Automation in the Life Science Research Laboratory

Automation of 3D digital rolling circle amplification using a 3D-printed liquid handler

Automation of liquid handling is indispensable to improve throughput and reproducibility in biochemical assays. However, the incorporation of automated systems into laboratory workflows is often hindered by the high cost and complexity associated with building robotic liquid handlers. Here, we report a 3D-printed liquid handler based on a fluidic manifold, thereby obviating the need for complex robotic mechanisms. The fluidic manifold, termed a dispensing and aspirating (DA) device, comprises parallelized multi-pipette structures connected by distribution and aspiration channels, enabling the precise supply and removal of reagents, respectively. Leveraging the versatility of 3D printing, the DA device can be custom-designed and printed to fit specific applications. As a proof-of-principle, we engineered a 3D-printed liquid handler dedicated for 3D digital rolling circle amplification (4DRCA), an advanced biochemical assay involving multiple sample preparation steps such as antibody incubation, cell fixation, nucleic acid amplification, probe hybridization, and extensive washing. We demonstrate the efficacy of the 3D-printed liquid handler to automate the preparation of clinical samples for the simultaneous, in situ analysis of oncogenic protein and transcript markers in B-cell acute lymphoblastic leukemia cells using 4DRCA. This approach provides an effective and accessible solution for liquid handling automation, offering high throughput and reproducibility in biochemical assays.

Development and transfer of automated methods in neuroscience: The DADTA

In the second half of the 20th century, neuroscientists across North America developed automated systems for use in their research laboratories. Their decisions to do so were complex and contingent, partly a result of global reasons, such as the need to increase efficiency and flexibility, and partly a result of local reasons, such as the need to amend perceived biases of earlier research methodologies. Automated methods were advancements but raised several challenges. Transferring a system from one location to another required that certain components of the system be standardized, such as the hardware, software, and programming language. This proved difficult as commercial manufacturers lacked incentives to create standardized products for the few neuroscientists working towards automation. Additionally, investing in automated systems required massive amounts of time, labor, funding, and computer expertise. Moreover, neuroscientists did not agree on the value of automation. My brief history investigates Karl Pribram's decisions to expand his newly created automated system by standardizing equipment, programming, and protocols. Although he was an eminent Stanford neuroscientist with strong institutional support and computer know-how, the development and transfer of his automated behavioral testing system was riddled with challenges. For Pribram and neuroscience more generally, automation was not so automatic.

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV-vis Spectroscopy: Implication for Understanding of Heterogeneous Processes

The development of in situ techniques to quantitatively characterize the heterogeneous reactions is essential for understanding physicochemical processes in aqueous phase. In this work, a new approach coupling in situ UV-vis spectroscopy with a two-step algorithm strategy is developed to quantitatively monitor heterogeneous reactions in a compact closed-loop incorporation. The algorithm involves the inverse adding-doubling method for light scattering correction and the multivariate curve resolution-alternating least squares (MCR-ALS) method for spectral deconvolution. Innovatively, theoretical spectral simulations are employed to connect MCR-ALS solutions with chemical molecular structural evolution without prior information for reference spectra. As a model case study, the aqueous adsorption kinetics of bisphenol A onto polyamide microparticles are successfully quantified in a one-step UV-vis spectroscopic measurement. The practical applicability of this approach is confirmed by rapidly screening a superior adsorbent from commercial materials for antibiotic wastewater adsorption treatment. The demonstrated capabilities are expected to extend beyond monitoring adsorption systems to other heterogeneous reactions, significantly advancing UV-vis spectroscopic techniques toward practical integration into automated experimental platforms for probing aqueous chemical processes and beyond.

