Rapid and accurate detection of Escherichia coli growth by fluorescent pH-sensitive organic nanoparticles for high-throughput screening applications

Si, Yang; Grazon, Chloé; Clavier, Gilles; Rieger, Jutta; Audibert, Jean-Frédéric; Sclavi, Bianca; Méallet‐Renault, Rachel

doi:10.1016/j.bios.2015.08.028

Cited by 47 publications

(42 citation statements)

References 43 publications

(38 reference statements)

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“…With high accuracy, any suspension culture of mixed populations could be maintained in log phase growth for days in order to study transient invaders into microbial communities 33 or even microbiome system dynamics 34 . The advent of small molecule fluorescent reporters for metabolic fitness 35 , pH 36,37 , and CO2 38 , in addition to the hundreds of fluorescent protein sensors available to the synthetic biology community at large 39,40 , also impresses the seemingly unlimited potential of being able to multiplex and quantify changes in growth, gene expression, and the environment in real-time.…”

Section: Discussionmentioning

confidence: 99%

Flexible open-source automation for robotic bioengineering

Chory

Gretton

DeBenedictis

et al. 2020

Preprint

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INTRODUCTIONLiquid handling robots have become a biotechnology staple 1,2 , allowing laborious or repetitive protocols to be executed in highthroughput. However, software narrowly designed to automate traditional hand-pipetting protocols often struggles to harness the full capabilities of robotic manipulation. Here we present Pyhamilton, an open-source Python package that eliminates these constraints, enabling experiments that could never be done by hand. We used Pyhamilton to double the speed of automated bacterial assays over current software and execute complex pipetting patterns to simulate population dynamics. Next, we incorporated feedback-control to maintain hundreds of remotely monitored bacterial cultures in log-phase growth without user intervention. Finally, we applied these capabilities to comprehensively optimize bioreactor protein production by maintaining and monitoring fluorescent protein expression of nearly 500 different continuous cultures to explore the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate Pyhamilton's empowerment of existing hardware to new applications ranging from biomanufacturing to fundamental biology. MAIN TEXTAutomation has been widely implemented in biotechnology 3 to facilitate routine tasks involved in DNA sequencing 4 , chemical synthesis 5 , drug discovery 6 , and molecular biology 7 . In principle, flexibly programmable robots could enable diverse experiments beyond the capabilities of human researchers, across a range of disciples within the sciences. Existing robotic software easily automates protocols designed for hand pipettes, but struggles to enable more specialized or sophisticated methods. As such, truly custom robot manipulation remains out of reach for most laboratories 2 , even those with well-established automation infrastructures.Bioautomation lags behind the rapidly advancing field of manufacturing, where robots are expected to be task-flexible, responsive to new situations, and interactive with humans or remote management systems when ambiguous situations or errors arise 2 . A key limitation is the lack of a comprehensive, suitably abstract, and accessible software ecosystem 8-10 . Though bioinformatics is becoming increasingly opensourced 11,12 , bioautomation has been slow to adopt key practices such as modularity, version control, and asynchronous programming.To address these issues, we developed Pyhamilton, a Python package that not only facilitates high-throughput operations within the laboratory, but also allows liquid-handling robots to execute previously unimaginable and increasingly impressive methods. With this package, users can use process scheduling, run simulations for experimental planning, implement error handling for straightforward troubleshooting, and easily integrate robots with external laboratory equipment. Design of Pyhamilton SoftwarePyhamilton enables Hamilton STAR and STARlet liquid handling robots to be programmed using standard Python. This allows for robotic method development to benefit from standa...

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Section: Discussionmentioning

confidence: 99%

Flexible open-source automation for robotic bioengineering

Chory

Gretton

DeBenedictis

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…The calculated pKa for both types of FNP was 6.55±0.02. 40 The number of grafted FA was 530 and 340 for FNP1FA and FNP2FA respectively ( Table 1).…”

Section: Spectroscopic Properties Of the Fnpmentioning

confidence: 99%

“…c Hydrodynamic diameter and dispersity factor given in brackets. * Already published data [40] Fluorescent nanoparticles, in which BODIPY is covalently linked to the polymer chains, FNP1BOD and FNP2BOD, were obtained. The FNP were well-defined in size as shown by DLS measurements (mean hydrodynamic diameter: 76 and 77 nm respectively; (σ: 0.12 and 0.10 respectively, Table S1).…”

Section: Synthesis Of Fnpmentioning

confidence: 99%

“…BODIPY-free non-fluorescent nanoparticles, but otherwise similar in composition to FNP1BOD and FNP2BOD, were also prepared using the same protocol and used as reference nanoparticles FNP1 and FNP2 in Table S1). Fluoresceinamine has already successfully been grafted on this blank nanoparticles and their sensitivity to pH measurement demonstrated, 40 providing thus good reference samples.…”

Section: Synthesis Of Fnpmentioning

confidence: 99%

“…The spectroscopic properties of BODIPY-free nanoparticles grafted with fluoresceinamine (FA), FNPFA, have already been studied 40 and are recalled in Table 3 and in Figure 1 (pH=8), S9 and S10. They were shown to respond to pH variation similarly to fluorescein dissolved in water ( Figures S9 and S10).…”

Section: Spectroscopic Properties Of the Fnpmentioning

confidence: 99%

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