Melanoma Single-Cell Biology in Experimental and Clinical Settings
Abstract
:1. Melanoma Biology, Clinics and Treatment
2. Clonal Heterogeneity in Melanoma
3. Single-Cell Technology
3.1. Principles of Single-Cell Individualization
3.2. Single-Cell Data Processing and Analysis
- i.
- Preprocessing aims at removing the effect of all factors without relevance for the expected biological effects. It includes read alignment, expression quantification, quality control, technical bias correction, and normalization. Mapping tools originally developed for bulk RNA-seq are mostly applicable also to scRNA-seq data. Further steps include quality control and filtering of unwanted genes and cells (e.g., cells expressing only a few genes); imputing missing values, batch correction (reducing systematic measurement biases between different runs and/or treatment groups), and normalizing gene expression (reducing unwanted variance between cells due to capture efficiency, sequencing depth, dropouts, and other technical effects). Technical noise of scRNA-seq is a common problem due to the low starting material and challenging experimental protocols. Detailed descriptions and recommendations of suited program-tools have recently been published.
- ii.
- Cell typing and diversity analysis aims at disentangling cell identities and their functional impact in the respective tissues. It includes clustering of cellular transcriptomes and their assignment to cell types (also called populations) in a supervised or unsupervised way. The former classification approach uses cell-type gene signatures taken from previous studies to assign the new data to these ‘known’ cell types. The latter class-discovery approach splits new data into de-novo groups of cells. In a second step, these new groups are related to known cell types by applying previous cell type signatures and statistical enrichment techniques, thus linking unsupervised with supervised approaches. Classifying cells into types or physiological states is essential for many secondary analyses to characterize the tumor microenvironment by composition of immune cells and/or to extract varying fractions of tumor cells from different developmental stages. For this task and scRNA-seq in general, reliable reference systems with a resolution down to cell states are required. Depending on the research question, even intermediate transition states might be of interest. Reference cell atlases of cell types of different healthy and cancer tissues, of immune cells and of melanoma-related cell types extracted from previous melanoma studies, have been published in a number of reports [50,59,60] and have put an emphasis on immune cells in these settings [61,62,63].
- iii.
- Gene module and marker extraction, functional analysis, and network inference aim at understanding gene regulation of cells on the gene level including aberrant effects due to genetic defects, external stimuli leading to treatment resistance and intrinsic evolutionary adaptations on tissue level. This task analyses co-expression of groups of genes, characterizes their functional context, and infers gene networks. Gene networks affect interactions between different cell types and/or signaling pathways. Beyond simple changes in average gene expression between cell types (or across bulk-collected libraries), scRNA-seq enables a high granularity of changes in expression. Particularly, cell type-specific alterations in cell state across samples are of special interest. These analyses deliver individual marker genes and sets of signature genes characterizing the different cell types and states, and, in addition, genes reflecting interactions between the cell types in the complex microenvironments of the respective tissue type. Appropriate methods to modularize transcriptional programs are non-negative matrix normalization (NMF) [64] or self-organizing-maps (SOM) [65].
- iv.
- Analysis of developmental trajectories in terms of pseudotime and RNA velocity aim at deducing time-dependent aspects of tissue development and cancer progression from cross-sectional scRNA-seq data. scRNA-seq experiments provide snapshot data, which resolves the molecular heterogeneity of cell cultures and tissues with single cell resolution under static conditions (see task (ii)). Given, that each cell is measured only once, one needs computational methods to deduce developmental trajectories on cellular level from time-independent data. The pseudotime model assumes that single-cell transcriptomes can be understood as a series of microscopic states of cellular development that exist in parallel at the same (real) time in the cell culture or tissue under study. Moreover, the model assumes that the temporal development smoothly and continuously changes transcriptional states in small and densely distributed steps so that the similarity of transcriptional characteristics can serve as a proxy of time, called pseudotime. It scales development in units of values between zero and unities for the start and end points, respectively. The pseudotime algorithm typically aligns the cells along a trajectory in reduced multi-dimensional space where a large variety of projection algorithms can be applied, differing regarding criteria such as cellular ordering, topology, scalability and usability [66]. Each method has its own characteristics in terms of the underlying algorithm, produced outputs and regarding the topology of the pseudotime trajectory (e.g., predefined linear, multibranched, cyclic, or ‘inferred from the data’). ‘RNA-velocity’ provides another independent approach to infer developmental trajectories from static scRNA-seq data [67,68]. It directly ‘forecasts’ the transcriptional state of a cell based on the relation between spliced and unspliced mRNA in terms of a directional change of cell state in cell-diversity space (task (v)). RNA-velocity provides a vector-field reflecting transcriptional changes of each cell, which can be transferred into developmental trajectories joining sources and sinks of mRNA abundance in cell-state and gene-state space.
- v.
- Dimension reduction, visualization of cell- and gene-state space, and data portrayals aim at enabling the intuitive perception of complex data in order to extract ‘hidden’ information and to support hypothesis development and testing. scRNA-seq data are high-dimensional data (ten-thousands of transcripts multiplied with ten-to-hundred thousand of cells multiplied with a multitude of biological conditions), which is difficult to visualize in its original form. Dimension reduction and appropriate visualization are therefore important challenges in all four tasks listed above. Conceptually, two perpendicular types of information have to be considered, namely cell- and gene-centered views on the scRNA transcriptomes as addressed in tasks II and III, respectively [61,69]. For the view on cell diversity, different methods projecting multidimensional cell-transcriptomes data into two dimensions are in use, such as Principal Component Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) [70,71]. These methods produce point clouds in cell similarity space visualizing mutual similarities between the single-cell transcriptomes in terms of colored clusters of cells (task II) and/or of colored expression levels of selected gene markers and signatures in the individual cell transcriptomes (task III). For visualization of transcriptomic landscapes in gene state space, we developed the expression portrayal method based on self-organizing map (SOM) machine learning. Such landscapes support identification of modules of co-expressed genes, of their mutual network topology and of their functional context [72,73].