A robotic system for automated chemical synthesis of therapeutic agents

The development of novel compounds for tissue-specific targeting and imaging is often impeded by a lack of lead compounds and the availability of reliable chemistry. Automated chemical synthesis systems provide a potential solution by enabling reliable, repeated access to large compound libraries for screening. Here we report an integrated solid-phase combinatorial chemistry system created using commercial and customized robots. Our goal is to optimize reaction parameters, such as varying temperature, shaking, microwave irradiation, aspirating and dispensing large-sized solid beads, and handling different washing solvents for separation and purification. This automated system accommodates diverse chemical reactions such as peptide synthesis and conventional coupling reactions. To confirm its functionality and reproducibility, 20 nerve-specific contrast agents for biomedical imaging were systematically and repeatedly synthesized and compared to other nerve-targeted agents using molecular fingerprinting and Uniform Manifold Approximation and Projection, which lays the foundation for creating reliable and reproductive chemical libraries in bioimaging and nanomedicine.

Nanomaterials in the treatment and diagnosis of rheumatoid arthritis: Advanced approaches

Rheumatoid arthritis (RA), a chronic inflammatory condition that affects persons between the ages of 20 and 40, causes synovium inflammation, cartilage loss, and joint discomfort as some of its symptoms. Diagnostic techniques for RA have traditionally been split into two main categories: imaging and serological tests. However, significant issues are associated with both of these methods. Imaging methods are costly and only helpful in people with obvious symptoms, while serological assays are time-consuming and require specialist knowledge. The drawbacks of these traditional techniques have led to the development of novel diagnostic approaches. The unique properties of nanomaterials make them well-suited as biosensors. Their compact dimensions are frequently cited for their outstanding performance, and their positive impact on the signal-to-noise ratio accounts for their capacity to detect biomarkers at low detection limits, with excellent repeatability and a robust dynamic range. In this review, we discuss the use of nanomaterials in RA theranostics. Scientists have recently synthesized, characterized, and modified nanomaterials and biomarkers commonly used to enhance RA diagnosis and therapy capabilities. We hope to provide scientists with the promising potential that nanomaterials hold for future theranostics and offer suggestions on further improving nanomaterials as biosensors, particularly for detecting autoimmune disorders.

Piston-driven automated liquid handlers

Laboratory capacities are often limited by time-consuming manual repetitive procedures rather than analysis time itself. While modern instruments are typically equipped with an autosampler, sample preparation often follows manual procedures including many labor-intensive, monotonous tasks. Particularly, for a high number of samples, well plates, and low microliter pipetting, manual preparation is error-prone often requiring repeated experiments. Sampling and sample preparation can account for greater analytical variability than instrument analysis. Repetitive tasks such as liquid handling benefit strongly from technological advances and led to the increasing applications of various automated liquid handlers (ALHs). In this review, we discuss the considerations for ALHs in the microliter range and highlight advantages and challenges when transforming from manual to automated workflows. We strongly focused on differences in liquid handling and outlined advantages due to sensor-controlled pipetting. ALHs can substantially improve costs-effectiveness and laboratory capacity. This is a consequence of increased efficiency, and throughput of laboratories while simultaneously raising data quality. Additionally, ALHs can improve safety, documentation of data, and sustainability. While automation requires careful consideration and resource demanding implementation, we believe it offers numerous advantages and can help to transform modern laboratories.

Achieving near-zero particle generation by simplicity of design-A compliant-mechanism-based gripper for clean-room environments

Lab Automation facilitates high-throughput processes and improves reproducibility and efficiency while removing human action, primary source of contaminating particles. Handling poses a risk of contamination due to close contact with the objects. We propose a novel gripper (CrocoGrip) relying on compliant mechanisms to reduce the amount of contaminating particles generated by the gripper rather than preventing their emission, the latter being the common approach in current grippers. Our novel gripper is actuated by linear solenoids and purely relies on deformation for its motion. As a result, abrasive behavior and, therefore, the generation of particles is reduced without the need for additional sealing. We experimentally proved that only particles smaller than 3.0µm are emitted by the gripper, with a large proportion of the particles being generated by the actuation. The CrocoGrip fulfills the demands of ISO14644 class 5. The gripping relies on the deformation energy of the compliant mechanism, making the gripping energy-efficient and safe. The maximum gripping force achieved by the CrocoGrip was 5.5N. Because the force transmitted to the handling object depends on the design of the gripping jaws, which are interchangeable, the force can be reduced for more sensible handling objects. Using three different sets of jaws, CrocoGrip was able to handle a microplate in SBS-standard, a 50mL Falcon tube, and a Ø60mm Petri dish using a robotic arm. Due to the monolithic design of the CrocoGrip and, as a result, the need for few components, we achieve a simplicity of design, making cleaning, sterilization and maintenance easy, even for nonexperts. The CrocoGrip exploits the advantages of compliant mechanisms, especially for applications requiring clean-room environments. This approach of compliant-mechanism-based grippers enables an increase in the cleanliness of handling processes without an increase in system complexity of the gripper to facilitate the lab automation of highly sensible processes, such as in tissue engineering.

