466.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 10

Similar Datasets

Project description:To probe the phenotypic heterogeneity found in cell populations, we developed an image-guided genomics technique termed spatiotemporal genomic and cellular analysis (SaGA) that allows for precise selection and amplification of living and rare cells. SaGA was used on collectively invading 3-D cancer cell packs to create purified leader and follower cell lines. The leader cell cultures are phenotypically stable and highly invasive in contrast to follower cultures, which show phenotypic plasticity over time and minimally invade in a sheet-like pattern. Genomic and molecular interrogation reveals an atypical VEGF-based vasculogenesis signaling that facilitates recruitment of follower cells but not for leader cell motility itself, which instead utilizes focal adhesion kinase-fibronectin signaling. While leader cells provide an escape mechanism for followers, follower cells in turn provide leaders with increased growth and survival. These data support a symbiotic model of collective invasion where phenotypically distinct cell types cooperate to promote their escape. A single tumor can harbor distinct genetic and epigenetic cellular sub-populations that drive tumor initiation and progression. This intratumor heterogeneity is proposed to be one of the major confounding factors of treatment causing relapse and poor clinical outcome1. Genomic instability and epigenetic modifications generate intratumor heterogeneity creating distinct genetic and epigenetic sub-populations or clones. A branched tumor evolutionary architecture can emerge containing the plasticity to progress under harsh environmental conditions and thwart therapeutic attempts to eradicate the tumor. It can be argued that until we discover how intratumor heterogeneity can be circumvented, precision oncology initiatives may fall short of expectations. Single cell sequencing methodologies have improved the genomic, transcriptomic, and epigenomic resolution of clonal tumor populations; however, the phenotypic implications of these alterations remain unclear. This is partly due to experimental challenges and is compounded by phenotypic plasticity that allows cancer cells to adapt to local changes in the microenvironment, without changes to the genome itself (e.g., epithelial to mesenchymal transition). Despite repeated observations that a small number of rare cancer cells or clones, hidden within a larger tumor population can drive tumor growth and spread, studies linking single cell or clonal phenotypes with genomic data have been limited. To probe the biology of a rare and phenotypically heterogeneous cell populations, single cells or subclones need to be isolated based upon user-defined criteria, instead of a random isolation approach; therefore, we developed a technique to image live cells within a biologically relevant 3-D environment, select a cell or cellular group based upon user-defined criteria, extract the cell(s), and subject the cell(s) to genomic and molecular analyses. In this way, we can purify, amplify, and systematically dissect the biologies of rare cells. This new technique, termed spatiotemporal genomic and cellular analysis (SaGA), was used to dissect the phenotypic heterogeneity of collective cancer cell invasion in a 3-D lung cancer model. These data incorporate the first SaGA-derived leader and follower cell lines to reveal that leader cells utilize atypical vasculogenesis signaling machinery by secreting VEGF to attract follower cells in invasive cell chains. In contrast, follower cells support leader cell growth by increasing their mitotic efficiency. This relationship argues for a cellular symbiosis within the collective invasion pack. Furthermore, these data provide proof of concept that SaGA is a powerful technology for dissecting phenotypic heterogeneity within cancer cell populations.

Dataset Information

Characterizing phenotypically distinct T cell populations

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets