We are grateful to Dr. Ruth Wilkins, Radiobiology and Protection Division at Health Canada, and Farrah Flegal, Canadian Nuclear Laboratories and their laboratory personnel for access to metaphase image data from their cytogenetic biodosimetry laboratories. This paper was supported by a contract from Build in Canada Innovation Program to CytoGnomix (Serial No. EN579-172270/001/SC). The initial version of ADCI and development of algorithms were supported by the Western Innovation Fund; the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant 371758-2009); US Public Health Service (DART-DOSE CMCR, 5U01AI091173-0); the Canadian Foundation for Innovation; Canada Research Chairs, and CytoGnomix Inc.

<ol>
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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+discrimination+of+dicentric+and+monocentric+chromosomes+by+machine+learning-based+image+processing.">Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.</a> Microsc Res Tech. 79 (5), 393-402 (2016).</li><li>Subasinghe, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Centromere+detection+of+human+metaphase+chromosome+images+using+a+candidate+based+method.">Centromere detection of human metaphase chromosome images using a candidate based method.</a> F1000Res. 5, 1565 (2016).</li><li>Wilkins, R. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+annual+Canadian+biodosimetry+network+intercomparisons.">Evaluation of the annual Canadian biodosimetry network intercomparisons.</a> Int J Radiat Biol. 91 (5), 443-451 (2015).</li><li>Liu, J., Li, Y., Wilkins, R., Flegal, F., Knoll, J. H. M., Rogan, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+Cytogenetic+Biodosimetry+Through+Automation+Of+Dicentric+Chromosome+Curation+And+Metaphase+Cell+Selection.">Accurate Cytogenetic Biodosimetry Through Automation Of Dicentric Chromosome Curation And Metaphase Cell Selection.</a> F1000Research. 6, 1396 (2017).</li><li>Schunck, C., Johannes, T., Varga, D., L&#246;rch, T., Plesch, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+developments+in+automated+cytogenetic+imaging:+unattended+scoring+of+dicentric+chromosomes,+micronuclei,+single+cell+gel+electrophoresis,+and+fluorescence+signals.">New developments in automated cytogenetic imaging: unattended scoring of dicentric chromosomes, micronuclei, single cell gel electrophoresis, and fluorescence signals.</a> Cytogenet Genome Res. 104 (1-4), 383-389 (2004).</li><li>Ramakumar, A., Subramanian, U., Prasanna, P. G. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+sample+processing+and+sample+management;+the+functional+evolution+of+classical+cytogenetic+assay+towards+automation.">High-throughput sample processing and sample management; the functional evolution of classical cytogenetic assay towards automation.</a> Mutat Res Genet Toxicol Environ Mutagen. 2015, 132-141 (2015).</li><li>Deperas, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CABAS:+a+freely+available+PC+program+for+fitting+calibration+curves+in+chromosome+aberration+dosimetry.">CABAS: a freely available PC program for fitting calibration curves in chromosome aberration dosimetry.</a> Radiat Prot Dosimetry. 124 (2), 115-123 (2007).</li><li>Ainsbury, E. A., Lloyd, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dose+estimation+software+for+radiation+biodosimetry.">Dose estimation software for radiation biodosimetry.</a> Health Phys. 98 (2), 290-295 (2010).</li></ol>

PKR and JHMK cofounded CytoGnomix, which is commercializing ADCI and holds related patents. YL and BCS are employees of CytoGnomix. ADCI is copyrighted, and the centromere localization method in ADCI is patented (US Pat. No. 8,605,981; German Pat. No. 112011103687).

