Visual barcodes for clonal-multiplexing of live microscopy-based assays

While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation, we develop visual barcodes that discriminate the clonal identity of single cells by different fluorescent proteins that are targeted to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then use visual barcodes to generate ‘Signalome’ cell-lines by mixing 12 clones of different live reporters into a single population, allowing simultaneous monitoring of the activity in 12 branches of signaling, at clonal resolution, over time. Using the ‘Signalome’ we identify two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological systems.

The ability to multiplex samples has revolutionized science as well as medical practice. Genetic barcoding applications enabled unprecedented multiplexing, followed by parallel processing and analysis of dozens to hundreds of thousands of samples in applications like scRNA-Seq or functional CRISPR/shRNA/open-reading frame (ORF) screens. In contrast, in the field of image-based screens, high-order multiplexing is limited by the small number of channels that can be practically separated with common microscopy equipment. To address this, we developed visual barcodes that enable multiplexing of microscopy-based applications and used them to multiplex live-cell reporters for the study of signaling pathways dynamics in cancer cells.

To maintain growth, individual cells must balance a network that integrates information from numerous branches of signaling. In cancer, genetic and non-genetic alterations in major signaling pathways have been tightly linked to tumor initiation, progression, and response to anticancer therapies. It was also demonstrated that many of these alterations can be classified into a dozen signaling pathways, which together regulate core cellular processes such as cell fate, cell survival, and genome maintenance^1,2.

To facilitate understanding of cancer signaling, previous studies have developed genetically tagged activity reporters, i.e., fluorescent proteins that exhibit changes in abundance or localization in response to particular signaling activities^3,4. Unlike endpoint assays, which necessitate ending the experiment in order to measure a phenotype, these reporters helped reveal the intricate dynamics of individual branches of signal transduction pathways, in live cells, at single-cell resolution. However, since it is difficult to multiplex fluorescence reporters, mutual dependencies between these separate branches of signaling remained less explored.

In this study, we describe the development and application of visual barcodes, a technology that enables multiplexing live-cell imaging applications. The visual barcodes are used as labels of the clonal identity of single cells in mixed populations. To advance the understanding of cancer signaling pathways and their crosstalk, we used visual barcodes to multiplex 12 live reporters of major signaling pathways, generating an experimental system that we term, the “Signalome”.

Using the Signalome, we investigated the coordinated (multiplexed) dynamics of 12 signal transduction pathways in cancer cells that were challenged with well-characterized chemical perturbants. Our results show that multiplexing 12 reporter lines not only increases throughput but also eliminates noise associated with well-to-well variations in high-throughput drug screens. Surprisingly, our results also identify a previously undescribed binary partitioning of cancer signaling into two distinct clusters.

To maintain homeostasis, proliferating cells require mechanisms that coordinate rates of cell division with appropriate rates of biosynthesis. To sustain the proliferative demands of oncogenes, cancer cells require precisely tuned rates of biosynthesis⁵. If cells fail to double their mass between consecutive cell divisions, cell mass will progressively diminish. Conversely, rates of biosynthesis that exceed rates of cell division can result in cellular enlargement and senescence^6,7,8. We show that the two clusters of signaling pathways identified by the Signalome system represent a general stress response that correlates with the cells’ need to balance between growth and proliferation. Implementing Signalome in cancer cell lines thus revealed the previously under-appreciated importance of growth homeostasis as a central organizing principle in cancer signaling.

Our findings thus demonstrate that the Signalome is a robust technology that can help study the dynamics of signaling pathways in a single-cell resolution and the interconnection between different signaling branches. As the system is highly modular, replacing reporters can be easily achieved to allow the study of many other questions across the different fields of biology. In addition, the visual barcodes can be used in numerous in vitro and in vivo applications in which visual deconvolution of multiplexed cell lines is of need.

Developing visual barcodes to allow multiplexing of live imaging applications

To construct visual barcodes, we first stably infected the A375 melanoma cell line with a lentivirus containing the nuclear marker histone 2A fused to iRFP (iRFP-H2A) to accurately demarcate nuclear and cytoplasmic compartments (Fig. 1A). Next, iRFP-H2A cancer cells were used to generate five subclones, each labeled with a cyan fluorescent protein (CFP) tagged with unique cellular localization sequences, targeting the CFP into one of five subcellular locations: nucleus, endoplasmic reticulum (ER), cytoplasm (NES), peroxisomes (Peroxi), and whole cell (WC) (Fig. 1B). CFP localization was thus used as a visual barcode that can discriminate clonal identity.

