scholarly journals Single‐cell transcriptome analysis of tumor‐infiltrating B cells reveals their clinical implications in non‐small cell lung cancer

2020 ◽  
Vol 12 (1) ◽  
pp. 5-7
Author(s):  
Xia‐Yao Diao ◽  
Fang Wang
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhong-Yin Huang ◽  
Ming-Ming Shao ◽  
Jian-Chu Zhang ◽  
Feng-Shuang Yi ◽  
Juan Du ◽  
...  

AbstractThe complex interactions among different immune cells have important functions in the development of malignant pleural effusion (MPE). Here we perform single-cell RNA sequencing on 62,382 cells from MPE patients induced by non-small cell lung cancer to describe the composition, lineage, and functional states of infiltrating immune cells in MPE. Immune cells in MPE display a number of transcriptional signatures enriched for regulatory T cells, B cells, macrophages, and dendritic cells compared to corresponding counterparts in blood. Helper T, cytotoxic T, regulatory T, and T follicular helper cells express multiple immune checkpoints or costimulatory molecules. Cell-cell interaction analysis identifies regulatory B cells with more interactions with CD4+ T cells compared to CD8+ T cells. Macrophages are transcriptionally heterogeneous and conform to M2 polarization characteristics. In addition, immune cells in MPE show the general up-regulation of glycolytic pathways associated with the hypoxic microenvironment. These findings show a detailed atlas of immune cells in human MPE and enhance the understanding of potential diagnostic and therapeutic targets in advanced non-small cell lung cancer.


2021 ◽  
Vol 16 (3) ◽  
pp. S569
Author(s):  
M. Vanderputten ◽  
F. Aboubakar ◽  
C. Bouzin ◽  
D. Hoton ◽  
C. Stanciu Pop ◽  
...  

Author(s):  
Dylan L. Schaff ◽  
Shambhavi Singh ◽  
Kee-Beom Kim ◽  
Matthew D. Sutcliffe ◽  
Kwon-Sik Park ◽  
...  

AbstractSmall-cell lung cancers derive from pulmonary neuroendocrine cells, which have stemlike properties to reprogram into other cell types upon lung injury. It is difficult to uncouple the plasticity of these transformed cells from heritable changes that evolve in primary tumors or select in metastases to distant organs. Approaches to single-cell profiling are also problematic if the required sample dissociation activates injury-like signaling and reprogramming. Here, we defined cell-state heterogeneities in situ through laser capture microdissection-based 10-cell transcriptomics coupled with stochastic-profiling fluctuation analysis. Using labeled cells from a small-cell lung cancer mouse model initiated by neuroendocrine deletion of p53 and Rb, we profiled cell-to-cell transcriptional-regulatory heterogeneity in spheroid cultures and liver colonies seeded intravenously. Fluctuating transcripts in vitro were partly shared with other epithelial-spheroid models, and candidate heterogeneities increased considerably when cells were delivered to the liver. Colonization of immunocompromised animals drove the fractional appearance of alveolar type II-like markers and poised cells for paracrine stimulation from immune cells and hepatocytes. Immunocompetency further exaggerated the fragmentation of tumor states in the liver, yielding mixed stromal signatures evident in bulk sequencing from autochthonous tumors and metastases. We identified dozens of transcript heterogeneities that recur irrespective of biological context; their mapped orthologs brought together observations of murine and human small-cell lung cancer. Candidate heterogeneities recurrent in the liver also stratified primary human tumors into discrete groups not readily explained by molecular subtype. We conclude that heterotypic interactions in the liver and lung are an accelerant for intratumor heterogeneity in small-cell lung cancer.Statement of significanceThe single-cell regulatory heterogeneity of small-cell lung cancer becomes increasingly elaborate in the liver, a common metastatic site for the disease.


2018 ◽  
Vol 246 (2) ◽  
pp. 154-165 ◽  
Author(s):  
Masafumi Horie ◽  
Naoya Miyashita ◽  
Johanna Sofia Margareta Mattsson ◽  
Yu Mikami ◽  
Martin Sandelin ◽  
...  

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