Candida albicans exhibits heterogeneous and adaptive cytoprotective responses to antifungal compounds
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eLife assessment
The valuable study by Dumeaux et al examines the transcriptional response to antifungal treatment in the major opportunistic human fungal pathogen Candida albicans. Using solid methodology, including a novel droplet-based single cell transcriptomics platform, the authors report that fungal cells exhibit heterogeneity in their transcriptional response to antifungal drug treatment. The ability to study the trajectories of individual cells in a high-throughput manner provides a novel perspective on studying the emergence of drug tolerance and resistance in fungal pathogens.
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Abstract
Candida albicans, an opportunistic human pathogen, poses a significant threat to human health and is associated with significant socio-economic burden. Current antifungal treatments fail, at least in part, because C. albicans can initiate a strong drug tolerance response that allows some cells to grow at drug concentrations above their minimal inhibitory concentration. To better characterize this cytoprotective tolerance program at the molecular single-cell level, we used a nanoliter droplet-based transcriptomics platform to profile thousands of individual fungal cells and establish their subpopulation characteristics in the absence and presence of antifungal drugs. Profiles of untreated cells exhibit heterogeneous expression that correlates with cell cycle stage with distinct metabolic and stress responses. At 2 days post-fluconazole exposure (a time when tolerance is measurable), surviving cells bifurcate into two major subpopulations: one characterized by the upregulation of genes encoding ribosomal proteins, rRNA processing machinery, and mitochondrial cellular respiration capacity, termed the Ribo-dominant ( Rd ) state; and the other enriched for genes encoding stress responses and related processes, termed the Stress-dominant ( Sd ) state. This bifurcation persists at 3 and 6 days post-treatment. We provide evidence that the ribosome assembly stress response (RASTR) is activated in these subpopulations and may facilitate cell survival.
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Author Response
Reviewer #1 (Public Review):
This study applies state-of-the-art single-cell transcriptome analysis to investigate the nature of drug tolerance, a phenomenon distinct from drug resistance, and a problem of considerable importance in the treatment of C. albicans infections. The authors first show that their transcriptomics platform can reveal sub-populations of untreated cells that display distinct transcription profiles related to metabolic and stress responses that are coupled with cell cycle regulation. They note the consistency of these findings with previous work indicating connections between cell cycle phase and expression of genes related to stress responses and metabolism and argue that this validates their experimental approach, which relies on a complex statistical analysis of sparse data from a relatively …
Author Response
Reviewer #1 (Public Review):
This study applies state-of-the-art single-cell transcriptome analysis to investigate the nature of drug tolerance, a phenomenon distinct from drug resistance, and a problem of considerable importance in the treatment of C. albicans infections. The authors first show that their transcriptomics platform can reveal sub-populations of untreated cells that display distinct transcription profiles related to metabolic and stress responses that are coupled with cell cycle regulation. They note the consistency of these findings with previous work indicating connections between cell cycle phase and expression of genes related to stress responses and metabolism and argue that this validates their experimental approach, which relies on a complex statistical analysis of sparse data from a relatively small number of single cells. They then proceed to analyze drug-treated cells, mostly focusing on fluconazole (FCZ; which targets ERG11, thus disrupting ergosterol biosynthesis and membrane integrity) and examining individual cells at 2-, 3-, and 6-days following treatment. Their primary finding is the identification of two major classes of cells, one of which they call the α response, characterized by high ribosomal protein (RP) gene expression and the absence of either heat shock or hyperosmotic stress gene expression as well as low expression of glycolytic, carbohydrate reserve pathway, and histone genes. The second survival state on day 2 (called the β response) instead displays low RP gene expression and high heat-shock stress response. Interestingly, the proportion of β cells clearly increases on day 3. In addition, responses to caspofungin (CSP) and rapamycin (RAPA) are examined and compared to FCZ or untreated cells. The main conclusion that the authors draw from their data is that the initial α response transitions to the β response, which is similar to a recently characterized ribosome assembly stress response (RASTR) in the budding yeast S. cerevisiae. They argue that the transcriptional state in α cells provokes the transition to the β state.
This manuscript presents an enormous amount of complex data whose significance will be difficult to evaluate for those (e.g., this reviewer) not immersed in the specialized analytical techniques used here. Taken at face value, however, the experimental findings are consistent with the authors' main conclusions. Nevertheless, and consistent with the complexity of the responses observed, there are many findings that remain to be explored in mechanistic detail and for which conclusions are less precise.
