Peak emission wavelength and fluorescence lifetime are coupled in far-red, GFP-like fluorescent proteins.

The discovery and use of fluorescent proteins revolutionized cell biology by allowing the visualization of proteins in living cells. Advances in fluorescent proteins, primarily through genetic engineering, have enabled more advanced analyses, including Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) and the development of genetically encoded fluorescent biosensors. These fluorescence protein-based sensors are highly effective in cells grown in monolayer cultures. However, it is often desirable to use more complex models including tissue explants, organoids, xenografts, and whole animals. These types of samples have poor light penetration owing to high scattering and absorption of light by tissue. Far-red light with a wavelength between 650-900nm is less prone to scatter, and absorption by tissues and can thus penetrate more deeply. Unfortunately, there are few fluorescent proteins in this region of the spectrum, and they have sub-optimal fluorescent properties including low brightness and short fluorescence lifetimes. Understanding the relationships between the amino-acid sequences of far-red fluorescence proteins and their photophysical properties including peak emission wavelengths and fluorescence lifetimes would be useful in the design of new fluorescence proteins for this region of the spectrum. We used both site-directed mutagenesis and gene-shuffling between mScarlet and mCardinal fluorescence proteins to create new variants and assess their properties systematically. We discovered that for far-red, GFP-like proteins the emission maxima and fluorescence lifetime have a strong inverse correlation.

High-Content Screening Approaches That Minimize Confounding Factors in RNAi, CRISPR, and Small Molecule Screening.

Screening arrayed libraries of reagents, particularly small molecules began as a vehicle for drug discovery, but the in last few years it has become a cornerstone of biological investigation, joining RNAi and CRISPR as methods for elucidating functional relationships that could not be anticipated, and illustrating the mechanisms behind basic and disease biology, and therapeutic resistance. However, these approaches share some common challenges, especially with respect to specificity or selectivity of the reagents as they are scaled to large protein families or the genome. High-content screening (HCS) has emerged as an important complement to screening, mostly the result of a wide array of specific molecular events, such as protein kinase and transcription factor activation, morphological changes associated with stem cell differentiation or the epithelial-mesenchymal transition of cancer cells. Beyond the range of cellular events that can be screened by HCS, image-based screening introduces new processes for differentiating between specific and nonspecific effects on cells. This chapter introduces these complexities and discusses strategies available in image-based screening that can mitigate the challenges they can bring to screening.

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.

Rapid Assessment and Visualization of Normality in High-Content and Other Cell-Level Data and Its Impact on the Interpretation of Experimental Results.

When investigators monitor effects on a population of cells following a perturbation, these events rarely occur in a classical normal (or Gaussian) distribution. A normal distribution is, however, explicitly assumed for events within a single well, in which mean values per well are used as an assay metric and, in general, measures of assay robustness, such as the Z' score and the V factor. Such analysis is not possible for many technologies; however, high-content screening (HCS) measures events of individual cells, which are averaged over the well. These individual cell-level measurements may be analyzed separately. This study quantifies the extent of nonnormality in experimental samples and their effects on determining the EC50 of a test compound and the assay robustness statistics. The results, based on five sets of publicly available data, indicate that the Z' or V-factor score can be improved by as much as 0.44 more than standard calculations, and the EC50 of a dose-response curve can be lowered by as much as fivefold when nonparametric methods are used, but not all data sets show a significant improvement. The effect on analysis depends in part on whether the greatest shift from normality occurs in the upper or lower range of the dose-response curve.

siRNA off-target effects can be reduced at concentrations that match their individual potency.

