The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications. (Journal of Biomolecular Screening 2010:726-734)

Human neutrophils (polymorphonuclear leukocytes [PMNs]) generate inflammatory responses within the joints of gout patients upon encountering monosodium urate (MSU) crystals. Neutrophil extracellular traps (NETs) are found abundantly in the synovial fluid of gout patients. The detailed mechanism of MSU crystal-induced NET formation remains unknown. Our goal was to shed light on possible roles of purinergic signaling and neutrophil migration in mediating NET formation induced by MSU crystals. Interaction of human neutrophils with MSU crystals was evaluated by high-throughput live imaging using confocal microscopy. We quantitated NET levels in gout synovial fluid supernatants and detected enzymatically active neutrophil primary granule enzymes, myeloperoxidase, and human neutrophil elastase. Suramin and PPADS, general P2Y receptor blockers, and MRS2578, an inhibitor of the purinergic P2Y6 receptor, blocked NET formation triggered by MSU crystals. AR-C25118925XX (P2Y2 antagonist) did not inhibit MSU crystal-stimulated NET release. Live imaging of PMNs showed that MRS2578 represses neutrophil migration and blocked characteristic formation of MSU crystal-NET aggregates called aggregated NETs. Interestingly, the store-operated calcium entry channel inhibitor (SK&F96365) also reduced MSU crystal-induced NET release. Our results indicate that the P2Y6/store-operated calcium entry/IL-8 axis is involved in MSU crystal-induced aggregated NET formation, but MRS2578 could have additional effects affecting PMN migration. The work presented in the present study could lead to a better understanding of gouty joint inflammation and help improve the treatment and care of gout patients.

Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, because respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary disease necessitating organ transplant. The visual examination of nasal or bronchial biopsies is critical for the diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone. Although ciliary beat frequency can be computed, this metric cannot sensitively characterize ciliary motion defects. Furthermore, PCD can present without any ultrastructural defects, limiting the use of other detection methods, such as electron microscopy. Therefore, an unbiased, computational method for analyzing ciliary motion is clinically compelling. We present a computational pipeline using algorithms from computer vision and machine learning to decompose ciliary motion into quantitative elemental components. Using this framework, we constructed digital signatures for ciliary motion recognition and quantified specific properties of the ciliary motion that allowed high-throughput classification of ciliary motion as normal or abnormal. We achieved >90% classification accuracy in two independent data cohorts composed of patients with congenital heart disease, PCD, or heterotaxy, as well as healthy controls. Clinicians without specialized knowledge in machine learning or computer vision can operate this pipeline as a “black box” toolkit to evaluate ciliary motion.