Single cell profiling framework reveals metabolic subpopulations as drivers of bioproduction heterogeneity

Domain: physics
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
Heterogeneity within clonal cell populations remains a critical bottleneck within bioprocess engineering, notably by undermining bioproduction yields. Efforts to mitigate its impact have, however, been hampered by technological difficulties quantifying metabolism at the single-cell level. Here, we propose a framework based on single-cell biosensor analysis that enables robust characterisation of cell’s metabolic states, leveraging it to detect and isolate isogenic heterogeneity in response to environmental perturbations and within microbial cell factories. We identify acute and gradual glucose depletion to induce differentiation of metabolically distinct subpopulations and reveal these subpopulations to exhibit differential production capabilities, with lower intracellular pH subpopulations exhibiting enhanced product accumulation within violacein-producing strains but reduced yields within lycopene-producing strains. Lastly, we highlight galactose cultivation as a method to modulate subpopulation dynamics towards higher-producing lycopene phenotypes. Altogether, our research provides insights into subpopulation differentiation and establishes promising avenues for the engineering of more robust and higher-producing strains.

Introduction
Isogenic, or phenotypic, heterogeneity refers to phenotypic cell-to-cell variations observed within genetically identical cell populations1. It is a widespread phenomenon which plays a key role in the fitness of many organisms-from promoting survival when faced with environmental perturbations through bet-hedging2–7 to allowing for division of labour strategies to occur8–10. Within bioproduction and the artificial environments that are bioreactors, however, isogenic heterogeneity is primarily associated with the emergence of low-producing subpopulations which impair product yields11–16 and likely promote strain instability1,17,18. To date, efforts to counter the occurrence of such low-producing subpopulations have predominantly relied on the use of feedback loops to actively select for high-producing cells13,16,18. While this approach has proven effective in some cases, it poses several limitations. Most notably, the construction of such feedback loops requires orthogonal product-responsive transcription factors, which are only available for a few select pathways. These feedback loops will also compete with the native metabolism and engineered pathways for cellular resources and thus, their incurred cost and subsequent impact on bioproduction yield needs to be carefully evaluated. Instead, a promising approach involves studying the underlying causes of isogenic heterogeneity to inform the design of more robust and higher-producing strains.

To that end, a comprehensive overview of single cells’ metabolic states is essential. Metabolites serve as dynamic indicators of cellular activity, responding promptly to environmental cues and engaging in complex bidirectional regulation with gene networks. Monitoring of metabolites can thus be used as a proxy for isogenic heterogeneity, enabling its detection in response to both environmental and cellular triggers, and providing valuable insight into its biological function. Quantification of metabolites at the single-cell level, however, is technically challenging due to the low sensitivity of existing metabolomics platforms. Indeed, while efforts to address this are underway, single-cell metabolomics remain in their infancy and are thus prohibitively costly, low throughput, and not readily accessible19. Instead, the most valuable tools we have at our disposal are metabolite-responsive genetically encoded fluorescent biosensors. These, in combination with single-cell detection methods such as flow cytometry and microscopy, enable for the relative concentrations of metabolites to not only be quantified at single-cell level but also assessed in real-time through transient fluctuations. For instance, the use of an L-valine biosensor within microfluidics chambers allowed Mustafi et al. to track the lineage of low-producing L-valine subpopulations in C. glutamicum11, while a pH biosensor in combination with flow cytometry enabled Bagamery et al. to better understand the bet-hedging phenotypes which emerged in response to glucose starvation in S. cerevisiae6. Despite the numerous valuable biosensors present within the literature20–23, few have attempted to standardise biosensor parts, focusing primarily on population-level characterisation24 and thus leaving single-cell evaluation of biosensor behaviour largely unexplored. Part standardisation adds uniformity and robustness to construct design which is crucial for improving both their predictability and reproducibility. This is especially significant when studying isogenic heterogeneity as factors such as stochasticity in gene expression25,26 and uneven plasmid distribution12 create cell-to-cell variability in biosensor expression levels that cannot be averaged out across the population and instead, inadvertently introduce biases.

Here, we propose a modular and standardised framework based on robust characterisation of single cells’ metabolic states to facilitate the study of isogenic heterogeneity (Fig. 1). We isolate biosensors from the literature which target a range of different core intracellular metabolites and integrate them as functional parts within the widely used S. cerevisiae MoClo YTK toolkit27, thereby establishing an extension to the toolkit which we refer to as YTK-ScBiosense. We then leverage the extended toolkit to systematically quantify single-cell metabolic responses to environmental challenges, specifically investigating acute and gradual depletion of glucose as a primary trigger of differentiation into metabolic subpopulations and examining the impact these subpopulations have on bioproduction heterogeneity. Lycopene and violacein are two naturally occurring pigments with important applications as colouring agents within the textile and food industry, but also as therapeutic agents within healthcare28–30. Using their bioproduction as case studies and intracellular pH (pHi) sensing signal as a subpopulation marker, we show depletion of glucose within batch fermentation to induce the emergence of subpopulations with differential production efficiencies. We demonstrate trends in efficiencies to be product-dependent, with cells from the lower pHi subpopulation displaying improved product accumulation within violacein-producing strains but reduced yields within lycopene-producing strains, and highlight cultivation strategies as a potential method to mitigate subpopulations’ impact on bioproduction yields. Collectively, these results demonstrate our framework’s versatility within the study of isogenic heterogeneity, providing valuable insights into its environmental triggers, showcasing the role of pHi by extension the stress response within the differentiation process, and highlighting its use as a proxy for production efficiency.

Results
Sensing and reporter part selection
Nine different biosensors, each with previously demonstrated functionality in yeast, were selected for the proposed YTK-ScBiosense toolkit (Supplementary Table 1). They were the following: pHluorin231, 2.6 C45-HRRz (referred to as FBPs)32, pRPL28, pRPL31B, pHXT533, pGPD234, pYRE35, yAT1.0336 and PercevalHR37 sensing pH, fructose-1,6-bisphosphate (FBP), growth, NADH, NADPH, absolute ATP, and the ratio of ATP:ADP respectively, to collectively provide a comprehensive overview of a cell’s metabolic state. More specifically, pHi serves as a valuable indicator of cellular stress, with pHi homeostasis being vital to maintaining cellular function and transient drops in pHi often playing a crucial role in initiating cells’ stress responses38–40. Growth is similarly representative of cellular fitness, with its regulation being fundamental to maintaining viability33. FBP is an intermediate of the glycolytic pathway whose abundance directly correlates with glycolytic flux, a core component of carbon metabolism41, while absolute ATP levels and the ratio of ATP:ADP are reflective of a cell’s energy status36,37,42,43. Lastly, NAD+:NADH and NADP+:NADPH ratios determine the redox balance of cells, which is critical in allowing many enzymatic reactions to occur and thus, in regulating many biological processes44.

Genetically encoded biosensors can be broadly broken down into two distinct components: a sensing unit which detects and transduces a given input signal, such as the presence of a target metabolite, and a reporter unit, which translates the sensing unit’s signal into a measurable output. To integrate our selected sensors as parts within the YTK toolkit27, we first isolated their core sensing units from literature-obtained constructs and integrated them within Level 0 (L0) YTK backbones, in accordance with each of the sensors’ specifications (Supplementary Notes 1). We then screened a selection of 15 commonly used fluorescent proteins (FPs) to identify the ones best suited for use as complementary reporter units (Supplementary Table 2). Specifically, we grouped our selected FPs according to their spectral properties and evaluated their respective in vivo brightness, with bright FPs promoting response strength and, by proxy, functionality of sensing constructs. We observed mTagBFP2, mNeonGreen (referred to as mNeon), eCitrine and mScarletI to consistently display the strongest BFP, GFP, YFP, and RFP signals respectively (Supplementary Fig. 1), and thus integrated them within their own L0 backbones to be used as reporters moving forward. While FP brightness is critical, it is worth noting that other FP properties may also need to be considered for a given application. For example, an FP’s pKa should be taken into account when implemented within potentially acidic conditions or its photostability within time-lapse experiments45.

With each of the sensing units and selected reporters integrated as standardised L0 parts, modular reconstitution of operational sensing constructs was achieved through the hierarchical assembly of promoter, coding sequences (CDS) and terminator parts into transcription units. This modular approach allows for much flexibility in the design of sensing constructs, especially when leveraging the numerous readily available YTK-parts. For instance, separation of the sensing and reporter parts enables interchangeability of reporters, which is paramount to ensure no signal overlap when multiplexing of multiple sensing constructs. Similarly, the large catalogue of available YTK promoter parts facilitates fine-tuning of sensing construct’s expression levels, which is especially pertinent when optimisation of its sensitivity (i.e., detection range) and dynamic range (i.e., response strength) is required within a given experimental set-up or strain. Of note, the degree of modularity will be dependent on the specifics of a sensing unit, with, for example, modularity in reporters of FRET and FRET-like sensors being restricted due to their sensing mechanism and being intrinsically linked to their fluorescent properties.

Sensing units part characterisation
To ensure that the isolated sensing parts retained functionality within our YTK-ScBionsense toolkit, each was assembled into operational sensing constructs (Supplementary Fig. 2) and validated at the population-level using assays tailored to the targeted metabolites. mNeon was leveraged as a reporter for TF-based and RNA-based constructs, while pTEF1, a robust high-expression promoter, was used for construct expression when applicable. Readout was normalised to mScarletI signal, placed downstream of the sensing unit and under constitutive pTEF2 expression, to correct for any cell-to-cell variation in expression levels which may otherwise bias sensing output. Such precautions were not included for growth sensing constructs as constitutive promoters, including pTEF2, tend to themselves be impacted by growth. This was also not required for FRET and FRET-like constructs as they are ratiometric by design, with variations in their expression levels inherently accounted for in their output. All sensing constructs were integrated within the genome to mitigate any heterogeneity which may arise from uneven plasmid expression12.

pHluorin2 and pYRE-mNeon, targeting pHi and NADPH levels respectively, were validated by exposing construct-expressing cells to either a weak acid or diamide treatment. Exposure of cells to weak acids, including sorbic and acetic acid, induces acidification of cells’ pHi46,47, while exposure to diamide, a thiol oxidising agent, depletes their NADPH pools35. In both cases, a dose-dependent sensing response was observed, with pHluorin2 signal dropping 38% and 47% upon addition of 5mM and 10mM sorbic acid respectively and a strong positive correlation (Pearson coefficient, ρ=0.97) being observed between diamide concentrations and pYRE-mNeon signal 2h post-treatment, coherent with pYRE expression being induced by reduced NADPH levels (Fig. 2A, B).

mNeon-FBPs was validated by shifting cells pre-grown on glucose to media with either glucose or pyruvate as a carbon source. Glucose is broken down to pyruvate through the glycolytic pathway as part of both fermentation and respiration. Glucose-grown cells thus have a greater dependency on glycolysis for energy, resulting in higher glycolytic flux, elevated FBP levels and by consequence, reduced FBPs signal32,48. Despite an initial drop, mNeon-FBPs signal in pyruvate-shifted cells steadily increased over time, reaching levels 65% higher than those of glucose-shifted cells 5h post shift, in accordance with the sensing construct’s expected behaviour (Fig. 2D, left). Pyruvate-shifted cells encoding for pFBP-2_6. sensor, Ortega et al. original construct used here as our positive control32, displayed a similar trend though its signal increase was more muted, at 53% 5h post shift, implying a weaker dynamic range (Fig. 2D, right). Control constructs in which the functional FBP-sensitive ribozyme was absent showed no significant differences in sensing signal upon shift, confirming the trends observed to indeed occur as a result of ribozyme activity (Supplementary Fig. 3).

