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- Herausgeber: Wiley-VCH GmbH
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book provides an introduction into laboratory automation and then continues to present an overview of the necessary devices and systems. The structure follows the specific requirements the automation needs to fulfil such as liquid delivery, low volume delivery, solid delivery and sample preparation and follows with an overview on robots and mobile robots. Finally, common interfaces in laboratory automation are discussed.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
1 Introduction
1.1 A Short Definition of Laboratory Automation
1.2 Short History of Laboratory Automation
1.3 Laboratory Applications and Requirements
1.4 The Goal of this Book
References
2 Basic Concepts and Principles of Laboratory Automation
2.1 The LUO Concept in Laboratory Automation
2.2 Advantages and Limitations of Laboratory Automation
2.3 Economic Potential of Laboratory Automation
References
3 Formats in Laboratory Automation
3.1 Formats in Biological Applications
3.2 Formats in Clinical Applications
3.3 Formats in Classical Analytical Applications
3.4 Automated Handling of Labware
References
4 Liquid Handling in Laboratory Automation
4.1 Introduction
4.2 Liquid Handling Technologies
4.3 Critical Liquid Handling Parameters and Error Sources in Liquid Handling
4.4 Market Potential and Systems
References
Note
5 Low–Volume Liquid Delivery
5.1 Introduction
5.2 Contact‐Based Dispenser Technologies
5.3 Contactless Dispenser Technologies
5.4 Application Areas and Requirements for Low‐Volume Dispensing
5.5 Overview of Low‐Volume Dispensers
References
6 Solid Dispensing
6.1 Introduction
6.2 Factors Influencing the Dosing of Solids
6.3 Solid‐Dispensing Technologies
6.4 Solid Dispensing Systems
References
7 Devices for Sample Preparation
7.1 Introduction
7.2 Automated Heating, Cooling, and Mixing
7.3 Automated Incubation
7.4 Automated Centrifugation
7.5 Automated Filtration
7.6 Automated Solid Phase Extraction
7.7 Automated Sonication
7.8 Automated Evaporation
References
8 Robots in Laboratory Automation
8.1 Robots – A Definition
8.2 Stationary Robots in Laboratory Automation
8.3 Mobile Robots
8.4 Gripper Systems
8.5 Safety Aspects in Laboratory Automation
References
9 Analytical Measurement Systems
9.1 Absorption‐Based Methods
9.2 Fluorescence‐Based Methods
9.3 Market Situation and Available Reader Systems
9.4 Mass Spectrometric Methods
References
10 Sample Identification in Laboratory Automation
10.1 Introduction
10.2 Barcode Technology
10.3 RFID Technology
References
11 Interfaces in Laboratory Automation
11.1 Introduction
11.2 Analog Interfaces
11.3 Digital Interfaces
11.4 Standardization in Laboratory Automation
References
12 Laboratory Automation Software
12.1 Introduction
12.2 System Control Software/Process Control Systems
12.3 Laboratory Information Management Systems
12.4 Electronic Laboratory Notebooks
12.5 Laboratory Execution Systems (LES)
12.6 Scientific Data Management Systems (SDMS)
12.7 Additional Laboratory Automation Software
References
Notes
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Clinically relevant parameters and clinical areas of application....
Table 1.2 Selection of classic narcotics.
Table 1.3 Enzymatic determination of ingredients in food.
Table 1.4 Parameter and method in food analysis.
Table 1.5 Frequent parameters in environmental analysis.
Chapter 3
Table 3.1 Wells and volumes of microplates.
Table 3.2 Surface modifications for cell culture applications.
Table 3.3 Application and requirements for microplates.
Table 3.4 Cavities and filling volume for deepwell plates.
Table 3.5 Applications of filter plates.
Table 3.6 Different sealing materials with main properties.
Table 3.7 Selection of microtiter plates.
Table 3.8 Selected heat‐sealing products.
Table 3.9 Selection of sealing mats.
Table 3.10 Venous blood sampling systems, anticoagulants, color coding, and ...
Table 3.11 Labware for blood analysis.
Table 3.12 Sample formats in urine analysis.
Table 3.13 Purity grades and purity criteria.
Table 3.14 Selection of commercially available sealers and peelers.
Chapter 4
Table 4.1 Recommended pipette tips for different compound classes.
Table 4.2 Pipetting technologies for different samples.
Table 4.3 Precision and accuracy for selected liquid handling systems.
Table 4.4 Surface tension, viscosity, and vapor pressure of selected substan...
Table 4.5 Synonymous liquid classes.
Table 4.6 Dielectric constants of selected liquids (20 °C unless otherwise s...
Table 4.7 Advantages and disadvantages of single and multichannel systems.
Table 4.8 Selected liquid handling systems with one to eight channels.
Table 4.9 Time to fill 96‐well plate
a)
(in minutes).
Table 4.10 Selected liquid handling systems with more than eight channels. ...
Chapter 5
Table 5.1 Comparison of the valve and displacement‐based dispenser.
Table 5.2 Use of low‐volume delivery technologies by technology [3].
Table 5.3 Required droplets size in different applications.
Table 5.4 Properties of contact‐based dispensers.
Table 5.5 Properties of contactless dispensers.
Table 5.6 Broad application areas of LVD.
Table 5.7 Positive displacement dispenser SPT Labtech.
Table 5.8 Dosable solvents
sciDROP PICO
.
Table 5.9 Dosable solvents GeSIM.
Table 5.10 Selection of acoustic dispensers.
Chapter 6
Table 6.1 Application areas of solid dispensing [6].
Table 6.2 Motivation for using automated solid dispensers (according to [6])...
Table 6.3 Physical parameters of different compounds [9].
Table 6.4 Dosing characteristics for different compounds [26].
Table 6.5 Automated solid dispensers.
Chapter 7
Table 7.1 Liquid extraction methods.
Table 7.2 Available heating/cooling systems.
Table 7.3 Selected thermo cyclers for automated workflows.
Table 7.4 Selected solutions for automated shaking.
Table 7.5 Selected automated stirrers.
Table 7.6 Combined shaking and tempering devices.
Table 7.7 Selection of automatable incubators.
Table 7.8 Selection of partially automatable incubators.
Table 7.9 Typical applications of centrifuges with centrifugation speed.
Table 7.10 Functionality of different centrifuges.