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

An open-source FACS automation system for high-throughput cell biology

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

Noninvasive total counting of cultured cells using a home-use scanner with a pattern sheet

The inherent variability in cell culture techniques hinders their reproducibility. To address this issue, we introduce a comprehensive cell observation device. This new approach enhances the features of existing home-use scanners by implementing a pattern sheet. Compared with fluorescent staining, our method over- or underestimated the cell count by a mere 5%. The proposed technique showcased a strong correlation with conventional methodologies, displaying R2 values of 0.91 and 0.99 compared with the standard chamber and fluorescence methods, respectively. Simulations of microscopic observations indicated the potential to estimate accurately the total cell count using just 20 fields of view. Our proposed cell-counting device offers a straightforward, noninvasive means of measuring the number of cultured cells. By harnessing the power of deep learning, this device ensures data integrity, thereby making it an attractive option for future cell culture research.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Fully automated high-throughput immuno-µPlaque assay for live-attenuated tetravalent dengue vaccine development

Dengue fever has remained a continuing global medical threat that impacts half of the world's population. Developing a highly effective dengue vaccine, with live-attenuated tetravalent vaccines as leading candidates, remains essential in preventing this disease. For the development of live virus vaccines (LVVs), potency measurements play a vital role in quantifying the active components of vaccine drug substance as well as drug product during various stages of research, development, and post-licensure evaluations. Traditional plaque-based assays are one of the most common potency test methods, but they generally take up to weeks to complete. Less labor and time-intensive potency assays are thus called for to aid in the acceleration of vaccine development, especially for multivalent LVVs. Here, we introduce a fully automated, 96-well format µPlaque assay that has been optimized as a high-throughput tool to evaluate process and formulation development of a live-attenuated tetravalent dengue vaccine. To the best of our knowledge, this is the first report of a miniaturized viral plaque method for dengue with full automation via an integrated robotic system. Compared to the traditional manual plaque assay, this newly developed method substantially reduces testing time by approximately half and allows for the evaluation of over ten times more samples per run. The fully automated workflow, from cell culture to plaque counting, significantly minimizes analyst hands-on time and improves assay repeatability. The study presents a pioneering solution for the rapid measurement of LVV viral titers, offering promising prospects for advancing vaccine development through high-throughput analytics.

Automated glycan-bead coupling for high throughput, highly reproducible anti-glycan antibody analysis

Automation of diagnostic assays generally aims to increase reproducibility and throughput while decreasing human errors and hands-on time. Here, we introduce a protocol for the automated chemical conjugation of glycans to color-coded magnetic beads using the KingFisher Flex magnetic particle processor. The resulting glycan-coupled magnetic beads allow the detection of anti-glycan antibodies of different isotypes from various species. By generating anti-glycan antibody profiles, monoclonal antibodies can be screened for their specificity and cross-reactivity, while anti-glycan antibody profiles from different human body fluids can aid in predicting response to treatment or outcome of disease. This efficient, scalable protocol can also be adapted to attach proteins and other biomolecules to beads, making it useful for a wider range of applications that require bead-based laboratory methods.

A dynamic knowledge graph approach to distributed self-driving laboratories

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

The Duckbot: A system for automated imaging and manipulation of duckweed

Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.

