Single-Cell Informatics for Tumor Microenvironment and Immunotherapy
Abstract
:1. Introduction
2. Single-Cell Technologies
2.1. Single-Cell Sequencing
2.2. Spatial Single-Cell Technologies
3. Bioinformatics Tools for Single-Cell Data Analysis
3.1. Bioinformatics Dissection of Tumor–Immune Cell Microenvironment
3.1.1. Quality Control
3.1.2. Gene Count Normalization
3.1.3. Confounder Correction
3.1.4. Feature Selection
3.1.5. Dimensionality Reduction
3.1.6. Cell Clustering
3.1.7. Annotation
3.2. Bioinformatics Analysis of Tumor–Immune Cell Communication
3.2.1. Ligand–Receptor Interaction
3.2.2. Intracellular Signaling Communication
3.2.3. Spatial-Based Communication Inference
3.2.4. Experimental Validation of the Inferred CCC
4. Application of Single-Cell Bioinformatics Tools in Understanding Patient Heterogeneity and Treatment Responses
4.1. Utilizing Bioinformatics Analysis to Explore the Tumor Microenvironment (TME)
4.2. Application of Bioinformatics Analysis Regarding Tumor–Immune Cell Communication
5. Challenges and Future Directions
5.1. Challenges Faced by TME Dissection via Single-Cell Technologies
5.2. Challenges Faced by Cell–Cell Interaction Analysis via Single-Cell Technologies
5.3. Challenges of Integrating Diverse Single-Cell Datasets
5.4. Outlook: Translational Insights of Single-Cell Technologies in Clinical Application
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Cancer Type | Single-Cell Technique(s) | Platform(s) | Analysis Tool(s) | Key Finding(s) | Reference(s) |
---|---|---|---|---|---|
Breast | scRNA-seq | Smart-Seq2 | Seurat | Micrometastases display an upregulated OXPHOS pathway with elevated levels of promoting metabolites compared to primary tumors. OXPHOS inhibition greatly attenuates metastasis in lung cancer models, potentiating its value in breast cancer. | [25] |
Melanoma | scRNA-seq | 10x Genomics Chromium | Seurat | CCR7-SELL-CD4+ T cell subtype strongly correlates to developing severe immune-related adverse events. | [26] |
Prostate | Spatial transcriptomic | Slide-seq | Conos | Stromal cells, including endothelial cells and pericytes, are more chaotically aligned in cancerous prostate; interactions between IGF1+ fibroblasts and IGF1R+ tumor cells are suggested by their colocalization. | [27] |
Head and neck | Spatial transcriptomic and proteomic | 10x Genomics Visium | Seurat | Two distinct tumor phenotypes were identified: one situated at the leading edge characterized by an accumulation of highly proliferative tumor cells, and the other located at the core with a high infiltration of immune cells. | [28] |
Colon | scRNA-seq; Spatial proteomic | 10x Genomics Chromium; PenoCycler | Seurat; CACTI | Single-cell analysis discovered the interactions occurring between SPP1+ macrophages and cancer-associated fibroblasts (CAFs) which are high in integrin receptors; spatial analysis demonstrates the spatial proximity between CAFs and SPP1+ macrophages, further suggesting their interactions. | [29] |
Lung | scRNA-seq; Spatial transcriptomic | 10x Genomics Chromium; 10x Genomics Visium | Seurat | Four cancer subpopulations were identified. The UBE2C+ cell type is strongly correlated with the invasion of lung adenocarcinoma, reflected by its constant increase during the invasion process. The UBE2C+ subpopulation is predominantly located within the peritumoral region and is associated with highly active tumor activities. | [30] |
scRNA-seq Data Analysis Step | Method | Package(s) | Advantage(s) | Limitation(s) | Example Study | Platform | Ref. |
---|---|---|---|---|---|---|---|
Quality control | Ambient RNA removal | SoupX, CellBender |
|
| [48,49] | R (SoupX) and Terra (CellRender) | [50,51] |
Doublet removal | scDblFinder |
|
| [52] | R | [53] | |
Normalization | Scaling-based | sctransform |
|
| [54] | R | [55] |
Regression-based | SCnorm |
|
| [56] | R | [57] | |
Spike-in RNA-based | BASiCS |
|
| [58] | R | [59] | |
Confounder correction | Technical-based | scVI, Harmony |
|
| [60,61] | R (Harmony) and Python (scVI and scGen) | [62,63] |