A Representative image of the A375 cell line with an iRFP-H2A nuclear marker. B Representative images of five CFP localizations in the A375 cell line: Whole Cell (WC), Nuclear Export Signal (NES), Nuclei, Peroxisome (Peroxi), and Endoplasmic Reticulum (ER). C Heatmap of false detection rate (FDR) for barcode calling for the five CFP localizations in the A375 cell line. DRepresentative images of all 12 visual barcodes used in A375 cell line. E Average miss rate of barcode calling for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39). Numbers in the diagonal represent the sensitivity for each barcode. F Violin plots showing miss rate for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39) or with drugs (n = 75). G–I Scatter plot showing the separation of nine A375 clones with visual barcodes by the ImageStream system according to their fluorescent color and localization. The separation by localization is only demonstrated for GFP-positive clones (I). J Representative images from ImageStream of all nine clones. Two cells from each clone are presented. Scale bar in (A and B) is 50 µm, in (D) 100 µm and in (J) 7 µm. Source data are provided with this paper.

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To test the accuracy of the visual barcodes, we imaged each of the five subclones separately with both phase-contrast as well as in iRFP and CFP channels. We then used CellProfiler⁹ to: (1) segment the cells, (2) identify both nucleus and cytoplasmic compartments, and (3) extract texture, shape, and intensity-based features in the CFP channel for each cell (Sup code). CellProfiler Analyst¹⁰ was used to classify single cells based on visual barcode readouts. In our implementation, we used 70% of the cells as a training set, with the remaining 30% as a validation set. The average false detection rate (false-positive barcode labeling) was 1.15%, while miss rate (false-negative rate) was 1.17%, representing very high precision and recall rates respectively (Fig. 1C and Supplementary Fig. 1A). The highest false detection rate and miss rate were both detected between nuclear and ER localizations, which were thus deprioritized from additional follow-up.

To scale up the dimensionality of visual barcodes to a 12-barcode system, we fused three of the localization signals to four fluorescent proteins (CFP, BFP, GFP, and YFP), resulting with 12 distinct visual barcodes (Fig. 1D). This increase in barcode number still maintained a very high accuracy with an average false detection rate of 1.45% and a miss rate of 1.34% (Fig. 1E and Supplementary Fig. 1B). Most detection errors were between subclones with the same fluorescence color, with the highest false detection rate detected between whole-cell and peroxisomal BFP (Fig. 1E). To explore the robustness of the system under different types of perturbations, we treated each of the subclones separately with 75 drugs (Supplementary Data 1). We found that after 48 h of drug treatment, both precision and recall were still very high in almost all drugs with only eight drugs (12%) having a miss rate of above 3%, five of which strongly affecting cell proliferation and viability. (Fig. 1F and Supplementary Fig. 1B, C).

Note that one option to further increase the number of barcodes is to combine two barcode proteins into a single cell. By using four fluorescent proteins and five localizations, up to 160 different visual barcodes can be generated. Indeed, we were able to demonstrate a very high precision and recall rate for barcode calling even when barcodes with the same localization but different colors were combined (Supplementary Fig. 1D, E).

Lastly, we showed that the clones can also be separated when cells are in suspension, using the Imagestream high-resolution microscopy and flow cytometry system (Fig. 1G–J). As our system lacked a laser to detect YFP, we only multiplexed nine of the subclones. To demonstrate that the visual barcodes can also be used in vivo, we mixed the nine clones and implanted them subcutaneously in a nude mouse. When the tumor reached a diameter of 8 mm it was excised, dissociated into single cells, and analyzed by the Imagestream system, demonstrating that all nine clones could be detected (Supplementary Fig. 1F–J).

Generating the “Signalome” reporting cell lines

To generate the Signalome, we assembled 12 previously published and well-characterized reporter constructs (Supplementary Fig. 2A), each associated with the activity of a different cancer-related signaling pathway. To enable their multiplexing, we replaced the original tagged fluorescent protein in each of the 12 reporter vectors with the mStrawberry fluorescent protein. We used two different types of reporters: reporters that drive the expression of the fluorescent protein by a specific transcription response element (TRE) or kinase translocation reporters (KTRs) that translocate the fluorescent protein from the nucleus to the cytoplasm upon activation of upstream signaling (Supplementary Fig. 2A).

We then infected each of the 12 A375 subclones that have visual barcodes with one of the 12 reporters (Fig. 2A). Next, we validated that the proliferation rate of the single-cell derived reporter subclones is not different from that of the parental cell line (Supplementary Fig. 2B). We also validated that all 12 subclones are as sensitive to the BRAF inhibitor, vemurafenib, as the parental A375 cell line (Supplementary Fig. 2C). Lastly, we pooled together all 12 subclones generating the A375 signalome cell line, and demonstrated that the proportions of the 12 different clones remained constant over 48 h of culture (Fig. 2B).

