Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels
Abstract
:Simple Summary
Abstract
1. Introduction
2. Genomic Profiling
2.1. Traditional Genomic Profiling
2.2. Single-Cell Genomic Profiling
3. Transcriptional Profiling
3.1. Traditional Transcriptional Profiling
3.1.1. mRNA Expression Profiling in Breast Cancer Heterogeneity
3.1.2. mRNA Half-Life Detection in Breast Cancer Heterogeneity
3.1.3. Functional RNAs in Breast Cancer Heterogeneity
3.2. Single-Cell Transcriptome Profiling
4. Protein Profiling
4.1. Traditional Protein Profiling
4.2. Single-Cell Proteomic Profiling
- Flow cytometry, which is an approach that quantifies the fluorescence characteristics of individual cells or particles within a fluid stream when exposed to light sources [154]. Cells labeled with fluorescent antibodies are rapidly channeled through a detection region within a flow chamber. Subsequently, these stained cells are stimulated by lasers, and a detector captures the intensity of the emitted fluorescence. Over the decades, since its inception in the late 1960s, flow cytometry has evolved significantly. It has progressed from an initial capacity to measure 1–2 fluorescent substances within cells, to now being capable of analyzing 10–15 fluorescent substances within a single cell, enabling the assessment of entire cellular pathways.
- Single-cell mass spectrometry (MS), which is a method that offers the potential for a label-free quantitative analysis of the full proteome of a single cell, inclusive of proteins, peptides, and PTMs [155]. One key advantage of MS is that it does not necessitate molecular labeling, and it can attain sensitivity to the femtomolar level for pure proteins. Various mass spectrometry techniques, such as electrospray MS, laser/desorption/ionization MS, and secondary ion MS, are deployed in single-cell research. However, the utilization of MS for single-cell protein analysis faces challenges, primarily due to an inadequate sensitivity to detect the low-abundance proteins that are typically present in single cells.
- Reverse-phase protein array (RPA), which is a miniaturized protein imprinting technique that facilitates quantitative monitoring of protein expression in hundreds or even thousands of samples concurrently [156]. This method involves archiving whole-cell lysates in a microarray format for detecting proteins of interest via immunological detection. Notably, RPA obviates the need for protein sample separation via electrophoresis, thereby enabling the concurrent analysis of multiple samples. Additionally, RPA requires only a minimal sample volume for multiplex protein detection.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Gene | Discovery | Involved Process | Mutation Risk | Reference |
---|---|---|---|---|
PTEN | 1997 | apoptosis, cell cycle, and signal transduction | activation of proliferation and survival signals | [23] |
STK11 | 1997 | cell cycle, metabolism, and energy balance | activation of cell proliferation and metabolic pathways | [24] |
CHEK2 | 1999 | DNA repair and cell apoptosis | impairments in DNA repair and cell apoptosis processes | [25] |
PIK3CA | 2004 | regulation of signaling pathway | activation of survival signals | [26] |
AKT1 | 2007 | regulation of signaling pathway | activation of cell proliferation and survival signals | [27] |
BARD1 | 2010 | DNA repair and cell apoptosis | increased susceptibility to breast cancer | [28] |
NF1 | 2015 | regulation of signaling pathway | increased rate of developing breast cancer | [29] |
Omics Field | Analysis Type | Advantages | Limitations | Refs. |
---|---|---|---|---|
Genomics | Bulk | Lower cost; matured analytical methods; provides comprehensive sequence information. | Averages over cell populations; misses information about rare cell populations; limited prediction of the ultimate biological effect. | [19,22,23,24,25] |
Single cell | Detects mutations and structural variations in individual cells; highlights cell-to-cell heterogeneity and rare cell populations; enables study of intra-tumoral heterogeneity in cancer. | Requires substantial sequencing depth for accurate results; higher costs; greater complexity of data analysis; limited information on the ultimate biological effect. | [7,43,44,45] | |
Transcriptomics | Bulk | Lower cost; matured techniques and analytical methods; global expression analysis; detects all splice variants. | Averages over cell populations; misses cell-to-cell heterogeneity; only represents an intermediate step; correlation with protein levels is not always linear. | [32,57,58,59,82,83,84] |
Single cell | Captures cell-to-cell variability in gene expression; detects all splice variants; sensitive, high dynamic range, and quantitative; parses cell-specific transcriptomes in single-cell experiments. | Data can be noisy; more complex data analysis; only represents an intermediate step; correlation with protein levels is not always linear. | [107,110,116,117,118] | |
Proteomics | Bulk | Comprehensive coverage of the proteome; mature techniques; resolves the final regulatory level. | Averages over cell populations; less sensitivity to low-abundance proteins; certain proteins difficult to isolate; high dynamic range of proteome makes detection difficult. | [137,141,145,147] |
Single cell | Potential to capture protein-level heterogeneity across individual cells; proteins are the main effectors of cellular function. | Technically challenging; limited coverage of the proteome; less mature techniques; certain proteins difficult to isolate; high dynamic range of proteome makes detection difficult; post-translational modifications may greatly influence activity but can be challenging to analyze. | [160,161,162,163,170,175,180] |
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Share and Cite
Zhu, Z.; Jiang, L.; Ding, X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers 2023, 15, 4164. https://doi.org/10.3390/cancers15164164
Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers. 2023; 15(16):4164. https://doi.org/10.3390/cancers15164164
Chicago/Turabian StyleZhu, Zijian, Lai Jiang, and Xianting Ding. 2023. "Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels" Cancers 15, no. 16: 4164. https://doi.org/10.3390/cancers15164164