Table 7.11 Automated centrifuges.
Table 7.12 Systems for partially parallel solid phase extraction.
Table 7.13 Automated SPE systems with limited parallelity.
Table 7.14 Automated SPE systems with limited parallelity.
Table 7.15 Systems for fully automated parallel solid phase extraction.
Table 7.16 Column types in SPE.
Table 7.17 Examples for solid phase extraction plates (compact format).
Table 7.18 Examples for solid‐phase extraction plates (modular format).
Table 7.19 Automated ultrasonic devices.
Table 7.20 Automated evaporation systems.
Chapter 8
Table 8.1 Robot types.
Table 8.2 Comparison of industrial and collaborative robots.
Table 8.3 Selected laboratory robots.
Table 8.4 Selection of mobile robots.
Table 8.5 Selection of available grippers.
Table 8.6 Overview of safety requirements for using collaborative robots.
Chapter 9
Table 9.1 Selected clinical enzyme tests.
Table 9.2 Examples of different fluorophores with associated excitation and ...
Table 9.3 Fluorescence plate reader for 1536 and more wells.
Table 9.4 Selection of commercial reader systems.
Table 9.5 Mass resolution for different resolution powers.
Table 9.6 Mass spectrometric applications in life sciences.
Table 9.7 Selection of mass spectrometer by application.
Table 9.8 Selected mass spectrometric systems – ICP/MS.
Table 9.9 Selected mass spectrometric systems – single MS.
a)
Table 9.10 Selected mass spectrometric systems – hybrid systems.
Chapter 10
Table 10.1 Typical linear barcodes.
Table 10.2 Maximum capacity of selected 2D barcodes.
Table 10.3 Examples for barcode reading solutions on liquid handling platfor...
Table 10.4 Selection of microplate print and apply solutions.
Table 10.5 Selected automated tube rack barcode reader.
Table 10.6 Properties of selected camera barcode reader.
Table 10.7 Classification of RFID systems by used frequencies.
Table 10.8 Market situation RFID systems for applications in life sciences....
Chapter 11
Table 11.1 Overview of parallel interfaces.
Table 11.2 Overview of serial interfaces.
Table 11.3 Interfaces of laboratory devices.
Table 11.4 Functions and description of devices in SiLA.
Table 11.5 Available devices and products with SiLA standard.
Chapter 12
Table 12.1 Status windows SAMI EX runtime.
Table 12.2 Selected Products and Features for System Control Software.
Table 12.3 Advantages of SaaS LIMS solutions [28].
Table 12.4 Vendors laboratory information management systems. Data from lims...
Table 12.5 Open‐source LIMS solutions.
Table 12.6 Vendors of electronic laboratory notebooks, USA and Canada.
Table 12.7 Vendors of electronic laboratory notebooks, Europe.
Table 12.8 Vendors of electronic laboratory notebooks, Asia.
Table 12.9 Open‐source solutions ELN.
Table 12.10 Vendors of laboratory execution systems.
Table 12.11 Vendors of scientific data management systems.
List of Illustrations
Chapter 1
Figure 1.1 Classical process of enzymatic and cellular assays in drug discov...
Figure 1.2 Sample preparation process of blood for the determination of vita...
Figure 1.3 Process flow for classical analytical applications.
Chapter 2
Figure 2.1 Linear structure of an integrated system.
Figure 2.2 Fully automated system with a central robot as the system integra...
Figure 2.3 Fully automated system with a flexible dual‐arm robot. Different ...
Figure 2.4 Distributed automation system, with different robots. Different s...
Figure 2.5 Distributed automation system with different robots, and complete...
Figure 2.6 Distributed automation system with mobile flexible robots. The ro...
Figure 2.7 Gross expenditures for R&D projects in selected emerging countrie...
Figure 2.8 Market potential for laboratory automation.
Figure 2.9 Projected market potential for laboratory automation in Europe fo...
Figure 2.10 Projected market potential for laboratory automation worldwide 2...
Figure 2.11 Laboratory automation market potential by region, forecast 2024....
Figure 2.12 Projected market potential worldwide 2024 (projected revenues of...
Figure 2.13 Projected growth rates (compound annual growth rate, CAGR) in pe...
Figure 2.14 Laboratory automation by application.
Figure 2.15 Market share by country and application field, forecast by 2026 ...
Figure 2.16 Market share by country and user, forecast by 2026 in %.
Figure 2.17 Global laboratory automation systems market revenue, by companie...
Chapter 3
Figure 3.1 Standard MTP Format.
Figure 3.2 Bottom profiles of microplates.
Figure 3.3 Racks for different labware formats in MTP footprint (a) 50 ml ce...
Figure 3.4 Automated cappers for different tube sizes: (a) for 15 ml centrif...
Figure 3.5 (a) Rack for automated handling of safe lock vials on liquid hand...
Chapter 4
Figure 4.1 First automated liquid handling systems. (a) Tecan 500 (1985), (b...
Figure 4.2 Air displacement technology (a) and positive displacement technol...
Figure 4.3 Predicted market potential for automated workstations in Mio USD ...
Figure 4.4 Predicted market potential for liquid handling market in Mio USD ...
Figure 4.5 Configurations of liquid handling systems.
Figure 4.6 Single‐channel handling systems. (a) Myra (Bio Molecular Sciences...
Figure 4.7 Open‐source liquid handling systems. (a) OT‐2 (Opentrons), (b) Op...
Figure 4.8 Reservoir types in liquid handling.
Chapter 5
Figure 5.1 Low dispensing technologies [1].
Figure 5.2 Working principle acoustic dispenser [18]..
Figure 5.3 Application areas of low‐volume dispensing.
Chapter 6
Figure 6.1 Selected systems for solid dispensing. (a) Junior (Unchained Labs...
Figure 6.2 Dosing methods: Classic Hopper and SV Hopper.
Chapter 7
Figure 7.1 Proprietary chilling solutions on liquid handler deck: Tube Freez...
Figure 7.2 Automated vortexer for sample preparation (celisca, Rostock, Germ...
Figure 7.3 Vacum Filtration unit on Liquid Handler Manifold with destination...
Figure 7.4 Automated Filtration System for higher Volumes: Device integrated...
Figure 7.5 Labware types and adapter formats (amplius GmbH, Rostock, Germany...
Chapter 8
Figure 8.1 Classification of robots.
