Controlling Heterogeneity and Increasing Titer from Riboswitch-Regulated Bacillus subtilis Spores for Time-Delayed Protein Expression Applications

Abstract

Sporulated cells have potential as time-delayed expression chassis of proteins for applications such as 'on-demand' biologics production, whole cell biosensors, or oral vaccines. However, the desired attributes of high expression rates and low product variances are difficult to maintain from germinated spores. In this work we study the effect of an integrating vs. theta replicating plasmid in a wild-type Bacillus subtilis and two PolY mutants. The cells were engineered to produce a fluorescent reporter protein (RFP) under the control of a riboswitch activated by theophylline. This allowed for greater sensitivity to point mutations. The fluorescence and cell growth curves were fit with a custom kinetic model and a peak kinetic rate (LKP max ) was extracted for each clonal population (n = 30 for all cell, vector, and growth combinations). Plasmid based expression yields higher (8.7x) expression rates due to an increased copy number of the expression cassette (10x over integrated). The variance of LKP max values increased 2.07x after sporulation for the wild type strain. This increase in variance from sporulation is very similar to what is observed with UV exposure. This effect can be partially mitigated by the use of PolY knockouts observed in suspended cell growths and adherent biofilms.

Automated Classification of Bacterial Cell Sub-Populations with Convolutional Neural Networks

“…All flow cytometry experiments were performed on the factory-direct unmodified BD FACSCanto flow cytometer (San Jose, CA). We used 488 nm laser for extinction and detected fluorescence with a 525/550 nm as well as 610/620 nm bandpass filters as described previously 8 .…”

“…The phenotypic heterogeneity of cells needs to be controlled for precise functional materials such as sensors or bacterial therapies 6,7 . Classification of heterogeneity is currently assessed via manual inspection of microscope images, as we did before with B. subtilis endospore activated as fluorescent reporters ( Figure 1A) 8 . Accurate, higher throughput quantification of cell sub-populations is necessary for improved design of cell-based materials, sensors, and therapies.…”

“…While flow-assisted cell counting (FACS) can address larger, eukaryotic, cells the method cannot be as easily applied to sorting bacterial cells which can be up to 1,000 time smaller than eukaryotes. Moreover, bacteria cells can rapidly transition through development states, such as B. subtilis which repeatedly transitions between vegetative cell, forespore, and spore states ( Figure 1A) 8 . Moreover, bacteria can form clusters of cells, especially biofilm forming B. subtilis ( Figure 1B), which further confounds FACS based analysis which can misrepresent a cluster of cells (or cluster of various cell types) as a single cell ( Figure 1C) 9 .…”

Automated classification of bacterial cell sub-populations with convolutional neural networks

“…The phenotypic heterogeneity of cells needs to be controlled for precise functional materials such as sensors or bacterial therapies [6,7]. Classification of heterogeneity is currently assessed via manual inspection of microscope images, as we did before with B. subtilis endospore activated as fluorescent reporters ( Fig 1A) [8]. Accurate, higher throughput quantification of cell subpopulations is necessary for improved design of cell-based materials, sensors, and therapies.…”

“…While flow-assisted cell counting (FACS) can address larger, eukaryotic, cells the method cannot be as easily applied to sorting bacterial cells which can be up to 1,000 time smaller than eukaryotes. Moreover, bacteria cells can rapidly transition through development states, such as B. subtilis which repeatedly transitions between vegetative cell, forespore, and spore states ( Fig 1A) [8]. Moreover, bacteria can form clusters of cells, especially biofilm forming B. subtilis (Fig 1B), which further confounds FACS based analysis which can misrepresent a cluster of cells (or cluster of various cell types) as a single cell ( Fig 1C) [9].…”