Can I benefit from laboratory automation? A decision aid for the successful introduction of laboratory automation

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.

Physical Laboratory Automation in Synthetic Biology

Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

Deep learning-based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra

Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilised DLAP to analyse subtypes of tyrosine hydroxylase (TH)-positive dopaminergic midbrain neurons in mouse and human brain-sections. These neurons modulate complex behaviour, and are differentially affected in Parkinson's and other diseases. DLAP allows the analysis of large cell numbers, and facilitates the identification of small cellular subpopulations. Using DLAP, we identified a small subpopulation of TH-positive neurons (~5%), mainly located in the very lateral Substantia nigra (SN), that was immunofluorescence-negative for the plasmalemmal dopamine transporter (DAT), with ~40% smaller cell bodies. These neurons were negative for aldehyde dehydrogenase 1A1, with a lower co-expression rate for dopamine-D2-autoreceptors, but a ~7-fold higher likelihood of calbindin-d28k co-expression (~70%). These results have important implications, as DAT is crucial for dopamine signalling, and is commonly used as a marker for dopaminergic SN neurons.

High-throughput process development from gene cloning to protein production

In the post-genomic era, the demand for faster and more efficient protein production has increased, both in public laboratories and industry. In addition, with the expansion of protein sequences in databases, the range of possible enzymes of interest for a given application is also increasing. Faced with peer competition, budgetary, and time constraints, companies and laboratories must find ways to develop a robust manufacturing process for recombinant protein production. In this review, we explore high-throughput technologies for recombinant protein expression and present a holistic high-throughput process development strategy that spans from genes to proteins. We discuss the challenges that come with this task, the limitations of previous studies, and future research directions.

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

Development of a high-throughput minimum inhibitory concentration (HT-MIC) testing workflow

The roots of the minimum inhibitory concentration (MIC) determination go back to the early 1900s. Since then, the test has undergone modifications and advancements in an effort to increase its dependability and accuracy. Although biological investigations use an ever-increasing number of samples, complicated processes and human error sometimes result in poor data quality, which makes it challenging to replicate scientific conclusions. Automating manual steps using protocols decipherable by machine can ease procedural difficulties. Originally relying on manual pipetting and human vision to determine the results, modern broth dilution MIC testing procedures have incorporated microplate readers to enhance sample analysis. However, current MIC testing procedures are unable to simultaneously evaluate a large number of samples efficiently. Here, we have created a proof-of-concept workflow using the Opentrons OT-2 robot to enable high-throughput MIC testing. We have further optimized the analysis by incorporating Python programming for MIC assignment to streamline the automation. In this workflow, we performed MIC tests on four different strains, three replicates per strain, and analyzed a total of 1,152 wells. Comparing our workflow to a conventional plate MIC procedure, we find that the HT-MIC method is 800% faster while simultaneously boasting a 100% accuracy. Our high-throughput MIC workflow can be adapted in both academic and clinical settings since it is faster, more efficient, and as accurate than many conventional methods.

Detecting anomalies from liquid transfer videos in automated laboratory setting

In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting.

StressMatic: A Novel Automated System to Induce Depressive- and Anxiety-like Phenotype in Rats

Major depressive disorder (MDD) is a multidimensional psychiatric disorder that is estimated to affect around 350 million people worldwide. Generating valid and effective animal models of depression is critical and has been challenging for neuroscience researchers. For preclinical studies, models based on stress exposure, such as unpredictable chronic mild stress (uCMS), are amongst the most reliable and used, despite presenting concerns related to the standardization of protocols and time consumption for operators. To overcome these issues, we developed an automated system to expose rodents to a standard uCMS protocol. Here, we compared manual (uCMS) and automated (auCMS) stress-exposure protocols. The data shows that the impact of the uCMS exposure by both methods was similar in terms of behavioral (cognition, mood, and anxiety) and physiological (cell proliferation and endocrine variations) measurements. Given the advantages of time and standardization, this automated method represents a step forward in this field of preclinical research.

