This multi-lineage resolution allows researchers to investigate region- and cell-type–specific signatures of neurodegeneration, providing a dynamic view of cellular turnover in the central nervous system (CNS).
Resonant’s assay quantifies cfDNA originating from six key neural and glial populations:
Cortical neurons
Dopaminergic neurons
Spinal motor neurons
Astrocytes
Microglia
Schwann cells
To evaluate platform performance at various concentrations, we created in silico admixtures, mixing fragmented neuronal and glial gDNA reads into blood plasma reads (0%, 0.1%, 1%, 10%, 15%, 25%, 50%, 75%, and 100%).
The classifiers demonstrated strong linearity across all six primary cell types, suggesting cfDNA signals scale proportionally with biological input rather than assay noise. This supports their potential utility for tracking cell type-specific degeneration, monitoring therapeutic responses, and assessing disease dynamics over time.
We analyzed cfDNA from plasma samples collected from controls and patients with Alzheimer’s, Parkinson’s, ALS. These clinical samples enabled real-world assessment of across neurodegenerative conditions.
We developed multivariate models that integrate cfDNA signals from six CNS cell types. This approach outperformed single-marker strategies, capturing coordinated, disease-specific patterns of cellular injury and turnover.
Our integrated models enabled >98% classification accuracy across AD, PD, and ALS cohorts. Leveraging the full spectrum of cell-type inputs, the platform distinguished disease presence and type in research-use settings.
Individual neuron classifiers detected elevated cortical, dopaminergic, and spinal motor neuron-derived cfDNA in the plasma of patients with Alzheimer’s disease, Parkinson’s disease, and ALS, respectively, reflecting cell type-specific neurodegeneration. Integrating these three neuronal signatures into a single multivariate cell-of-origin model further improved disease detection accuracy, outperforming both individual neuron classifiers and iPSC-derived models across all disease contexts.
These findings suggest that cfDNA signatures derived from specific neural and glial populations reflect the cellular hallmarks of distinct neurodegenerative diseases. By modeling contributions from multiple neural and glial cell types, Resonant’s platform supports high-resolution detection of disease-relevant biology—which can be applied to both the identification and differentiation of neurodegenerative conditions.
This capability provides researchers and clinical partners with a powerful molecular tool to study mechanisms of disease, stratify cohorts, and assess therapeutic effects through a non-invasive, cell-type–specific lens.
Resonant’s platform is for Research Use Only and is not intended for clinical diagnostic purposes.
As brain cells degenerate, fragments of DNA are released into the circulation. This cell-free DNA (cfDNA) carries molecular features that reflect its cell of origin. By decoding those patterns, Resonant identifies which neuronal and glial populations are affected, and to what degree.
This approach links measurable cfDNA in blood to underlying neurodegenerative processes, enabling researchers to study disease biology with greater resolution and less invasiveness relative to conventional tools.
Expanded cell types, including cortical projection and hippocampal neurons, astrocytes, and microglia.
Frameworks to integrate cfDNA signals with imaging, fluid biomarkers, and clinical phenotyping.
Improved detection of rare or low-abundance neuronal signals to capture early or subtle changes.
Designed to support exploratory endpoints, from biomarker discovery to pharmacodynamic readouts.
