Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27241242_1.fastq.gz | Fastqsanger.gz | |||
| SRR27241242_2.fastq.gz | Fastqsanger.gz | |||
| SRR27241243_1.fastq.gz | Fastqsanger.gz | |||
| SRR27241243_2.fastq.gz | Fastqsanger.gz | |||
| SRR27241244_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 32 |
Similar Datasets
Project description:These research areas concentrate on stress induced proteases in recombinant Escherichia coli, glycosylation heterogeneity due to bioprocess conditions produced in mammalian cells, and metabolic engineering of E. coli. The hypothesis of this project is that recombinant protein glycosylation is inefficient under normal bioreactor conditions since the additional glycosylation reactions necessary for the recombinant protein exceed the metabolic capacity of the cells. Normal bioreactor conditions have been optimized for cell growth, and sometimes for protein productivity. Only recently has it been accepted that optimal glycosylation may not occur under optimal growth or protein productivity conditions. Specific Aim: Determine the relationship between bioreactor conditions and glycosylation gene expression in NS0 cells.
2011-04-29 | GSE28920 | GEO
Project description:We studied cardiomyocytes maturation through extended in vitro culture and also used the adult rat as a bioreactor,
2023-06-02 | GSE190758 | GEO
Project description:Although cell therapies require large numbers of quality-controlled hPSCs, existing technologies are limited in their ability to efficiently grow and scale stem cells. We report here that cell-state conversion from primed-to-naïve pluripotency enhances the biomanufacturing of hPSCs. Naïve hPSCs exhibit superior growth kinetics and aggregate formation characteristics in stirred suspension bioreactors compared to their primed counterparts. Moreover, we demonstrate the role of the bioreactor mechanical environment in the maintenance of naïve pluripotency, through transcriptomic enrichment of mechano-sensing signaling for cells in the bioreactor along with a decrease in expression of lineage-specific and primed pluripotency hallmarks. Bioreactor-cultured, naïve hPSCs maintain epigenetic hallmarks of naïve pluripotency, exhibit robust production of naïve pluripotency metabolites, and display reduced expression of primed pluripotency cell surface markers. We also show that these cells retain the ability to undergo targeted differentiation into beating cardiomyocytes, hepatocytes and neural rosettes. They additionally display faster kinetics of teratoma formation compared to their primed counterparts. Naïve bioreactor hPSCs also retain structurally stable chromosomes. Our research corroborates that converting hPSCs to the naïve state enhances hPSC manufacturing and indicates a potentially important role for the bioreactor’s mechanical environment in maintaining naïve pluripotency.
2020-09-18 | GSE144656 | GEO
Project description:Bioreactor bile acids experiment via Broad Institute (Ramnik Xavier lab)
2018-08-08 | MSV000082802 | MassIVE
Project description:We report RNAseq data derived from GM12878 cell line exposed to defined pH treatments using enhanced gasses based bioreactor system
2022-10-06 | GSE203180 | GEO
Project description:We reported the microbial communities in wastewater between conventional membrane bioreactor (MBR) system and biofilm MBR system using Illumina sequencing.
2019-08-14 | GSE135797 | GEO
Project description:Non-targeted metabolomics of nose microbiota cultivated in a bioreactor under controlled conditions
2024-05-13 | MSV000094744 | MassIVE
Project description:There is a great need for setting novel measurable attributes at the cell physiological level in a scalable biopharmaceutical production process to be able to predict the process outcomes and improve process understanding. In a biologic production process, changes in culture environment due to several factors such as shear and bubble induced damage from gas sparging and agitation are known to occur. There is a gap in the knowledge of cellular response due to varying bioreactor environment itself during the course of cell culture, from lag-phase to log-phase to stationary-phase in culture. With the emergence of micro-arrays as tools for exploring cell physiological changes, it opens the possibility for studying the effect of bioreactor culture environment itself on the cell substrate. Such information could be eventually used to designate gene transcripts as biomarkers for cell status in a controlled bioreactor system. A model 5L bench-scale bubble aerated and impeller agitated bioreactor system was used to study gene expression profiles of a hybridoma cell line during the time-course of batch culture. Gene expression profiles that were variable from early-to-late in batch culture, as well as invariant gene profiles were summarized using microarray findings. Typical cellular functions studied were oxidative stress response, DNA damage response, apoptosis, antioxidant activity, cellular metabolism, and protein folding. These findings were also verified with a more rigorous semi-quantitative RT-PCR technique. The results of this study suggest that under predefined bioreactor culture conditions, significant gene changes from lag to log to stationary phase could be identified, which could then be used to track the culture state.
2012-10-01 | GSE33062 | GEO
Project description:Many studies have characterized the results of shear stress changes on cultured endothelial cells in different bioreactor systems. However it is still unclear how an invasive intervention like stent procedure may influence the transcriptional response of endothelium. To study the simultaneous effects of shear stress changes and stent application on endothelial gene expression, we have used an experimental apparatus of laminar flow bioreactor (LFB) system with human cultured endothelial cells exposed or not exposed to stent procedure with different flow conditions. Microarray analysis was evaluated in each experimental protocol.
2013-03-16 | GSE45225 | GEO
Project description:There is a great need for setting novel measurable attributes at the cell physiological level in a scalable biopharmaceutical production process to be able to predict the process outcomes and improve process understanding. In a biologic production process, changes in culture environment due to several factors such as shear and bubble induced damage from gas sparging and agitation are known to occur. There is a gap in the knowledge of cellular response due to varying bioreactor environment itself during the course of cell culture, from lag-phase to log-phase to stationary-phase in culture. With the emergence of micro-arrays as tools for exploring cell physiological changes, it opens the possibility for studying the effect of bioreactor culture environment itself on the cell substrate. Such information could be eventually used to designate gene transcripts as biomarkers for cell status in a controlled bioreactor system. A model 5L bench-scale bubble aerated and impeller agitated bioreactor system was used to study gene expression profiles of a hybridoma cell line during the time-course of batch culture. Gene expression profiles that were variable from early-to-late in batch culture, as well as invariant gene profiles were summarized using microarray findings. Typical cellular functions studied were oxidative stress response, DNA damage response, apoptosis, antioxidant activity, cellular metabolism, and protein folding. These findings were also verified with a more rigorous semi-quantitative RT-PCR technique. The results of this study suggest that under predefined bioreactor culture conditions, significant gene changes from lag to log to stationary phase could be identified, which could then be used to track the culture state. We ran consecutive 5L bioreactor runs, each with an independent vial thaw, to achieve multiple biological replicates per time-point. Bioreactors were sampled approximately every 12 hours for RNA extraction. For the 5L bioreactors, microarray samples were run for day 1 (n=2), day 2 (n=2), day 3 (n=3), and day 3.5 (n=3). Here 2 or 3 of the three biological replicates run for each time-point were included in the analysis, based on >70% genes found. We define early exponential as day 1, peak exponential as day 2 and day 3 and late stationary as day 3.5.
2012-10-01 | E-GEOD-33062 | biostudies-arrayexpress