We thank Reviewer #1 for their excellent questions. The manuscript does have a large amount of complex data so this version of the manuscript has a tighter focus on the major findings (i.e. 𝛼/Rd versus β/Sd subpopulations in response to FCZ). We have tried to explore these subpopulations in greater depth with supporting data from complementary technologies and additional bioinformatic analyses. We agree that there still remains several observations in the manuscript that are not explored in mechanistic detail. We have tried our best to clearly delineate the evidence that we have for these findings in addition to their potential significance.
Towards the simplification of the manuscript, we have moved the discussion regarding “comets” to Appendix 2 [Changes L837-897] along with the detailed analysis of the response of cells to rapamyacin and caspofungin [Changes L899-963]. We have also removed from the manuscript a paragraph (and associated Figure 2 - figure supplement 5 in the original manuscript) from the Discussion that described our inability to assign DNA level chromosomal aberrations to either the Rd or Sd subpopulations using whole genome sequencing. Figures 5 and 6 of the original manuscript depicted GO analysis that compared changes in the molecular processes between 𝛼/Rd and β/Sd subpopulations at day 3 and 6 respectively. Although interesting, the figures do not advance the main findings of the manuscript and have been removed from this version.
Reviewer #2 (Public Review):
In this manuscript, Dumeaux et al. assess the heterogeneous cellular response of the fungal pathogen Candida albicans to antifungal agents, using single-cell RNA sequencing. The researchers develop and optimized single-cell transcriptomics platform for C. albicans, and exploit this technique to monitor the cellular response to treatment with three distinct antifungal agents. Through this analysis, they identify two distinct subpopulations of cells that undergo differential transcriptomic responses to antifungal treatment: one involving upregulation of translation and respiration, and the other involving stress responses. This work monitors how different and prolonged antifungal exposure alters and shifts fungal cell populations between these responses. This is an innovative study that exploits novel single-cell transcriptomic techniques to address a very interesting question regarding the heterogeneous nature of the fungal response to antifungal drug treatment. This work optimizes a protocol for single-cell RNA sequencing, which is a significant contribution to the fungal research community and will bolster future research efforts in this area. The identification of two distinct subpopulations of fungal cells with differential responses to antifungal treatment is an exciting and novel finding. While there are aspects of this manuscript that are of significant interest, there are also limitations to this work.
The research is framed as a method to study antifungal drug tolerance, but it is not clear how it does so, based on the methods. This work also compares very different populations of cells (rapidly growing untreated cells compared with cells grown in antifungal for several days), making it difficult to assess the role of antifungal treatment specifically in this analysis. This manuscript is also written with a great deal of highly technical language that makes it difficult to dissect the major findings and outcomes from the study.
We sincerely thank the reviewer for these comments and for making the effort to evaluate the manuscript. We have tried to address these criticisms by improving the introduction to better explain fungal drug tolerance [Changes L53-61] and to explain how our experimental design allows us to investigate this phenomenon (for example for UT cells L184-187, L142-149). We have also re-written subsections of the results to more intuitively explain technical concepts (especially surrounding single cell technologies and analyses) [L250-257, L368-373, L699-707]. Some subsections of the results have been moved to the appendices in order to better emphasize the major findings and outcomes (e.g. comets L837-897 and in depth analysis of RAPA and CSP treatment L899-963). We address each of the specific concerns below. We have also removed some complicated analyses that did not directly advance the major findings of the manuscript including the GO analysis in Figures 5 and 6 of the original manuscript.
Before proceeding, we would like to take this opportunity to underscore that these experiments were not primarily designed to investigate the differences between untreated (UT) and treated cells. The major findings (of the 𝛼/Rd and β/Sd subpopulations) are not dependent on the UT profiles. That is, the 𝛼/Rd and β/Sd subpopulations would be evident even if the UT profiles were removed from the manuscript entirely. Rather, the UT profiles/analyses are intended to contribute to the manuscript by helping establish the technical efficacy of the sc-profiling technique. For example, we might expect - a priori - that a large component of cell to cell heterogeneity in isogenic UT cells should correspond to differences in cell cycle, and, indeed, this is what we found.
Indeed, we did embed (via UMAP) and cluster (via Leiden clustering) the UT data alongside data for the drug-treated cells (Figure 3), which reveals that UT cells largely cluster separately from drug-treated cells. The reviewer is absolutely correct to question the sources underlying this separation; in addition to differential cellular responses to the drug itself, some of the separation may be due to differences in the amount of growth media, for example. (The fact that different drugs (FCZ, RAPA and CSP) largely separate from UT cells and from each other may suggest that at least some of this separation could be due to differences in the mode of action of each drug rather than to issues related to, for example, media depletion. However, this difference is not a major finding of the manuscript. Rather, we agree with the reviewer that “The identification of two distinct subpopulations of fungal cells with differential responses to antifungal treatment is an exciting and novel finding”. As such, the major results begin with data in panels 3D and E that reveal the two distinct cell types within the FCZ-treated sample (a distinction that is not dependent on the status of the UT cells).
