Proteomic identification and validation of novel neuronal EV-based markers for Alzheimers disease biomarker discovery

Ahmed, W.; Nogueras-Ortiz, C.; Sagar, R.; Dong, D.; Boyd, R. J.; Yao, P. J.; Iliuk, A.; Lyketsos, C. G.; Witwer, K. W.; Kapogiannis, D.; Mahairaki, V.

doi:10.64898/2026.04.26.720870

bioRxiv · 10.64898/2026.04.26.720870

Proteomic identification and validation of novel neuronal EV-based markers for Alzheimers disease biomarker discovery

Ahmed, W.·Nogueras-Ortiz, C.·Sagar, R.·Dong, D.·Boyd, R. J.·Yao, P. J.·Iliuk, A.·Lyketsos, C. G.·Witwer, K. W.·Kapogiannis, D.·Mahairaki, V.

View original source ↗Export metadata ↓

Abstract

Extracellular vesicles (EVs) circulate in biofluids and carry tissue-specific molecular cargo, offering significant potential for the discovery of minimally invasive biomarkers. However, translation in neurodegenerative diseases has been hindered by the lack of validated neuronal EV surface markers that enable selective isolation from plasma. We hypothesized that proteomic profiling of EVs released from human induced pluripotent stem cell (hiPSC)-derived neurons would identify 1. robust Alzheimers disease (AD)-associated signatures that reflect disease pathogenesis, and 2. surface-accessible neuronal markers capable of enriching disease-relevant cargo. Neurons differentiated from AD patients and age-matched cognitively normal (CN) individuals were used to isolate EVs, which were characterized and analyzed by LC-MS proteomics in both total and membrane-enriched fractions. Proteomic profiling identified numerous dysregulated proteins, with a subset validated across independent AD datasets. We identified CNTNAP2 and STX1B as neuronal, brain-enriched EV surface proteins accessible for selective capture and confirmed their presence in EVs from post-mortem human brain, supporting them as bona fide brain-derived EV markers. Immuno-isolation of plasma EVs showed that CNTNAP2-positive EVs had a robust AD-associated increase in phosphorylated tau, identifying CNTNAP2 as a highly discriminative brain-derived EV marker and supporting its potential for blood-based AD diagnostics. Graphical AbstractPrevious studies indicate that extracellular vesicles (EVs) released from neurons carry disease-relevant cargo, yet the search for the optimal neuronal surface markers for the selective isolation of EVs pertinent to Alzheimers disease (AD) from blood is ongoing. To address this need, we performed proteomic profiling of EVs derived from hiPSC-neurons (iNEVs) of AD patients and cognitively normal individuals (CN). LC-MS analysis of whole and membrane-enriched EV fractions revealed robust protein dysregulation and identified CNTNAP2 and STX1B as surface-exposed neuronal EV proteins, confirmed in human brain-derived EVs (BDEVs). Immuno-isolation of plasma EVs demonstrated that CNTNAP2-positive EVs are enriched for phosphorylated tau in AD, whereas the previously used markers; NrCAM and ATP1A3-positive EVs showed limited discrimination. These findings establish a robust framework for proteomic discovery and nominate CNTNAP2 as a promising EV selection marker for blood-based AD diagnostics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/720870v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@754b45org.highwire.dtl.DTLVardef@a0a566org.highwire.dtl.DTLVardef@cb0134org.highwire.dtl.DTLVardef@1bc0275_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ahmed, W., Nogueras-Ortiz, C., Sagar, R., Dong, D., Boyd, R. J., Yao, P. J., Iliuk, A., Lyketsos, C. G., Witwer, K. W., Kapogiannis, D., Mahairaki, V.. 2026-04-29. Proteomic identification and validation of novel neuronal EV-based markers for Alzheimers disease biomarker discovery. https://doi.org/10.64898/2026.04.26.720870

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗

bioRxiv2026

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗

bioRxiv2026

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