Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC

Kumar, Nilesh; Mishra, Bharat; Athar, Mohammad; Mukhtar, Shahid

doi:10.1007/978-1-0716-1534-8_10

Cited by 36 publications

(32 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A lot of research reported that the IQCB1 and PDZD11 expressions positively correlate with macrophages and CD8 + T cell infiltration in colon cancer 20,21 . WAS is an important regulator of the actin cytoskeleton in hematopoietic cells and lymphocyte homeostasis 22 . MYO1F was involved in the M1‐polarization of macrophages 23 .…”

Section: Discussionmentioning

confidence: 99%

“…The gene regulatory network was constructed using pySCENIC. 22 In detail, we used the singularity to run the image of pySCENIC (version 0.12.0). First, we used "GRNBoost2" to obtain the TFs and their target genes, defining the regulons.…”

Section: Gene Regulatory Network Constructionmentioning

confidence: 99%

“…To investigate the potential mechanism of how HPV influences host immune cells, we used pySCENIC to construct the regulatory relationship between transcription factor (TF) to targets. 22 Detailly, pySCENIC aims to infer TF-targets sets (regulons) with relatively higher activity in one cell type compared to other cell types, which is mainly based on coexpression association between TFs, targets, and TF binding motif in the promoter region of the targets. Based on the complex gene regulatory network, we hoped to determine whether there were TFs involved in the transcriptional regulation of HPVs infection.…”

Section: Hpv16 Might Regulate the Infiltration Of Immune Cells Mainly...mentioning

confidence: 99%

See 2 more Smart Citations

Single‐cell transcriptomic analysis of the role of HPV16‐positive macrophages in cervical cancer prognosis

Wang

Liu

et al. 2022

Journal of Medical Virology

View full text Add to dashboard Cite

Almost all cases of cervical cancer (CC) can be attributed to high‐risk human papillomavirus (HPVs) infections in keratinocytes. However, it is unknown whether HPV invades immune cells such as macrophages and T cells. We analyzed the single‐cell transcriptome of the CC and its adjacent tissues and found that HPV16 genes, including E1, E6, and E7, expressed in the macrophages and CD8+ T cells in addition to the malignant cells. HPV16+ macrophages highly expressed the genes that promote cell adhesion and the favorable genes such as WAS, IQCB1, MYO1F, and PDZD11 in CC prognosis. The transcription factor KLF5 potentially accounted for the induction of these protective genes and thus facilitated the infiltration of the immune cells in tumor tissues. Our single‐cell transcriptome analysis suggests the potential value of the HPV16+ macrophage in CC prognosis. However, extensive experimental studies investigating the characteristics and functions of the HPV+ immune cells are still required.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Gene Regulatory Network Constructionmentioning

confidence: 99%

Section: Hpv16 Might Regulate the Infiltration Of Immune Cells Mainly...mentioning

confidence: 99%

See 1 more Smart Citation

Single‐cell transcriptomic analysis of the role of HPV16‐positive macrophages in cervical cancer prognosis

Wang

Liu

et al. 2022

Journal of Medical Virology

View full text Add to dashboard Cite

show abstract

“…The pySCENIC package (version 0.9.0), a Python-based implementation of the SCENIC pipeline was used to investigate the gene regulatory network of transcription factors (TFs) in CRC [ 20 ]. Two gene-motif rankings (hg19-tss-centered-10 kb and hg19-500 bp-upstream) from the RcisTarget database were used to detect the transcription start site (TSS) and the gene regulatory networks in the scRNA-seq data in CRC.…”

Section: Methodsmentioning

confidence: 99%

Single-cell N6-methyladenosine regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy

et al. 2022

View full text Add to dashboard Cite

Background N6-methyladenosine (m6A) RNA methylation plays a critical role in key genetic events for various cancers; yet, how m6A functions within the tumor microenvironment (TME) remains to be elucidated. Methods A total of 65,362 single cells from single-cell RNA-seq data derived from 33 CRC tumor samples were analyzed by nonnegative matrix factorization (NMF) for 23 m6A RNA methylation regulators. CRC and Immunotherapy cohorts from public repository were used to determine the prognosis and immune response of TME clusters. Results The fibroblasts, macrophages, T and B cells were respectively grouped into 4 to 5 subclusters and then classified according to various biological processes and different marker genes. Furthermore, it revealed that the m6A RNA methylation regulators might be significantly related to the clinical and biological features of CRC, as well as the pseudotime trajectories of main TME cell types. Bulk-seq analysis suggested that these m6A-mediated TME cell subclusters had significant prognostic value for CRC patients and distinguished immune response for patients who underwent ICB therapy, especially for the CAFs and macrophages. Notably, CellChat analysis revealed that RNA m6A methylation-associated cell subtypes of TME cells manifested diverse and extensive interaction with tumor epithelial cells. Further analysis showed that ligand-receptor pairs, including MIF − (CD74 + CXCR4), MIF − (CD74 + CD44), MDK–NCL and LGALS9 − CD45, etc. mediated the communication between m6A associated subtypes of TME cells and tumor epithelial cells. Conclusions Taken together, our study firstly revealed the m6A methylation mediated intercellular communication of the tumor microenvironment in the regulation of tumor growth and antitumor immunomodulatory processes.

show abstract

“…Regulatory gene network inference was performed using pySCENIC (v0.11.2) (Kumar et al , 2021) with default parameters in Phython (v3.7), following the previously described protocol (Aibar et al , 2017; Kumar et al , 2021). In brief, potential regulatory interactions were inferred based on the expression of predefined human transcription factors and their target genes in the preprocessed gene expression data from keratinocyte clusters defined by the single‐cell multi‐omics approach, using the GRNBoost2 algorithm.…”

Section: Methodsmentioning

confidence: 99%

Differentiation‐related epigenomic changes define clinically distinct keratinocyte cancer subclasses

Solé-Boldo

Raddatz

Gutekunst

et al. 2022

Molecular Systems Biology

View full text Add to dashboard Cite

Keratinocyte cancers (KC) are the most prevalent malignancies in fair-skinned populations, posing a significant medical and economic burden to health systems. KC originate in the epidermis and mainly comprise basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC). Here, we combined single-cell multi-omics, transcriptomics, and methylomics to investigate the epigenomic dynamics during epidermal differentiation. We identified ~3,800 differentially accessible regions between undifferentiated and differentiated keratinocytes, corresponding to regulatory regions associated with key transcription factors. DNA methylation at these regions defined AK/cSCC subtypes with epidermal stem cell-or keratinocyte-like features. Using cell-type deconvolution tools and integration of bulk and single-cell methylomes, we demonstrate that these subclasses are consistent with distinct cells-of-origin. Further characterization of the phenotypic traits of the subclasses and the study of additional unstratified KC entities uncovered distinct clinical features for the subclasses, linking invasive and metastatic KC cases with undifferentiated cells-of-origin.Our study provides a thorough characterization of the epigenomic dynamics underlying human keratinocyte differentiation and uncovers novel links between KC cells-of-origin and their prognosis.

show abstract

Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC

Cited by 36 publications

References 30 publications

Single‐cell transcriptomic analysis of the role of HPV16‐positive macrophages in cervical cancer prognosis

Single‐cell transcriptomic analysis of the role of HPV16‐positive macrophages in cervical cancer prognosis

Single-cell N6-methyladenosine regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy

Differentiation‐related epigenomic changes define clinically distinct keratinocyte cancer subclasses

Contact Info

Product

Resources

About