Specification of FBG sensor system.
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65519",title:"Vital Sign Measurement Using FBG Sensor for New Wearable Sensor Development",doi:"10.5772/intechopen.84186",slug:"vital-sign-measurement-using-fbg-sensor-for-new-wearable-sensor-development",body:'\nIn Japan, there is surge in demand for medical care of the elderly as their population continues to increase [1]. This is causing a serious concern especially considering the prevailing shortage of medical staff. Meanwhile, since the Tokyo Olympic Games will be held in 2020, there is a high need for self-healthcare management among healthy people. A simple home health system to monitor the vital signs in elderly people is becoming an absolute necessity, as there is increasing demand for their self-health management on a daily basis. Vital signs are fundamental indicators of human health. These indicators include heart rate, respiration rate, blood pressure, body temperature, level of consciousness.
\nIn order to meet such needs, wearable sensors are being developed by manufacturers to monitor vital signs [2, 3, 4]. These sensors are glasses or wristwatch type, they have a characteristic that can measure vital signs continuously. Most of these sensors are of photoelectric pulse wave type measuring the changes in light absorption caused by hemoglobin in blood vessels. These sensors are compact, portable, and easy to install on a human body. However, there are a few issues with these sensors: moisture noise caused by perspiration, skin damage due to the probe pressure [5], and dependence of signal strength on probe mounting position [6]. In addition, people have psychologically stressful for people who do not use wrist watches or eyeglasses from attaching these type wearable sensor. Many photoelectric pulse wave sensors can measure only the pulse rate and cannot measure blood pressure. The currently used measuring many sphygmomanometers are of stationary type and therefore cannot be carried by hand. Accordingly, they are not suitable for home use and continuous monitoring.
\nThe FBG sensor is an optical fiber type highly accurate strain sensor. The FBG sensor has a feature that a plurality of sensors can be installed with one interrogator, the optical fiber length is 1 km or more. From these features, FBG sensors are used in building and civil engineering fields. Tam et al. have introduced FBG sensors in railway rail monitoring systems [7, 8]. There are research studies reporting measurement of vital signs using FBG sensors [9, 10, 11]. Furthermore, since the sensor part is an optical fiber, it can be introduced into a textile product [12]. Therefore, the FBG sensor is introduced into the wristband or the sleeve of the shirt, and the sensor can be installed on the living body simply by wearing the textile product.
\nThe authors propose that the FBG sensor is installed to the pulsation point of the skin surface and the vital sign can be calculated from the measured signal. The vital signs such as pulse rate, respiratory rate, stress load, and blood pressure are calculated from the measured signal of FBG sensor. In this paper, the details of the strain signal measured at a pulsation point of a human body with the FBG sensor, method of calculation from the measured signal, and measurement accuracy for each vital sign are described.
\nAn FBG sensor system is composed of an interrogator part with a light source and a detector, and a sensor part with an optical fiber. The schematic and the specifications of the FBG sensor system used in this study are shown in Figure 1 and Table 1, respectively. We used the FPG interrogator system, named PF25-S01 (Nagano Keiki Co., Ltd.) [13]. This interrogator is equipped with an ASE light source that emits near infrared light with a wavelength of 1525–1570 nm that passes through the core of the optical fiber.
\nFBG sensor system.
Interrogator | \nOptical fiber | \n||
---|---|---|---|
Size D × W × H (mm) | \n230 × 330 × 100 | \nMaterial | \nSilica glass (Core: Ge) | \n
Weight (kg) | \n4 | \nMode | \nSingle mode | \n
Light source | \nASE | \nFiber diameter (μm) | \n250 | \n
Wavelength (nm) | \n1525–1570 | \nCladding diameter (μm) | \n145 | \n
Power (mW) | \n30 | \nCore diameter (μm) | \n10.5 | \n
Sampling rate (kHz) | \n10 | \nDetection range (nm) | \n1550 ± 0.5 | \n
Detector | \nInGaAs PIN PD | \n\n | \n |
Wavelength resolution (pm) | \n±0.1 | \n\n | \n |
Specification of FBG sensor system.
In the FBG sensor, a diffraction grating is formed when the refractive index is periodically changed along the axis of the core of the optical fiber. The FBG sensor reflects only a specific wavelength corresponding to the interval period of the diffraction grating. The wavelength of the reflected light from the FBG is called Bragg wavelength that follows the Eq. (1),
where, \n
The Bragg wavelength reflected by the sensor portion passes through a circulator and is directed to a detection device that is a Mach-Zehnder interferometer. The optical path difference of the interferometer is approximately 5 mm. The homodyne detection method using the Mach-Zehnder interferometer detects the shift length of Bragg wavelength as interference phase shift [14, 15]. The Mach-Zehnder interferometer provides the light outputs through three detectors.
\nIn each detector, the detected light is photo-electrically converted into an electric signal that is further converted to a digital signal by an analog/digital converter. Subsequently, the phase angle is demodulated, and a wavelength shift (proportional to the displacement and distortion) is calculated. The method has an advantage in that the resolution of wavelength measurement is finer compared with other methods. By this method, the pressure of the FBG sensor part is detected by measuring the displacement in Bragg wavelength.
\nExperiments were performed using a biological model (Figure 2) to study the signals measured by an FBG sensor attached to a living body. A piston was employed to simulate the heart, and a blood-mimicking fluid (manufactured by CIRS) was used to simulate the blood. The movement of the piston was controlled to set the flow rate of the pseudo blood passing through a 500-mm-long acrylic pipe (inner diameter 8 mm) that simulated a blood vessel. For a phantom biological model, Flow Phantom (Supertech, Inc., ATS 524), was used. A flow phantom, made of a rubber material, was used to simulate the resilience of a living body (artificial skin), and it was provided with a hole of 8 mm diameter located at a depth of 15 mm from the top surface. A pipe of 8 mm inner diameter, made of vinyl chloride, and a sensor to measure the pressure of the pseudo blood were installed at the rear of the flow phantom. The pseudo blood, discharged from the piston, passed through the acrylic pipe, flow phantom, and vinyl chloride pipe in this order. An FBG sensor was attached on top of the flow phantom perpendicular to the direction of flow of the pseudo blood. The sensor part of an ultrasonic tomographic image measurement apparatus, installed parallel to the direction of flow of the pseudo blood and covering the FBG sensor as shown in Figure 2, captures the image of the inner details of the flow phantom [16].
\nSchematic of a biological mode.
During the flow of the pseudo blood, the changes in the inner diameter of the pseudo artery were measured by the FBG sensor and the tomographic apparatus. The FBG sensor signal for a flow rate of 30 mL of pseudo blood in 0.5 s, the diameter of the simulated artery from the tomographic image, and the result of the pressure gauge are shown in Figure 3. It is evident that the FBG sensor signal is closely similar to the diameter of the simulated artery and the pressure of the fluid. In addition, Figure 4 shows the FBG sensor signal for various conditions of the pressure of the pseudo blood. It is evident that the larger the pressure of the simulated blood is, the larger the amplitude of the FBG sensor becomes. In this way, it was confirmed that the FBG sensor signal could measure the variations in the artery diameter caused by the blood pressure. In other words, as the strain at the pulsation point is changed by the pressure of the blood flow, the magnitude of the strain change is measured by the FBG sensor; this observation indicates that the FBG sensor signal contains information on blood pressure.
\nResult of flow phantom diameter and FBG sensor signal.
Result of FBG sensor signal while changing the pressure.
In Section 3, it was found that the variation in diameter of the blood vessel could be measured by the FBG sensor. In this section, we discuss the relation between the FBG sensor signal and heartbeat by installing the FBG sensor at a pulsation point of the subject.
\nThe FBG sensor was installed perpendicular to the direction of blood flow in the radial artery at the wrist of the subject. An electrocardiograph (Nihon Kohden Corp., PVM-2701) was installed at the chest of the subject, and the electrocardiogram was measured simultaneously. The subject was male in their twenties, their posture at the time of measurement was supine, the sampling rate was 10 kHz, and the duration of measurement was 30 s. The peak-to-peak interval (PPI) of the FBG sensor signal and the R-to-R interval (RRI) of the electrocardiogram were calculated.
\nThe result of the RRI and PPI of the subject is shown in Figure 5, where the horizontal axis is the measurement time, and the vertical axes are the time intervals of PPI and RRI [17]. The heart rates of subject was ~51 times per min. It is evident that the RRI and PPI plots are almost identical for the subject. In other words, the FBG sensor signal corresponds to the heartbeat vibration as it represents the variation in the diameter of the arterial blood vessel caused by the flow rate (or pressure) that in turn is related to the heartbeat.
\nResults of RRI and PPI.
