Local generation and efficient evaluation of numerous drug combinations in a single sample
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eLife assessment
In this manuscript, a method to test a large number of drug combinations in a single cell culture sample is presented. The strength of the evidence lies in their multiple experiments with different combinations of agents. The paper suggests that results from this application are feasible and the methodology could be applied in other laboratories to use drug combinations for defined outcomes.
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Abstract
We develop a method that allows one to test a large number of drug combinations in a single-cell culture sample. We rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combination of fluorescently barcoded drugs. We create independent transient drug gradients across the cell culture sample to produce heterogeneous local drug combinations. After the incubation period, the ensuing phenotype and corresponding drug barcodes for each cell are recorded. We use these data for statistical prediction of the treatment response to the drugs in a macroscopic population of cells. To further application of this technology, we developed a fluorescent barcoding method that does not require any chemical drug(s) modifications. We also developed segmentation-free image analysis capable of handling large optical fields containing thousands of cells in the sample, even in confluent growth condition. The technology necessary to execute our method is readily available in most biological laboratories, does not require robotic or microfluidic devices, and dramatically reduces resource needs and resulting costs of the traditional high-throughput studies.
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Author Response
Reviewer #3 (Public Review):
A large body of work in the literature has established that the diversity in cells of identical genetic background occurs due to two components: 1) intrinsic noise - such as stochastic fluctuations in gene expression - as well as 2) extrinsic noise - variability that arises from sources that are external to the biochemical process of gene expression, such as abundances of ribosomes or stage in the cell cycle. Note that this widely-accepted definition does not separate intrinsic and extrinsic from intracellular and extracellular. The authors cite a few of these seminal papers (which focus on noise introduced to gene expression) but then define their interpretation of intrinsic noise much more broadly "... intrinsic noise as phenotype(s) fluctuations across isogenic cell populations cultured …
Author Response
Reviewer #3 (Public Review):
A large body of work in the literature has established that the diversity in cells of identical genetic background occurs due to two components: 1) intrinsic noise - such as stochastic fluctuations in gene expression - as well as 2) extrinsic noise - variability that arises from sources that are external to the biochemical process of gene expression, such as abundances of ribosomes or stage in the cell cycle. Note that this widely-accepted definition does not separate intrinsic and extrinsic from intracellular and extracellular. The authors cite a few of these seminal papers (which focus on noise introduced to gene expression) but then define their interpretation of intrinsic noise much more broadly "... intrinsic noise as phenotype(s) fluctuations across isogenic cell populations cultured under the same conditions. Measurement noise in some cases can also be thought of as intrinsic noise. Fluctuations in cellular phenotype(s) driven by the global environment will be referred to as extrinsic noise." This misuse of widely accepted terminology creates significant confusion in the interpretation of the results.
A point of contention with redefining noise as the authors have done is that they are lumping all processes unique to the cell as intrinsic and all environmental factors as extrinsic. Thus, when statements are made such as "external factors that contribute to noise are principally manifest through convection" (line 40-41, page 2) the veracity of these assumptions must be established. For example, when a ligand binds and unbinds from a receptor due to thermal energy, that "noise" in cellular stimulation is not convection-based, yet an example of how extrinsic noise can influence cellular responses. The definition is important because the underlying premise for the pipeline presented is that "While intrinsic cell variability can be significant, we believe that it is the extrinsic factor(s) that drive sample variability in most experimental cellular systems" (lines 42-43, page 4).
We thank the referee for this very important critical comment. The referee correctly points out that the terminology (intrinsic vs. extrinsic noise) used in the cited papers has to be adapted and more clearly stated.
We wish to point out that the autonomous system in Michael Elowitz and colleagues’ original paper was a single protein within a single cell. The noise that was measured in these experiments was driven by temporal fluctuations. An example of extrinsic noise for this system is, indeed, as pointed out by the referee, ligand binding and unbinding from a receptor.
