Tools and Protocols for High-Content Imaging and Analysis

Integrating hardware, software, and fluorescent labels for optimized HCA assay development

• Chambers KM, Mandavilli BS, Dolman NJ, Janes MS (2018) General staining and segmentation procedures for high-content imaging and analysis. Methods Mol Biol 1683:21–31.
• Mandavilli BS, Aggeler RJ, Chambers KM (2018) Tools to measure cell health and cytotoxicity using high-content imaging and analysis. Methods Mol Biol 1683:33–46.
• Mandavilli BS, Yan M, Clarke S (2018) Cell-based high-content analysis of cell proliferation and apoptosis. Methods Mol Biol 1683:47–57.
• Dolman NJ, Samson BA, Chambers KM, Janes MS, Mandavilli BS (2018) Tools to measure autophagy using high-content imaging and analysis. Methods Mol Biol 1683:59–71.

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High-content imaging and analysis transforms fluorescence microscopy into a high-throughput, quantitative tool for investigating spatial and temporal aspects of cell biology [1]. Automation—not only of the image acquisition but also of the analysis—allows millions of cells to be analyzed and reveals the heterogeneity of responses that exist within cell populations. These cellular responses can then be assessed across a range of manipulations, whether they are genome-wide screens or small-molecule library analyses [2].

To achieve a seamless high-content workflow, automation is required at every step, from image capture to the hardware for scanning microtiter plates and integrating with robotic plate-handling systems. Furthermore, analysis of the acquired images must also be automated; with Thermo Scientific HCS Studio software, this analysis can occur immediately after capture, producing visible data outputs even as the microplate is scanned.

For those entering the field of high-content imaging, the hardware, software, and reagent considerations can be overwhelming. In a series of recently published reviews in Methods in Molecular Biology, Mandavilli and colleagues introduce the essential elements of the high-content imaging and analysis process [3-6]. These reviews provide direction for hardware considerations and software settings, as well as optimized protocols for labeling cells with fluorescent probes. A range of fluorescent probes are discussed, from dyes for labeling cell structures that underpin segmentation to reagents that form the basis of the most commonly used functional assays.

Segmentation: The cornerstone of high content

High-content imaging and analysis provides automated quantitation of images captured on a fluorescence microscope. Even before the first image is acquired, the high-content analysis (HCA) system must recognize a cell in the field, and this recognition starts with segmentation. Segmentation—the identification of specific elements of the cell—can be achieved through imaging cells stained with fluorescent dyes that selectively label either the nucleus (e.g., Invitrogen HCS NuclearMask stains) or the entire cell (Invitrogen HCS CellMask stains). Once the nucleus or cell is recognized as an object to analyze, the software can quantify additional fluorescent reporters for various cellular processes.

Segmentation based on nuclear labeling enables the HCA software to identify what is or is not a cell. Moreover, the central location of the nucleus within a cell allows cytoplasmic segmentation to be routinely performed without additional labels in the majority of cell types. Chambers et al. [3] provide protocols for labeling cells with the HCS NuclearMask stains, along with optimization approaches based on the types of algorithms selected within HCS Studio software (Table 1). While essential for segmentation, these probes can also offer insight into biological processes. For example, DNA-binding dyes can report DNA content, providing a basis for cell cycle analysis. In addition, the HCS CellMask stains can report changes in cell shape as a function of compound treatment (Figure 1).

Table 1. Nuclear, whole cell, and plasma membrane stains for segmentation.

HCA assays for cell health and cytotoxicity

One of the most common applications of high-content imaging and analysis is to provide a multiparametric readout of cell health and cytotoxicity. Overall viability assessed using Invitrogen LIVE/DEAD reagents can be combined with readouts requiring a spatial element, such as probes for mitochondrial membrane potential. Mandavilli et al. describe protocols and troubleshooting steps for implementing multiparametric probe sets for viability and mitochondrial health, as well as fluorescent probes for determining reactive oxygen species and phospholipidosis/ steatosis within cells [4]. For example, the Invitrogen HCS Mitochondrial Health Kit provides the reagents required for simultaneous measurement of cell number (blue-fluorescent dye), mitochondrial membrane potential (orange-fluorescent dye), and cell viability (green-fluorescent dye). This approach provides important data when screening for compound toxicity, and even prelethal toxicity. Loss of mitochondrial membrane potential is a common precursor of cell death and is therefore a useful indicator of drug cytotoxicity.

