Effect of blood sample storage on the study of gene expression in response to ionizing radiation exposure
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ABSTRACT: For around ten years, microarrays have been suggested for the diagnosis of ionizing radiation exposure. We assessed for the first time the relevance of gene expression profiling in a real accidental case. This study was performed on peripheral blood mononuclear cells of 41 potential victims. The different strategies of analysis highlighted a huge effect of the blood sample handling on the gene expression profiles. This effect was so high that it could mask specific modulations as a potential effect of ionizing radiation exposure. Thus, we assessed a new way of blood sampling adapted to gene expression analysis: PAXgene. With this method, more than 70% of the modulations of gene expression induced 3 hours after an ex vivo exposure to 0.5 Gy were preserved even in a 24-hour delayed analysis (as for transportation of blood sample from the accident location to the laboratory). We validated a new methodology in order to propose a new strategy of blood sampling and handling for gene profiling. This system could be used in case of accidental overexposure to study whether gene expression is a relevant biomarker of ionizing radiation exposure. Radiation induced gene expression in human blood was measured at 3 hours after exposure to doses of 0 and 0.5 grays. Following the incubation of 3 hours at 37°C, the RNA extractions were performed either immediately or 24h later (as for transportation of blood sample at room temperature). Two different blood preservation methods were compared: classical anticoagulant and PAXgene Blood RNA System. Venous blood samples of 6 donors were used (3 for anticoagulant study, 3 for Paxgene study). Each sample was hybridized twice.
Project description:One of the most likely risks astronauts on long duration space missions face is exposure to ionizing radiation associated with highly energetic and charged heavy (HZE) particles. Since access to medical expertise on such a mission is limited at best, early diagnosis and mitigation of such exposure is critical. In order to accurately determine the dosage within 1 hour post-exposure, dose-dependent âbiomarkersâ are needed. Therefore, we performed a dose-course transcriptional analysis for radiation exposure at 0, 0.3, 1.5, and 3.0 Gy with corresponding time point at 1 hour (hr) post-exposure using Affymetrix® GeneChip® Human Gene 1.0 ST v1 Array chips. The analysis of our data suggests a set of sensitive genetic biomarkers specific to each radiation level as well as generic radiation response biomarkers. Upregulated biomarkers can then be used within lab-on-a-chip (LOC) systems to detect exposure to ionizing radiation. A total of sixteen human samples representing radiation exposure at levels 0 Gy, 0.3 Gy, 1.5 Gy and 3.0 Gy at time point 1 hour (hr) post-exposure were constructed. Blood samples were extracted from four human volunteers, and were irradiated. Leukocytes were extracted, and gene expression was measured. Samples for all four volunteers were measured at 1 hr for all four dose levels, resulting in four replicates at each dose level. Thus, a total of 4 samples at each of the four radiation levels were sampled, yielding the total of 16 samples.
Project description:Gene expression profiles of peripheral blood samples from C57BL/6 mice exposed with ionizing radiation. We used mice as model animal to study biologial recovery response after radiation damage. Therefore, we obtained gene expression profiles from C57BL/6 mice exposed with various levels of ionizing radiation, including low and high doses and control groups. In order to measure recovery rate, we collected peripheral blood samples at different time durations after the exposure. In order to obtain robust signatures specific to radiation response, we are interested in knowing if the radiation signarures will be present in the presence of confounders. Therefore, mice were given intraperitoneal injections of lipopolysaccharide endotoxin (LPS), or treated with granulocyte colony-stimulating factor (GCSF), otherwise no treatment after ionizing radiation exposure. The underlying mechanism of confounder treatment is that LPS induces strong immune response resembling the effect of infection, and GCSF stimulates mobilization of HSCs. Exploratory analysis shows that the confounding effects did affect the radiation signature to some extent. This study provides insights into the molecular basis of time- and dose- dependent response to ionizing radiation in mice hematopoietic system. A total of 536 C57BL/6 mice peripheral blood gene expression profiles were measured in 3 different batches using the Affymetrix mouse 430A 2.0 microarray. The experiment is designed to assess blood gene expression changes after exposure to ionizing radiation of 0, 100, 150, 200, 300, 450, 600, 800 and 1050 cGy. Samples were collected at 6, 24, 48, 72, 120 and 168hrs after a single dose exposure.
