Multiplexed high content screening assays create a systems cell biology approach to drug discovery

Unraveling the genetics of arsenic toxicity with cellular morphology QTL

The health risks that arise from environmental exposures vary widely within and across human populations, and these differences are largely determined by genetic variation and gene-by-environment (gene-environment) interactions. However, risk assessment in laboratory mice typically involves isogenic strains and therefore, does not account for these known genetic effects. In this context, genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening because they provide a way to introduce genetic variation in risk assessment without increasing animal use. Cell lines from genetic reference populations of laboratory mice offer genetic diversity, power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

Multiplex Hextuple Luciferase Assaying

We recently expanded the commonly used dual luciferase assaying method toward multiplex hextuple luciferase assaying, allowing monitoring the activity of five experimental pathways against one control at the same time. In doing so, while our expanded assay utilizes a total of six orthogonal luciferases instead of two, this assay, conveniently, still utilizes the well-established reagents and principles of the widely used dual luciferase assay. Three quenchable D-luciferin-consuming luciferases are measured after addition of D-Luciferin substrate, followed by quenching of their bioluminescence (BL) and the measurement of three coelenterazine (CTZ)-consuming luciferases after addition of CTZ substrate, all in the same vessel. Here, we provide detailed protocols on how to perform such multiplex hextuple luciferase assaying to monitor cellular signal processing upstream of five transcription factors and their corresponding transcription factor-binding motifs, using a constitutive promoter as normalization control. The first protocol is provided on how to perform cell culture in preparation toward genetic or pharmaceutical perturbations, as well as transfecting a multiplex hextuple luciferase reporter vector encoding all luciferase reporter units needed for multiplex hextuple luciferase assaying. The second protocol details on how to execute multiplex hextuple luciferase assaying using a microplate reader appropriately equipped to detect the different BLs emitted by all six luciferases. Finally, the third protocol provides details on analyzing, plotting, and interpreting the data obtained by the microplate reader.

Examining multiple cellular pathways at once using multiplex hextuple luciferase assaying

Sensitive simultaneous assessment of multiple signaling pathways within the same cells requires orthogonal reporters that can assay over large dynamic ranges. Luciferases are such genetically encoded candidates due to their sensitivity, versatility, and cost-effectiveness. We expand luciferase multiplexing in post-lysis endpoint luciferase assays from two to six. Light emissions are distinguished by a combination of distinct substrates and emission spectra deconvolution. All six luciferase reporter units are stitched together into one plasmid facilitating delivery of all reporter units through a process we termed solotransfection, minimizing experimental errors. We engineer a multiplex hextuple luciferase assay to probe pathway fluxes through five transcriptional response elements against a control constitutive promoter. We can monitor effects of siRNA, ligand, and chemical compound treatments on their target pathways along with the four other probed cellular pathways. We demonstrate the effectiveness and adaptiveness of multiplex luciferase assaying, and its broad application across different research fields.

Multiplexed detection of luciferase-based sensors in the same sample is challenging and limited by the substrates’ emission spectra. Here the authors establish a system based on three different luciferases and sequential detection to achieve measurements of up to six parameters within the same experiment.

Pitch-tunable pillar arrays for high-throughput culture and immunohistological analysis of tumor spheroids

Tumor spheroids are multicellular, three-dimensional (3D) cell culture models closely mimicking the microenvironments of human tumors in vivo, thereby providing enhanced predictability, clinical relevancy of drug efficacy and the mechanism of action. Conventional confocal microscopic imaging remains inappropriate for immunohistological analysis due to current technical limits in immunostaining using antibodies and imaging cells grown in 3D multicellular contexts. Preparation of microsections of these spheroids represents a best alternative, yet their sub-millimeter size and fragility make it less practical for high-throughput screening. To address these problems, we developed a pitch-tunable 5 × 5 mini-pillar array chip for culturing and sectioning tumor spheroids in a high throughput manner. Tumor spheroids were 3D cultured in an alginate matrix using a twenty-five mini-pillar array which aligns to a 96-well. At least a few tens of spheroids per pillar were cultured and as many as 25 different treatment conditions per chip were evaluated, which indicated the high throughput manner of the 5 × 5 pillar array chip. The twenty-five mini-pillars were then rearranged to a transferring pitch so that spheroid-containing gel caps from all pillars can be embedded into a specimen block. Tissue array sections were then prepared and stained for immunohistological examination. The utility of this pitch-tunable pillar array was demonstrated by evaluating drug distribution and expression levels of several proteins following drug treatment in 3D tumor spheroids. Overall, our mini-pillar array provides a novel platform that can be useful for culturing tumor spheroids as well as for immunohistological analysis in a multiplexed and high throughput manner.

