More recently, the advancement of deep learning techniques enables reinforcement learning (RL) to handle high-dimensional decision-making problems. It brings increasing attention in transport areas. Some work has applied them to solve the intractable traffic signal control (TSC) problem and achieved promising results. However, very few research comprehensively investigates the impacts of key design elements, including state definitions, reward functions and deep reinforcement learning (DRL) methods, on TSC performance. To fill this research gap, this paper first selects commonly used design elements from existing literature. Then, we compare their learning stability and control performance at an isolated intersection under different scenarios via simulation experiments. The experimental results show that the quantitative state (e.g., the number of vehicles on lanes) and image-like state (e.g., vehicle position and speed) have no significant impact on the performance under different traffic demands. However, the impact of various reward functions on performance is vivid, especially under high traffic demand. Also, high-resolution vehicular network data may not be possible to improve control performance versus ordinary camera data. In addition, the value-based DRL algorithms outperform the policy-based algorithm and traditional TSC control methods. The findings of this research would provide insights and guidance for transport engineers to design an efficient DRL-based TSC system in a real traffic environment.