A Illustration of the 12 clones that were used to generate the A375 Signalome cell line. A375 cells were first infected with iRFP-H2A to mark the cell nucleus. Then, 12 clones were generated with 12 visual barcodes. Lastly, a different live reporter was added to each of the clones. Transcription activating reporters are represented by gold while translocation reporters are represented in red. Binding partners in the nucleus are represented in purple. B Relative number of cells from each clone in a DMSO control wells over time. C Scatter plot showing the correlation between the reporter activity scores, for all 12 clones when grown separately or as part of the Signalome cell line, in response to 75 drugs. D–F Reporter activity plots of the A375 signalome cell line in response to DMSO, vemurafenib (1 µM) or trametinib (0.125 µM) over time. Blue and red backgrounds represent activation or inhibition scores above 0.2 or below −0.2, respectively. The average cell count per reporter in (D–F) is: 656, 545, and 530 cells, respectively. Source data are provided with this paper.

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An advantage of single-cell measurements is that perturbations can be characterized not only for their influence on population average but also on the full distribution (i.e., the frequency of cells with low, medium or high signaling activity). To quantify condition-dependent differences in the distribution of reporter activity, we used the Kolmogorov–Smirnov (KS) test as it is a nonparametric test that can also detect changes in the distribution that are not reflected by the mean of the distribution (Supplementary Fig. 2D, E)^11,12. To add to the KS score, activity scores were assigned a positive or negative sign based on the change in direction of the mean of these distributions. We used a threshold of ± 0.2 to mark a significant change in activity as it represents the 0.5 percentile of reporter activity across our DMSO control wells (Supplementary Fig. 2H). To validate the reporters’ activity, we first used known positive or negative regulators for each of the reporters demonstrating a perfect correlation between the reporter’s score and the expected drug effect (Supplementary Fig. 2F). We also compared the A375 signalome cell line activity scores to the activity of all 12 reporters when tested separately and found a very strong correlation between the two (r = 0.75, P < 10⁻¹⁶) (Fig. 2C). As expected, we also found that Vemurafenib and the MEK inhibitor Trametinib, both inhibitors of the MAPK pathway, exerted highly similar effects on the A375-Signalome cell line (Fig. 2D–F). Finally, to validate our reporters’ activity using different methods, we used western blot analysis demonstrating full agreement between both techniques (Supplementary Fig. 2G).

An advantage of multiplexed, time-dependent measurements on signaling is the ability to differentiate direct and indirect drug influences. For example, while MAPK inhibitors (vemurafenib and trametinib) promoted detectable changes in measured ERK signaling that were observed 1.5 h into drug treatment, an influence of these same drugs on other pathways was also observed, albeit at much later times (Fig. 2D–F). This is in agreement with the direct effect of these drugs on the MAPK pathway and the subsequent adaptive response of the other pathways to the inhibition of the MAPK pathway. Indeed, previous reports already described activation of PKA¹³, NFkB¹⁴, HIF¹⁵, and YAP/TAZ¹⁶ in response to vemurafenib and suggested that these adaptations can contribute to resistance to MAPK inhibition. In addition, we observed a significant upregulation of retinoic acid receptor activity 48 h after BRAF or MEK inhibition (P value < 6 × 10⁻⁵). Interestingly, examination of three independent cohorts of melanoma patients demonstrated that patients with high activity of the RAR/RXR pathway, as calculated from expression data by PathOlogist^17,18, had an overall better survival (Supplementary Fig. 2I–K). Therefore, it may be of interest to continue and better explore the role of RAR in melanoma and its response to therapy. These measurements also demonstrate the efficiency and throughput of the Signalome system: with one 384-well plate, we screened the influence of 75 drugs (in triplicates) and 39 DMSO controls on 12 branches of signaling, in multiple time points, and at single-cell resolution. A single signalome plate thus provided measurements on half a million cells from each of the different time points.

To demonstrate that the visual barcodes and a signalome system can be readily applied to other cell lines we generated two more signalome cell lines using the PC9 EGFR-mutated non-small-cell lung cancer cell line and the SK-MEL-5 BRAF-mutated melanoma cell line. We were able to demonstrate that our barcode precision and recall rates are also very high in these cell lines (Supplementary Fig. 3A–D) and that an early inhibition of ERK by both inhibitors can be detected followed by cell-line-specific adaptive mechanisms. For example, while inhibition of BRAF or MEK in A375 melanoma cell line resulted in activation of the YAP/TAZ pathway, the effect of BRAF/MEK inhibition in the SK-MEL-5 cell line resulted in inhibition of the YAP/TAZ pathway (Supplementary Fig. 3E–H). Here, again we found that EGFR inhibition in the PC9 cells also drives upregulation of RAR activity.

