Omid Cameron Farokhzad

Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how datapoints per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of datapoints per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics – not identifications alone – by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

In mass spectrometry-based proteomics, robust and efficient search engines are essential for accurate peptide and protein identification and quantification. Advances in sample preparation and instrumentation have increased the demand for highly scalable processing tools, with datasets comprising hundreds or thousands of samples in single-cell and population studies. Here we present Radiant DIA, a novel Data-Independent Acquisition search engine which achieves 4x faster processing and 10x lower cloud compute costs for large experiments while ensuring rigorous control of false discovery rate (FDR) and maintaining similar sensitivity, precision, and quantitative accuracy. The Radiant DIA search engine is paired with a modular pipeline deployable on cloud and desktop environments comprising individual modules for distributed re-scoring, FDR estimation, protein inference and quantification. Unlike traditional monolithic applications, this architecture enables high-performance, cloud-scale analysis without sacrificing local usability. Together, the Radiant DIA and Fulcrum Pipeline tools enhance computational efficiency to facilitate biological discovery in large-scale proteomics, as demonstrated by analyses of real-world experiments up to thousands of MS acquisitions.

The effectivity of cancer immunotherapies is hindered by immunosuppressive tumour microenvironments that are poorly infiltrated by effector T cells and natural killer cells. In infection and autoimmune disease, the recruitment and activation of effector immune cells is coordinated by pro-inflammatory T helper 17 (T_H17) cells. Here we show that pathogen-mimicking hollow nanoparticles displaying mannan (a polysaccharide that activates T_H17 cells in microbial cell walls) limit the fraction of regulatory T cells and induce T_H17-cell-mediated anti-tumour responses. The nanoparticles activate the pattern-recognition receptor Dectin-2 and Toll-like receptor 4 in dendritic cells, and promote the differentiation of CD4⁺ T cells into the T_H17 phenotype. In mice, intra-tumoural administration of the nanoparticles decreased the fraction of regulatory T cells in the tumour while markedly increasing the fractions of T_H17 cells (and the levels of T_H17-cell-associated cytokines), CD8⁺ T cells, natural killer cells and M1-like macrophages. The anti-tumoural activity of the effector cells was amplified by an agonistic antibody against the co-stimulatory receptor OX40 in multiple mouse models. Nanomaterials that induce T_H17-cell-mediated immune responses may have therapeutic potential.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle–protein interface, driven by the relationship between protein–NP affinity and protein abundance. This enables scalable systems that leverage protein–nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP‐surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP‐based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2–1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large‐scale NP‐based deep proteomic studies.

Introduction: Comprehensive assessment of the proteome remains elusive because of proteoforms arising from alternative splicing, allelic variation, and protein modifications. Characterization of the variable protein forms, or proteoforms will expand our understanding of the molecular mechanisms underlying diseases, however requires unbiased protein coverage at sufficient scale. Scalable, deep and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of Proteograph in a proof-of-concept proteogenomic analysis of 80 healthy controls and 61 early-stage non-small-cell lung cancer (NSCLC) samples to dissect differences between protein isoforms arising from alternative gene splicing, as well as the identification of novel peptides arising from allelic variation. Materials, Methods and Results: Processing the 141 plasma samples with Proteograph yielded 21,959 peptides corresponding to 2,499 protein groups. Using peptides with significant abundance differences (p < 0.05; Benjamini-Hochberg corrected), we extracted proteins comprised of peptides where at least one peptide had significantly higher plasma abundance, and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of putative proteoforms. For three of these proteins, the abundance variation is possibly explained by underlying protein isoforms. To identify protein variants, we performed exome sequencing on 29 individuals from the NSCLC study, created personalized mass spectrometry search libraries for each individual, and identified 464 protein variants. Conclusions: Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of protein variants and peptides present in plasma, at a scale sufficient to enable population-scale proteomic studies. Citation Format: Margaret Donovan, Henry Huang, John Blume, Marwin Ko, Ryan Benz, Theodore Platt, Juan Cuevas, Serafim Batzoglou, Asim Siddiqui, Omid Farokhzad. Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6340.

Significance: Deep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle–protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.

The pathway of the protein corona. From understanding toward exploitation through its use for biomarkers discovery.