Chinese researchers have developed a new platform that enables the comprehensive characterization of individual neurons by simultaneously capturing their gene-expression profile, morphology and functional activity, a breakthrough that could advance understanding of how the brain works and how neurological disorders develop.

The study, led by researchers from the Chinese Academy of Sciences, was published on Thursday in the journal Cell.

Neurons, the brain's basic signaling units, can be understood through three key dimensions: how they function, how they are structured and connected, and which genes they express. However, scientists have long faced a major challenge: no technology could comprehensively measure all three types of information from the same neuron.

To address this, the research team developed a trimodal platform called IMC, which integrates functional imaging, morphological reconstruction and gene-expression profiling within a single workflow.

The platform combines two proprietary technologies. One is a high-resolution multiphase parallelized two-photon microscope, which enables researchers to reconstruct a neuron's brain-wide projection pattern without cutting brain tissue. The other is a dual-color expansion fluorescence in situ hybridization technique, which precisely maps gene molecules within cells and can detect six genes simultaneously.

Using the platform, researchers conducted experiments in awake mice. They first recorded how individual neurons responded to visual stimuli and facial movements through in vivo calcium imaging. They then reconstructed the same neurons' long-range projections across the brain and finally mapped the distribution and abundance of genes within those cells. Throughout the process, the neurons' spatial locations were preserved, allowing the three datasets to be accurately aligned.

The team has already collected trimodal datasets from more than 100 neurons. By combining gene-expression and morphological information with functional data, the researchers found they could predict neuronal responses more accurately than with any single type of data alone.

The study also showed that the spatial distribution of genes within a cell can serve as an important marker for distinguishing different neuron types.

In addition, the researchers identified a previously uncharacterized subtype of excitatory neuron that expresses molecular markers typically associated with inhibitory neurons while exhibiting distinctive responses to visual stimuli. The finding could provide new insights into how neurons are classified and organized within brain circuits.

Researchers say the platform offers a powerful new tool for linking neuronal activity, structure and gene expression at single-cell resolution, paving the way for a more complete understanding of the brain's complex networks.