Haakan N. Joensson

Micro- and nanoplastics have become increasingly relevant as contaminants to be monitored due to their potential health effects and environmental impact. Nanoplastics, in particular, have been shown to be difficult to detect in drinking water, requiring new capture technologies. In this work, we applied the acoustofluidic seed particle method to capture nanoplastics in an optimized, tilted grid of silica clusters even at the high flow rate of 5 mL/min. Moreover, we achieved, using this technique, the enrichment of nanoparticles ranging from 500 nm to 25 nm as a first in the field. We employed fluorescence to observe the enrichment profiles according to size, using a washing buffer flow at 0.5 mL/min, highlighting the size-dependent nature of the silica seed particle release of various sizes of nanoparticles. These results highlight the versatility of acoustic trapping for a wide range of nanoplastic particles and allow further study into the complex dynamics of the seed particle method at these size ranges. Moreover, with reproducible size-dependent washing curves, we provide a new window into the rate of nanoplastic escape in high-capacity acoustic traps, relevant to both environmental and biomedical applications.

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

Droplet microfluidics utilize a monodisperse water-in-oil emulsion, with an expanding toolbox offering a wide variety of operations on a range of droplet sizes at high throughput. However, translation of these capabilities into applications for non-expert laboratories to fully harness the inherent potential of microscale manipulations is woefully trailing behind. One major obstacle is that droplet microfluidic setups often rely on custom fabricated devices, costly liquid actuators, and are not easily set up and operated by non-specialists. This impedes wider adoption of droplet technologies in, e.g., the life sciences. Here, we demonstrate an easy-to-use minimal droplet production setup with a small footprint, built exclusively from inexpensive commercially sourced parts, powered and controlled by a laptop. We characterize the components of the system and demonstrate production of droplets ranging in volume from 3 to 21 nL in a single microfluidic device. Furthermore, we describe the dynamic tuning of droplet composition. Finally, we demonstrate the production of droplet-templated cell spheroids from primary cells, where the mobility and simplicity of the setup enables its use within a biosafety cabinet. Taken together, we believe this minimal droplet setup is ideal to drive broad adoption of droplet microfluidics technology.

3D cell culture models are an important tool in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Here, we present a high-throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification, benchmark it against more conventional image analysis, and characterize spheroid assembly determining optimal surfactant concentrations and incubation times for spheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.

Cyanobacteria are model organisms for photosynthesis and are attractive for biotechnology applications. To aid investigation of genotype-phenotype relationships in cyanobacteria, we develop an inducible CRISPRi gene repression library in Synechocystis sp. PCC 6803, where we aim to target all genes for repression. We track the growth of all library members in multiple conditions and estimate gene fitness. The library reveals several clones with increased growth rates, and these have a common upregulation of genes related to cyclic electron flow. We challenge the library with 0.1 M L-lactate and find that repression of peroxiredoxin bcp2 increases growth rate by 49%. Transforming the library into an L-lactate-secreting Synechocystis strain and sorting top lactate producers enriches clones with sgRNAs targeting nutrient assimilation, central carbon metabolism, and cyclic electron flow. In many examples, productivity can be enhanced by repression of essential genes, which are difficult to access by transposon insertion.

We developed an inducible CRISPRi gene repression library in the cyanobacteriumSynechocystissp. PCC 6803, where all annotated genes are targeted for repression. We used the library to estimate gene fitness in multiple conditions. The library revealed several mutants with increased specific growth rates (up to 17%), and transcriptomics of these mutants revealed common upregulation of genes within photosynthetic electron flow. We challenged the library with L-lactate stress to find more tolerant mutants. Repression of the peroxiredoxin Bcp2 increased growth rate by 49% in the presence of 0.1 M L-lactate. Finally, the library was transformed into a L-lactate-secreting strain, and droplet microfluidics sorting of top producers enriched sgRNAs targeting nutrient assimilation, redox modulation, and cyclic-electron flow. Several clones showed increased productivity in batch cultivations (up to 75%). In some cases, tolerance or productivity was enhanced by partial repression of essential genes, which are difficult to access by transposon insertion.

The cellular machinery that supports protein synthesis and secretion lies at the foundation of cell factory-centered protein production. Due to the complexity of such cellular machinery, the challenge in generating a superior cell factory is to fully exploit the production potential by finding beneficial targets for optimized strains, which ideally could be used for improved secretion of other proteins. We focused on an approach in the yeast Saccharomyces cerevisiae that allows for attenuation of gene expression, using RNAi combined with high-throughput microfluidic single-cell screening for cells with improved protein secretion. Using direct experimental validation or enrichment analysis-assisted characterization of systematically introduced RNAi perturbations, we could identify targets that improve protein secretion. We found that genes with functions in cellular metabolism ( YDC1 , AAD4 , ADE8 , and SDH1 ), protein modification and degradation ( VPS73 , KTR2 , CNL1 , and SSA1 ), and cell cycle ( CDC39 ), can all impact recombinant protein production when expressed at differentially down-regulated levels. By establishing a workflow that incorporates Cas9-mediated recombineering, we demonstrated how we could tune the expression of the identified gene targets for further improved protein production for specific proteins. Our findings offer a high throughput and semirational platform design, which will improve not only the production of a desired protein but even more importantly, shed additional light on connections between protein production and other cellular processes.