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
Figure 1. Spike-in dilution series demonstrate strong linearity across biologically relevant DNA concentrations for primary neurons. Cortical (A, R² = 0.998) and dopaminergic (B, R² = 0.998) neuron-derived DNA were reliably detected in plasma. In contrast, iPSC-derived DNA was less predictive of cell origin.
To evaluate platform performance, we conducted a spike-in dilution study using primary neurons and healthy donor plasma. The experiment assessed whether our proprietary process and deconvolution pipeline maintains quantitative accuracy across a physiologically relevant range of concentrations.
The assay demonstrated strong linearity across all six cell types, reinforcing its suitability for studies involving variable cfDNA input, whether across individuals, timepoints, or disease states. These results indicate that measured cfDNA levels scale with biological input, not assay noise, supporting applications in group comparisons, stratification, and therapeutic response studies.
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.
Distinct molecular signatures from cortical, dopaminergic, and spinal motor neurons are elevated in Alzheimer’s, Parkinson’s, and ALS cohorts, respectively, reflecting disease-relevant cellular changes (A–C). A corresponding performance matrix shows accurate classification across conditions based on these cell-type–specific profiles (D).
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.
Identify cell-specific signatures of degeneration in Alzheimer’s, Parkinson’s, ALS, and related conditions
Monitor CNS cell turnover in response to therapeutics and explore pharmacodynamic signals over time.
