Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for dentifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease’s pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.