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In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling automation to make the sequencing workflow more efficient and cost effective. The question remains as to its suitability and return on investment. A number of points need to be carefully considered before introducing robots into biological laboratories. Here, we describe the state-of-the-art technology of both sophisticated and do-it-yourself (DIY) robotic liquid-handlers and provide a practical review of the motivation, implications and requirements of laboratory automation for genome sequencing experiments.

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand‐pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open‐source Python platform that can execute complex pipetting patterns required for custom high‐throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log‐phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real‐time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology. SYNOPSIS An open‐source Python platform enables advanced liquid handling robots to perform a variety of complex high‐throughput experiments that could never be performed manually. Bioautomation can benefit from flexible, easily shared protocols in a widely used language. Custom techniques such as plaque assays can be readily automated. 480 bacterial turbidostats using 100 different media compositions areused to map the metabolic fitness landscape for recombinant protein production. Graphical Abstract An open‐source Python platform enables advanced liquid handling robots to perform a variety of complex high‐throughput experiments that could never be performed manually.

Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vascular perfusion. Here, we describe an ‘interrogator’ that employs liquid-handling robotics, custom software and an integrated mobile microscope for the automated culture, perfusion, medium addition, fluidic linking, sample collection and in situ microscopy imaging of up to ten organ chips inside a standard tissue-culture incubator. The robotic interrogator maintained the viability and organ-specific functions of eight vascularized, two-channel organ chips (intestine, liver, kidney, heart, lung, skin, blood–brain barrier and brain) for 3 weeks in culture when intermittently fluidically coupled via a common blood substitute through their reservoirs of medium and endothelium-lined vascular channels. We used the robotic interrogator and a physiological multicompartmental reduced-order model of the experimental system to quantitatively predict the distribution of an inulin tracer perfused through the multi-organ human-body-on-chips. The automated culture system enables the imaging of cells in the organ chips and the repeated sampling of both the vascular and interstitial compartments without compromising fluidic coupling. A system employing liquid-handling robotics and an integrated mobile microscope enables the automated culture, sample collection and in situ microscopy imaging of up to ten fluidically coupled organ chips within a standard tissue-culture incubator.

The interdisciplinary research field of microfluidics has the potential to revolutionize current technologies that require the handling of a small amount of fluid, a fast response, low costs and automation. Microfluidic platforms that handle small amounts of liquid have been categorised as continuous-flow microfluidics and digital microfluidics. The first part of this paper discusses the recent advances of the two main and opposing applications of liquid handling in continuous-flow microfluidics: mixing and separation. Mixing and separation are essential steps in most lab-on-a-chip platforms, as sample preparation and detection are required for a variety of biological and chemical assays. The second part discusses the various digital microfluidic strategies, based on droplets and liquid marbles, for the manipulation of discrete microdroplets. More advanced digital microfluidic devices combining electrowetting with other techniques are also introduced. The applications of the emerging field of liquid-marble-based digital microfluidics are also highlighted. Finally, future perspectives on microfluidic liquid handling are discussed.

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot’s movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy—using a robotic system (RS)—is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS.

The large volumes of samples to be analysed every day would be impossible to manage without laboratory automation. As laboratory procedures have progressed, so have the tasks of laboratory personnel. With this feature article, we would like to provide (bio)chemical practitioners with little or no knowledge of laboratory automation with a guide to help them decide whether to implement laboratory automation and find a suitable system. Especially in small- and medium-sized laboratories, operating a laboratory system means having bioanalytical knowledge, but also being familiar with the technical aspects. However, time, budget and personnel limitations allow little opportunity for personnel to get into the depths of laboratory automation. This includes not only the operation, but also the decision to purchase an automation system. Hasty investments do not only result in slow or non-existent cost recovery, but also occupy valuable laboratory space. We have structured the article as a decision tree, so readers can selectively read chapters that apply to their individual situation. This flexible approach allows each reader to create a personal reading flow tailored to their specific needs. We tried to address a variety of perspectives on the topic, including people who are either supportive or sceptical of laboratory automation, personnel who want or need to automate specific processes, those who are unsure whether to automate and those who are interested in automation but do not know which areas to prioritize. We also help to make a decision whether to reactivate or discard already existing and unused laboratory equipment.

Abstract As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design–Build–Test–Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering. Graphical Abstract

Commercial liquid handling robots are rarely appropriate when tasks change often, which is the case in the early stages of biochemical research. In order to address it, we have developed EvoBot, a liquid handling robot, which is open-source and employs a modular design. The combination of an open-source and a modular design is particularly powerful because functionality is divided into modules with simple, well-defined interfaces, hence customisation of modules is possible without detailed knowledge of the entire system. Furthermore, the modular design allows end-users to only produce and assemble the modules that are relevant for their specific application. Hence, time and money are not wasted on functionality that is not needed. Finally, modules can easily be reused. In this paper, we describe the EvoBot modular design and through scientific experiments such as basic liquid handling, nurturing of microbial fuel cells, and droplet chemotaxis experiments document how functionality is increased one module at a time with a significant amount of reuse. In addition to providing wet-labs with an extendible, open-source liquid handling robot, we also think that modularity is a key concept that is likely to be useful in other robots developed for scientific purposes.

Liquids are the primary environments in which chemical, physical, and biological processes occur. Considering a liquid bridge as liquid unit volume (LUV) element, it is highly desirable to develop reliable tools for handling such volumes. Herein, a sort of intelligent microfluidic platform based on the pyroelectric‐electrohydrodynamics (EHD) is shown for manipulating liquid bridges and thus performing multiple functions in a flexible and simple way. Several basic operations with liquid bridges using an EHD‐pin matrix based on the pyroelectric effect engineered in ferroelectric crystals are demonstrated. By activating pyro‐EHD effect in predetermined positions (pins of the array), the locomotion and handling of single or multiple LUVs simultaneously are controlled. In particular, multiple operations such as lift, displacement, mixing, stretching, and carrying vector for microparticles, are shown. These tweezers based on a pyro‐EHD matrix can open the route for a multipurpose platform driven by physical intelligence and can be used for driving locomotion and operate manifolds functionalities in many areas of science and technology at microscale as well as nanoscale with advantages to be activated by the sole thermal stimulus, controlled remotely, and in noncontact mode. An intelligent microfluidic platform, based on pyroelectric‐electrohydrodynamics (pyro‐EHD) is developed for the handling of liquid bridges as liquid unit volume (LUV). Using an array of microelectrodes on the surface of a lithium niobate crystal, individually controlled, the pyroelectric field is generated in different positions attracting the LUV toward the active microelectrode.