Capabilities and limitations of the software
The protocol described in this paper introduces the typical step-wise procedure used in ADCI to import and process cytogenetic metaphase images, create radiation calibration curves, and estimate biological dose in individuals or samples exposed to unknown radiation levels. However, it is not necessary to carry out these instructions sequentially. For example, many test samples of unknown dose can be processed and analyzed using the same precomputed calibration curve. Furthermore, once processing is complete, the image selection and DC filtering models can be iterated by the user. Application of an appropriate image selection model depends on the characteristics and quality of the metaphase image data, which in turn relies both on the laboratory protocol used to prepare cells and the stringency criteria used to select cells with automated metaphase capture systems. Chromosome morphologies will differ among biodosimetry and cytogenetic laboratories, and thus, the image selection models should be evaluated by the user to determine whether the predefined image selection models supplied with the software will be adequate to produce accurate dose estimates, or whether custom models with user-defined thresholds need to be created. Based on our experience, the effectiveness of image selection models is influenced by the source and quality of the cell images. Users can design their own image selection criteria using different combinations of filters to eliminate false positive DCs and image selection models, and the corresponding threshold values to select desired images. There is flexibility in input of calibration curves and dose estimation, as coefficients of the linear-quadratic curve and DC frequencies can be modified or manually inputted.
Although the software is fully automated, images can be manually reviewed and selected. This capability is available to include or remove individually processed images through the Microscope Viewer function in the main GUI. Nevertheless, due to automation, the software is significantly more efficient compared with the manual scoring of metaphase images and counting DCs. A sample consisting of 1000 images can be processed in 20 (tiff) to 40 (jpg) min on a multi-core performance workstation. This software will be particularly useful in time-critical or labor-intensive situations, such as events in which multiple individuals have been exposed or were suspected to have been exposed to radiation, or where time-sensitive diagnoses and treatment decisions are critical.
Precise and accurate high throughput detection of DCs as well as dose estimation are necessary for unattended radiation assessment. Other available alternatives to the software do not fulfill both of these requirements. A user-assisted, image-based cytogenetic analysis (DCScore, Metasystems17) system requires manual verification of candidate DCs, due to a high error rate attributable to uncorrected overlaps between chromosomes, and the system does not determine radiation dose. DCScore would not be as effective as ADCI in a radiation event involving a large number of potentially exposed individuals. Large aperture microscope systems can collect images of multiple metaphase cells18, however, they do not analyze them. &#34;CABAS&#34;19 and &#34;Dose Estimate&#34;20 software can generate calibration curves and estimate dose, but do not score DCs. Other biodosimetry assays that are not based on DC analysis include H2AX fluorescence, fluorescence in situ hybridization with DNA probes targeted to specific chromosomes, gene expression, micronucleus assay, and urine and respiratory biomarkers. These methods are less specific and less sensitive for ionizing radiation, can be more costly, in some instances, are more time consuming, and have generally not been standardized across multiple reference laboratories. Most of these techniques do not detect stable radiation responses, so they cannot be used for long term assessment (&#62;7 days post-exposure) of radiation dose. By contrast, this can evaluate individuals up to 90 days post-exposure, and can process data from any cytogenetics laboratory microscope imaging system. However, if a sample is drawn &#62;4 weeks post-exposure, sensitivity is reduced due to the decay of dicentric aberrations1,2,3 and the software does not currently correct DC frequencies for delays in sampling exposed individuals.
This software has some limitations. Existing image selection models select mostly acceptable metaphase images, but in some instances, fail to eliminate unsatisfactory images, which can reduce the accuracy of DC detection. It is still an open question how to design a satisfactory image selection model that eliminates all unsuitable metaphase cells. The software provides accurate estimates for samples exposed to higher radiation doses (&#8805; 2 Gy). Despite considerable progress in reducing the number of false positive DCs16, these objects have not been eliminated. Lower quality metaphase cells at low radiation dose (especially &#60; 1 Gy) are more prone to false positive DC detection. Therefore, low dose samples were not included when generating the calibration curve used for dose estimation of HC test samples. However, if a curve containing low dose samples is desired, a lower SVM Sigma value reduces false positive counts in low dose samples but may result in lower DC yields in high dose samples. Figure 8 compares the HC curve used for dose estimation (Sigma = 1.5) with a calibration curve fit with additional low dose samples at lower SVM sigma value (1.0). In samples with insufficient numbers of metaphase cells and/or poor quality metaphase images, it may not be possible to precisely estimate biological exposures at low dose, potentially resulting in deviations from physical dose exceeding 0.5 Gy.
The software may not accurately assess radiation types if their dose-response curves best fit a linear or near-linear model. Thus far, it has been tested only with samples exposed to X- and Gamma rays. If another radiation source is examined, users must ensure both calibration and test samples are exposed to the same type of radiation. The software uses either maximum likelihood or least squares fitting to construct a dose-response curve using a linear-quadratic model. There is currently no option to impose a strict linear curve fit, appropriate for high energy particle exposures, however such functionality will be available in the future.
Future development
Our ongoing efforts are focused on improving image selection models and accurate dose measurement, in particular of samples exposed to low radiation doses. Subsequent software versions will provide standard error measurements on dose estimates and confidence intervals on calibration curves. In addition, a high-performance-computing version of the software for the Blue Gene (BG/Q, IBM) supercomputer is under development for timely evaluation of individuals exposed in a mass-casualty radiation event. Some components of the software have already been tested and deployed on this platform11.

