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Biology subjects

Ruggiero, K.

Publications and source records attributed to Ruggiero, K..

2 recordsLinked to original sources

End-To-End, Fully Automated Manufacturing of Hematopoietic Stem and Progenitor Cell Therapy Via CCR5-Targeted Integration

Gene therapies utilizing hematopoietic stem and progenitor cells (HSPCs) have curative potential across hematologic diseases, but manual manufacturing workflows are costly and difficult to scale, limiting patient access. Here, we demonstrated automation of an HSPC gene knock-in protocol on a high-throughput manufacturing platform designed for clinical-scale production. The platform executed all steps, from post-thaw HSPC inoculation through Cas9 electroporation, AAV6 transduction, and harvest, producing gene-edited HSPCs (geHSPCs) carrying an anti-HIV antibody transgene at the CCR5 locus with high purity, preserved differentiation potential, and editing efficiencies comparable to small-scale processing. Following transplantation into mice, geHSPCs engrafted and reconstituted multiple hematopoietic lineages, demonstrating that repopulation capacity was preserved. This work shows that clinical-scale doses of geHSPCs meeting release criteria can be produced on an automated, high-throughput platform, while preserving key product attributes and in vivo function, providing a path toward scalable and cost-effective manufacturing that could broaden access to HSPC gene therapies.

genetics↗

A Bayesian approach to multivariate and multilevel modelling with non-random missingness for hierarchical clinical proteomics data

High throughput mass-spectrometry-based proteomics data from clinical studies brings challenges to statistical analysis. The challenges originate from the hierarchical levels of protein abundance data and interactions between clinical study design and experimental design. The non-random missingness of the measurements from a vast amount of information also adds complexity in data analysis. We propose multivariate multilevel models to analyse protein abundances and to handle abundance-dependent missingness within a Bayesian framework. The proposed model enables the variance decomposition at different levels of the data hierarchy and provides shrinkage of protein-level estimates for a group of proteins. A logistic missingness and censored model with informative prior is used to handle incomplete data. Hamiltonian MC/No-U-Turn Sampling and Gibb MCMC algorithms are created to derive the posterior distribution of study parameters; Hamiltonian MC is demonstrated to gain more efficiency for these high-dimensional correlated data. Improvements of the proposed missing data model is compared to the univariate mixed effect model and the multivariate-multilevel model using complete data in a simulated study and a clinical proteomics study. The proposed model framework can be used in other types of data with similar structure and Non Random Missingness mechanism (MNAR).

bioinformatics↗