Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

Single-cell genomics has the potential to map cell states and their dynamics in an unbiased way in response to perturbations like disease. However, elucidating the cell-state transitions from healthy to disease requires analyzing data from perturbed samples jointly with unperturbed reference samples. Existing methods for integrating and jointly visualizing single-cell datasets from distinct contexts tend to remove key biological differences or do not correctly harmonize shared mechanisms. We present Decipher, a model that combines variational autoencoders with deep exponential families to reconstruct derailed trajectories (https://github.com/azizilab/decipher). Decipher jointly represents normal and perturbed single-cell RNA-seq datasets, revealing shared and disrupted dynamics. It further introduces a novel approach to visualize data, without the need for methods such as UMAP or TSNE. We demonstrate Decipher on data from acute myeloid leukemia patient bone marrow specimens, showing that it successfully characterizes the divergence from normal hematopoiesis and identifies transcriptional programs that become disrupted in each patient when they acquire NPM1 driver mutations.

Over the last decade, the benefits of drug vectors to treat cancer have been well recognized. However, drug delivery and vector distribution differences in tumor-associated capillary bed at different stages of disease progression are not well understood. To obtain further insights into drug vector distribution changes in vasculature during tumor progression, we combined intra-vital imaging of metastatic tumors in mice, microfluidics-based artificial tumor capillary models, and Computational Fluid Dynamics (CFD) modeling. Microfluidic and CFD circulation models were designed to mimic tumor progression by escalating flow complexity and chaoticity. We examined flow of 0.5 and 2μm spherical particles, and tested the effects of hematocrit on particle local accessibility to flow area of capillary beds by co-circulating red blood cells (RBC). Results showed that tumor progression modulated drug vector distribution in tumor-associated capillaries. Both particles shared 80-90% common flow area, while 0.5 and 2μm particles had 2-9% and 1-2% specific flow area, respectively. Interestingly, the effects of hematocrit on specific circulation area was opposite for 0.5 and 2μm particles. Dysfunctional capillaries with no flow, a result of tumor progression, limited access to all particles, while diffusion was shown to be the only prevailing transport mechanism. In view of drug vector distribution in tumors, independent of formulation and other pharmacokinetic aspects, our results suggest that the evolution of tumor vasculature during progression may influence drug delivery efficiency. Therefore, optimized drug vectors will need to consider primary vs metastatic tumor setting, or early vs late stage metastatic disease, when undergoing vector design.