Project description:Our in vivo study measures miRNA and gene expression changes in human blood cells in response to ionizing radiation in order to develop miRNA signatures that can be used as biomarkers for radiation exposure. Blood was collected from 8 radiotherapy patients immediately prior to and at 4 h after total body irradiation with 1.25 Gy X-rays. Both miRNA and gene expression changes were measured by means of quantitative PCR and microarray hybridization, respectively. Patients were in complete remission at the time of blood collection. Out of 223 differentially expressed genes, 37 were both downregulated and predicted targets of the upregulated miRNAs. Both miRNA and gene control of biological processes such as hemopoiesis and the immune response is increased after irradiation, whereas metabolic processes are underrepresented among all differentially expressed genes and the genes controlled by miRNAs. Sixteen human whole-blood samples were included in this study. Eight samples were collected shortly before total body irradiation. The other 8 samples were collected at 4 h after total body irradiation with 1.25 Gy X-rays.
Project description:For around ten years, microarrays have been suggested for the diagnosis of ionizing radiation exposure. We assessed for the first time the relevance of gene expression profiling in a real accidental case. This study was performed on peripheral blood mononuclear cells of 41 potential victims. The different strategies of analysis highlighted a huge effect of the blood sample handling on the gene expression profiles. This effect was so high that it could mask specific modulations as a potential effect of ionizing radiation exposure. Thus, we assessed a new way of blood sampling adapted to gene expression analysis: PAXgene. With this method, more than 70% of the modulations of gene expression induced 3 hours after an ex vivo exposure to 0.5 Gy were preserved even in a 24-hour delayed analysis (as for transportation of blood sample from the accident location to the laboratory). We validated a new methodology in order to propose a new strategy of blood sampling and handling for gene profiling. This system could be used in case of accidental overexposure to study whether gene expression is a relevant biomarker of ionizing radiation exposure.
Project description:After defining a gene expression signature that predicted radiation exposure dose with high accuracy in human peripheral white blood cells irradiated ex vivo, we now demonstrate the predictive power of gene expression signatures in blood from patients undergoing total body irradiation. Using whole genome microarray analysis, we have identified genes that respond to radiation exposure in cancer patients in vivo. A 3-nearest neighbor classifier built from these genes correctly predicted samples as exposed to 0, 1.25 or 3.75 Gy with 94% accuracy even when samples from healthy donor controls were included. The same samples were classified with 98% accuracy using a signature previously defined from ex vivo irradiation data. The samples could also be classified as exposed or not exposed with 100% accuracy using multiple methods. The demonstration that ex vivo irradiation is an appropriate model that can provide meaningful prediction of in vivo exposure, and that the signatures are robust across diverse disease states, is an important advance in the application of gene expression for biodosimetry. Translation of these signatures to a fully automated “lab-on-a-chip” device will enable high-throughput screening for large-scale radiological emergencies, as well as making such tests practical for clinical uses. Radiation induced gene expression was measured in vivo in TBI patients at 4 hours after 1.25Gy exposure or at 24 hours after 3.75Gy exposure with three 1.25Gy split doses (approximately 4 hours apart). A total of 18 TBI patients, diagnosed with a variety of cancers were used in this study. Blood from 14 healthy control individuals was also used for comparison.
Project description:Using a large representative sample of postmenopausal women in the Norwegian Women and Cancer (NOWAC) postgenome study, we investigated blood gene expression changes due to intra-technical variability, normal inter-individuality (age, body mass index, fasting status), and exposure variables (smoking, hormone therapy and medication use) at proportion and level of real life situation revealing mechanistic insights of these effects mirrored in blood. We used a representative sample of postmenopausal women (N=286) in the NOWAC postgenome study. We investigated blood gene expression changes due to intra-technical variability, normal inter-individuality (age, body mass index, fasting status), and exposure variables (smoking, hormone therapy and medication use). A total of 304 arrays, including 18 technical replicates, were analyzed. We filtered out samples which had less than 40% probes with a signal to noise ratio (S/N) greater than or equal to 3. When a technical replicate was conducted, the array with the least number of probes with S/N greater than or equal to 3 was excluded. After samples filtration, a total of 286 arrays were analyzed. The following samples were filtered out; they were not used in the normalization processing, and none of the study conclusions are based on these samples. NWbAB_ 1 NWbAB_ 2 NWbAB_ 3 NWbAB_ 4 NWbAB_ 5 NWbAB_ 7 NWbAB_ 8 NWbAB_ 61 NWbAB_ 63 NWbAB_ 116 NWbAB_ 120 NWbAB_ 121 NWbAB_ 180 NWbAB_ 183 NWbAB_ 214 NWbAB_ 249 NWbAB_ 254 NWbAB_ 299 Sample records (GSMxxxx) are not provided for the filtered-out samples. However, the metadata for the filtered-out samples is included in the Series supplementary file GSE15289_filtered_metadata.txt. The raw data for both the filtered-out samples and non-filtered-out samples (GSM381832-GSM382117) are included in the Series supplementary file GSE15289_raw.txt.