Since the FBG sensor corresponds to heartbeat vibration, the vital sign can be calculated from the FBG sensor signal. In this section, the pulse rate is calculated from the FBG sensor signal. An FBG sensor was installed perpendicular to the direction of blood flow in the radial artery at the left wrist of the subject. In order to measure the reference pulse rate, an electronic sphygmomanometer was installed at the upper arm of the subject. The measurements using the FBG sensor and the electronic blood pressure were performed at the same time. The peak interval (PPI) of the FBG sensor signal was measured, and the pulse rate per min was calculated from Eq. (2).
where, \n
A scatter diagram between the reference pulse rate and the pulse rate calculated from the FBG sensor signal is shown in Figure 6. The correlation coefficients for the subjects A, B, and C were observed to be 0.67, 0.87, and 0.56, respectively, while the respective measurement accuracies were 2.1, 2.6, and 1.9 bpm. These results indicate that the pulse rate could be measured from the FBG sensor signal with high accuracy. Thus, it is evident that the pulse rate can be calculated if the peak of the FBG sensor signal is detected accurately.
\nResult of calculated pulse rate from FBG sensor signal.
The pulse rate was calculated from the FBG sensor signal in Section 5.1. This section describes the measurement and calculation of respiration rate. When a person breathes, a physiological phenomenon called respiratory dynamic arrhythmia occurs, whereby the pulse rate rises during inspiration and decreases during expiration. In other words, the PPI decreases during inspiration, and increases during expiration. Therefore, when breathing is repeated, the PPI cycles up and down, and thus, the period of a breath cycle can be deduced from a PPI cycle. The respiratory rate per min is calculated from Eq. (3),
where, \n
Experiments on breathing rate were conducted with three subjects. To measure the reference respiratory rate, a medical face mask attached with a temperature data logger (Ishikura Shoten Co., Ltd., SK-L200 THII) was used. The purpose of the face mask was to prevent the breath from leaking out. Breath temperature is known to be higher than the atmospheric temperature. At the time of inspiration, the temperature of the atmosphere is measured with a data logger. On the contrary, as the breath temperature was measured at exhalation, the value was relatively higher. Thus, a constant periodic temperature change was measured every time the subject breathes. One cycle of this temperature change was used as reference respiration time, and the reference respiratory rate was calculated from Eq. (3). The FBG sensor signal was measured under the same conditions as those for the experiment on pulse rate presented in Section 5.1. The subjects were in sitting posture at the time of measurement.
\nThe results of the temperature data logger and PPI from the FBG sensor signal are shown in Figure 7. It is evident that both curves are very similar to each other. In addition, it could be confirmed that the cycle varies depending on the number of breaths. The respiratory rates calculated from the temperature data logger and the FBG sensor signal are plotted against one another in Figure 8 for the three subjects. The measurement accuracies observed for the three subjects were 0.4, 0.6, and 0.4 per min, which were considered to be reasonably good. The high measurement accuracy was observed even for different respiration rates of the same subject and for different subjects. The change in pulse rate (change in PPI interval) due to respiratory dynamic arrhythmia was very small; however, since the sampling rate of the FBG sensor was 10 kHz, it is considered that the calculated respiratory rate was accurate. This measurement method can calculate a respiratory rate in the range of 6–10 bpm; therefore, it is suitable for measurement of slow breathing (~12 bpm or less). It is evident from the above results that the high accuracy of measurement of respiratory rate is attributable to the high sampling rate.
\nResults of the PPI from the temperature data logger and the FBG sensor.
Result of calculating respiratory rate.
In this section, the blood pressure is calculated from the waveform of the FBG sensor signal. As shown in Section 3, the FBG sensor signal is measured representing the pressure of the blood flow that causes a change in the diameter of the blood vessel. Pulsation is a distortion that causes an arterial distortion on the skin surface. Therefore, information on blood pressure is considered to be present in the FBG sensor signal from which a distortion is measured.
\nA signal measured with a general photoelectric pulse wave sensor is a volume pulse wave signal indicating the volume of blood. A signal obtained by second derivative of the volume pulse wave signal is an acceleration plethysmogram. The basic shape of acceleration plethysmogram includes five peaks [18]. The A-wave to the E-wave are called initial systolic positive wave, initial contraction negative wave, mid-systole re-elevation wave, post-contraction descent wave, and expansion initial positive wave, respectively. Therefore, an acceleration pulse wave contains information on systole and diastole of the heart. The first derivative signal of the FBG sensor is similar to the acceleration pulse wave in shape [13]. Since an FBG sensor signal indicates the displacement of the Bragg wavelength due to strain, time change of the volume pulse wave is measured. Therefore, the first derivative signal of the FBG sensor is similar to the second derivative signal of the volume pulse wave signal. Furthermore, since the first derivative waveform of an FBG sensor signal includes information on the systole and diastole of the heart, the blood pressure can be calculated from this waveform.
\nWhen blood pressure was calculated from FBG sensor signal, PLS regression analysis, which is a widely known multivariate analysis among others, was used. The PLS regression analysis can construct calibration curves from the explanatory and objective variables. At this time, it is a feature to construct a calibration curve on the premise that an explanatory variable and an objective variable contain errors. The explanatory variable is the FBG sensor signal waveform, and the objective variable is the blood pressure measured simultaneously by the electronic sphygmomanometer. The FBG sensor signal is processed through the following steps [19].
The FBG sensor signal is processed with a band pass filter of 0.5–5 Hz.
The ‘A’ peak (‘A’ wave) is detected from the signal waveform of the band pass filter.
The signal is divided for each detected peak.
The divided signals are averaged.
At the vertical axis of the averaged signal, the first point (peak of ‘A’ wave) is normalized to “1”, and the lowest point (peak of ‘B’ wave) is set to “0”.
The horizontal axis of all FBG sensor signal waveforms processed up to the 5th term is cut out in the shortest time, and the length of the horizontal axis is unified.
The step 1 of the signal processing a range for covering a signal with a pulse rate of 30–300 times/min. In step 4 of the signal processing, considering that the measurement time of the electronic sphygmomanometer is ~30 s, the average is calculated for the pulse wave signals measured within that time. The step 5 of signal processing is to cancel the vertical axis fluctuations caused by pressure while installing the FBG sensor in humans. The step 6 of signal processing is to cancel the pulse rate fluctuations caused by respiratory sinus arrhythmia.
\nThe FBG sensor signal processed through the aforementioned signal processing steps is used as an explanatory variable, the blood pressure value of the electronic sphygmomanometer measured simultaneously is used as a target variable, and a calibration curve is constructed by PLS regression analysis. The newly measured FBG sensor signal is substituted into this calibration curve to calculate the blood pressure.
\nThis experiment was performed on three subjects. A schematic of the experimental blood pressure measurement is shown in Figure 9 [19]. The posture of the subject was supine, and the FBG sensor was installed at the pulsation point of the radial artery of the right wrist. The reference blood pressure value (objective variable) was measured simultaneously with the electronic sphygmomanometer installed at the left upper arm. Systolic and diastolic blood pressures were measured with an electronic sphygmomanometer. In the calculation of the systolic blood pressure, the signal-processed FBG sensor signal waveform was used as an explanatory variable, and the systolic blood pressure measured simultaneously with the electronic blood pressure monitor was used as the objective variable. Similarly, in the calculation of the diastolic blood pressure, the same FBG sensor signal waveform and the diastolic blood pressure measured simultaneously with electronic sphygmomanometer were used. The measurement time was 30 s, while the number of measurements was 75 times. Whereas 50 data points were used for construction of calibration curve, the remaining 25 data points were assigned to the calibration curve and used as verification data for blood pressure calculation. The target measurement accuracy was ±5 mmHg.
\nExperimental image of blood pressure measurement.
Table 2 shows the calibration curve construction data sets of systolic and diastolic blood pressures in each subject [19]. A calibration curve for calculating systolic blood pressure or diastolic blood pressure was constructed using the data sets of each subject. Table 3 shows verification data sets for calculation of systolic and diastolic blood pressures for each subject. The verification data set is substituted into the constructed calibration curve, and systolic and diastolic blood pressures were calculated. For example, a calibration curve was constructed with 50 data points of the systolic blood pressure of the subject A; the 25 data points of the systolic blood pressure of verification data of the subject A were substituted into the calibration curve, and the systolic blood pressure of subject A was calculated. Similarly, a calibration curve was constructed with 50 data points of the diastolic blood pressure of the subject B in Table 2; the 25 data points of validation data of the diastolic blood pressure of the subject B in Table 3 were substituted into the calibration curve, and the diastolic blood pressure of subject B was calculated.
\nSubject | \nNumber | \nMax (mmHg) | \nMin (mmHg) | \nAve (mmHg) | \n
---|---|---|---|---|
(a) Systolic blood pressure data set | \n||||
A | \n50 | \n125 | \n100 | \n111.3 | \n
B | \n50 | \n136 | \n113 | \n123.1 | \n
C | \n50 | \n111 | \n93 | \n100.9 | \n
(b) Diastolic blood pressure data set | \n||||
A | \n50 | \n79 | \n46 | \n62.7 | \n
B | \n50 | \n80 | \n56 | \n68.2 | \n
C | \n50 | \n60 | \n46 | \n53.7 | \n
Calibration curve construction data sets.