Validation of the ATP-sensing constructs was performed by treating pRS425-PercevalHR (pPercervalHR), PercevalHR, and yAT1.03-expressing cells pre-grown on ethanol with antimycin A (AA), an inhibitor of respiration. pPercevalHR refers to the original plasmid-expressed version of the biosensor, while PercevalHR refers to our genome-integrated variant. PercevalHR and yAT1.03 respectively displayed a 1.69- and 1.19-fold reduction in sensing signal from their pre-exposure baselines upon AA treatment, consistent with ethanol-grown cells primarily relying on respiration for energy generation and thus losing their ability to maintain ATP levels upon treatment (Fig. 2F, G). pPercevalHR, by contrast, showed no significant change in signal following exposure to AA, likely due to the strong noise present within its sensing output (Fig. 2G, Supplementary Fig. 4A). No significant signal change was either observed upon exposure to solvent with no AA for any of the sensing constructs, confirming signal reduction to indeed be due to AA activity (Supplementary Fig. 4B).

Lastly, validation of growth-responsive constructs was performed by growing pRPL28-, pRPL31B-, or pHXT5-mNeon expressing cells to saturation, with growth rate and signal change being recorded at every time point of growth curve, while pGPD2-mNeon validation involved cloning of the construct into WT and gpd1Δgpd2Δ KO strains and comparing their resulting sensing signals. While pRPL28-mNeon showed a positive correlation between its signal change and growth rate, in line with expectations, pHXT5-mNeon and pRPL31B-mNeon both yielded signals too weak for a meaningful correlation to be detected and were therefore dropped from further consideration (ρ=0.50, 0.00, and 0.09 respectively, Fig. 2C). gpd1Δgpd2Δ KO cells encoding for pGPD2-mNeon exhibited a signal 1.20x stronger than that of WT cells, indicating increased NADH levels (Fig. 2E). This aligns with previous studies which have shown gpd1Δgpd2Δ double mutants to exhibit a reduced capacity to convert NADH back to NAD+, leading to accumulation of NADH49, and thereby confirms pGPD2-mNeon functionality.

Our YTK-ScBiosense toolkit thus retains 7 sensing parts which can be combined with 4 FP reporters, 23 promoters and 13 integration vectors, 3 readily available within the YTK toolkit and another 10 targeting neutral sites from the MYT toolkit50 (Supplementary Fig. 5). It is worth noting that the evaluation of sensing units in combination with alternate FPs as reporters, that is, mTagBFP2, eCitrine and mScarletI, revealed variability in their performance, thereby highlighting the importance of assaying functionality before use of any newly constructed biosensor (Supplementary Notes 2, Supplementary Figs. 6, 7, 8). The expression of all tested constructs had a minimal impact on cell growth kinetics, suggesting minimal burden (Supplementary Fig. 9).

Systematic analysis of metabolic environmental responses
As isogenic heterogeneity is primarily associated with promoting survival when faced with environmental perturbations, we next sought to use our sensing constructs to systematically quantify the metabolic response of single cells when shifted from rich YPD media to 10 different media conditions, each presenting a range of different metabolic challenges51. These included minimal media with both excess (SMD) and limited (NLIM_PRO, NLIM_GLN, NLIM_UREA, NLIM_NH4) nitrogen, with the nitrogen sources for the latter varying in their preferential utilisation by S. cerevisiae52. Conditions in which fermentation (YPD, SMD) vs respiration (YPEtOH, SMEtOH) are favoured were also tested, in addition to one carbon starvation condition (CSTRAVE), and one condition in which the TOR signalling pathway, a key regulator of growth53, was inhibited by rapamycin (RAPA).

Population-level analysis of cells’ metabolic response showed a reduction in pHi upon shift to CSTRAVE and maximal growth following shift to YPD when compared to all other conditions, consistent with previous well-established findings (Fig. 3A, Supplementary Figs. 10, 11)54–57. Results from pGPD2 and pYRE also aligned with recent findings on cofactor abundance under respiratory vs fermentative growth58, with cells displaying altered NADH dynamics upon shift to YPEtOH when compared to YPD, as evidenced by a decrease in pGPD2 signal, while NADP+:NADPH levels remained constant across conditions. Interestingly, we also observed elevated FBP levels, as indicated by reduced mNeon-FBPs signals, upon shift to CSTRAVE and SMEtOH, which may indicate increased gluconeogenesis to compensate for the low glucose levels present within these conditions or, alternatively, could arise as a result of pHi biasing reporter output. Indeed, the pHi of stressed cells has been reported to reach pHs as low as 4.755 while the pKa of mNeon hovers at 5.445. It is thus possible that the drop in pHi observed within certain CSTRAVE and SMEtOH-shifted cells may be inducing deprotonation of mNeon, resulting in artificially low and heterogeneous reporter outputs – as was observed within strains encoding for mNeon under unregulated, constitutive expression (Supplementary Fig. 12). mScarletI, by contrast, has a pKa of 4.045 and displayed robustness to changes in pHi, prompting us to repeat the shift to these conditions using sensing constructs which made use of mScarletI as a reporter – when feasible – to ensure no bias carried over to our analysis of heterogeneity.

Isogenic cell-to-cell variations can be broadly categorised into micro- vs macro-heterogeneity. Micro-heterogeneity refers to the broad distribution of phenotypes that may occur along a given metric, while macro-heterogeneity refers to the clustering of phenotypes into distinct subpopulations. While micro-heterogeneity is typically quantified using robust coefficient of variance (rCV), a metric of a given distribution’s spread59, macro-heterogeneity may be quantified using the Hartigan’s dip test, a measure of multimodality60 (see Methods). Using the dip test statistic (Dn) as a measure of macro-heterogeneity, we observe a uniform metabolic response following shift to most media conditions, as evidenced by shifted cells displaying a unimodal signal distribution with low Dn values across all sensing constructs (Fig. 3B, Supplementary Fig. 10). Deviations did, however, arise, the most significant of which occurred in response to acute depletion of glucose. Indeed, cells shifted to CSTRAVE and SMEtOH, two conditions characterised by sudden glucose starvation-induced stress, consistently differentiated into stable, metabolically distinct subpopulations across multiple metabolites, including pHi and absolute ATP levels. Cells shifted to YPEtOH did not yield the same differentiation pattern despite the lack of glucose, possibly due to elements of the nutrient-rich YP- media compensating or, alternatively, delaying the onset of heterogeneity. The latter is supported by the observation of a skewed pHi distribution 8h post shift to YPEtOH, typically indicative of an emerging subpopulation (Supplementary Fig. 10).

Characterisation of CSTRAVE-induced pHi subpopulations
Macro-heterogeneity in response to acute depletion of glucose was most pronounced within pHluorin2’s sensing signal and upon shift to CSTRAVE media, prompting us to further investigate glucose-induced subpopulation differentiation using these conditions as a case study. Clustering of cells based on their pHi, as determined by pHluorin2 signal, revealed two dominant subpopulations, with approximately ~20% of cells exhibiting a significant drop in pHi post shift (p1), while the remaining majority maintained pHi levels closer to those observed under optimal conditions (p2). Co-expression of pHluorin2 and pRPL28-mScarletI sensing constructs showed the subpopulations to also follow distinct growth patterns, with p2 displaying greater growth capacity compared to p1 4h post shift (pRPL28 signal = 0.94 and 0.85 a.u. respectively, Fig. 3C). p2’s pRPL28-mScarletI signal later decreased to match that of p1 at 8h post shift, suggesting p2’s ability to maintain growth could not be sustained within prolonged starvation conditions.

A closer look at differentiation dynamics using time-lapse microscopy revealed all cells to experience a slight drop in pHi immediately post shift, with the pHi of p2 cells bouncing back as time progressed while that of p1 cells continued to decrease, until it stabilised at around 4h post shift (Fig. 3D, Supplementary Fig. 13A). These results also showed the subpopulations to remain stable throughout the observation period, persisting 15h post shift with no cells transitioning between the subpopulations. Sorting of cells based on their pHluorin2 signal post shift further revealed p2 to have increased viability, as evidenced by a higher CFU count when plated on rich YPD media (424 and 132 CFUs for p2 and p1 respectively), and greater persistence, with p2 cells showing a 3.35-fold increase in survival rate when stored in PBS for 7 days (26.5% vs 7.63% survival rate for p2 vs p1 respectively, Supplementary Fig. 13B, C). p2 cells also displayed better regrowth dynamics when shifted back to rich YPD media, as demonstrated by them reaching saturation faster, thereby suggesting an improved capacity to resume growth when the right conditions reappear (Supplementary Fig. 13D). Lastly, the sorted subpopulations were plated on rich YPD media to reset their phenotype before being shifted back to CSTRAVE media. Doing so resulted in the emergence of p1 and p2 within all of the colony isolates tested, irrespective of the subpopulation’s history, thereby confirming the subpopulations to occur as a result of isogenic heterogeneity and not recurring spontaneous mutations (Fig. 3E).

Together, these results align with previous reports of glucose starvation-induced subpopulation differentiation. For instance, in their model of cellular differentiation, Davidson et al. posit that cells enter either a state of quiescence or non-quiescence upon depletion of glucose, with quiescent cells retaining greater reproductive capabilities, in line with the behaviour identified from p261. Bagamery et al.'s bet-hedging hypothesis similarly describes two distinct phenotypic responses to glucose starvation, correlating fermentation pre-glucose starvation to a drop in pHi, collapsed mitochondria and cellular arrest6,62. This is in contrast to cells undergoing respiration which, despite having a slower growth rate under optimal conditions, are posited to have a greater ability to adapt and thus resume growth upon depletion of glucose. These observations parallel the behaviour observed from p1 and p2 subpopulations, with p2 not only exhibiting improved growth but also displaying greater viability and persistence in response to glucose depletion.

pHi subpopulation dynamics within batch cultivation
Fermentation of industrially relevant products often requires extended cultivation, beyond glucose depletion and density saturation, to allow for product accumulation and yield maximisation63–65. Accordingly, and given the widespread occurrence of low-producing subpopulations within bioproduction, we next pondered whether the pHi subpopulations we previously observed would also emerge within batch cultivation as a result of gradual glucose depletion and what impact they may have on the production efficiency of microbial cell factories (MCFs). To that end, pHluorin2 was introduced within strains encoding for either the violacein- (Fig. 4A) or lycopene- (Fig. 4B) production pathway and cultivated in flasks mimicking batch fermentation conditions. Fully differentiated subpopulations appeared within both MCFs following 24h of flask batch fermentation and persisted throughout the cultivation process (Fig. 4D, Supplementary Fig. 14).