Figure 8.2 Serial (a) and parallel (b) kinematic in laboratory robotics.
Figure 8.3 Degrees of freedom of a body in free space (forward/back, up/down...
Figure 8.4 Classification of different robotic systems.
Figure 8.5 Collaborative robot market shares 2019.
Figure 8.6 Gripper types. (a) 2‐finger gripper, (b) 3‐finger gripper, and (c...
Figure 8.7 Adaptive rotating lever gripper.
Figure 8.8 Different labware sizes in adaptive rotating lever gripper. (a) 1...
Chapter 9
Figure 9.1 Redox reaction (a) and absorption spectrum (b) of NAD.
Figure 9.2 Schematic reaction of WST‐1 test.
Figure 9.3 Schematic reaction of MTT test.
Figure 9.4 Microplate reader market 2018–2026 (Mio USD) [65].
Figure 9.5 Basic principle of mass spectrometry.
Figure 9.6 Scheme of chemical ionization.
Chapter 10
Figure 10.1 Classification of Auto‐ID identification systems.
Figure 10.2 Camera positions around the barcode label.
Figure 10.3 Captured image and result.
Figure 10.4 3D CAD of the advanced tube barcode reader.
Chapter 11
Figure 11.1 Functional principle serial interface.
Figure 11.2 Examples for parallel interfaces.
Figure 11.3 Serial signal transmission (synchronous, asynchronous).
Figure 11.4 SiLA 2 client–server concept.
Chapter 12
Figure 12.1 Market potential laboratory automation software [1].
Figure 12.2 Software control structure in integrated systems.
Figure 12.3 Cellario protocol designer.
Figure 12.4 Cellario Analyze.
Figure 12.5 SAMI EX Editor.
Figure 12.6 SAMI EX Runtime.
Figure 12.7 SAMI Process Management.
Figure 12.8 Market potential laboratory information management systems, by c...
Guide
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
End User License Agreement
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Devices and Systems for Laboratory Automation
Kerstin ThurowSteffen Junginger
Authors
Prof. Dr.-Ing. habil. Kerstin Thurow
University of Rostock
Center for Life Science Automation
Friedrich‐Barnewitz‐Straße 8
18119 Rostock
Germany
Dr.‐Ing. Steffen Junginger
University of Rostock
Institute of Automation
Friedrich‐Barnewitz‐Straße 8
18119 Rostock
Germany
Cover Image: © sergeyryzhov/Getty Images
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1Introduction
1.1 A Short Definition of Laboratory Automation
The term “automation” first appeared in 1936. Harder described automation as “the transfer of work tasks to machines in a production process without human intervention” [1]. In 1946, he while working as Vice President founded the Automation Department of Ford Motor Company. After World War II, two books by Diebold (1926–2005) appeared in 1952, describing automation as “automatic operation or a process for the automatic production of material goods.” Diebold defined two main meanings of automation. On the one hand, he defined automation as an automatic control through feedback. On the other hand, automation for him was also the integration of a different number of machines [2]. The Diepold concept was further developed by Bright, who described the various stages of mechanization and automation [3], and Drucker, who recognized automation as “a conceptual system beyond technology.” These three theories form the basis for understanding the concept and importance of automation [4]. “Automation” can be seen as an abbreviation for “automation technology” or “automatic operation.” Alternatively, automation is also a combination of the Greek “automotos” (means “to move yourself”) and the Latin “‐ion” (means “a state”). “Mechanization” is the replacement of physical labor with machines; however, machine operation is controlled by human operators. “Automation” also replaces these control measures with machines, i.e. it replaces the physical and mental activities of humans with machines.
Laboratory automation is part of automation technology and aims to develop and optimize technologies for the automation of classic laboratories. This includes a wide variety of laboratories in the fields of medical diagnostics, environmental analysis, or quality control, for example, in the pharmaceutical industry, food monitoring, or industrial production. Laboratory automation is a strongly multidisciplinary field. The main goal of automating laboratory processes has not changed since the first steps in this area and consists of increasing the number of processed samples (and thus productivity), reducing the processing times required per sample, and improving the quality of those obtained experimental data or the creation of opportunities for examinations that would not be possible without suitable laboratory automation.
Laboratory automation can today be defined as a highly complex integration of robotics, liquid handling systems, sample processing, and analyzing devices and computers for process control. The most important part of laboratory automation is laboratory robotics, which develops robots and robotic solutions adapted to the specificity of laboratory processes. Since the robots in laboratory automation systems generally only take on transport tasks, the development of suitable devices and components for the automatic execution of laboratory processes (e.g. dosing, shaking, incubating, etc.) is of immense importance. Suitable software algorithms are required to control the individual systems and to evaluate the data collected.
1.2 Short History of Laboratory Automation
The main drivers of the development of automation solutions are often the development of special branches of industry as well as new and more complex requirements for specific analytical processes. Very often, the impulses for the development of new solutions result from the end‐users who are confronted with several problems and inadequacies in their everyday laboratory work. For a long time, the requirements of industrial process control drove the development of automated systems.
1.2.1 Early Developments in Laboratory Automation
The first reports on the use of automated devices can be traced back to 1875 [5]. The first steps that have been made accessible to automation seem very simple from today's perspective: washing filtration residues on filter paper or liquid extractions. In 1875, Stevens described a device that made it possible to wash filter residues with water at a controlled flow rate. The wash solution was in a closed reservoir, through which air was passed through an opening. The flow rate could be controlled by the size of the opening [6]. This concept was further developed by Mitchel [7] and Lathrop [8]. In the analysis of fertilizers, the samples were washed successively with 10 ml water each until a total volume of 2500 ml was reached in order to wash out the soluble components. For this purpose, Horne developed a device for the automatic washing of the samples [9]. The first automatic burette for laboratories with recurring titrations was described by Squibb in 1894 [10]. In the same year, Greiner presented an automatic pipette, which was used for the Babcock milk test [11]. The previous developments were not suitable for slow extractions over several hours; therefore, Hibbard developed a suitable system with which flow rates of approximately 40 drops/minute were possible. A further reduction in the dripping speed could be achieved by installing a splitter [12]. The first liquid–liquid extractors were used for botanical studies. By spraying the extraction solvent into the aqueous phase, the efficiency of the extraction could be increased considerably by increasing the surface area [13]. The first devices were developed by different scientists, who were faced with different problems in the laboratory. They were very fragile systems that could be easily broken and very difficult to clean; therefore, the solutions were proprietary and did not find widespread use.