Subject | \nNumber | \nMax (mmHg) | \nMin (mmHg) | \nAve (mmHg) | \n
---|---|---|---|---|
(a) Systolic blood pressure data set | \n||||
A | \n25 | \n131 | \n100 | \n110.1 | \n
B | \n25 | \n138 | \n112 | \n122.2 | \n
C | \n25 | \n110 | \n93 | \n100.4 | \n
Average systolic blood pressure in data set (mmHg) | \n110.9 | \n|||
(b) Diastolic blood pressure data set | \n\n | |||
A | \n25 | \n77 | \n46 | \n60.3 | \n
B | \n25 | \n76 | \n58 | \n68.0 | \n
C | \n25 | \n65 | \n39 | \n53.1 | \n
Average diastolic blood pressure in data set (mmHg) | \n60.5 | \n
Verification data set in each subjects.
Figure 10 shows a scatter plot of reference blood pressure and calculated blood pressure during systole and diastole of each subject. Table 4 shows the results of blood pressure calculation, whereby it was observed that the calculation accuracy of systolic and diastolic blood pressures were ±5 mmHg, and it was calculated with the same blood pressure value as that of a commercially available blood pressure monitor. In the case of the systolic blood pressure, the average value of the verification data for blood pressure calculation was 110.9 mmHg, while the average value of the calculation accuracy was 2.9 mmHg (~2.6%). Similarly, in the diastolic blood pressure, the average value of the verification data for blood pressure calculation was 60.5 mmHg, while the average value of the calculation accuracy was 2.8 mmHg (~4.7%). These results indicate that the calculation accuracy of diastolic blood pressure is lower than that of systolic blood pressure. This is due to step 6 of signal processing, whereby the canceling of the pulse rate fluctuation was performed in order to calculate the FBG sensor signal by the PLS regression analysis. The so-called latter half portion of a single beat of the FBG sensor signal is truncated. There is a peak of expansion initial positive wave in this part, which represents the diastole of the heart. Therefore, it is considered that the deletion of the diastolic information from the FBG sensor signal caused a decline in the calculation accuracy. In other words, in this calculation method, since the negative characteristics of the signal processing is reflected in the result, the blood pressure is calculated from the movement of the heart included in the FBG sensor signal. However, considering that the results of all blood pressure calculations were ±5 mmHg, it is considered that the blood pressure was calculated with high calculation accuracy. Therefore, it is established that the blood pressure can be calculated with high calculation accuracy by constructing the calibration curve by PLS regression analysis of the waveform of the FBG sensor signal. It is evident from this result that it is possible to calculate blood pressure from the same FBG sensor signal in addition to pulse rate, respiratory rate, and stress load.
\nScatter plot of reference blood pressure and calculated blood pressure during systolic and diastolic. (A) Calculated result in systolic blood pressure. (B) Calculated result in diastolic blood pressure.
\n | Subject | \nA | \nB | \nC | \nAverage | \n
---|---|---|---|---|---|
Calculating accuracy (mmHg) | \nSystolic | \n3.4 | \n2.8 | \n2.6 | \n2.9 | \n
Diastolic | \n4.1 | \n1.8 | \n2.6 | \n2.8 | \n
Results of calculating blood pressure.
In this paper, the method of calculating each vital sign from the FBG sensor signal was described. An experiment using a biological model demonstrated that the FBG sensor signal was influenced by the change in diameter of the tube through which a fluid (pseudo blood) was allowed to flow by a simulated pressure. This model was replaced with a living body, and a change in the diameter of the artery caused by a change in the flow rate of blood related to the movement of the heart was measured. The FBG sensor signal was measured at a high temporal resolution of 10 kHz; therefore, the pulse rate could be calculated with a high measurement accuracy. Based on the phenomenon of respiratory sinus arrhythmia, the respiration rate could be calculated from the cycle of pulse rate change during expiration and inspiration. On the other hand, the first derivative waveform of the FBG sensor signal was found to be similar to the acceleration pulse wave, which was the second derivative of the volume pulse wave signal; therefore, it was considered that the systolic and diastolic information of the heart was present in the signal. Subsequent to the signal processing of this primary derivative waveform of FBG sensor signal, a calibration curve was constructed by PLS regression analysis for calculating the blood pressure, and accordingly, the blood pressures (systolic and diastolic) were calculated with high accuracy. All these vital signs were calculated from a measured FBG sensor signal. Therefore, considering that only the analysis method is different, it is possible to calculate a plurality of vital signs simultaneously from one measurement signal. Since the FBG sensor signal is continuously measured, the vital signs can also be calculated on a continuous basis. In addition, the signal is measured simply by installing the FBG sensor at the pulsation point. Apart from the wrist, there are other pulsation points such as at the elbow, neck, temple, and ankle of the living body. Therefore, it is possible to select a part of interest that can be easily measured. Since the FBG sensor is an optical fiber, it can be introduced into a textile product. Cover the optical fiber with multiple silk thread in technic of braid. Unevenness is formed on the surface of the optical fiber by this silk thread, and frictional force is occurred. The optical fiber covered with this silk thread is embedded into the knitted fabric by inlay knitting. With this method, the optical fiber can be fixed without slipping in the knitted fabric. If FBG sensor can be introduced into textile products, it can be applied to wristbands, long-sleeved shirts, socks, and various other products. However, there are issues such as miniaturization of the FBG interrogator.
\nThere are two approaches to applying the FBG sensor system in the clinical setting. First method, place the current FBG interrogator in the corner of the room, extend the optical fiber and install the FBG sensor in the hospitalized patient. With this method power supply can be supplied stably. The second method is to develop a compact FBG interrogator that can be attached to the human body. We are developing the 1/12 downsizing prototype FBG interrogators (74 × 97 × 57 mm, 175 g) using commercially available optical components. This interrogator is battery-powered and wirelessly communicates measurement signals. If electronic circuits etc. are changed from commercial type to specialization type, further size down is predicted. This prototype FBG interrogator can be operated at minimum 5 hours with battery. However, it is necessary to take measures against the temperature caused by heat. In our study, we employed the FBG sensor commonly used in applications ranging from infrastructure to living organisms. While we used only one FBG sensor for all our experiments, the length of the optical fiber was also very short. The currently available commercial FBG interrogators have been specifically developed for use in large-scale applications employing many sensors. Our research is a highly original theme that is entirely different from the current application areas of FBG sensors. An FBG sensor can measure a distortion on a continuous basis with high accuracy and sensitivity, and the sensor section is compact owing to the use of optical fiber. Furthermore, it facilitates measurement of a pulse wave signal by a very simple method of fixing it at a pulsation point. Except for a few issues, the FBG sensors having such characteristics are expected to be used as wearable sensors successfully in future.
\nCurrently, there are very few products that can continuously measure blood pressure without using a cuff. In addition, pulse rate and stress load can be detected with commercially available wearable sensors. However, at the same time, there is no wearable sensor using which both breathing rate and blood pressure can be calculated. This is because of the issues related to the time resolution and detection sensitivity of the measurement signal. In contrast, the FBG sensor can calculate these vital signs with high accuracy. It can be used as a precious blood pressure monitor. Moreover, considering that hospitalized patients need to be monitored on a continuous basis in respect of the vital signs, it would be a major breakthrough if the sensors can be installed in textile products for ease of handling and installation. The above discussions lead to the conclusion that it is possible to use an FBG sensor for monitoring of vital signs with high accuracy.
\nThis work was supported by JSPS KAKNHI Grant Number JP16H01805 and the Wearable vital signs measurement system development project at Shinshu University. This research is (partially) supported by the Creation of a development platform for implantable/wearable medical devices by a novel physiological data integration system of the Program on Open Innovation Platform with Enterprises, Research Institute and Academia (OPERA) from the Japan Science and Technology Agency (JST).
\nThe authors declare no conflict of interest.
Past efforts in epidemiological (nuclear power industry, atomic bomb explosions, nuclear reactor accidents, etc.) and clinical (diagnostic imaging, radiation oncology, radiation therapy planning etc.) research strongly contributed to the current understanding of ionizing radiation effects on human organs, tissues, and cells [1, 2]. In principle radiation biology is based on effects of instantaneous (10−18 s) [3], stochastic damaging interactions of ionizing radiation with cells, a main target being the genetic material, i.e. chromatin in the cell nucleus [4]. In this context, radio-sensitivity and radio-resistance as opposing terms describe the extent of individual cellular susceptibility or ‘response’ upon radiation exposure which are highly dependent on physical (e.g., radiation type, dose, dose rate, etc.), chemical (e.g., hydroxyl radicals, etc.) and biological (e.g., developmental and proliferative state of the affected cell type) factors. As the overall organismal radiation response results from the entirety of all individual radiation responses on the single cell level, deeper understanding of the underlying, complex molecular mechanisms and dynamics of radiation induced DNA damaging and repair on the cellular level is highly relevant for fundamental and applied radiation biology (for review see [1, 2, 5]).