Interestingly, the onset of differentiation occurred earlier within violacein MCFs, with subpopulations emerging following 16h of cultivation, in direct correlation with cultures reaching maximum carrying capacity and glucose levels being fully consumed (Supplementary Fig. 15). Lycopene MCFs, by contrast, grew at a slower rate, with remaining glucose levels being 41 times that of violacein-producing cultures at 16h (0.83 g L−1 as opposed to 0.02 g L−1), seemingly resulting in delayed differentiation by comparison. No subpopulations emerged from fed-batch-like flask fermentation, characterised by glucose pulses being periodically supplemented to cultures, thereby reinforcing the importance of glucose depletion within the differentiation process (Supplementary Fig. 14).

Lycopene- and violacein-producing strains both exhibited subpopulation dynamics comparable to those observed following shift to CSTRAVE media, with differentiation triggering a sharp decline in p1’s pHi whereas p2 maintained levels closer to those of the population pre-differentiation (Supplementary Fig. 16). p1’s signal remained steady upon differentiation, regardless of the product or sampling time, whereas p2’s signal showed a gradual decline, dropping from 1.23 and 1.29 to 1.00 and 1.06 a.u. following 24h to 72h of cultivation within violacein and lycopene MCFs respectively (Supplementary Fig. 14).

Curiously, while the relative frequency of p1 when compared to p2 remained stable throughout the cultivation process, it differed between the products, persisting at approximately ~20% and ~40% of the total violacein and lycopene producing population, respectively. This discrepancy may arise from a range of factors, most notable of which is the differences in metabolic loads imposed by the two production pathways, with them pulling resources away from distinct metabolic pathways, which could promote the activation of different stress responses or influence the cell’s predispositions to enter a stress state.

pHi subpopulations as a source of production heterogeneity
Clustering of violacein-producing cells based on their pHluorin2 signal showed p1 to directly correlate with increased violacein production, as indicated by increased detection of violacein autofluorescence (Fig. 4E–G). Indeed, p1 displayed an enhanced ability to produce and accumulate violacein, with its autofluorescence measurements increasing 7.36-fold from 0.28 a.u. to 2.06 a.u. following 24h and 72h of cultivation respectively (Fig. 4F). The accumulation of violacein within p2 was, by contrast, much less pronounced, showing an 82% reduction in efficiency when compared to p1 at the end of the cultivation period. Microscopy analysis of pHluorin2-sorted subpopulations confirmed these findings, with violacein autofluorescence being detected exclusively within cells belonging to the p1 subpopulation, but not from p2 (Fig. 4G). Strikingly, the trend observed within lycopene-producing cells was reversed (Fig. 4I). Single-cell Raman microscopy of sorted subpopulations revealed lycopene peaks to be 3.71x more pronounced within p2 compared to p1, with a peak area of 0.074 a.u. for p2 compared to 0.020 a.u. for p1 (Fig. 4I left). Consistent with these measurements, the pellet of p1 was noticeably lighter in colour relative to that of p2 (Fig. 4I right). The mixed population, containing both the p1 and p2 subpopulations, showed a 40% reduction in lycopene production when compared to p2, in line with the subpopulation ratios and further highlighting the importance of implementing strategies aimed at eliminating p1 for yield optimisation.

Lycopene is derived from acetyl-coA, an intermediate of respiration66, while violacein is derived from L-tryptophan, an amino acid which has been shown to promote tolerance to a range of different stressors67–69. The distinct behaviours in product accumulation may thus arise from the distinct metabolic responses between the subpopulations, with p2 displaying increased mitochondrial staining, indicative of enhanced respiratory capabilities (Supplementary Fig. 17), and p1 displaying greater cellular stress, as indicated by its lower pHi.

Modulating subpopulation dynamics to promote production
Having demonstrated the presence of subpopulations to impair overall production efficiency of industrially relevant metabolites, we next decided to investigate whether the implementation of informed cultivation conditions could be used to fine-tune subpopulation ratios — with the goal of optimising production levels. Specifically, we explored the potential of galactose as a carbon source to alleviate the burden imposed by p1 only copene production, by favouring differentiation to p2. Galactose-grown cells have been shown to display reduced overflow metabolism and increased respiration levels70,71, which we hypothesised could contribute to increased p2 frequency, in line with previous observations by Bagamery et al.6. To test this hypothesis, lycopene-producing strains were grown within deep-well plates with either 2% galactose or 2% glucose as a carbon source, with the resulting subpopulation dynamics and lycopene production analysed. Results showed the relative frequency of p1 to drop significantly under galactose conditions, with p1 comprising 69.8% of the total cell population when grown on glucose and dropping to 31.6% under galactose cultivation conditions (Fig. 5A, E). This altered subpopulation dynamics paralleled the changes observed in single-cell lycopene production under both culture conditions, whereby galactose cultivation prompted a shift from low- to high- producing phenotypes when compared to glucose cultivation (Fig. 5B). The decrease in p1 frequency also coincided with enhanced lycopene production, reaching 2.12 mg L−1 within galactose cultivation, a 1.94-fold increase from the 1.09 mg L-1 seen under glucose cultivation, and enhanced production relative to biomass, increasing from 0.021 mg g−1 to 0.035 mg g−1 under glucose and galactose cultivation respectively (Fig. 5C, D). This carbon source-dependent pattern persisted within micro-bioreactor cultivation and under pHi buffering conditions, confirming robustness of the observed subpopulation dynamics, including to changes in external pH (Fig. 5E). Trends in production under these conditions were also consistent with those observed within deep-well plates, although the reduced culture volumes and biomass of micro-bioreactors promoted variability within the resulting production measurements (Supplementary Fig. 18A). Of note, switching carbon source from glucose to galactose had a similar effect of the pHi dynamics of violacein-producing populations, with their relative p1 frequencies dropping 22% from glucose to galactose cultivation (Supplementary Fig. 18B).

Together, these results exemplify how tuning subpopulation dynamics toward a desired phenotype may improve production efficiency, and thereby highlight the importance of understanding these subpopulations and their differentiation mechanism to inform the cultivation and engineering of better producing strains. This may be all the more crucial to take into account during scale-up as uneven mixing within bioreactors creates micro-environments that are deprived of glucose, which may be sufficient to trigger differentiation and thus further amplify the phenomenon72.

In conclusion, we report a standardised and modular framework which leverages the YTK-ScBiosense toolkit, comprised of 7 different sensing parts, each targeting different core intracellular metabolites, to enable robust characterisation of single cell’s metabolic state. We demonstrated this framework’s ability to not only systematically quantify single cells’ metabolic responses to environmental perturbations but also to identify and isolate metabolically distinct subpopulations, with depletion of glucose in particular emerging as a strong trigger of subpopulation differentiation. Further characterisation of the observed subpopulations using pHi as a subpopulation marker corroborated previous evidence of a bet-hedging strategy, and revealed pHi subpopulations as drivers of bioproduction heterogeneity and markers of bioproduction efficiency – thereby bypassing the need for single-cell product quantification to guide bioprocess design. Lastly, we highlight fine-tuning of cultivation strategies as a method to modulate subpopulation dynamics towards a desired phenotype, correlating this change in subpopulation abundance with improved bioproduction yields. Future multi-omics works aimed at elucidating the documented divergent product-dependent responses and their underlying mechanisms – including to account for potential reversible genetic mutations – could not only inform additional cultivation strategies but also engineering targets to enhance both strain productivity and stability.

Methods
Strain and growth conditions
The S. cerevisiae strain used throughout this study was BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0). CEN.PK2-1C WT (MATa his3 D1 leu2-3_112 ura3-52 trp1-289 MAL2-8c SUC2) and its gpd1Δgpd2Δ KO (gpd1::TRP1 gpd2::LEU2 his3::HIS3) were used for characterisation of pGPD2 sensing constructs. With the exception of the shift experiments and unless stated otherwise, strains were grown on either rich YPD media – 20g L−1 peptone (Merck), 10g L−1 yeast extract (Sigma), 2% glucose (VWR) – or selective SC-ULH media – 6.7g L−1 yeast nitrogen base without amino acids (Sigma), 1.4g L−1 yeast synthetic dropout supplement lacking uracil, leucine and histidine (Sigma), 1% glucose.

Assembly of sensing constructs
Plasmids were constructed using Golden Gate assembly, as outlined by Lee et al.27. Fluorescent proteins and sensing units of interest were isolated from either genomic DNA (pRPL28, pRPL31B, pHXT5 and pGPD2), pCfB4170 (Addgene: #126750, pYRE, mKate2), pDRF1-GW yAT1.03 (Addgene: #132781, yAT1.03v1), pRS425-PercevalHR (NBRP: BYP9646, PercevalHR), pFBP-2_6.sensor (Addgene: #162800), pCggRO-reporter (Addgene: #124582, eCitrine), gblocks (sequences from Mahon et al.31, pHluorin2, and from Shaner et al.73, mNeonGreen), or plasmids readily available within the RLA collection (all remaining FPs), via PCR amplification using primers encoding for BsmBI restriction site overhangs. Amplified sequences were purified using a PCR clean up kit (NEB) and assembled within their corresponding backbones via L0 Golden Gate reactions (Supplementary Notes 1). Golden Gate reactions were prepared as follows: 50 fmol of each DNA insert, 25 fmol of backbone, 1 μL T4 DNA ligase buffer (NEB), 0.5 μL T7 DNA ligase (NEB), 0.5 μL BsmBI restriction enzyme (NEB), and ddH2O (Invitrogen) to bring the final volume to 10 μL, and incubated using the following thermocycler program: 30 cycles alternating between digestion and ligation (42°C for 2 min, 16°C for 5 min) followed by a final digestion step (60°C for 10 min) and a heat inactivation step (80°C for 10 min). They were then transformed into E. coli using a standard heat shock transformation protocol, with assembled plasmids later extracted via miniprep using QIAprep spin miniprep kit (QIAGEN). Of note, mTurquoise2, Venus, and mRuby2 were already available as L0 parts of the YTK toolkit27.

Higher order assembly of transcriptions units and multi-cassette constructs were completed using L1 and L2 Golden Gate reactions. These were prepared identically to L0 reactions, with the exception of DNA inserts and backbones which were replaced by L0 parts or L1 cassettes for L1 and L2 inserts respectively, and by integration vectors targeting desired integration loci for the backbone. BsmBI was also replaced by BsaI (NEB) for L1 reactions. When multi-cassette assembly was desired, transcription units were first cloned into pre-assembled intermediary vectors containing the appropriate combination of ConL sequences.

A summary of all L0 parts constructed for the YTK-ScBionsense toolkit can be found in Supplementary Table 2, while final sensing constructs and sensing strains are available within Supplementary Data 1 and 2.

