Plasma membrane assays and three-compartment image cytometry for high content screening

Increasing the Content of High-Content Screening: An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising.

In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.

We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

Automated methods for the analysis of skeletal muscle fiber size and metabolic type

It is of interest to quantify the size, shape, and metabolic subtype of skeletal muscle fibers in many areas of biomedical research. To do so, skeletal muscle samples are sectioned transversely to the length of the muscle and labeled for extracellular or membrane proteins to delineate the fiber boundaries and additionally for biomarkers related to function or metabolism. The samples are digitally photographed and the fibers "outlined" for quantification of fiber cross-sectional area (CSA) using pointing devices interfaced to a computer, which is tedious, prone to error, and can be nonobjective. Here, we review methods for characterizing skeletal muscle fibers and describe new automated techniques, which rapidly quantify CSA and biomarkers. We discuss the applications of these methods to the characterization of mitochondrial dysfunctions, which underlie a variety of human afflictions, and we present a novel approach, utilizing images from the online Human Protein Atlas to predict relationships between fiber-specific protein expression, function, and metabolism.

Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management

The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.

Iterative tensor voting for perceptual grouping of ill-defined curvilinear structures

In this paper, a novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures. Our approach builds upon the tensor voting and the iterative voting frameworks. Its efficacy lies on iterative refinements of curvilinear structures by gradually shifting from an exploratory to an exploitative mode. Such a mode shifting is achieved by reducing the aperture of the tensor voting fields, which is shown to improve curve grouping and inference by enhancing the concentration of the votes over promising, salient structures. The proposed technique is validated on delineating adherens junctions that are imaged through fluorescence microscopy. However, the method is also applicable for screening other organisms based on characteristics of their cell wall structures. Adherens junctions maintain tissue structural integrity and cell-cell interactions. Visually, they exhibit fibrous patterns that may be diffused, heterogeneous in fluorescence intensity, or punctate and frequently perceptual. Besides the application to real data, the proposed method is compared to prior methods on synthetic and annotated real data, showing high precision rates.

Quantification of hormone sensitive lipase phosphorylation and colocalization with lipid droplets in murine 3T3L1 and human subcutaneous adipocytes via automated digital microscopy and high-content analysis

Lipolysis in adipocytes is associated with phosphorylation of hormone sensitive lipase (HSL) and translocation of HSL to lipid droplets. In this study, adipocytes were cultured in a high-throughput format (96-well dishes), exposed to lipolytic agents, and then fixed and labeled for nuclei, lipid droplets, and HSL (or HSL phosphorylated on serine 660 [pHSLser660]). The cells were imaged via automated digital fluorescence microscopy, and high-content analysis (HCA) methods were used to quantify HSL phosphorylation and the degree to which HSL (or pHSLser660) colocalizes with the lipid droplets. HSL:lipid droplet colocalization was quantified through use of Pearson's correlation, Mander's M1 Colocalization, and the Tanimoto coefficient. For murine 3T3L1 adipocytes, isoproterenol, Lys-γ3-melanocyte stimulating hormone, and forskolin elicited the appearance and colocalization of pHSLser660, whereas atrial natriuretic peptide (ANP) did not. For human subcutaneous adipocytes, isoproterenol, forskolin, and ANP activated HSL phosphorylation/colocalization, but Lys-γ3-melanocyte stimulating hormone had little or no effect. Since ANP activates guanosine 3',5'-cyclic monophosphate (cGMP)-dependent protein kinase, HSL serine 660 is likely a substrate for cGMP-dependent protein kinase in human adipocytes. For both adipocyte model systems, adipocytes with the greatest lipid content displayed the greatest lipolytic responses. The results for pHSLser660 were consistent with release of glycerol by the cells, a well-established assay of lipolysis, and the HCA methods yielded Z' values >0.50. The results illustrate several key differences between human and murine adipocytes and demonstrate advantages of utilizing HCA techniques to study lipolysis in cultured adipocytes.

