scholarly journals Batch effects and the effective design of single-cell gene expression studies

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Po-Yuan Tung ◽  
John D. Blischak ◽  
Chiaowen Joyce Hsiao ◽  
David A. Knowles ◽  
Jonathan E. Burnett ◽  
...  

2019 ◽  
Author(s):  
Anuja Sathe ◽  
Sue Grimes ◽  
Billy T. Lau ◽  
Jiamin Chen ◽  
Carlos Suarez ◽  
...  

ABSTRACTPurposeThe tumor microenvironment (TME) consists of a heterogenous cellular milieu that can influence cancer cell behavior. The characteristics of the cellular TME have a dramatic impact on treatments such as immunotherapy. These features can be revealed with single-cell RNA sequencing (scRNA-seq). We hypothesized that single cell gene expression studies of gastric cancer (GC) together with paired normal tissue and peripheral blood mononuclear cells (PBMCs) would identify critical elements of cellular dysregulation not apparent with other approaches.MethodsSingle cell gene expression studies were conducted on seven patients with GC and one patient with intestinal metaplasia. We sequenced 56,167 cells comprising GC (32,407 cells), paired normal tissue (18,657 cells) and PBMCs (5,103 cells). Protein expression of genes of interest was validated by multiplex immunofluorescence.ResultsTumor epithelium had copy number alterations and a distinct gene expression program compared to normal with intra-tumor heterogeneity. The GC TME was significantly enriched for stromal cells, macrophages, dendritic cells (DCs) and Tregs. TME-exclusive stromal cells expressed extracellular matrix components distinct from normal tissue. Macrophages were transcriptionally heterogenous and did not conform to a binary M1/M2 paradigm. Gene expression program of tumor DCs was unique from PBMC DCs. TME-specific cytotoxic T cells comprised of two exhausted heterogenous subsets. Helper, cytotoxic T, Treg and NK cells expressed multiple immune checkpoint or costimulatory molecules. Receptor-ligand analysis revealed TME-exclusive inter-cellular communication.ConclusionsSingle cell gene expression studies revealed widespread reprogramming across multiple cellular elements in the milieu of the GC TME. Cellular remodeling was delineated by changes in cell numbers, transcriptional states and inter-cellular interactions. This characterization facilitates understanding of tumor biology and enables the identification of novel molecular targets including for cancer immunotherapy.STATEMENT OF TRANSLATIONAL RELEVANCEWe leveraged the power of single-cell genomics to characterize the heterogenous cell types and states in the tumor microenvironment (TME). By profiling thousands of single cells from surgical resections of gastric cancer together with paired normal mucosa and peripheral blood mononuclear cells (PBMCs), we determined the deviations in the TME from physiological conditions. Our analysis revealed a cellular reprogramming of the TME compared to normal mucosa in immune and stromal lineages. We detected transcriptional heterogeneity within macrophages and a TME-specific gene expression program in dendritic cells. Cytotoxic T cells in the TME had heterogenous profiles of exhaustion and expression of multiple immune checkpoint and costimulatory molecules. We constructed a receptor-ligand based inter-cellular communications network that was exclusive to tumor tissue. These discoveries provide information at a highly granular cellular resolution enabling advances in cancer biology, biomarker discovery and identification of treatment targets such as for immunotherapy.



2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Anissa Guillemin ◽  
Angélique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon


2016 ◽  
Author(s):  
Po-Yuan Tung ◽  
John D. Blischak ◽  
Chiaowen Joyce Hsiao ◽  
David A. Knowles ◽  
Jonathan E. Burnett ◽  
...  

AbstractSingle cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.



2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Lan Jiang ◽  
Huidong Chen ◽  
Luca Pinello ◽  
Guo-Cheng Yuan


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