High-content imaging characterization of cell cycle therapeutics throughin vitroandin vivosubpopulation analysis

Affimer-mediated locking of p21-activated kinase 5 in an intermediate activation state results in kinase inhibition

Kinases are important therapeutic targets, and their inhibitors are classified according to their mechanism of action, which range from blocking ATP binding to covalent inhibition. Here, a mechanism of inhibition is highlighted by capturing p21-activated kinase 5 (PAK5) in an intermediate state of activation using an Affimer reagent that binds in the P+1 pocket. PAK5 was identified from a non-hypothesis-driven high-content imaging RNAi screen in urothelial cancer cells. Silencing of PAK5 resulted in reduced cell number, G1/S arrest, and enlargement of cells, suggesting it to be important in urothelial cancer cell line survival and proliferation. Affimer reagents were isolated to identify mechanisms of inhibition. The Affimer PAK5-Af17 recapitulated the phenotype seen with siRNA. Co-crystallization revealed that PAK5-Af17 bound in the P+1 pocket of PAK5, locking the kinase into a partial activation state. This mechanism of inhibition indicates that another class of kinase inhibitors is possible.

Broad Spectrum Activity of the Checkpoint Kinase 1 Inhibitor Prexasertib as a Single Agent or Chemopotentiator Across a Range of Preclinical Pediatric Tumor Models

PURPOSE

Checkpoint kinase 1 (CHK1) inhibitors potentiate the DNA-damaging effects of cytotoxic therapies and/or promote elevated levels of replication stress, leading to tumor cell death. Prexasertib (LY2606368) is a CHK1 small-molecule inhibitor under clinical evaluation in multiple adult and pediatric cancers. In this study, prexasertib was tested in a large panel of preclinical models of pediatric solid malignancies alone or in combination with chemotherapy.

EXPERIMENTAL DESIGN

DNA damage and changes in cell signaling following in vitro prexasertib treatment in pediatric sarcoma cell lines were analyzed by Western blot and high content imaging. Antitumor activity of prexasertib as a single agent or in combination with different chemotherapies was explored in cell line-derived (CDX) and patient-derived xenograft (PDX) mouse models representing nine different pediatric cancer histologies.

RESULTS

Pediatric sarcoma cell lines were highly sensitive to prexasertib treatment in vitro, resulting in activation of the DNA damage response. Two PDX models of desmoplastic small round cell tumor and one malignant rhabdoid tumor CDX model responded to prexasertib with complete regression. Prexasertib monotherapy also elicited robust responses in mouse models of rhabdomyosarcoma. Concurrent administration with chemotherapy was sufficient to overcome innate resistance or prevent acquired resistance to prexasertib in preclinical models of neuroblastoma, osteosarcoma, and Ewing sarcoma, or alveolar rhabdomyosarcoma, respectively.

CONCLUSIONS

Prexasertib has significant antitumor effects as a monotherapy or in combination with chemotherapy in multiple preclinical models of pediatric cancer. These findings support further investigation of prexasertib in pediatric malignancies.

Abemaciclib Is Active in Preclinical Models of Ewing Sarcoma via Multipronged Regulation of Cell Cycle, DNA Methylation, and Interferon Pathway Signaling

PURPOSE

Ewing sarcoma (ES) is a rare and highly malignant cancer that occurs in the bone and surrounding tissue of children and adolescents. The EWS/ETS fusion transcription factor that drives ES pathobiology was previously demonstrated to modulate cyclin D1 expression. In this study, we evaluated abemaciclib, a small-molecule CDK4 and CDK6 (CDK4 and 6) inhibitor currently under clinical investigation in pediatric solid tumors, in preclinical models of ES.

EXPERIMENTAL DESIGN

Using Western blot, high-content imaging, flow cytometry, ELISA, RNA sequencing, and CpG methylation assays, we characterized the in vitro response of ES cell lines to abemaciclib. We then evaluated abemaciclib in vivo in cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) mouse models of ES as either a monotherapy or in combination with chemotherapy.

