Biodosimetry: A Future Tool for Medical Management of Radiological Emergencies

Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice

Following a large-scale radiological event, timely collection of samples from all potentially exposed individuals may be precluded, and high-throughput bioassays capable of rapid and individualized dose assessment several days post-exposure will be essential for population triage and efficient implementation of medical treatment. The objective of this work was to validate the performance of a biomarker panel of radiosensitive intracellular leukocyte proteins (ACTN1, DDB2, and FDXR) and blood cell counts (CD19+ B-cells and CD3+ T-cells) for retrospective classification of exposure and dose estimation up to 7 days post-exposure in an in-vivo C57BL/6 mouse model. Juvenile and adult C57BL/6 mice of both sexes were total body irradiated with 0, 1, 2, 3, or 4 Gy, peripheral blood was collected 1, 4, and 7-days post-exposure, and individual blood biomarkers were quantified by imaging flow cytometry. An ensemble machine learning platform was used to identify the strongest predictor variables and combine them for biodosimetry outputs. This approach generated successful exposure classification (ROC AUC = 0.94, 95% CI: 0.90-0.97) and quantitative dose reconstruction (R2 = 0.79, RMSE = 0.68 Gy, MAE = 0.53 Gy), supporting the potential utility of the proposed biomarker assay for determining exposure and received dose in an individual.

Organ-specific Biodosimetry Modeling Using Proteomic Biomarkers of Radiation Exposure

In future mass casualty medical management scenarios involving radiation injury, medical diagnostics to both identify those who have been exposed and the level of exposure will be needed. As almost all exposures in the field are heterogeneous, determination of degree of exposure and which vital organs have been exposed will be essential for effective medical management. In the current study we sought to characterize novel proteomic biomarkers of radiation exposure and develop exposure and dose prediction algorithms for a variety of exposure paradigms to include uniform total-body exposures, and organ-specific partial-body exposures to only the brain, only the gut and only the lung. C57BL6 female mice received a single total-body irradiation (TBI) of 2, 4 or 8 Gy, 2 and 8 Gy for lung or gut exposures, and 2, 8 or 16 Gy for exposure to only the brain. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrating significant (FDR<0.05 and |logFC|>0.2) changes in expression after radiation exposure was characterized. All proteins were used for feature selection to build 7 different predictive models of radiation exposure using different sample cohort combinations. These models were structured according to practical field considerations to differentiate level of exposure, in addition to identification of organ-specific exposures. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. The overall predictive accuracy for all models was 100% at the model training level. When tested with reserved samples Model 1 which compared an "exposure" group inclusive of all TBI and organ-specific partial-body exposures in the study vs. control, and Model 2 which differentiated between control, TBI and partials (all organ-specific partial-body exposures) the resulting prediction accuracy was 92.3% and 95.4%, respectively. For identification of organ-specific exposures vs. control, Model 3 (only brain), Model 4 (only gut) and Model 5 (only lung) were developed with predictive accuracies of 78.3%, 88.9% and 94.4%, respectively. Finally, for Models 6 and 7, which differentiated between TBI and separate organ-specific partial-body cohorts, the testing predictive accuracy was 83.1% and 92.3%, respectively. These models represent novel predictive panels of radiation responsive proteomic biomarkers and illustrate the feasibility of development of biodosimetry algorithms with utility for simultaneous classification of total-body, partial-body and organ-specific radiation exposures.

BAX and DDB2 as biomarkers for acute radiation exposure in the human blood ex vivo and non-human primate models

There are currently no available FDA-cleared biodosimetry tools for rapid and accurate assessment of absorbed radiation dose following a radiation/nuclear incident. Previously we developed a protein biomarker-based FAST-DOSE bioassay system for biodosimetry. The aim of this study was to integrate an ELISA platform with two high-performing FAST-DOSE biomarkers, BAX and DDB2, and to construct machine learning models that employ a multiparametric biomarker strategy for enhancing the accuracy of exposure classification and radiation dose prediction. The bioassay showed 97.92% and 96% accuracy in classifying samples in human and non-human primate (NHP) blood samples exposed ex vivo to 0-5 Gy X-rays, respectively up to 48 h after exposure, and an adequate correlation between reconstructed and actual dose in the human samples (R2 = 0.79, RMSE = 0.80 Gy, and MAE = 0.63 Gy) and NHP (R2 = 0.80, RMSE = 0.78 Gy, and MAE = 0.61 Gy). Biomarker measurements in vivo from four NHPs exposed to a single 2.5 Gy total body dose showed a persistent upregulation in blood samples collected on days 2 and 5 after irradiation. The data indicates that using a combined approach of targeted proteins can increase bioassay sensitivity and provide a more accurate dose prediction.

G0-PCC-FISH derived multi-parametric biodosimetry methodology for accidental high dose and partial body exposures

High dose radiation exposures are rare. However, medical management of such incidents is crucial due to mortality and tissue injury risks. Rapid radiation biodosimetry of high dose accidental exposures is highly challenging, considering that they usually involve non uniform fields leading to partial body exposures. The gold standard, dicentric assay and other conventional methods have limited application in such scenarios. As an alternative, we propose Premature Chromosome Condensation combined with Fluorescent In-situ Hybridization (G0-PCC-FISH) as a promising tool for partial body exposure biodosimetry. In the present study, partial body exposures were simulated ex-vivo by mixing of uniformly exposed blood with unexposed blood in varying proportions. After G0-PCC-FISH, Dolphin's approach with background correction was used to provide partial body exposure dose estimates and these were compared with those obtained from conventional dicentric assay and G0-PCC-Fragment assay (conventional G0-PCC). Dispersion analysis of aberrations from partial body exposures was carried out and compared with that of whole-body exposures. The latter was inferred from a multi-donor, wide dose range calibration curve, a-priori established for whole-body exposures. With the dispersion analysis, novel multi-parametric methodology for discerning the partial body exposure from whole body exposure and accurate dose estimation has been formulated and elucidated with the help of an example. Dose and proportion dependent reduction in sensitivity and dose estimation accuracy was observed for Dicentric assay, but not in the two PCC methods. G0-PCC-FISH was found to be most accurate for the dose estimation. G0-PCC-FISH has potential to overcome the shortcomings of current available methods and can provide rapid, accurate dose estimation of partial body and high dose accidental exposures. Biological dose estimation can be useful to predict progression of disease manifestation and can help in pre-planning of appropriate & timely medical intervention.

Calibration curve for radiation dose estimation using FDXR gene expression biodosimetry - premises and pitfalls

PURPOSE

Radiation-induced alterations in gene expression show great promise for dose reconstruction and for severity prediction of acute health effects. Among several genes explored as potential biomarkers, FDXR is widely used due to high upregulation in white blood cells following radiation exposure. Nonetheless, the absence of a standardized protocols for gene expression-based biodosimetry is a notable gap that warrants attention to enhance the accuracy, reproducibility and reliability. The objective of this study was to evaluate the sensitivity of transcriptional biodosimetry to differences in protocols used by different laboratories and establish guidelines for the calculation of calibration curve using FDXR expression data.

MATERIAL AND METHODS

Two sets of irradiated blood samples generated during RENEB exercise were used. The first included samples irradiated with known doses including: 0, 0.25, 0.5, 1, 2, 3 and 4 Gy. The second set consisted of three 'blind' samples irradiated with 1.8 Gy, 0.4 Gy and a sham-irradiated sample. After irradiation, samples were incubated at 37 °C over 24 h and sent to participating laboratories, where RNA isolation and FDXR expression analysis by qPCR were performed using sets of primers/probes and reference genes specific for each laboratory. Calibration curves based on FDXR expression data were generated using non-linear and linear regression and used for dose estimation of 'blind' samples.