Reviewer #3 (Public Review):
The authors described their extensive single-cell analysis of Candida undergoing (sub-inhibitory) antibiotic treatment versus no treatment. To do so, the authors used a microfluidics platform they had previously developed, and they optimized, characterized, and validated it for this particular application. Their findings included: (a) the transcription of untreated cells is driven mostly by cell cycle phase, (b) treated cells can be clustered into several major groups and a few outlier groups that the authors termed comets, (c) cells undergoing FCZ treatment can adopt one of two different states (possibly bistability). I found the results interesting and the approach to be sound, and much of the results confirmed my prior expectations. The authors provide a detailed depiction of what is going on in the transcriptome during sub-inhibitory treatment, although this did not always lead to a mechanistic explanation. The clinical relevance was unclear to me beyond a proof of concept application for single-cell transcriptomics. In my opinion, an interesting follow-up would be to follow the transcriptional trajectory of lineages undergoing antimicrobial switching (on and off). The main issues I identified were the author's use of the term tolerance versus resistance, interpretation of "comets", clustering approach, description of fitness, and comparison between time points.
We thank the reviewer for their time and effort with this manuscript. In the revised manuscript, we expanded the introduction to better delineate between resistance and tolerance, moved the “comets” section to the appendices, as it distracted from the major results and we provided more interpretive analysis of the findings. We also better defined the bioinformatic approaches. (Changes e.g. comets L837-897 and in depth analysis of RAPA and CSP treatment L899-963). With respect to comparisons between time points, we now address these concerns throughout the Response to Reviewer document. We have also moved a comparison of UT versus FCZ cells to Appendix 2 L828-836 as it was perhaps misleading readers of our intention. We only performed this comparison as a sort of “sanity” check to see if the single cell (sc)-profiling would detect differences between UT and drug treated cells.
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eLife assessment
The valuable study by Dumeaux et al examines the transcriptional response to antifungal treatment in the major opportunistic human fungal pathogen Candida albicans. Using solid methodology, including a novel droplet-based single cell transcriptomics platform, the authors report that fungal cells exhibit heterogeneity in their transcriptional response to antifungal drug treatment. The ability to study the trajectories of individual cells in a high-throughput manner provides a novel perspective on studying the emergence of drug tolerance and resistance in fungal pathogens.
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Reviewer #1 (Public Review):
This study applies state-of-the-art single-cell transcriptome analysis to investigate the nature of drug tolerance, a phenomenon distinct from drug resistance, and a problem of considerable importance in the treatment of C. albicans infections. The authors first show that their transcriptomics platform can reveal sub-populations of untreated cells that display distinct transcription profiles related to metabolic and stress responses that are coupled with cell cycle regulation. They note the consistency of these findings with previous work indicating connections between cell cycle phase and expression of genes related to stress responses and metabolism and argue that this validates their experimental approach, which relies on a complex statistical analysis of sparse data from a relatively small number of …
Reviewer #1 (Public Review):
This study applies state-of-the-art single-cell transcriptome analysis to investigate the nature of drug tolerance, a phenomenon distinct from drug resistance, and a problem of considerable importance in the treatment of C. albicans infections. The authors first show that their transcriptomics platform can reveal sub-populations of untreated cells that display distinct transcription profiles related to metabolic and stress responses that are coupled with cell cycle regulation. They note the consistency of these findings with previous work indicating connections between cell cycle phase and expression of genes related to stress responses and metabolism and argue that this validates their experimental approach, which relies on a complex statistical analysis of sparse data from a relatively small number of single cells. They then proceed to analyze drug-treated cells, mostly focusing on fluconazole (FCZ; which targets ERG11, thus disrupting sphingolipid biosynthesis and membrane integrity) and examining individual cells at 2-, 3-, and 6-days following treatment. Their primary finding is the identification of two major classes of cells, one of which they call the α response, characterized by high ribosomal protein (RP) gene expression and the absence of either heat shock or hyperosmotic stress gene expression as well as low expression of glycolytic, carbohydrate reserve pathway, and histone genes. The second survival state on day 2 (called the β response) instead displays low RP gene expression and high heat-shock stress response. Interestingly, the proportion of β cells clearly increases on day 3. In addition, responses to caspofungin (CSP) and rapamycin (RAPA) are examined and compared to FCZ or untreated cells. The main conclusion that the authors draw from their data is that the initial α response transitions to the β response, which is similar to a recently characterized ribosome assembly stress response (RASTR) in the budding yeast S. cerevisiae. They argue that the transcriptional state in α cells provokes the transition to the β state.