RESULTS

Abemaciclib induced quiescence in ES cell lines via a G₁ cell-cycle block, characterized by decreased proliferation and reduction of Ki-67 and FOXM1 expression and retinoblastoma protein (RB) phosphorylation. In addition, abemaciclib reduced DNMT1 expression and promoted an inflammatory immune response as measured by cytokine secretion, antigen presentation, and interferon pathway upregulation. Single-agent abemaciclib reduced ES tumor volume in preclinical mouse models and, when given in combination with doxorubicin or temozolomide plus irinotecan, durable disease control was observed.

CONCLUSIONS

Collectively, our data demonstrate that the antitumor effects of abemaciclib in preclinical ES models are multifaceted and include cell-cycle inhibition, DNA demethylation, and immunogenic changes.

Morphological single cell profiling of the epithelial-mesenchymal transition

Single cells respond heterogeneously to biochemical treatments, which can complicate the analysis of in vitro and in vivo experiments. In particular, stressful perturbations may induce the epithelial-mesenchymal transition (EMT), a transformation through which compact, sensitive cells adopt an elongated, resistant phenotype. However, classical biochemical measurements based on population averages over large numbers cannot resolve single cell heterogeneity and plasticity. Here, we use high content imaging of single cell morphology to classify distinct phenotypic subpopulations after EMT. We first characterize a well-defined EMT induction through the master regulator Snail in mammary epithelial cells over 72 h. We find that EMT is associated with increased vimentin area as well as elongation of the nucleus and cytoplasm. These morphological features were integrated into a Gaussian mixture model that classified epithelial and mesenchymal phenotypes with >92% accuracy. We then applied this analysis to heterogeneous populations generated from less controlled EMT-inducing stimuli, including growth factors (TGF-β1), cell density, and chemotherapeutics (Taxol). Our quantitative, single cell approach has the potential to screen large heterogeneous cell populations for many types of phenotypic variability, and may thus provide a predictive assay for the preclinical assessment of targeted therapeutics.

Fibroblast growth factor signaling is required for early somatic gonad development in zebrafish

The vertebrate ovary and testis develop from a sexually indifferent gonad. During early development of the organism, primordial germ cells (the gamete lineage) and somatic gonad cells coalesce and begin to undergo growth and morphogenesis to form this bipotential gonad. Although this aspect of development is requisite for a fertile adult, little is known about the genetic regulation of early gonadogenesis in any vertebrate. Here, we provide evidence that fibroblast growth factor (Fgf) signaling is required for the early growth phase of a vertebrate bipotential gonad. Based on mutational analysis in zebrafish, we show that the Fgf ligand 24 (Fgf24) is required for proliferation, differentiation, and morphogenesis of the early somatic gonad, and as a result, most fgf24 mutants are sterile as adults. Additionally, we describe the ultrastructural elements of the early zebrafish gonad and show that distinct somatic cell populations can be identified soon after the gonad forms. Specifically, we show that fgf24 is expressed in an epithelial population of early somatic gonad cells that surrounds an inner population of mesenchymal somatic gonad cells that are in direct contact with the germ cells, and that fgf24 is required for stratification of the somatic tissue. Furthermore, based on gene expression analysis, we find that differentiation of the inner mesenchymal somatic gonad cells into functional cell types in the larval and early juvenile-stage gonad is dependent on Fgf24 signaling. Finally, we argue that the role of Fgf24 in zebrafish is functionally analogous to the role of tetrapod FGF9 in early gonad development.

