Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.06.23.660079

Single Cell Proteomics in the Developing Human Brain

Abstract

Proteins are the functional effectors of virtually all biological processes, and accurately measuring their abundance and dynamics is essential for understanding development and disease. Although mRNA levels have historically been used as proxies for protein expression, growing evidence, especially from studies of the human cerebral cortex, has revealed widespread discordance between transcript and protein abundance. To directly address this limitation, we developed a rigorously optimized workflow that combines single-cell mass spectrometry with precise sample preparation to resolve, for the first time, quantitative proteomes of individual cells from the developing human brain. Our platform achieved deep proteomic coverage ([~]800 proteins per cell) even in immature prenatal human neurons (5-10 m diameter, [~]100 pg of protein per cell), capturing major brain cell types and enabling proteome-wide characterization at single-cell resolution. This approach revealed extensive transcriptome-proteome discordance across cell types, with particularly strong discrepancies in genes associated with neurodevelopmental disorders, a finding validated through orthogonal experiments. Proteins exhibited markedly higher cell-type specificity than their mRNA counterparts, underscoring the importance of proteomic-level analysis for resolving cellular identity and function. By reconstructing developmental trajectories from radial glia to excitatory neurons at the proteomic level, we identified dynamic stage-specific protein co-expression modules and pinpointed the intermediate progenitor-to-neuron transition as a molecularly vulnerable phase linked to autism. Altogether, by enabling single cell proteomics, this study establishes a foundational resource and technological advance for developmental neuroscience. It demonstrates that single-cell proteomics can capture critical developmental events and disease mechanisms that are undetectable at the transcript level. As this technology continues to improve in sensitivity and scalability, single-cell proteomics will become an indispensable tool for uncovering the molecular logic of brain development and for illuminating pathophysiological processes underlying neurodevelopmental disorders.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wu, T., jiang, l., Mukhtar, T., Wang, L., jian, r., Wang, C., Trinh, T., Kriegstein, A., Snyder, M., Li, J.. 2025-06-24. Single Cell Proteomics in the Developing Human Brain. https://doi.org/10.1101/2025.06.23.660079

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