RESULTS

Dose estimates for sham-irradiated sample (0.020-0.024 Gy) and sample irradiated with 0.4 Gy (0.369-0.381 Gy) showed remarkable consistency across all laboratories, closely approximating the true doses regardless variation in primers/probes and reference genes used. For sample irradiated with 1.8 Gy the dose estimates were less precise (1.198-2.011 Gy) but remained within an acceptable margin for triage within the context of high dose range.

CONCLUSION

Methodological differences in reference genes and primers/probes used for FDXR expression measurement do not have a significant impact on the dose estimates generated, provided that all reference genes performed as expected and the primers/probes target a similar set of transcript variants. The preferred method for constructing a calibration curve based on FDXR expression data involves employing linear regression to establish a function that describes the relationship between the logarithm of absorbed dose and FDXR ΔCt values. However, one should be careful with using non-irradiated sample data as these cannot be accurately represented on a logarithmic scale. A standard curve generated using this approach can give reliable dose estimations in a dose range from 50 mGy to 4 Gy at least.

Comparison of Novel Proteomic Expression Profiles for Radiation Exposure in Male and Female C57BL6 Mice

There is a need for point-of-care diagnostics for future mass casualty events involving radiation exposure. The development of radiation exposure and dose prediction algorithms for biodosimetry is needed for screening of large populations during these scenarios, and exploration of the potential effects which sex, age, genetic heterogeneity, and physiological comorbidities may have on the utility of biodosimetry diagnostics is needed. In the current study, proteomic profiling was used to examine sex-specific differences in age-matched C57BL6 mice on the blood proteome after radiation exposure, and the usefulness of development and application of biodosimetry algorithms using both male and female samples. Male and female mice between 9-11 weeks of age received a dose of total-body irradiation (TBI) of either 2, 4 or 8 Gy and plasma was collected at days 1, 3 and 7 postirradiation. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrated significant (FDR < 0.05 and |logFC| > 0.2) changes in expression after radiation exposure. All proteins were used for feature selection to build predictive models of radiation exposure using different sample and sex-specific cohorts. Both binary (prediction of any radiation exposure) and multidose (prediction of specific radiation dose) model series were developed using either female and male samples combined or only female or only male samples. The binary series (models 1, 2 and 3) and multidose series (models 4, 5 and 6) included female/male combined, female only and male only respectively. Detectable values were obtained for all ∼7,000 proteins included in the SomaScan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies in the binary model series was ∼100% at the model training level, and when tested with fresh samples, 97.9% for model 1 (female and male) and 100% for model 2 (female only) and model 3 (male only). When sex-specific models 2 and 3 were tested with the opposite sex, the overall predictive accuracy rate dropped to 62.5% for model 2 and remained 100% for model 3. The overall predictive accuracy rate in the multidose model series was 100% for all models at the model training level and, when tested with fresh samples, 83.3%, 75% and 83.3% for Multidose models 4-6, respectively. When sex-specific model 5 (female only) and model 6 (male only) were tested with the opposite sex, the overall predictive accuracy rate dropped to 52.1% and 68.8%, respectively. These models represent novel predictive panels of radiation-responsive proteomic biomarkers and illustrate the utility and necessity of considering sex-specific differences in development of radiation biodosimetry prediction algorithms. As sex-specific differences were observed in this study, and as use of point-of-care radiation diagnostics in future mass casualty settings will necessarily include persons of both sexes, consideration of sex-specific variation is essential to ensure these diagnostic tools have practical utility in the field.

Interlaboratory comparison of high-throughput protein biomarker assay quantifications for radiation exposure classification

In the event of a widespread radiological incident, thousands of individuals will require rapid assessment of exposure using validated biodosimetry assays to inform clinical triage. In this scenario, multiple biodosimetry laboratories may be necessary for large-volume sample processing. To meet this need, we have developed a high-throughput assay for the rapid measurement of intracellular protein biomarkers in human peripheral blood samples using an Imaging Flow Cytometry (IFC) platform. The objective of this work was to harmonize and validate the reproducibility of our blood biomarker assay for radiation exposure across three IFC instruments, two located at Columbia University (CU) and the third at Health Canada. The Center for Radiological Research (CRR) at CU served as the central laboratory and reference instrument, where samples were prepared in triplicate, labeled with two radiation responsive leukocyte biomarkers (BAX and phosphor-p53 (Ser37)), and distributed for simultaneous interrogation by each IFC. Initial tests showed that significantly different baseline biomarker measurements were generated on each instrument when using the same acquisition settings, suggesting that harmonization of signal intensities is necessary. Subsequent tests harmonized biomarker measurements after irradiation by modulating laser intensity using two reference materials: unstained samples and standardized rainbow beads. Both methods generated measurements on each instrument without significant differences between the new and references instruments, allowing for the use of one master template to quantify biomarker expression across multiple instruments. Deming regression analyses of 0-5 Gy dose-response curves showed overall good correlation of BAX and p53 values across new and reference instruments. While Bland-Altman analyses indicated low to moderate instrument biases, ROC Curve analyses ultimately show successful discrimination between exposed and unexposed samples on each instrument (AUC values > 0.85).

Biodosimetry Based on Gamma-H2AX Quantification in Human Peripheral Blood Lymphocytes after Partial-body Irradiation

ABSTRACT

Quantification of gamma-H2AX foci can estimate exposure to ionizing radiation. Most nuclear and radiation accidents are partial-body irradiation, and the doses estimated using the total-body irradiation dose estimation formula are often lower than the actual dose. To evaluate the dose-response relation of gamma-H2AX foci in human peripheral blood lymphocytes after partial-body irradiation and establish a simple and high throughput model to estimate partial-body irradiation dose, we collected human peripheral blood and irradiated with 0-, 0.5-, 1-, 2-, 3-, 4-, 5-, 6-, and 8-Gy gamma rays to simulate total-body irradiation in vitro. Gamma-H2AX foci were quantitated by flow cytometry at 1 h after irradiation, and a dose-response curve was established for total-body irradiation dose estimation. Then, a partial-body irradiation dose-response calibration curve was established by adding calibration coefficients based on the Dolphin method. To reflect the data distribution of all doses more realistically, the partial-body irradiation dose-response calibration curve was divided into two sections. In addition, partial-body irradiation was simulated in vitro, and the PBI data were substituted into curves to verify the accuracy of the two partial-body irradiation calibration curves. Results showed that the dose estimation variations were all less than 30% except the 25% partial-body irradiation group at 1 Gy, and the partial-body irradiation calibration dose-response curves were YF 1 = - 3.444 x 2 + 18.532 x + 3.109, R 2 = 0.92 (YF ≤ 27.95); YF 2 = - 2.704 x 2 + 37.97 x - 56.45, R 2 = 0.86 (YF > 27.95). Results also suggested that the partial-body irradiation dose-response calibration curve based on the gamma-H2AX foci quantification in human peripheral blood lymphocytes is a simple and high throughput model to assess partial-body irradiation dose.

Applications of Premature Chromosome Condensation technique for genetic analysis

Cytogenetic techniques are used to detect aberrations in the genetic material and such techniques have a wide range of applications including for disease diagnosis, drug discovery and for the detection and quantification of mutagenic exposures. Although different types of cytogenetic techniques are in use, the Premature Chromosome Condensation (PCC) is one which is unique by virtue of it not requiring culture of peripheral blood mononucleate cells (PBMNCs) to detect chromatid and chromosomal aberrations. Such an advantage is useful in situations where rapid assessments of genetic damage is required, for example, during radiation exposures. PCC utilizes condensation of interphase chromatin by either biological or chemical means. The most widely used application of PCC is for biodosimetry. However, the rapidness of aberration detection has made PCC a useful technique for other applications such as for cancer diagnosis, drug-induced genotoxicity and preimplantation or assisted reproductive techniques. Also, PCC can be utilized for understanding the fundamental cellular mechanisms involved in chromatin condensation and chromosome morphologies. We present here the various approaches to obtain PCC, its applications and the endpoints which are used while using PCC as a cytogenetic technique.

DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns

In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker-related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry.

Evaluating transport conditions of conventional, widely used EDTA blood tubes for gene expression analysis in comparison to expensive, specialized PAXgene tubes in preparedness for radiological and nuclear events

PURPOSE

Gene expression (GE) analysis of a radio-sensitive gene set (FDXR, DDB2, WNT3, POU2AF1) has been introduced in the last decade as an early and high-throughput prediction tool of later developing acute hematologic radiation syndrome (H-ARS) severity. The use of special tubes for RNA extraction from peripheral whole blood (PAXgene) represent an established standard in GE studies, although uncommonly used in clinics and not immediately available in the quantities needed in radiological/nuclear (R/N) incidents. On the other hand, EDTA blood tubes are widely utilized in clinical practice.

MATERIAL AND METHODS

Using blood samples from eleven healthy donors, we investigated GE changes associated with delayed processing of EDTA tubes up to 4 h at room temperature (RT) after venipuncture (simulating delays caused by daily clinical routine), followed by a subsequent transport time of 24 h at RT, 4 °C, and -20 °C. Differential gene expression (DGE) of the target genes was further examined after X-irradiation with 0 Gy and 4 Gy under optimal transport conditions.

RESULTS

No significant changes in DGE were observed when storing EDTA whole blood samples up to 4 h at RT and subsequently kept at 4 °C for 24 h which is in line with expected DGE. However, other storage conditions, such as -20 °C or RT, decreased RNA quality and/or (significantly) caused changes in DGE exceeding the known methodological variance of the qRT-PCR.

CONCLUSION

Our data indicate that the use of EDTA whole blood tubes for GE-based H-ARS severity prediction is comparable to the quality of PAXgene tubes, when processed ≤ 4 h after venipuncture and the sample is transported within 24 hours at 4 °C.

Comparison of Isolated Lymphocyte and Whole Blood-Based CBMN Assays for Radiation Triage

Following a mass-casualty nuclear/radiological event, there will be an important need for rapid and accurate estimation of absorbed dose for biological triage. The cytokinesis-block micronucleus (CBMN) assay is an established and validated cytogenetic biomarker used to assess DNA damage in irradiated peripheral blood lymphocytes. Here, we describe an intercomparison experiment between two biodosimetry laboratories, located at Columbia University (CU) and Health Canada (HC) that performed different variants of the human blood CBMN assay to reconstruct dose in human blood, with CU performing the assay on isolated lymphocytes and using semi-automated scoring whereas HC used the more conventional whole blood assay. Although the micronucleus yields varied significantly between the two assays, the predicted doses closely matched up to 4 Gy - the range from which the HC calibration curve was previously established. These results highlight the importance of a robust calibration curve(s) across a wide age range of donors that match the exposure scenario as closely as possible and that will account for differences in methodology between laboratories. We have seen that at low doses, variability in the results may be attributed to variation in the processing while at higher doses the variation is dominated by inter-individual variation in cell proliferation. This interlaboratory collaboration further highlights the usefulness of the CBMN endpoint to accurately reconstruct absorbed dose in human blood after ionizing radiation exposure.

Development of a human peripheral blood ex vivo model for rapid protein biomarker detection and applications to radiation biodosimetry

In the event of a widespread radiological incident, thousands of people may be exposed to a wide range of ionizing radiation. In this unfortunate scenario, there will be a need to quickly screen a large number of people to assess the amount of radiation exposure and triage for medical treatment. In our earlier work, we previously identified and validated a panel of radiosensitive protein biomarkers in blood leukocytes, using the humanized-mouse and non-human primate (NHP) models. The objective of this work was to develop a high-throughput imaging flow-cytometry (IFC) based assay for the rapid measurement of protein biomarker expression in human peripheral blood samples irradiated ex vivo. In this assay design, peripheral human blood samples from healthy adult donors were exposed to 0-5 Gy X-irradiation ex vivo and cultured for up to 2 days. Samples were stained with a cocktail of surface antigens (CD66b, CD20, and CD3), fixed and permeabilized, and intracellularly stained for BAX (Bcl-2-associated X) protein, used here as a representative biomarker. Samples were interrogated by IFC, and a uniform analysis template was created to measure biomarker expression in heterogeneous and specific leukocyte subtype populations at each time point. In this human blood ex vivo model, we show that within gated populations of leukocyte subtypes, B-cells are highly radiosensitive with the smallest surviving fraction, followed by T-cells and granulocytes. Dose-dependent biomarker responses were measured in the lymphocytes, B-, and T-cell populations, but not in the granulocytes, with dose-response curves showing increasing fold changes in BAX protein expression up to Day 2 in lymphocyte populations. We present here the successful use of this ex vivo model for the development of radiation dose-response curves of a candidate protein biomarker towards future applications of dose reconstruction and biodosimetry.

Establishment and validation of a calibration curve for dicentric chromosome induced by 6MV X-ray

Radiation during radiotherapy and nuclear accidents is currently one of the biggest concerns for the international community. Biological dosimetry examines the amount of damage caused by radiation at the cellular level by quantifying a radiation biomarker. In particular, the dicentric chromosome assay is a biodosimetric technique that can quantify radiation damage by correlating radiation dose exposure with the frequency of dicentric chromosomes in the peripheral lymphocytes extracted from exposed individuals. This study aims to present of the reference dose-response calibration curve for biodosimetry laboratory of Mashhad University of Medical Sciences (north-east of Iran). In all, 40 samples of peripheral blood from four healthy volunteers were irradiated at doses of 0-5 Gray in a customised water phantom using a 6 MV X-rays at dose rate of 2 Gy/min from a linear accelerator. The irradiated samples were cultured and analysed according to the International Atomic Energy Agency Cytogenetic Dosimetry Protocol (2011) with some modifications. Linear-quadratic model curve fitting and further statistical analysis were done using Chromosome Aberration Calculation Software Version 2.0 and Dose Estimate (Version 5.2). The curve equation obtained was ${Y}_{dic}=0.0533{D}^2+0.0231D+0.0001$ and was in the range of other studies. Validation of the calibration curve was done by estimating the dose of blind samples.

Candidate biomarkers and persistent transcriptional responses after low and high dose ionizing radiation at high dose rate

PURPOSE

Development of an integrated time and dose model to explore the dynamics of gene expression alterations and identify biomarkers for biodosimetry following low- and high-dose irradiations at high dose rate.

MATERIAL AND METHODS

We utilized multiple transcriptome datasets (GSE8917, GSE43151, and GSE23515) from Gene Expression Omnibus (GEO) for identifying candidate biological dosimeters. A linear mixed-effects model with random intercept was used to explore the dose-time dynamics of transcriptional responses and to functionally characterize the time- and dose-dependent changes in gene expression.

RESULTS

We identified genes that are correlated with dose and time and discovered two clusters of genes that are either positively or negatively correlated with both dose and time based on the parameters of the model. Genes in these two clusters may have persistent transcriptional alterations. Twelve potential transcriptional markers for dosimetry-ARHGEF3, BAX, BBC3, CCDC109B, DCP1B, DDB2, F11R, GADD45A, GSS, PLK3, TNFRSF10B, and XPC were identified. Of these genes, BAX, GSS, and TNFRSF10B are positively associated with both dose and time course, have a persistent transcriptional response, and might be better biological dosimeters.

CONCLUSIONS

With the proposed approach, we may identify candidate biomarkers that change monotonically in relation to dose, have a persistent transcriptional response, and are reliable over a wide dose range.