The Checkpoint Kinase 1 Inhibitor Prexasertib Induces Regression of Preclinical Models of Human Neuroblastoma

Purpose: Checkpoint kinase 1 (CHK1) is a key regulator of the DNA damage response and a mediator of replication stress through modulation of replication fork licensing and activation of S and G₂-M cell-cycle checkpoints. We evaluated prexasertib (LY2606368), a small-molecule CHK1 inhibitor currently in clinical testing, in multiple preclinical models of pediatric cancer. Following an initial assessment of prexasertib activity, this study focused on the preclinical models of neuroblastoma.Experimental Design: We evaluated the antiproliferative activity of prexasertib in a panel of cancer cell lines; neuroblastoma cell lines were among the most sensitive. Subsequent Western blot and immunofluorescence analyses measured DNA damage and DNA repair protein activation. Prexasertib was investigated in several cell line-derived xenograft mouse models of neuroblastoma.Results: Within 24 hours, single-agent prexasertib promoted γH2AX-positive double-strand DNA breaks and phosphorylation of DNA damage sensors ATM and DNA-PKcs, leading to neuroblastoma cell death. Knockdown of CHK1 and/or CHK2 by siRNA verified that the double-strand DNA breaks and cell death elicited by prexasertib were due to specific CHK1 inhibition. Neuroblastoma xenografts rapidly regressed following prexasertib administration, independent of starting tumor volume. Decreased Ki67 and increased immunostaining of endothelial and pericyte markers were observed in xenografts after only 6 days of exposure to prexasertib, potentially indicating a swift reduction in tumor volume and/or a direct effect on tumor vasculature.Conclusions: Overall, these data demonstrate that prexasertib is a specific inhibitor of CHK1 in neuroblastoma and leads to DNA damage and cell death in preclinical models of this devastating pediatric malignancy. Clin Cancer Res; 23(15); 4354-63. ©2017 AACR.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

High-content analysis to leverage a robust phenotypic profiling approach to vascular modulation

Phenotypic screening seeks to identify substances that modulate phenotypes in a desired manner with the aim of progressing first-in-class agents. Successful campaigns require physiological relevance, robust screening, and an ability to deconvolute perturbed pathways. High-content analysis (HCA) is increasingly used in cell biology and offers one approach to prosecution of phenotypic screens, but challenges exist in exploitation where data generated are high volume and complex. We combine development of an organotypic model with novel HCA tools to map phenotypic responses to pharmacological perturbations. We describe implementation for angiogenesis, a process that has long been a focus for therapeutic intervention but has lacked robust models that recapitulate more completely mechanisms involved. The study used human primary endothelial cells in co-culture with stromal fibroblasts to model multiple aspects of angiogenic signaling: cell interactions, proliferation, migration, and differentiation. Multiple quantitative descriptors were derived from automated microscopy using custom-designed algorithms. Data were extracted using a bespoke informatics platform that integrates processing, statistics, and feature display into a streamlined workflow for building and interrogating fingerprints. Ninety compounds were characterized, defining mode of action by phenotype. Our approach for assessing phenotypic outcomes in complex assay models is robust and capable of supporting a range of phenotypic screens at scale.

Inhibition of microglia activation as a phenotypic assay in early drug discovery

Complex biological processes such as inflammation, cell death, migration, proliferation, and the release of biologically active molecules can be used as outcomes in phenotypic assays during early stages of drug discovery. Although target-based approaches have been widely used over the past decades, a disproportionate number of first-in-class drugs have been identified using phenotypic screening. This review details phenotypic assays based on inhibition of microglial activation and their utility in primary and secondary screening, target validation, and pathway elucidation. The role of microglia, both in normal as well as in pathological conditions such as chronic neurodegenerative diseases, is reviewed. Methodologies to assess microglia activation in vitro are discussed in detail, and classes of therapeutic drugs known to decrease the proinflammatory and cytotoxic responses of activated microglia are appraised, including inhibitors of glutaminase, cystine/glutamate antiporter, nuclear factor κB, and mitogen-activated protein kinases.