Radiation biodosimetry uses biological markers, mostly chromosomal aberrations such as dicentric chromosomes (DCs) and chromosome translocations to measure radiation doses that individuals are exposed to. A biologically absorbed dose may be different from the physical dose measured by instruments due to variability between individuals. Similarly, radiation of a certain physical dose can produce different biological exposures due to underlying physiological or environmental conditions. Knowledge of the biological dose is of particular importance for both diagnosis and treatment.
The DC assay is the gold standard of the World Health Organization (WHO) and International Atomic Energy Agency (IAEA) for assessing biological radiation exposure in people. It was the first assay recommended by the IAEA and WHO for radiation dose assessment. DC frequency is relatively stable for approximately 4 weeks after radiation exposure1 and their quantitative correlation with emitted radiation dose is accurate, which make DCs the ideal biomarker. The relationship between radiation dose (referenced in Gray [Gy] units), and DC frequency (referenced as number of DCs per cell) can be expressed as a linear-quadratic function.
The cytogenetic DC assay has been the industry standard for about 55 years2. It has been performed manually, requiring 1 - 2 days to analyze microscope data from a single blood sample. Several hundred to several thousand images are needed to accurately estimate radiation exposure depending on the dose3. At doses exceeding 1 Gy, IAEA recommends a minimum of 100 DCs be detected. Examination of 250 - 500 metaphase images is common practice in biodosimetry cytogenetic laboratories. For samples with exposures &#60;1 Gy, 3,000 - 5,000 images are suggested due to the lower probabilities of DC formation. In either case, it is a labor-intense task.
Cytogenetic biodosimetry laboratories create their own in vitro radiation biodosimetry calibration curves before assessing biological doses in test samples. Blood samples from normal, control individuals are exposed to radiation and lymphocytes are then cultured and prepared for metaphase chromosome analysis. Using these samples, biological doses received are calibrated to the known physical doses emitted by a standard radiation source. After metaphase cell images are recorded, experts examine images, count DCs and calculate DC frequencies for each sample. A calibration curve is built by fitting a linear-quadratic curve to the DC frequencies at all doses. Then, exposures in test sample from individuals can be inferred by matching the DC frequencies to the calibrated doses on the curve or by specifying them in the corresponding linear quadratic formula.
We have automated both the detection of DCs and dose determination to expedite this procedure using software. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) uses machine learning-based image processing techniques to detect and discriminate dicentric chromosomes (DCs) from monocentric chromosomes (MCs) and other objects and automates radiation dose estimation. The software aims to significantly reduce or eliminate the necessity for manual verification of DC counts and to accelerate dose estimation through automation. It has been developed with the involvement of reference biodosimetry laboratories at Health Canada (HC) and Canadian Nuclear Laboratories (CNL). Their feedback will ensure that performance will continue to meet IAEA criteria for this assay.
The software performs the following functions: 1) filtering DCs and selecting optimal metaphase cell images for analysis, 2) chromosome recognition, DC detection, and DC frequency determination, and 3) estimating radiation dose from dose-calibrated, cytogenetic radiation data. This software processes groups of metaphase images from the same individual (termed a sample), counts the number of DCs in each using image processing techniques, and returns the estimated radiation dose received by each sample in units of Grays (Gy).
The software has been designed to handle a range of chromosome structures, counts, and densities. However, the algorithm performs optimally in metaphase images containing a near complete complement of well-separated, linear chromosomes4. Images containing highly overlapped sets of chromosomes, multiple cells, incomplete metaphase cells, sister chromatid separation, nuclei, non-chromosomal objects, and other defects can reduce the accuracy of the algorithm. Dedicated image selection models and other object segmentation thresholds can filter out the majority of sub-optimal images and false positive DCs.
Dicentric chromosome detection is performed when an image is processed. The algorithm attempts to determine which objects in an image are chromosomes and then locates the two regions most likely to be centromeres on each chromosome. Then, a series of different Support Vector Machine (SVM) learning models distinguish chromosomes as either DCs or normal, monocentric chromosomes. The SVM models differ in sensitivity and specificity of DC detection (see Step 3.1.4 below), which can affect the DC frequencies that are determined in a sample.
ADCI processes sets of Giemsa- (or DAPI-) stained metaphase digital images (in TIFF or JPG format) for one or more samples. The software analyzes DCs in both calibration samples and test samples. The physical doses (in Gy) of calibration samples are known and are used in the generation of a calibration curve. The physical and biological doses of individuals with unknown exposures are inferred by the software from the machine-generated calibration curve. Although laboratories use comparable techniques, the calibration curves from different laboratories often vary3. Both calibration curve and test samples from the same laboratory should be processed for accurate dose estimation in test samples.
