RESUMEN
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.
Asunto(s)
Neoplasias de la Mama/inmunología , Regulación Neoplásica de la Expresión Génica , Receptores de Antígenos de Linfocitos T/metabolismo , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Microambiente Tumoral/inmunología , Teorema de Bayes , Neoplasias de la Mama/patología , Análisis por Conglomerados , Biología Computacional , Femenino , Perfilación de la Expresión Génica , Humanos , Sistema Inmunológico , Inmunoterapia/métodos , Ganglios Linfáticos , Linfocitos Infiltrantes de Tumor , Macrófagos/metabolismo , Fenotipo , TranscriptomaRESUMEN
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, and it pinpoints bifurcation points by 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.