Highly multiplexed phenotypic imaging for cell proliferation studies

The application of multiplexed imaging technologies in phenotypic drug discovery (PDD) enables profiling of complex cellular perturbations in response to drug treatment. High-content analysis (HCA) is among the most pursued approaches in PDD, with a proven capability to identify compounds with a given cellular mechanism of action (MOA), as well as to unveil unexpected drug cellular activities. The ability of fluorescent image-based cytometric techniques to dissect the phenotypic heterogeneity of cell populations depends on the degree of multiplexing achievable. At present, most high-content assays employ up to four cellular markers separately detected in distinct fluorescence channels. We explored the possibility to increase HCA multiplexing through analysis of multiple proliferation markers in the same fluorescence channel by taking advantage of the different timing of antigen appearance during the cell cycle, or differential intracellular localization. Simultaneous analysis of DAPI staining and five immunofluorescence markers (BrdU incorporation, active caspase-3, phospho-histone H3, phospho-S6, and Ki-67) resulted in the first six-marker high-content assay readily applicable to compound MOA studies. This approach allows detection of rare cell subpopulations, unveiling a high degree of phenotypic heterogeneity in exponentially growing cell cultures and variability in the individual cell response to antiproliferative drugs.

Multiparametric Cell-Based Assay for the Evaluation of Transcription Inhibition by High-Content Imaging

Loss of normal cell cycle regulation is a hallmark of human cancer. Cyclin-dependent kinases (CDKs) are key regulators of the cell cycle and have been actively pursued as promising therapeutic targets. Likewise, members of the CDK family are functionally related to transcriptional modulation, a molecular pathway suitable for therapeutic intervention. We used a set of 2500 compounds in the U2OS cell line to evaluate its effect in the cell division process. Interestingly, out of this analysis, we identified a subpopulation of compounds that are able to inhibit RNA polymerase activity, thus interfering with gene transcription processes. After this finding, we developed, validated, and fully automated a multiparameter high-content imaging (HCI) assay to measure phosphorylation of the RNA polymerase II carboxyl terminal domain (pCTD). Simultaneously, we measured both the DNA content and cell proliferation index in the treated cells. The linear regression analysis comparing the IC50 for pCTD and the 4N EC50 for DNA content or IC50 for cell proliferation showed an excellent agreement ( r2 = 0.84 and r2 = 0.94, respectively). Our results confirm that this method allows discriminating between cell cycle and transcription inhibition and confirms HCI as a powerful technology for the identification of compounds with an effective and selective pathway phenotype.

High content image based analysis identifies cell cycle inhibitors as regulators of Ebola virus infection

Viruses modulate a number of host biological responses including the cell cycle to favor their replication. In this study, we developed a high-content imaging (HCI) assay to measure DNA content and identify different phases of the cell cycle. We then investigated the potential effects of cell cycle arrest on Ebola virus (EBOV) infection. Cells arrested in G1 phase by serum starvation or G1/S phase using aphidicolin or G2/M phase using nocodazole showed much reduced EBOV infection compared to the untreated control. Release of cells from serum starvation or aphidicolin block resulted in a time-dependent increase in the percentage of EBOV infected cells. The effect of EBOV infection on cell cycle progression was found to be cell-type dependent. Infection of asynchronous MCF-10A cells with EBOV resulted in a reduced number of cells in G2/M phase with concomitant increase of cells in G1 phase. However, these effects were not observed in HeLa or A549 cells. Together, our studies suggest that EBOV requires actively proliferating cells for efficient replication. Furthermore, multiplexing of HCI based assays to detect viral infection, cell cycle status and other phenotypic changes in a single cell population will provide useful information during screening campaigns using siRNA and small molecule therapeutics.