This software offers speed, accuracy and scalability which addresses the productivity required to handle an event in which many individuals must simultaneously be tested. It was developed from 2008 - 20174,5,6,7,8,9,10,11,12,13. Using recent computer hardware, this desktop PC software can process and estimate radiation dose in a patient sample of 500 metaphase genome equivalents in 10 - 20 min 4. The code is based on a set of proprietary image segmentation and machine learning algorithms for chromosome analysis. Expert analysis of each chromosome exposed to 3 Gy radiation gave comparable accuracies to ADCI. In a set of 6 samples of unknown exposures (previously used in an international proficiency exercise), the software estimated doses within 0.5 Gy of the values obtained by manual review of the same data by HC and CNL, fulfilling the IAEA&#39;s requirements for triage biodosimetry. Furthermore, inter-laboratory standardization and ultimately reproducibility of dose estimates benefit from having a common, automated DC scoring algorithm. Nevertheless, the software permits customization of image filtering and selection criteria, enabling differences in chromosome preparation methods and radiation calibration sources to be taken into account.
This software is a graphical user interface (GUI)-based system which analyzes sets of chromosome images containing Giemsa (or DAPI)-stained metaphase cells for abnormalities that result from exposure to ionizing radiation. The image sets are digitally photographed with a light (or epifluorescent) microscope system and each set corresponds to a different sample. The software utilizes image processing techniques to detect and discriminate DCs from MCs and other objects. Empirically-derived segmentation filters then automatically eliminate false positive DCs without affecting true DCs. Finally, the software automatically filters out undesirable images based on various image properties found poor quality metaphase images with precomputed (or user-specified) image selection models. These images include those containing excessive or insufficient numbers of &#34;noisy&#34; objects, multiple overlapping chromosomes, images lacking metaphase chromosomes, excessive numbers of sister chromatids4. The automatically curated image data are used to generate dose calibration curve from samples of known radiation dose and are used to estimate exposures of test samples exposed to unknown doses.
Output of the software can be viewed and saved as: 1) text-based output viewed in the console, 2) plots which can be saved as images, and 3) reports in HTML format. Many aspects of the software are customizable to suit the specific needs of different laboratories. Individual laboratories usually provide both calibration and test samples prepared and collected based on the cytogenetic protocol validated in that laboratory. This maintains uniformity of sample preparation and allows calibration curves generated from calibration samples to be meaningfully applied to test samples derived using the same protocol. Calibration curves may also be created from either curve coefficients or DC frequencies at defined doses. The most accurate dose estimates are obtained by filtering out lower quality images and false positive DCs (FPs). Selection of optimal image subsets within each sample is accomplished using &#39;Image selection models&#39; that eliminate subpar images which tend to introduce FPs. A series of pre-validated models are included with the software, however additional models with customized thresholds and filters can be created and saved, by the user.
Once the software successfully loads, the main graphical user interface (GUI) is presented (see Figure 1). From this interface, samples, each consisting of a folder of metaphase cell image files, may be selected and processed to identify DCs, calibration curves may be created and compared, and radiation exposure dose of samples may be determined.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56245/56245fig1.jpg" /> 
Figure 1: The Major Sectors of the Graphical User Interface Include: a list of samples (1), a list of calibration curves (2), the process queue (3), which monitors the status of DC detection in each set of images of each sample, a plot display (4), which summarizes statistical or other quantitative properties of a set of images in samples or calibration curves, and a console (5) which contains descriptive text as outputs of each operation performed by the program. <a href="//cloudflare.jove.com/files/ftp_upload/56245/56245fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table>
	<tbody>
		<tr>
			<td>Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI)</td>
			<td>CytoGnomix</td>
			<td>NA</td>
			<td>ADCI software is released in a binary installation package file for Microsoft Windows 7, 8, 8.1 and 10; 235 Mb of disk storage are required for a typical installation. The software has been tested with Intel or AMD x86-64 processors; at least 1 Gb RAM is recommended. Analyses have been benchmarked on a computer configured with an Intel I7 processor and 16 Gb RAM. Operation of ADCI requires an active license and a USB-based hardware dongle, which must remain plugged in while the software is executing. The dongle encodes the software expiry date. Each time the software is started, this date is read. The software will allow access to the program if the current date and time precedes the expiration time-date stamp. Extending an expired software license can be accomplished by obtaining a new dongle or by renewing the license with an updated key at startup.</td>
		</tr>
		<tr>
			<td>Digital images of metaphase cell nuclei</td>
			<td>Examples: Metasystems, Leica Microsystems</td>
			<td>M-Search (Metasystems), Cytovision (Leica) software</td>
			<td>High resolution TIFF format; typically &#62;250 digital images generated with a microscope imaging capture system (minimum 63x magnification objective, 10x magnification ocular).</td>
		</tr>
		<tr>
			<td>MSI Leopard Pro (recommended, optional)</td>
			<td>Micro-Star International</td>
			<td>MSI GP62 6QF 480CA Leopard Pro</td>
			<td>Multi-core performance workstation.</td>
		</tr>
	</tbody>
</table>