\nHence, cytometric analyses based on fluorescence microscopy have become the method of choice to study damaging effects of ionizing radiation and DNA repair. This has contributed a lot to today’s knowledge. However, conventional fluorescence microscopy is limited to average lateral resolutions around 200 nm laterally and 600 nm axially [6] and thus is limited to the bulk analysis of molecular cellular processes and structures. In parallel, super-resolution fluorescence microscopy techniques have rapidly evolved during the last few decades and turned out to be powerful tools to study cellular structures and molecular architectures on the nanoscale (for review see [6, 7, 8]). Methods based on stochastic reversible photobleaching [9, 10, 11, 12, 13, 14, 15] of single molecules called Single Molecule Localization Microscopy (SMLM) [16] reach effective resolutions down to 10 nm and have become popular among modern super-resolution imaging techniques as their realization is highly practical and straightforward using established specimen preparation methods of standard fluorescence microcopy [17]. As such resolutions allow the detection of single molecules, such as nucleosomes [18], proteins [19, 20], receptors and junction proteins [21, 22], or even single chromatin loops [23] etc., super-resolution microscopy opens new avenues for the research of radiation induced damaging and repair processes [5, 24].
\nWith this article, we attempt to introduce the novel SMLM approach to radiation biophysics and radiation biology. We start with a brief summary on the basics of ionizing radiation, induction of DNA damage and damage repair mechanisms, to follow up with some standard radiobiology analysis methods. We further provide an overview of the working principles of selected sub-diffraction microscopy techniques with a focus on SMLM. Finally, the successful application of localization microscopy in radiation biology research is demonstrated along examples of current works.
\nIonizing radiation penetrates through material and deposits enough energy to ionize molecules or atoms by liberating electrons. The effects of ionizing radiation on biological materials are highly dependent on the dose, the dose rate and type of radiation. In living cells, ionizing radiation hits all kinds of biomolecules, such as desoxyribonuleic acids (DNAs), aminoacids (proteins), lipids (membranes), carbohydrates, etc. However, most harmful consequences to living organisms show damages inflicted to their genomic DNA, especially in the form of DNA double-strand breaks (DSBs) [25, 26]. Especially follow-up effects of false strand repair may lead to significant dysfunctional development as for instance tumor genesis.
\nIonizing radiation (IR) includes all high energy/speed (> 1% speed of light) ions (e.g. carbon ions), atom nuclei (e.g. alpha particles), subatomic particles (e.g. beta particles, protons or neutrons) and high-energy electromagnetic waves (e.g. high energy ultraviolet (UV) rays, X-rays and gamma rays), that carry enough energy to directly or indirectly ionize atoms or molecules by liberating electrons from them, and to break molecular bonds [27].
\nThe most common types of ionizing radiation occurring under environmental circumstances are caused by radioactive decay and can be divided into three groups: alpha, beta and gamma radiation [27]. Alpha radiation is made up of particles comprising two protons and two neutrons (helium nucleus) that carry energies in the range of up to several MeV. Due to its large particle size, alpha radiation has the lowest penetration depth through biological materials and the highest energy deposition per distance traveled. Beta particles are made up of electrons or positrons, thus exerting higher penetration depths and lower energy depositions compared to alpha particles. Gamma rays are high energy electromagnetic waves that exhibit the highest penetration and compared to particles, lowest energy deposition per track unit in biomaterials among these three types of IR. Due to the dispersed and low energy deposition in tissue, gamma and beta radiation are often referred to as low linear energy transfer (LET) radiation, whereas alpha particles belong to high LET radiation [27].
\nFor clinical diagnosis and therapy in radiology or radiation oncology [28, 29], typically artificial radiation sources are applied, as for instance to produce X-rays in the energy range of keV to MeV, electrons and positrons, protons, and heavy ions (carbon or nitrogen). Like alpha particle, protons and heavy ions belong to high LET radiation. The advantage of protons and especially heavy ions is based on the characteristic absorbance with a Bragg peak at the end of the particle track where most of the particle energy is deposited. This energy positioning peak can exactly be localized in the tumor volume so that intact cells and tissues in the tumor surroundings are excluded from radiation damaging [30].
\nThe absorbed dose D of ionizing radiation is quantified by the amount of energy deposited per unit mass of the penetrated material and is measured in units of Joule per kilogram (J/kg) or Gray (Gy) [27]. It describes an universal energy absorption for all types of ionizing radiation and is most commonly used in radio-physical research, whereas a radiation type specific dose also called the equivalent dose H calculated by multiplication with a weighting factor WR (e.g. WR = 1 for gamma radiation and WR = 20 for alpha radiation) is often used in radio-biology, radio-medicine, or radiation protection and safety. The equivalent dose can be further weighted by a tissue weighting factor WT to result in the effective dose E, which describes radiobiological effects considering the used radiation type and the tissue/organ of interest. Both, the equivalent dose and effective dose are quantified in units of Sievert (Sv) and do not represent physically measurable quantities but rather a value based on clinical and epidemiological outcome that is typically used in radiation safety [31].
\nAmong all kinds of ionizing radiation induced biological effects, damages to chromatin especially the DNA molecules in the nucleus of cells are thought to be the most severe with respect to cellular survival and carcinogenesis [2, 5, 32, 33]. DNA base oxidation, single strand breaks (SSBs) and double strand breaks (DSBs) are the most common ionizing radiation induced damages to the DNA molecule, that affect genome integrity and DNA biochemistry [34].
\nDSBs of DNA belong to the most complex and severe types of DNA damages as they directly affect genome integrity and the way of cellular survival [35, 36, 37]. Single strand breaks (SSBs) induced by ionizing radiation and base damages occur more frequently than double strand breaks [34]. It can be estimated to about 40 DSBs/Gy and about 1,000 SSBs/Gy. SSBs are less severe to genome integrity as an intact template strand is still available for complementarity-aided, error-free repair of the lesion. But DSBs are also simply formed by two or more opposing SSBs in close proximity or combinations of different DNA damage types [26].
\nInduction of DSBs in native chromatin is rapidly followed up by phosphorylation of nearby histones of the H2A variant H2AX at serine residues at position 139 [38]. This results in the generation of plenty γH2AX molecules around a DSB damage site, where about 2 Mbp of DNA are usually phosphorylated [39]. This leads to the formation of focus structures of sizes in the range of micrometers, which can be visualized under a fluorescence microscope [40]. These phosphorylated histones serve as signal and anchor points for many downstream recruited proteins of certain DNA damage response and repair machineries [41]. As the number of γH2AX foci is quantitative for DNA damage, counting of specifically labeled foci has been established as a measure for dose-efficiency and correlated to cell survival [42].
\nSingle ionizing radiation induced DNA lesions can be caused by direct or indirect hits [43]. Ionizing radiation penetrating through a cell nucleus can hit and ionize atoms in a DNA molecule itself with a certain probability. However, the most prominent primary reaction underlying all ionizing radiation induced DNA damages is the radiolysis mediated formation of reactive oxygen species (ROS), e.g. •OH radicals, O2•- radicals and H2O2, which can further inflict reducing damage and thus lesion to the DNA [44]. Ionizing radiation, especially high LET radiation, is known for its property to efficiently induce highly complex damages to DNA. Such complex DNA damage sites composed of multiple lesions in close proximity on both strands are also termed locally multiple damage sites (LMDS) [45].
\nLiving organisms developed highly efficient and customized ways to repair the severe damages inflicted to their genome. The DNA DSB sites are rapidly (within seconds to minutes) recognized and marked by proteins of an initial response, which serve as signals and docking sites for more specialized proteins of DNA repair pathways. The fate of repair type depends on the concerted presence of pathway specific damage response proteins [1, 2, 46, 47, 48]. The main two ways by which cells respond to DNA double-strand breaks are non-homologous end joining (NHEJ; also called canonical NHEJ = cNHEJ) and homologous recombination (HR). NHEJ mediated DSB repair is fast and can be error-prone, but it can be flexibly performed throughout all cell cycle phases. HR works error-free, but is mostly restricted to late S and G2 phases as a homologous sister chromatid is required as a repair template [49, 50, 51, 52]. Recent data, however, have suggested that active genes may employ HR also in G1 phase, by utilizing the nascent RNA as a template for precise repair (reviewed in [53]). As the DNA-end resection is inhibited in G1 cells, an alternative model with cNHEJ taking the advantage of the same principle (RNA-templated repair) has also been proposed. Interestingly, DNA repair by HR is preferred in lower eukaryotic life forms, whereas NHEJ is predominantly observed in mammalian organisms. Alternative low abundant DSB repair pathways are the alternative end joining pathways (a-NHEJ; also called back-up EJ), micro-homology mediated EJ (MMEJ) and single strand annealing (SSA). One main difference between all DSB repair mechanisms is the extent of initial DNA end-resection at the damage site [26, 54, 55, 56, 57, 58, 59]. The DNA damage response (DDR) against DSBs is subject to intensive radiobiological investigation and fluorescence microscopy of in situ DSB repair proteins serves as state of the art biological dosimetry.