Biomarker integration for improved biodosimetry of mixed neutron + photon exposures

There is a persistent risk of a large-scale malicious or accidental exposure to ionizing radiation that may affect a large number of people. Exposure will consist of both a photon and neutron component, which will vary in magnitude between individuals and is likely to have profound impacts on radiation-induced diseases. To mitigate these potential disasters, there exists a need for novel biodosimetry approaches that can estimate the radiation dose absorbed by each person based on biofluid samples, and predict delayed effects. Integration of several radiation-responsive biomarker types (transcripts, metabolites, blood cell counts) by machine learning (ML) can improve biodosimetry. Here we integrated data from mice exposed to various neutron + photon mixtures, total 3 Gy dose, using multiple ML algorithms to select the strongest biomarker combinations and reconstruct radiation exposure magnitude and composition. We obtained promising results, such as receiver operating characteristic curve area of 0.904 (95% CI: 0.821, 0.969) for classifying samples exposed to ≥ 10% neutrons vs. < 10% neutrons, and R2 of 0.964 for reconstructing photon-equivalent dose (weighted by neutron relative biological effectiveness) for neutron + photon mixtures. These findings demonstrate the potential of combining various -omic biomarkers for novel biodosimetry.

Longitudinal multi-omic changes in the transcriptome and proteome of peripheral blood cells after a 4 Gy total body radiation dose to Rhesus macaques

BACKGROUND

Non-human primates, such as Rhesus macaques, are a powerful model for studies of the cellular and physiological effects of radiation, development of radiation biodosimetry, and for understanding the impact of radiation on human health. Here, we study the effects of 4 Gy total body irradiation (TBI) at the molecular level out to 28 days and at the cytogenetic level out to 56 days after exposure. We combine the global transcriptomic and proteomic responses in peripheral whole blood to assess the impact of acute TBI exposure at extended times post irradiation.

RESULTS

The overall mRNA response in the first week reflects a strong inflammatory reaction, infection response with neutrophil and platelet activation. At 1 week, cell cycle arrest and re-entry processes were enriched among mRNA changes, oncogene-induced senescence and MAPK signaling among the proteome changes. Influenza life cycle and infection pathways initiated earlier in mRNA and are reflected among the proteomic changes during the first week. Transcription factor proteins SRC, TGFβ and NFATC2 were immediately induced at 1 day after irradiation with increased transcriptional activity as predicted by mRNA changes persisting up to 1 week. Cell counts revealed a mild / moderate hematopoietic acute radiation syndrome (H-ARS) reaction to irradiation with expected lymphopenia, neutropenia and thrombocytopenia that resolved within 30 days. Measurements of micronuclei per binucleated cell levels in cytokinesis-blocked T-lymphocytes remained high in the range 0.27-0.33 up to 28 days and declined to 0.1 by day 56.

CONCLUSIONS

Overall, we show that the TBI 4 Gy dose in NHPs induces many cellular changes that persist up to 1 month after exposure, consistent with damage, death, and repopulation of blood cells.

Dicentric chromosome assay using a deep learning-based automated system

The dicentric chromosome assay is the "gold standard" in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently developed automated system was evaluated for the dicentric chromosome assay. A previously constructed deep learning-based automatic dose-estimation system (DLADES) was used to construct dose curves and calculate estimated doses. Blood samples from two donors were exposed to cobalt-60 gamma rays (0-4 Gy, 0.8 Gy/min). The DLADES efficiently identified monocentric and dicentric chromosomes but showed impaired recognition of complete cells with 46 chromosomes. We estimated the chromosome number of each "Accepted" sample in the DLADES and sorted similar-quality images by removing outliers using the 1.5IQR method. Eleven of the 12 data points followed Poisson distribution. Blind samples were prepared for each dose to verify the accuracy of the estimated dose generated by the curve. The estimated dose was calculated using Merkle's method. The actual dose for each sample was within the 95% confidence limits of the estimated dose. Sorting similar-quality images using chromosome numbers is crucial for the automated dicentric chromosome assay. We successfully constructed a dose-response curve and determined the estimated dose using the DLADES.

A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays

A large-scale malicious or accidental radiological event can expose vast numbers of people to ionizing radiation. The dicentric chromosome (DCA) and cytokinesis-block micronucleus (CBMN) assays are well-established biodosimetry methods for estimating individual absorbed doses after radiation exposure. Here we used machine learning (ML) to test the hypothesis that combining automated DCA and CBMN assays will improve dose reconstruction accuracy, compared with using either cytogenetic assay alone. We analyzed 1349 blood sample aliquots from 155 donors of different ages (3-69 years) and sexes (49.1% males), ex vivo irradiated with 0-8 Gy at dose rates from 0.08 Gy/day to ≥ 600 Gy/s. We compared the performances of several state-of-the-art ensemble ML methods and found that random forest generated the best results, with R2 for actual vs. reconstructed doses on a testing data subset = 0.845, and mean absolute error = 0.628 Gy. The most important predictor variables were CBMN and DCA frequencies, and age. Removing CBMN or DCA data from the model significantly increased squared errors on testing data (p-values 3.4 × 10-8 and 1.1 × 10-6, respectively). These findings demonstrate the promising potential of combining CBMN and DCA assay data to reconstruct radiation doses in realistic scenarios of heterogeneous populations exposed to a mass-casualty radiological event.

NEW EXPERIMENTAL APPROACH IN BIODOSIMETRY: EX VIVO APOPTOSIS DETECTION

This study establishes a new experimental approach for retrospective biodosimetric assessment by apoptosis detection ex vivo. For this purpose, we used mononuclear blood leukocytes isolated from the peripheral blood of irradiated Wistar rats and cultured them ex vivo for posterior analysis. Using flow cytometry, we distinguished apoptotic lymphocyte subsets individual biodosimetric potential at different time periods after exposure: B-lymphocytes 6-8 h (0-7 Gy), natural killer cells 24 h (0-7 Gy) and T-lymphocytes 24 h (0-1 Gy). This novel experimental design innovates through the need of a single blood sample from irradiated individuals for a complete biodosimetric assessment.

Establishment of in vitro Calibration Curve for 60Co-γ-rays Induced Phospho-53BP1 Foci, Rapid Biodosimetry and Initial Triage, and Comparative Evaluations With γH2AX and Cytogenetic Assays

A rapid and reliable method for biodosimetry of populations exposed to ionizing radiation in the event of an incident or accident is crucial for initial triage and medical attention. DNA-double strand breaks (DSBs) are indicative of radiation exposure, and DSB-repair proteins (53BP1, γH2AX, ATM, etc.) are considered sensitive markers of DSB quantification. Phospho-53BP1 and γH2AX immunofluorescence technique serves as a sensitive, reliable, and reproducible tool for the detection and quantification of DSB-repair proteins, which can be used for biological dose estimations. In this study, dose-response curves were generated for ⁶⁰Co-γ-rays induced phospho-53 Binding Protein 1 (phospho-53BP1) foci at 1, 2, 4, 8, 16, and 24 h, post-irradiation for a dose range of 0.05–4 Gy using fluorescence microscopy. Following ISO recommendations, minimum detection limits (MDLs) were estimated to be 16, 18, 25, 40, 50, and 75 mGy for dose-response curves generated at 1, 2, 4, 8, 16, and 24 h post-irradiation. Colocalization and correlation of phospho-53BP1 and γH2AX were also measured in irradiated peripheral blood lymphocytes (PBLs) to gain dual confirmation. Comparative evaluation of the established curve was made by γH2AX-immunofluorescence, dicentric chromosome assay (DCA), and reciprocal translocation (RT) assays by reconstructing the dose of 6 dose-blinded samples. Coefficients of respective in-house established dose-response curves were employed to reconstruct the blind doses. Estimated doses were within the variation of 4.124%. For lower doses (0.052 Gy), phospho-53BP1 and γH2AX assays gave closer estimates with the variation of −4.1 to + 9% in comparison to cytogenetic assays, where variations were −8.5 to 24%. For higher doses (3 and 4 Gy), both the cytogenetic and immunofluorescence (phospho-53BP1 and γH2AX), assays gave comparable close estimates, with −11.3 to + 14.3% and −10.3 to −13.7%, variations, respectively.