expedited radiation biodosimetry by automated dicentric chromosome identification (adci) and dose estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Watch this Scientific Journal Video about Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation at JoVE.com

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

The cytogenetic dicentric chromosome (DC) assay quantifies exposure to ionizing radiation. The Automated Dicentric Chromosome Identifier and Dose Estimator software accurately and rapidly estimates biological dose from DCs in metaphase cells. It distinguishes monocentric chromosomes and other objects from DCs, and estimates biological radiation dose from the frequency of DCs.

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome ...

expedited-radiation-biodosimetry-automated-dicentric-chromosome

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Genetics

15.6K Views.  CytoGnomix Inc.. The cytogenetic dicentric chromosome (DC) assay quantifies exposure to ionizing radiation. The Automated Dicentric Chromosome Identifier and Dose Estimator software accurately and rapidly estimates biological dose from DCs in metaphase cells. It distinguishes monocentric chromosomes and other objects from DCs, and estimates biological radiation dose from the frequency of DCs.

Video: Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice

Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially compromised, can easily be identified by conventional chromosome staining techniques. However, detection of stable aberrations, which involve exchange or translocation of genetic materials without considerable modification in the chromosome morphology, requires sophisticated chromosome painting techniques that rely on in situ hybridization of fluorescently labeled DNA probes, a chromosome painting technique popularly known as fluorescence in situ hybridization (FISH). FISH probes can be specific for whole chromosome/s or precise sub-region on chromosome/s. The method not only allows visualization of stable aberrations, but it can also allow detection of the chromosome/s or specific DNA sequence/s involved in a particular aberration formation. A variety of chromosome painting techniques are available in cytogenetics; here two highly sensitive methods, multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY), are discussed to identify inter-chromosomal stable aberrations that form in the bone marrow cells of mice after exposure to total body irradiation. Although both techniques rely on fluorescent labeled DNA probes, the method of detection and the process of image acquisition of the fluorescent signals are different. These two techniques have been used in various research areas, such as radiation biology, cancer cytogenetics, retrospective radiation biodosimetry, clinical cytogenetics, evolutionary cytogenetics, and comparative cytogenetics.

Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization mFISH and Spectral Karyotyping SKY in Irradiated Mice

The present protocol describes the usefulness of multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY) in identifying inter-chromosomal stable aberrations in the bone marrow cells of mice after exposure to total body irradiation.

Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially ...

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control in model organisms. Technologies including short hairpin RNAs (shRNAs) and clustered regularly interspaced short palindromic repeats (CRISPR) have enabled such screens in diploid mammalian cells. Here we describe a large-scale shRNA screen for regulators of X-chromosome inactivation (XCI), using a murine cell line with firefly luciferase and hygromycin resistance genes knocked in at the C-terminus of the methyl CpG binding protein 2 (MeCP2) gene on the inactive X-chromosome (Xi). Reactivation of the construct in the reporter cell line conferred survival advantage under hygromycin B selection, enabling us to screen a large shRNA library and identify hairpins that reactivated the reporter by measuring their post-selection enrichment using next-generation sequencing. The enriched hairpins were then individually validated by testing their ability to activate the luciferase reporter on Xi.