Magnetic Nanoparticle-Based Semi-automated Panning for High-Throughput Antibody Selection

Bio-panning is a common process involved in recombinant antibody selection against defined targets. The biopanning process aims to isolate specific antibodies against an antigen via affinity selection from a phage display library. In general, antigens are immobilized on solid surfaces such as polystyrene plastic, magnetic beads, and nitrocellulose. For high-throughput selection, semi-automated panning selection allows simultaneous panning against multiple target antigens adapting automated particle processing systems such as the KingFisher Flex. The system setup allows for minimal human intervention for pre- and post-panning steps such as antigen immobilization, phage rescue, and amplification. In addition, the platform is also adaptable to perform polyclonal and monoclonal ELISA for the evaluation process. This chapter will detail the protocols involved from the selection stage until the monoclonal ELISA evaluation with important notes attached at the end of this chapter for optimization and troubleshooting purposes.

Geographical distribution of authorship for leading cardiothoracic surgery journals

BACKGROUND

Evolution of surgical practice is influenced by publications in the leading journals of that field. If the authorship of a publication lacks geographical diversity, this could create bias and limit generalizability of the evidence. Accordingly, we conducted a geographical analysis of the leading Cardiothoracic Surgery journals worldwide.

METHODS

Using 2020 Impact Factor, we searched the leading Cardiothoracic Surgery journals over the past decade. Only original articles were included. Data regarding first, second and last authors were extracted from every article. From this, we analysed country of affiliation, highest academic degree obtained and author location by metropolitan or rural setting.

RESULTS

A total of 12,706 original articles were published in the top 5 ranked Cardiothoracic journals between 2011 and 2020. Authors originated from 69 countries, with the majority being from North America and Western Europe. The United States was the most common country of affiliation (42.8%) in all five journals, with New York City the most prominent city. A total of  63.7% of the authorship originated from large metropolitan areas (estimated as population greater than 500,000 residents), and the most common degrees obtained by authors were MD and PhD.

CONCLUSION

The prominent Cardiothoracic authorship is predominantly located in Western countries, most commonly large metropolitan centers in the United States. This raises questions as to whether the literature adequately reflects populations in other geographical areas such as the continents of South America and Africa and rural settings. Leading journals should consider policies which encourage publication by authors from geographical locations that are underrepresented globally.

Automation of yeast spot assays using an affordable liquid handling robot

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation.

High—throughput and automated screening for COVID-19

The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres.

Effects of DNA template preparation on variability in cell-free protein production

DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.

Open-source personal pipetting robots with live-cell incubation and microscopy compatibility

Liquid handling robots have the potential to automate many procedures in life sciences. However, they are not in widespread use in academic settings, where funding, space and maintenance specialists are usually limiting. In addition, current robots require lengthy programming by specialists and are incompatible with most academic laboratories with constantly changing small-scale projects. Here, we present the Pipetting Helper Imaging Lid (PHIL), an inexpensive, small, open-source personal liquid handling robot. It is designed for inexperienced users, with self-production from cheap commercial and 3D-printable components and custom control software. PHIL successfully automates pipetting (incl. aspiration) for e.g. tissue immunostainings and stimulations of live stem and progenitor cells during time-lapse microscopy using 3D printed peristaltic pumps. PHIL is cheap enough to put a personal pipetting robot within the reach of most labs and enables users without programming skills to easily automate a large range of experiments.

