Results obtained from the serial dilution of fluorescently labeled MIN6 cells demonstrated that the imaging platform has a significant advantage over both plate readers in terms of dynamic range and sensitivity. Specifically, the imaging platform was able to discern a 32-fold cell titer range in the three colors analyzed. Furthermore, slope and regression analyses showed that the imaging platform has a strong performance advantage in quantifying commonly used red and green fluorescent proteins. All three platforms resolved low-end signal comparably in the brightest channel (blue, DAPI). The intense DAPI signal results from high-affinity and high-occupancy binding of the probe to large concentrations of nucleic acid. Fluorescent protein intensity is typically weaker, primarily because of the lower numbers of fluorophores when used as reporters of gene expression or metabolic processes in living cells. The same is true for common fluorophore-conjugated antibodies as used in the bimodal TNF-α/VCAM-1 assay. For such reagents, the sensitivity and dynamic range of the imager were found to be essential to resolve low-end signal in our performance comparison in a primary screen to detect small molecule inducers or inhibitors of VCAM-1 by TNF-α. All three platforms detected a core number of TNF-α/VCAM-1 pathway agonists, consistent with their similar abilities to detect bright signals. However, the imaging platform had a higher Z′ (0.41) than did the plate readers (0.16 for the EnVision) and outperformed the plate readers in the detection of TNF-α/VCAM-1 inhibitors.
It is important to emphasize that data analysis was handled as equitably as possible given the different types of output from the imager and reader platforms. For plate readers, very little data filtering can be performed, with the exception of removing extreme outliers. This was done in the plate reader data from the TNF-α/VCAM-1 screen by removing wells that had extremely high or low levels of DAPI intensity. Although relatively insensitive, this nuclear channel intensity flag succeeded in removing some undesirable sample wells, such as those caused by debris and toxic compounds. Flagging and removal of extremely low cell density wells were critical for the TNF-α/VCAM-1 assay since VCAM-1 expression depends on cell–cell contact and was significantly reduced in wells with sparse cell seeding, toxicity, or growth inhibition. For the imaging dataset, IN Cell Developer Toolbox image analysis metrics were used to flag wells based on parameters of low total collected object areas, aberrant nuclear profiles, and aberrant SD among objects in a well. Specifically, inhibitors were rejected based on statistically low collected nuclear area, aberrant nuclear profile, or statistically low number of identified nuclei. Similarly, false agonists, such as molecular precipitates, inherently fluorescent molecules, and debris, were rejected based on high signal SD within the well. Thus, an additional benefit of the imager platform is its ability to use multiparametric data to classify samples, resulting in fewer false-positives.
We noted that the imager measurements showed greater well-to-well variance than did plate readers in sample wells with low cell titers. This was due primarily to variation in the total number of cells in the image area at low cell titers and, potentially, differences in defining the areas of interest (morphometry), rather than in quantification of pixel intensity per se.
It was possible to reduce such variance by normalizing integrated density values relative to nuclear DAPI area and object counts (). Cell-by-cell image cytometry, for which higher-resolution images are used to create better cell-by-cell segmentation, might further improve normalization by area and/or object counts and could provide additional intra-well filtering/gating, both of which might further improve the imaging results. In addition, use of a higher-resolution objective (e.g.
, 20×) would further increase the sensitivity of the imager because of the increased NA (fluorescent intensity is proportional to NA4
in a microscope).11
The possible advantages of higher-resolution images for any assay must always be balanced with the decrease in speed that may result if more images have to be acquired to measure the same number of cells.
In conclusion, this report has characterized the performance differences between two commonly used plate readers and a plate imager in the context of a fluorescent cellular bioassay. Although plate readers have strong advantages in speed and simplicity, automated microscopy platforms can provide superior performance for exacting resolution of fluorescent cellular bioassays. The EnVision and DTX platforms can scan a 384-well microplate in three colors in about 5 and 15
min, respectively and the data require no further processing besides normalization and analysis. In contrast, three-color imaging of a 384-well microplate by the IN Cell 1000 at one image per well requires approximately 25
min, and the downstream analysis of image data requires a rigorous process that is labor intensive to develop and validate. Furthermore, the complexity of image analysis algorithms increases for multiplexed assays, but this increased workload also brings substantial opportunities. Multiparametric analysis affords greater assay sensitivity and selectivity because of the ability to introduce filters/gates to refine hit calling. In some cases, this process can provide flexibility during assay development by increasing the range of parameters available to improve sensitivity and dynamic range or to correct and normalize data.
Lastly, image-based screening could lead to additional benefits. For instance, image datasets queried with one algorithm for one purpose could be revisited to quantify additional features. Thus, simple re-analysis of the existing dataset could provide selectivity or other useful information that might not have been foreseen prior to running the assay. Moreover, analysis using algorithms designed to detect features unrelated to the intent of the primary screen represents a new screen at marginal additional cost. While some assays are inappropriate (e.g.
, liquid phase/homogeneous assays) for an imaging platform, others are inappropriate for plate reader platforms (e.g.
, object morphometry; low-level signal detection as shown here). Ultimately, the choice of platform depends on assay demands and the investigator's resources and experience. In summary, image-based screening is a powerful technology that extends the potential of cell-based assays and will become more widespread as throughput and analysis techniques improve.12