Screening of miRNAs in White Blood Cell as a Radiation Biomarkers for Rapid Assessment of Acute Radiation Injury

Accidental radiation exposure is a threat to human health that necessitates effective clinical diagnosis. Suitable biomarkers are urgently needed for early assessment of exposure dose. Existing technologies being used to assess the extent of radiation have notable limitations. As a radiation biomarker, miRNA has the advantages of simple detection and high throughput. In this study, we screened for miRNAs with dose and time dependent responses in peripheral blood leukocytes via miRNA sequencing in establishing the animal model of acute radiation injury. Four radiation-sensitive and stably expressed miRNAs were selected out in the 24 h group of leukocyte miRNAs: mmu-miR-130b-5p, mmu-miR-148b-5p, mmu-miR-184-3p, mmu-miR-26a-2-3p, and five were screened in the 48 h group of leukocyte miRNAs: mmu-miR-130b-5p, mmu-miR-423-5p, mmu-miR-676-3p, mmu-miR-150-5p, mmu-miR-342-3p.The correlation curves between their expression and irradiation dose were plotted. Then, the results were validated by RT-qPCR in mouse peripheral blood. As a result, mmu-miR-150-5p and mmu-miR-342-3p showed the highest correlation at 48h after irradiation, and mmu-miR-130b-5p showed good correlation at both 24 h and 48 h after irradiation. In a conclusion, the miRNAs that are sensitive to ionizing radiation with dose dependent effects were selected out, which have the potential of forming a rapid assessment scheme for acute radiation injury.

Assessment of bone dose response using ATR-FTIR spectroscopy: A potential method for biodosimetry

The health care application of ionizing radiation has expanded worldwide during the last several decades. While the health impacts of ionizing radiation improved patient care, inaccurate handling of radiation technology is more prone to potential health risks. Therefore, the present study characterizes the bone dose response using bovine femurs from a slaughterhouse. The gamma irradiation was designed into low-doses (0.002, 0.004 and 0.007 kGy) and high-doses (1, 10, 15, 25, 35, 50 and 60 kGy), all samples received independent doses. The combination of FTIR spectroscopy and PLS-DA allows the detection of differences in the control group and the ionizing dose, as well as distinguishing between high and low radiation doses. In this way, our findings contribute to future studies of the dose response to track ionizing radiation effects on biological systems.

NIH Policies and Regulatory Pathways to U.S. FDA licensure: Strategies to Inform Advancement of Radiation Medical Countermeasures and Biodosimetry Devices

The Radiation and Nuclear Countermeasures Program within the National Institute of Allergy and Infectious Diseases (NIAID), is tasked with the mandate of identifying biodosimetry tests to assess exposure and medical countermeasures (MCMs) to mitigate/treat injuries to individuals exposed to significant doses of ionizing radiation from a radiological/nuclear incident, hosted. To fulfill this mandate, the Radiation and Nuclear Countermeasures Program (RNCP), hosted a workshop in 2018 workshop entitled "Policies and Regulatory Pathways to U.S. FDA licensure: Radiation Countermeasures and Biodosimetry Devices." The purpose of the meeting was to facilitate the advancement of MCMs and biodosimetry devices by assessing the research devices and animal models used in preclinical studies; government policies on reproducibility, rigor and robustness; regulatory considerations for MCMs and biodosimetry devices; and lessons learned from sponsors of early stage MCM or biodosimetry devices. Meeting presentations were followed by a NIAID-led, open discussion among academic investigators, industry researchers and U.S. government representatives.

Peripheral Blood Transcript Signatures after Internal 131I-mIBG Therapy in Relapsed and Refractory Neuroblastoma Patients Identifies Early and Late Biomarkers of Internal 131I Exposures

131I-metaiodobenzylguanidine (131I-mIBG) is a targeted radiation therapy developed for the treatment of advanced neuroblastoma. We have previously shown that this patient cohort can be used to predict absorbed dose associated with early 131I exposure, 72 h after treatment. We now expand these studies to identify gene expression differences associated with 131I-mIBG exposure 15 days after treatment. Total RNA from peripheral blood lymphocytes was isolated from 288 whole blood samples representing 59 relapsed or refractory neuroblastoma patients before and after 131I-mIBG treatment. We found that several transcripts predictive of early exposure returned to baseline levels by day 15, however, selected transcripts did not return to baseline. At 72 h, all 17 selected pathway-specific transcripts were differentially expressed. Transcripts CDKN1A (P < 0.000001), FDXR (P < 0.000001), DDB2 (P < 0.000001), and BBC3 (P < 0.000001) showed the highest up-regulation at 72 h after 131I-mIBG exposure, with mean log2 fold changes of 2.55, 2.93, 1.86 and 1.85, respectively. At day 15 after 131I-mIBG, 11 of the 17 selected transcripts were differentially expressed, with XPC, STAT5B, PRKDC, MDM2, POLH, IGF1R, and SGK1 displaying significant up-regulation at 72 h and significant down-regulation at day 15. Interestingly, transcripts FDXR (P = 0.01), DDB2 (P = 0.03), BCL2 (P = 0.003), and SESN1 (P < 0.0003) maintained differential expression 15 days after 131I-mIBG treatment. These results suggest that transcript levels for DNA repair, apoptosis, and ionizing radiation-induced cellular stress are still changing by 15 days after 131I-mIBG treatment. Our studies showcase the use of biodosimetry gene expression panels as predictive biomarkers following early (72 h) and late (15 days) internal 131I exposure. Our findings also demonstrate the utility of our transcript panel to differentiate exposed from non-exposed individuals up to 15 days after exposure from internal 131I.

Early molecular markers for retrospective biodosimetry and prediction of acute health effects

Radiation-induced biological changes occurring within hours and days after irradiation can be potentially used for either exposure reconstruction (retrospective dosimetry) or the prediction of consecutively occurring acute or chronic health effects. The advantage of molecular protein or gene expression (GE) (mRNA) marker lies in their capability for early (1-3 days after irradiation), high-throughput and point-of-care diagnosis, required for the prediction of the acute radiation syndrome (ARS) in radiological or nuclear scenarios. These molecular marker in most cases respond differently regarding exposure characteristics such as e.g. radiation quality, dose, dose rate and most importantly over time. Changes over time are in particular challenging and demand certain strategies to deal with. With this review, we provide an overview and will focus on already identified and used mRNA GE and protein markers of the peripheral blood related to the ARS. These molecules are examined in light of 'ideal' characteristics of a biomarkers (e.g. easy accessible, early response, signal persistency) and the validation degree. Finally, we present strategies on the use of these markers considering challenges as their variation over time and future developments regarding e.g. origin of samples, point of care and high-throughput diagnosis.

An integrative chemometric approach and correlative metabolite networking of LC-MS and 1H NMR based urine metabolomics for radiation signatures

The increasing threat of nuclear terrorism or radiological accident has made high throughput radiation biodosimetry a requisite for the immediate response for triage. Owing to detection of subtle alterations in biological pathways before the onset of clinical conditions, metabolomics has become an important tool for studying biomarkers and the related mechanisms for radiation induced damage. Here, we have attempted to combine two detection techniques, LC-MS and ¹H NMR spectroscopy, to obtain a comprehensive metabolite profile of urine at 24 h following lethal (7.5 Gy) and sub-lethal (5 Gy) irradiation in mice. Integrated data analytics using multiblock-OPLSDA (MB-OPLSDA), correlation networking and pathway analysis was used to identify metabolic disturbances associated with radiation exposure. MB-OPLSDA revealed better clustering and separation of irradiated groups compared with controls without overfitting (p-value of CV-ANOVA: 1.5 × 10^-3). Metabolites identified through MB-OPLSDA, namely, taurine, creatine, citrate and 2-oxoglutarate, were found to be dose independent markers and further support and validate our earlier findings as potential radiation injury biomarkers. Integrated analysis resulted in the enhanced coverage of metabolites and better correlation networking in energy, taurine, gut flora, L-carnitine and nucleotide metabolism observed post irradiation in urine. Our study thus emphasizes the major advantage of using the two detection techniques along with integrated analysis for better detection and comprehensive understanding of disturbed metabolites in biological pathways.

