Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment

Abstract

SUMMARYKnowledge of the phenotypic states of immune cells in the tumor microenvironment is essential for understanding immunological mechanisms of cancer progression and immunotherapy responses, as well as the development of novel treatments. By combining single-cell RNA-seq data from over 45,000 immune cells collected from eight primary breast carcinomas, as well as matched normal breast tissue, peripheral blood, and lymph node, we created an immune map of breast cancer. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address the computational challenges inherent to single-cell RNA-seq data, enabling integration of data across patients. This atlas revealed significant similarity between normal and tumor tissue resident immune cells. However, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Our results argue against discrete activation states in T cells and the polarization model of macrophage activation in cancer, with important implications for characterizing tumorinfiltrating immune cells.

Single Cell Transcriptomics of Pancreatic Cancer Precursors Demonstrates Epithelial and Microenvironmental Heterogeneity as an Early Event in Neoplastic Progression

“…clear separation based on their individual transcriptomes remains difficult. This high overlap of myeloid derived subpopulations mirrors recent findings in breast cancer where previously proposed M1 and M2 macrophage associated genes are frequently expressed within the same cells among these two clusters[48]. As Azizi et al, describes, this suggests that distinct prototypical macrophages may not be prevalent within tumors and may in fact exist within the spectrum of these two phenotypes.…”

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

“…[35] Recent studies have also revealed an unprecedented heterogeneity within T-cell and myeloid cell types, where subsets of these exhibited a diverse spectrum of states. [19,36,37] These states present as complex phenotypes not mainly defined by marker genes, but rather the expression profile of a larger gene set. When the finer tiers of these two cell types were examined, several trends of co-localization could be observed; such as weak positive signals between cDC2:CD1C, Mø1:EGR1, and pDC:IRF7 with several CD4+ T-cell populations, including…”

Adult Human Glioblastomas Harbor Radial Glia-like Cells

“…scRNA-seq libraries were prepared according to 10X Genomics specifications. The Sequence Quality Control (Azizi et al, 2018) package was used to process raw scRNA-seq reads to a transcript count matrix, including de-multiplexing, alignment, barcode and UMI error correction, and generation of a digital expression matrix. Alignment was performed to the hg38 annotation restricted to transcribed, polyadenylated RNA of length >200 nucleotides to increase mapping specificity.…”