New review highlights spatial technologies in advanced brain mapping

Understanding how molecular changes shape brain function and disease requires more than measuring genes in bulk tissue. The brain contains highly diverse cell populations organized into complex anatomical and microenvironmental niches, making it difficult to capture its biology with conventional approaches alone.

Published in EXO – Beyond the Cell, a review examines how advances in single-cell and spatial multi-omics are helping researchers address this challenge by integrating multiple molecular layers while preserving cellular or tissue context.

Led by researchers from the University of Campinas (UNICAMP), the Federal University of São Paulo, and collaborating institutions in Brazil, the review, titled "Single-Cell and Spatial Multi-omics for Mapping the Brain Across Molecular Layers," surveys recent applications spanning transcriptomics, epigenomics, proteomics, metabolomics, and spatial profiling.

The authors highlight how single-cell multi-omics can reveal regulatory relationships that are difficult to resolve from transcriptomic data alone. In studies of Alzheimer's disease, for example, integrated epigenomic and transcriptomic analyses have linked changes in chromatin organization to neuronal vulnerability and disease progression. In Parkinson's disease and psychiatric disorders, multi-omic studies have helped characterize cell-type- and region-specific changes involving neurons, microglia, and oligodendrocytes.

Spatial technologies add another critical dimension by showing where molecular states occur within tissue. Approaches ranging from sequencing-based platforms to high-resolution imaging and mass spectrometry imaging can help distinguish molecular environments across brain regions and pathological niches. In stroke and other neurological conditions, such spatial information has revealed differences between lesion cores, peri-lesional regions, and relatively preserved tissue.

The review also emphasizes that higher resolution does not automatically translate into better biological understanding. Dissociation bias, postmortem tissue variability, data sparsity, segmentation errors, limited molecular coverage, high costs, and differences between analytical pipelines can all affect interpretation and reproducibility. These challenges are particularly important in the brain, where long neuronal processes and intricate tissue architecture complicate the assignment of molecular signals to individual cells.

Looking ahead, the authors identify several emerging directions, including artificial intelligence for cross-modal data integration, single-cell proteomics and metabolomics, morphomics, and spatiotemporal approaches capable of capturing molecular processes across time. They also call for greater representation of biologically and environmentally diverse populations in multi-omic datasets.

Rather than viewing multi-omics simply as a collection of increasingly sophisticated technologies, the review highlights its broader potential to connect molecular state, spatial context, cellular phenotype, and disease progression-while emphasizing that standardized workflows, reproducibility, scalability, and independent validation will be essential for translating these advances into neuroscience and clinical research.