Large-scale correlations in signaling suggest a generalized response that is compound independent

To investigate interdependencies in the cancer signaling, we treated the pooled A375 signalome cell line with a library of 422 well-characterized chemical perturbants (Supplementary Data 2, Supplementary Data 3, and Supplementary Fig. 4A). Of all tested compounds, 122 (28.9%) promoted significant changes in at least one of the reported pathways (KS absolute score > 0.2) (Fig. 3A, Supplementary Fig. 4I, Supplementary Data 4, and Supplementary Data 5). As expected, different drugs with similar targets displayed similar patterns of reporters’ changes in response to these drugs (Supplementary Fig. 4B–G).

A Unsupervised hierarchical clustering of A375 signalome cells treated by 122 active drugs according to their activity scores. Due to technical error, Geminin reporter was not measured for all drugs and was thus discarded. Results from all 12 reporters can be seen in Supplementary Fig. 4I for the subset of drugs that for which Geminin data are available. B Scatter plot showing the correlation between the activities of the p38 and p53 reporters after 48 h of treatment with 122 active drugs. C Heatmap showing the pairwise Pearson correlations between the different A375 Signalome clones, after 48 h of treatment with 122 active drugs. D A model proposing how drugs with different mechanisms may converge into two major signaling states. While each of the drugs has different targets, many of the targets affect the same sensing mechanism that later governs the p53- vs p38-signaling states. Source data are provided with this paper.

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Surprisingly, in addition to target-specific signatures, unsupervised clustering of the reporters’ activity scores also suggested a higher structure that partitions the signal transduction signatures into three clusters, two of which seem anticorrelated (Fig. 3A and Supplementary Fig. 4I). Cluster A groups compounds that seem to all activate the pathways PKA, AKT, ERK, p38, and JNK while inhibiting WNT, p53, NFkB, RAR, HIF, and YAP\TAZ; while cluster B contains drugs that orchestrate the opposite response. Cluster C contained drugs that did not follow this dichotomy. The large number of drugs with varying mechanisms of action that result in signaling clusters A and B, suggests a coordinated response to drug treatment that involves all of our measured branches of signaling and is surprisingly independent of drug target. As a case in point, Fig. 3B shows a negative correlation between the activities of p53 and p38 which is persistent across a wide diversity of chemical perturbations. Drugs that diminished p38 activity correlate with equivalent increase in the activity of p53 and vice versa (Pearson’s r = −0.517, P  <  2.2 × 10⁻¹⁶) (Fig. 3B). More generally, drugs promoted positive correlations among pathways within cluster A or B (intracluster correlations) and negative correlations when comparing pathways from cluster A to pathways from cluster B (intercluster correlations). For simplicity, we will refer to these two clusters as the p38 signaling state (cluster A) and the p53-signaling state (Cluster B) (Fig. 3A).

Chemical perturbations can generate correlated influences by simultaneously affecting more than one target. Such correlated influences, however, should be compound-specific, relating to target affinities that differ from one compound to another. By contrast, we found that the same pairs of pathways are positively, or negatively correlated, across a large number of drugs (n = 122) that have multiple and highly different targets. (Fig. 3B, C). To demonstrate that this partition is not cell line-specific, we treated the PC9 signalome cell line with 247 drugs and found very similar bifurcation into two anticorrelated clusters of pathways (Supplementary Fig. 4H and Supplementary Data 6). The question, therefore, is as follows: how can such a wide variety of perturbations, each associated with different targets, converge onto only two main outcomes? We reasoned that the partitioning of the signaling pathways into two clusters suggests a regulatory process that is common and upstream of all of our measured branches of signaling (Fig. 3D).

Large-scale correlations in signaling are present pre-treatment and increase over time by multiple drugs

To gain insight into the nature of this upstream regulatory process, we first performed time-course measurements to ask, how soon after drug treatments do the pairwise correlations become apparent? We found that pairwise correlations in pathway activity became more and more prominent throughout the 48 h following drug treatments (Fig. 4A, B). The segregation of the two signaling states post drug treatment is also apparent from visual inspection of time-course measurements (Fig. 4C–H). Trajectories of activity of the reporters in response to six representative drugs that are chemically distinct and associated with different targets, demonstrated that while three drugs promoted the p38 signaling state (Fig. 4C–E), the other three activated the p53-signaling state (Fig. 4F–H). Altogether, these results demonstrate the binary partitioning of the signaling pathways by showing that a variety of different drugs, each associated with different targets, converge to promote two main signaling states outcomes.

A Scatter plots of the activity scores of p38 and p53 reporters in the A375 signalome cell line before and at multiple time points after treatment with 122 active drugs. Each dot represents a different drug. Pearson’s r is depicted for each of the time points. B Pearson correlation coefficients were calculated for each pair of pathways in the A375 signalome cell line before and at different time points after treatment with 122 active drugs. Each datapoint represents a correlation value for one given pair of pathways over all 122 active drugs. The correlation between the activity score of p38 and p53 pathways is marked by a red circle. C–H Locally weighted smoothing (Lowess) regression of all reporters in each of the two signaling states is shown for six representative drug treatments, each associated with different drug targets. While three of the drugs drive the p53-signaling state (C–E), the other three drugs drive the p38 signaling state (F–H). The bold (red and blue) lines and the gray sleeves represent mean values and + /− SEM respectively. Source data are provided with this paper.