FP brightness measurements
Single FP-expressing colonies were grown to saturation in SC-ULH, diluted 1:50 in 2 mL of fresh medium, and incubated at 30°C for 4h, with 250 rpm shaking, until an OD ~0.6 was reached. 30 μL of culture was then diluted in 200 μL PBS (Invitrogen) and analysed using Attune NxT flow cytometer (ThermoFisher), while another 500 μL was pelleted, resuspended in 5 μL PBS, and transferred to microscopy glass slides for analysis on Eclipse Ti microscope fitter with a Hamamatsu ORCA Flash4 Camera with CoolLED p300 light source (Nikon). The Attune’s VL1-H, BL1-H, and YL2-H channels and the Nikon’s DAPI, GFP, and Cy3 filter cubes were used for BFP, GFP, and RFP data collection respectively. Brightfield microscopy images were also collected for cell segmentation purposes, performed using a pipeline adapted from YeaZ model74. For microplate measurements, overnight cultures were diluted to an OD of 0.1 in 150 μL of fresh media within wells of black μClear 96-well plates (Greiner). Plates were then sealed with Breathe-Easy membranes (Sigma) and incubated within Spark microplate reader (Tecan), set at 30°C with 200 rpm orbital shaking. OD600 and fluorescence measurements were taken following 20h of incubation, with 400nm (20), 485nm (20), 560nm (20) excitation and 465nm (35), 520nm (10) and 620nm (20) emission filter settings being used for BFP, GFP and RFP detection respectively.

Functionality assays
For microplate perturbation, KO, and growth assays, sensing isolates were grown to saturation in SC-ULH before being diluted to an OD of 0.1 in 150 μL of fresh, pre-aliquoted media within wells of 96-well plate. Plates were then sealed and setup within microplate reader (30°C, 200 rpm orbital shaking), with OD600 and fluorescence at excitation 400nm (20) and 485nm (20) and emission 520nm (10) – corresponding to pHluorin2 signal – on top of regular BFP, GFP and RFP measurements recorded every 30 min. At OD ~0.4, equivalent to exponential phase, sorbic acid (final concentration 10mM and 5mM) and acetic acid (12mM) was added to pHluorin2-expressing strains, while diamide (final concentration 0, 0.5, 1 and 2mM) was added to pYRE-expressing strains. Plates were then placed back into microplate reader and measurements pursued every 15 min for up to two hours.

To obtain growth dynamics and for validation of pGPD2-expressing strains, no interruption or treatment was introduced and, instead, measurements continued until saturation was reached. Exponential phase of pGPD2-expressing strains was determined post-experiment from analysis of growth curves, while stationary phase points were excluded from analysis of pRPL28-, pRPL31B- and pHXT5-results as to avoid skewing data with an abundance of points at µ≈0. Negative growth rates were also excluded from pRPL28-, pRPL31B-, and pHXT5-results as these indicate cell death, which could bias the sensing results.

For condition shift assays, single FBPs-expressing colonies were grown to saturation in SC-ULH, diluted 1:50 in 2 mL of fresh medium and incubated at 30°C for 4h, with 250 rpm shaking, until an OD ~0.6 was reached. Cells were then pelleted, washed twice in PBS, and resuspended to an OD of 0.1 in fresh SC-ULH media with either 1% glucose or 1% pyruvate (Sigma) as a carbon source. 150 μL of resuspended culture was then aliquoted into wells of 96-well plate, which was sealed and setup within microplate reader (30°C, 200 rpm orbital shaking). OD600, BFP, GFP, and RFP fluorescence measurements were recorded every 15 min for up to 5h.

Lastly, tracking of ATP levels in response to AA followed a protocol adapted from Luzia et al.75. Single ATP-sensing colonies were grown in SC-ULH with 1% EtOH (Fisher Scientific) for 48h, to let cells acclimate to their new carbon source, before being passaged 1:20 in 2 mL fresh medium for another 24h. 120 μL of culture was then sampled, diluted in 800 μL fresh media, and analysed using the Attune NxT flow cytometer. VL1-H, VL2-H, VL3-H and BL1-H channels were used for signal detection, with VL3-H:VL1-H and BL1-H:VL2-H corresponding to yAT1.03 and PercevalHR signals respectively. Flow rate was set to 25 μL/min and constrained to 2000 events/sec. Samples were left to run for 2 min to determine baseline signal before being treated with 100 μL of 0.5mM AA (Sigma) diluted in 5% EtOH. Samples were then left to run for another 5 min to track signal change. In between samples, 1×2 min and 2×2 min rinses were performed with 100% EtOH and MQH2O, respectively, to wash off any remaining AA and EtOH.

When applicable, fluorescent measurements were normalised to OD600, and fold increase to control condition was calculated. This allowed for response magnitude of different sensing construct variants to be compared.

Medium shift
Single sensing colonies were grown to saturation in YPD before being diluted to an OD of 0.1 in 5 mL of fresh media and left to incubate at 30°C for 4h, with 250 rpm shaking. Cells were then pelleted, washed twice in PBS, and resuspended to an OD of 20 in PBS. 10 μL of resuspended culture was then diluted in 2 mL of condition media (YPD, RAPA, YPEtOH, SMD, SMEtOH, NLIM_PRO, NLIM_GLN, NLIM_NH4, NLIM_UREA, CSTRAVE) within 14 mL glass culture tubes, and placed back to incubate at 30°C. SM-media were composed of 6.7g L−1 YNB base without amino acid and 38mM (NH4)2SO4 (Sigma). NLIM-media were composed of 1.7g L−1 YNB base without amino acid or ammonium sulphate (Sigma), supplemented with 0.8mM of the relevant nitrogen source, while CSTRAVE media was composed of the same base with 1mM (NH4)2SO4 as a nitrogen source. 1% glucose or 1% EtOH was used as a carbon source, with the exception of CSTRAVE, for which no carbon source was provided. 4h and 8h post shift, 20 μL of cultures were aliquoted and resuspended in 200 μL of PBS. Fluorescence of 10,000 events was quantified using Attune NxT, using a flow rate of 100 μL/min. pHluorin2 signal was determined using VL2-H:BL1-H ratio, while BL1-H:YL2-H enabled detection of mNeon-FBPs, pGPD2-mNeon, and pYRE-mNeon signals. pRPL28 signal (BL1-H or YL2-H when multiplexed with pHluorin2) was normalised to cell size, as determined by FSC-H, while yAT1.03 and PercevalHR signals were quantified using VL3-H:VL1-H and BL1-H:VL2-H respectively. YRE-mScarletI signal (YL2-H) in CSTRAVE and SMEtOH-shifted cells was also normalised to cell size to mitigate bias from pHi effects on mNeon control output, with cell size displaying a direct correlation with constitutive FP expression (Supplementary Fig. 12). For RAPA condition, cells were first grown in YPD, with 200 ng.mL−1 rapamycin (Sigma) treatment added 30 min prior to 4h time point.

Time lapse microscopy
CSTRAVE-shifted pHluorin2-expressing cells were immediately transferred to black μClear 96-well plates and coated with concanavalin A to restrict cell movement. Cells were then imaged every 10 min for a total of 15h using the Eclipse Ti2 Twin-Cam-TIRF microscope fitted with two Prime95B cameras with CooLED 4000 light source (Nikon) and an environmental chamber to maintain temperature at 30°C. Resulting pHluorin2 signal was quantified by overlaying emission signals detected at 535nm following excitation at 375nm and 480nm. Signal from single cells were tracked using the TrackMate plugin in ImageJ/Fiji, using the LoG (Laplacian of Gaussian) detector for cell segmentation and a simple LAP tracker.

Subpopulation characterisation
Sorting of pHluorin2-expressing cells into p1 and p2 subpopulations was performed using the CytoFLEX SRT Cell Sorter (Beckman Coulter), based on manually defined FSC-A:FSC-H gates (filtering of debris, doublet, and budding yeast) and V525-H:B525-H fluorescence gates (pHluorin2 subpopulation classification). Analysis of 5,000 events was completed prior to sorting to allow for gate adjustment when necessary. To obtain CFU counts, 1,000 cells per subpopulation were sorted, resuspended in 100 μL of PBS, plated on YPD, and incubated at 30°C overnight. Resulting colony count performed using Reshape Biotech.

For chronological lifespan assay, 500,000 cells per subpopulation were sorted into distinct 5 mL tubes, sealed with parafilm to prevent evaporation, and stored at 30°C. Viability of cells after 0, 3, and 7 days was determined by quantifying permeability to propidium iodide (PI, Merck). 1 μL of 1 mg/mL PI was added to 200 μL of sorted cell sample, with the resulting fluorescence being analysed using Attune NxT (YL2-H). Live (cells at exponential phase) and dead (cells resuspended in 70% EtOH and incubated at room temperature for at least 30 min) controls were used to determine fluorescence thresholds of viability.

To determine regrowth dynamics, 10,000 cells per subpopulation were directly sorted into 8 wells of 96-well plate containing 200 μL of pre-aliquoted YPD. Empty well buffers were placed between each sorted subpopulation, ensuring no cross-contamination due to splashing. Plates were then sealed using a Breathe-Easy membrane and analysed using the CLARIOstar microplate reader (BMG Labtech), with OD600 being recorded every 30 min for 24h. Incubation was set at 30°C, with 250 rpm double orbital shaking. Lastly, to analyse re-shift dynamics, 1,000 cells were sorted per subpopulation, resuspended in 100 μL of PBS, before being plated on YPD. Single colony was then inoculated in YPD to be shifted back to CSTRAVE media, as described in “Medium shift” section.

Production strain cultivation
Lycopene and violacein production pathways were introduced within pHluorin2-expressing strains, with resulting strains detailed in Supplementary Data 3. Flask fermentation was achieved by inoculating single colony isolates to saturation before diluting them to an OD of 0.1 in 20 mL of fresh YPD within 100 mL flasks. Flasks were then placed to incubate at 30°C, with 250 rpm shaking. Every 24h, 10g L−1 glucose was added to fed-batched -like culture flasks while batched culture flasks remained untouched. For determination of heterogeneity onset, 160 μL of culture was sampled at each of the specified time point, with 10 μL being diluted in 200 μL PBS and analysed for pHluorin2 signal quantification using Attune NxT (VL2-H:BL1-H), 50 μL diluted in 950 μL fresh YPD for OD600 recording using spectrophotometer, and the supernatant of the remaining 100 μL extracted and analysed for glucose quantification using HPLC.

To compare impact of glucose vs galactose cultivation, deep-well plates and micro-bioreactor were leveraged. Single colony isolates were grown to saturation before being diluted to an OD of 0.1 in 3 mL or 1 mL of fresh YP- media, with either 2% glucose or 2% galactose (Sigma), within 48 deep-well plates (Enzyscreen) or BioLector XT/Microfluidic Flower plates (Beckman Coulter) respectively. Plates then being sealed using Aeraseal film (Sigma) and placed to incubate at 30°C, with 700 rpm shaking within Multitron Standard incubators (Infors HT) for deep-well plates, or with 800 rpm shaking within BioLector XT Microbioreactor fitted with microfluidic module, for Flower plates. When specified, culture pH was monitored every 10 min, with 1M NaOH or 1M HCl automatically supplemented to maintain a constant culture pH of 5.0. After 72h of cultivation, 10 μL was diluted in 200 μL PBS for analysis of pHluorin2 signal on Attune NxT (VL2-H:BL1-H).

MitoTracker staining
pHluorin2-expressing lycopene MCF was grown under batch flask conditions for 72h before 50 μL of culture was sampled, washed twice in PBS, and resuspended in 1 mL PBS. 0.5 μL of 1 mM MitoTrackerTM Red FM (ThermoFisher) dye was then added, with cells being left to incubate in the dark at 30°C to allow for mitochondrial staining to occur. Post-incubation, cells were washed in PBS to remove any excess dye. 20 μL of washed sample was then diluted in 100 μL PBS and analysed using the Attune NxT flow cytometer, with MitoTrackerTM Red FM signal being detected in the YL2-H channel and normalised to FSC-H to account for variations in cell size. 5 μL of cells were also transferred to slide for analysis on Ti2 Twin-Cam-TIRF microscope, with detection of MitoTrackerTM Red FM being achieved using Cy5 HQ filter cube.

