Dataset Information

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq.

ABSTRACT: Tumor-infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA-seq (scRNA-seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adjacent normal tissues from 4 early-stage non-small cell lung cancer (NSCLC) patients. Our scRNA-seq analyses identified 11 485 cells that varied in identity and gene expression traits between normal and tumor tissues. Among these, myeloid cell populations exhibited the most diverse changes between tumor and normal tissues, consistent with tumor-mediated reprogramming. Through trajectory analysis, we identified a differentiation path from CD14+ monocytes to M2 macrophages (monocyte-to-M2). This differentiation path was reproducible across patients, accompanied by increased expression of genes (eg, MRC1/CD206, MSR1/CD204, PPARG, TREM2) with significantly enriched functions (Oxidative phosphorylation and P53 pathway) and decreased expression of genes (eg, CXCL2, IL1B) with significantly enriched functions (TNF-? signaling via NF-?B and inflammatory response). Our analysis further identified a co-regulatory network implicating upstream transcription factors (JUN, NFKBIA) in monocyte-to-M2 differentiation, and activated ligand-receptor interactions (eg, SFTPA1-TLR2, ICAM1-ITGAM) suggesting intratumoral mechanisms whereby epithelial cells stimulate monocyte-to-M2 differentiation. Overall, our study identified the prevalent monocyte-to-M2 differentiation in NSCLC, accompanied by an intricate transcriptional reprogramming mediated by specific transcriptional activators and intercellular crosstalk involving ligand-receptor interactions.

SUBMITTER: Song Q

PROVIDER: S-EPMC6558497 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq.

Song Qianqian Q Hawkins Gregory A GA Wudel Leonard L Chou Ping-Chieh PC Forbes Elizabeth E Pullikuth Ashok K AK Liu Liang L Jin Guangxu G Craddock Lou L Topaloglu Umit U Kucera Gregory G O'Neill Stacey S Levine Edward A EA Sun Peiqing P Watabe Kounosuke K Lu Yong Y Alexander-Miller Martha A MA Pasche Boris B Miller Lance D LD Zhang Wei W

Cancer medicine 20190429 6

Tumor-infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA-seq (scRNA-seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adj ...[more]

PMID: 31033233

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Project description:Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.

Dataset Information

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq.

Publications

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq.

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