We report a small hairpin RNA (shRNA) and next generation sequencing-based protocol for identifying regulators of X-chromosome inactivation in a murine cell line with firefly luciferase and hygromycin resistance genes fused to the methyl CpG binding protein 2 (MeCP2) gene on the inactive X chromosome.

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control ...

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules.

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

A Non-random Mouse Model for Pharmacological Reactivation of Mecp2 on the Inactive X Chromosome

X chromosome inactivation (XCI) is the random silencing of one X chromosome in females to achieve gene dosage balance between the sexes. As a result, all females are heterozygous for X-linked gene expression. One of the key regulators of XCI is Xist, which is essential for the initiation and maintenance of XCI. Previous studies have identified 13 trans acting X chromosome inactivation factors (XCIFs) using a large-scale, loss-of-function genetic screen. Inhibition of XCIFs, such as ACVR1 and PDPK1, using short-hairpin RNA or small molecule inhibitors, reactivates X chromosome-linked genes in cultured cells. But the feasibility and tolerability of reactivating the inactive X chromosome in vivo remains to be determined. Towards this goal, a Xist&#916;:Mecp2/Xist:Mecp2-Gfp mouse model has been generated with non-random XCI due to deletion of Xist on one X chromosome. Using this model, the extent of inactive X reactivation was quantitated in the mouse brain following treatment with XCIF inhibitors. Recently published results show, for the first time, that pharmacological inhibition of XCIFs reactivates Mecp2 from the inactive X chromosome in cortical neurons of the living mouse brain.

Here, we describe a protocol to generate a viable female murine model with non-random X chromosome inactivation, i.e., the maternally-inherited X chromosome is inactive in 100% of the cells. We also describe a protocol to test feasibility, tolerability, and safety of pharmacological reactivation of the inactive X chromosome in vivo.

X chromosome inactivation (XCI) is the random silencing of one X chromosome in females to achieve gene dosage balance between the sexes. As a result, all ...

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT employs two purification steps. First, immunoprecipitation of a tagged RNP subunit is followed by mild RNase digestion and subsequent non-denaturing affinity elution. A second immunoprecipitation of another RNP subunit allows for enrichment of a defined complex. Following a denaturing elution of RNAs and proteins, the RNA footprints are converted into high-throughput DNA sequencing libraries. Unlike the more popular ultraviolet (UV) crosslinking followed by immunoprecipitation (CLIP) approach to enrich RBP binding sites, RIPiT is UV-crosslinking independent. Hence RIPiT can be applied to numerous proteins present in the RNA interactome and beyond that are essential to RNA regulation but do not directly contact the RNA or UV-crosslink poorly to RNA. The two purification steps in RIPiT provide an additional advantage of identifying binding sites where a protein of interest acts in partnership with another cofactor. The double purification strategy also serves to enhance signal by limiting background. Here, we provide a step-wise procedure to perform RIPiT and to generate high-throughput sequencing libraries from isolated RNA footprints. We also outline RIPiT&#39;s advantages and applications and discuss some of its limitations.

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Here, we present a protocol to enrich endogenous RNA binding sites or &#34;footprints&#34;&#160;of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT ...

Transforming, Genome Editing and Phenotyping the Nitrogen-fixing Tropical Cannabaceae Tree Parasponia andersonii

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae). Together with 4 additional species, it forms the only known non-legume lineage able to establish a nitrogen-fixing nodule symbiosis with rhizobium. Comparative studies between legumes and P. andersonii could provide valuable insight into the genetic networks underlying root nodule formation. To facilitate comparative studies, we recently sequenced the P. andersonii genome and established Agrobacterium tumefaciens-mediated stable transformation and CRISPR/Cas9-based genome editing. Here, we provide a detailed description of the transformation and genome editing procedures developed for P. andersonii. In addition, we describe procedures for the seed germination and characterization of symbiotic phenotypes. Using this protocol, stable transgenic mutant lines can be generated in a period of 2-3 months. Vegetative in vitro propagation of T0 transgenic lines allows phenotyping experiments to be initiated at 4 months after A. tumefaciens co-cultivation. Therefore, this protocol takes only marginally longer than the transient Agrobacterium rhizogenes-based root transformation method available for P. andersonii, though offers several clear advantages. Together, the procedures described here permit P. andersonii to be used as a research model for studies aimed at understanding symbiotic associations as well as potentially other aspects of the biology of this tropical tree.

