Online, single-cell monitoring in bioreactors is possible by integrating a flow cytometer, a setup that several labs have already tried. Another option for real-time (but offline) measurements is possible by analyzing samples taken manually. [Credit: Nicola Tree/Getty Images]
Much of the discussion around process analytics involves sensors: which ones are compatible, how they interface, and their in-, at-, and off-line capabilities. A recent paper from Chalmers University (Sweden) describes a platform for creating noninvasive biosensors from fluorophores engineered into yeast cells.
The paper reports on how measuring several parameters simultaneously may be possible by assigning a different fluorophore to each, for example pH, ATP levels, oxidative stress, glycolytic flux, and ribosome production.
Lead author Luca Torello Pianale discussed the introduction of separate fluorophores (fluorescent proteins, in this case) for each parameter—oxidative stress, glycolytic flux, and ribosome production—without affecting the fluorescent emission or the cells’ normal functioning, but only if pairs of biosensors are inserted. “It is not possible to create all combinations because of limitations of the excitation-emission spectra of the fluorescent proteins used,” he said.
One partial fix, for single-cell analysis, is to co-culture strains harboring different biosensors.
“Since the fluorescent biosensor proteins are different, this creates a fluorescent footprint, which we describe in the paper,” continues Torello Pianale, and which allows simultaneous detection of multiple parameters through flow cytometry.
In the case of intracellular pH and ATP, for example, fluorescence is directly sensitive to those parameters, Torello Pianale tells GEN.
“For the oxidative stress biosensor, transcription of the fluorescent protein (ymYPET) is regulated by a promoter highly responsive to YAP1, the master transcriptional factor involved in the oxidative stress response in yeast. Ribosome levels are detected by tagging a protein, RPL13A, on the ribosome surface with a fluorescent protein, mTurquoise2. Sensing glycolytic flux is achieved thanks to an RNA aptamer sequence at the end of the coding sequence of mTurquoise2, whose transcription is driven by a constitutive promoter. This aptamer stabilizes or degrades mTurquoise2 mRNA based on the presence or absence of fructose-bis-phosphate, which directly correlates to glycolytic flux.”
For oxidative stress biosensors, glycolytic flux, and ribosome levels, a normalization construct was integrated with each biosensor to normalize fluorescence from the biosensor.
“This is crucial, especially in single-cell analysis,” emphasizes Torello Pianale, “since cells in different growth phases—but also within the same growth phase, due to population heterogeneity—show different metabolic activities that may affect biosensor transcription or translation.”
All the biosensors have been integrated into the genome using the CRISPR-Cas9 genome editing, which improves genome integration.
“Our lab uses an efficient, established CRISPR-Cas9 protocol in Saccharomyces cerevisiae. The tricky part is designing a good sgRNA to target the sequence. We demonstrated genome integration efficiency above 80% for laboratory and industrial Saccharomyces cerevisiae strains using the sgRNA I designed,” continues Torello Pianale.
The publication described fluorescence detection using the Biolector from m2p-labs, which allows online detection of growth and fluorescence at bulk population levels. Analysis within the Biolector device occurs in 48- or 96-well microplates. However, molecular biosensors can easily be followed microscopically or through flow cytometry.
“We are also working on single-cell analysis using these biosensors,” Torello Pianale says, noting that at-line, PAT-worthy deployments are also in the works.
“Online, single-cell monitoring in bioreactors is possible by integrating a flow cytometer, a setup that several labs have already tried. Another option for real-time (but offline) measurements is possible by analyzing samples taken manually,” he continues.
The advantage of genetically encoded biosensors becomes apparent, as cells are ready for analysis immediately after sampling without sample preparation. “As we showed,” he says, “these biosensors do not affect the cells’ growth rate or yields of key metabolites, so they should not stress cells.
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