This manuscript presents an enormous amount of complex data whose significance will be difficult to evaluate for those (e.g., this reviewer) not immersed in the specialized analytical techniques used here. Taken at face value, however, the experimental findings are consistent with the authors' main conclusions. Nevertheless, and consistent with the complexity of the responses observed, there are many findings that remain to be explored in mechanistic detail and for which conclusions are less precise.
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Reviewer #2 (Public Review):
In this manuscript, Dumeaux et al. assess the heterogeneous cellular response of the fungal pathogen Candida albicans to antifungal agents, using single-cell RNA sequencing. The researchers develop and optimized single-cell transcriptomics platform for C. albicans, and exploit this technique to monitor the cellular response to treatment with three distinct antifungal agents. Through this analysis, they identify two distinct subpopulations of cells that undergo differential transcriptomic responses to antifungal treatment: one involving upregulation of translation and respiration, and the other involving stress responses. This work monitors how different and prolonged antifungal exposure alters and shifts fungal cell populations between these responses. This is an innovative study that exploits novel …
Reviewer #2 (Public Review):
In this manuscript, Dumeaux et al. assess the heterogeneous cellular response of the fungal pathogen Candida albicans to antifungal agents, using single-cell RNA sequencing. The researchers develop and optimized single-cell transcriptomics platform for C. albicans, and exploit this technique to monitor the cellular response to treatment with three distinct antifungal agents. Through this analysis, they identify two distinct subpopulations of cells that undergo differential transcriptomic responses to antifungal treatment: one involving upregulation of translation and respiration, and the other involving stress responses. This work monitors how different and prolonged antifungal exposure alters and shifts fungal cell populations between these responses. This is an innovative study that exploits novel single-cell transcriptomic techniques to address a very interesting question regarding the heterogeneous nature of the fungal response to antifungal drug treatment. This work optimizes a protocol for single-cell RNA sequencing, which is a significant contribution to the fungal research community and will bolster future research efforts in this area. The identification of two distinct subpopulations of fungal cells with differential responses to antifungal treatment is an exciting and novel finding. While there are aspects of this manuscript that are of significant interest, there are also limitations to this work. The research is framed as a method to study antifungal drug tolerance, but it is not clear how it does so, based on the methods. This work also compares very different populations of cells (rapidly growing untreated cells compared with cells grown in antifungal for several days), making it difficult to assess the role of antifungal treatment specifically in this analysis. This manuscript is also written with a great deal of highly technical language that makes it difficult to dissect the major findings and outcomes from the study.
-
Reviewer #3 (Public Review):
The authors described their extensive single-cell analysis of Candida undergoing (sub-inhibitory) antibiotic treatment versus no treatment. To do so, the authors used a microfluidics platform they had previously developed, and they optimized, characterized, and validated it for this particular application. Their findings included: (a) the transcription of untreated cells is driven mostly by cell cycle phase, (b) treated cells can be clustered into several major groups and a few outlier groups that the authors termed comets, (c) cells undergoing FCZ treatment can adopt one of two different states (possibly bistability). I found the results interesting and the approach to be sound, and much of the results confirmed my prior expectations. The authors provide a detailed depiction of what is going on in the …
Reviewer #3 (Public Review):
The authors described their extensive single-cell analysis of Candida undergoing (sub-inhibitory) antibiotic treatment versus no treatment. To do so, the authors used a microfluidics platform they had previously developed, and they optimized, characterized, and validated it for this particular application. Their findings included: (a) the transcription of untreated cells is driven mostly by cell cycle phase, (b) treated cells can be clustered into several major groups and a few outlier groups that the authors termed comets, (c) cells undergoing FCZ treatment can adopt one of two different states (possibly bistability). I found the results interesting and the approach to be sound, and much of the results confirmed my prior expectations. The authors provide a detailed depiction of what is going on in the transcriptome during sub-inhibitory treatment, although this did not always lead to a mechanistic explanation. The clinical relevance was unclear to me beyond a proof of concept application for single-cell transcriptomics. In my opinion, an interesting follow-up would be to follow the transcriptional trajectory of lineages undergoing antimicrobial switching (on and off). The main issues I identified were the author's use of the term tolerance versus resistance, interpretation of "comets", clustering approach, description of fitness, and comparison between time points.
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