Glioblastoma multiforme (GBM) comprises several molecular subtypes including proneural GBM. Most therapeutic approaches targeting glioma cells have failed. An alternative strategy is to target cells in the glioma microenvironment, such as tumor-associated macrophages and microglia (TAMs). Macrophages depend upon colony stimulating factor (CSF)-1 for differentiation and survival. A CSF-1R inhibitor was used to target TAMs in a mouse proneural GBM model, which dramatically increased survival, and regressed established tumors. CSF-1R blockade additionally slowed intracranial growth of patient-derived glioma xenografts. Surprisingly, TAMs were not depleted in treated mice. Instead, glioma-secreted factors including GM-CSF and IFN-γ facilitated TAM survival in the context of CSF-1R inhibition. Alternatively activated/ M2 macrophage markers decreased in surviving TAMs, consistent with impaired tumor-promoting functions. These gene signatures were associated with enhanced survival in proneural GBM patients. Our results identify TAMs as a promising therapeutic target for proneural gliomas, and establish the translational potential of CSF-1R inhibition for GBM.

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 RNA sequencing (scRNA-seq) 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—treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime 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 scRNA-seq 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 generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems.

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-seq data, as input and orders cells according to their developmental progression by pinpointing bifurcation points and labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

Summary To delineate the ontogeny of the mammalian endoderm, we generated 112,217 single-cell transcriptomes representing all endoderm populations within the mouse embryo until midgestation. By employing graph-based approaches, we modelled differentiating cells for spatio-temporal characterization of developmental trajectories and defined the transcriptional architecture that accompanies the emergence of the first (primitive or extra-embryonic) endodermal population and its sister pluripotent (embryonic) epiblast lineage. We uncovered a relationship between descendants of these two lineages, whereby epiblast cells differentiate into endoderm at two distinct time-points, before and during gastrulation. Trajectories of endoderm cells were mapped as they acquired embryonic versus extra-embryonic fates, and as they spatially converged within the nascent gut endoderm; revealing them to be globally similar but retaining aspects of their lineage history. We observed the regionalized identity of cells along the anterior-posterior axis of the emergent gut tube, reflecting their embryonic or extra-embryonic origin, and their coordinate patterning into organ-specific territories.