Search

An inexpensive single-arm robot is widely utilized for recent laboratory automation solutions. The integration of a single-arm robot as a transfer system into a semi-automatic liquid dispenser without a transfer system can be realized as an inexpensive alternative to a fully automated liquid handling system. However, there has been no quantitative investigation of the positional accuracy of robot arms required to transfer microplates. In this study, we constructed a platform comprising aluminum frames and digital gauges to facilitate such measurements. We measured the position repeatability of a robot arm equipped with a custom-made finger by repeatedly transferring microplates. Further, the acceptable misalignment of plate transfer was evaluated by adding an artificial offset to the microplate position using this platform. The results of these experiments are expected to serve as benchmarks for the selection of robot arms for laboratory automation in biology. Furthermore, all information for replicating this device will be made publicly available, thereby allowing many researchers to collaborate and accumulate knowledge, hopefully contributing to advances in this field.

During laboratory automation of life science experiments, coordinating specialized instruments and human experimenters for various experimental procedures is important to minimize the execution time. In particular, the scheduling of life science experiments requires the consideration of time constraints by mutual boundaries (TCMB) and can be formulated as the “scheduling for laboratory automation in biology” (S-LAB) problem. However, existing scheduling methods for the S-LAB problems have difficulties in obtaining a feasible solution for large-size scheduling problems at a time sufficient for real-time use. In this study, we proposed a fast schedule-finding method for S-LAB problems, SAGAS (Simulated annealing and greedy algorithm scheduler). SAGAS combines simulated annealing and the greedy algorithm to find a scheduling solution with the shortest possible execution time. We have performed scheduling on real experimental protocols and shown that SAGAS can search for feasible or optimal solutions in practicable computation time for various S-LAB problems. Furthermore, the reduced computation time by SAGAS enables us to systematically search for laboratory automation with minimum execution time by simulating scheduling for various laboratory configurations. This study provides a convenient scheduling method for life science automation laboratories and presents a new possibility for designing laboratory configurations.

Abstract The retinal pigment epithelium (RPE) is essential for the survival and function of retinal photoreceptor cells. RPE dysfunction causes various retinal diseases including age-related macular degeneration (AMD). Clinical studies on ES/iPS cell-derived RPE transplantation for RPE dysfunction-triggered diseases are currently underway. Quantification of the diseased RPE area is important to evaluate disease progression or the therapeutic effect of RPE transplantation. However, there are no standard protocols. To address this issue, we developed a 2-step software that enables objective and efficient quantification of RPE-disease area changes by analyzing the early-phase hyperfluorescent area in fluorescein angiography (FA) images. We extracted the Abnormal region. This extraction was based on deep learning-based discrimination. We scored the binarized extracted area using an automated program. Our program’s performance for the same eye from the serial image captures was within 3.1 ± 7.8% error. In progressive AMD, the trend was consistent with human assessment, even when FA images from two different visits were compared. This method was applicable to quantifying RPE-disease area changes over time, evaluating iPSC-RPE transplantation images, and a disease other than AMD. Our program may contribute to the assessment of the clinical course of RPE-disease areas in routine clinics and reduce the workload of researchers.

Understanding evolutionary constraints in antibiotic resistance is crucial for prediction and control. Here, the authors use high-throughput laboratory evolution of Escherichia coli alongside machine learning to identify trade-off relationships associated with drug resistance.

It is unclear whether strategies involving antibiotic cycling can efficiently control the emergence of antibiotic-resistant bacteria. Here, Yoshidaet al. show that the evolution of multi-drug-resistant bacteria in vitrocan be manipulated by administering pairs of antibiotics and switching between them.