Automated Bioprocess Feedback Operation in a High-Throughput Facility via the Integration of a Mobile Robotic Lab Assistant

The development of biotechnological processes is challenging due to the diversity of process parameters. For efficient upstream development, parallel cultivation systems have proven to reduce costs and associated timelines successfully while offering excellent process control. However, the degree of automation of such small-scale systems is comparatively low, and necessary sample analysis requires manual steps. Although the subsequent analysis can be performed in a high-throughput manner, the integration of analytical devices remains challenging, especially when cultivation and analysis laboratories are spatially separated. Mobile robots offer a potential solution, but their implementation in research laboratories is not widely adopted. Our approach demonstrates the integration of a small-scale cultivation system into a liquid handling station for an automated cultivation and sample procedure. The samples are transported via a mobile robotic lab assistant and subsequently analyzed by a high-throughput analyzer. The process data are stored in a centralized database. The mobile robotic workflow guarantees a flexible solution for device integration and facilitates automation. Restrictions regarding spatial separation of devices are circumvented, enabling a modular platform throughout different laboratories. The presented cultivation platform is evaluated on the basis of industrially relevant E. coli BW25113 high cell density fed-batch cultivation. The necessary magnesium addition for reaching high cell densities in mineral salt medium is automated via a feedback operation loop between the analysis station located in the adjacent room and the cultivation system. The modular design demonstrates new opportunities for advanced control options and the suitability of the platform for accelerating bioprocess development. This study lays the foundation for a fully integrated facility, where the physical connection of laboratory equipment is achieved through the successful use of a mobile robotic lab assistant, and different cultivation scales can be coupled through the common data infrastructure.

An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Modelling Human Physiology on-Chip: Historical Perspectives and Future Directions

For centuries, animal experiments have contributed much to our understanding of mechanisms of human disease, but their value in predicting the effectiveness of drug treatments in the clinic has remained controversial. Animal models, including genetically modified ones and experimentally induced pathologies, often do not accurately reflect disease in humans, and therefore do not predict with sufficient certainty what will happen in humans. Organ-on-chip (OOC) technology and bioengineered tissues have emerged as promising alternatives to traditional animal testing for a wide range of applications in biological defence, drug discovery and development, and precision medicine, offering a potential alternative. Recent technological breakthroughs in stem cell and organoid biology, OOC technology, and 3D bioprinting have all contributed to a tremendous progress in our ability to design, assemble and manufacture living organ biomimetic systems that more accurately reflect the structural and functional characteristics of human tissue in vitro, and enable improved predictions of human responses to drugs and environmental stimuli. Here, we provide a historical perspective on the evolution of the field of bioengineering, focusing on the most salient milestones that enabled control of internal and external cell microenvironment. We introduce the concepts of OOCs and Microphysiological systems (MPSs), review various chip designs and microfabrication methods used to construct OOCs, focusing on blood-brain barrier as an example, and discuss existing challenges and limitations. Finally, we provide an overview on emerging strategies for 3D bioprinting of MPSs and comment on the potential role of these devices in precision medicine.

Development and validation of a miniaturized bacteriophage host range screening assay against antibiotic resistant Pseudomonas aeruginosa

Antimicrobial resistance is a current global health crisis, and the increasing emergence of multidrug resistant infections has led to the resurgent interest in bacteriophages as an alternative treatment. Prior to clinical application, phage suitability is assessed, via susceptibility testing and breadth of host range to bacteriophage, however, these are both large-scale manual processes and labor-intensive. The aim of the study was to establish and validate a scaled down methodology for high-throughput screening to reduce procedural footprint. In this paper, we describe a scaled-down adapted methodology that can successfully screen bacteriophages, isolated and purified from wastewater samples. Furthermore, we describe a miniaturized host range assay against clinical Pseudomonas aeruginosa isolates using a spot test (2 μL/ drop) that was found to be both sensitive (94.6%) and specific (94.7%). It also demonstrated a positive predictive value (PPV) of 86.4% and negative predictive value (NPV) of 98%. The breadth of host range of bacteriophages that exhibited lytic activity on P. aeruginosa isolates was corroborated using the scaled down assay. The high correlation achieved in this study confirms miniaturization as the first step in future automation that could test phage diversity and efficacy as antimicrobials.