By contrast, our autonomous system is an ensemble of cells isolated from other samples but still subject to fluctuations in the external environment. We did not continuously measure temporal fluctuations in individual cells, but recorded snapshot(s) of cellular phenotype(s) within a single sample. The source of noise in these measurements is variability between individual cells, and we referred to this type of noise as intrinsic because it driven by the processes within the sample. We denoted as extrinsic noise that which is driven by external factors to this autonomous system (a particular sample), such as variability between different samples due to temperature, humidity, etc.
All of these external factors (to the best of our knowledge) are related to movement and gradient formation of fluid or gas and, hence, from a physicochemical perspective, driven by convection process(es). The initial cell seeding that eventually leads to unique microenvironment formation can also be thought as an example of extrinsic noise using this terminology. The process of cell sedimentation and attachment is driven by advection, as the referee correctly points out. We have, therefore, adjusted the text accordingly.
We hope that clarifying the intrinsic/extrinsic terminology in the "Introduction" section of the manuscript (line 37) should be sufficient to avoid the confusion the referee discusses. We are open (very reluctantly) to switching terminology to terms internal and external noise.
Throughout, figures lack labels and sufficient explanation for interpretation, as well as the number of experiments used to generate the data that is processed through the pipeline for each condition. For a study designed to eliminate replicate culture conditions, the onus is on the authors to show that replicates are in fact fully recapitulated in the population variance after statistical binning/processing.
To address this comment, we modified the figure legends and labels of most of the figures.
We wish to emphasize that each point-injection experiment we performed is unique due to randomness in the local delivery method. This is due to the variability in the manual micro-injection release rate and direction of the initial flow. Several experiments (3+) were performed to improve the width of the label(s) distribution(s) and their mixing condition, and the results of the better optimized local delivery were selected as representative for the manuscript. Sample selection was independent of the outcome of drugs action and based on initial label distribution only. An experimental improvement of our method, similar to initialization of the pseudo-random number generator in numerical experiments, is required to achieve systematic reproducibility of drug(s) distribution(s). One way to do so is robotically, but certainly the best is to design a system that utilizes a predictably constant drug gradient within a sample that contains large enough cells, a topic that will be the subject of future experiments.
Ultimately, when the paper presents results such as Figure 9 as the culmination of the pipeline as applied to cell viability studies, it is unclear how useful insight is extracted from this methodology. Four drugs are applied in combination to adherent HeLa cells and time-dependent local cell density is provided as a proxy for cell viability. While it is stated that "The absolute drug concentration can be determined using the homogeneous delivery method discussed above" (line 421-422, page 19), this analysis is not performed, and I am left unsure of whether extrinsic factors are truly driving sample variability under this context. It is unclear to the reader how the point injections were administered, and no discussion of how the confounding factors of synergy or antagonism will be addressed through this methodology.
We attempted to explain that data shown in Figure 9 were not meant to be the climactic point of the entire pipeline (rather, the data shown in Figure 6 represent our key achievement). In this four-drug experiment, we exhausted the fluorescent spectrum bandwidth necessary to distinguish drug labels (i.e., using commonly available microscopy tools). In order to estimate local cell density, we had to rely on bright field imaging data which is not the most accurate possible implementation (see further response to your comment below). More importantly, we had to wash samples between the measurements to remove detached (dead) cells and cell debris. This step can (and usually does) influence local cell density in a non-uniform fashion, since both media removal and deposition are performed locally by pipetting (cells in the vicinity of aspiration/media deposit sites can be washed off regardless of the drug treatment.)
To clarify how point injections were administered, we added a detailed description in the Methods section. Please see section Drug labeling and delivery, pages 11-12.
In this manuscript, we wished to establish possible applications of our method and avoid in depth analysis or biological interpretation of a specific drug combination that is dependent on the cell line or on a particular experimental condition. We added a paragraph in the "Discussion" section suggesting the necessity of future research dedicated to methodology and analytical interpretation of high-dimensional context-dependent drug interaction data.
