iPipet: sample handling using a tablet

How the past is shaping the future of life science: The influence of automation and AI on biology

Automation has been a transformative force for many industries, including manufacturing and chemistry. While the term traditionally referred to mechanical operations to produce physical objects, the definition has since expanded: 1) it can now mean both physical and/or information automation; and 2) it can now produce physical and/or conceptual outputs. While automation has yet to fully revolutionize life science research, much of which still relies on manual processes, we show that biology automation is the ultimate mixture of the concepts listed above - it involves automation of physical and data processing, and production of physical samples as well as conceptual data outputs. Here, we explore the history of automation and what it can - and cannot - teach us about the future of automated life science experimentation. We examine the current state of automated biology, its major successes, and the remaining barriers to wider adoption. Unlike in other fields, however, automation is reaching broader integration in life science at a time when both biology and AI are reaching their adolescence. At The Align Foundation, we are anticipating this change and hoping to leverage this inflection as a way to accelerate and democratize research. We anticipate that this novel combination of automation, AI, and life science learning will impact the trajectory of biological research, including the design and execution of high-throughput experiments and the analysis of resulting large-scale data.

Engineering biology and automation-Replicability as a design principle

Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale-as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.

High capacity clinical SARS-CoV-2 molecular testing using combinatorial pooling

BACKGROUND

The SARS-CoV-2 pandemic led to unprecedented testing demands, causing major testing delays globally. One strategy used for increasing testing capacity was pooled-testing, using a two-stage technique first introduced during WWII. However, such traditional pooled testing was used in practice only when positivity rates were below 2%.

METHODS

Here we report the development, validation and clinical application of P-BEST - a single-stage pooled-testing strategy that was approved for clinical use in Israel.

RESULTS

P-BEST is clinically validated using 3636 side-by-side tests and is able to correctly detect all positive samples and accurately estimate their Ct value. Following regulatory approval by the Israeli Ministry of Health, P-BEST was used in 2021 to clinically test 837,138 samples using 270,095 PCR tests - a 3.1fold reduction in the number of tests. This period includes the Alpha and Delta waves, when positivity rates exceeded 10%, rendering traditional pooling non-practical. We also describe a tablet-based solution that allows performing manual single-stage pooling in settings where liquid dispensing robots are not available.

CONCLUSIONS

Our data provides a proof-of-concept for large-scale clinical implementation of single-stage pooled-testing for continuous surveillance of multiple pathogens with reduced test costs, and as an important tool for increasing testing efficiency during pandemic outbreaks.

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.

Pipette Show: An Open Source Web Application to Support Pipetting into Microplates

Despite increasing automation, manual pipetting remains a daily important task in life science laboratories. However, the creation of an efficient work plan is often time-consuming, and its completion is error-prone. Here, we present Pipette Show, a free Vue.js based application that optimizes the generation of an efficient work plan for pipetting into microplates and supports its reliable execution by visual guidance. The basis forms a graphical web interface with a module for building workflows as well as a module displaying the information for each pipetting step by illuminating wells of microplates placed on a tablet.

Use of some cost-effective technologies for a routine clinical pathology laboratory

Classically, the need for highly sophisticated instruments with important economic costs has been a major limiting factor for clinical pathology laboratories, especially in developing countries. With the aim of making clinical pathology more accessible, a wide variety of free or economical technologies have been developed worldwide in the last few years. 3D printing and Arduino approaches can provide up to 94% economical savings in hardware and instrumentation in comparison to commercial alternatives. The vast selection of point-of-care-tests (POCT) currently available also limits the need for specific instruments or personnel, as they can be used almost anywhere and by anyone. Lastly, there are dozens of free and libre digital tools available in health informatics. This review provides an overview of the state-of-the-art on cost-effective alternatives with applications in routine clinical pathology laboratories. In this context, a variety of technologies including 3D printing and Arduino, lateral flow assays, plasmonic biosensors, and microfluidics, as well as laboratory information systems, are discussed. This review aims to serve as an introduction to different technologies that can make clinical pathology more accessible and, therefore, contribute to achieve universal health coverage.