Automated microscopy and image analysis for androgen receptor function

Systems-level approaches have emerged that rely on analytical, microscopy-based technology for the discovery of novel drug targets and the mechanisms driving AR signaling, transcriptional activity, and ligand independence. Single cell behavior can be quantified by high-throughput microscopy methods through analysis of endogenous protein levels and localization or creation of biosensor cell lines that can simultaneously detect both acute and latent responses to known and unknown androgenic stimuli. The cell imaging and analytical protocols can be automated to discover agonist/antagonist response windows for nuclear translocation, reporter gene activity, nuclear export, and subnuclear transcription events, facilitating access to a multiplex model system that is inherently unavailable through classic biochemical approaches. In this chapter, we highlight the key steps needed for developing, conducting, and analyzing high-throughput screens to identify effectors of AR signaling.

Multidimensional profiling of cell surface proteins and nuclear markers

Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.

Quantification of lipid droplets and associated proteins in cellular models of obesity via high-content/high-throughput microscopy and automated image analysis

Intracellular lipid droplets are associated with a myriad of afflictions including obesity, fatty liver disease, coronary artery disease, and infectious diseases (eg, HCV and tuberculosis). To develop high-content analysis (HCA) techniques to analyze lipid droplets and associated proteins, primary human preadipocytes were plated in 96-well dishes in the presence of rosiglitazone (rosi), a PPAR-(c) agonist that promotes adipogenesis. The cells were then labeled for nuclei, lipid droplets, and proteins such as perilipin, protein kinase C (PKC), and hormone-sensitive lipase (HSL). The cells were imaged via automated digital microscopy and algorithms were developed to quantify lipid droplet (Lipid Droplet algorithm) and protein expression and colocalization (Colocalization algorithm). The algorithms, which were incorporated into Vala Science Inc's CyteSeer((R)) image cytometry program, quantified the rosi-induced increases in lipid droplet number, size, and intensity, and the expression of perilipin with exceptional consistency (Z' values of 0.54-0.71). Regarding colocalization with lipid droplets, Pearson's correlation coefficients of 0.38 (highly colocalized), 0.16 (moderate), and -0.0010 (random) were found for perilipin, PKC, and HSL, respectively. For hepatocytes (AML12, HuH-7, and primary cells), the algorithms also quantified the stimulatory and inhibitory effect of oleic acid and triacsin C on lipid droplets (Z's > 0.50) and ADFP expression/colocalization. Oleic acid-induced lipid droplets in HeLa cells and macrophages (THP-1) were also well quantified. The results suggest that HCA techniques can be utilized to quantify lipid droplets and associated proteins in many cell models relevant to a variety of diseases.

Exploration of chromatic aberration for multiplanar imaging: proof of concept with implications for fast, efficient autofocus

Image-based autofocus determines focus directly from the specimen (as opposed to reflective surface positioning with an offset), but sequential acquisition of a stack of images to measure resolution/sharpness and find best focus is slower than reflective positioning. Simultaneous imaging of multiple focal planes, which is also useful for 3D imaging of live cells, is faster but requires complicated optics. With color CCD cameras and white light sources commonly available, we asked if axial chromatic aberration can be utilized to acquire multiple focal planes simultaneously, and if it can be controlled through a range sufficient for practical use. For proof of concept, we theoretically and experimentally explored the focal differences between three narrow wavelength bands on a 3-chip color CCD camera with and without glass inserts of various thicknesses and dispersions. Ray tracing yielded changes in foci of 0.65-0.9 microm upon insertion of 12.5-mm thick glass samples for green (G, 522 nm) vs. blue (B, 462 nm) and green vs. red (G-R, 604 nm). On a microscope: (1) With no glass inserts, the differences in foci were 2.15 microm (G-B) and 0.43 microm (G-R); (2) With glass inserts, the maximum change in foci for G vs. B was 0.44 microm and for G vs. R was 0.26 microm; and (3) An 11.3 mm thick N-BK7 glass insert shifted the foci 0.9 microm (R), 0.6 microm (G), and 0.35 microm (B), such that the B and R foci were farther apart (2.1 microm vs. 1.7 microm) and the R and G foci were closer together (0.25 microm vs. 0.45 microm). The slopes of the differences in foci were dependent on thickness, index of refraction, and dispersion. The measured differences in foci are comparable to the axial steps of 0.1-0.24 microm commonly used for autofocus, and focal plane separation can be altered by inserting optical elements of various dispersions and thicknesses. By enabling acquisition of multiple, axially offset images simultaneously, chromatic aberration, normally an imaging pariah, creates a possible mechanism for efficient multiplanar imaging of multiple spectral bands from white light illumination.