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These results suggest that the p38- and p53-signaling states are mediated by a process that, in response to drug treatments, gradually increases its influence, or activity, over time. Since it is likely that the drug treatments only promoted the activity of an already existing process, we were curious as to why the correlations shown in Fig. 3C seem absent in unperturbed cells (Fig. 4B)? One possibility is that, prior to drug treatments, signaling pathways are subjected to the simultaneous influence of several competing regulatory demands, each pulling in a different direction. According to this interpretation, drug treatments promote correlated signaling by increasing the relative weight of one particular regulatory process—most likely a stress response—such that its effect is no longer averaged by competing influences. This model suggests a testable hypothesis: the binary partitioning of the 12 pathways should become apparent in untreated cells if the measurements are normalized for independently existing correlations.

To this end, we used Principal component analysis (PCA), a technique that transforms a dataset into a linear combination of independently existing multivariate correlations. As expected, PCA confirmed the high degree of correlations by identifying a single principal component (PC1) that explains almost 50% of the variance after 48 h of treatment (Fig. 5A). Further, the first principal component clearly identified the two signaling states; drugs that promote the p38-signaling or p53-signaling states are characterized by negative or positive values of PC1 respectively (Fig. 5B). Next, we repeated the PCA, but this time on measurements collected prior to drug treatments (time zero). Note that in this latter implementation of PCA, variation in measured activities did not reflect differences in drug response, as no drugs were yet applied, but rather, small well-to-well variations like differences in evaporation rate or oxygen concentrations. In early landmark studies^19,20, such well-to-well (or sample-to-sample) variation in cell division rates and in cell size served as pivotal evidence for the functioning of cell size checkpoints in animal cells²¹. Since the signalome provides measurements on 12 pathways in each well, it can test whether these small variations will lead to a well-specific shift in the signaling states that can be detected by PCA. Indeed, PCA significantly identified both p38- and p53-signaling states also in the unperturbed cells (Fig. 5C).

A PCA of the activity scores of 11 signaling pathways after 48 h of treatment with 122 drugs. The color of each drug is indicating its cluster in Fig. 3A. B, C Bar plots representing the PC1 loading of each pathway after 48 h of drug treatment (B) or pretreatment (C). D Variance of pathway activity, when projected on the principal components calculated from measurements on cells that were not exposed to drug treatment. Source data are provided with this paper.

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To further explore whether the p38- and p53-signaling states precede drug treatments, we tested whether measurements on cells that were not exposed to drug treatments can predict the specific correlations observed post drug treatments (Supplementary Fig. 5). This analysis identified dynamics that are classically characteristic of homeostasis (Fig. 5D). In the first hour post drug treatment, chemical perturbations effectively eliminated correlated activities that linked the different branches of signaling in the unperturbed cells. Several hours into drug treatment, however, correlated activities resumed and, in fact, gained more prominence. These results suggest a general stress program that: (A) had functioned in cells that were not subject to drug treatment (B) was effectively eliminated in the first hour of drug treatment and (C) had resumed activity in the hours following drug treatment. This observation can also be visualized in Fig. 4C–H, in which the two signaling states are apparent at time zero (pre-drug treatment), lost at one hour, and gains significance at the latter time points.

The p38- and p53-signaling states are linked to perturbations in cell size

The constancy of the two signaling states, in the face of diverse chemical perturbations, suggests that the large-scale correlations described by these signaling states function to support some process that is critical in our cell lines. Since our measurements were performed on cancer cells, we further reasoned that the process in question may relate to demands imposed by continuous cell divisions. To maintain homeostasis, proliferating cells must double their mass between consecutive cell divisions. In cancer, this requirement may be more critical^22,23,24. If cancer cells fail to match the proliferative demands of an oncogene with equivalent increases in biosynthesis, cell size will decrease over time²¹. We therefore wondered whether the observed pattern of coordinated signaling results from stress-sensing systems that respond to changes in cell size resulting from imbalances in cell growth and cell division (Fig. 6A).

