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Infants cry when separated from their mothers. Here, we show that POMC neurons in the arcuate nucleus of the hypothalamus—via ß-endorphin production and signaling—modulate the crying behavior of mouse pups. The effect of POMC neurons on vocal behavior depends on the expression of µ-opioid receptors, the main receptor for ß-endorphin. Thus, POMC neurons in the hypothalamus modulate infant cry through opioid signaling.

The behavior of offspring results from the combined expression of maternal and paternal genes. Genomic imprinting silences some genes in a parent-of-origin specific manner, a process that, among all animals, occurs only in mammals. How genomic imprinting affects the behavior of mammalian offspring, however, remains poorly understood. Here, we studied how the loss of the paternally inherited gene Magel2 in mouse pups affects the emission of separation-induced ultrasonic vocalizations (USV). Using quantitative analysis of more than 1000 USVs, we characterized the rate of vocalizations as well as their spectral features from postnatal days 6-12 (P6-P12), a critical phase of mouse development that covers the peak of vocal behavior in pups. Our analyses show that Magel2 deficient offspring emit separation-induced vocalizations at lower rates and with altered spectral features mainly at P8. We also show that dams display altered behavior towards their own Magel2 deficient offspring at this age. In a test to compare the retrieval of two pups, dams retrieve wildtype control pups first and faster than Magel2 deficient offspring. These results suggest that the loss of Magel2 impairs the expression of separation-induced vocalization in pups as well as maternal behavior at a specific age of postnatal development, both of which support the pups' growth and development.

Mice emit ultrasonic vocalizations (USV) to transmit socially-relevant information. To detect and classify these USVs, here we describe the development of VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameter tuning. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a dataset of >4,000 USVs emitted by mice, VocalMat detected more than >98% of the USVs and accurately classified ≈86% of USVs when considering the most likely label out of 11 different USV types. We then used Diffusion Maps and Manifold Alignment to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat allows accurate and highly quantitative analysis of USVs, opening the opportunity for detailed and high-throughput analysis of this behavior.