Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

Single-cell and spatial multi-omics for mapping the brain across molecular layers

Understanding how molecular mechanisms occur and shape brain function and dysfunction remains a central challenge in neuroscience. Although bulk omics methods have contributed significantly to the field, they fail to address the cellular and spatial heterogeneity of the brain. Single-cell and spatial multi-omics approaches emerged to address these limitations by enabling integrated, high-resolution profiling of molecular layers while preserving cellular and tissue context. However, despite their impact on basic neuroscience, the clinical translation of these methods remains limited by cost, technical complexity, and analytical challenges. In this review, we summarize recent advances in single-cell and spatial multi-omics applied to brain research, critically evaluating their technological capabilities, translational potential, and current limitations. We further highlight emerging directions, including spatiotemporal integration, morphomics, improved reproducibility, and the expansion of multi-omics research to biologically and environmentally diverse populations.

Single Cell Proteomics to Spatial Multiomics in Cardiovascular Disease: Foundations, Technologies, and Biological Insights.

It is now apparent that cardiac form and function are governed at a system level by an integrated collective of heterogeneous single-cell programs that together regulate tissue homeostasis, drive disease emergence, and shape individualized responses to therapy. Current advances in single-cell proteomics and spatial multiomics by mass spectrometry imaging allow systematic dissection of clinically defined cardiovascular tissues with an unprecedented molecular resolving power, yet remain relatively underutilized in cardiovascular research. This compendium review works to stimulate new research into spatial regulation of the heart, emphasizing integration of single-cell proteomic regulation with comprehensive multiomic mass spectrometry imaging studies. In this context, we outline conceptual foundations, technological innovations, and biological insights that have resulted in the current success of single-cell proteomic and spatial multiomic analyses in cardiovascular disease. We provide an experimental design knowledge base of critical components in single-cell proteomics and spatial workflows by mass spectrometry imaging, essential for generating robust and reproducible data sets that are interpretable by advanced computational methods. Key cardiovascular discoveries by single-cell proteomics and multiomic mass spectrometry imaging are reviewed, highlighting how these approaches have provided new molecular insights into cardiac cell programming.

Integrative single-cell and spatial transcriptomics combined with machine learning to discover complement-associated biomarkers in temporal lobe epilepsy with hippocampal sclerosis

Deep axonal proteomics of human iPSC-derived neurons by microfluidic separation and DIA-MS

One of the defining features of neurons is their compartmentalisation into soma, dendrites and axon. Axons are highly specialised for the transmission of signals over long distances but also exhibit unique vulnerabilities in the context of neurodegeneration, implying a specialised axonal proteome. However, in-depth proteomic characterisation of axons has been limited by the difficulty of isolating pure axonal material in sufficient quantities for conventional mass spectrometry analysis. Here, we combine microfluidic-based axon–soma separation with data-independent acquisition mass spectrometry (DIA-MS) on an Orbitrap Astral mass spectrometer for deep profiling of compartment-resolved proteomes of human induced pluripotent stem cell (iPSC)-derived cortical neurons. We quantify over 9,000 proteins in the somatodendritic compartment and ∼6,000 proteins in the axonal compartment, to our knowledge, representing the deepest human axonal proteome reported to date. Differential abundance analysis identifies 1,250 axon-enriched proteins with strong enrichment of axon-related pathways including vesicle-mediated transport, synaptic vesicle dynamics, and cytoskeletal organisation. Extending this workflow to iPSC-derived lower motor neurons, we reveal a core set of 417 axon-enriched proteins shared between cortical and lower motor neurons, alongside subtype-specific axonal signatures whose functional differences become evident only through compartment-restricted analysis. We show broad coverage of genes for the neurodegenerative diseases hereditary spastic paraplegia and amyotrophic lateral sclerosis, establishing a quantitative reference for interpreting how disease-linked mutations may differentially affect axonal versus somatodendritic proteostasis. This workflow and resource are readily applicable to other neuronal subtypes and disease models, paving the way for studying axonal proteome dynamics in health and disease. Highlights Deepest human axonal proteome with ∼6,000 proteins from iPSC-derived neurons Microfluidic separation paired with DIA mass spectrometry Axon-specific signatures masked in whole-cell analyses are revealed Core set of 417 axon-enriched proteins shared across neuron subtypes Framework for studying axon-selective vulnerability in neurodegeneration Graphical abstract

A Review of Neuroproteomics in Neurological Disorders: The Use of Machine Learning and Deep Learning

Proteomics has emerged as a powerful tool for advancing our understanding of brain disorders by enabling large-scale characterization of protein expression, post-translational modifications, and interaction networks. Neurological conditions are often characterized by complex and dynamic molecular changes that are not fully captured by traditional diagnostic approaches. Proteomic technologies, particularly mass spectrometry-based and affinity-based methods, offer the ability to identify disease-specific protein signatures and elucidate underlying pathophysiological mechanisms, including neurodegeneration, neurodevelopment and neuroinflammation and alterations happening to the extracellular matrix and body fluid homeostasis. In recent years, artificial intelligence has emerged as a powerful tool to proteomics, enabling improved analysis of complex biological datasets. This integration has significantly enhanced the discovery of biomarkers for early diagnosis, disease stratification, and monitoring of therapeutic responses. Thus, cerebrospinal fluid and blood-based proteomic analyses have revealed promising candidates for neurological diseases. This review summarizes current advances in proteomics across a range of brain disorders, highlighting key molecular pathways, biomarker discovery efforts, and evolving clinical applications. Furthermore, it outlines future directions, including the application of machine learning for improved biomarker identification and precision medicine.

VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.