Itch and pain are distinct sensations that share anatomically similar pathways: from the periphery to the brain. Over the last decades, several itchspecific neural pathways and molecular markers have been identified at the peripheral and spinal cord levels. Although the perception of sensation is ultimately generated at the brain level, how the brain separately processes the signals is unclear. The primary somatosensory cortex (S1) plays a crucial role in the perception of somatosensory information, including touch, itch, and pain. In this study, we investigated how S1 neurons represent itch and pain differently. First, we established a spontaneous itch and pain mouse model. Spontaneous itch or pain was induced by intradermal treatment with 5-HT or capsaicin on the lateral neck and confirmed by a selective increase in scratching or wiping-like behavior, respectively. Next, in vivo two-photon calcium imaging was performed in awake mice after four different treatments, including 5-HT, capsaicin, and each vehicle. By comparing the calcium activity acquired during different sessions, we distinguished the cells responsive to itch or pain sensations. Of the total responsive cells, 11% were both responsive, and their activity in the pain session was slightly higher than that in the itch session. Itch- and painpreferred cells accounted for 28.4% and 60.6%, respectively, and the preferred cells showed the lowest activity in their counter sessions. Therefore, our results suggest that S1 uses a multiplexed coding strategy to encode itch and pain, and S1 neurons represent the interaction between itch and pain.

Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson’s disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.