A A model demonstrating how sensing of cell size can affect both cell growth and proliferation to keep homeostasis of cell size. B Scatter plot showing the initial and long-term effects of rapamycin or SNS-032 on the average cellular growth rate and division rate of Rpe1 cells. Data points indicate the average growth and division rates measured: (1) during the first 24 h of drug treatment and (2) during 24–60 h of drug treatment. C Average growth rate vs. division rate in five cell lines (Rpe1, HeLa, U2OS, SAOS2, 16HBE) treated with either growth inhibitors (red) or cdk1/2 inhibitors (blue). Measurements in each cell line were normalized by the values measured for untreated control samples (gray) of the same cell line. The growth inhibitors used were: Cycloheximide, Torin-2, and Rapamycin, at varying doses (de- tailed in “Methods”). The cdk1/2 inhibitors used were: SNS-032, PHA848125, Cdk2 Inhibitor III, and Dinaciclib, at varying doses (detailed in “Methods”). D, E The average cell size for a given drug correlated with its PC1 value calculated on the reporters’ activity for data from Kaufman et al. (D) and Liu at al. (E). FAverage growth rate vs. division rate for A375 cells in Signalome screen. Each circle represents one screened condition (drug treatment). The circle’s color indicates the value of PC1 in that condition. Contour lines show the average value of PC1 as a function of growth rate and division rate. G The average level of p38 (top) and p53 (bottom) activity as a function of growth rate and cell cycle length. H A model proposing how drugs which affect cell division activate the p53-signaling state while drugs that affect cell growth activate the p38-signaling state. Each state in return actives a compensation mechanism resulting in a new equilibrium. Source data are provided with this paper.

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As a first step, we asked whether drugs that selectively interfere with rates of biosynthesis trigger compensatory mechanisms that will help the cell to reach a new steady-state between growth and proliferation rates. We first used the Retinal epithelial line RPE1, an hTERT immortalized non-cancerous cell line, as it is highly suitable for studies of cell size²¹. To inhibit cell division rate, we used various chemical inhibitors of Cyclin-dependent kinases (CDK) like SNS-032²¹ while to lower biosynthetic activity (cell growth), we either inhibited protein synthesis by Cycloheximide or mTOR activity by Rapamycin and Torin. In all cases, drug doses were carefully optimized to ensure that cells are still proliferating and are not undergoing complete cell cycle arrest. For quantitative measurements of cell growth (protein synthesis per unit time), we followed a previously described protocol for single-cell measurements of total macromolecular protein mass using fluorescently labeled succinimidyl ester (SE) that label all proteins (Supplementary Methods)^21,25,26.

Our results demonstrate that during the initial hours of Rapamycin treatment, while cell growth was rapidly inhibited, the rates of cell division were relatively unaffected (Fig. 6B). Conversely, the CDK2 inhibitor SNS-032 lowered rates of cell division but did not affect cell growth (Fig. 6B). At the later time points, however, a coordination of growth and proliferation was re-established, but at a slightly different setpoint. Cells with inhibited rates of biosynthesis adapted by promoting longer periods of biosynthetic activity (longer cell cycles). Yet, this lengthening of the cell cycle fell short of a perfect adaptation, resulting in paired values of growth and division that fell slightly below the proportionality line. Similarly, to adapt to the longer growth periods imposed by CDK2 inhibitors, cells lowered the amount of protein synthesized per unit time. Yet, here too, the compensation was incomplete, resulting in paired values that lay above the line. Extending these results to multiple cell cycle or cell growth inhibitors across five cell lines demonstrated that incomplete compensation of the growth and proliferation rate is a general phenomenon (Fig. 6C). In conclusion, our results suggest that: (A) Drugs that perturb rates of biosynthesis trigger compensatory changes in division rates, and vice versa; (B) The adaptation of growth rates and division rates to drug treatments is typically incomplete, resulting in paired values of growth and division rates that lie above and below the proportionality line.

To investigate the possibility that the p38- and p53-signaling states that we observed are related to the homeostasis of cell size, we asked whether drugs that induce these two states differ in their influence on cell size. To that end, we scored each compound for the extent that it promoted the p38-signaling vs p53-signaling states (Supplementary Methods). We then used the signalome single-cell resolution measurements to calculate the influence of each drug on cell division rate and cell size. Consistent with our hypothesis, we found that drug treatments that promote the p38 state correlated with a smaller cell size while drugs that promote the p53 state correlated with increased cell size (Pearson’s r = −0.625, P = 1.68 × 10⁻²³) (Fig. 6D).

To further strengthen our model, we asked whether drug targets that promote the p53- and p38-signaling states were previously characterized as regulators of cell-size homeostasis. In mammalian cells, a comprehensive screen for mechanisms of cell-size homeostasis was described in Liu et al.²⁵. In that study, highly quantitative cell-size measurements were performed on cells that were interrogated with the NIBR MoA drug library²⁷—a highly characterized chemical library designed to interrogate mechanisms of action. To extract target-specific cell-size influences, we implemented a linear regression model (Supplementary Methods). This resulted with a list of genes, each with a predicted influence on rates of cell division and on cell size. Figure 6E reveals a significant correlation between the influence of drug targets on cell size (as quantified in Liu et al., Supplementary Data 7). Specifically, targets that were characterized in Liu et al. as inhibitors of cellular growth were independently identified in our study as targets that promote the activation of the p38-signaling state. Conversely, targets characterized by Liu et al.²⁵ to slow the rates of cell division were independently identified in our study as targets that promote the activation of the signalome reporters associated with the p53-signaling state. Notice, that while the signalome data were produced on A375 melanoma cells, the data of Liu et al.²⁵ was generated using HeLa cervical cancer cells. These correlations between signal transduction measurements on A375 cells and cell-size measurements on HeLa cells indicate that the link of the two states with cell size is common across different cell lines.