Biological-based | Seurat or Scanpy build-ins, Tricycle |
|
| [64] | R (Seurat) and Python (Scanpy and Tricycle) | [46,65,66] | |
Feature selection | Deviance-based | sctransform |
|
| [67] | R | [55] |
Highly variable gene-based | Seurat |
|
| [68] | R | [65] | |
Highly expressed gene-based | Monocle |
|
| [69] | R | [70] | |
Dimensionality reduction | PCA | Seurat, Scanpy |
|
| [71] | R, Python | [46,65] |
t-SNE | Seurat, Scanpy |
|
| [72] | R, Python | [46,65] | |
UMAP | Seurat, Scanpy |
|
| [73] | R, Python | [46,65] | |
Clustering | Louvain | Seurat, Scanpy |
|
| [74] | R, Python | [46,65] |
Leiden | Seurat, Scanpy |
|
| [75] | R, Python | [46,65] | |
Annotation | Classifier-based | CellTypist, Clustifyr |
|
| [76,77] | Python, R | [78,79] |
Reference-based | Azimuth, SingleR |
|
| [26,80] | R, Python | [81,82] |
CCC Analysis Tool | Approach | Advantage | Limitation | Platform | Example Study | Ref. |
---|---|---|---|---|---|---|
iTALK | Identifies general interaction patterns between different cell types based on gene expression profiles. | Enables the prediction of interactions from multiple samples. | The scoring scheme accounts for only abundant genes and thus likely overlooks interactions between less abundant genes. | R | [108] | [101] |
PyMINEr | Integrates multi-omics data to identify activating and inhibitory interactions; primarily focuses on identifying metabolic pathways. | Offers the full pipeline from clustering to visualization; there is no requirement for a reference database due to automatic generation; provides additional information on activator and inhibitor. | Signaling pathway-based CCC inference may lead to false prediction due to poor understanding of pathway components, increasing false positive rates. | Python | [109] | [103] |
CellPhoneDB | Calculates the permutation-based LR scores to identify significantly up- and downregulated interactions. | Gives strengthened inflammation- and proliferation-related gene sets; reduces false positive rates by using heteromeric complex-based inference. | Shortened in epithelial–mesenchymal transition gene sets; may increase false negative rates. | Python; Python web interface | [110] | [104] |
CellChat | Computes the communication score of each LR pair’s interaction strength that are permuted to identify significant interactions. | Reduces false positive rates by using heteromeric complex-based inference; integrates information of mediators and influencers; enables CCC inference in continuous cell states. | Lacks predictions within cell groups; pairwise comparisons limit analysis under different conditions. | R | [111] | [105] |
ICELLNET | Calculates both individual and global communication scores to assess cellular communications among single cells or cell types of interests. | Particularly useful in predicting cytokine interactions; enables the incorporation of gene expressions from different datasets; implements experimental validation of the predicted interactions. | Comprises less interactions in the database; lacks information of signaling pathways and gene regulatory networks. | R | [106] | [106] |
SingleCellSignalR | Uses regularized product scores to stabilize the noise and variability present in the dataset. | The regularized product score generates a stable threshold to reduce false positive rates. | Unable to integrate information from multiple samples. | R | [112] | [107] |
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Tian, J.; Bai, X.; Quek, C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int. J. Mol. Sci. 2024, 25, 4485. https://doi.org/10.3390/ijms25084485
Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. International Journal of Molecular Sciences. 2024; 25(8):4485. https://doi.org/10.3390/ijms25084485
Chicago/Turabian StyleTian, Jiabao, Xinyu Bai, and Camelia Quek. 2024. "Single-Cell Informatics for Tumor Microenvironment and Immunotherapy" International Journal of Molecular Sciences 25, no. 8: 4485. https://doi.org/10.3390/ijms25084485