The better understanding of combustion processes and the steadily growing production of electrical energy at the end of the nineteenth century revolutionized power generation. The development of automation was therefore decisively driven by the coal and power generation industry since at the beginning of the twentieth century, there was an increasing need for more precise knowledge of the quality of coal (calorific value). The first commercial laboratory automation device was therefore a device for grinding coal samples. The Sturtevant Automatic Coal Crasher was operated by an external motor, and it made it possible to provide representative samples [14]. Another important parameter in industrial production was the determination of carbon dioxide in flue gases for the optimization of combustion processes. A commercial system was introduced to the market by Simmance and Abady. The system could be operated unattended for longer periods of time, but only provided intermittent values. A continuous variant was proposed by Stache et al. with the development of the autolyser [15]. Taylor and Hugh developed a system for the automated determination of carbon monoxide, which was based on a change in conductivity of a solution when the gas was passed through [16]. Conductivity measurements have also been reported for the control of sulfuric acid content in papermaking. Edelmann developed a device that enabled the automatic supply of sulfuric acid based on the measured values. This had previously been done manually and therefore represented an enormous source of errors [17].
The first commercial automated laboratory devices were developed during the First World War due to an increased need for rapid gas analysis. Such systems could now be used for the detection of chemical warfare agents in armed conflicts. The first systems were based on the measurement of changes in the conductivity of a heating wire. Since there was no chromatographic separation of the components prior to the measurement, clear identification of substances was not possible. Commercial variants were sold by the Cambridge Instrument Company and others [18].
In the 1920s, new requirements came from the sugar and paper industries, where there was an increasing need for pH determinations in different production steps. An essential step is the liming of sugar cane juice to remove non‐sugars, for which an automated system was first developed in 1928 [19]. This system marked the beginning of the era of the development of electrodes for pH control. The electrodes available at the time required too long equilibration times, were too complicated for use in an industrial environment and were too susceptible to poisoning from sulfur dioxide, which was used in the process. Balch and Kane used tungsten‐calomel electrodes for their developments, but it turned out that these exhibited variabilities in calibration, did not last long, and were also susceptible to poisoning [20].
In 1929, the first automated titration systems were introduced, which used a photocell to detect the color change in the solution. After the color change was detected, a valve was automatically closed so that no further titrant was dosed. The authors reported that “the device was 165 times more sensitive than the human eye” [21]. Hickman and Sanford developed a much more sophisticated titration device at Eastman Kodak. The device had an option to empty the previous sample to avoid contamination. In addition, the indicator was automatically supplied [22].
With the beginning of World War II, there was a further boost in the development of automation solutions in process control. This resulted from increased demands on the production of war‐relevant goods and a lack of qualified workers. Automated devices were also used to enable unskilled workers to perform complex tasks [23]. Particular attention was paid to the development of semi‐automated distillation equipment; Ferguson developed a corresponding system for petroleum fractionation [24]. The automatic mercaptan titrator (Shell Oil Company) for the analysis of gasoline was also a typical example of an automation solution that arose due to the existing shortage of skilled workers. Because the system was used in a refinery, the device was locked in an explosion‐proof housing. To ensure overpressure in the system and to prevent the penetration of explosive gases, compressed air was fed into the housing. A potentiometric method developed in 1941 was automated in 1943. The device could easily be operated by unskilled workers [25]. In contrast to manual titration, in which the rate of addition is adjusted around the end point, the titrant was kept constant.
At the end of World War II, the use of automated systems in the chemical industry had already become routine; thus, there was an increasing need for appropriately trained specialists. New devices were developed for fraction collectors for chromatography or distillations. Electronic components were increasingly used to control valves, for example, for an automated system for paper chromatography [26]. The development of automated titrators was advanced. In 1948, a device was created that used a motor‐driven syringe to add the titrant. The motor speed could be adapted for the respective titration applications and the titration curve could be printed [27]. The automated Karl Fischer titration was introduced in 1952 by the Merck company. Since this method works without water, it was not possible to use classic potentiometric methods to determine the end point. Instead, a polarization process with depolarization of the platinum electrodes used at the end point was chosen [28]. The automated coulometric Karl Fischer titration, which made it possible to recover the Karl Fischer reagent [29], represents a significant development. A summary of automated titration techniques and systems using photometric, amperometric, conductometric, thermal, and potentiometric methods can be found in Ewing [30]. The first reviews of automation technologies appeared in the 1950s [31]. From 1952, the “Instrument Engineer” journal was devoted to special automation topics.
Computers related to automation were first described in 1948. The “office‐size electronic computer” presented by Reeves Instrument Corporation gave researchers an opportunity to simulate their processes for the first time [32]. The first use of digital computers was described as a system for the mass spectrometric determination of hydrocarbon mixtures (Atlantic Refining Company) [33]. In the following period, computers quickly found diverse uses in laboratory automation. Cerda and Ramis described, among other things, the automation of potentiometric titrations with a Commodore VIC‐20 microcomputer and with an IBM PC. In some cases, separate computers were used for data handling due to the limited storage capacity. The latter system has been described for the titration of studies on chemical equilibria as well as for titrations to determine equivalence points. A system consisting of two burettes, an autosampler, a potentiometer, and an Acer 710 to control the entire system enabled the automatic determination of boron in industrial samples. A system for ion‐selective potentiometry has also been described. The authors also described automatic systems for conductometric, photometric, spectrophotometric‐potentiometric, fluorometric, and thermometric titrations [34].
In addition to the development of computers, the introduction of transistors also revolutionized laboratory automation.
Innovative technologies in the dosing of liquids were essential for further development of laboratory automation [35]. In 1957, Schnitger developed a new type of pipette that already had all the features of modern piston‐operated pipettes today. It had a spring‐loaded piston, a second coaxial spring for blowing out liquid residues, and replaceable plastic pipette tips. An air buffer separated the liquid from the reciprocating piston. The Eppendorf company (Hamburg, Germany) secured exclusive production and marketing rights and introduced the first industrially manufactured piston‐operated pipette into the market in 1961 [36]. Today's mechanically adjustable micropipettes are based on a model developed by Gilson, which he patented in 1974 [37].