Small interfering RNAs (siRNAs) are routinely used to reduce mRNA levels for a specific gene with the goal of studying its function. Several studies have demonstrated that siRNAs are not always specific and can have many off-target effects. The 3' UTRs of off-target mRNAs are often enriched in sequences that are complementary to the seed-region of the siRNA. We demonstrate that siRNA off-targets can be significantly reduced when cells are treated with a dose of siRNA that is relatively low (e.g. 1 nM), but sufficient to effectively silence the intended target. The reduction in off-targets was demonstrated for both modified and unmodified siRNAs that targeted either STAT3 or hexokinase II. Low concentrations reduced silencing of transcripts with complementarity to the seed region of the siRNA. Similarly, off-targets that were not complementary to the siRNA were reduced at lower doses, including up-regulated genes that are involved in immune response. Importantly, the unintended induction of caspase activity following treatment with a siRNA that targeted hexokinase II was also shown to be a concentration-dependent off-target effect. We conclude that off-targets and their related phenotypic effects can be reduced for certain siRNA that potently silence their intended target at low concentrations.

Single cell cytometry of protein function in RNAi treated cells and in native populations.

BACKGROUND: High Content Screening has been shown to improve results of RNAi and other perturbations, however significant intra-sample heterogeneity is common and can complicate some analyses. Single cell cytometry can extract important information from subpopulations within these samples. Such approaches are important for immune cells analyzed by flow cytometry, but have not been broadly available for adherent cells that are critical to the study of solid-tumor cancers and other disease models. RESULTS: We have directly quantitated the effect of resolving RNAi treatments at the single cell level in experimental systems for both exogenous and endogenous targets. Analyzing the effect of an siRNA that targets GFP at the single cell level permits a stronger measure of the absolute function of the siRNA by gating to eliminate background levels of GFP intensities. Extending these methods to endogenous proteins, we have shown that well-level results of the knockdown of PTEN results in an increase in phospho-S6 levels, but at the single cell level, the correlation reveals the role of other inputs into the pathway. In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3. In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level. CONCLUSION: We find that single cell analysis methods are generally applicable to a wide range of experiments in adherent cells using technology that is becoming increasingly available to most laboratories. It is well-suited to emerging models of signaling dysfunction, such as oncogene addition and oncogenic shock. Single cell cytometry can demonstrate effects on cell function for protein levels that differ by as little as 20%. Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

Expanding the repertoire of RNA interference screens for developing new anticancer drug targets.

RNA interference (RNAi) screening for cancer drug target identification has been growing, in both the number of laboratories carrying out screens and in the scale of the screens themselves, from the first screens that were published a few years ago. This growth is directly related to the significant new insights into cancer cell biology that have been defined by relatively few studies. Recently, such screens have moved from general studies of cancer cell function (finding new mechanisms of malignancy and tumor suppression), to screens that explain the clinical problems, such as resistance to chemotherapeutics. In light of the progression observed in these published studies, it is now possible to consider how RNAi screening can be used to characterize other areas of cancer research that have been proposed to explain the development of clinical cancers. Examples include: oncogene addiction/oncogenic shock, cancer stem cells, lineage dependency and the epithelial-mesenchymal transition. RNAi screening can enable critical evaluations of both the roles of these concepts in tumor development and provide starting points for new therapeutic programs targeting emerging areas of cancer cell biology.

Increasing the robustness and validity of RNAi screens.

RNAi screening in mammalian cells has become a valuable method to identify and describe genetic relationships in both basic biology and disease mechanisms. Multiple efforts are underway to standardize how RNAi screening data are reported, including establishing experimental criteria for defining a validated hit from a screen, and the extent to which the primary screening data themselves are reported. These discussions have identified several key areas that require consistency, or at least understanding, before RNAi screening data can be used generally. Successfully addressing these targeted areas would broaden the use of RNAi screening data beyond advancing one or a few hits into validation experiments, to enable verification of primary screening data, and to facilitate comparisons between sample groups based on screening profiles. Areas for improving RNAi screening include general guidelines for validating hits from screens, the creation of standardized reporting structures for RNAi screening data, such as Minimum Information About an RNAi Experiment (MIARE), statistical methods for analyzing screening data that explicitly account for differences between screening RNAi reagents versus small molecules, and technical improvements to RNAi screening that improve the analysis of gene knockdowns, including multiparametric approaches, such as high-content screening. This review will discuss how these approaches can improve RNAi screening data at the community level and for an individual researcher trying to manage an RNAi screen.