Next, we used the signalome to test whether the two signaling states correlate with imbalances in cell growth and division rates. We found that drugs that disproportionately decrease growth rates (i.e., data points below the diagonal) were associated with the p38-signaling state, while drugs that disproportionately decrease proliferation (i.e., points above the diagonal) induced the p53-signaling state (Fig. 6F, G).

Additional support for the distinct association of the p38- and p53-signaling states with cell growth and cell division came from functional annotation enrichment analysis using the STRING database (http://string-db.org/)^28,29,30,31. Relying on the list of the drugs that selectively promote the p38- or p53-signaling states, we asked whether drug targets that promote the p38- or p53-signaling states are functionally distinct. Indeed, we found that while drugs that promote the activation of the p53-signaling state were enriched for inhibitors of the cell division cycle, drugs that promoted the activation of the p38 state were significantly enriched for regulators of anabolic activity, including the Insulin/mTORC1 pathway, as well as regulators of glucose, glycogen and carbohydrate metabolism (Supplementary Fig. 6A, B).

In cancer, disproportional changes in growth and division can spontaneously result from intracellular genetic changes or from external stresses, including nutrient or growth factor deprivation. To test if the p38- and p53-signaling states are represented in human cancers, we mined the TCGA (https://www.cancer.gov/tcga) to retrieve proteomic measurements from 8167 human tumors that span 32 different types of cancer^32,33. To compare signalome reporters with TCGA, we assembled a list of eight proteins or phosphoproteins that are known to correlate with the activity of pathways included in the signalome. Using these proteins as a surrogate for pathway activity we separately analyzed each of the 32 cancers for correlated signaling. Specifically, for each cancer, we calculated all pairwise-correlation coefficients related to the signalome pathways. Our results demonstrated that, with the exception of cholangiocarcinoma, similar signaling bifurcation was present in all of the other cancer types as demonstrated in Supplementary Fig. 6F. This further indicates a common regulatory process upstream of all of our measured branches of signaling (Fig. 3D).

As a final question, we asked what is different about the drugs in cluster C (Fig. 3A) that allows them to escape the mutually exclusive association with either the p53- or the p38-signaling states? One possible hypothesis is that drugs in this cluster target mechanisms that are essential for the maintenance of cell-size homeostasis by uncoupling the cellular feedback mechanisms that balance cell proliferation and cell growth. In this context, it is intriguing to point out that CDK4 inhibitors are among the highest-scoring drugs in this group. Indeed, recently, we and others have shown that cell-size homeostasis critically depends on the CDK4³⁴ (but not CDK2) which may thus explain why CDK4 inhibitors do not adhere to the p38- pr p53-signaling states (Supplementary Figs. 6C–E and 7). Further studies are needed to better understand the mechanisms that drive other drugs to cluster C.

Live reporters are widely used to study signaling dynamics in cells. However, currently, the ability to multiplex live reporters together is limited by the number of reporters that can be multiplexed and the ability to follow multiplexed reporters for multiple days rather than hours³⁵. The integration of information from multiple signaling branches is critical for the understanding of complex biological processes. In this study we introduced visual barcodes, a fluorescent protein coupled to a specific subcellular localization peptide, which allows multiplexing cells in live imaging applications. We demonstrate that visual barcodes are robust to perturbations, have a high precision and recall rates and are applicable for multiplexing both in vitro and in vivo. Multiplexing of different subclones not only increases the throughput of experiments but also reduces cost and well-to-well or animal-to-animal variation³⁶. Adding more fluorescent proteins or cellular localizations to the system can augment its multiplexing potential, and we predict that the system can be easily expanded to 20-plex combinations if only one barcode is used per cell and more than a 200-plex if two visual barcodes are used per cell. Deconvolution of the visual barcodes was done using freeware, thus enabling the use of the system without licensing limitations. The visual barcodes system can be used for a very wide variety of applications such as competition assays between clones with different perturbations in vitro or in vivo, live tracking of cells with reduced risk of switching between subclones, as well as multiplexing of live reporters, as we demonstrated by the Signalome cell lines.