Transforming, Genome Editing and Phenotyping the Nitrogen-fixing Tropical Cannabaceae Tree Parasponia andersonii

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae) and can form nitrogen-fixing root nodules in association with the rhizobium. Here, we describe a detailed protocol for reverse genetic analyses in P. andersonii based on Agrobacterium tumefaciens-mediated stable transformation and CRISPR/Cas9-based genome editing.

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae). Together with 4 additional species, it forms the ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

Identification of Host Pathways Targeted by Bacterial Effector Proteins using Yeast Toxicity and Suppressor Screens

Intracellular bacteria secrete virulence factors called effector proteins into the host cytosol that act to subvert host proteins and/or their associated biological pathways to the benefit of the bacterium. Identification of putative bacterial effector proteins has become more manageable due to advances in bacterial genome sequencing and the advent of algorithms that allow in silico identification of genes encoding secretion candidates and/or eukaryotic-like domains. However, identification of these important virulence factors is only an initial step. Naturally, the goal is to determine the molecular function of effector proteins and elucidate how they interact with the host. In recent years, techniques like the yeast two-hybrid screen and large-scale immunoprecipitations coupled with mass spectrometry have aided in the identification of protein-protein interactions. Although identification of a host binding partner is the crucial first step toward elucidating the molecular function of a bacterial effector protein, sometimes the host protein is found to have multiple biological functions (e.g., actin, clathrin, tubulin), or the bacterial protein may not physically bind host proteins, depriving the researcher of crucial information about the precise host pathway being manipulated. A modified yeast toxicity screen coupled with a suppressor screen has been adapted to identify host pathways impacted by bacterial effector proteins. The toxicity screen relies on a toxic effect in yeast caused by the effector protein interfering with the host biological pathways, which often manifests as a growth defect. Expression of a yeast genomic library is used to identify host factors that suppress the toxicity of the bacterial effector protein and thus identify proteins in the pathway that the effector protein targets. This protocol contains detailed instructions for both the toxicity and suppressor screens. These techniques can be performed in any lab capable of molecular cloning and cultivation of yeast and Escherichia coli.

Bacterial pathogens secrete proteins into the host that target crucial biological processes. Identifying the host pathways targeted by bacterial effector proteins is key to addressing molecular pathogenesis. Here, a method using a modified yeast suppressor and toxicity screen to elucidate host pathways targeted by toxic bacterial effector proteins is described.

Intracellular bacteria secrete virulence factors called effector proteins into the host cytosol that act to subvert host proteins and/or their associated ...

Preparation of Meiotic Chromosome Spreads from Zebrafish Spermatocytes

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding the chromosome events that take place during meiotic prophase, including the pairing, synapsis, and recombination events that ensure proper chromosome segregation. While the mouse has been an important model for understanding the molecular mechanisms underlying these processes, not all meiotic events in this system are analogous to human meiosis. We recently demonstrated the exciting potential of the zebrafish as a model of human spermatogenesis. Here we describe, in detail, our methods to visualize meiotic chromosomes and associated proteins in chromosome spread preparations. These preparations have the advantage of allowing high resolution analysis of chromosome structures. First, we describe the procedure for dissecting testes from adult zebrafish, followed by cell dissociation, lysis, and spreading of the chromosomes. Next, we describe the procedure for detecting the localization of meiotic chromosome proteins, by immunofluorescence detection, and nucleic acid sequences, by fluorescence in situ hybridization (FISH). These techniques comprise a useful set of tools for the cytological analysis of meiotic chromatin architecture in the zebrafish system. Researchers in the zebrafish community should be able to quickly master these techniques and incorporate them into their standard analyses of reproductive function.

Nuclear surface spreads are an indispensable tool for studying chromosome events during meiosis. Here we demonstrate a method to prepare and visualize meiotic chromosomes during prophase I from zebrafish spermatocytes.

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding ...