At VIS 2024, Ozette’s former intern, Trevor Manz, presented work conducted in collaboration with Nils Gehlenborg’s lab at Harvard Medical School. The presentation outlined our work on a general framework for comparing embedding visualizations across class-label hierarchies. The paper is published in the IEEE TVCG journal. Ozette uses this methodology to identify and visualize meaningful compositional differences between two single-cell datasets.

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At SITC 2024, Ozette’s partner, Compass, presented findings from the monotherapy arm of their CTX-471 phase 1 trial. A novel anti-CD137 agonist antibody, CTX-471 treatment elicited changes consistent with increased anti-tumor immunity, including changes in natural killer cell activation status and tumor-specific expression of markers. These data identify potential biomarkers for selection of patients with a higher probability of responding to CTX-471 therapy. Read more here.

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At SITC 2024, Ozette’s Head of Immunology, Ashley Wilson, presented how we use our 48-color spectral cytometry assay and AI-driven analysis platform to better identify optimal donors for allogeneic cell therapy. Analyzing immunophenotypes across different blood products from healthy donors, we found that donor characteristics, such as chronic viral infection and mobilization efficacy, can impact the suitability of blood products for allogeneic cell therapy development. Learn more by downloading the event poster here.

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At SITC 2024, Ozette’s Zoey Zhou presented how Ozette applied our AI-driven analysis platform to streamline the analysis of paired spectral flow cytometry and CITE-seq datasets and enhance immune cell profiling. By analyzing PBMC samples from healthy donors, we identified robust immunophenotypes to inform CITE-seq differential expression analysis, revealing distinct gene expression profiles associated with defined protein expression phenotypes. Download the event poster here to learn more.

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This year at CYTO, Ozette showcased "An automated approach to high-parameter spectral flow cytometry assay validation using a novel AI/ML platform to assess the precision and stability of predefined biomarker endpoints in the context of a 48-color pan-immune profiling panel." If you weren't able to catch us at the conference or want to take a closer look, download the event poster here.

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Methods such as CITE-seq simultaneously measure RNA and protein expression at a single cell level, but the protein expression data is frequently underutilized. Download our preprint to learn how we harness Ozette Discovery™ to achieve instantaneous in silico sorting of granular cell phenotypes, enabling identification of COVID-19-associated cell populations and precise RNA-seq profiling in mixed samples.

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