\nAfter the induction of a DSB, damage response proteins are rapidly recruited and accurately determine the fate of the DSB towards a repair pathway that best deals with the damage site in a certain genomic and cellular context. The chromatin remodeling p53-binding protein (53BP1) protects the break site from extensive end resection [60], thereby promoting repair by non-homologous end joining [61], whereas BRCA1 facilitates extensive end resection for repair by homologous recombination [52, 62].
\nThe NHEJ repair pathway is initiated with binding of the Ku70-Ku80 heterodimer complex to the DNA ends of the DSB site, which serves as a linkage between damage site and further damage response proteins [61, 63]. In a second step, DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is recruited to the Ku complex forming the DNA-PK complex. On-going recruitment of X-ray complex (XRCC4)/DNA Ligase IV (X4LIG4) complex and XLF to the DNA-PK complex forms the core NHEJ complex [64]. DNA-PKcs sterically protects the break site for repair and phosphorylates other repair proteins [65, 66] and H2AX [41]. Furthermore, DNA-PK auto-phosphorylation results in a conformational change of the core complex, thereby enabling DNA end processing by nucleases and dissociation of the DNA-PKcs subunit [67, 68]. Finally, ligation of the DNA ends is mediated by the X-ray complex (XRCC4)/DNA Ligase IV (X4LIG4) complex and XLF [69, 70, 71]. Artemis endonuclease [72, 73], polynucleotide kinase (PNK) [74], DNA polymerase (pol) μ and λ can be additionally involved in NHEJ repair depending on the chemical properties of the DNA damage site [75].
\nTo initiate repair by HR, the free damaged DNA ends at the DSB site must be sensed and bound by a protein complex comprised of MRE11, RAD50 and NBS1 (MRN complex) [76]. Next, the protein kinase Ataxia Telangiectasia Mutated (ATM) [77] is recruited to the MRN complex at the damage site [78], which auto-phosphorylates and phosphorylates components of the neighboring chromatin. Most prominent phosphorylations are those of the histone variant H2AX (γH2AX), one of the earliest and a very sensitive marker of cellular response to DSBs [38]. End resection is initiated by the single-strand endonuclease and exonuclease activity of the Mre11 protein [52, 79] of the MRN complex. RAD50 further stimulates Mre11 nuclease activity and Nbs1 interacts with CtIP [80], another protein that is essential for the initiation of MRN complex mediated end resection [81]. Exonuclease 1 (Exo1) and Dna1/BLM are recruited by CtIP to continue end resection [82, 83, 84] until it gets attenuated by RPA coating of resected ssDNA ends [85]. BRCA2 in combination with BRCA1 and PALB2 dismantles the ssDNA ends from RPA coats enabling binding and forming of the RAD51 nucleoprotein filament, which stimulates homology search and strand invasion [86]. Sister chromatid strand recombination via Holiday junctions is further facilitated by RAD54A and its paralog RAD54B [87, 88], finally resulting in conservative repair of the DNA lesion.
\na-NHEJ or b-NHEJ has been described in slightly different ways which are not well distinguished [56, 57, 58]. Mostly, in the presence of short micro-homologies (>4 bp) after CtIP-MRN mediated end resection, repair via an alternative end joining (MMEJ) can take place [89]. This is initiated by Poly(ADP-ribose) polymerase 1 (PARP1) and followed up by DNA polymerase θ (pol θ) mediated strand extension starting at the paired micro-homology site. Ligase1 and Ligase2 are supposed to perform the final ligation of DNA ends [90, 91].
\nWhen the damage site is flanked by larger regions with non-allelic sequence homologies, repair by single-strand annealing is also possible. The absence of Ku proteins and even more extensive end resection to expose the homologous regions as single strands are necessary for SSA repair [92]. Again, RAP binding to the resected ends promotes RAD52 mediated annealing of homologous regions. Nuclease XPF-ERCC1 trims the remaining non-homologous overhangs and DNA Ligase 1 connects the DNA ends [93].
\nSeveral studies indicate, that damaged genomic Alu elements use micro-homologies for single-strand annealing, thereby often leading to translocations [94, 95]. Such nonconventional damage repair processes might explain a significant portion of the observed deletion events associated with malignancies [59]. In fact, in vitro model systems could already demonstrate Alu mediate non-allelic homology dependent DSB repair [96].
\nRadio-sensitivity can be assessed on different scales ranging from whole organs and tissues over single cells to molecular markers and mechanisms. Based on clinical and experimental findings on DNA damage induction and response mechanisms, the dose effect of ionizing radiation on biological material is commonly described with a linear-quadratic model [97, 98]. At low dose ranges (below 1 Gy), radiation damages are supposed to linearly increase with the applied dose, whereas at higher doses the probability for multiple hits increases and complex DNA damage spots dominate.
\nIn this chapter, we summarize some established methods to study cellular and molecular effects of ionizing radiation. In the past, sophisticated assays were developed to detect and quantify radiation induced damages to the cell and nuclear DNA ranging from techniques to assess overall cell survivability, large-scale chromosomal damages and rearrangements over sensitive detection of DNA break sites to modern state of the art technologies that can visualize the formation of damage foci in situ with the help of suitable biomarkers.
Colony forming assays (CFAs) based on clonogenic survival (also called clonogenic assay) (see [99] and citations therin) as a method to quantify cell survival after radiation exposure was firstly described by Puck and Marcus in 1956 [100]. CFAs measure the ability of cells to divide after treatment with agents that impair cellular reproduction, e.g. radiation (Figure 1). Since then, colony forming assays were improved for many different cell types and are widely used as a “gold standard” in radiobiological studies. In practice, irradiated cells are plated at higher dilutions so that single cells are well separated. Upon incubation colonies form each originating from a single cell. Thereby, colonies comprising 50 cells or more are considered for estimating the survival fraction. Treatments such as exposure to ionizing radiation damages the reproductive survivability of cells and thus results in a lower number of colony formation events at the same number of plated cells [101].
A fraction of ionizing radiation induced DNA double strand breaks results in heavy genomic rearrangements that can be detected on metaphase chromosomes. False rearrangements of multiple centromeric regions between chromosomes can lead to dicentric, acentric, centric ring conformations [104, 105] that can be visualized under a conventional fluorescence microscope (Figure 2a). The good reproducibility and comparability lets the so called dicentric assay stand among the gold standards of biological dosimetry [106]. Nowadays dicentric assays are further developed towards biological dosimetry in the low dose range (< 500 mGy).
The micronucleus test is a method to assess and detect chromosomal breakages in interphase nuclei developed by Schmid et al. in 1975 [107]. Radiation damage can result in major chromosomal aberrations and loss on the centromeric region by wrong rearrangement of DNA double strand breaks (Figure 2a). These heavily damaged acentric chromosomes can form separated globular structures outside the main nucleus in interphase. As micronucleus formation can be readily detected in interphase nuclei, analysis can be performed much faster and serves as an efficient alternative for the analysis of instable chromosome aberrations [108].
In 1984, Ostling and Johanson published a micro-electrophoresis technique that could visualize DNA damages in single cells [109]. First, cells are embedded in agarose and lysed with non-ionic detergents under high salt concentrations, so that only nucleoids (supercoiled DNA loops attached to the nuclear matrix) remain. Ionizing radiation-induced breaks relax and locally unwind the supercoiled DNA structure, thereby partly linearizing the strand at the break site. When voltage is applied linearized DNA segments (SSB) protrude from the nucleoid and migrate faster towards the anode while the nucleoid core remains assembled. The nucleoid and its tail resemble a comet, when stained with 4′,6-diamidino-2-phenylindole (DAPI) or other quantitative DNA fluorescence dyes and visualized under the fluorescence microscope, thus leading to the term ‘comet assay’ [110, 111]. Alkaline variations of the comet assay [110] were introduced, that can detect DNA damages over an extended dose range (0.25 Gy to 2 Gy) than under neutral pH conditions (1 Gy - 3 Gy) [111]. Modern approaches extend the method by automatization of experimental procedures and image analysis [112, 113, 114], thereby enabling statistically robust high-throughput detection of DNA damages for potential clinical applications.
DNA damage response proteins like γH2AX, 53BP1, RAD51 etc. accumulate at initial damage sites and rapidly form foci-like structures in the nucleus (see for example Figure 2b). Antibody staining and fluorescence microscopy of such damage response proteins is an established tool to visualize and quantify DNA damage repair foci at single cell resolution. One advantage of this technique is the ability to assess molecular dynamics of DNA damage repair by visual observation of foci formation at different time points after irradiation.
(A) Example of colony formation after cell exposure to different doses of X-ray irradiation. (B) Typical survival curves for cell colonies after irradiation with different types of photon and particle radiation. Linear-quadratic cell survival curves are fitted and can be used to calculate the relative biological effectiveness. Note: These figures are modified and were originally published under CC BY license in [102, 103].