The technical advances in the development of small motors and valves led to the introduction of semi‐automated syringe‐based pipetting systems in the 1970s. In 1971, the Digital Dilutor (Hamilton, Reno, NV) was introduced, which used two calibrated syringes as pipetting plungers. The establishment of microprocessor technology made it possible to create program sequences for controlling the motors and valves and this led to the first fully automatic pipetting systems. The first automated liquid handling systems emerged in the 1980s as a result of further electromechanical developments. The development of these systems has been driven by clinical radioimmunoassays. Hamilton (Reno, NV) and Tecan (Männedorf, Switzerland) cooperated in the late 1970s in the joint development of the Hamilton AMICA system, which was the basis for the later pipetting systems Hamilton 2000 Series and Tecan Sampler 500/RSP 5000 Series Workstation. Both systems were based on Cartesian robotic platforms and enabled single‐channel pipetting. A short time later, systems with two separate Cartesian arms and a second pipetting channel were also available. With the Zymark Z510 Master Laboratory Station, Zymark (Hopkinton, MA) developed its own pipetting system for integration into more complex Zymark robot systems.
1.2.2 Advances in the Automation of Clinical Laboratories
Medical and clinical applications and requirements largely drove the development of laboratory automation. The first real automated systems with automated loading of samples into the system and then fully automated measurement appeared in medical laboratories in the mid‐1950s. The AutoAnalyzer (Technicon), presented in December 1956, was able to determine the concentrations of urea, sugar, and calcium in blood samples within 2.5 minutes [38]. The concentration was determined by color changes that were read out using photocells [39]. The AutoAnalyzer I used flow analysis technology to increase sample throughput. Later versions enabled the simultaneous determination of 20 analytes, with a throughput of 150 samples per hour. The AutoAnalyzer started a long development in clinical automation. Devices such as the Sequential Multiple Analyzer (SMA, 1969) and Sequential Multiple Analyzer with Computer (1974) increased the throughput further [40]. The AutoAnalyzer was the first batch analyzer in clinical laboratories and led to numerous other batch analyzers, which could usually examine up to 100 samples continuously for individual analytes. In the early 1980s, the introduction of the photodiode for spectrometers with grating monochromators led to the development of systems that enabled simultaneous determination of different analytes in a sample using different specific wavelengths [41].
Another approach was followed by the Research Specialites Co., Richmond, CA, which presented the Robot Chemist in 1959 [42]. Although the Robot Chemist was able to take over all manual steps in sample preparation and enabled analysis with conventional cuvettes, it was not successful in the long term due to its excessive mechanical complexity; production stopped in 1969. The principle of batch sample processing has increasingly been replaced by discrete systems that work with positive displacement pipettes. The solutions were appropriately mixed by the dispensing steps themselves or by means of magnetic or mechanical stirrers. Temperature monitoring was implemented, as well as washing steps between the individual sub‐steps. Permanent (glass) or disposable (plastic) cuvettes were used. Depending on the application, different analyzers with different lamps, including tungsten, quartz halogen, mercury, xenon, or laser, were used. The monochromators used interference filters, prisms, or diffraction gratings. The signal detection was usually carried out with photodiodes since a wide range of wavelengths can be covered in this way [43].
In addition to the development of automated analysis systems, another important step was the introduction of ready‐made kits for carrying out analytical determinations, which contained all the necessary solvents and reagents as well as the corresponding work instructions. Sigma Chemical Company introduced the first kit of its kind in the 1950s. This eliminated the need for the manual production of reagents in the laboratory, which, in addition to reducing the workload, also led to considerable improvements in the quality of the analytical tests.
With the beginning of the 1970s, the introduction of robots into clinical laboratories and with it the era of total automation began. A revolution in this area occurred in the 1980s when Sasaki opened the first fully automated laboratory [44, 45]. As professor and director of the Department of the Clinical Laboratory at Kochi Medical School (Kochi, Japan), he and his team built conveyor belts, robots for loading and unloading analyzers and developed the first process control software [46]. The automation efforts at this time resulted from extensive savings in technical personnel for the implementation of clinical‐chemical investigations [47]. Through close cooperation with industrial partners, his ideas led to commercial products that were used in numerous clinical laboratories across Japan. Further, 72% of all university hospitals in Japan installed and used such systems [47]. In the 1990s, there were several commercial suppliers of fully automated systems for clinical laboratories [48]. Regardless of the success of these first laboratory automation systems, they remained stand‐alone solutions that could not be used for smaller laboratories and institutions, particularly due to the high costs. In addition, different interfaces of devices from different manufacturers limited the general use, since communication between different devices was not possible in this way. Sasaki et al., therefore, recommended the introduction of binding standards and sizes of racks as well as the use of more flexible robotic technologies in order to achieve plug‐and‐play functionality in automation systems [47]. Some laboratories developed in‐house solutions, but these were very proprietary systems and required a lot of maintenance.
Dr. Rod Markin (University of Nebraska Medical Center) developed one of the first clinical laboratory automation management systems. His system later enabled the “plug‐and‐play” integration of automation systems and clinical analyzers for managing and testing patient samples. His idea was to develop an automated transport system with which various test processes with commercially available test systems are possible. He paid particular attention to the management of the test processes, which resulted in greater efficiency, improved reporting, and lower laboratory costs.
1.2.3 Developments in Pharmaceutical Research
In addition to the requirements of clinical laboratories, the development of high‐throughput screening (HTS) methods in the pharmaceutical industry has been of particular importance for the development of laboratory automation since the 1980s [49, 50]. Due to the lack of drugs for numerous diseases (especially cancer and viral diseases), the increasing resistance of microorganisms to known antibiotics and the expiry of important patents, there was great pressure for faster development and testing of new potential active ingredients. In addition to the synthesis of new active ingredients, their testing with regard to biological activity, carcinogenicity, mutagenicity, and metabolism behavior is the focus of interest. The early identification of toxic properties of the potential drug candidates contributes significantly to reducing the costs of drug development and increasing safety. The main goal of HTS is to increase the number of samples processed per unit of time. The number of samples to be examined has increased dramatically. While in the 1980s, a sample volume of around 10 000 compounds was processed per year, at the beginning of the 1990s it was already 10 000 samples per month. Only five years later, there was a requirement to process the same number of samples within a week [51]. Today, HTS can include the processing of several thousand samples per day. In the area of ultra‐HTS, up to 100 000 samples have to be processed per day [52, 53]. Since processing numerous of samples is associated with considerable costs for reagents, solvents, and consumables, there is great interest in minimizing these costs by miniaturizing the experimental approaches [53]. In the period from 1998 to 2006, Novartis (Basel, Switzerland) succeeded in significantly increasing the number of compounds examined while at the same time drastically reducing the cost per substance.