A BMP7 variant inhibits the tumorigenic potential of glioblastoma stem-like cells

Glioblastoma multiforme (GBM) is among the most aggressive tumor types and is essentially an incurable malignancy characterized by resistance to chemo-, radio-, and immunotherapy. GBM is maintained by a hierarchical cell organization that includes stem-like, precursor, and differentiated cells. Recurrence and maintenance of the tumor is attributed to a small population of undifferentiated tumor-initiating cells, defined as glioblastoma stem-like cells (GSLCs). This cellular hierarchy offers a potential treatment to induce differentiation of GSLCs away from tumor initiation to a more benign phenotype or to a cell type more amenable to standard therapies. Bone morphogenetic proteins (BMPs), members of the TGF-β superfamily, have numerous biological activities including control of growth and differentiation. In vitro, a BMP7 variant (BMP7v) decreased primary human GSLC proliferation, endothelial cord formation, and stem cell marker expression while enhancing neuronal and astrocyte differentiation marker expression. In subcutaneous and orthotopic GSLC xenografts, which closely reproduce the human disease, BMP7v decreased tumor growth and stem cell marker expression, while enhancing astrocyte and neuronal differentiation compared with control mice. In addition, BMP7v reduced brain invasion, angiogenesis, and associated mortality in the orthotopic model. Inducing differentiation of GSLCs and inhibiting angiogenesis with BMP7v provides a potentially powerful and novel approach to the treatment of GBM.

Knockdown of ubiquitin ligases in glioblastoma cancer stem cells leads to cell death and differentiation

The cancer stem cell (CSC) hypothesis proposes that a subpopulation of CSCs is frequently responsible for chemotherapy resistance and metastasis and is now a point of attack for research into the next generation of therapeutics. Although many of these agents are directed at inducing CSC apoptosis (as well as the bulk tumor), some agents may also decrease cell "stemness" possibly through induction of differentiation. Ubiquitin ligases, critical to virtually all cellular signaling systems, alter the degradation or trafficking of most proteins in the cell, and indeed broad perturbation of this system, through inhibition of the proteosome, is a successful cancer treatment. The authors examined several glioblastoma stem cell isolates pre- and postdifferentiation to elucidate the phenotypic effects following shRNA knockdown of ubiquitin ligases. The results were analyzed using high-content imaging (HCI) and identified ubiquitin ligases capable of inducing both CSC differentiation and apoptosis. Quite often these effects were specific to CSCs, as ubiquitin ligase knockdown in terminally differentiated progeny yielded markedly different results. The resolution of HCI at the subpopulation level makes it an excellent tool for the analysis of CSC phenotypic changes induced by shRNA knockdown and may suggest additional methods to target these cells for death or differentiation.

Tasisulam sodium, an antitumor agent that inhibits mitotic progression and induces vascular normalization

LY573636-sodium (tasisulam) is a small molecule antitumor agent with a novel mechanism of action currently being investigated in a variety of human cancers. In vitro, tasisulam induced apoptosis via the intrinsic pathway, resulting in cytochrome c release and caspase-dependent cell death. Using high content cellular imaging and subpopulation analysis of a wide range of in vitro and in vivo cancer models, tasisulam increased the proportion of cells with 4N DNA content and phospho-histone H3 expression, leading to G(2)-M accumulation and subsequent apoptosis. Tasisulam also blocked VEGF, epidermal growth factor, and fibroblast growth factor-induced endothelial cell cord formation but did not block acute growth factor receptor signaling (unlike sunitinib, which blocks VEGF-driven angiogenesis at the receptor kinase level) or induce apoptosis in primary endothelial cells. Importantly, in vivo phenocopying of in vitro effects were observed in multiple human tumor xenografts. Tasisulam was as effective as sunitinib at inhibiting neovascularization in a Matrigel plug angiogenesis assay in vivo and also caused reversible, non G(2)-M-dependent growth arrest in primary endothelial cells. Tasisulam also induced vascular normalization in vivo. Interestingly, the combination of tasisulam and sunitinib significantly delayed growth of the Caki-1 renal cell carcinoma model, whereas neither agent was active alone. These data show that tasisulam has a unique, dual-faceted mechanism of action involving mitotic catastrophe and antiangiogenesis, a phenotype distinct from conventional chemotherapies and published anticancer agents.