(a) Example of a lymphocyte metaphase plate with centromeres highlighted by FISH. The cells were irradiated with 3 Gy X-rays. The big arrows show two dicentric chromosomes. The small arrow heads label the corresponding acentric fragments. (b) Typical examples of fibroblast nuclei (stained with a specific DNA dye) with γH2AX foci after exposure to high dose irradiation. The foci are labeled by specific antibodies.
Fluorescence microscopy of potent marker labels is a powerful analysis tool to assess cellular effects of ionizing radiation on the single cell level by optical examination. Due to past efforts, a myriad of fluorescent probes exists for the molecular labeling of almost any known biological target structure (e.g. specific antibodies against γH2AX, 53BP1, MRE11, RAD51 or other repair proteins as well as against heterochromatin or euchromatin etc.). This opens the door to analyze molecular mechanisms underlying fundamental biological functions by optical investigation, e.g. DNA damage response and repair dynamics upon ionizing radiation exposure [1, 2, 5].
\nA variety of novel super-resolution microscopy techniques were invented in the last few decades [115]. With the help of novel super-resolution microscopy techniques, the molecular effects of ionizing radiation in single cells can be studied on the nanoscale. Nano-labeled molecular structures can be resolved in biological specimens down to a precision of 10 nm (1/50 of the wavelength of visible light), which is in the range of single nucleosomes, antibodies, receptors, etc. (see for example [20, 21]).
\nIn order to improve the resolution in light microscopy, a prerequisite is to circumvent the diffraction limit of light, a physical phenomenon firstly described by Ernst Karl Abbe and John William Strutt, 3rd Baron Rayleigh, during the late 19th and early 20th century [115]. In diffraction limited fluorescence/light microscopy, the Abbe or Rayleigh criterion (Figure 3) is commonly used to define a resolution measure describing the minimal distance D between two point-like light sources with wavelength λ that can be resolved:
\nRayleigh criterion for the diffraction of two point-like light sources and single-molecule localization microscopy techniques to circumvent the diffraction limit. (A) The resolution limit of two adjacent point-like sources of light is defined by the distance between these two light points; the first intensity minimum of one light point overlaps with the main intensity maximum of the other light point. (B) The diffraction limited resolution of fluorescence microscopy illustrated by an example of three point-like signal sources within a distance below the resolvable range. (C) Working principle of SPDM by spectral isolation of labeling molecules. Here the spatial positions of three point-like fluorescent light sources can be separated by three different colors green, red and blue (from left to right). (D) Working principles of most single molecule localization microscopy methods rely on spectral modulation that switches most fluorophores into a dark state in a stochastic manner. Thereby, detection of only a sparse subpopulation of labels that are either totally isolated or lie apart at distances greater than the diffraction limit is possible. A series of acquisitions, each representing another stochastic sparse subpopulation of signals, can be summarized to result in a complete image below diffraction limit. (E) Minimal Jablonski diagram showing the electronic states and transitions involved in the intrinsic stochastic blinking of fluorophores. Note: These figures are modified and were originally published under CC BY license in [116, 117].
Therein λ is the wavelength and NA the Numerical Aperture of the objective lens (NA = n sin(α/2); n = refraction index; α = lens aperture angle). Conventional fluorescence microscopy techniques that use objective lenses with high numerical aperture NA (≥ 1.4) are available today. In confocal laser scanning microscopes, they typically achieve resolutions down to 200 nm in lateral and 600 nm in axial direction. However, modern super-resolution microscopy using the same objective lenses circumvent this physical limit by sophisticated interaction with fluorescence signals so that they can visualize biological specimen down to resolutions in the order of 10 nm, which is in the range of single nucleosomes, antibodies, receptors, etc. [16].
\nA complete overview of super-resolution microscopy techniques is beyond the aim of this article. However, we want to mention some meanwhile very well established ones:
\nSophisticated near-field super-resolution methods, e.g. total internal reflection fluorescence (TIRF) microscopy (TIRFM) [118, 119] or near-field scanning optical microscopy (SNOM, NSOM) [120, 121], belong to the first techniques breaking the diffraction limit by novel techniques working in the optical near field of fine crystal tips probing the specimen without an microscope objective lens. Unfortunately, near-field techniques are technically restricted to the visualization of surfaces of cells, membranes or isolated organelles [122, 123, 124].
\nMore recently evolved far-field super-resolution fluorescence microscopy techniques use objective lenses available from establishes microscope manufactures and can be separated into two principle approaches. The first is based on the spatially modulated excitation of fluorophores, e.g. by point spread function engineering as in stimulated emission depletion (STED) [125] or by excitation through a series of illumination patterns as in structured illumination microscopy (SIM) [126]. A second group of super-resolution techniques is based on optical isolation of fluorescent molecules through switchable intensities [17] or intrinsic differences in spectral signatures [127]. The latter techniques often referred to as single molecule localization microscopy (SMLM) in general, can be practically implemented with customary microscope parts and standard objective lenses [16]. Spectral precision distance microscopy (SPDM) an early development of the 1990s [128] is the one and only localization microscopy method, that establishes optical isolation of molecular labels through constant differences in absorption and emission spectra of different fluorophores, that are applied in a combinatory labeling strategy [127, 129]. Most localization microscopy methods, however, rely on stochastic spectral modulations of single fluorophore molecules, such as photo-activated localization microscopy (PALM) [12], fluorescence PALM (FPALM) [13], stochastic optical reconstruction microscopy (STORM) [15, 130], direct STORM (dSTORM) [131], ground state depletion microscopy followed by individual molecule return (GSDIM) [132], SPDM with physically modifiable fluorophores (SPDMphymod) [14, 17], etc. In the following chapters, we will describe single molecule localization microscopy in more details as being applied in radiation biophysics and we will provide examples indicating wide applications in nano-probing biomolecules and molecular mechanisms.
\nSingle molecule localization microscopy is one the most popular super-resolution techniques, because it can be practically realized with standard optical setup and standard specimen preparation methods using commercially available fluorophore labels. Fundamental to all SMLM techniques is the stochastic sampling of signals. The intrinsic blinking nature of a variety of available fluorophores at excitation with high laser powers (in the range of several kW/cm2), enables SMLM with conventional dyes like GFP / YFP, Alexa488, Alexa568, etc. [16, 17, 134].
\nApart from conventional fluorescence, which is based on rapid, repetitive excitation (10–15 s) and red-shifted emission (10–9 s) between the ground state S0 and excited singlet state S1, fluorescent molecules additionally undergo intersystem crossing (ISC) [135] from S1 to dark triplet states T1 [136]. From there, fast relaxation (10–3 s) to the original S0 ground state enables re-entry to new cycles of normal fluorescence (Figure 3E, left). Further transition from the T1 state into a second dark state D also occurs, which takes longer (ms to min) to recover to the ground state S0 (Figure 3E, right) [116, 137]. This reversible photobleaching via the long lived dark state D results in a limited number of stochastically blinking fluorochromes at resolvable time scales that can be used in single molecule localization microscopy to determine sub-diffraction positions of single fluorescing molecules [134].
\nFor image acquisition, a time series of raw diffraction limited images (several hundreds to a few thousand) from the same region of interest are registered and efficiently searched for blinking events under a user-defined intensity threshold to discriminate signals from background. Then, the intensity profile of each blinking event is fitted by a Gaussian curve and the barycenter point of the signal source is calculated. Notably, the localization precision of such a point merely depends on its intensity/background ratio [138].
\nA major advantage of SMLM approaches lies in the data format. The point matrix containing the lateral x and y coordinates of each localization signal allows all kinds of mathematical and statistical analyses (Figure 4). Most prominent are analyses based on Ripley’s point-to-point distance information which can be used for the elucidation of signal densities, cluster formation, and spatial organization of labels [139]. Recently, novel mathematical approaches like persistent homology determinations were introduced to investigate topological similarities [133]. Computation of the coordinate matrix into an image with user-defined resolution and visual enhancements is then possible. If provided, multi-color analyses on the single molecule level can be performed to study more complex molecular mechanisms and dynamics.
\nGeneral workflow of single molecule localization microscopy and data analysis. Serial images are acquired from the same region of interest (i). The point-spread function of each blinking event in each single image is gauss fitted to estimate the intensity maximum (ii), which represents the idealized lateral coordinates of the signal source (iii). The result is a data table containing the coordinates of all detected signal points. The matrix representation of data allows mathematical and stiatistical analysis of clustering, distance distributions, signal densities, multi-color signal distributions, enhanced visualization and topology (iv). Note: These figures are modified and were originally published under CC BY license in [16, 117, 133].
