To help obtain both intracellular and intercellular spatial information and produce a linkage between phenotypes and genotypes of cells, we developed technologies of image-guided cell sorters based on 3D imaging flow cytometers capable of producing cell tomography at a throughput of 1000 cells/s. With the AI capabilities with real-time AI inference, the systems have shown superb capabilities for classification, cell type discovery, and spatial biology studies.

Spatially-resolved single-cell analysis of lymphoma identifies markers of therapeutic resistance. Molecular subtypes that associate with changes in the tumor architecture will be presented. Comparison between immune checkpoint response and resistant lymphoma identify potential biomarkers of treatment response. Implications for engineered cellular therapies such as CAR T will be discussed.

In this talk, I will introduce multiplex imaging modalities (genomics, proteomics, and metabolomics) to decipher the spatial and temporal decision-making of single-cells at macromolecular resolution in engineered organoids and human tissues for systems immuno-engineering, subcellular precision oncology, and personalized regenerative medicine applications. Automated machine learning algorithms in this single-cell big data impact biomedical practice and clinical care. In the last part of the talk, digital technologies interfacing cellular interactive media will be presented using the virtual reality of 3D spatial omics. Single-cell biotechnologies and digital cellular media tools synergistically complement each other for next-generation bioengineering, crowd-sourced education, and collaborative discovery platforms.

Using the data architecture for the Sloan Digital Sky Survey, we have recently developed a platform that allows for multiplex immunofluorescence (mIF) histopathologic maps at single-cell resolution across whole slides named ‘AstroPath.' This technology is applied to the whole slide mIF mapping of samples from pre- and on-treatment samples from patients treated with immune checkpoint inhibitors (ICIs), enabling the development of biomarkers of response and resistance to ICIs.

Immune checkpoint blockade therapies have drastically improved patient survival in numerous cancer types, yet only a subset of patients respond favorably. Current study uses a multi-omics (integrated scRNA and scTCR sequencing) approach to identify distinct cellular states of CD8+ T cells and develop predictive signatures with clinical benefit identified using retrospective analysis. Intended impact of this study is to develop predictive gene signatures from preclinical models which could help improve patient selection in clinical trials and ultimately enhance patient benefit.

Single-cell sequencing is a quantitative technology frequently used to position or classify cell characteristics based on multiple molecular features. We have utilized such a cutting-edge tool to build our internal disease landscapes and, by cross-comparing with the data generated from in vitro and in vivo models, also provided informative insights in understanding disease biology and revealing potential MoA of proposed or tested therapeutic target. Through the practice, the therapeutic strategy should be able to be carefully proposed or polished.

Developing a successful immunotherapy to reverse pulmonary arterial hypertension (PAH) pathology requires a comprehensive mapping of the immune cell landscape in pulmonary arteries. Our analyses revealed immune microenvironments as well as vessel-immune cell interactions that have allowed us to propose a relationship with the severity of the PAH lesions and to understand how specific immune cells can drive smooth muscle cell proliferation, ultimately guiding therapeutic efforts to regress neointimal cells.