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.

3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. 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 for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid 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.

The future of the life sciences is linked to automation and microfluidics. As robots start working side by side with scientists, robotic automation of microfluidics in general, and droplet microfluidics in particular, will significantly extend and accelerate the life sciences. Here, we demonstrate the automation of droplet microfluidics using an inexpensive liquid-handling robot to produce human scaffold-free cell spheroids at high throughput. We use pipette actuation and interface the pipetting tip with a droplet-generating microfluidic device. In this device, we produce highly monodisperse droplets with a diameter coefficient of variation (CV) lower than 2%. By encapsulating cells in these droplets, we produce cell spheroids in droplets and recover them to standard labware containers at a throughput of 85,000 spheroids per microfluidic circuit per hour. The viability of the cells in spheroids remains high throughout the process and decreases by >10% (depending on the cell line used) after a 16 h incubation period in nanoliter droplets and automated recovery. Scaffold-free cell spheroids and 3D tissue constructs recapitulate many aspects of functional human tissue more accurately than 2D or single-cell cultures, but assembly methods for spheroids (e.g., hanging drop microplates) have limited throughput. The increased throughput and decreased cost of our method enable spheroid production at the scale needed for lead discovery drug screening, and approach the cost at which these microtissues could be used as building blocks for organ-scale regenerative medicine.

The future of life science is linked to automation and microfluidics. Here we present a robotic lab assistant, a general automation platform for droplet microfluidics including a conversational mobile interface. We demonstrate the automated production of human cancer microtissues in droplets at a throughput of 85000 spheroids per microfluidic circuit per hour. The capability of automated spheroid generation is directly applicable to precision medicine and drug screening. Multiple cell lines were successfully tested, including cancer cell lines, co-cultures, and primary cells. The 3D-microtissues/spheroids were automatically assembled-incubated-retrieved with high viability for further drug screening analysis - the platform interfaces with standard labware.

The unique properties of nanomaterials enable the creation new analytical devices. Polyaniline (PANI) micro- and nanofiber network, freestanding in the gap between two gold microelectrodes, has been used in a new nanodetector for metal ions in solutions. The gold electrodes were modified with the aid of alkanethiols, forming a self-assembled monolayer (SAM), which is able to block the ion current flow, but also to interact with metal ions when specific functional molecules are incorporated into the layer. The electric field of the trapped metal ions induces change of the electrical conductivity of polyaniline nanofibers in vicinity. A small injected sample (75 mu L) of a solution of salt (about 0.5 mu g of salt) was enough to induce a reproducible change in the electrical conductivity of polyaniline nano-network, which was registered as a function of time within 10-20 s. The response was proportional to the concentration of ions. It also depends on properties of ions, e.g., the ionic radius, which allows for identification of metal ions by analyzing the parameters of the signal: the retention time (RT), half width (HW), amplitude (A) and integral intensity (INT). The advantage of the new device is the instant responsiveness and easy operation, but also the simple construction based on organic (polymer) technology. The system is "open"-when learned and calibrated adequately, other metal ions can be analyzed. The nanodetector can be used in cases where monitoring of the presence and concentration of metal ions is important.

Droplet microfluidics enables high throughput cell processing, analysis and screening by miniaturizing the reaction vessels to nano- or pico-liter water-in oil droplets, but like many other microfluidic formats, droplet microfluidics have not been interfaced with or automated by laboratory robotics. Here we demonstrate automation of droplet microfluidics based on an inexpensive liquid handling robot for the automated production of human scaffold-free cell spheroids, using pipette actuation and interfacing the pipetting tip with a droplet generating microfluidic chip. In this chip we produce highly mono-disperse 290μm droplets with diameter CV of 1.7%. By encapsulating cells in these droplets, we produce cell spheroids in droplets and recover them to standard formats at a throughput of 85000 spheroids per microfluidic circuit per hour. The viability of the cells in spheroids remains high after recovery only decreased by 4% starting from 96% after 16 hours incubation in nanoliter droplets. Scaffold-free cell spheroids and 3D tissue constructs recapitulate many aspects of functional human tissue more accurately than 2D or single cell cultures, but assembly methods for spheroids, e.g. hanging drop micro-plates, has had limited throughput. The increased throughput and decreased cost of our method enables spheroid production at the scale needed for lead discovery drug screening and approaches the cost where these micro tissues could be used as building blocks for organ scale regenerative medicine.

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.