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.

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here, we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types typically seen in response to lung injury, and by striking infidelity amongst transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9 , and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9 -dependent resistance to Natural Killer (NK) cells. Loss of developmental stage-specific constraint in macrometastases triggered by NK cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Realizing the therapeutic potential of human induced pluripotent stem (iPS) cells will require robust, precise and safe strategies for genetic modification, as cell therapies that rely on randomly integrated transgenes pose oncogenic risks. Here we describe a strategy to genetically modify human iPS cells at ‘safe harbor’ sites in the genome, which fulfill five criteria based on their position relative to contiguous coding genes, microRNAs and ultraconserved regions. We demonstrate that ~10% of integrations of a lentivirally encoded β-globin transgene in β-thalassemia-patient iPS cell clones meet our safe harbor criteria and permit high-level β-globin expression upon erythroid differentiation without perturbation of neighboring gene expression. This approach, combining bioinformatics and functional analyses, should be broadly applicable to introducing therapeutic or suicide genes into patient-specific iPS cells for use in cell therapy.