Phosphorylated histone variant γH2AX molecules at the site of DSBs and their accumulation into γH2AX-foci are well-established markers of DNA damage response and repair. Most recent studies performed SMLM of γH2AX specific antibodies in HeLa cells that were exposed to different doses of γ-radiation and fixed at different time points after radiation exposure [140]. Quantitative analysis resulted in a linear quadratic increase in measured γH2AX localization signal points and cluster numbers with increasing doses of radiation exposure (Figure 5A,B). With increasing repair time, the number of γH2AX clusters decreases; thereby successfully demonstrating repair dynamics and cell recovery by γH2AX-cluster relaxation on the molecular level. As dose responses and molecular dynamics for γH2AX clusters and raw γH2AX signal points well correlate with past observations, this study can serve as a benchmark standard for future super-resolution radiobiology experiments.
\n(A) “Visualization of cluster formation from the SMLM image of cell nuclei after 2 Gy radiation exposure. Left column: Density image obtained from the coordinate matrix and the next neighbor distance. The point intensity (see intensity scale bar) refers to the next neighbor frequency. Right column: Resulting clusters. The points belonging to a cluster are represented by a closed area (colored spots) and reflect nano-clusters within γ-H2AX foci. Top > Bottom: 30 min, 1 h, 3 h, 8h post irradiation. (B) Numbers of γ-H2AX clusters per cell vs. dose and repair time. The boxplots show the mean cluster number per nucleus (small black square boxes), the median (red line), the lower and upper quantile (big box), and the value range within ± 2 standard deviations (dashed line). The black crosses refer to values that are differing more than 3 box lengths from the median.” These figures together with the text of the relevant figure legend are reproduced from [140] with permission from the Royal Society of Chemistry.
Similar studies indicated that the γH2AX cluster size remained constant during repair also at later times post irradiation, i.e., at later times only the number of clusters reduced. This typical size was about 400 nm in diameter after photon irradiation and nearly independent from dose or the cell types analyzed [140, 141]. For α-particle irradiation from radioactive decay [142], the γH2AX cluster size along the particle track was about 200–300 nm; this size could be also observed for γH2AX clusters induced by environmental stress as for instance the deficiency of folat during long time culturing [143].
\nAfter exposure to photon radiation (different doses and energies) the SMLM analysis of heterochromatin around γH2AX clusters using specific antibodies against H3K9me3 methylation sites, revealed a fast relaxation of the chromatin and slower re-condensation after finishing the repair processes [144]. The degree of relaxation was independent of the dose which is in good relation to the equally sized γH2AX clusters [140, 141]. In contrast the euchromatin density increased during repair followed by a decrease after finishing the repair processes [144]. However, in total the chromatin showed an increasing clustering during repair followed by a reduction of clusters dependent on the energy of the damaging photons (unpublished). In general it can be assumed that DNA damaging by ionizing radiation does not only induce a reorganization of chromatin at the damaged sites but may also induce long range chromatin rearrangements for repair processes. Whether such chromatin rearrangements are random or directed to improve repair protein recruitment will be subject of future investigations.
\nBeyond γH2AX cluster formation, foci and sub-foci clusters of repair proteins were investigated after photon or particle irradiation [141, 142, 145, 146, 147]. In the following, some typical examples are shown taken from ongoing projects:
53BP1 foci were investigated in differently radio-resistant cell types, the moderately radio-resistant neonatal human dermal fibroblast cell line (NHDF) and highly radio-resistant U87 glioblastoma cell line. Specimens of both cell types were exposed to high-LET 15N-ion radiation of doses of 1.3 Gy (in a 10° irradiation scheme) and 4.0 Gy (in a 90° irradiation scheme) at the particle irradiation facility of the Joint Institute for Nuclear Research, Dubna, Russia [145, 146].
At given time points up to 24 h post irradiation, SMLM of fluorescently tagged 53BP1 molecules was performed and the coordinate data of each labeled molecule were quantitatively evaluated [138, 139, 140]. Clusters of these tags were determined as sub-units of repair foci (Figure 6a) and the formation and relaxation of these clusters revealed a higher ratio of 53BP1 proteins being recruited into clusters in NHDF cells (less radio-resistant) as compared to U87 cells (more radio-resistant) with different levels of distribution prior to DNA damage induction. This relation of 53BP1 inside and outside particle track clusters (Figure 6b) remained different for both cell types during the repair time observed. This could be seen as a measure of the “just-in-time” availability of 53BP1 proteins but did not reflect the absolute number of 53BP1 proteins available. The speed of cluster formation and relaxation differed for the two cell types (Figure 6c) indicating the recruitment of the existing proteins in the cell nucleus (higher in U87 cells) rather than a de novo production [147].
\n(a) 2D density SMLM images of 53BP1 repair proteins. Typical examples are shown for fluorescently-labeled 53BP1 proteins in NHDF cells (a) and U87 cells (B) “after 1.3 Gy tangential 15N-irradiation (10° angle between the ion beam and the cell layer). The time values indicate the period post irradiation when the samples were taken as aliquots of the same irradiated culture and fixed. For comparison, examples of non-irradiated control cells are presented. The left columns are merged images of SMLM data and wide-field images. In the right columns the SMLM images clusters and cluster areas are shown. The scale bars equal to 1 μm.” (b) relative amounts of 53BP1 signals detected within (blue) and outside (orange) repair clusters. “Graphs: Mean values and margins given by the standard deviation are depicted in gray. The values are always normalized to the mean number of signals detected at a given time point. The data are presented for NHDF fibroblasts (A) and U87 cells (B) after 1.3 Gy tangential 15N-irradiation (10° angle between the ion beam and the cell layer). Images: The pointillist images represent examples of sections of cell nuclei with labelling points inside (blue) and outside (orange) clusters at the given time points. The samples were taken as aliquots of the same culture at different time points (from 5 min to 24 hrs) after irradiation. For comparison, examples of non-irradiated control cells are presented (= 0 min).” (c) Ripley distance frequency analysis. The relative frequencies of pairwise distances are presented for the aliquots of the irradiated cell samples at different time points post irradiaton (color label of curves); (A) NHDF and (B) U87cells irradiated under 10° irradiation angle. Note: These figures are modified and the parts of the text written in “...” are reproduced from the original figures which were originally published under CC BY license in [146, 147].
A certain number of the clusters remained persistent, even longer than 24 h post irradiation (Figure 6b); thereby the number of these remaining clusters varied in each cell line. The heavily damaged cell nuclei maintained repair activity in order to process the complex damage patterns caused by high-LET 15N-radiation. This long-standing repair activity of 53BP1 proteins was shown in both cell types and the behavior of the cells could causatively be linked to the cell-type specific radio-resistance.
\nThe dynamics and cluster formation of tagged 53BP1 molecules showed that these clusters were embedded within a random distribution of points. After irradiation, a fast formation of 53BP1 clusters was observed (Figure 6c). During the early repair time of about 30 min - 1 h after radiation exposure some clusters were dispersed while others persisted and the amount of randomly distributed proteins was growing. The latter clusters that were persistent did not disappear until the end of the repair period being studied (24 h).
Another study performed two-color SMLM of immunostained γH2AX and Mre11 proteins [141] and revealed significantly delayed foci formation by Mre11 compared to γH2AX. While γH2AX clusters are already established at 30 min after radiation exposure (Figure 7, left), Mre11 is still ubiquitously distributed in the nucleus. Mre11 cluster formation is maximal at around 180 min after irradiation with significant association to γH2AX clusters (Figure 7, right).
Overview of the results obtained from SMLM measurements. Left panels show the data obtained after radiation exposure for MCF-7 breast cancer cell nuclei (“MCF-7”) in comparison to cell nuclei of CCD-1059SK fibroblasts (“Fibis”); right panels show the data obtained without radiation treatment, i.e., the natural occurrence of MRE11 clusters in these cells. The columns of each panel represent the mean values calculated from 20 nuclei each. The error bars on top of the column indicate the standard deviation. For each time step after the irradiation process, the data are given for cells exposed to 2 Gy ionizing radiation and for cells subjected to the same culturing procedure but not to radiation treatment. Level of significance between the corresponding values: *** = 0.1%. Note: These figures and their legends in “..” are slightly modified and were originally published under CC BY license in [141].
The reason to apply topological analyses is to record properties of point patterns, which are invariant under certain deformations of the object. Mathematically these deformations correspond to continuous transformations of the topological space defined by the structures. Here we have considered two properties, the number of “components” (explained below), which are independent from each other in such sense that connections between points only exist within the respective components and the number of “holes” of the structures inside the components (explained below, Figure 8a). In algebraic topology, these properties are called the Betti numbers for zero and one -dimensional simplicial complexes [148].
\nLeft: Illustration of the barcode data representation. (A) Continuously growing spheres, exemplarily depicted at 5 different scales α, around the point data illustrate the idea of the α-shape filtration. (B) As the growing spheres mutually embed the Centre of each-other the corresponding centres are connected by an edge. Whenever a triangle is formed, it is included in the complex as a face element. (C) Barcodes (Betti numbers) of dimension 0 (D0) and 1 (D1) corresponding to connected components and holes. Right: Heat map depicting the Jaccard indices averaged from components and holes for similarity of (non-)heterochromatin associated γH2AX clusters. Note: These figures are modified and were originally published under CC BY license in [133].