Automation in the Life Science Research Laboratory

Protocols in the academic life science laboratory are heavily reliant on the manual manipulation of tools, reagents and instruments by a host of research staff and students. In contrast to industrial and clinical laboratory environments, the usage of automation to augment or replace manual tasks is limited. Causes of this 'automation gap' are unique to academic research, with rigid short-term funding structures, high levels of protocol variability and a benevolent culture of investment in people over equipment. Automation, however, can bestow multiple benefits through improvements in reproducibility, researcher efficiency, clinical translation, and safety. Less immediately obvious are the accompanying limitations, including obsolescence and an inhibitory effect on the freedom to innovate. Growing the range of automation options suitable for research laboratories will require more flexible, modular and cheaper designs. Academic and commercial developers of automation will increasingly need to design with an environmental awareness and an understanding that large high-tech robotic solutions may not be appropriate for laboratories with constrained financial and spatial resources. To fully exploit the potential of laboratory automation, future generations of scientists will require both engineering and biology skills. Automation in the research laboratory is likely to be an increasingly critical component of future research programs and will continue the trend of combining engineering and science expertise together to answer novel research questions.

Improving Reproducibility in Synthetic Biology

Synthetic biology holds great promise to deliver transformative technologies to the world in the coming years. However, several challenges still remain to be addressed before it can deliver on its promises. One of the most important issues to address is the lack of reproducibility within research of the life sciences. This problem is beginning to be recognised by the community and solutions are being developed to tackle the problem. The recent emergence of automated facilities that are open for use by researchers (such as biofoundries and cloud labs) may be one of the ways that synthetic biologists can improve the quality and reproducibility of their work. In this perspective article, we outline these and some of the other technologies that are currently being developed which we believe may help to transform how synthetic biologists approach their research activities.

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.

Expression of Prokaryotic Integral Membrane Proteins in E. coli

Production of prokaryotic membrane proteins for structural and functional studies in E. coli can be parallelized and miniaturized. All stages from cloning, expression, purification to detergent selection can be investigated using high-throughput techniques to rapidly and economically find tractable targets.

An accurate clone-based haplotyping method by overlapping pool sequencing

Chromosome-long haplotyping of human genomes is important to identify genetic variants with differing gene expression, in human evolution studies, clinical diagnosis, and other biological and medical fields. Although several methods have realized haplotyping based on sequencing technologies or population statistics, accuracy and cost are factors that prohibit their wide use. Borrowing ideas from group testing theories, we proposed a clone-based haplotyping method by overlapping pool sequencing. The clones from a single individual were pooled combinatorially and then sequenced. According to the distinct pooling pattern for each clone in the overlapping pool sequencing, alleles for the recovered variants could be assigned to their original clones precisely. Subsequently, the clone sequences could be reconstructed by linking these alleles accordingly and assembling them into haplotypes with high accuracy. To verify the utility of our method, we constructed 130 110 clones in silico for the individual NA12878 and simulated the pooling and sequencing process. Ultimately, 99.9% of variants on chromosome 1 that were covered by clones from both parental chromosomes were recovered correctly, and 112 haplotype contigs were assembled with an N50 length of 3.4 Mb and no switch errors. A comparison with current clone-based haplotyping methods indicated our method was more accurate.

Highly efficient de novo mutant identification in a Sorghum bicolor TILLING population using the ComSeq approach

Screening large populations for carriers of known or de novo rare single nucleotide polymorphisms (SNPs) is required both in Targeting induced local lesions in genomes (TILLING) experiments in plants and in screening of human populations. We previously suggested an approach that combines the mathematical field of compressed sensing with next-generation sequencing to allow such large-scale screening. Based on pooled measurements, this method identifies multiple carriers of heterozygous or homozygous rare alleles while using only a small fraction of resources. Its rigorous mathematical foundations allow scalable and robust detection, and provide error correction and resilience to experimental noise. Here we present a large-scale experimental demonstration of our computational approach, in which we targeted a TILLING population of 1024 Sorghum bicolor lines to detect carriers of de novo SNPs whose frequency was less than 0.1%, using only 48 pools. Subsequent validation confirmed that all detected lines were indeed carriers of the predicted mutations. This novel approach provides a highly cost-effective and robust tool for biologists and breeders to allow identification of novel alleles and subsequent functional analysis.