During the past decade, emerging studies using electrochemistry and nanoscale imaging have demonstrated that partial exocytotic release is prevailing in neuroendocrine cell models. However, due to complicated structure and culture process, few studies have been carried out using neurons, especially human neurons. Here, dopamine (DA) release from individual vesicles and DA content stored within vesicles were quantified from induced pluripotent stem cell-derived DA neurons with electrochemical techniques. The results indicate that around 61% of the total vesicular DA content is released from these neurons during exocytosis. The vesicular content quantified in DA neurons is significantly higher than that in undifferentiated neural progenitor cells, owing to the increased appearance of dense-core vesicles that are able to store more DA molecules than the clear vesicles. When the neurons are differentiated with BAY-K8644, which stimulates neuronal maturation as well as DA release, the release fraction rises to 91%. The use of BAY-K8644 can be considered as chronic stimulation and leads to similar effects on exocytosis as repetitive stimulation, which triggers short-term plasticity. This study demonstrates partial release in DA transmission in human neurons and provides a link between neuronal maturation and the formation of plasticity. Furthermore, this work suggests that the fraction of release in exocytosis at human neurons may be a factor in determining plasticity.

Real-time, in situ monitoring of neurochemical dynamics in intact neural circuits is critical for elucidating brain function. Recent innovations in micro- and nanoelectrode engineering have markedly advanced our ability to detect neurotransmitter and neuromodulator release with high spatiotemporal resolution, while the application of machine learning (ML) has facilitated the development of next-generation electrodes and enhanced signal processing capabilities. Here, we outline a vision for the potential directions for electrode interface design and the deepening integration of ML in in situ neurochemical sensing, illustrating how breakthroughs over the past decade have illuminated these opportunities.

Mass spectrometry (MS) and mass spectrometry imaging (MSI) are powerful tools for spatial molecular analysis of complex biological systems. However, fundamental and practical constraints on spatial resolution have traditionally limited their effective use in single-cell and subcellular studies. An emerging solution to this limitation is gel-assisted sample embedding and expansion, a method that physically infiltrates and magnifies biological specimens with a swellable hydrogel. This approach has enabled the mapping of key biomolecules, such as lipids and proteins, within intact specimens at single-cell and subcellular resolution without requiring hardware and software modifications. Here, we discuss the principles and applications of gel-assisted MS and MSI methods, highlighting their early developments, validations, and applications. We further examine the current challenges and necessary improvements for advancing single-cell and subcellular spatial omics using this approach.

This work offers a comprehensive approach to understanding the phenomena underlying vesicular exocytosis, a process involved in vital functions of living organisms such as neuronal and neuroendocrine signaling. The kinetics of release of most neuromediators that modulate these functions in various ways can be efficiently monitored using single-cell amperometry (SCA). Indeed, SCA at ultramicro- or nanoelectrodes provides the necessary temporal, flux, and nanoscale resolution to accurately report on the shape and intensity of single exocytotic spikes. Rather than characterizing amperometric spikes using standard descriptive parameters (e.g., amplitude and half-width), however, this study summarizes a modeling approach based on the underlying biology and physical chemistry of single exocytotic events. This approach provides deeper insights into intravesicular phenomena that control vesicular release dynamics. The ensuing model’s intrinsic parsimony makes it computationally efficient and friendly, enabling the processing of large amperometric traces to gain statistically significant insights.