The use of a centrifuge-free RABiT-II system for high-throughput micronucleus analysis

Objectives and achievements of the HUMN project on its 26th anniversary

Micronuclei (MN) are a nuclear abnormality that occurs when chromosome fragments or whole chromosomes are not properly segregated during mitosis and consequently are excluded from the main nuclei and wrapped within nuclear membrane to form small nuclei. This maldistribution of genetic material leads to abnormal cellular genomes which may increase risk of developmental defects, cancers, and accelerated aging. Despite the potential importance of MN as biomarkers of genotoxicity, very little was known about the optimal way to measure MN in humans, the normal ranges of values of MN in healthy humans and the prospective association of MN with developmental and degenerative diseases prior to the 1980's. In the early 1980's two important methods to measure MN in humans were developed namely, the cytokinesis-block MN (CBMN) assay using peripheral blood lymphocytes and the Buccal MN assay that measures MN in epithelial cells from the oral mucosa. These discoveries greatly increased interest to use MN assays in human studies. In 1997 the Human Micronucleus (HUMN) project was founded to initiate an international collaboration to (i) harmonise and standardise the techniques used to perform the lymphocyte CBMN assay and the Buccal MN assay; (ii) establish and collate databases of MN frequency in human populations world-wide which also captured demographic, lifestyle and environmental genotoxin exposure data and (iii) use these data to identify the most important variables affecting MN frequency and to also determine whether MN predict disease risk. In this paper we briefly describe the achievements of the HUMN project during the period from the date of its foundation on 9th September 1997 until its 26th Anniversary in 2023, which included more than 200 publications and 23 workshops world-wide.

RABiT-III: an Automated Micronucleus Assay at a Non-Specialized Biodosimetry Facility

Micronuclei, detected through the cytokinesis-block micronucleus assay, are valuable indicators of ionizing radiation exposure, especially in short-term lymphocyte cultures. The peripheral human blood lymphocyte assay is recognized as a prime candidate for automated biodosimetry. In a prior project at the Columbia University Center for Radiological Research, we automated this assay using the 96-well ANSI/SLAS microplate standard format and relied on established biotech robotic systems named Rapid Automated Biodosimetry Tool (RABiT). In this study, we present the application of a similar automated biotech setup at an external high-throughput facility (RABiT-III) to implement the same automated cytokinesis-block micronucleus assay. Specifically, we employed the Agilent BRAVO liquid-handling system and GE IN Cell Analyzer 6000 imaging system in conjunction with the PerkinElmer Columbus image data storage and analysis system. Notably, this analysis system features an embedded PhenoLOGIC machine learning module, simplifying the creation of cell classification algorithms for CBMN assay image analysis and enabling the generation of radiation dose-response curves. This investigation underscores the adaptability of the RABiT-II CBMN protocol to diverse RABiT-III biotech robotic platforms in non-specialized biodosimetry centers. Furthermore, it highlights the advantages of machine learning in rapidly developing algorithms crucial for the high-throughput automated analysis of RABiT-III images.

The NIAID/RNCP Biodosimetry Program: An Overview

Established in 2004, the Radiation and Nuclear Countermeasures Program (RNCP), within the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health has the central mission to advance medical countermeasure mitigators/therapeutics, and biomarkers and technologies to assess, triage, and inform medical management of patients experiencing acute radiation syndrome and/or the delayed effects of acute radiation exposure. The RNCP biodosimetry mission space encompasses: (1) basic research to elucidate novel approaches for rapid and accurate assessment of radiation exposure, (2) studies to support advanced development for US Food and Drug Administration (FDA) clearance of promising triage or treatment devices/approaches, (3) characterization of biomarkers and/or assays to determine degree of tissue or organ dose that can predict outcome of radiation injuries (i.e., organ failure, morbidity, and/or mortality), and (4) outreach efforts to facilitate interactions with researchers developing cutting edge biodosimetry approaches. Thus far, no biodosimetry device has been FDA cleared for use during a radiological/nuclear incident. At NIAID, advancement of radiation biomarkers and biodosimetry approaches is facilitated by a variety of funding mechanisms (grants, contracts, cooperative and interagency agreements, and Small Business Innovation Research awards), with the objective of advancing devices and assays toward clearance, as outlined in the FDA's Radiation Biodosimetry Medical Countermeasure Devices Guidance. The ultimate goal of the RNCP biodosimetry program is to develop and establish accurate and reliable biodosimetry tools that will improve radiation preparedness and ultimately save lives during a radiological or nuclear incident.

Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells

Background

Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay.

Methods

Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes.

Results

The parameter F res , the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84-0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined F res in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94-0.99]), an increase of 13 percentage points over age alone.

Conclusions

If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.

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.

ESTABLISHMENT OF THE IN VITRO DOSE-RESPONSE CALIBRATION CURVE FOR X-RAY-INDUCED MICRONUCLEI IN HUMAN LYMPHOCYTES

The cytokinesis-block micronucleus assay has proven to be a reliable technique for biological dosimetry. This study aimed to establish the dose-response curve for X-ray-induced micronucleus. Peripheral blood samples from three healthy donors were irradiated with various doses and scoring criteria by the micronuclei (MN) in binucleated cells. The results showed that the frequency of MN increased with the elevation of radiation dose. CABAS and Dose Estimate software were used to fit the MN and dose into a linear quadratic model, and the results were compared. The linear and quadratic coefficients obtained by the two software were basically the same and were comparable with published curves of similar radiation quality and dose rates by other studies. The dose-response curve established in this study can be used as an alternative method for in vitro dose reconstruction and provides a reliable tool for biological dosimetry in accidental or occupational radiation exposures.

Rapid and automatic detection of micronuclei in binucleated lymphocytes image

Cytokinesis block micronucleus (CBMN) assay is a widely used radiation biological dose estimation method. However, the subjectivity and the time-consuming nature of manual detection limits CBMN for rapid standard assay. The CBMN analysis is combined with a convolutional neural network to create a software for rapid standard automated detection of micronuclei in Giemsa stained binucleated lymphocytes images in this study. Cell acquisition, adhesive cell mass segmentation, cell type identification, and micronucleus counting are the four steps of the software's analysis workflow. Even when the cytoplasm is hazy, several micronuclei are joined to each other, or micronuclei are attached to the nucleus, this algorithm can swiftly and efficiently detect binucleated cells and micronuclei in a verification of 2000 images. In a test of 20 slides, the software reached a detection rate of 99.4% of manual detection in terms of binucleated cells, with a false positive rate of 14.7%. In terms of micronuclei detection, the software reached a detection rate of 115.1% of manual detection, with a 26.2% false positive rate. Each image analysis takes roughly 0.3 s, which is an order of magnitude faster than manual detection.

Challenges and Strategies in the Development of Radiation Biodosimetry Tests for Patient Management

The public health and medical response to a radiological or nuclear incident requires the capability to sort, assess, treat, triage and ultimately discharge, as well as to refer or transport people to their next step in medical care. The Public Health Emergency Medical Countermeasures Enterprise (PHEMCE), directed by the U.S. Department of Health and Human Services (HHS), facilitates a comprehensive, multi-agency effort to develop and deploy radiation biodosimetry tests. Within HHS, discovery and development of biodosimetry tests includes the National Institute of Allergy and Infectious Diseases (NIAID) National Institutes of Health (NIH), the Office of the Assistant Secretary of Preparedness and Response (ASPR), Biomedical Advanced Research and Development Authority (BARDA), and the Food and Drug Administration (FDA) as primary partners in this endeavor. The study of radiation biodosimetry has advanced significantly, with expansion into the fields of cytogenetics, genomics, proteomics, metabolomics, lipidomics and transcriptomics. In addition, expansion of traditional cytogenetic assessment methods using automated platforms, and development of laboratory surge capacity networks have helped to advance biodefense preparedness. This article describes various programs and coordinating efforts between NIAID, BARDA and FDA in the development of radiation biodosimetry approaches to respond to radiological and nuclear threats.

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.