Vascular normalization in orthotopic glioblastoma following intravenous treatment with lipid-based nanoparticulate formulations of irinotecan (Irinophore C™), doxorubicin (Caelyx®) or vincristine

Background

Chemotherapy for glioblastoma (GBM) patients is compromised in part by poor perfusion in the tumor. The present study evaluates how treatment with liposomal formulation of irinotecan (Irinophore C™), and other liposomal anticancer drugs, influence the tumor vasculature of GBM models grown either orthotopically or subcutaneously.

Methods

Liposomal vincristine (2 mg/kg), doxorubicin (Caelyx^®; 15 mg/kg) and irinotecan (Irinophore C™; 25 mg/kg) were injected intravenously (i.v.; once weekly for 3 weeks) in Rag2M mice bearing U251MG tumors. Tumor blood vessel function was assessed using the marker Hoechst 33342 and by magnetic resonance imaging-measured changes in vascular permeability/flow (K_trans). Changes in CD31 staining density, basement membrane integrity, pericyte coverage, blood vessel diameter were also assessed.

Results

The three liposomal drugs inhibited tumor growth significantly compared to untreated control (p < 0.05-0.001). The effects on the tumor vasculature were determined 7 days following the last drug dose. There was a 2-3 fold increase in the delivery of Hoechst 33342 observed in subcutaneous tumors (p < 0.001). In contrast there was a 5-10 fold lower level of Hoechst 33342 delivery in the orthotopic model (p < 0.01), with the greatest effect observed following treatment with Irinophore C. Following treatment with Irinophore C, there was a significant reduction in K_trans in the orthotopic tumors (p < 0.05).

Conclusion

The results are consistent with a partial restoration of the blood-brain barrier following treatment. Further, treatment with the selected liposomal drugs gave rise to blood vessels that were morphologically more mature and a vascular network that was more evenly distributed. Taken together the results suggest that treatment can lead to normalization of GBM blood vessel the structure and function. An in vitro assay designed to assess the effects of extended drug exposure on endothelial cells showed that selective cytotoxic activity against proliferating endothelial cells could explain the effects of liposomal formulations on the angiogenic tumor vasculature.

The Interweaving of Cheminformatics and HTS

The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.

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.

Knockdown of cancer testis antigens modulates neural stem cell marker expression in glioblastoma tumor stem cells

The cancer stem cell hypothesis posits that a subpopulation of cancer stem cells is frequently responsible for a tumor's progression and resistance to treatment. The differential cellular morphology and gene expression between cancer stem cells and the majority of the tumor is becoming a point of attack for research into the next generation of therapeutic agents that may work through an induction of differentiation rather than apoptosis. Advances in the field of high-content imaging (HCI), combined with modern shRNA technology and subpopulation analysis tools, have created an ideal screening system to detect these morphological changes in a subset of cells upon gene knockdown. The authors examined several glioblastoma stem cell isolates pre- and postdifferentiation to elucidate the phenotypic effects caused by both serum differentiation and gene knockdown. Neural markers were first characterized in these cells at varying states of differentiation using HCI and immunoblots. The authors then chose one of these isolates, in both the pre- and postdifferentiated forms, for further analysis and screened for morphological changes upon shRNA knockdown of a panel of cancer testis antigens (CTAs). CTAs are a family of proteins that are normally expressed in male germ cells as well as heterogeneously expressed in some metastatic tumors. This gene family has also been implicated in the differentiation of normal human stem cells, therefore making it an ideal candidate for modulation in tumor stem cells. Using their approach, the authors identified the differential effects of gene knockdown in both cell types leading to either changes in neural stem cell marker expression or a decreased cell density likely due to growth arrest or cell death. The resolution that HCI brings to a screen at the subpopulation level makes it an excellent tool for the analysis of phenotypic changes induced by shRNA knockdown in a variety of tumor stem cells.