Naturally, some studies require additional assessment of cytotoxicity, and for this reason Mandavilli et al. also provide a detailed discussion regarding the use of Invitrogen CellROX reagents and Invitrogen HCS LipidTox stains for measuring reactive oxygen species generation and phospholipidosis/ steatosis, respectively, in high-content imaging and analysis applications [4].

HCA assays for cell proliferation, apoptosis, and autophagy

In concert with a suite of advanced fluorescent probes, high-content imaging and analysis can be used to explore mechanistic aspects of cell health, including cell proliferation, apoptosis [5], and autophagy [6]. Proliferation and apoptosis are two key readouts for assessing cell health and are included in established probe combinations for measuring cytotoxicity during compound development and screening [7].

Mandavilli et al. describe the use of a fluorogenic apoptosis probe, Invitrogen CellEvent Caspase-3/7 Green Reagent, to monitor the early stages of apoptosis [5]. The CellEvent reagent comprises the DEVD peptide—which contains the recognition site for caspase-3 and -7—conjugated to a nucleic acid–binding dye. Because the DEVD peptide inhibits the ability of the dye to bind DNA, CellEvent Caspase-3/7 Green Reagent is intrinsically nonfluorescent. In the presence of activated caspase-3/7, the dye is cleaved from the DEVD peptide and free to bind DNA, producing a bright green-fluorescent signal (fluorescence emission maximum ~520 nm) indicative of apoptosis (Figure 2A). An important advantage of a fluorogenic probe is that cells do not need to be washed (to remove unbound or unincorporated probe) following incubation. This protocol simplification not only allows preservation of the entire apoptotic population, including fragile cells, which can be lost during wash steps, but also facilitates time-lapse imaging studies, as cells can be imaged in the presence of the probe.

In addition, HCA protocols are described for measuring cell proliferation by labeling cells with the thymidine analog 5-ethynl-2´-deoxyuridine (EdU) and subsequent detection by click chemistry of EdU incorporated into newly synthesized DNA [5], as well as for monitoring the induction of autophagy by immunolabeling cells with an antibody to the autophagosomal marker LC3B [6]. Autophagy is a key pro-survival mechanism implicated in a variety of disease states, including lysosomal storage disorders, neurodegenerative diseases, cancers, and Parkinson’s disease [8]. Dolman et al. describe protocols for immunolabeling cells and quantifying LC3B-positive puncta that appear after either blockade of autophagic flux with compounds such as chloroquine or bafilomycin A1, or induction of autophagy through nutrient deprivation or mTOR inhibition (Figure 2B) [6]. Approaches for validating the specificity of autophagosomal labeling using genetic knockout of critical autophagy genes (by CRISPR-Cas9 genome editing) are discussed.

References

1. Zanella F, Lorens JB, Link W (2010) Trends Biotechnol 28:237–245. 
2. Nickischer D, Elkin L, Cloutier N et al. (2018) Methods Mol Biol 1683:165–191. 
3. Chambers KM, Mandavilli BS, Dolman NJ et al. (2018) Methods Mol Biol 1683:21–31. 
4. Mandavilli BS, Aggeler RJ, Chambers KM (2018) Methods Mol Biol 1683:33–46. 
5. Mandavilli BS, Yan M, Clarke S (2018) Methods Mol Biol 1683:47–57. 
6. Dolman NJ, Samson BA, Chambers KM et al. (2018) Methods Mol Biol 1683:59–71. 
7. Towne DL, Nicholl EE, Comess KM et al. (2012) J Biomol Screen 17:1005–1017. 
8. Jiang P, Mizushima N (2014) Cell Res 24:69–79.