Transcriptional Profiling Reveals Kidney Neutrophil Heterogeneity in Both Healthy People and ccRCC Patients

Abstract

Neutrophil is known to critically impact the development of renal diseases (e.g., the clear cell renal cell carcinoma (ccRCC)), whereas the heterogeneity of neutrophils in ccRCC remains unclear. In the present study, kidney biopsies from healthy donors and ccRCC tissues were collected for single-cell RNA sequencing (scRNA-seq). In addition, the subpopulations of neutrophils in a healthy kidney and in the tumor microenvironment (TME) of ccRCC were expressed and then analyzed. The genes reported previously were mapped to all subpopulations identified here. On that basis, biological theme comparison and Gene Set Enrichment Analysis (GSEA) were employed to reveal and compare relevant biological functions. In a healthy kidney, neutrophils exhibit two subpopulations: one is more associated with renal autoimmunity, probably acting as therapeutic target; the other is suggested to resist infectious microorganisms. It is noteworthy that six subpopulations were identified in ccRCC biopsy, and two were more relevant to autoimmunity, while the other four are more relevant to the tumor pathology. Besides, ccRCC neutrophil could resist anticancer immune therapies of ipilimumab and pembrolizumab for their low/no expressions of CTLA-4, PD-1, and PD-L1. Thus, this study can help understand the heterogeneity and pathological significance of neutrophils in renal diseases.

High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways

“…Several studies using single-cell (sc-) or single-nucleus (sn-) RNA sequencing of human and mouse kidney in health and disease have been performed (Schroeder et al, 2020;Subramanian et al, 2021;Wu et al, 2017;Park et al, 2018;Young et al, 2018;Lake et al, 2019;Ransick et al, 2019;Stewart et al, 2019;Menon et al, 2020;Chen et al, 2021b;Chen et al, 2021aChen et al, , 2021cKaur and Advani, 2021;Meng et al, 2021;Muto et al, 2021;Sidhom et al, 2021;Clark et al, 2022) and have used spatial validation methods including immunofluorescence microscopy, fluorescence in situ hybridization, and targeted panels of a few dozen RNA probes or antibodies. However, these spatial methods are hampered by relatively low throughput (Choi et al, 2018;Goltsev et al, 2018;Kishi et al, 2019;Xia et al, 2019aXia et al, , 2019bSchu ¨rch et al, 2020;Alon et al, 2021).…”

Transcriptomic Signatures of Airway Epithelium Infected With SARS-CoV-2: A Balance Between Anti-infection and Virus Load

“…The corresponding transcriptome analysis data were compared to screen DEGs, and to increase the efficiency of the study, among the mentioned candidate DEGs and the parameters used were min.pct > 0.25 and | Log2 (FC) | > 0.5. The DEGs underwent pathway enrichment analysis, and their gene expression profiles were analyzed using the Gene Set Enrichment Analysis (GSEA) method together with data sets from the MSigDB database ( Meng et al, 2021 ; Xing and Song, 2021 ).…”

External Validation of the Prognostic Value of an Immune-Associated Gene Panel for Clear Cell Renal Cell Carcinomas

“…TCGA-KIRC (kidney renal clear cell carcinoma) and GSE29609 ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29609 ) datasets were downloaded in our study ( Giraldo et al, 2017 ). Immune-related genes were obtained from the ImmPort database ( https://www.immport ), and transcription factor–related genes were downloaded from the CISTROME project ( http://www.cistrome.org/ ) ( Meng et al, 2021 ). In addition, the tumor mutation burden (TMB) was calculated according to the number of gene mutations in each tumor sample ( Li et al, 2020 ).…”