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Single cell sequencing of out of thaw bone marrow derived MSC cells

ABSTRACT: Purpose: A Multiomics-based Unbiased Computational Modeling Platform Identifies Predictive Attributes of Mesenchymal Stromal Cell Immunomodulatory Potency Methods: scRNAseq from MSCs with dropseq method on BioRad ddseq platform Results: framework for identifying putative CQAs and generating MoA hypotheses for therapeutic cells and may help overcome current challenges in advancing large-scale, quality-driven manufacturing of cell therapies for broad clinical use. We used SureCell app to align the reads to human reference genome and used SEURAT for downstream analysis. Conclusions: Cell therapies are complex “living medicines” with potential to treat chronic, incurable diseases. Despite their success and promise, quality-driven reproducible manufacturing and identification of Mechanisms-of-Action (MoA) remain significant challenges. Specifically, it is difficult to identify which set of cell attributes, among the thousands of proteins, RNA, lipids, and metabolites, are most correlative to their function in a specific disease setting. Here, we report a multiomics-driven unbiased computational platform to identify multivariate features that are predictive of their immunomodulatory functions.

ORGANISM(S): Homo sapiens

PROVIDER: GSE195999 | GEO | 2025/12/02

REPOSITORIES: GEO

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Project description:Next-generation T-cell-directed vaccines for COVID-19 aim to induce durable T-cell immunity against circulating and future hypermutated SARS-CoV-2 variants. Mass Spectrometry (MS)based immunopeptidomics holds promise for guiding vaccine design, but computational challenges impede the precise and unbiased identification of conserved T-cell epitopes crucial for vaccines against rapidly mutating viruses. We introduce a computational framework and analysis platform integrating a novel machine learning algorithm, immunopeptidomics, intra-host data, epitope immunogenicity, and geo-temporal CD8+ T-cell epitope conservation analyses. Central to our approach is MHCvalidator, a novel artificial neural network algorithm enhancing MS-based immunopeptidomics sensitivity by modeling antigen presentation and sequence features. MHCvalidator identified a novel nonconventional SARS-CoV-2 T-cell epitope presented by B7 supertype molecules, originating from a +1-frameshift in a truncated Spike (S) antigen, supported by ribo-seq data. Intra-host analysis of SARS-CoV-2 proteomes from ~100,000 COVID-19 patients revealed a prevalent S antigen truncation in ~51% of cases, exposing a rich source of frameshifted viral antigens. Our framework includes EpiTrack, a new computational pipeline tracking global mutational dynamics of MHCvalidator-identified SARS-CoV-2 CD8+ epitopes from vaccine BNT162b4. While most vaccine-encoded CD8+ epitopes exhibit global conservation from January 2020 to October 2023, a highly immunodominant A*01-associated epitope, especially in hospitalized patients, undergoes substantial mutations in Delta and Omicron variants. Our approach unveils unprecedented SARS-CoV-2 T-cell epitopes, elucidates novel antigenic features, and underscores mutational dynamics of vaccine-relevant epitopes. The analysis platform is applicable to any viruses, and underscores the need for continual vigilance in T-cell vaccine development against the evolving landscape of hypermutating SARS-CoV-2 variants.

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Single cell sequencing of out of thaw bone marrow derived MSC cells

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