Single-cell high-throughput microscopy images contain key biological information underlying normal and pathological cellular processes. Image-based analysis and profiling are powerful and promising for extracting this information but are made difficult due to substantial complexity and heterogeneity in cellular phenotype. Hand-crafted methods and machine learning models are popular ways to extract cell image information. Representations extracted via machine learning models, which often exhibit good reconstruction performance, lack biological interpretability. Hand-crafted representations, on the contrary, have clear biological meanings and thus are interpretable. Whether these hand-crafted representations can also generate realistic images become an interesting question. In this paper, we propose a CellProfiler to image (CP2Image) model that can directly generate realistic cell images from CellProfiler representations. We also demonstrate most biological information encoded in the CP representations are well-preserved in the generating process. This is the first time hand-crafted representations be shown to have generative ability and provide researchers an intuitive way for their further analysis.