Performance and automation of ancient DNA capture with RNA hyRAD probes

DNA hybridization-capture techniques allow researchers to focus their sequencing efforts on preselected genomic regions. This feature is especially useful when analysing ancient DNA (aDNA) extracts, which are often dominated by exogenous environmental sources. Here, we assessed, for the first time, the performance of hyRAD as an inexpensive and design-free alternative to commercial capture protocols to obtain authentic aDNA data from osseous remains. HyRAD relies on double enzymatic restriction of fresh DNA extracts to produce RNA probes that cover only a fraction of the genome and can serve as baits for capturing homologous fragments from aDNA libraries. We found that this approach could retrieve sequence data from horse remains coming from a range of preservation environments, including beyond radiocarbon range, yielding up to 146.5-fold on-target enrichment for aDNA extracts showing extremely low endogenous content (<1%). Performance was, however, more limited for those samples already characterized by good DNA preservation (>20%-30%), while the fraction of endogenous reads mapping on- and off-target was relatively insensitive to the original endogenous DNA content. Procedures based on two instead of a single round of capture increased on-target coverage up to 3.6-fold. Additionally, we used methylation-sensitive restriction enzymes to produce probes targeting hypomethylated regions, which improved data quality by reducing post-mortem DNA damage and mapping within multicopy regions. Finally, we developed a fully automated hyRAD protocol utilizing inexpensive robotic platforms to facilitate capture processing. Overall, our work establishes hyRAD as a cost-effective strategy to recover a set of shared orthologous variants across multiple ancient samples.

Automation assisted anaerobic phenotyping for metabolic engineering

BACKGROUND

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.

RESULTS

In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.

CONCLUSIONS

Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Microfluidic Systems for Isolation of Spermatozoa from Testicular Specimens of Non-Obstructive Azoospermic Men: Does/Can It Improve Sperm Yield?

Intracytoplasmic sperm injection (ICSI) has allowed reproduction options through assisted reproductive technologies (ARTs) for men with no spermatozoa within the ejaculate (azoospermia). In men with non-obstructive azoospermia (NOA), the options for spermatozoa retrieval are testicular sperm extraction (TESE), testicular sperm aspiration (TESA), or micro-surgical sperm extraction (microTESE). At the initial time of spermatozoa removal from the testis, spermatozoa are immobile. Independent of the means of spermatozoa retrieval, the subsequent steps of removing spermatozoa from seminiferous tubules, determining spermatozoa viability, identifying enough spermatozoa for oocyte injections, and isolating viable spermatozoa for injection are currently performed manually by laboratory microscopic dissection and collection. These laboratory techniques are highly labor-intensive, with yield unknown, have an unpredictable efficiency and/or success rate, and are subject to inter-laboratory personnel and intra-laboratory variability. Here, we consider the potential utility, benefits, and shortcomings of developing technologies such as motility induction/stimulants, microfluidics, dielectrophoresis, and cell sorting as andrological laboratory add-ons to reduce the technical burdens and variabilities in viable spermatozoa isolation from testicular samples in men with NOA.

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.

Automating Laboratory Processes by Connecting Biotech and Robotic Devices—An Overview of the Current Challenges, Existing Solutions and Ongoing Developments

The constantly growing interest and range of applications of advanced cell, gene and regenerative therapies raise the need for efficient production of biological material and novel treatment technologies. Many of the production and manipulation processes of such materials are still manual and, therefore, need to be transferred to a fully automated execution. Developers of such systems face several challenges, one of which is mechanical and communication interfaces in biotechnological devices. In the present state, many devices are still designed for manual use and rarely provide a connection to external software for receiving commands and sending data. However, a trend towards automation on the device market is clearly visible, and the communication protocol, Open Platform Communications Data Access (OPC DA), seems to become established as a standard in biotech devices. A rising number of vendors offer software for device control and automated processing, some of which even allow the integration of devices from multiple manufacturers. The high, application-specific need in functionalities, flexibility and adaptivity makes it difficult to find the best solution and, in many cases, leads to the creation of new custom-designed software. This report shall give an overview of existing technologies, devices and software for laboratory automation of biotechnological processes. Furthermore, it presents an outlook for possible future developments and standardizations.