Comparative analysis of three studies measuring fluorescence from engineered bacterial genetic constructs

Flow Cytometry Quantification of Transient Transfections in Mammalian Cells

Flow cytometry is a powerful quantitative assay supporting high-throughput collection of single-cell data with a high dynamic range. For flow cytometry to yield reproducible data with a quantitative relationship to the underlying biology, however, requires that (1) appropriate process controls are collected along with experimental samples, (2) these process controls are used for unit calibration and quality control, and (3) data are analyzed using appropriate statistics. To this end, this chapter describes methods for quantitative flow cytometry through the addition of process controls and analyses, thereby enabling better development, modeling, and debugging of engineered biological organisms. The methods described here have specifically been developed in the context of transient transfections in mammalian cells but may in many cases be adaptable to other categories of transfection and other types of cells.

Interlaboratory Reproducibility in Growth and Reporter Expression in the Cyanobacterium Synechocystis sp. PCC 6803

In recent years, a plethora of new synthetic biology tools for use in cyanobacteria have been published; however, their reported characterizations often cannot be reproduced, greatly limiting the comparability of results and hindering their applicability. In this interlaboratory study, the reproducibility of a standard microbiological experiment for the cyanobacterial model organism Synechocystis sp. PCC 6803 was assessed. Participants from eight different laboratories quantified the fluorescence intensity of mVENUS as a proxy for the transcription activity of the three promoters PJ23100, PrhaBAD, and PpetE over time. In addition, growth rates were measured to compare growth conditions between laboratories. By establishing strict and standardized laboratory protocols, reflecting frequently reported methods, we aimed to identify issues with state-of-the-art procedures and assess their effect on reproducibility. Significant differences in spectrophotometer measurements across laboratories from identical samples were found, suggesting that commonly used reporting practices of optical density values need to be supplemented by cell count or biomass measurements. Further, despite standardized light intensity in the incubators, significantly different growth rates between incubators used in this study were observed, highlighting the need for additional reporting requirements of growth conditions for phototrophic organisms beyond the light intensity and CO2 supply. Despite the use of a regulatory system orthogonal to Synechocystis sp. PCC 6803, PrhaBAD, and a high level of protocol standardization, ∼32% variation in promoter activity under induced conditions was found across laboratories, suggesting that the reproducibility of other data in the field of cyanobacteria might be affected similarly.

Engineering Escherichia coli to Utilize Erythritol as Sole Carbon Source

Erythritol, one of the natural sugar alcohols, is widely used as a sugar substitute sweetener in food industries. Humans themselves are not able to catabolize erythritol and their gut microbes lack related catabolic pathways either to metabolize erythritol. Here, Escherichia coli (E. coli) is engineered to utilize erythritol as sole carbon source aiming for defined applications. First, the erythritol metabolic gene cluster is isolated and the erythritol-binding transcriptional repressor and its DNA-binding site are experimentally characterized. Transcriptome analysis suggests that carbohydrate metabolism-related genes in the engineered E. coli are overall upregulated. In particular, the enzymes of transaldolase (talA and talB) and transketolase (tktA and tktB) are notably overexpressed (e.g., the expression of tktB is improved by nearly sixfold). By overexpression of the four genes, cell growth can be increased as high as three times compared to the cell cultivation without overexpression. Finally, engineered E. coli strains can be used as a living detector to distinguish erythritol-containing soda soft drinks and can grow in the simulated intestinal fluid supplemented with erythritol. This work is expected to inspire the engineering of more hosts to respond and utilize erythritol for broad applications in metabolic engineering, synthetic biology, and biomedical engineering.

PLATERO: A calibration protocol for plate reader green fluorescence measurements

One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.

Multicolor Plate Reader Fluorescence Calibration

Plate readers are commonly used to measure cell growth and fluorescence, yet the utility and reproducibility of plate reader data is limited by the fact that it is typically reported in arbitrary or relative units. We have previously established a robust serial dilution protocol for calibration of plate reader measurements of absorbance to estimated bacterial cell count and for green fluorescence from proteins expressed in bacterial cells to molecules of equivalent fluorescein. We now extend these protocols to calibration of red fluorescence to the sulforhodamine-101 fluorescent dye and blue fluorescence to Cascade Blue. Evaluating calibration efficacy via an interlaboratory study, we find that these calibrants do indeed provide comparable precision to the prior calibrants and that they enable effective cross-laboratory comparison of measurements of red and blue fluorescence from proteins expressed in bacterial cells.

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site

Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.

A FAIR-compliant parts catalogue for genome engineering and expression control in Saccharomyces cerevisiae

The synthetic biology toolkit for baker's yeast, Saccharomyces cerevisiae, includes extensive genome engineering toolkits and parts repositories. However, with the increasing complexity of engineering tasks and versatile applications of this model eukaryote, there is a continued interest to expand and diversify the rational engineering capabilities in this chassis by FAIR (findable, accessible, interoperable, and reproducible) compliance. In this study, we designed and characterised 41 synthetic guide RNA sequences to expand the CRISPR-based genome engineering capabilities for easy and efficient replacement of genomically encoded elements. Moreover, we characterize in high temporal resolution 20 native promoters and 18 terminators using fluorescein and LUDOX CL-X as references for GFP expression and OD600 measurements, respectively. Additionally, all data and reported analysis is provided in a publicly accessible jupyter notebook providing a tool for researchers with low-coding skills to further explore the generated data as well as a template for researchers to write their own scripts. We expect the data, parts, and databases associated with this study to support a FAIR-compliant resource for further advancing the engineering of yeasts.