SMLM images as for instance of γH2AX foci/clusters are point-sets for which components and holes can be defined. A geometric relationship among the points is defined by growing spheres of radius α around each of them. Whenever two spheres mutually embed each-other’s center, these centers of the growing spheres are connected and the connected points belong to the same component. With increasing radii, the number of components is reducing. At the end of the procedure, a single component is remaining, the whole γH2AX cluster. For the definition of holes, a polygon is appropriate. Whenever the edges form a closed area, a hole is counted until another line closes a triangle separated from the original hole [147, 148].
\nThe results are presented as “barcodes” to track the formation and disappearance of components and holes with increasing α (Figure 8, left panel). These barcodes offer easy comparison of different sets of barcodes and their similarity can be calculated by the Jaccard index [149]. The Jaccard index results is a value between 0 and 1, where 0 is equal to no overlap of two bars and 1 describes two identical bars. Barcodes of different dimensions are defined as similar, if the averages of the individual similarity indices fulfill the Jaccard index conditions of similarity. Importantly, topological comparisons are independent of the scale so that it is possible to compare variably large clusters.
\nThe barcode transfers the examined structures into a form of visualization that is scale invariant. The formation and dissolution of small scaled complexes is recorded alongside the lifetime of large scaled complexes. Consequently, for γH2AX clusters, the barcodes contain bars representing components and holes in the nanometer but also in the micrometer scale ranges. In Figure 8 (right panel) a representative result of a heat map of Jaccard Indices is shown for SkBr3 breast cancer cells. 200 heterochromatin associated γH2AX clusters and 200 non-heterochromatin associated γH2AX clusters were selected by determining those with the highest and lowest heterochromatic densities in the environment and examined according to their topological similarity. For the average similarity for components and holes, heterochromatin associated γH2AX clusters showed a clear similarity whereas non-heterochromatin associated γH2AX clusters did not. This means that by topological analysis the heterochromatin associated γH2AX clusters could be discriminated as those clusters of high topological similarity [133]. The proximity of γH2AX clusters to heterochromatin seems to have a significant measurable impact on its structure. Interestingly, the non-heterochromatin associated γH2AX clusters and heterochromatin associated γH2AX clusters were more similar than the non-heterochromatin associated γH2AX clusters themselves. It can be clearly seen that the proximity to heterochromatin influences the structure of the clusters.
\nFor particle irradiated NHDF cells and U87 cells, the similarity values obtained by averaging of components and holes values for each 53BP1 cluster were determined and the clusters of the 10° irradiations scheme were compared. The Jaccard indices revealed values between 0.55 and 0.82 for U87 and NHDF cells. The broad frequency distribution did not show a peak for NHDF cells whereas for U87 cells a clear peak at 0.64 was found. If the clusters of the 90° irradiation scheme were compared, the peak was located at 0.63 for U87 cells. This value was the same, if the 10° with the 90° irradiation scheme was compared. For these two comparisons (90° vs. 90°, 10° vs. 90°), NHDF cells revealed a bimodal peak distribution where one peak was located at 0.67 and the other one at 0.72 (Figure 9). Thus, it can be concluded that in case of the more radio-sensitive NHDF cells a higher topological similarity in 53BP1 clustering was identified than the case the less radio-sensitive U87 cells.
\n“Normalized histograms of the frequencies of similarity values of barcodes (Jaccard indices) of 53BP1 clusters in NHDF and U87 cells irradiated under 10° or 90° irradiation angle and fixed 2h post irradiation. The distributions of the average similarity of dimension 0 and 1 barcodes of 53BP1 clusters in NHDF and U87 cells are shown. The similarity distributions of clusters in cells irradiated under an angle of 10° are shown in blue, the similarity distributions of clusters in cells irradiated under 90° are shown in orange, and the similarity distributions obtained when comparing clusters in cells irradiated with 10° to clusters in cells irradiated with 90° are depicted in green”. Note: These figures are modified and were originally published together with the cited figure legend under CC BY license in [147].
Alu short interspersed elements (SINEs) make up 11% of the human genome with over 1 million copies [150]; thereby making them ideal markers for assessing global chromatin architecture and dynamics by SMLM. Despite their involvement in many diseases of modern human [151, 152, 153, 154, 155, 156, 157, 158] and post-transcriptional regulation [159, 160, 161, 162, 163, 164, 165, 166], evidence grows that Alu elements are significantly regulating genome integrity and stability as a response to environmental stress. Concordantly, RNA Pol III transcriptional activation of Alu elements upon chemically and radiation induced DNA damage was observed [167] and epigenetic changes, such as DNA hypomethylation, in Alu elements are differently induced in human cell lines, when exposed to different types of radiation [168].
\nSMLM of irradiated breast cancer cells stained by combinatorial fluorescence in situ hybridization (COMBO-FISH) [169, 170] with a unique, short 17-mer oligonucleotide specific for genomic Alu elements (Figure 10A) resulted in a negative linear quadratic decline of the dose efficiency curve of localization signal points in the 0.5 Gy to 4 Gy dose range (Figure 10C) [170]. Furthermore, differential association of Alu signals with H3K9me3 heterochromatin between irradiated and non-irradiated cells could be revealed (Figure 10B). The heterochromatin relaxed after irradiation. However, the extension of this relaxation was independent of the dose. Alu dosimetry was also applied to the low dose range (< 0.5 Gy) (Figure 10D) [16], thereby opening new paths to study the molecular mechanisms underlying the controversial low dose radiation effects [171, 172, 173, 174, 175], which are difficult to assess due to a lack of appropriate biomarkers for the <0.5 Gy dose range [176].
\n(A) Single molecule localization microscopy analysis of Alu clustering and dose dependent effects of numbers of Alu labelling points after exposure to ionizing photon radiation. (B) Density distribution of heterochromatin labelling in concentric rings around the center of ALU clusters. The reduction of the density peak corresponding to heterochromatin relaxation around the Alu clusters was independent of the dose. (C) Linear quadratic dose response observed by SMLM of specific oligonucleotide nanoprobe labeling of Alu elements in SkBr3 cells after exposure to different doses of γ-radiation. (D) Linear dose response observed by SMLM of specific oligonucleotide nanoprobe labeling of Alu elements in SkBr3 cells after exposure to low doses of γ-radiation. Note: These figures are modified and were originally published under CC BY license in [16, 167].
With this article, we have addressed scientists, researchers, and clinicians working in interdisciplinary fields, which are searching for a brief introduction to current radiobiology, its fundamental principles and methodologies. We would further like to have caught the attention of radiation biologists in laboratories, clinics, and industry by demonstrating novel super-resolution microscopy techniques that have the potential to drive radiobiology to a next generation. Single molecule localization allows geometrical and topological analyses on the meso- and nano-scale at the single-cell level in situ with the advantages of easy practice and the applicability to already existing experimental methods (e.g. immunostaining, FISH). As super-resolution microscopy techniques are still not a wide-spread routine in molecular biology laboratories, the long history of fluorescence microscopy data from radiobiological studies provides a solid basis for validation. We have shown that radiobiology can be an application of SMLM based nanoscopy and its versatile data analysis method which allow the investigation of new perspectives of DNA damage induction and repair. It can even help to discover novel markers of biological dosimetry as demonstrated by our recent studies assessing dose dependent effects on retrotransposon Alu availability. Nano-scaled analysis of repair foci architecture and dynamics by assessing foci like 53BP1, Mre11, etc. will give further insight into the molecular mechanisms of DNA damage response and fate of repair pathway of individual damage sites in single cells. Indeed, evidence grows that nanostructure and function of chromatin are highly interdependent aspects that govern the fundamentals of molecular genetics, such as cell type differentiation, gene expression, DNA damage repair and reproduction. Thus, super-resolution radiobiology could serve as a general proof of principle for many other molecular biology applications in future. Finally, we believe that single-molecule localization microscopy will develop to a standard application of radiation biology and might even add to the repertoire of diagnostic technologies in clinical facilities in the future.
\nThe successful collaborations with Felix Bestvater, German Cancer Research Center (DKFZ), Heidelberg, Christoph Cremer, Institute for Pharmacy and Molecular Biotechnology, Heidelberg University, Dieter W. Heermann, Institute for Theoretical Physics, Heidelberg University, Harry Scherthan, Institute for Radiobiology of the Bundeswehr, Munich, and Martin Falk, Institute of Biophysics, Czech Academy of Sciences, Brno, are gratefully acknowledged. The work was supported by the project grant (“Einflüsse strahleninduzierter, multipler und einzelner spezifisch-targetierter DNA-Strangschäden auf die übergeordnete meso- und nanoskalige Chromatinarchitektur und die Topologie von Reparaturfoci” (NANOSTRANG)) of the Federal Ministry of Education and Research.
\nThere is no conflict of interest for any of the authors.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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