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eLife assessment
In this manuscript, a method to test a large number of drug combinations in a single cell culture sample is presented. The strength of the evidence lies in their multiple experiments with different combinations of agents. The paper suggests that results from this application are feasible and the methodology could be applied in other laboratories to use drug combinations for defined outcomes.
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Reviewer #1 (Public Review):
In this paper, the authors developed a method that allows one to test a large number of drug combinations in a single cell culture sample. In principle, the experiments rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatments. They used a single sample containing thousands of cells treated with a combination of fluorescent barcoded drugs, and created transient drug gradients. They also developed segmentation- free image analysis capable of handling optical fields with a substantial number of cells. The major strength of this work is the demonstration of the feasibility of testing drug combinations in a relatively straightforward manner that could be used by many laboratories. As such this paper could have a significant impact on the early drug discovery of …
Reviewer #1 (Public Review):
In this paper, the authors developed a method that allows one to test a large number of drug combinations in a single cell culture sample. In principle, the experiments rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatments. They used a single sample containing thousands of cells treated with a combination of fluorescent barcoded drugs, and created transient drug gradients. They also developed segmentation- free image analysis capable of handling optical fields with a substantial number of cells. The major strength of this work is the demonstration of the feasibility of testing drug combinations in a relatively straightforward manner that could be used by many laboratories. As such this paper could have a significant impact on the early drug discovery of combinatorial therapy. One of the weaknesses in this manuscript is the absence of studies beyond just HeLa cells. In addition, the phenotype tested is cell death, which might limit the application to other drug interactions that might look at other phenotypes; e.g inhibition of cell proliferation or changes in differentiation phenotypes. Finally, there is a basic assumption that drug leakage does not occur or is minimal, but secondary uptake of the drug is likely and may not be homogeneous. Notwithstanding, the approach is feasible and likely will be applied in several laboratories.
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Reviewer #2 (Public Review):
This manuscript explores a novel technique to use dyes co-injected with various pharmaceutical reagents, like chemotherapic agents, to assess cellular effects in a cell culture model.
The major premise is that dye diffusion can be detected through fluorescent microscopy and be used as a measure of co-injected drug concentration. In chemotherapy commonly multiple drugs are given simultaneously, however, understanding how to tailor the concentrations of a multi-drug cocktail to each individual is largely trial and error. The authors surmise that perhaps using a cell culture model whereby cancer cells are cultured and then exposed to dye-tracked molecules an optimal multi-drug combination and concentrations can be determined. In other words, the intermixing of various connected drugs can then be fluorescently …
Reviewer #2 (Public Review):
This manuscript explores a novel technique to use dyes co-injected with various pharmaceutical reagents, like chemotherapic agents, to assess cellular effects in a cell culture model.
The major premise is that dye diffusion can be detected through fluorescent microscopy and be used as a measure of co-injected drug concentration. In chemotherapy commonly multiple drugs are given simultaneously, however, understanding how to tailor the concentrations of a multi-drug cocktail to each individual is largely trial and error. The authors surmise that perhaps using a cell culture model whereby cancer cells are cultured and then exposed to dye-tracked molecules an optimal multi-drug combination and concentrations can be determined. In other words, the intermixing of various connected drugs can then be fluorescently monitored to elucidate optimal concentrations of multi-drug combinations.
The concept overall is interesting but is relatively preliminary in its proof of concept. The authors note that varying free-diffusion of drugs out of the cell could complicate interpretation and that most of the analysis was done on a relatively short time basis and not longer evaluation periods that were more typical of chemotherapy.
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Reviewer #3 (Public Review):
The ability to rapidly test a large combination of drug cocktails on patient cells in culture would enhance personalized therapeutic regimens. Currently, testing 10 concentrations of 3 drugs in combination is intractable. Elgart & Loscalzo propose to take advantage of diverse drug responses within a single dish to streamline the exploration of multi-drug combinations. By sampling the population variation in uptake of multiple dyes within individual cells and delivering the dyes by a variety of modes (i.e. point injection, sequential homogenous mixing), a pipeline is developed for estimating a "response space" that arises from the complex intersections of multiple drug/dye concentration gradients.