A pitch-tunable 5 × 5 mini-pillar array chip was developed for culturing and sectioning tumor spheroids (TSs) in a high throughput manner. TSs were cultured on the chip aligned to 96-well. TS array sections were prepared following pitch rearrangement.

Ultraminiaturized assay for rapid, low cost detection and quantification of clinical and biochemical samples

Herein, we report a simple, sensitive, rapid and low-cost ultraminiaturized assay technique for quantitative detection of 1 μl of clinical or biochemical sample on a novel ultraminiaturized assay plate (UAP). UAP is prepared by making tiny cavities on a polypropylene sheet. As UAP cannot immobilize a biomolecule through absorption, we have activated the tiny cavities of UAP by 1-fluoro-2-nitro-4-azidobenzene in a photochemical reaction. Activated UAP (AUAP) can covalently immobilize any biomolecule having an active nucleophilic group such as amino group. Efficacy of AUAP is demonstrated by detecting human IgE, antibody of hepatitis C virus core antigen and oligonucleotides. Quantification is performed by capturing the image of the colored assay solution and digitally quantifying the image by color saturation without using costly NanoDrop spectrophotometer. Image - based detection of human IgE and an oligonucleotide shows an excellent correlation with absorbance - based assay (recorded in a NanoDrop spectrophotometer); it is validated by Pearson's product-moment correlation with correlation coefficient of r = 0.9545088 and r = 0.9947444 respectively. AUAP is further checked by detecting hepatitis C virus Ab where strong correlation of color saturation with absorbance with respect to concentration is observed. Ultraminiaturized assay successfully detects target oligonucleotides by perfectly hybridizing with their respective complementary oligonucleotide probes but not with a random oligonucleotide. Ultraminiaturized assay technique has substantially reduced the requirement of reagents by 100 times and assay timing by 50 times making it a potential alternative to conventional method.

Effect-Size Measures as Descriptors of Assay Quality in High-Content Screening: A Brief Review of Some Available Methodologies

The field of high-content screening (HCS) typically uses measures of screen quality conceived for fairly straightforward high-throughput screening (HTS) scenarios. However, in contrast to HTS, image-based HCS systems rely on multidimensional readouts reporting biological responses associated with complex cellular phenotypes. Not only is the dimensionality in which the screens operate higher, but also the scale of the individual features describing the quantified phenotypic changes is often smaller than what is seen in one-dimensional HTS platforms. Therefore, the use of HTS-type quality measures to characterize HCS screens may severely underestimate the detection power of the assays used, and it may mislead the screening scientists regarding the necessary screen optimization. Also, traditional HTS-based measures are typically reported without any estimation of precision, which makes them unsuitable for computation of confidence intervals or for meta-analysis. This review summarizes the well-established statistical techniques for reporting effect sizes and argues that this broadly accepted methodology could be seamlessly integrated into HCS quality reporting, supplanting or even replacing measures such as Z' or S_w (assay window).

Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases.

In vitro and in vivo mouse models for pharmacogenetic studies

The identification of causative genes underlying biomedically relevant phenotypes, particularly complex multigenic traits, is of vital interest to modern medicine. Using genome-wide association analysis, many studies have successfully identified thousands of loci (called quantitative trait loci or QTL), some of these associating with drug response phenotypes. However, the determination and validation of putative genes has been much more challenging. The actions of drugs, both efficacious and deleterious, are complex phenotypes that are controlled or influenced in part by genetic mechanisms.Investigation for genetic correlates of complex traits and pharmacogenetic traits is often difficult to perform in human studies due to cost, availability of relevant sample population, and limited ability to control for environmental effects. These challenges can be circumvented with the use of mouse models for pharmacogenetic studies. In addition, the mouse can be treated at sub- and supratherapeutic doses and subjected to invasive procedures, which can facilitate measures of drug response phenotypes, making identification of pharmacogenetically relevant genes more feasible. The availability of multiple mouse genetic and phenotypic resources is an additional benefit to using the mouse for pharmacogenetic studies.Here, we describe the contribution of animal models, specifically the mouse, towards the field of pharmacogenetics. In this chapter, we describe different mouse models, including the knockout mouse, recombinant mouse inbred strains, in vitro mouse cell-based assays, as well as novel experimental approaches like the Collaborative Cross recombinant mouse inbred panel, which can be applied to preclinical pharmacogenetics research. These approaches can be used to assess drug response phenotypes that are difficult to model in humans, thereby facilitating drug discovery, development, and application.