Small Molecule Responses to Sequential Irradiation with Neutrons and Photons for Biodosimetry Applications: An Initial Assessment

Mass casualty exposure scenarios from an improvised nuclear device are expected to be far more complex than simple photons. Based on the proximity to the explosion and potential shielding, a mixed field of neutrons and photons comprised of up to approximately 30% neutrons of the total dose is anticipated. This presents significant challenges for biodosimetry and for short-term and long-term medical treatment of exposed populations. In this study we employed untargeted metabolomic methods to develop a biosignature in urine and serum from C57BL/6 mice to address radiation quality issues. The signature was developed in males and applied to samples from female mice to identify potential sex differences. Thirteen urinary (primarily amino acids, vitamin products, nucleotides) and 18 serum biomarkers (primarily mitochondrial and fatty acid β oxidation intermediates) were selected and evaluated in samples from day 1 and day 7 postirradiation. Sham-irradiated groups (controls) were compared to an equitoxic dose (3 Gy X-ray equivalent) from X rays (1.2 Gy/min), neutrons (∼1 Gy/h), or neutrons-photons. Results showed a time-dependent increase in the efficiency of the signatures, with serum providing the highest levels of accuracy in distinguishing not only between exposed from non-exposed populations, but also between radiation quality (photon exposures vs. exposures with a neutron component) and in between neutron-photon exposures (5, 15 or 25% of neutrons in the total dose) for evaluating the neutron contribution. A group of metabolites known as acylcarnitines was only responsive in males, indicating the potential for different mechanisms of action in baseline levels and of neutron-photon responses between the two sexes. Our findings highlight the potential of metabolomics in developing biodosimetric methods to evaluate mixed exposures with high sensitivity and specificity.

Making the Case for Absorbed Radiation Response Biodosimetry - Utility of a High-Throughput Biodosimetry System

There is an unmet need to provide medical personnel with a Food and Drug Administration (FDA)-approved biodosimetry method for quantifying individualized absorbed dose response to inform treatment decisions for a very large patient population potentially exposed to ionizing radiation in the event of a nuclear incident. Validation of biodosimetry devices requires comparison of absorbed dose estimates to delivered dose as an indication of accuracy; however, comparison to delivered dose does not account for biological variability or an individual's radiosensitivity. As there is no FDA-cleared gene-expression-based biodosimetry method for determining biological response to radiation, results from accuracy comparisons to delivered dose yield relatively wide tolerance intervals or uncertainty. The Arizona State University Biodesign Institute is developing a high-throughput, automated real-time polymerase chain reaction (RT-PCR)-based biodosimetry system that provides absorbed dose estimates for patients exposed to 0-10 Gy from blood collected 1-7 days postirradiation. While the absorbed dose estimates result from a calibration against the actual exposed dose, the reported dose estimate is a measure of response to absorbed dose based on the exposure models used in developing the system. A central concern with biodosimetry test evaluation is how variability in the dose estimate results could affect medical decision-making, and if the biodosimetry test system performance is quantitatively sufficient to inform effective treatment. A risk:benefit analysis of the expected system performance in the proposed intended use environment was performed to address the potential medical utility of this biodosimetry system. Uncertainty analysis is based on biomarker variability in non-human primate (NHP) models. Monte Carlo simulation was employed to test multiple groups of biomarkers and their potential variation in response to determine uncertainty associated with dose estimate results. Dose estimate uncertainty ranges from ±1.2-1.7 Gy depending on the exposure dose over a range of 2-10 Gy. The risk:benefit of individualized absorbed dose estimates within the context of medical interventions after a nuclear incident is considered and the application of the biodosimetry system is evaluated in this framework. NHP dose-response relationships, as measured by clinical outcome end points, show expected biological and radiosensitivity responses in the primate populations tested and corroborate the biological variability observed in the reported absorbed dose estimate. Performance is examined in relationship to current clinical management and treatment recommendations, with evaluation of potential patient risk in over- and underestimating absorbed dose.

The future of biological dosimetry in mass casualty radiation emergency response, personalized radiation risk estimation and space radiation protection

PURPOSE

The aim of this brief personal, high level review is to consider the state of the art for biological dosimetry for radiation routine and emergency response, and the potential future progress in this fascinating and active field. Four areas in which biomarkers may contribute to scientific advancement through improved dose and exposure characterization, as well as potential contributions to personalized risk estimation, are considered: emergency dosimetry, molecular epidemiology, personalized medical dosimetry, and space travel.

CONCLUSION

Ionizing radiation biodosimetry is an exciting field which will continue to benefit from active networking and collaboration with the wider fields of radiation research and radiation emergency response to ensure effective, joined up approaches to triage; radiation epidemiology to assess long term, low dose, radiation risk; radiation protection of workers, optimization and justification of radiation for diagnosis or treatment of patients in clinical uses, and protection of individuals traveling to space.

Two-miRNA-based finger-stick assay for estimation of absorbed ionizing radiation dose

Nuclear radiation and radioactive fallouts resulting from a nuclear weapon detonation or reactor accidents could result in injuries affecting multiple sensitive organs, defined as acute radiation syndrome (ARS). Rapid and early estimation of injuries to sensitive organs using markers of radiation response is critical for identifying individuals who could potentially exhibit ARS; however, there are currently no biodosimetry assays approved for human use. We developed a sensitive microRNA (miRNA)-based blood test for radiation dose reconstruction with ±0.5 Gy resolution at critical dose range. Radiation dose-dependent changes in miR-150-5p in blood were internally normalized by a miRNA, miR-23a-3p, that was nonresponsive to radiation. miR-23a-3p was not highly expressed in blood cells but was abundant in circulation and was released primarily from the lung. Our assay showed the capability for dose estimation within hours to 1 week after exposure using a drop of blood from mice. We tested this biodosimetry assay for estimation of absorbed ionizing radiation dose in mice of varying ages and after exposure to both improvised nuclear device (IND)-spectrum neutrons and gamma rays. Leukemia specimens from patients exposed to fractionated radiation showed depletion of miR-150-5p in blood. We bridged the exposure of these patients to fractionated radiation by comparing responses after fractionated versus single acute exposure in mice. Although validation in nonhuman primates is needed, this proof-of-concept study suggests the potential utility of this assay in radiation disaster management and clinical applications.

Role of blood derived cell fractions, temperature and sample transport on gene expression-based biological dosimetry

PURPOSE

For triage purposes following a nuclear accident or a terrorist event, gene expression biomarkers in blood have been demonstrated to be good bioindicators of ionizing radiation (IR) exposure and can be used to assess the dose received by exposed individuals. Many IR-sensitive genes are regulated by the DNA damage response pathway, and modulators of this pathway could potentially affect their expression level and therefore alter accurate dose estimations. In the present study, we addressed the potential influence of temperature, sample transport conditions and the blood cell fraction analyzed on the transcriptional response of the following radiation-responsive genes: FDXR, CCNG1, MDM2, PHPT1, APOBEC3H, DDB2, SESN1, P21, PUMA, and GADD45.