Transcriptional changes associated with breast cancer occur as normal human mammary epithelial cells overcome senescence barriers and become immortalized.

BACKGROUND: Human mammary epithelial cells (HMEC) overcome two well-characterized genetic and epigenetic barriers as they progress from primary cells to fully immortalized cell lines in vitro. Finite lifespan HMEC overcome an Rb-mediated stress-associated senescence barrier (stasis), and a stringent, telomere-length dependent, barrier (agonescence or crisis, depending on p53 status). HMEC that have overcome the second senescence barrier are immortalized. METHODS: We have characterized pre-stasis, post-selection (post-stasis, with p16 silenced), and fully immortalized HMEC by transcription profiling and RT-PCR. Four pre-stasis and seven post-selection HMEC samples, along with 10 representatives of fully immortalized breast epithelial cell lines, were profiled using Affymetrix U133A/B chips and compared using both supervised and unsupervised clustering. Datasets were validated by RT-PCR for a select set of genes. Quantitative immunofluorescence was used to assess changes in transcriptional regulators associated with the gene expression changes. RESULTS: The most dramatic and uniform changes we observed were in a set of about 30 genes that are characterized as a "cancer proliferation cluster," which includes genes expressed during mitosis (CDC2, CDC25, MCM2, PLK1) and following DNA damage. The increased expression of these genes was particularly concordant in the fully immortalized lines. Additional changes were observed in IFN-regulated genes in some post-selection and fully immortalized cultures. Nuclear localization was observed for several transcriptional regulators associated with expression of these genes in post-selection and immortalized HMEC, including Rb, Myc, BRCA1, HDAC3 and SP1. CONCLUSION: Gene expression profiles and cytological changes in related transcriptional regulators indicate that immortalized HMEC resemble non-invasive breast cancers, such as ductal and lobular carcinomas in situ, and are strikingly distinct from finite-lifespan HMEC, particularly with regard to genes involved in proliferation, cell cycle regulation, chromosome structure and the DNA damage response. The comparison of HMEC profiles with lines harboring oncogenic changes (e.g. overexpression of Her-2neu, loss of p53 expression) identifies genes involved in tissue remodeling as well as proinflamatory cytokines and S100 proteins. Studies on carcinogenesis using immortalized cell lines as starting points or "normal" controls need to account for the significant pre-existing genetic and epigenetic changes inherent in such lines before results can be broadly interpreted.

High-content screening moves to the front of the line.

High-content screening (HCS) has been used in late-stage drug discovery for a decade. In the past few years, technological advances have expanded the role of HCS into the early stages of drug discovery, including high-throughput screening and hit-to-lead studies. More recently, computational advances in image analysis and technological advancements in general cell biology have extended the utility of HCS into target validation and basic biological studies, including RNAi screening. The use of HCS in target validation is expanding the work that can be done at this stage, especially the range of targets that can be characterized, and putting it into a more biological context.

Anticancer drug development incorporating high-content screening and RNAi: synergistic approaches to improve target identification and validation.

RNA interference (RNAi) and high-content screening (HCS) are powerful technologies that have converged on the early drug discovery process within the last year. RNAi emerged from basic science, where it has become a standard and accepted method for examining gene function. RNAi has achieved this level of recognition because it is a robust technology; however, it is not simple to use, and care needs to be taken to manage the sources of artifacts that can occur when using RNAi. HCS was developed for advanced drug development studies, particularly for toxicology. Recently developed HCS systems are tailored to target validation and high-throughput screening applications. These newer platforms are both faster, and capable of studying many more cellular events than previously possible. It follows that combining RNAi, particularly the screening of RNAi libraries, with HCS would be an obvious step. However, obvious is not synonymous with simple, and combining the technologies requires an understanding of strengths and challenges to each. This review describes RNAi and HCS technologies as they apply to drug target validation, and discusses efforts to integrate them. Particular focus is applied to aspects of HCS that mitigate some of the challenges inherent to RNAi.