The paper is in places very rigorous in establishing bounds in which this pipeline may have utility by defining …
Reviewer #3 (Public Review):
The ability to rapidly test a large combination of drug cocktails on patient cells in culture would enhance personalized therapeutic regimens. Currently, testing 10 concentrations of 3 drugs in combination is intractable. Elgart & Loscalzo propose to take advantage of diverse drug responses within a single dish to streamline the exploration of multi-drug combinations. By sampling the population variation in uptake of multiple dyes within individual cells and delivering the dyes by a variety of modes (i.e. point injection, sequential homogenous mixing), a pipeline is developed for estimating a "response space" that arises from the complex intersections of multiple drug/dye concentration gradients.
The paper is in places very rigorous in establishing bounds in which this pipeline may have utility by defining the linearity of two-drug co-delivery, explicitly illustrating the pre-processing/binning performed on the data, reporting distributions of uptake under different environmental dye gradients, and finding a tight correlation between dye and drug response to justify the surrogate use of dye characterization for the end-goal of drug cocktail formulation. I am particularly impressed with the results depicted in Figure 6 and the associated supplemental figures as a demonstration of an application of this approach for nanocarrier-based combinatorial siRNA delivery. However, there are major weaknesses in interpretability and underlying assumptions.
A large body of work in the literature has established that the diversity in cells of identical genetic background occurs due to two components: 1) intrinsic noise - such as stochastic fluctuations in gene expression - as well as 2) extrinsic noise - variability that arises from sources that are external to the biochemical process of gene expression, such as abundances of ribosomes or stage in the cell cycle. Note that this widely-accepted definition does not separate intrinsic and extrinsic from intracellular and extracellular. The authors cite a few of these seminal papers (which focus on noise introduced to gene expression) but then define their interpretation of intrinsic noise much more broadly "... intrinsic noise as phenotype(s) fluctuations across isogenic cell populations cultured under the same conditions. Measurement noise in some cases can also be thought of as intrinsic noise. Fluctuations in cellular phenotype(s) driven by the global environment will be referred to as extrinsic noise." This misuse of widely accepted terminology creates significant confusion in the interpretation of the results.
A point of contention with redefining noise as the authors have done is that they are lumping all processes unique to the cell as intrinsic and all environmental factors as extrinsic. Thus, when statements are made such as "external factors that contribute to noise are principally manifest through convection" (line 40-41, page 2) the veracity of these assumptions must be established. For example, when a ligand binds and unbinds from a receptor due to thermal energy, that "noise" in cellular stimulation is not convection-based, yet an example of how extrinsic noise can influence cellular responses. The definition is important because the underlying premise for the pipeline presented is that "While intrinsic cell variability can be significant, we believe that it is the extrinsic factor(s) that drive sample variability in most experimental cellular systems" (lines 42-43, page 4).
Throughout, figures lack labels and sufficient explanation for interpretation, as well as the number of experiments used to generate the data that is processed through the pipeline for each condition. For a study designed to eliminate replicate culture conditions, the onus is on the authors to show that replicates are in fact fully recapitulated in the population variance after statistical binning/processing.
Ultimately, when the paper presents results such as Figure 9 as the culmination of the pipeline as applied to cell viability studies, it is unclear how useful insight is extracted from this methodology. Four drugs are applied in combination to adherent HeLa cells and time-dependent local cell density is provided as a proxy for cell viability. While it is stated that "The absolute drug concentration can be determined using the homogeneous delivery method discussed above" (line 421-422, page 19), this analysis is not performed, and I am left unsure of whether extrinsic factors are truly driving sample variability under this context. It is unclear to the reader how the point injections were administered, and no discussion of how the confounding factors of synergy or antagonism will be addressed through this methodology.
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