Subpopulation-level product quantification
Following 72h of batched cultivation, cells were diluted 1:50 in 5 mL of PBS and analysed on the CytoFLEX SRT Cell Sorter (Beckman Coulter), with sorting performed using manually defined FSC-A:FSC-H gates and V525-H:B525-H gates. Violacein quantification was performed in tandem to pHluorin2 signal acquisition (V525-H:B525-H), through detection of its autofluorescence signal using the R712-H:FSC-H channels – in line with spectral characterisation obtained from Aurora Spectral flow cytometer (Cytek, Supplementary Fig. 19A, B). Signal of 10,000 events were recorded, with clustering of cells based on their pHluorin2 and autofluorescence signals – as detailed in “Subpopulation clustering” section – allowing for quantification of production at the subpopulation-level. Visual confirmation of phenotypes was achieved using fluorescent microscopy. 1.5 x 10^6 cells per subpopulation were sorted, pelleted, and resuspended in 10 μL of PBS. 2 μL of resuspended culture was then transferred to a slide and analysed using the Ti2 Twin-Cam-TIRF microscope, with detection of violacein autofluorescence achieved using Cy5 HQ filter cube.

For subpopulation quantification of lycopene, Raman microscopy was used (Supplementary Fig. 19C). 5 x 10^6 cells per subpopulation were sorted and fixed through resuspension in 1 mL 70% EtOH. Resuspended cells were incubated at room temperature for 5 min, washed twice in PBS, resuspended in 2 μL of PBS, and plated on a fused silica coverslip (UQG Optics). Cells were then left to dry for a couple of minutes before being covered by a second coverslip, to ensure a dense uniform layer of single cells for analysis. Raman spectra were collected using a system comprised of an inverted optical microscope (DMIRB/E; Leica) equipped with a 40×/0.85 objective lens (Leica), a 660nm wavelength laser (Gem, Novanta Photonics), a spectrometer (IsoPlane 320; Princeton Instruments), a back-illuminated deep depletion 1600×200 EMCCD (DU970P; Andor Technology), and an automated sample stage (H30 XYZ ProScan II; Prior Scientific). A minimum of 300 measurements per subpopulation were collected by raster scanning with a 5 μm step size, as to ensure no cell was measured twice. Pre-processing of Raman spectra involved quality control by visual inspection, smoothing using SG filter, and baseline correction. Quantification of lycopene was achieved by calculating the sum of the area under three lycopene peaks centred at 1,000, 1,148 and 1,508 cm−1 with 30 cm−1 window on either side, defined using positive control, using integration via trapezoidal method.

Population-level lycopene quantification
A total of 1 mL of deep-well plate culture was sampled, washed twice in PBS, and resuspended in 250 μL acetone (Sigma). 425–600 μm glass beads (Sigma) were added to resuspended samples before lysing them using the Evolution Tissue Homogeniser (Precellys), with 8 cycles of 60 sec at 8,000 rpm and 15 sec of rest. Samples then pelleted to allow for isolation of lycopene-containing supernatant, which was analysed using HPLC. Another 1 mL of culture was pelleted, its supernatant removed, and its pellet left to dry overnight at 60°C for dry cell weight (DCW) measurements. Lycopene production to biomass was calculated by taking ratio of lycopene production to DCW.

Single cell-level lycopene quantification
5 μL of culture was diluted in 900 mL buffer A and 100 mL buffer B (proprietary, Single-Cell Biotech) and processed on yeast microfluidic chip with flow rate of 40 μL/min using Flow-mode Raman-activated Cell Sorter (FlowRACS, Single-Cell Biotech). Approximately 750 cell spectra were collected per condition, with lycopene peaks for lycopene quantification centred at 1,002, 1,154 and 1,513 cm–1 with 30 cm–1 window on either side, in line with lycopene standard and positive control.

Heterogeneity detection
Detection of macro-heterogeneity from flow cytometry data was performed as follows. Debris, doublet, and budding yeasts were filtered out by FSC-A:SSC-A and FSC-A:FSC-H gating in FlowJo (BD Life Sciences). Compensation was applied to relevant sensing data using flowCore package76 in R, with compensation matrix generated from mNeon and mScarletI single-color control analysis on Attune NxT. Heterogeneity within signal distributions was quantified using Hartigans’ dip test statistic, as calculated using the R diptest package77. Hartigan’s dip test works by assessing the likelihood that a given distribution reflects more than one mode. The null hypothesis is that of a uniform distribution, reflective of homogeneity, while deviations from the null hypothesis are quantified through increasing dip test statistics (Dn), with high Dn implying distinct subpopulations.

Subpopulation clustering
When applicable, clustering into subpopulations was performed by fitting two Gaussian distributions of variable shape, volume, and orientation onto multivariate data using R’s mClust78, with best suited model being evaluated and selected based on the Bayesian Information Criterion (BIC). Z-score standardisation of the signals was performed prior to clustering to ensure equal weighing of each clustering variable. Clustering was primarily performed to extract relative p1 frequency values.

