Gangliosides (GGs), a large group of sialylated glycosphingolipids, are considered biomarkers of human brain development, aging and certain diseases. Determination of individual GG components in complex mixtures extracted from a human brain represents a fundamental prerequisite for correlating their specificity with the specialized function of each brain area. In the context of modern glycomics, detailed investigation of GG expression and structure in human brain requires a continuous development and application of innovative methods able to improve the quality of data and speed of analysis. In this work, for the first time, a high-throughput mapping and sequencing of gangliosides in human fetal brain was performed by a novel mass spectrometry (MS)-based approach developed recently in our laboratory. Three GG mixtures extracted and purified from different regions of the same fetal brain in the 36^th gestational week: frontal neocortex (NEO36), white matter of the frontal lobe (FL36) and white matter of the occipital lobe (OL36) were subjected to comparative high-throughput screening and multi-stage fragmentation by fully automated chip-based nanoelectrospray ionization (nanoESI) high capacity ion trap (HCT) MS. Using this method, in only a few minutes of signal acquisitions, over 100 GG and asialo-GG species were detected and identified in the three mixtures. Obtained data revealed for the first time that differences in GG expression in human fetal brain are dependent on phylogenetic development rather than topographic factors. While a significant variation of GG distribution in NEO36 vs FL36 was observed, no significant differences in GG expression in white matter of frontal vs occipital lobe were detected. Additionally, the largest number of species was identified in NEO36, which correlates with the functional complexity of neocortex as the newest brain region. In the last stage of analysis, using MS²–MS³ molecular ion fragmentation at variable amplitudes, a NEO36-associated GD1b isomer could clearly be discriminated. Present results indicate that the combination of fully automated chipESI with HCT MS ⁿ is able to provide ultra-fast, sensitive and reliable analyses of complex lipid-linked carbohydrates from which the pattern of their expression and structure in a certain type of bio-matrix can be determined.