A comparative analysis of standard microtiter plate reading versus imaging in cellular assays

We evaluated the performance of two plate readers (the Beckman Coulter [Fullerton, CA] DTX and the PerkinElmer [Wellesley, MA] EnVision) and a plate imager (the General Electric [Fairfield, CT] IN Cell 1000 Analyzer) in a primary fluorescent cellular screen of 10,000 Molecular Libraries Screening Center Network library compounds for up- and down-regulation of vascular cell adhesion molecule (VCAM)-1, which has been shown to be up-regulated in atherothrombotic vascular disease and is a general indicator of chronic inflammatory disease. Prior to screening, imaging of a twofold, six-step titration of fluorescent cells in a 384-well test plate showed greater consistency, sensitivity, and dynamic range of signal detection curves throughout the detection range, as compared to the plate readers. With the same 384-well test plate, the detection limits for fluorescent protein-labeled cells on the DTX and EnVision instruments were 2,250 and 560 fluorescent cells per well, respectively, as compared to 280 on the IN Cell 1000. During VCAM screening, sensitivity was critical for detection of antagonists, which reduced brightness of the primary immunofluorescence readout; inhibitor controls yielded Z' values of 0.41 and 0.16 for the IN Cell 1000 and EnVision instruments, respectively. The best 1% of small molecule inhibitors from all platforms were visually confirmed using images from the IN Cell 1000. The EnVision and DTX plate readers mutually identified approximately 57% and 21%, respectively, of the VCAM-1 inhibitors visually confirmed in the IN Cell best 1% of inhibitors. Furthermore, the plate reader hits were largely exclusive, with only 6% agreement across all platforms (three hits out of 47). Taken together, the imager outperformed the plate readers at hit detection in this bimodal assay because of superior sensitivity and had the advantage of speeding hit confirmation during post-acquisition analysis.

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.

Dynamic autofocus for continuous-scanning time-delay-and-integration image acquisition in automated microscopy

Efficient image cytometry of a conventional microscope slide means rapid acquisition and analysis of 20 gigapixels of image data (at 0.3-microm sampling). The voluminous data motivate increased acquisition speed to enable many biomedical applications. Continuous-motion time-delay-and-integrate (TDI) scanning has the potential to speed image acquisition while retaining sensitivity, but the challenge of implementing high-resolution autofocus operating simultaneously with acquisition has limited its adoption. We develop a dynamic autofocus system for this need using: 1. a "volume camera," consisting of nine fiber optic imaging conduits to charge-coupled device (CCD) sensors, that acquires images in parallel from different focal planes, 2. an array of mixed analog-digital processing circuits that measure the high spatial frequencies of the multiple image streams to create focus indices, and 3. a software system that reads and analyzes the focus data streams and calculates best focus for closed feedback loop control. Our system updates autofocus at 56 Hz (or once every 21 microm of stage travel) to collect sharply focused images sampled at 0.3x0.3 microm(2)/pixel at a stage speed of 2.3 mms. The system, tested by focusing in phase contrast and imaging long fluorescence strips, achieves high-performance closed-loop image-content-based autofocus in continuous scanning for the first time.