Abstract Background The emergence and spread of antibiotic resistance in bacteria is becoming a global public health problem. Combination therapy, i.e., the simultaneous use of multiple antibiotics, is used for long-term treatment to suppress the emergence of resistant strains. However, the effect of the combinatorial use of multiple drugs on the development of resistance remains elusive, especially in a quantitative assessment. Results To understand the evolutionary dynamics under combination therapy, we performed laboratory evolution of Escherichia coli under simultaneous addition of two-drug combinations. We demonstrated that simultaneous addition of a certain combinations of two drugs with collateral sensitivity to each other could suppress the acquisition of resistance to both drugs. Furthermore, we found that the combinatorial use of enoxacin, a DNA replication inhibitor, with Chloramphenicol can accelerate acquisition of resistance to Chloramphenicol. Genome resequencing analyses of the evolved strains suggested that the acceleration of resistance acquisition was caused by an increase of mutation frequency when enoxacin was added. Conclusions Integration of laboratory evolution and whole-genome sequencing enabled us to characterize the development of resistance in bacteria under combination therapy. These results provide a basis for rational selection of antibiotic combinations that suppress resistance development effectively.

Transcription activator-like effector nuclease (TALEN) is an artificial nuclease that causes DNA cleavage at the target site and induces few off-target reactions because of its high sequence specificity. Powerful and variable tools using TALENs can be used in practical applications and may facilitate the molecular breeding of many plant species. We have developed a convenient construction system for a plant TALEN vector named the Emerald Gateway TALEN system. In this study, we added new properties to this system, which led to an increase in the efficiency of targeted mutagenesis. Rice dMac3 is a translational enhancer that highly increases the efficiency of translation of the downstream ORF. We inserted dMac3 into the 5' untranslated region of the TALEN gene. In the cultured rice cells to which the TALEN gene was introduced, the frequency of targeted mutagenesis was highly increased compared with those altered using the conventional system. Next, the promoter for the TALEN gene was replaced with iPromoter, and its expression was stringently controlled by a GVG transcription factor that was activated in the presence of glucocorticoid. This conditional expression system worked effectively and led to a higher frequency of targeted mutagenesis than that by the constitutive expression system, while no mutagenesis was detected without glucocorticoid treatment. These results suggest that our system can be applied to genome editing to create the desired mutation.

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.

PURPOSE:Although the lamina cribrosa (LC) is the primary site of axonal damage in glaucoma, adequate methods to image and measure it are currently lacking. Here, we describe a noninvasive, in vivo method of evaluating the LC, based on swept-source optical coherence tomography (SS-OCT), and determine this method's ability to quantify LC thickness. METHODS:This study comprised 54 eyes, including normal (n = 18), preperimetric glaucoma (PPG; n = 18), and normal tension glaucoma (NTG; n = 18) eyes. We used SS-OCT to obtain 3 x 3 mm cube scans of an area centered on the optic disc, and then synchronized reconstructed B- and en-face images from this data. We identified the LC in these B-scan images by marking the visible borders of the LC pores. We marked points on the anterior and posterior borders of the LC in 12 B-scan images in order to create a skeleton model of the LC. Finally, we used B-spline interpolation to form a 3D model of the LC, including only reliably measured scan areas. We calculated the average LC thickness (avgLCT) in this model and used Spearman's rank correlation coefficient to compare it with circumpapillary retinal nerve fiber layer thickness (cpRNFLT). RESULTS:We found that the correlation coefficient of avgLCT and cpRNFLT was 0.64 (p < 0.01). The coefficient of variation for avgLCT was 5.1%. AvgLCT differed significantly in the groups (normal: 282.6 ± 20.6 μm, PPG: 261.4 ± 15.8 μm, NTG: 232.6 ± 33.3 μm). The normal, PPG and NTG groups did not significantly differ in age, sex, refractive error or intraocular pressure (IOP), although the normal and NTG groups differed significantly in cpRNFLT and Humphrey field analyzer measurements of mean deviation. CONCLUSION:Thus, our results indicate that the parameters of our newly developed method of measuring LC thickness with SS-OCT may provide useful and important data for glaucoma diagnosis and research.