A systems approach for analysis of high content screening assay data with topic modeling

Background

High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements.

Method

In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment.

Results

We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration.

Conclusions

The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.

Strategies and challenges for systematically mapping biologically significant molecular pathways regulating carcinoma epithelial-mesenchymal transition

Enormous progress has been made towards understanding the role of specific factors in the process of epithelial-mesenchymal transition (EMT); however, the complex underlying pathways and the transient nature of the transition continues to present significant challenges. Targeting tumour cell plasticity underpinning EMT is an attractive strategy to combat metastasis. Global gene expression profiling and high-content analyses are among the strategies employed to identify novel EMT regulators. In this review, we highlight several approaches to systematically interrogate key pathways involved in EMT, with particular emphasis on the features of multiparametric, high-content imaging screening strategies that lend themselves to the systematic discovery of highly significant modulators of tumour cell plasticity.

Statistical issues in subpopulation analysis of high content imaging data

High content imaging (HCI) is an increasingly important method for elucidating changes in cellular biology. The combination of validated immuno-fluorescent assays, availability of automated microscopy, and advances in image analysis software has led to increased exploitation of this technology in a variety of settings ranging from cellular signaling pathways to drug discovery. Recent advances in HCI have made the collection of multi-parametric datasets from high-throughput screening routine, but analysis of these data remains a challenge. The few existing analytical tools used to analyze HCI data, usually provided by HCI platform vendors, require extensive operator interaction and, more importantly, lack statistical power. This results in serious under-utilization of the information available from this powerful technology. As HCI applications become increasingly complex, measurements to estimate the composition of the cell population and thus the underlying data structure also becomes more complicated, and the analysis to facilitate the understanding of the biological system will rely heavily on analytical expertise that requires advanced statistical training. The aim of this article is to review the major statistical issues in HCI data analysis, with a focus on quantitative analysis of cell subpopulations.

Image-based assessment of growth and signaling changes in cancer cells mediated by direct cell-cell contact

Background

Many important biological processes are controlled through cell-cell interactions, including the colonization of metastatic tumor cells and the control of differentiation of stem cells within their niche. Despite the crucial importance of the cellular environment in regulating cellular signaling, in vitro methods for the study of such interactions are difficult and/or indirect.

Methodology/Principal Findings

We report on the development of an image-based method for distinguishing two cell types grown in coculture. Furthermore, cells of one type that are in direct contact with cells of a second type (adjacent cells) can be analyzed separately from cells that are not within a single well. Changes are evaluated using population statistics, which are useful in detecting subtle changes across two populations. We have used this system to characterize changes in the LNCaP prostate carcinoma cell line when grown in contact with human vascular endothelial cells (HUVECs). We find that the expression and phosphorylation of WWOX is reduced in LNCaP cells when grown in direct contact with HUVECs. Reduced WWOX signaling has been associated with reduced activation or expression of JNK and p73. We find that p73 levels are also reduced in LNCaP cells grown in contact with HUVECs, but we did not observe such a change in JNK levels.

Conclusions/Significance

We find that the method described is statistically robust and can be adapted to a wide variety of studies where cell function or signaling are affected by heterotypic cell-cell contact. Ironically, a potential challenge to the method is its high level of sensitivity is capable of classifying events as statistically significant (due to the high number cells evaluated individually), when the biological effect may be less clear. The methodology would be best used in conjunction with additional methods to evaluate the biological role of potentially subtle differences between populations. However, many important events, such as the establishment of a metastatic tumor, occur through rare but important changes, and methods such as we describe here can be used to identify and characterize the contribution of the environment to these changes.

Information management for high content live cell imaging

BACKGROUND

High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments.

RESULTS

We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data.