MATERIALS AND METHODS

Whole blood from healthy donors was exposed to a 2 Gy X-ray dose with a dose rate of 0.5 Gy/min (output 13 mA, 250 kV peak, 0.2 mA) and incubated for 24 h at either 37, 22, or 4 °C. For mimicking the effect of transport conditions at different temperatures, samples incubated at 37 °C for 24 h were kept at 37, 22 or 4 °C for another 24 h. Comparisons of biomarker responses to IR between white blood cells (WBCs), peripheral blood mononuclear cells (PBMCs) and whole blood were carried out after a 2 Gy X-ray exposure and incubation at 37 °C for 24 hours.

RESULTS

Hypothermic conditions (22 or 4 °C) following irradiation drastically inhibited transcriptional responses to IR exposure. However, sample shipment at different temperatures did not affect gene expression level except for SESN1. The transcriptional response to IR of specific genes depended on the cell fraction used, apart from FDXR, CCNG1, and SESN1.

CONCLUSION

In conclusion, temperature during the incubation period and cell fraction but not the storing conditions during transport can influence the transcriptional response of specific genes. However, FDXR and CCNG1 showed a consistent response under all the different conditions tested demonstrating their reliability as individual biological dosimetry biomarkers.

Establishing a Reference Dose-Response Calibration Curve for Dicentric Chromosome Aberrations to Assess Accidental Radiation Exposure in Saudi Arabia

In cases of nuclear and radiological accidents, public health and emergency response need to assess the magnitude of radiation exposure regardless of whether they arise from disaster, negligence, or deliberate act. Here we report the establishment of a national reference dose–response calibration curve (DRCC) for dicentric chromosome (DC), prerequisite to assess radiation doses received in accidental exposures. Peripheral blood samples were collected from 10 volunteers (aged 20–40 years, median = 29 years) of both sexes (three females and seven males). Blood samples, cytogenetic preparation, and analysis followed the International Atomic Energy Agency EPR-Biodosimetry 2011 report. Irradiations were performed using 320 kVp X-rays. Metafer system was used for automated and assisted (elimination of false-positives and inclusion of true-positives) metaphases findings and DC scoring. DC yields were fit to a linear–quadratic model. Results of the assisted DRCC showed some variations among individuals that were not statistically significant (homogeneity test, P = 0.66). There was no effect of age or sex (P > 0.05). To obtain representative national DRCC, data of all volunteers were pooled together and analyzed. The fitted parameters of the radiation-induced DC curve were as follows: Y = 0.0020 (±0.0002) + 0.0369 (±0.0019) ^*D + 0.0689 (±0.0009) ^*D². The high significance of the fitted coefficients (z-test, P < 0.0001), along with the close to 1.0 p-value of the Poisson-based goodness of fit (χ² = 3.51, degrees of freedom = 7, P = 0.83), indicated excellent fitting with no trend toward lack of fit. The curve was in the middle range of DRCCs published in other populations. The automated DRCC over and under estimated DCs at low (<1 Gy) and high (>2 Gy) doses, respectively, with a significant lack of goodness of fit (P < 0.0001). In conclusion, we have established the reference DRCC for DCs induced by 320 kVp X-rays. There was no effect of age or sex in this cohort of 10 young adults. Although the calibration curve obtained by the automated (unsupervised) scoring misrepresented dicentric yields at low and high doses, it can potentially be useful for triage mode to segregate between false-positive and near 2-Gy exposures from seriously irradiated individuals who require hospitalization.

The RABiT-II DCA in the Rhesus Macaque Model

An automated platform for cytogenetic biodosimetry, the "Rapid Automated Biodosimetry Tool II (RABiT-II)," adapts the dicentric chromosome assay (DCA) for high-throughput mass-screening of the population after a large-scale radiological event. To validate this test, the U.S. Federal Drug Administration (FDA) recommends demonstrating that the high-throughput biodosimetric assay in question correctly reports the dose in an in vivo model. Here we describe the use of rhesus macaques (Macaca mulatta) to augment human studies and validate the accuracy of the high-throughput version of the DCA. To perform analysis, we developed the 17/22-mer peptide nucleic acid (PNA) probes that bind to the rhesus macaque's centromeres. To our knowledge, these are the first custom PNA probes with high specificity that can be used for chromosome analysis in M. mulatta. The accuracy of fully-automated chromosome analysis was improved by optimizing a low-temperature telomere PNA FISH staining in multiwell plates and adding the telomere detection feature to our custom chromosome detection software, FluorQuantDic V4. The dicentric frequencies estimated from in vitro irradiated rhesus macaque samples were compared to human blood samples of individuals subjected to the same ex vivo irradiation conditions. The results of the RABiT-II DCA analysis suggest that, in the lymphocyte system, the dose responses to gamma radiation in the rhesus macaques were similar to those in humans, with small but statistically significant differences between these two model systems.

A New Smartphone Application to Predict Hematologic Acute Radiation Syndrome Based on Blood Cell Count Changes-The H-module App

Treatment regimens for acute radiation syndrome have been improved over the past years. The application of appropriate therapy relies on rapid and high-throughput tests ideally conducted in the first 3 d after a radiation exposure event. We have examined the utility of blood cell counts (BCCs) 3 d post irradiation to predict clinical outcome for hematologic acute radiation syndrome (HARS). The BCCs and HARS severity information originated from data available in the System-for-Evaluation-and-Archiving-of-Radiation Accidents-based-on-Case-Histories (SEARCH). We found an almost complete discrimination of unexposed (HARS score H0) vs. irradiated individuals during model development and validation (negative predictive value > 94%) when using BCC data for all 3 d. We also found that BCC data increased the correct prediction of exposed individuals from day 1 to day 3. We developed spreadsheets to calculate the likelihood of correct diagnoses of the worried-well, requirement of hospitalization (HARS 2-4), or development of severe hematopoietic syndrome (HARS 3-4). In two table-top exercises, we found the spreadsheets were confusing and cumbersome, so we converted the spreadsheets into a smartphone application, named the H-module App, designed for ease of use, wider dissemination, and accommodation of co-morbidities in the HARS severity prediction algorithm.

Radiation Exposure Biomarkers in the Practice of Medical Radiology: Cooperative Research and the Role of the International Atomic Energy Agency (IAEA) Biodosimetry/Radiobiology Laboratory

The strategy toward personalized medicine in radiation oncology, nuclear medicine, and diagnostic and interventional radiology demands a specific set of assays for individualized estimation of radiation load for safety concerns and prognosis of normal tissue reactions caused by ionizing radiation. Apparently, it seems reasonable to use validated radiation dosimetric biomarkers for these purposes. However, a number of gaps in knowledge and methodological limitations still have to be resolved until dosimetric biomarkers will start to play a valuable role in clinical practice beyond radiation protection and radiation medicine. An extensive international multicenter research is necessary to improve the methodology of clinical applications of biodosimetry. That became a rationale for launching the IAEA Coordinated Research Project E35010 MEDBIODOSE: "Applications of Biological Dosimetry Methods in Radiation Oncology, Nuclear Medicine, and Diagnostic and Interventional Radiology." At the 2 Coordination Meeting on MEDBIODOSE (18-22 February 2019, Recife, Brazil), participants reported progress in the usage of biological dosimetry for genotoxicity assessment and/or individualization of radiotherapy treatment plans. Another avenue of research was the prognosis of normal tissue toxicity and cancer risk prediction using biomarkers' yield measured in vivo or after ex vivo irradiation of patients' cells. Other important areas are mechanisms of cytogenetic radiation response, validation of new radiation biomarkers, development of innovative techniques, automated and high-throughput assays for biodosimetry, and the overall improvement of biodosimetry service. An important aspect of clinical application of biodosimetry is standardization of techniques and unification of approaches to data interpretation. The new IAEA Biodosimetry/Radiobiology Laboratory, which is being established, will provide support for this activity. The declared lab's mission includes, among other tasks, a harmonization of the biodosimetry applications with relevant international standards, guidelines on good laboratory practice, and the IAEA EPR-Biodosimetry manual.