Statistical analysis and reproducibility
All flow cytometry experiments were performed with at least three biological replicates, while characterisation experiments included a minimum of two biological replicates, with the number of technical replicates varying depending on experimental protocol. Visualisation of phenotypes using microscopy was usually performed only once, as a validation of the trends observed using flow cytometry. All attempts at replication were successful, unless specified otherwise. Statistical analysis was performed using the t.test function or wilcox.test test in R, depending on assessed normality.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents an innovative biosensor-based toolkit integrated with flow cytometry and clustering algorithms to investigate phenotypic heterogeneity in engineered yeast. By leveraging this platform, the authors dissected pH-based subpopulations and metabolite production states, offering a modular strategy to characterize cell-to-cell variation in microbial cell factories. The study is useful in synthetic biology and bioprocess optimization. However, several critical aspects require further validation, deeper exploration, and broader contextualization to enhance the study’s impact and reproducibility. The following comments should be addressed before it can be considered for publication.\n1. The authors suggest that the identified subpopulation system is suitable for industrial biochemical production. Have the authors tested this approach in scaled-up fermentations for lycopene or violacein production under optimized conditions? Data from larger-scale bioreactor experiments would strengthen the claim of industrial applicability.\n2. The subpopulation dynamics were analyzed at 4 and 8 hours. Have the authors investigated longer-term dynamics (e.g., 24 or 48 hours)? Time-resolved data over extended culture durations could reveal population stability, transitions, or emergence of new subpopulations relevant for bioproduction.\n3. The study reports differential production of lycopene and violacein between the p1 and p2 subpopulations. Have the authors performed untargeted or targeted metabolite profiling to identify other metabolic differences between these subpopulations? This could help elucidate the underlying metabolic shifts and regulatory networks.\n4. The authors should consider incorporating continuous or time-resolved tracking of subpopulation dynamics using microfluidic platforms or time-lapse microscopy. Such approaches could provide finer resolution of how individual cells transition between states, improving mechanistic insights.\n5. Since pH is a controllable parameter in bioreactor systems or flask fermentations using buffer systems, have the authors tested how buffering influences biosensor performance and subpopulation behavior? It would be useful to assess how pH control affects metabolite yields and population distribution.\n6. The authors compared lycopene production under glucose and galactose conditions. Was a similar comparison performed for violacein production? This would help determine whether the observed metabolic subpopulations are carbon source–dependent across different production modules.\n7. Have the authors quantified violacein production across different subpopulations? Quantitative data would allow for a more precise correlation with biosensor outputs and strengthen the conclusions regarding violacein-linked metabolic heterogeneity.\n8. Given the importance of redox cofactors like NADH and NADPH in biochemical production, have the authors considered engineering or characterizing subpopulations based on redox biosensors? Comparing NADH- or NADPH-linked subpopulations under different carbon sources (glucose vs. galactose) could uncover additional layers of heterogeneity impacting productivity.\n9. The authors should include a clearer and more quantitative comparison between the presented biosensor toolkit and existing platforms such as metabolomics or transcriptomics. They should also highlight the advantages in terms of temporal resolution, throughput, sensitivity, and cost to emphasize the novelty and utility of their approach.\n10. The authors should provide more detailed validation of each biosensor used in the study. Specifically, please include dose-response curves, dynamic range, linearity, response time, and potential cross-reactivity. This information is critical to assess biosensor robustness and reproducibility.", "reviewer_2": "In this study, the authors used biological sensors to detect a range of intracellular conditions in yeast at the single-cell level, aiming to characterize heterogeneity among the cell population with a homogenous genetic background that is triggered by stress. This work demonstrates the use of genetic sensors to differentiate cells in different biological states, which has the potential to identify intracellular conditions that facilitate specific cellular activities, such as biosynthesis. The manuscript provides a clear and detailed description of the characterization of genetic parts, which supports the synthetic biology community in using these genetic tools. Here are several concerns and suggestions:\n1. In Figure 3, some stresses in this study, such as nutrient depletion, may affect gene expression activities (transcription and/or translation). As genetic sensors involve gene expression, it is critical to validate that the expression rates of sensors and reporters are not affected by the changing conditions. One common way of validating a genetic sensor is to replace the genetic sensor gene with a fluorescent protein gene and show that the expression of that fluorescent protein is not affected by the changing condition.\n2. Based on the results in Figure 4, the authors hypothesize that intracellular pH affects the biosynthesis of violacein and lycopene. To validate this hypothesis, they changed the carbon source to galactose to alter cellular pH. However, a change in carbon source also altered metabolic flux, which may affect the activity of biosynthetic pathways. As demonstrated in Figure 2, intracellular pH can be modulated by a supply of a weak acid. The authors should perform a new set of experiments, providing an acid, base, or buffer to adjust the cellular pH for improving biosynthesis yield, which will generate direct, strong support for their hypothesis and show the utility of their single-cell level genetic sensing approach.\n3. There are some typos and missing characters throughout the manuscript. For example, in the Figure 2 legend, “(B) Change in pYRE-mNeon signal following exposure to varying concentrations of diamide dashed line) …...” is missing an open bracket: “(B) Change in pYRE-mNeon signal following exposure to varying concentrations of diamide (dashed line)”. In Figure 3A, an x-axis marker is missing.", "reviewer_3": "This is a very solid paper addressing (1) a framework about how to uncover metabolic heterogeneity, (2) finding a link between heterogenetiy in pHi and bioproduction, and (3) how to use that information to improve bioproduction. The first part is general and is based largely on the testing of a standardized set of intracellular reporters and associated fluorescent proteins. The second and third parts are a specific exemplar case whereby low glucose conditions trigger a split into distinctly different metabolic phenotypes that affect, in opposite ways, production of lycopene and violacein.\nThis question is rather important and is only just beginning to build traction in the research community. While it is applied here to bioproduction in yeast, single-cell variability has been seen to be critical to many areas of microbiology, ranging from pathogenesis to antibiotic treatment. This paper excels in terms of providing a useful toolkit for yeast and in providing a key example to point to for why this matters. I have often found people can be convinced heterogeneity exists (it does!) but are less inclined to believe it actually matters for a phenotype of their interest. There we often do not know and have limited examples to use that can come across as anecdotes. This paper is important in providing a pathway to more systematically rooting those out. For both the work on the tools and the pH connection to production of these pigments, the work well-supports the claims.\nOne suggestion I have would be to be a bit more clear in the writing of the paper of this duality between a general set of tools and approach and then transition to an exemplar that was enabled because several internal states were followed across several conditions to then explore what effect that might have on bioproduction. Having read the title, I was a bit surprised by the flow that ended up occurring and making this more explicit could be useful.\nMy one concern with the data is that the rapid re-establishment of two phenotypes does not necessarily preclude a genetic basis. Normal point mutations would not have the potential to do this, but hyper-variable genetics can. This could be either regions that flip (phase variation, like what is used with Flp-FRT) or copy-number variations of local duplications (which can be rapidly selected upon and either be as large as the size of gene(s) or as small as single nucleotides). This can be easily addressed: send your populations for sequencing IMMEDIATELY after sorting and use an appropriate analysis pipeline that can detect these “other” events (like breseq). Pay particular attention to novel junctions or coverage along the genome. Even if you do find a change in this manner, the phenomenon is the same and it is just as important to identify. That said, it would have a different basis (genetic) and could be addressed differently (change the genome to prevent flipping or try to constrain repeat expansion/contraction)."}, "author_response": "Point-by-point Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThis manuscript presents an innovative biosensor-based toolkit integrated with flow cytometry and clustering algorithms to investigate phenotypic heterogeneity in engineered yeast. By leveraging this platform, the authors dissected pH-based subpopulations and metabolite production states, o?ering a modular strategy to characterize cell-to-cell variation in microbial cell factories. The study is useful in synthetic biology and bioprocess optimization. However, several critical aspects require further validation, deeper exploration, and broader contextualization to enhance the study’s impact and reproducibility. The following comments should be addressed before it can be considered for publication.\nWe would like to thank reviewer 1 for their thorough and insightful comments.\n1. The authors suggest that the identified subpopulation system is suitable for industrial biochemical production. Have the authors tested this approach in scaled-up fermentations for lycopene or violacein production under optimized conditions? Data from larger-scale bioreactor experiments would strengthen the claim of industrial applicability.\nScale-up is indeed a critical consideration for bioproduction, especially within the context of industrial applicability, and the reviewer is justified in pointing this out as, while heterogeneity is believed to be amplified in large-scale bioreactors due to uneven mixing, little empirical data exists to demonstrate this in practice. Unfortunately, large-scale fermentation experiments were not feasible within our timeframe; but we have now updated our manuscript to include data which tracks subpopulation dynamics within the BioLector micro-bioreactors. Although these remain small-scale, they mimic industrial bioreactor shaking conditions and are thus more representative of industrial conditions, which we believe strengthen this industrial applicability1. Future works aimed at further investigating the impact of scale-up on heterogeneity using our framework are currently under consideration for future works.\nThe new text describing the results reads:\nThis carbon source-dependent pattern persisted within micro-bioreactor cultivation and under pH bu7ering conditions, confirming robustness of the observed subpopulation dynamics, including to changes in external pH (Fig. Supplementary Figure 18A).\nModified Supplementary Fig. 18A shows:\nSupplementary Figure 18 – Further exploration of pHi subpopulation dynamics on glucose vs galactose. (A) E?ect switching carbon source from glucose to galactose had on the pHi dynamics, as determined by pHluorin2 signal, of lycopene-producing strains following 72h of cultivation within deep-well plate, BioLector micro-bioreactor, and BioLector with bu?ering at pH = 5.0 cultivation, across 3 biological replicates. Significance scores denote p-value p < 0.05 (*) based on Kruskal-Wallis test. Error bars represent standard error to the mean.\n2. The subpopulation dynamics were analysed at 4 and 8 hours. Have the authors investigated longer-term dynamics (e.g., 24 or 48 hours)? Time-resolved data over extended culture durations could reveal population stability, transitions, or emergence of new subpopulations relevant for bioproduction.\nWe have explored long-term subpopulation dynamics under batch flask fermentation following 24h, 48h, and 72h of cultivation, with the results displayed in Supplementary Fig. 14. The subpopulations remained stable in their frequency throughout the cultivation period, with p1 representing approximately 20% and 40% of the whole population within the violacein and lycopene microbial cell factories respectively, in accordance with what was observed following shift to CSTRAVE media.\nText reads:\nFully di7erentiated subpopulations appeared within both microbial cell factories following 24 hours of flask batch fermentation and persisted throughout the cultivation process (Fig. 4D, Supplementary Fig. 14).\n…\np1's signal remained steady upon di7erentiation, regardless of the product or sampling time, whereas p2's signal showed a gradual decline, dropping from 1.23 and 1.29 to 1.00 and 1.06 a.u. following 24h to 72h of cultivation within violacein and lycopene MCFs respectively (Supplementary Fig. 14). Curiously, while the relative frequency of p1 when compared to p2 remained stable throughout the cultivation process, it di7ered between the products, enduring at approximately ~20% and ~40% of the total violacein and lycopene producing population respectively.\nSupplementary Fig. 14 shows:\nSupplementary Figure 14 – Evolution of pH dynamics within lycopene and violacein i bioproduction. Cell co-transformed with pHluorin2 and either the lycopene- or violacein- production pathway were grown in batched vs fed-batched-like, characterised by glucose pulses, YPD flask cultures, with their pHluorin2 signal being recorded and analysed every 24h using flow cytometry.\n3. The study reports di?erential production of lycopene and violacein between the p1 and p2 subpopulations. Have the authors performed untargeted or targeted metabolite profiling to identify other metabolic di?erences between these subpopulations? This could help elucidate the underlying metabolic shifts and regulatory networks.\nWe concur with the reviewer’s feedback that a comprehensive metabolic profile of the subpopulations is crucial to gain a better understanding of the di?erentiation mechanism. Traditional untargeted or targeted metabolite profiling, however, require a considerable amount of biomass which can be quite challenging to acquire within the study of subpopulations as we are constrained by the frequency of the less abundant subpopulation, the sorting rate of the FACS we have at our disposal, and the stability of pHluorin2 signal under prolonged non-experimental conditions required for FACS sorting. Performing metabolic profiling is thus not straight forward and requires further methodological or equipment development, something that we are exploring for future works.\n4. The authors should consider incorporating continuous or time-resolved tracking of subpopulation dynamics using microfluidic platforms or time-lapse microscopy. Such approaches could provide finer resolution of how individual cells transition between states, improving mechanistic insights.