CONCLUSION

Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

Modification of Kolmogorov-Smirnov test for DNA content data analysis through distribution alignment

The Kolmogorov-Smirnov (K-S) test is a statistical method often used for comparing two distributions. In high-throughput screening (HTS) studies, such distributions usually arise from the phenotype of independent cell populations. However, the K-S test has been criticized for being overly sensitive in applications, and it often detects a statistically significant difference that is not biologically meaningful. One major reason is that there is a common phenomenon in HTS studies that systematic drifting exists among the distributions due to reasons such as instrument variation, plate edge effect, accidental difference in sample handling, etc. In particular, in high-content cellular imaging experiments, the location shift could be dramatic since some compounds themselves are fluorescent. This oversensitivity of the K-S test is particularly overpowered in cellular assays where the sample sizes are very big (usually several thousands). In this paper, a modified K-S test is proposed to deal with the nonspecific location-shift problem in HTS studies. Specifically, we propose that the distributions are "normalized" by density curve alignment before the K-S test is conducted. In applications to simulation data and real experimental data, the results show that the proposed method has improved specificity.

A high-content glucocorticoid receptor translocation assay for compound mechanism-of-action evaluation

Ligand-induced cytoplasm to nucleus translocation is a critical event in the nuclear receptor (NR) signal transduction cascade. The development of green fluorescent proteins and their color variants fused with NRs, along with the recent developments in automated cellular imaging technologies, has provided unique tools to monitor and quantify the NR translocation events. These technology developments have important implications in the mechanistic evaluation of NR signaling and provide a powerful tool for drug discovery. The unique challenges for developing a robust NR translocation assay include cytotoxicity accompanied with chronic overexpression of NRs, basal translocation induced by serum present in culture medium, and interference from endogenous NRs, as well as subcellular dynamics. The authors have developed a robust assay system for the glucocorticoid receptor (GR) that was applied to a panel of nuclear receptor ligands. Using a high-content imaging system, ligand-induced, dose-dependent GR nuclear translocation was quantified and a correlation with other conventional assays established.

Image-based chemical screening

Technological advances have made it feasible to conduct high-throughput small-molecule screens based on visual phenotypes of individual cells, using automated imaging and analysis. These screens are rapidly moving from being small, proof-of-principle tests to robust and widespread screens of hundreds of thousands of compounds. Automated imaging screens maximize the information obtained in an initial screen and improve the ability to select high-quality leads. In this Perspective, I highlight the key steps necessary for conducting a high-throughput image-based chemical compound screen.

Systems cell biology based on high-content screening

A new discipline of biology has emerged since 2004, which we call "systems cell biology" (SCB). Systems cell biology is the study of the living cell, the basic unit of life, an integrated and interacting network of genes, proteins, and myriad metabolic reactions that give rise to function. SCB takes advantage of high-content screening platforms, but delivers more detailed profiles of cellular systemic function, including the application of advanced reagents and informatics tools to sophisticated cellular models. Therefore, an SCB profile is a cellular systemic response as measured by a panel of reagents that quantify a specific set of biomarkers.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

Integrating molecular design resources within modern drug discovery research: the Roche experience

Various computational disciplines, such as cheminformatics, ADME modeling, virtual screening, chemogenomics search strategies and classic structure-based design, should be seen as one multifaceted discipline contributing to the early drug discovery process. Although significant resources enabling these activities have been established, their true integration into daily research should not be taken for granted. This article reviews value-adding activities from target assessment to lead optimization, and highlights the technical and process-related aspects that can be considered essential for performance and alignment within the research organization.

Systems Cell Biology Knowledge Created from High Content Screening

High content screening (HCS), the large-scale automated analysis of the temporal and spatial changes in cells and cell constituents in arrays of cells, has the potential to create enormous systems cell biology knowledge bases. HCS is being employed along with the continuum of the early drug discovery process, including lead optimization where new knowledge is being used to facilitate the decision-making process. We demonstrate methodology to build new systems cell biology knowledge using a multiplexed HCS assay, designed with the aid of knowledge-mining tools, to measure the phenotypic response of a panel of human tumor cell types to a panel of natural product-derived microtubule-targeted anticancer agents and their synthetic analogs. We show how this new systems cell biology knowledge can be used to design a lead compound optimization strategy for at least two members of the panel, (-)-laulimalide and (+)-discodermolide, that exploits cell killing activity while minimally perturbing the regulation of the cell cycle and the stability of microtubules. Furthermore, this methodology can also be applied to basic biomedical research on cells.