Parallel sample processing was increasingly used in the automation of bioscreening. The development of a uniform standardized format, the microtiter plate, played an important role. Depending on the format used (see Chapter 3), up to 384 or more samples can be processed in parallel today. This required the development of parallel working systems for the dosing of liquids, but also for the technical determination of the parameters by means of adsorption or fluorescence methods.
Microtiter plate‐based test methods were presented for the first time in 1986 at the Fourth International Symposium on Laboratory Robotics[54]. The systems used an early version of Zymark's microplate management system and, thanks to interchangeable hands, were able to carry out various laboratory processes such as pipetting, washing plates or adding reagents. The systems were referred to as “one‐armed chemists” [55] and were initially used for enzyme‐linked immunosorbent assays (ELISAs) investigations [56]. However, their throughput and unattended operation were severely limited.
The use of articulated robots (see Chapter 8) represented a very cost‐intensive variant of the automation of such processes and was therefore not generally applicable. Numerous companies, therefore, developed specialized liquid handling systems based on a Cartesian robot structure. The Cetus Propette, a 12‐channel pipetting system for the transfer of liquids in microtiter format, was introduced in 1996. The device originally developed for the automation of interleukin‐2 assays was later used extensively in polymerase chain reaction (PCR) analysis [57]. The Biomek 1000 (Beckman Coulter), originally a development by Infinitek, was launched in 1984. It enabled the single or parallel multi‐channel pipetting of several samples. The interchangeable pipetting heads were a special feature. Another Cartesian liquid handling platform, the Star 700, was introduced by Kemble (U.K.) in 1985. The MikrolabAT (Hamilton Company) was launched in 1987 for the batch screening of blood samples for HIV and hepatitis viruses. The system had 12 channels with variable span and used disposable pipette tips. The first 96‐channel pipetting system was the Quadra96, developed by TomTec in 1990 [58], later followed by a variant with a 384 pipetting head. In contrast to solutions with liquid‐handling hands‐on articulated robot arms, Cartesian systems enable a significantly faster and better quality liquid transfer. The liquid handling workstations available today represent all further developments of these early pipetting systems. Workstation technology quickly found its way into molecular biology and genomics, as both areas of science were characterized by low throughputs and numerous labor‐intensive liquid handling steps.
In order to avoid bottlenecks in the metrological determination of the samples, it was also necessary to develop parallel reading systems based on absorption or fluorescence methods. One of the first automated plate readers, the EL310, was introduced by BioTek in 1984 [59]. Today's plate readers enable the parallel reading of up to 1536 samples in microtiter plate format.
Various automated systems have been described for biological studies. One of the best‐known applications is the Tox21 Initiative, which was started in 2008 with the aim of determining the toxicity of environmentally relevant compounds. The Tox21 Screening System has been used to screen more than 10 000 compounds. To determine the reproducibility of the results, the substances were examined on three days each with three replicates in different well positions. Various devices such as incubators, contactless dispensers for liquid dosing in the nano range and fluorescence or luminescence‐based plate readers were positioned around a central robot [60]. Approximately 40 different assays were used for the biological testing, the parallel testing of the samples was performed in the 1536 format. All results have been made accessible in public databases and are thus available to scientists worldwide for further data evaluation, the formation of new hypotheses, and the establishment of reliable QSAR models.
The earliest automated systems in pharmaceutical screening were developed for finding biologically active compounds in natural products. The majority of these systems, if not all, were tailor‐made in‐house developments that were usually not published for reasons of competition. Therefore, no general formats and technologies could be derived and developed from these developments. One of the few published studies comes from Eli Lilly and Company (Indianapolis, IN). They used a PUMA 560 robot for inoculation of microbial colonies in sample vessels combined with a subsequent test of the antibiotic effect of the fermentation extracts [61].
Pfizer (Groton, CT) has also been using HTS methods since 1986 for the screening of natural products by replacing fermentation broths with dimethyl sulfoxide solutions of synthetic compounds using 96‐well plates and reduced assay volumes of 50–100 μl. After initially 800 compounds per week examined, a volume of 7200 compounds per week was already achieved in 1989. Autoradiography and image analysis were introduced for 125I receptor‐ligand screens. The coupling of reverse transcriptase (RT), quantitative PCR, and multiplexing enabled multiple targets to be addressed in a single assay. By 1992, around 40% of the hits were produced using HTS as starting materials for the discovery portfolio. In 1995 the HTS methodology was expanded to include ADMET (absorption, distribution, metabolism, excretion, toxicity) targets. ADMET examinations require the unique identification of every single compound, which leads to the development of an automated high‐throughput liquid chromatography‐mass spectrometry (LC‐MS). In 1996, the testing of approximately 90 compounds per week in microsomal, protein binding, and serum stability assays was possible. Until 1999, the HTS for ADME examinations was completely integrated into the drug discovery process.
Automated screening systems have also been used at the Genomic Institute of the Novartis Research Foundation (GNF). A system developed for genome screening was used for almost 200 genome screens from 60 000 to 100 000 wells. The system not only carried out the transports, but also enabled the plates to be transported between the liquid handlers, incubators, and plate readers. The actual measurement was carried out on an integrated ViewLux plate reader (Perkin Elmer) or, for fluorescence‐based assays, on a confocal Opera 384 well system, on which the cells can be displayed directly. In order to optimally use all genomic information generated for structural biology, an automated system was developed that enables the automatic expression and purification of bacterial cells, baculoviruses and mammalian cells. Bacterial proteins were expressed using a parallel fermentation system consisting of 96 arranged 100 ml culture tubes, which enabled high‐density cell growth and yields of 2–4 g cell pellet for each culture with minimal variation. Protein purification was performed using GNF's automated protein purification system, which included a 96‐tube centrifuge, sonication probes, and liquid handling and affinity purification functions. As a result, 10 mg of purified protein could be obtained per tube; the overall process took 96 hours [62].
