The classical small molecule neurotransmitters are essential for cell-cell signaling in the nervous system for regulation of behaviors and physiological functions. Metabolomics approaches are ideal for quantitative analyses of neurotransmitter profiles but have not yet been achieved for the repertoire of 14 classical neurotransmitters. Therefore, this study developed targeted metabolomics analyses by full scan gas chromatography/time-of-flight mass spectrometry (GC-TOF) and hydrophilic interaction chromatography-QTRAP mass spectrometry (HILIC-MS/MS) operated in positive ionization mode for identification and quantitation of 14 neurotransmitters consisting of acetylcholine, adenosine, anandamide, aspartate, dopamine, epinephrine, GABA, glutamate, glycine, histamine, melatonin, norepinephrine, serine, and serotonin. GC-TOF represents a new metabolomics method for neurotransmitter analyses. Sensitive measurements of 11 neurotransmitters were achieved by GC-TOF, and three neurotransmitters were analyzed by LC-MS/MS (acetylcholine, anandamide, and melatonin). The limits of detection (LOD) and limits of quantitation (LOQ) were assessed for linearity for GC-TOF and LC-MS/MS protocols. In neurotransmitter-containing dense core secretory vesicles of adrenal medulla, known as chromaffin granules (CG), metabolomics measured the concentrations of 9 neurotransmitters consisting of the catecholamines dopamine, norepinephrine, and epinephrine, combined with glutamate, serotonin, adenosine, aspartate, glycine, and serine. The CG neurotransmitters were constitutively secreted from sympathoadrenal chromaffin cells in culture. Nicotine- and KCl-stimulated release of the catecholamines and adenosine. Lithium, a drug used for the treatment of bipolar disorder, decreased the constitutive secretion of dopamine and norepinephrine and decreased nicotine-stimulated secretion of epinephrine. Lithium had no effect on other secreted neurotransmitters. Overall, the newly developed GC-TOF with LC-MS/MS metabolomics methods for analyses of 14 neurotransmitters will benefit investigations of neurotransmitter regulation in biological systems and in human disease conditions related to drug treatments.

BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages. MATERIALS AND METHODS: This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains. RESULTS: Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r = 0.996, P < .001). Normative samples of intracranial and brain volumes across gestational ages were developed. Eight of 9 pathologic fetal intracranial volumes were automatically predicted to be >2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages. CONCLUSIONS: Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.