An Unbiased Cell Morphology–Based Screen for New, Biologically Active Small Molecules

Abstract

We have implemented an unbiased cell morphology–based screen to identify small-molecule modulators of cellular processes using the Cytometrix (TM) automated imaging and analysis system. This assay format provides unbiased analysis of morphological effects induced by small molecules by capturing phenotypic readouts of most known classes of pharmacological agents and has the potential to read out pathways for which little is known. Four human-cancer cell lines and one noncancerous primary cell type were treated with 107 small molecules comprising four different protein kinase–inhibitor scaffolds. Cellular phenotypes induced by each compound were quantified by multivariate statistical analysis of the morphology, staining intensity, and spatial attributes of the cellular nuclei, microtubules, and Golgi compartments. Principal component analysis was used to identify inhibitors of cellular components not targeted by known protein kinase inhibitors. Here we focus on a hydroxyl-substituted analog (hydroxy-PP) of the known Src-family kinase inhibitor PP2 because it induced cell-specific morphological features distinct from all known kinase inhibitors in the collection. We used affinity purification to identify a target of hydroxy-PP, carbonyl reductase 1 (CBR1), a short-chain dehydrogenase-reductase. We solved the X-ray crystal structure of the CBR1/hydroxy-PP complex to 1.24 Å resolution. Structure-based design of more potent and selective CBR1 inhibitors provided probes for analyzing the biological function of CBR1 in A549 cells. These studies revealed a previously unknown function for CBR1 in serum-withdrawal-induced apoptosis. Further studies indicate CBR1 inhibitors may enhance the effectiveness of anticancer anthracyclines. Morphology-based screening of diverse cancer cell types has provided a method for discovering potent new small-molecule probes for cell biological studies and anticancer drug candidates.

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning

“…Other machine learning methods are likely to be equally effective, based on their performance in previous work (15)(16)(17)(18)(19)(20)(21)(22)(23)(24). The system then presents the researcher with a new batch of cells, which it has classified based on the tentative rule, and the researcher corrects errors.…”

“…However, many interesting phenotypes require the assessment of several measured features of cells. Machine learning methods that select and combine multiple features for automated cell classification have been used to score many phenotypes (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). These methods require the provision of example cells that do and do not display the morphology of interest (i.e., positive and negative cells).…”

“…Cell image analysis allows accurate identification and measurement of cells' features, enabling automated analysis of certain phenotypes that were previously intractable (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). However, many interesting phenotypes require the assessment of several measured features of cells.…”

Up-Regulation of Carbonyl Reductase 1 Renders Development of Doxorubicin Resistance in Human Gastrointestinal Cancers

“…A survey of previous literature revealed several CBR1 inhibitors, such as such as QUE, 12,33) hydroxy-PP, 34) epigallocatechin gallate, 35) chrysin and zearalenone analogues. 36) The IC 50 values of the inhibitors range from 0.2 to 10 µM, although the assay conditions are different among the reports.…”

Association between CBR1 polymorphisms and NSCLC in the Chinese population

“…CBR1 is also hypothesized to participate in cellular processes such as signal transduction (7), apoptosis (8), carcinogenesis (9) and drug resistance (10), and serves an important role in endometrial cancer (11), acute myeloid leukemia (12) and hepatocellular carcinoma (13). Schlager and Powis (14) reported that the level of CBR1 activity was significantly increased in lung tumors compared within normal tissue.…”