Interaction of Stem Cells and Their Niche: Behavior and Gene Expression Profiles of CD34+/CD38− Cells upon Co-Cultivation with AFT024.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1281-1281
Author(s):  
Wolfgang Wagner ◽  
Rainer Saffrich ◽  
Ute Wirkner ◽  
Volker Eckstein ◽  
Jonathon Blake ◽  
...  

Abstract Cell-cell contact between stem cells and cellular determinants of the microenvironment plays an essential role in the regulation of self-renewal and differentiation. The stromal cell line derived from murine fetal liver (AFT024) has been shown to support maintenance of primitive human hematopoietic progenitor cells (HPC) in vitro. We have studied the interaction between HPC (defined as CD34+/CD38− umbilical cord blood cells) and AFT024 and the impact of co-cultivation on the behavior and gene expression of HPC. By time lapse microscopy the mobility and behavior of CD34+/CD38− cells were monitored. Approximately 30% of the CD34+/CD38− cells adhered to the cellular niche through an uropod. CD44 and CD34 were co-localized at the site of contact. Gene expression profiles of CD34+/CD38− cells were then compared upon co-cultivation either with or without AFT024. After cultivation for 16h, 20h, 48h or 72h the HPC were separated form the feeder layer cells by a second FAC-Sort. Differential gene expression was analyzed using our Human Genome cDNA Microarray of over 51,145 ESTs. Among the genes with the highest up-regulation in contact with AFT024 were several genes involved in cell adhesion, proliferation and DNA-modification including tubulin genes, ezrin, complement component 1 q subcomponent 1 (C1QR1), proto-oncogene proteins c-fos and v-fos, proliferating cell nuclear antigen (PCNA), HLA-DR, gamma-glutamyl hydrolase (GGH), minichromosome maintenance deficient 6 (MCM6), uracil-DNA glycolase (UNG) and DNA-methyltransferase 1 (DNMT1). In contrast, genes that were down-regulated after contact with AFT024 included collagenase type iv (MMP2), elastin (ELN) and hemoglobin genes. Differential expression of six genes was confirmed by RT-PCR. Other authors have reported on the differential gene expression profiles of CD34+ cells derived from the bone marrow versus those from G-CSF mobilized blood. As CD34+ cells from the bone marrow might represent cells exposed to the natural HPC niche we have then compared our findings with these experiments. In these comparisons we identified several overlapping genes that are involved in regulation of cell cycle and DNA repair including PCNA, DNMT1, MCM6, MCM2, CDC28 protein kinase regulatory subunit 1B (CKS1B), Topoisomerase II (TOP2a), DNA Ligase 1 (LIG1) and DNA mismatch repair protein MLH1. All these genes were up-regulated among CD34+/CD38− cells upon co-culture with AFT024, as well as among CD34+ cells derived from the bone marrow versus those from peripheral blood. Our studies support the hypothesis that intimate contact and adhesive interaction of HPC with their niche profoundly influenced their proliferative potential and their differentiation program.

2016 ◽  
Vol 6 (1_suppl) ◽  
pp. s-0036-1582635-s-0036-1582635 ◽  
Author(s):  
Sibylle Grad ◽  
Ying Zhang ◽  
Olga Rozhnova ◽  
Elena Schelkunova ◽  
Mikhail Mikhailovsky ◽  
...  

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.


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