Single-cell technologies provide a new lens for understanding diseases & dugs: 90% of Sanofi's disease targets are credentialed using single-cell genomics. Applying single-cell technologies to drug discovery increases probability of success but requires infrastructure and advanced analytics, as scaling up workflows and developing novel methods is necessary for broader impact. Spatial technology adds an important additional dimension but brings new analytical challenges, which will require even greater investment in high-throughput analytics.

We developed a single-cell spatial imageomics pipeline with advanced analytic and modeling tools to analyze intricated cell-cell communication in heterogeneous tumor and brain microenvironments, incorporating single cell spatial transcriptomics, proteomics, and imaging data. In this talk, we will showcase its application in studying cancer and neurological disorders, focusing on microenvironments and crosstalk pathways in ovarian cancer, brain and bone metastases, and Alzheimer's disease.

The spatial organization of cells in tissues and subcellular networks provides a quantitative metric for determining health and disease states. In this talk, I will introduce spatial omics modalities (spatial genomics, spatial proteomics, and spatial metabolomics) to decipher and model the spatio-temporal decision-making of single cells at macromolecular resolution in engineered organoids and human tissues. Automated machine learning algorithms in this single-cell big data impact biomedical practice and clinical care.

My presentation will describe Regeneron’s approach to profile the tumor microenvironment of patients with renal cancer utilizing chromatin readouts. The composition of T cell-states, regulatory dynamics, and the rewired transcriptional program of NFkB in dysfunction will be discussed.

Mixed invasive ductal and lobular carcinoma (mDLC) is a rare subtype of breast cancer displaying both ductal and lobular morphologies, posing challenges for clinical management. It remains unclear whether these distinct morphologies have distinct biology and risk of recurrence. Here we present multi-omic (spatially-resolved transcriptomic, genomic, and single-cell) profiling of collision type mDLC cases and identify clinically significant differences between the underlying ductal and lobular tumor regions.

Heterogeneity is a fundamental property of multicellular organisms. In this talk, I will describe a new drug recommendation method called ASGARD, which computationally repurposes drugs over heterogeneous cell types in the single cell RNA-Seq data. Next I will go over new discoveries on a large population cohort of single-cell imaging mass cytometry data from breast cancer patients. We reveal novel breast cancer survival subtypes with atypic prognosis outcomes. Through these examples, we hope to provide tools to take advantage of inter-cellular heterogeneity in cancer treatment, and prognosis prediction.

To unravel the cancer, diabetes, and cardiovascular heterogeneity and its implications for patient outcomes, we use single-cell and spatial profiling techniques. In this presentation, I will delve into our efforts to comprehensively map the heterogeneity of hematological cancers in both adult and pediatric populations. Our goal is to elucidate the intricate interplay between tumor characteristics and the tumor microenvironment, shedding light on their associations with adverse clinical outcomes.

We investigate single-cell proteomic analysis using 2D image-guided cell sorters and 3D imaging flow cytometers (3D-IFCs) that possess high-throughput and high-content imaging in a single system. Both systems are empowered by artificial intelligence (AI) with convolutional neural network for label-free detection of DNA damages, protein translocations, and cell fate prediction. We will discuss: 1) Can AI outperform human to detect protein distribution through high dimensional features unrecognized by human vision? 2) Can AI "predict" the response of cells to stresses and perturbations?