RNAi and high-content screening in target identification and validation.

The development of effective novel therapeutic agents faces many significant challenges, such as demonstrating that a candidate target plays a critical role in disease progression. RNA interference (RNAi) has proven to be a robust and highly scalable technology, and as such, has become an essential method for studying targets in many disease models. High-content screening (HCS) is a platform for quantitatively measuring cellular features such as transcription factor localization. This is a more powerful method of measuring signal transduction than reporter assays because the image-based data of HCS can eliminate many sources of assay artifacts, and the associated statistical tools are highly effective. While it appears obvious that convergence of technologies is required to establish RNAi screening assays in HCS formats, some challenges arise when combining the approaches. However, combining RNAi and HCS provides significant and unique advantages to a target validation program.

Combinatorial synthesis of substituted 3-(2-indolyl)piperidines and 2-phenyl indoles as inhibitors of ZipA-FtsZ interaction.

The ZipA-FtsZ protein-protein interaction is a potential target for antibacterial therapy. The design and parallel synthesis of a combinatorial library of small molecules, which target the FtsZ binding area on ZipA are described. Compounds were demonstrated to bind to the FtsZ binding domain of ZipA by HSQC NMR and to inhibit cell division in a cell elongation assay.

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes, prepared without passaging through E. coli.

BACKGROUND: Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library. RESULTS: Analysis of candidate genes as Y2H fusion constructs has shown that, while stable in E. coli and yeast for genetic studies, they are rapidly lost in growth conditions for genomic libraries. This includes the rapid loss of a fragment of the E. coli cell division gene ftsZ which encodes the binding site for ZipA and FtsA. Expression of this clone causes slower growth in E. coli. This clone is also rapidly lost in yeast, when expressed from a GAL1 promoter, relative to a vector control, but is stable when the promoter is repressed. We have demonstrated in this report that the construction of libraries for the E. coli and B. subtilis genomes without passaging through E. coli is practical, but the number of transformants is less than for libraries cloned using E. coli as a host. Analysis of several clones in the libraries that are strongly growth inhibitory in E. coli include genes for many essential cellular processes, such as transcription, translation, cell division, and transport. CONCLUSION: Expression of Y2H clones capable of interacting with E. coli and yeast targets are rapidly lost, causing a loss of complexity. The strategy for preparing Y2H libraries described here allows the retention of genes that are toxic when inappropriately expressed in E. coli, or yeast, including many genes that represent potential antibacterial targets. While these methods are generally applicable to the generation of Y2H libraries from any source, including mammalian and plant genomes, the potential of functional clones interacting with host proteins to inhibit growth would make this approach most relevant for the study of prokaryotic genomes.

Genomics in anti-infective drug discovery--getting to endgame.

Whole chromosome sequence of prokaryotes has provided the availability of multiple bacterial pathogen sequence data and it has become a widely used tool in the drug discovery process. However the sequence data in itself is merely a starting point for drug discovery of novel antibacterial targets and, eventually, drugs. In order to leverage this large amount of data it is necessary to match an understanding of the microbial physiology of pathogenic bacteria to disease processes and identifying the gene products for which intervention may reduce or eliminate the infectious state. However, to date, the application of genomics to anti-infective drug discovery has not, since 1995 with the first complete sequence of a pathogenic bacterium, led to identification of a novel antibacterial agent. Here we review the field of bacterial genomics and how it is enabling the drug discovery process. Many new molecular-based technologies (proteomics, transcriptional profiling, studies of gene expression in vivo) have originated or have expanded into wider use, and have been made possible by the availability of complete bacterial genome sequence information and subsequent bioinformatic analytic tools. Taken together, these technologies, overlaid within an established drug discovery program, now affords the opportunity for the identification, validation, and process design for high-throughput target mining at unprecedented volumes and timeframes.