Integrated analysis of transcriptomic and metabolomic profiling reveal the p53 associated pathways underlying the response to ionizing radiation in HBE cells

Background

Radiation damage to normal tissues is a serious concern. P53 is a well-known transcription factor which is closely associated with radiation-induced cell damage. Increasing evidence has indicated that regulation of metabolism by p53 represents a reviving mechanism vital to protect cell survival. We aimed to explore the interactions of radiation-induced transcripts with the cellular metabolism regulated by p53.

Methods

Human bronchial epithelial (HBE) cell line was used to knockout p53 using CRISPR/cas9. Transcriptomic analysis was conducted by microarray and metabolomic analysis was conducted by GC–MS. Integrative omics was performed using MetaboAnalyst.

Results

326 mRNAs showed significantly altered expression in HBE p53-/- cells post-radiation, of which 269 were upregulated and 57 were downregulated. A total of 147 metabolites were altered, including 45 that increased and 102 that decreased. By integrated analysis of both omic data, we found that in response to radiation insult, nitrogen metabolism, glutathione metabolism, arachidonic acid metabolism, and glycolysis or gluconeogenesis may be dysregulated due to p53.

Conclusions

Our study provided a pilot comprehensive view of the metabolism regulated by p53 in response to radiation exposure. Detailed evaluation of these important p53-regulated metabolic pathways, including their roles in the response to radiation of cells, is essential to elucidate the molecular mechanisms of radiation-induced damage.

Micronucleus Assay: The State of Art, and Future Directions

During almost 40 years of use, the micronucleus assay (MN) has become one of the most popular methods to assess genotoxicity of different chemical and physical factors, including ionizing radiation-induced DNA damage. In this minireview, we focus on the position of MN among the other genotoxicity tests, its usefulness in different applications and visibility by international organizations, such as International Atomic Energy Agency, Organization for Economic Co-operation and Development and International Organization for Standardization. In addition, the mechanism of micronuclei formation is discussed. Finally, foreseen directions of the MN development are pointed, such as automation, buccal cells MN and chromothripsis phenomenon.

A High Throughput Approach to Reconstruct Partial-Body and Neutron Radiation Exposures on an Individual Basis

Biodosimetry-based individualized reconstruction of complex irradiation scenarios (partial-body shielding and/or neutron + photon mixtures) can improve treatment decisions after mass-casualty radiation-related incidents. We used a high-throughput micronucleus assay with automated scanning and imaging software on ex-vivo irradiated human lymphocytes to: a) reconstruct partial-body and/or neutron exposure, and b) estimate separately the photon and neutron doses in a mixed exposure. The mechanistic background is that, compared with total-body photon irradiations, neutrons produce more heavily-damaged lymphocytes with multiple micronuclei/binucleated cell, whereas partial-body exposures produce fewer such lymphocytes. To utilize these differences for biodosimetry, we developed metrics that describe micronuclei distributions in binucleated cells and serve as predictors in machine learning or parametric analyses of the following scenarios: (A) Homogeneous gamma-irradiation, mimicking total-body exposures, vs. mixtures of irradiated blood with unirradiated blood, mimicking partial-body exposures. (B) X rays vs. various neutron + photon mixtures. The results showed high accuracies of scenario and dose reconstructions. Specifically, receiver operating characteristic curve areas (AUC) for sample classification by exposure type reached 0.931 and 0.916 in scenarios A and B, respectively. R² for actual vs. reconstructed doses in these scenarios reached 0.87 and 0.77, respectively. These encouraging findings demonstrate a proof-of-principle for the proposed approach of high-throughput reconstruction of clinically-relevant complex radiation exposure scenarios.

The hunt for radiation biomarkers: current situation

Purpose: The possibility of a large-scale acute radiation exposure necessitates the development of new methods that could provide a rapid assessment of the doses received by individuals using high-throughput technologies. There is also a great interest in developing new biomarkers of dose exposure, which could be used in large molecular epidemiological studies in order to correlate estimated doses received and health effects. The goal of this review was to summarize current literature focused on biological dosimetry, namely radiation-responsive biomarkers.Methods: The studies involved in this review were thoroughly selected according to the determined criteria and PRISMA guidelines.Results: We described briefly recent advances in radiation genomics and metabolomics, giving particular emphasis to proteomic analysis. The majority of studies were performed on animal models (rats, mice, and non-human primates). They have provided much beneficial information, but the most relevant tests have been done on human (oncological) patients. By inspecting the radiaiton biodosimetry literate of the last 10 years, we identified a panel of candidate markers for each -omic approach involved.Conslusions: We reviewed different methodological approaches and various biological materials, which can be exploited for dose-effect prediction. The protein biomarkers from human plasma are ideal for this specific purpose. From a plethora of candidate markers, FDXR is a very promising transcriptomic candidate, and importantly this biomarker was also confirmed by some studies at protein level in humans.

Salivary Metabolomics of Total Body Irradiated Nonhuman Primates Reveals Long-Term Normal Tissue Responses to Radiation

PURPOSE

To identify metabolomic biomarkers of acute radiation exposure in saliva that show time-dependent changes.

METHODS AND MATERIALS

Nonhuman primates were exposed to 4 Gy of total body irradiation with γ-rays. Saliva was collected from 7 animals twice before and at days 1, 3, 5, 7, 15, 21, 28, and 60 after irradiation. Profiling was conducted with liquid chromatography time-of-flight mass spectrometry. Multivariate data analysis and potential biomarker identification was conducted through random Forests and the software MetaboAnalyst. Candidate biomarkers were validated through tandem mass spectrometry, and receiver operating characteristic curves were constructed to show the diagnostic ability of the signature over time.

RESULTS

Untargeted metabolomic analysis revealed significant and persistent effects up to the 60 days evaluated in this study. Biomarkers spanning primarily amino acids and nucleotides were identified, with a significant number showing long-term responses. Fifteen biomarkers showed high statistical significance in the first week after irradiation and 16 at >7 days after irradiation (false discovery rate-adjusted P < .05). The combination of the biomarkers in a single biosignature was able to accurately show the diagnostic ability of the signature in a binary classifier system with receiver operating characteristic curves.

CONCLUSIONS

Radiation can alter the metabolome in saliva, and metabolomics could effectively be used to monitor radiation responses, as a biodosimetry method, in the event of a radiological incident. Saliva metabolomics also has potential relevance in a clinical setting.

Candidate protein markers for radiation biodosimetry in the hematopoietically humanized mouse model

After a radiological incident, there is an urgent need for fast and reliable bioassays to identify radiation-exposed individuals within the first week post exposure. This study aimed to identify candidate radiation-responsive protein biomarkers in human lymphocytes in vivo using humanized NOD scid gamma (Hu-NSG) mouse model. Three days after X-irradiation (0–2 Gy, 88 cGy/min), human CD45+ lymphocytes were collected from the Hu-NSG mouse spleen and quantitative changes in the proteome of the human lymphocytes were analysed by mass spectrometry. Forty-six proteins were differentially expressed in response to radiation exposure. FDXR, BAX, DDB2 and ACTN1 proteins were shown to have dose-dependent response with a fold change greater than 2. When these proteins were used to estimate radiation dose by linear regression, the combination of FDXR, ACTN1 and DDB2 showed the lowest mean absolute errors (≤0.13 Gy) and highest coefficients of determination (R² = 0.96). Biomarker validation studies were performed in human lymphocytes 3 days after irradiation in vivo and in vitro. In conclusion, this is the first study to identify radiation-induced human protein signatures in vivo using the humanized mouse model and develop a protein panel which could be used for the rapid assessment of absorbed dose 3 days after radiation exposure.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.