HCS Road

The effective analysis and interpretation of high-content screening (HCS) data requires joining results to information on experimental treatments and controls, normalizing data, and selecting hits or fitting concentration-response curves. HCS data have unique requirements that are not supported by traditional high-throughput screening databases, including the ability to designate separate positive and negative controls for different measurements in multiplexed assays; the ability to capture information on the cell lines, fluorescent reagents, and treatments in each assay; the ability to store and use individual-cell and image data; and the ability to support HCS readers and software from multiple vendors along with third-party image analysis tools. To address these requirements, the authors developed an enterprise system for the storage and processing of HCS images and results. This system, HCS Road, supports target identification, lead discovery, lead evaluation, and lead profiling activities. A dedicated client supports experimental design, data review, and core analyses and displays images together with results for assay development, hit assessment, and troubleshooting. Data can be exported to third-party applications for further analysis and exploration. HCS Road provides a single source for high-content results across the organization, regardless of the group or instrument that produced them.

Precise fluorophore lifetime mapping in live-cell, multi-photon excitation microscopy

Fluorophore excited state lifetime is a useful indicator of micro-environment in cellular optical molecular imaging. For quantitative sensing, precise lifetime determination is important, yet is often difficult to accomplish when using the experimental conditions favored by live cells. Here we report the first application of temporal optimization and spatial denoising methods to two-photon time-correlated single photon counting (TCSPC) fluorescence lifetime imaging microscopy (FLIM) to improve lifetime precision in live-cell images. The results demonstrated a greater than five-fold improvement in lifetime precision. This approach minimizes the adverse effects of excitation light on live cells and should benefit FLIM applications to high content analysis and bioimage informatics.

Sub-population analysis based on temporal features of high content images

Background

High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods.

Results

In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering.

Conclusion

We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.

eIF4E activation is commonly elevated in advanced human prostate cancers and significantly related to reduced patient survival

Elevated eukaryotic translation initiation factor 4E (eIF4E) function induces malignancy in experimental models by selectively enhancing translation of key malignancy-related mRNAs (c-myc and BCL-2). eIF4E activation may reflect increased eIF4E expression or phosphorylation of its inhibitory binding proteins (4E-BP). By immunohistochemical analyses of 148 tissues from 89 prostate cancer patients, we now show that both eIF4E expression and 4E-BP1 phosphorylation (p4E-BP1) are increased significantly, particularly in advanced prostate cancer versus benign prostatic hyperplasia tissues. Further, increased eIF4E and p4E-BP1 levels are significantly related to reduced patient survival, whereas uniform 4E-BP1 expression is significantly related to better patient survival. Both immunohistochemistry and Western blotting reveal that elevated eIF4E and p4E-BP1 are evident in the same prostate cancer tissues. In two distinct prostate cancer cell models, the progression to androgen independence also involves increased eIF4E activation. In these prostate cancer cells, reducing eIF4E expression with an eIF4E-specific antisense oligonucleotide currently in phase I clinical trials robustly induces apoptosis, regardless of cell cycle phase, and reduces expression of the eIF4E-regulated proteins BCL-2 and c-myc. Collectively, these data implicate eIF4E activation in prostate cancer and suggest that targeting eIF4E may be attractive for prostate cancer therapy.

Phenotypic fingerprinting of small molecule cell cycle kinase inhibitors for drug discovery

Phenotypic drug discovery, primarily abandoned in the 1980’s in favor of targeted approaches to drug development, is once again demonstrating its value when used in conjunction with new technologies. Phenotypic discovery has been brought back to the fore mainly due to recent advances in the field of high content imaging (HCI). HCI elucidates cellular responses using a combination of immunofluorescent assays and computer analysis which increase both the sensitivity and throughput of phenotypic assays. Although HCI data characterize cellular responses in individual cells, these data are usually analyzed as an aggregate of the treated population and are unable to discern differentially responsive subpopulations. A collection of 44 kinase inhibitors affecting cell cycle and apoptosis were characterized with a number of univariate, bivariate, and multivariate subpopulation analyses demonstrating that each level of complexity adds additional information about the treated populations and often distinguishes between compounds with seemingly similar mechanisms of action. Finally, these subpopulation data were used to characterize compounds as they relate in chemical space.