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Diagnosing mental health conditions like major depressive disorder and bipolar disorder can be challenging. Both conditions share similar symptoms, especially periods of depression, which can lead to misdiagnosis and delays in choosing the right treatment. Researchers are now exploring new biological approaches to improve how these conditions are identified.
Scientists at the Universidad de Chile have developed a new diagnostic strategy that combines extracellular vesicles with artificial intelligence. Extracellular vesicles, also called exosomes when they are small, are tiny particles released by cells into the bloodstream. They carry molecular signals that reflect what is happening inside the body, including in the brain.
Detailed overview of the proposed image-based pipeline for morphological classification of cellular sensors (cultured rat microglial cells) using patient-derived sEVs
The pipeline is organized into three functional blocks. Block 1: sEV Isolation and Microglial Treatment. sEVs are isolated from patient blood plasma and applied to cultured microglia which serve as biosensors capable of developing disease-specific morphological responses. After exposure to sEVs, microglial cells are fixed and immunostained. Immunostaining is used to visualize nuclei (DAPI, blue), microglial cytoplasm (Iba-1, red), and β-actin filaments (green). Block 2: Image Preprocessing. Fluorescent images are analyzed using blob detection to extract individual microglial cells, which are grouped into structured image arrays of size M×M. Each array includes cells from a single subject and is augmented by applying random affine transformations, including flips and rotations. A total of P arrays are generated per subject, enhancing sampling diversity and generalization. Block 3: Deep Learning-Based Classification. Arrays are the input to a DenseNet121 CNN, pre-trained on ImageNet and fine-tuned for this task. The final layer of the CNN is adapted to classify each microglial morphology into one of three diagnostic categories Ω: BD, MDD, or CTRL. Confidence scores π are computed for each class, and final subject-level predictions are obtained through a weighted voting mechanism that aggregates scores across all the arrays from a given subject. This hierarchical framework improves diagnostic accuracy by incorporating both cellular diversity and subject-level context.
The researchers used these vesicles as messengers to influence microglial cells, which are immune cells in the brain. When exposed to patient-derived extracellular vesicles, the microglial cells changed shape in ways that differed depending on whether the vesicles came from individuals with depression, bipolar disorder, or healthy controls.
These changes were captured using fluorescence microscopy, generating images of individual cells. Instead of analyzing the images manually, the team used a deep learning model called a convolutional neural network. This type of AI is especially good at recognizing patterns in images. By organizing cell images into structured groups and analyzing them through the model, the system learned to detect subtle differences between conditions.
To improve reliability, multiple image sets were generated for each patient using transformations such as rotation and rearrangement. The final diagnosis was determined using a voting approach across all analyzed image groups.
The results were highly accurate. The model correctly classified nearly all individuals across multiple validation tests, showing strong potential for distinguishing between major depressive disorder and bipolar disorder.
This approach highlights how combining extracellular vesicles with advanced imaging and artificial intelligence could lead to more objective and precise diagnostic tools in psychiatry. As research progresses, such platforms may help clinicians make faster and more accurate decisions, ultimately improving patient care.