1.3 Laboratory Applications and Requirements
1.3.1 Bioscreening and Pharmaceutical Testing
As described above, the development of laboratory automation has been largely influenced by the needs of the pharmaceutical industry since the 1980s. The need to find new potential drugs and reliable early screening for biological activity remain critical. The essential processes in this area include enzyme and cell‐based assays, ELISAs, DNA/RNA extraction, purification and quantification, PCR and qPCR, gene expression experiments and next generation sequencing (NGS).
1.3.1.1 Enzymatic Assays
Enzymatic assays use the determination of enzyme activity and are used to determine substances that inhibit or activate certain enzymes as well as the enzyme kinetics. Usually, a blank value and a measured value of the sample are measured after 5–10 minutes of exposure and the extinction difference is calculated, from which quantitative statements can be derived.
Enzymatic reactions use optical measurement methods. As early as 1935, Warburg described an optical‐enzymatic test for measuring the enzyme activities of NAD+ reducing enzymes. A photometric measurement of the change in color intensity during the reduction from NAD+ to NADH was carried out [63]. This test was used to measure the activities of lactate dehydrogenase (LDH), malate dehydrogenase (MDH) and glutamate dehydrogenase (GLDH) [64]. The biochemical detection of enzyme activities is also possible using composite enzymatic tests. In this case, enzyme activity is measured for which no colored substrate is available. The combination of the reaction of the enzyme to be determined (indicator reaction) with a further enzymatic reaction (measurement reaction) with a change in color intensity enables the extension of the method. The second reaction partially uses the products of the first reaction. This indirectly determines the enzyme activity and quantifies it in comparison to a standard series. Examples of composite enzymatic tests are the glucose oxidase (GOD)‐horseradish peroxidase (HRP) test and the GPT‐LDH test. The measurement of cell metabolic activity, cytotoxicity, or cytostatic activity is of great importance in the process of drug development. The detection of cell vitality by means of the MTT test uses the reduction of the yellow, water‐soluble dye 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) into a blue‐violet, water‐insoluble formazan. The conversion takes place by NAD(P) H‐dependent cellular oxidoreductase, which is present in viable cells. This non‐radioactive, colorimetric assay system using MTT was first described by Mosmann T and improved in subsequent years by several other investigators [65–67].
Enzymatic assays can be carried out continuously or discontinuously. The timed (discontinuous) assay measures the enzyme concentration in fixed periods of time. A common timed test method is to use a microplate reader to read multiple concentrations of the solution. Multiple dilution series are examined, which contain dilution series for the substrate, the enzyme, and for the substrate and enzyme together. After the start of the reactions, the solutions are incubated for a specified period of time. A stop solution is then added to prevent a further enzyme reaction. Continuous assays measure the formation of a product or the conversion of a substrate in real‐time. The disadvantage of a continuous assay is that only one reaction can be measured at a time. The advantage, however, is the convenience of easily measurable reaction rates. Enzymatic reactions are widely used in drug development for early testing of potential drug candidates [68].
1.3.1.2 Cell‐Based Assays
A higher level of information about the biological relevance of active ingredients can be achieved through cellular assays. Investigations can take place either in the cell network or at the level of an individual cell. Cell‐based assays are therefore used extensively in drug development, where they make up more than half of all tests for target validation and ADMET [69]. Classically, proliferation, migration, invasion, apoptosis, etc. are examined. Cell‐based assays are analytical tools that can be used to study a mechanism or process of cell function. They typically include intact or fixed cells. The following important types of cell‐based assays can be defined [70]:
Intracellular signal transmission: It is an important mechanism by which cells can react to their environment and extracellular signals. Cells can perceive their environment and modify gene expression, mRNA splicing, protein expression, and protein modifications to respond to these extracellular influences.
Cell viability assays: These tests determine the ratio of living and dead cells. Cell viability tests are used to determine the cellular response of drug candidates as well as for the optimization of cell culture conditions.
Proliferation Assays: Cell proliferation describes the biological process in which the number of cells increases over time due to cell division. They thus monitor the growth rates of cell populations. Cell proliferation is important in the regular homeostasis of tissues and cells to ensure an optimized growth, development, and maintenance of the organism.
Cytotoxicity assays: These assays determine the number of living and dead cells in a population after treatment with a drug candidate or pharmacological agent.
Cell senescence assays: Assays for assessing cell health include, e.g. assays for determining the senescence of cells. One example is the detection of senescence markers associated with the activity of β‐galactosidase which reflects the integrity of the cell membrane.
Cell death assays:
Apoptosis (programmed cell death type 1): Apoptosis investigations are essential for the development, homeostasis, and pathogenesis of various diseases including cancer. Apoptotic cells appear in response to extrinsic or intrinsic signals. Typical signs of apoptotic cell death include the exposure of phosphatidylserine on the extracellular side of the plasma membrane, the activation of caspases, the disruption of the mitochondrial membrane potential, or the shrinkage of the cells. Other markers are DNA fragmentation and condensation.
Autophagy (programmed cell death type 2): Autophagy is defined as the selective degradation of intracellular targets that serve as an important homeostatic function. This process enables the destruction of misfolded proteins by ubiquitination followed by a breakdown via the lysosomal route.
Necrosis (programmed cell death type 3): Cell swelling and destruction of the plasma membrane and subcellular organelles are typical signs of necrosis. Necrotic cell death is a heterogeneous phenomenon including both, programmed and accidental cell death.
Antibody‐dependent cell‐mediated cytotoxicity
(
ADCC
): ADCC is an immunological mechanism in which an effector cell of the immune system destroys an antibody‐loaded target cell. NK cells, but also macrophages, dendritic cells, neutrophils, and eosinophils primarily take over the role of the effector cell. The ADCC thus represents a connection between the innate and the adaptive immune system.
Complement depending cytotoxicity:
Complement‐dependent cytotoxicity
(
CDC
) is an effector function of IgG and IgM antibodies. If they are bound to surface antigen on the target cell (e.g. bacterially or virally infected cell), the classic complement pathway is triggered by binding of the protein C1q to these antibodies. This leads to the formation of a
Membrane Attack Complex
(
MAC
) and lysis of the target cell. The complement system is efficiently activated by human IgG1, IgG3, and IgM antibodies, weakly by IgG2 antibodies and not by IgG4 antibodies
[71]
. It is a mechanism of action through which therapeutic antibodies
[72]
or antibody fragments
[73]
can achieve an antitumor effect
[74]
.