Whole blood gene expression within days after total-body irradiation predicts long term survival in Gottingen minipigs

Gottingen minipigs mirror the physiological radiation response observed in humans and hence make an ideal candidate model for studying radiation biodosimetry for both limited-sized and mass casualty incidents. We examined the whole blood gene expression profiles starting one day after total-body irradiation with increasing doses of gamma-rays. The minipigs were monitored for up to 45 days or time to euthanasia necessitated by radiation effects. We successfully identified dose- and time-agnostic (over a 1-7 day period after radiation), survival-predictive gene expression signatures derived using machine-learning algorithms with high sensitivity and specificity. These survival-predictive signatures fare better than an optimally performing dose-differentiating signature or blood cellular profiles. These findings suggest that prediction of survival is a much more useful parameter for making triage, resource-utilization and treatment decisions in a resource-constrained environment compared to predictions of total dose received. It should hopefully be possible to build such classifiers for humans in the future.

Transportation container for pre-processing cytogenetic assays in radiation accidents

We report a shipping container that enables a disruptive logistics for cytogenetic biodosimetry for radiation countermeasures through pre-processing cell culture during transportation. The container showed precise temperature control (< 0.01 °C) with uniform sample temperature (< 0.1 °C) to meet the biodosimetry assay requirements. Using an existing insulated shipping box and long shelf life alkaline batteries makes it ideal for national stockpile. Dose curve of cytogenetic biodosimetry assay using the shipping container showed clear dose response and high linear correlation with the control dose curve using a laboratory incubator (Pearson’s correlation coefficient: 0.992). The container’s ability of pre-processing biological samples during transportation could have a significant impact on radiation countermeasure, as well as potential impacts in other applications such as biobanking, novel molecular or cell-based assays or therapies.

Machine learning methodology for high throughput personalized neutron dose reconstruction in mixed neutron + photon exposures

We implemented machine learning in the radiation biodosimetry field to quantitatively reconstruct neutron doses in mixed neutron + photon exposures, which are expected in improvised nuclear device detonations. Such individualized reconstructions are crucial for triage and treatment because neutrons are more biologically damaging than photons. We used a high-throughput micronucleus assay with automated scanning/imaging on lymphocytes from human blood ex-vivo irradiated with 44 different combinations of 0-4 Gy neutrons and 0-15 Gy photons (542 blood samples), which include reanalysis of past experiments. We developed several metrics that describe micronuclei/cell probability distributions in binucleated cells, and used them as predictors in random forest (RF) and XGboost machine learning analyses to reconstruct the neutron dose in each sample. The probability of "overfitting" was minimized by training both algorithms with repeated cross-validation on a randomly-selected subset of the data, and measuring performance on the rest. RF achieved the best performance. Mean R² for actual vs. reconstructed neutron doses over 300 random training/testing splits was 0.869 (range 0.761 to 0.919) and root mean squared error was 0.239 (0.195 to 0.351) Gy. These results demonstrate the promising potential of machine learning to reconstruct the neutron dose component in clinically-relevant complex radiation exposure scenarios.

Gene Expression Profiles from Heart, Lung and Liver Samples of Total-Body-Irradiated Minipigs: Implications for Predicting Radiation-Induced Tissue Toxicity

In the event of a major accidental or intentional radiation exposure incident, the affected population could suffer from total- or partial-body exposures to ionizing radiation with acute exposure to organs that would produce life-threatening injury. Therefore, it is necessary to identify markers capable of predicting organ-specific damage so that appropriate directed or encompassing therapies can be applied. In the current work, gene expression changes in response to total-body irradiation (TBI) were identified in heart, lungs and liver tissue of Göttingen minipigs. Animals received 1.7, 1.9, 2.1 or 2.3 Gy TBI and were followed for 45 days. Organ samples were collected at the end of day 45 or sooner if the animal displayed morbidity necessitating euthanasia. Our findings indicate that different organs respond to TBI in a very specific and distinct manner. We also found that the liver was the most affected organ in terms of gene expression changes, and that lipid metabolic pathways were the most deregulated in the liver samples of non-survivors (survival time <45 days). We identified organ-specific gene expression signatures that accurately differentiated non-survivors from survivors and control animals, irrespective of dose and time postirradiation. At what point did these radiation-induced injury markers manifest and how this information could be used for applying intervention therapies are under investigation.