Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

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.

DNA Nanoparticles for Improved Protein Synthesis In Vitro.

The amplification and digital quantification of single DNA molecules are important in biomedicine and diagnostics. Beyond quantifying DNA molecules in a sample, the ability to express proteins from the amplified DNA would open even broader applications in synthetic biology, directed evolution, and proteomics. Herein, a microfluidic approach is reported for the production of condensed DNA nanoparticles that can serve as efficient templates for in vitro protein synthesis. Using phi29 DNA polymerase and a multiple displacement amplification reaction, single DNA molecules were converted into DNA nanoparticles containing up to about 10(4)  clonal gene copies of the starting template. DNA nanoparticle formation was triggered by accumulation of inorganic pyrophosphate (produced during DNA synthesis) and magnesium ions from the buffer. Transcription-translation reactions performed in vitro showed that individual DNA nanoparticles can serve as efficient templates for protein synthesis in vitro.

Deep generative model deciphers derailed trajectories in acute myeloid leukemia.

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.

Cancer cells deploy lipocalin-2 to collect limiting iron in leptomeningeal metastasis.

The tumor microenvironment plays a critical regulatory role in cancer progression, especially in central nervous system metastases. Cancer cells within the cerebrospinal fluid (CSF)-filled leptomeninges face substantial microenvironmental challenges, including inflammation and sparse micronutrients. To investigate the mechanism by which cancer cells in these leptomeningeal metastases (LM) overcome these constraints, we subjected CSF from five patients with LM to single-cell RNA sequencing. We found that cancer cells, but not macrophages, within the CSF express the iron-binding protein lipocalin-2 (LCN2) and its receptor SCL22A17. These macrophages generate inflammatory cytokines that induce cancer cell LCN2 expression but do not generate LCN2 themselves. In mouse models of LM, cancer cell growth is supported by the LCN2/SLC22A17 system and is inhibited by iron chelation therapy. Thus, cancer cells appear to survive in the CSF by outcompeting macrophages for iron.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

Author Correction: Characterization of cell fate probabilities in single-cell data with Palantir.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Tumor progression effects on drug vector access to tumor-associated capillary bed.

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.