Discovering new biomarkers for stroke: the role of L1CAM-positive extracellular vesicles

Stroke is a devastating medical condition that affects millions of people worldwide. Detecting and diagnosing stroke early is crucial for effective treatment and better outcomes. Scientists are continually searching for reliable biomarkers—biological indicators that can signal the presence or severity of a disease. One promising area of research is the study of L1CAM-positive extracellular vesicles (L1EVs).

What Are L1EVs?

L1EVs are tiny particles released by cells that carry various types of molecules, including proteins and small RNAs, which can provide information about the state of the cells they come from. These vesicles are particularly interesting because they are found in the blood and can be used to monitor brain health from a simple blood test. L1CAM is a protein found on the surface of neurons, making L1EVs a potential marker for neuronal damage.

The Study: Analyzing L1EVs in Stroke Patients

A team led by researchers at the University of Texas Health Science Center aimed to understand the role of small RNAs within L1EVs after a stroke and to determine if these RNAs could help diagnose stroke. They collected blood samples from 28 patients, some of whom had experienced a stroke, and isolated the L1EVs from these samples.

Small RNA Sequencing and Stroke Diagnosis

By sequencing the small RNAs in L1EVs, the researchers discovered that these vesicles contain microRNAs (miRNAs) that are typically found in the brain. This finding is significant because it suggests that L1EVs can carry information about brain health.

To analyze the data, the team used two sophisticated methods:

  1. Weighted Gene Co-Expression Network Analysis (WGCNA): This method helps identify groups of genes or RNA molecules that are co-expressed and might be functionally related. In this study, WGCNA revealed small RNA modules (groups of related RNAs) that were linked to stroke diagnosis, the severity of the stroke (measured by the NIH stroke scale), and the age of the patients.
  2. Random Forest (RF) Machine Learning Algorithms: RF is a type of artificial intelligence that can classify data and make predictions. The researchers used RF to develop RNA signatures from the L1EVs that could distinguish between stroke patients and control patients.

Differential small RNA expression analysis of L1EVs
from plasma of acute ischemic stroke (AIS) patients

Figure 1

Volcano plot demonstrating (A) miRNAs and other (B) ncRNA (i.e. lncRNA, snRNA, snoRNA) significantly regulated following AIS. (C,D) Heat maps with individual patient relative expression levels of the top differentially expressed up-regulated miRNAs and other ncRNAs after AIS (fold-change [FC] > 2, adjusted P value < 0.05; AIS = 16, non-stroke control = 12). (E) Pathway analysis using gene ontology (GO) terms of target genes for the differentially regulated miRNAs reveals significant regulation of miRNAs targeting transcription factors, intracellular signaling, cell death pathways, and inflammatory signaling. (F) Sankey plot of the subset of differentially regulated lncRNAs (left column) and miRNAs (middle column) that interact with each other and their potential gene targets (right column).

Results and Implications

The small RNA signatures derived from the L1EVs showed a high degree of accuracy in diagnosing acute ischemic stroke (AIS), with an area under the curve (AUC) ranging from 0.833 to 0.932. The AUC is a measure of the test’s accuracy, with values closer to 1 indicating better performance.

These findings are promising, suggesting that small RNA signatures from L1EVs could serve as reliable biomarkers for stroke. However, more research is needed to fully understand how these small RNAs contribute to the body’s response to brain injury and to validate these findings in larger patient populations.


The study of L1CAM-positive extracellular vesicles and their small RNA cargoes offers a new frontier in stroke diagnosis. By providing a non-invasive way to monitor brain health through blood tests, this research could lead to earlier detection and better management of stroke, ultimately improving outcomes for patients. As scientists continue to explore this exciting area, the potential for new diagnostic tools and treatments becomes ever more promising.

Manwani B, Brathaban N, Baqai A, Munshi Y, Ahnstedt HW, Zhang M, Arkelius K, Llera T, Amorim E, Elahi FM, Singhal NS. (2024) Small RNA signatures of acute ischemic stroke in L1CAM positive extracellular vesicles. Sci Rep 14(1):13560. [article]

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