Project description:Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. This dataset is the validation dataset used to test the csSAM gene expression deconvolution algorithm as reported in the accompanying paper. Whole blood gene expression measurements for 24 pediatric renal transplant patients were analyzed on human specific HGU133V2.0 (+) whole genome expression arrays. Blood drawn using PaxGene Blood RNA Tubes (PreAnalytiX, Qiagen).
Project description:The Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico was recently established to better understand the impacts of prenatal exposure to inorganic arsenic (iAs). In this study, we examined a subset (n = 40) of newborn cord blood samples for microRNA (miRNA) expression changes associated with in utero arsenic exposure. Levels of iAs in maternal drinking water (DW-iAs) and maternal urine were assessed. Levels of DW-iAs ranged from below detectable values to 236 µg/L (mean = 51.7 µg/L). Total arsenic in maternal urine (U-tAs) was defined as the sum of iAs and its monomethylated and dimethylated metabolites (MMAs and DMAs, respectively) and ranged from 6.2 to 319.7 µg/L (mean = 64.5 µg/L). Genome-wide miRNA expression analysis of cord blood revealed 12 miRNAs with increasing expression associated with U-tAs. Transcriptional targets of the miRNAs were computationally predicted and subsequently assessed using transcriptional profiling. Pathway analysis demonstrated that the U-tAs-associated miRNAs are involved in signaling pathways related to known health outcomes of iAs exposure including cancer and diabetes mellitus. Immune response-related mRNAs were also identified with decreased expression levels associated with U-tAs, and predicted to be mediated in part by the arsenic-responsive miRNAs. Results of this study highlight miRNAs as novel responders to prenatal arsenic exposure that may contribute to associated immune response perturbations. We assessed the impact of prenatal exposure to arsenic on genome-wide miRNA expression profiles and their potential influence on gene expression patterns in the Biomarkers of Exposure to ARsenic (BEAR) prospective pregnancy cohort. This cohort includes residents from Gómez Palacio, located in the state of Durango in the Lagunera region of Northern Mexico. A total of 200 pregnant women residing in Gómez Palacio, State of Durango, Mexico, were recruited at the General Hospital of Gómez Palacio to participate in the BEAR prospective pregnancy cohort. The present study focuses on miRNA expression profiles and utilizes 40 samples obtained from mother-newborn pairs selected from the larger cohort (n=200). The subcohort was selected to include subjects exposed to varying levels of arsenic as determined by both total arsenic in maternal urine (U-tAs) and inorganic arsenic in drinking water (DW-iAs). Cord blood samples were collected from the newborns immediately after infant delivery. Blood samples were collected using PreAnalytix PaxGene RNA tubes and extracted using the PAXgene RNA Kit, per standard protocol (Qiagen, Valencia, CA). Isolated RNA used for microarray analysis were amplified and labeled using the NuGEN Ovation Pico WTA System V2 and Encore Biotin Module, respectively (NuGEN, San Carlos, CA). RNA isolated from 40 cord blood samples were labeled and hybridized to the Agilent Human miRNA Microarray, based off miRBase v16.0.
Project description:Ionizing radiation exposure from a potential nuclear energy plant leak or detonation of a nuclear weapon can cause massive casualties to both warfighters and civilians. Biomarkers in biological specimens like blood and tissue, such as RNA, proteins, and metabolites, have shown potential to determine radiation dose levels. However, these biomarkers in blood and urine are short-lived, typically detectable only within hours or a few days. To address the need for stable, long-term radiation exposure biomarkers, we developed two LC-MS-based methods using non-invasive hair samples to identify radiation-induced biomarkers.
Project description:Ionizing radiation exposure from a potential nuclear energy plant leak or detonation of a nuclear weapon can cause massive casualties to both warfighters and civilians. Biomarkers in biological specimens like blood and tissue, such as RNA, proteins, and metabolites, have shown potential to determine radiation dose levels. However, these biomarkers in blood and urine are short-lived, typically detectable only within hours or a few days. To address the need for stable, long-term radiation exposure biomarkers, we developed two LC-MS-based methods using non-invasive hair samples to identify radiation-induced biomarkers