\nWe agree with the reviewer and have now updated our manuscript to include new time-lapse microscopy data which analyses the pH of single cells in response to shift to CSTRAVE media. i While we were unable to track pH of cells pre-shift due to the high autofluorescence of YPD i media, we did obtain robust data showing pH dynamics immediately post shift. In doing so, we i observed all cells to experience a slight drop in pHpost shift, with the primary di?erence between i cells from the two subpopulations seemingly being their ability to counteract this drop. Indeed, cells which ended up in p2 had their pH bounce back up shortly post being shifted while those i which ended up in p1 kept on experiencing a gradual decline in pH which eventually stabilised 4h i post shift.\nOur time-lapse microscopy results also showed the subpopulations to remain stable over time, with no cells transitioning between subpopulations during our 15h observation period. As cells do not divide under our shift condition, we were unable to confirm whether subpopulation’s phenotype is inherited by daughter cells, but we did anecdotally observe bud cells which had been forming pre-shift to display the same phenotype as mother cells, suggesting that it may indeed be an inheritable phenotype – in line with the observations from Bagamery et al2.\nNew text reads:\nA closer look at di7erentiation dynamics using time-lapse microscopy revealed all cells to experience a slight drop in pH immediately post shift, with the pH of p2 cells bouncing back as i i time progressed while that of p1 cells kept dropping, until it stabilised at around 4h post shift (Fig. 3D, E, Supplementary Fig. 13A). These results also showed the subpopulations to remain stable throughout the observation period, persisting 15h post shift with no cells transitioning between the subpopulations.\nFig. 3D and E and Supplementary Fig. 13A show:\nFigure 3 – Metabolic response to environmental perturbations. (D) Tracked changes in pHluorin2 signal of single cells post shift to CSTRAVE media (left). Tracked signals normalised to initial pHi (t = 0h) to reduce noise and allow for inter-cell comparison of pHi dynamics, with raw endpoint signals (t = 15h) for p1 and p2 subpopulations shown (right).\nSupplementary Figure 13 – Further characterisation of pH subpopulations in response to i CSTRAVE shift. (A) Timelapse microscopy of pHluorin2-expressing cells post shift to CSTRAVE media. pHluorin2 signal was determined by comparing cell’s relative fluorescence following 375nM and 480nM excitation, with increased signal following 375nM excitation compared to 480nM excitation indicative of greater pHluorin2 values.\n5. Since pH is a controllable parameter in bioreactor systems or flask fermentations using bu?er systems, have the authors tested how bu?ering influences biosensor performance and subpopulation behavior? It would be useful to assess how pH control a?ects metabolite yields and population distribution.\nAs part of our added BioLector experiment, discussed in comment (1), we have included a condition in which the pH of the media was bu?ered (pH = 5.0), in accordance with industry practices. In doing so, we observed that subpopulations dynamics persisted independent of bu?ering conditions, with bu?ering having a minimal impact on the relative frequency of p1 across both glucose and galactose cultivation. We believed this demonstrates the robustness of pHluorin2 as a subpopulation marker as well as the robustness of our observed subpopulation dynamics.\n6. The authors compared lycopene production under glucose and galactose conditions. Was a similar comparison performed for violacein production? This would help determine whether the observed metabolic subpopulations are carbon source–dependent across di?erent production modules.\nWe have carried out the experiment suggested by the reviewer, highlighting the e?ect of glucose vs galactose conditions on pH dynamics of violacein-producing strain, which can be found in i Supplementary Fig. 18B. Populations grown on galactose exhibited a 22% reduction in p1 frequency compared to glucose-grown cells, consistent with trends observed in lycopene- producing strains, albeit less pronounced potentially as a result of violacein-producing strains already having relatively low baseline p1 frequencies. While a drop was observed, it was found to be not statistically significant, potentially due to the low number of biological replicates (n = 3). This reduction in p1 frequencies likely resulted in reduced bioproduction e?iciencies, with cells grown on glucose producing on average 0.33 mg.L-1 of violacein while cells grown on galactose produced amounts too low to be reliably quantified using HPLC.\nNew text reads:\nOf note, switching carbon source from glucose to galactose had a similar e7ect of the pHi dynamics of violacein-producing populations, with its relative p1 frequency dropping 22% from glucose to galactose cultivation (Supplementary Fig. 18B).\nSupplementary Fig. 18B shows:\nSupplementary Figure 18 – Further exploration of pH subpopulation dynamics on glucose vs i galactose. (B) E?ect switching carbon source from glucose to galactose had on the pH i dynamics, as determined by pHluorin2 signal, of violacein-producing strains following 72h of BioLector cultivation, across 3 biological replicates. Significance scores denote p-value with p > 0.05 (ns) based on Kruskal-Wallis test. Error bars represent standard error to the mean.\n7. Have the authors quantified violacein production across di?erent subpopulations? Quantitative data would allow for a more precise correlation with biosensor outputs and strengthen the conclusions regarding violacein-linked metabolic heterogeneity.\nAs mentioned previously, quantification of metabolites, including of violacein, at a subpopulation-level can be challenging due to the biomass required for robust quantification using analytical methods. Instead, we opted to quantify violacein within the subpopulations using autofluorescence, having performed spectral characterisation of violacein-producing and non-producing strains and found that emission between 605nM and 707nM following red laser excitation resulted in the strongest autofluorescence detection signal. We have now added this data to the manuscript as validation of autofluorescence as a method for violacein quantification (see Supplementary Figure 19). As further validation of this methodology, we sampled violacein- producing cultures following 48h, 72h and 120h and compared the resulting population-averaged autofluorescence to violacein production as quantified by HPLC, showing a positive correlation between the two.\nNew methods reads:\nSingle-cell violacein autofluorescence quantification was performed in tandem to pHluorin2 signal quantification, using the R712-H:FSC-H, in line with spectral characterisation obtained from Aurora Spectral flow cytometer (Cytek, Supplementary Fig. 19A, B).\nSupplementary Figure 19 and Figure R1 show:\nSupplementary Figure 19 – Production quantification controls. (A) Spectral characterisation following excitation with yellow-green (YG) or red (R) lasers of strains co-expressing pHluroin2 within violacein production pathway compared to pHluorin2-expressing and pHLUM control strains. Channels 1 through 10 represent di?erent emission properties, with R3-A, corresponding to an emission wavelength between 688nM and 707nM, displaying the strongest signal in response to violacein expression. (B) Quantification of violacein autofluorescence within strains expressing the violacein production pathway when compared to pHLUM negative control strains using red laser excitation within Cytoflex.\n8000\n7000\n6000\n0.075 0.100 0.125 0.150\nYield..mg.g\nFigure R1 – Validation of autofluorescence for violacein quantification. Correlation between violacein quantification as determined using autofluorescence vs extraction and HPLC.\n8. Given the importance of redox cofactors like NADH and NADPH in biochemical production, have the authors considered engineering or characterizing subpopulations based on redox ecnecseroulfotuA biosensors? Comparing NADH- or NADPH-linked subpopulations under di?erent carbon sources (glucose vs. galactose) could uncover additional layers of heterogeneity impacting productivity.\nFollowing the suggestion from the reviewer, we analysed NADH and NADPH dynamics using the pGPD2-mNeon and YRE-mNeon sensing signal respectively. Results showed a homogeneous distribution of cofactors upon both glucose and galactose cultivation. The lack of distinguishable subpopulations does not, however, rule out a potential impact of redox co-factors and future works can look deeper into this.\nFigure 2R shows:\nFigure R2 – NADH and NADPH subpopulation dynamics under glucose vs galactose batch flask fermentation. pGPD2-mNeon and YRE-mNeon expressing cells were cultivated within batch flask fermentation using glucose vs galactose as a carbon source, with the resulting sensing signal distributions assayed following 24h, 48h and 72h of cultivation and analysed using flow cytometry.\n9. The authors should include a clearer and more quantitative comparison between the presented biosensor toolkit and existing platforms such as metabolomics or transcriptomics. They should also highlight the advantages in terms of temporal resolution, throughput, sensitivity, and cost to emphasize the novelty and utility of their approach.\nWe appreciate this suggestion and have now edited the introduction to better emphasize the advantages of genetically encoded biosensors. Of note, genetically encoded biosensors and traditional metabolomics tend to be seen as complementary rather than alternatives and, as such, quantitative comparison of the two is not prevalent within the literature. Both have their own targets, strengths and weaknesses which makes them more or less suited for certain uses.\nNew text reads:\nQuantification of metabolites at the single-cell level, however, is technically challenging due to the low sensitivity of existing metabolomics platforms. While e7orts to address this are underway, single-cell metabolomics remain in their infancy and are thus prohibitively costly, low throughput, and not readily accessible19.\n10. The authors should provide more detailed validation of each biosensor used in the study. Specifically, please include dose-response curves, dynamic range, linearity, response time, and potential cross-reactivity. This information is critical to assess biosensor robustness and reproducibility.\nWe agree that comprehensive characterisation of each biosensor would be valuable. Here, we employed biosensors that have been previously characterised within the literature and have provided clear references to the original publications for readers seeking further validation data. A comprehensive characterisation of biosensors presents significant technical challenges, necessitating a mix of in vitro and cell-free assays which require specialised expertise and extensive assay optimisation. Indeed, such data can often be omitted from publications, even those presenting novel biosensors, due to the substantial technical barriers posed, especially when targeting metabolites as complex and fiddly as core intracellular metabolites. Therefore, we believe that additional characterisation falls outside the scope of our current study.\nIn this work, we primarily aimed to showcase the value of previously characterised biosensors to detect and study isogenic heterogeneity. Upon publication of our work, will make our biosensors cassettes and strains available to anyone in the community who request them to perform further characterisation.\nReviewer #2 (Remarks to the Author):\nIn this study, the authors used biological sensors to detect a range of intracellular conditions in yeast at the single-cell level, aiming to characterize heterogeneity among the cell population with a homogenous genetic background that is triggered by stress. This work demonstrates the use of genetic sensors to di?erentiate cells in di?erent biological states, which has the potential to identify intracellular conditions that facilitate specific cellular activities, such as biosynthesis. The manuscript provides a clear and detailed description of the characterization of genetic parts, which supports the synthetic biology community in using these genetic tools. Here are several concerns and suggestions:\nWe thank reviewer 2 for their valuable comments and have now addressed these as described below, improving the manuscript accordingly.\n1. In Figure 3, some stresses in this study, such as nutrient depletion, may a?ect gene expression activities (transcription and/or translation). As genetic sensors involve gene expression, it is critical to validate that the expression rates of sensors and reporters are not a?ected by the changing conditions. One common way of validating a genetic sensor is to replace the genetic sensor gene with a fluorescent protein gene and show that the expression of that fluorescent protein is not a?ected by the changing condition.\nThanks for the comment. Three di?erent types of biosensors were used in this study, including transcription factor-based, ribozyme-based and FRET-based biosensors. FRET-based biosensors are ratiometric by design, measuring the fluorescence ratio following excitation or emission at distinct wavelengths, and thus, automatically taking into account any variation in expression levels. For our transcription factor-based and ribozyme-based biosensors, we followed the strategy mentioned by the reviewer, placing a control fluorescent protein under constitutive expression immediately downstream of the biosensor, with the resulting measured signal being the fluorescence of the sensing reporter normalised to that of the control fluorescent protein, and thereby also accounting for any variation in expression level. In the case of our ribozyme- based biosensor, the constitutive promoters used to express the sensing construct and control fluorescent protein were pTEF1 and pTEF2 respectively, two promoters which have comparable and correlated expression levels3.\n2. Based on the results in Figure 4, the authors hypothesize that intracellular pH a?ects the biosynthesis of violacein and lycopene. To validate this hypothesis, they changed the carbon source to galactose to alter cellular pH. However, a change in carbon source also altered metabolic flux, which may a?ect the activity of biosynthetic pathways. As demonstrated in Figure 2, intracellular pH can be modulated by a supply of a weak acid. The authors should perform a new set of experiments, providing an acid, base, or bu?er to adjust the cellular pH for improving biosynthesis yield, which will generate direct, strong support for their hypothesis and show the utility of their single-cell level genetic sensing approach.\nThe reviewer raises a thoughtful point, and we concur that the correlation we observe between improved bioproduction and altered subpopulations dynamics may also arise from factors independent of subpopulations dynamics, such as metabolic rewiring brought by the use of an alternative carbon source. The reviewer also makes insightful proposal to counter this bias, by using culture pH as a method of modulating subpopulation dynamics instead. To test this, we leveraged the BioLector mini bioreactor system and assayed the impact bu?ering of culture pH would have on subpopulation dynamics of lycopene-producing strains. Culture pH was constantly monitored, being bu?ered with either HCl or NaOH to ensure a pH of 5.0 was maintained. In doing so, we observed robust subpopulation di?erentiation independent of bu?ering conditions, with pH bu?ering having a minimal impact on p1 frequency across both glucose and galactose conditions. As suggested by the reviewer, we also tested the addition of 10mM acetic acid which did not impact subpopulation dynamics, further suggesting that the observed di?erentiation pattern is robust to, and thus unfortunately cannot be modulated, through culture pH.\nWhile it may be impossible to fully detangle metabolic rewiring from subpopulation dynamics, we have now included single-cell lycopene production data to assess the di?erences in production dynamics between glucose and galactose cultivation. In doing so, we show galactose cultivation to induce a shift from low- to higher- producing phenotypes when compared to glucose cultivation, with this shift paralleling that observed within the pH dynamics. We believe i this lends further credibility to the hypothesis that the increase in production e?iciency under galactose cultivation is, at least in part, driven by modulation of subpopulation dynamics.