Antibody‐dependent cell phagocytosis
(
ADCP
): ADCP is the mechanism by which antibody opsonized target cells activate the FcγRs on the surface of macrophages to induce phagocytosis, resulting in internalization and degradation of the target cell through phagosomal acidification.
1.3.1.3 ELISAs
ELISAs are antibody‐based detection methods that belong to the enzymatic immunosorbent methods and are based on an enzymatic color reaction. The antigen to be detected is adsorptively bound and enriched via a first antibody, an enzyme‐coupled second antibody (detection antibody) leads to the reaction of a dye substrate. With the help of the ELISA, proteins (e.g. SARS‐CoV‐2 antibodies [75]) and viruses (e.g. Zika virus [76]), but also low molecular weight compounds such as hormones [77], toxins [78], and pesticides [79] in a sample (blood serum, milk, urine, food, etc.) can be detected using the property of specific antibodies to bind to the substance to be detected (antigen). An antibody is previously marked with an enzyme. The reaction catalyzed by the reporter enzyme serves as proof of the presence of the antigen. The reporter enzymes often used are HRP, alkaline phosphatase (AP), or, less often, GOD. In the case of the alkaline phosphatase a dye substrate (synonym: chromogen), for example, p‐nitrophenyl phosphate (pNPP), is added, while for peroxidase o‐phenylenediamine (oPD) is mostly used. The alkaline phosphatase splits off the phosphate residue from the colorless nitrophenyl phosphate and p‐nitrophenol is formed, which is pale yellow. The change in concentration of the dye produced by the enzymatic reaction can be followed with a photometer according to Lambert–Beer's law. The color intensity changes with the concentration of the nitrophenol formed and thus also the concentration of the antigen to be determined in the sample in comparison with a dilution series with known concentrations [80].
1.3.1.4 DNA/RNA Extraction, Purification, and Quantification
DNA extraction is one of the methods of DNA purification and involves the process of extracting DNA from cells. Usually, in the first step, the cells are concentrated by means of centrifugation, followed by cell disruption. Different procedures are required depending on the type of cells used. Plant, fungal, and bacterial cells usually require additional enzymatic or mechanical steps. Chemical cell disruption (alkaline lysis) is usually used for plasmid preparation from bacteria. The homogenate is clarified by filtration or centrifugation. DNA from mitochondria or chloroplasts is separated from the DNA of the cell nucleus by cell fractionation. Hirt extraction is used to isolate extrachromosomal DNA such as viral DNA [81]. An RNAse digestion can be performed to remove RNA. DNA extractions are usually based on two‐phase extraction [82] or precipitation [83], the latter being carried out with additional selective adsorption onto a DNA‐binding matrix. Some extraction processes are also combined with one another. Final ethanol precipitation usually follows [84], in some cases with the addition of ammonium acetate [85].
The quantification of DNA is possible with different methods [86]. The classic diphenylamine method uses colorimetric detection [87]. It has a detection limit of 3 μg but is very labor‐intensive and time‐consuming. Absorption‐based methods typically use microvolume spectrophotometers and are simple and quick. Their low specificity and sensitivity to impurities are disadvantageous. The sensitivity is around 2 ng/μl. Fluorescence measurements have better detection limits (10–50 pg/μl depending on the kit used). They have high specificity but require very expensive reagents [88]. Sometimes a digital PCR is also used, which is very sensitive and specific [89].
1.3.1.5 PCR/RT‐PCR/q‐PCR
The PCR is a method to reproduce genetic material (DNA) in vitro[90]. PCR uses the enzyme DNA polymerase. The term chain reaction indicates that the products of previous cycles serve as starting materials for the next cycle and thus enable exponential replication. Kleppe et al. used first a process for the amplification of DNA sections in 1971 by Kleppe et al. [91]. The actual developer of the method is considered to be Mullis (1944–2019, Nobel Prize in Chemistry 1993). The reaction usually uses volumes of 10–200 μl in small reaction vessels (200–500 μl) in a thermal cycler. Today, PCR is one of the most important methods of modern molecular biology and is used in biological and clinical‐diagnostic laboratories for genetic fingerprints, parentage reports, the cloning of genes, or the detection of hereditary diseases [92] and viral infections (e.g. dengue virus) [93]. The PCR test is currently the gold standard among the SARS‐CoV‐2 test procedures [94, 95].
Real‐time quantitative PCR (qPCR or RTD‐PCR) is an amplification method for nucleic acids based on the principle of ordinary PCR. In addition, it also enables the quantification of the DNA obtained. The quantification is carried out with the help of fluorescence measurements, which are recorded in real‐time during a PCR cycle.
1.3.1.6 Gene Expression Analysis
The gene expression analysis examines the implementation of genetic information (gene expression) with molecular biological and biochemical methods. It enables qualitative and quantitative statements about the activity of genes and can be used for individual transcripts as well as the complete transcriptome. Typical qualitative questions are the general expression of a gene and the type of cells in which the expression takes place. In the case of quantitative analysis, the size of the difference in expression compared to a defined reference is determined. Applications can be found in cancer research [96] or the investigation of viral diseases such as Zika [97] or SARS‐CoV‐2 [98].
1.3.1.7 Next‐Generation Sequencing
NGS is an improved technology for DNA sequencing. In contrast to classic enzymatic (Sanger sequencing) or chemical sequencing (Maxam‐Gilbert method), this method allows higher speeds and thus enables the sequencing of a complete human genome within one day [99, 100]. The NGS processes are often automated; the results are obtained in parallel with the sequencing. In addition, the results can be compared with a human reference genome. In the first step, DNA fragments are generated with the help of enzymes or centrifugation. In the next step, specific adapter oligonucleotides are bound to the fragments and a DNA library is created. The DNA fragments are bound to solid reaction media (for example a chip) and amplified. Due to the division into clusters of identical DNA, in which the actual sequencing takes place, many sequencing processes can take place parallel in a very short time. The data obtained are stored in the form of a DNA chip and analyzed using bioinformatics methods [101]. For the sequencing of the human genome, Illumina sequencing [102, 103] and SOLiD sequencing [104, 105] are mainly used.