For generating the Signalome cell lines, we added a different fluorescent reporter for each of our 12 validated visual barcodes subclones, reporting for major signaling pathways in cancer cells. While we generated each of the Signalome subclones using three consecutive rounds of infections (nuclear marker, visual barcode, fluorescent reporter), we envision that a visual barcode and a reporter could be integrated into a single plasmid thus allowing one round of infection with a mix of plasmids on a cell line with a nuclear marker, allowing the generation of additional Signalome cell lines in days rather than weeks.

While the signalome technology allows multiplexing live reporters in the same cell (Supplementary Fig. 1D, E), adding more than a very small number of such reporters per cell may have a substantial effect on the cells. Indeed, to the best of our knowledge, previous works that combined multiple live reporters in a single cell, have not exceeded a maximum of five reporters per cell^{35,37,38,39,40}. To achieve a much greater multiplexing, we mixed clones of cells, each containing a single reporter. While we demonstrate the use of 12-plex signalome cell lines we believe that much higher multiplexing can be easily achieved by this technology. In addition to the advantage in throughput by hi-plex reporter cell lines, as the readout of all reporters comes from the same wells, a substantial reduction in well-to-well variability is expected when comparing between the effect of perturbations on the different reporters. On the other hand, there are clear limitations for not comparing between reporters that are present in the same cells. The most obvious limitation is the difficulty to differentiate between events that occur in the same cell or in different cells. For example, if following a perturbation a subset of the cells of two clones that report on two signaling pathways show inhibition of the activity in these pathways, follow-up experiments will need to be carried out to find out if inhibition of both pathways always accrue together in the same cells or maybe in different cells. In addition, as our signalome clones were frequently expanded from single cells, it is important to validate that the main findings also apply to the whole population and do not reflect a cell-specific phenotype. One way to overcome this limitation is that each signalome reporter cell line will originate from multiple cells rather than from a single one. It remains to be seen what effect of such an approach will have on the recall and precision rates of the visual barcodes.

To better understand the interdependencies of signaling pathways, we treated the A375 and PC9 signalome cell lines with hundreds of characterized chemical perturbants. Altogether, our results suggest an explanation as to how a chemically diverse collection of drugs converged onto a much smaller number of signaling states. Growth and division are fundamental processes that are subject to multiple mechanisms of homeostasis. While different drugs affect different intracellular mechanisms, an influence on growth or division is a common denominator of many different drug targets. According to this model, the question of whether a drug promotes the p38- or p53-signaling states is not answered by the affected drug targets but rather, by how that drug targets relates to growth and division, i.e., to cell size (Fig. 6H).

The association of p38 with changes in cell size is consistent with previous reports^25,41 whereby p38 MAPK was shown to selectively activate cells that are smaller than their target size. With the present study, we not only confirm the selective association of p38 activity with decreased cell size but further demonstrate that this small-cell-size-dependent signaling is not exclusive to p38 MAPK, as it is also observed with ERK, JNK, AKT, and PKA which together constitute the “p38-signaling state”. In addition, our findings also suggest large-cell-size-dependent signaling by members of the p53-signaling cluster. In the context of cell-size regulation, these findings on p53 may prove pivotal. While literature on cell-size checkpoints address mechanisms that are activated in inappropriately small cells, literature on mechanisms that are activated in cells that are larger than appropriate are sparse. Our findings on large-cell-size-dependent p53 activity was confirmed in both cancer cell lines as well as non-cancer cell lines. Both the association of p53 with cell-size homeostasis and the negative correlation of p53 and p38 activity are consistent with previous literature. In previous studies, a negative feedback circuitry linking p38 and p53 has been characterized^42,43. Specifically, these studies have shown that while p38 promotes the activation of p53, active p53 subsequently responds in negative feedback to inhibit p38, bringing about the resumption of homeostasis^42,43. Our findings on the cell-size-dependent p53/p38 dichotomy is interestingly consistent with the roles of these proteins in cell growth. While the small-cell-size-dependent activation of p38 is consistent with the mTORC1-activation^44,45 and growth-promoting influence of p38, the large-cell-size-dependent activation of p53 is consistent with the mTORC1-inhibiting and growth-inhibiting influence of p53^46,47.

While previous studies have established a p38-dependent cell-size checkpoint in mammalian cells²⁵, these studies did not focus on the critical need to monitor cell size in continuously proliferating populations. The present work links size sensing in proliferating cells with an adaptation to homeostasis of growth and cell division. It is also interesting to note that, while both p38 and p53 are well-established stress proteins, their physiological response to stress conditions is very distinct. Stress conditions that activate p38 typically promote inflammatory programs which promote growth and suppress apoptosis⁴⁸. By contrast, the activation of p53 is both pro-apoptotic and functions to suppress mTORC1-mediated biosynthesis⁴⁹.

Overall, the visual barcodes are an easy-to-implement system that can help researchers to multiplex cells for a very wide variety of applications. The system is highly modular and can serve to generate Signalome cell lines with different reporters and thus may be useful in the research of a very wide variety of biological fields.