Methods for target, biomarker, and patient selection discovery in
central nervous system disorders utilizing patient-derived cellular models, spatial
proteomics, and
machine learning. The method generates neural cells from forebrain regions from induced pluripotent stem cells, performs
cell-
type specific proteome profiling using
antibody-
enzyme conjugates and spatial
proteome profiling, and applies statistical data augmentation to sparse biological datasets.
Machine learning classifiers with SHAP-based feature importance identify ranked biomarkers from
mass spectrometry data. The platform enables
patient stratification by linking molecular signatures to symptom severity,
drug screening through biomarker modulation, and diagnostic applications. Kits comprising antibodies for biomarkers including antibodies for biomarkers identified by the method facilitate implementation. Applications include
autism spectrum disorder, rare neurodevelopmental disorders,
schizophrenia,
epilepsy, Alzheimer's
disease, and Parkinson's
disease.