\nNew text reads:\nResults showed the relative frequency of p1 to drop significantly under galactose conditions, with p1 comprising 70.6% of the total cell population when grown on glucose and dropping to 29.8% under galactose cultivation conditions (Fig. 5A, Supplementary Figure 18A). This altered subpopulation dynamics paralleled the changes observed in single-cell lycopene production under both culture conditions, whereby galactose cultivation prompted a shift from low- to high- producing phenotypes when compared to glucose cultivation (Fig. 5B).\n…\nThis carbon source-dependent pattern persisted within micro-bioreactor cultivation and under pH bu7ering conditions, confirming robustness of the observed subpopulation dynamics, including to changes in external pH (Fig. Supplementary Figure 18A).\nFig. 5AB, Supplementary Fig. 18A, Fig. 3R show:\nFigure 5 – Modulation of subpopulation dynamics through cultivation strategies. (A) E?ect switching carbon source from glucose to galactose had on the pH dynamics, as determined by i pHluorin2 signal, of lycopene-producing strains following 72h of cultivation. Clustering of cells into p1 and p2 subpopulations was performed according to Methods, with the relative frequency of p1 displayed. (B) Change in single-cell lycopene production dynamics, as determined by Raman peak AUC, following 72h of cultivation on glucose vs galactose.\nSupplementary Figure 18 – Further exploration of pHi subpopulation dynamics on glucose vs galactose. (A) E?ect switching carbon source from glucose to galactose had on the pHi dynamics, as determined by pHluorin2 signal, of lycopene-producing strains following 72h of cultivation within deep-well plate, BioLector micro-bioreactor, and BioLector with bu?ering at pH = 5.0 cultivation, across 3 biological replicates. Significance scores denote p-value p < 0.05 (*) based on Kruskal-Wallis test. Error bars represent standard error to the mean.\nControl 10mM Acetic Acid\n4\n3\n2\n1\n0\n0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5\npHluorin2 Signal (VL2-H/BL1-h)\nFigure 3R – Impact of weak acid supplementation on pH dynamics of lycopene-producing i strains. E?ect 10mM acetic acid supplementation had on the pH dynamics of lycopene- i producing strains, as determined by pHluorin2 signal, following 72h of cultivation.\n3. There are some typos and missing characters throughout the manuscript. For example, in the Figure 2 legend, “(B) Change in pYRE-mNeon signal following exposure to varying concentrations of diamide dashed line) …...” is missing an open bracket: “(B) Change in pYRE-mNeon signal following exposure to varying concentrations of diamide (dashed line)”. In Figure 3A, an x-axis marker is missing.\nWe thank the reviewer for bringing this to our attention. We have now reviewed the manuscript and removed all typos to the best of our ability.\nReviewer #3 (Remarks to the Author):\nThis is a very solid paper addressing (1) a framework about how to uncover metabolic heterogeneity, (2) finding a link between heterogeneity in pHi and bioproduction, and (3) how to use that information to improve bioproduction. The first part is general and is based largely on the testing of a standardized set of intracellular reporters and associated fluorescent proteins. The second and third parts are a specific exemplar case whereby low glucose conditions trigger a split into distinctly di?erent metabolic phenotypes that a?ect, in opposite ways, production of lycopene and violacein.\nThis question is rather important and is only just beginning to build traction in the research community. While it is applied here to bioproduction in yeast, single-cell variability has been seen to be critical to many areas of microbiology, ranging from pathogenesis to antibiotic treatment. This paper excels in terms of providing a useful toolkit for yeast and in providing a key example to point to for why this matters. I have often found people can be convinced heterogeneity exists (it does!) but are less inclined to believe it actually matters for a phenotype of their interest. There we often do not know and have limited examples to use that can come across as anecdotes. This paper is important in providing a pathway to more systematically rooting those out. For both the work on the tools and the pH connection to production of these pigments, the work well-supports the claims.\nWe would like to thank Reviewer 3 for sharing our enthusiasm with regards to our results and highlighting the importance of this work.\nOne suggestion I have would be to be a bit more clear in the writing of the paper of this duality between a general set of tools and approach and then transition to an exemplar that was enabled because several internal states were followed across several conditions to then explore what ytisneD e?ect that might have on bioproduction. Having read the title, I was a bit surprised by the flow that ended up occurring and making this more explicit could be useful.\nWe thank the reviewer for pointing this out and have now edited the title as well as the abstract and introduction in the hopes of better reflecting the flow of the paper. The new title now reads: “Metabolic subpopulations as drivers of bioproduction heterogeneity: From framework development to bioprocess optimisation”. To emphasise the tool development part of the work a bit further, we have now named the sensor toolkit: YTK-ScBiosense and referred to it throughout the document.\nMy one concern with the data is that the rapid re-establishment of two phenotypes does not necessarily preclude a genetic basis. Normal point mutations would not have the potential to do this, but hyper-variable genetics can. This could be either regions that flip (phase variation, like what is used with Flp-FRT) or copy-number variations of local duplications (which can be rapidly selected upon and either be as large as the size of gene(s) or as small as single nucleotides). This can be easily addressed: send your populations for sequencing IMMEDIATELY after sorting and use an appropriate analysis pipeline that can detect these “other” events (like breseq). Pay particular attention to novel junctions or coverage along the genome. Even if you do find a change in this manner, the phenomenon is the same and it is just as important to identify. That said, it would have a di?erent basis (genetic) and could be addressed di?erently (change the genome to prevent flipping or try to constrain repeat expansion/contraction).\nWe appreciate the reviewer's feedback on this point, which raises an important and legitimate concern. Addressing it through sequencing, however, is challenging due to the number of cells required for adequate coverage, our sorting rate being constrained by the frequency of the p1 subpopulation and the limitations of the FACS we have at our disposal, and the stability of pHluorin2 signal under prolonged sorting conditions. Instead, we have opted to use an alternative strategy to account for the potential impact of mutations, as was done previously by Shabestary et al.4, which consists of sorting subpopulations post shift based on pHand plating them on rich i YPD media to reset their phenotype. We then selected three separate colony isolates from both plated subpopulations, shifted them back to CSTRAVE media and, in doing so, showed both subpopulation phenotype to emerge irrespective of the isolates’ history, i.e. of whether they belonged to p1 or p2 in the initial sort. This suggests that the phenotype is indeed isogenic, as, if the cause was mutational, we would expect the subpopulation’s phenotype to have persisted upon repeat shift, with cells from p2 only giving rise to p2 phenotype and cells from p1 only giving rise to the p1 phenotype. While these results were initially included within our Supplementary Figures, this reviewer’s comment has prompted us to highlight it more within the manuscript, moving the plot to Fig. 3 and emphasizing its significance within the main text. Of note, we recognise that, while our current method is commonly accepted within the literature, it does not fully eliminate the potential impact of reversible genetics events and thus, future works can target the elucidation of such potential e?ects. We have now included a note in our conclusion to caveat for the potential of such an occurrence.\nNew text reads:\nLastly, the sorted subpopulations were plated on rich YPD media to reset their phenotype before being shifted back to CSTRAVE media. Doing so resulted in the emergence of p1 and p2 within all of the colony isolates tested, irrespective of the subpopulation’s history, thereby confirming the subpopulations to occur as a result of isogenic heterogeneity and not recurring spontaneous mutations (Fig. 3F).\n…\nFuture multi-omics works aimed at elucidating the documented divergent product-dependent responses and their underlying mechanisms – including to account for potential reversible genetic mutations – could not only inform additional cultivation strategies but also engineering targets to enhance both strain productivity and stability.\nFigure 3E shows:\nFigure 3 – Metabolic response to environmental perturbations. (E) pHluorin2-expressing cells were sorted into p1 vs p2 subpopulations 4h post shift to CSTRAVE media and plated on fresh YPD plates to reset phenotype. 3 separate colony isolates from each plated subpopulation were then shifted back to CSTRAVE media, with the resulting pHluorin2 signal distribution displayed. Assignment of cells to p1 vs p2 subpopulation performed according to Methods.\nReferences:\n1. Fink, M., Cserjan-Puschmann, M., Reinisch, D. & Striedner, G. High-throughput microbioreactor provides a capable tool for early stage bioprocess development. Sci Rep 11, 2056 (2021).\n2. Bagamery, L. E., Justman, Q. A., Garner, E. C. & Murray, A. W. A Putative Bet-Hedging Strategy Bu?ers Budding Yeast against Environmental Instability. Curr Biol 30, 4563-4578.e4 (2020).\n3. Peng, B., Williams, T. C., Henry, M., Nielsen, L. K. & Vickers, C. E. Controlling heterologous gene expression in yeast cell factories on di?erent carbon substrates and across the diauxic shift: a comparison of yeast promoter activities. Microb Cell Fact 14, 91 (2015).\n4. Shabestary, K. et al. Phenotypic heterogeneity follows a growth-viability tradeo? in response to amino acid identity. Nat Commun 15, 6515 (2024)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my previous comments. However, some aspects still need to be elaborated, and additional details should be provided to improve clarity.\n1. The authors should include the micro-bioreactor figure in the main text.\n2. Have the authors analyzed lycopene production in deep-well plates, BioLector, and BioLector under buffering conditions? The authors should provide lycopene production data under all three conditions.", "reviewer_2": "The revised manuscript has improved in clarity with supports from new experiments. All my comments are addressed.", "reviewer_3": "The responses to my comments (as well as the other reviewers) have addressed my concerns regarding both the title/selling point of the paper and to the concern that genetic changes could be involved. While the latter could still be occurring, I think now that it is recognized it is sufficiently dealt with and can represent further work in the future."}, "author_response": "Point-by-point Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThe authors have addressed my previous comments. However, some aspects still need to be elaborated, and additional details should be provided to improve clarity.\n1. The authors should include the micro-bioreactor figure in the main text.\nWe have now added the micro-bioreactor data to the main text figure.\nSee modified Figure 5E:\nFigure 5 – Modulation of subpopulation dynamics through cultivation strategies. (A) Effect switching carbon source from glucose to galactose had on the pH dynamics, as i determined by pHluorin2 signal, of lycopene-producing strains following 72h of cultivation. Clustering of cells into p1 and p2 subpopulations was performed according to Methods, with the relative frequency of p1 displayed. Resulting change in (B) single-cell lycopene production dynamics, as determined by Raman peak AUC, (C) lycopene production, as quantified using HPLC, and (D) lycopene production relative to biomass. Dry cell weight (DCW) used as a measure of biomass. (E) Carbon source-dependent shift in subpopulation dynamics conserved across 3 biological replicates within deep-well plate, BioLector micro-bioreactor, and BioLector with buffering at pH = 5.0 cultivation. Significance scores denote p-value with p < 0.05 (*) based Kruskal-Wallis test. Error bars represent standard error to the mean.\n2. Have the authors analysed lycopene production in deep-well plates, BioLector, and BioLector under buffering conditions? The authors should provide lycopene production data under all three conditions.\nAs suggested by the reviewer, we have now included this data and referenced it within the main text. These experiments showed the expected trend, although the biolector data presented larger variability compared to the previous results obtained in deep well plates. This variability is likely due to the smaller volumes that can be run in the biolector, which only generate 5-10 mg DCW, while deep well plate cultivation produces over 50 mg DCW. The smaller amount of DCW amplifies errors during the extraction and quantification of lycopene, which is attributed to technical challenges, including the difficulty in achieving homogeneous pigment extraction, low pigment stability upon extraction, and less accurate dry cell weight (DCW) measurements. These factors contribute to the observed dispersion of the data. Future work to study in more detail the effect of pH control on subpopulations and production should consider a larger number of replicates and optimise the extraction protocol for smaller volumes. Due to limitations in accessing the equipment, we have been unable to perform additional experiments on the biolector.\nNew text reads:\nTrends in production under these conditions were also consistent with those observed within deep-well plates, although the reduced culture volumes and biomass of micro-bioreactors promoted variability within the resulting production measurements (Supplementary Fig. 18A).\nSee modified Supplementary Fig. 18:\nSupplementary Figure 18 – Further exploration of pH subpopulation dynamics i following glucose vs galactose cultivation. (A) Effect switching carbon source from glucose to galactose had on the lycopene production relative to DCW of lycopene-producing strains following 72h of cultivation within BioLector micro-bioreactor and BioLector with buffering at pH = 5.0 cultivation. (B) Shift in subpopulation dynamics following 72h of BioLector cultivation on glucose vs galactose conserved within violacein-producing strains. Significance scores denote p-value with p > 0.05 (ns) based on Kruskal-Wallis test. Error bars represent standard error to the mean, across 3 biological replicates.\nReviewer #2 (Remarks to the Author):\nThe revised manuscript has improved in clarity with supports from new experiments. All my comments are addressed.\nWe appreciate the reviewer's time and feedback, and are pleased that we could address their concerns to their satisfaction.\nReviewer #3 (Remarks to the Author):\nThe responses to my comments (as well as the other reviewers) have addressed my concerns regarding both the title/selling point of the paper and to the concern that genetic changes could be involved. While the latter could still be occurring, I think now that it is recognized it is sufficiently dealt with and can represent further work in the future.\nWe appreciate the reviewer's time and feedback, and are pleased that we could address their concerns to their satisfaction."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed my comments."}, "author_response": ""}]

Decision letter