3.3. Single-Cell Exome-Seq
4. Single-Cell Analyses in Melanoma
4.1. Primary Melanomas, Lymph Node Metastases and Cell Lines
4.2. Treatment Resistance Under Immune Checkpoint Inhibition
4.3. Treatment Resistance under Targeted Treatment
5. Spatial Sequencing in Melanoma
6. Single-Cell Sequencing of Copy Number Variations
7. Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No | Melanoma Samples | Experimental or Clinical Set-Up | Characteristics of Clonal Structure | Main Findings | References |
---|---|---|---|---|---|
1. | Primary melanomas and metastases (n = 19) | Untreated | Clonal signatures of cell cycle, spatial context, drug-resistance programs | Presence of AXL-high/MITF-low population in a AXL-low/ MITF-high cluster; single-cell signatures with prognostic relevance | [80] |
2. | Melanoma cell lines representing different stages of differentiation (n = 8) | Untreated | Cell clones with SOX9 and SOX10 high expression and transitional cells, knockdown of SOX10 affects clonal structure | Transition between gene networks instead of selection of individual clones (transcriptional plasticity) | [82] |
3. | Melanoma short-term cultures (BRAF and/or NRAS mutant) (n = 3) | Untreated | Clonal structure of cell cycle, stromal, OxPhos, pigmentation genes | Four different clonal structures with additional subclonal structures and stem cell-like subclones | [65] |
4. | Samples from 32 metastatic melanoma patients (n = 48) | Anti-PD1 inhibitor treatment of patients, either alone or in combination with anti-CTLA4 treatment | CD8+ T cells clones consisted of memory/survival (TCF7+) and exhaustion (CD38+) clones, respectively | TCF7+/CD8+ T cells are crucial for treatment response | [83] |
5. | Human melanoma samples (n = 33) | Clinical samples under anti-CTLA4 treatment | Clonal immune exclusion program: CDK4/CDK6 expression, JAK-STAT3 signaling, TNF pathway, senescence-associated programs, Myc targets | CDK4/CDK6 inhibitor treatment of resistant clones improved survival of mice in a murine melanoma model | [84] |
6. | Human melanoma samples (n = 25) | Anti-PD-1 inhibitor treatment of patients, either alone or in combination with anti-CTLA4 treatment | CD4+/CD8+ T cells with clusters of resting, transitional and exhausted T cells | Dysfunctional (exhausted) CD8+ T cells are still proliferative and showed tumor reactivity ex vivo | [85] |
7. | Tumor tissue of melanoma cell line mouse xenografts (minimal residual disease) (n = 3) | Murine xenograft model, BRAFi treatment | Minimal residual disease with 4 different transcriptional subpopulations (pigmented, SMC, NCSC, invasive cells) | Enrichment of neuronal stem cells population after BRAFi treatment; successful treatment with retinoid receptor inhibitor | [86] |
8. | A375 and 451Lu melanoma cell lines (n = 2) | BRAFi treatment | Patterns of resistance are present in parental cells and vice versa | Identification of a pre-resistant state at the tip of the parental population | [64] |
9. | Melanoma cell line A375 (n = 1) | BRAFi treatment after CRISPR/Cas interference with MAPK pathway | Clonal selection of treatment resistant clones | Resistance-mediating positions in MAPK genes were mostly located around MEK1E203K or KRASQ61 | [87] |
10. | BRAF-mutant melanoma cell lines (n = 3) | BRAFi treatment; testing of 13 different proteomic markers with single-cell barcode chip technology | Increased clonal heterogeneity under treatment | Activation of MEK/ERK and NF-κB p65 signaling in resitant clones; NF-κB inhibitor increased sensitivity of cells | [88] |
11. | BRAF-mutant melanoma cell line (n = 1) | BRAFi treatment; testing of 19 different proteomic markers with single-cell barcode chip technology | Drug-induced clonal cell states changes with NGFR/AXL or MITF, MART1 patterns | Two different trajectories of treatment resistance of MITF-high and MITF- low cells | [89] |
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Binder, H.; Schmidt, M.; Loeffler-Wirth, H.; Mortensen, L.S.; Kunz, M. Melanoma Single-Cell Biology in Experimental and Clinical Settings. J. Clin. Med. 2021, 10, 506. https://doi.org/10.3390/jcm10030506
Binder H, Schmidt M, Loeffler-Wirth H, Mortensen LS, Kunz M. Melanoma Single-Cell Biology in Experimental and Clinical Settings. Journal of Clinical Medicine. 2021; 10(3):506. https://doi.org/10.3390/jcm10030506
Chicago/Turabian StyleBinder, Hans, Maria Schmidt, Henry Loeffler-Wirth, Lena Suenke Mortensen, and Manfred Kunz. 2021. "Melanoma Single-Cell Biology in Experimental and Clinical Settings" Journal of Clinical Medicine 10, no. 3: 506. https://doi.org/10.3390/jcm10030506