Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies

scRNA-seq: Methods overview

Single cells are captured for measurement of their transcriptional landscape using either plate-based or microfluidics-based methods. Plate-based methods involve sorting of cells into separate wells (e.g., in a 96-well plate) via fluorescence-activated cell sorting, followed by RNA-seq protocols applied to each of the wells and pooling of samples following cell barcoding (different methods pool at different steps; Table 1). This approach enables freedom with respect to the RNA-seq protocol used and allows for index-sorting (quantification of protein expression in the cells sorted into individual wells) but is limited with respect to the number of cells that can be processed due to its time-consuming nature. Initially, microfluidics-based methods used microfluidic chips to capture single cells into individual chambers (e.g., Fluidigm) followed by lysis, reverse transcription, and amplification for library generation. Microfluidics have been used to pair within droplets single cells with beads carrying cell-identifying barcodes (3). Microfluidic-based capture of single cells and beads carrying cell-identifying barcodes into chambers has been implemented in Seq-well, which is a portable and low-cost alternative to droplet-based methods (4). Droplet-based methods are of higher throughput than microfluidic chip-based and plate-based methods, generating thousands of single-cell transcripts at relatively low cost, but are restricted to either 3′ or 5′ end sequencing protocols.

Two methodologies (SPLiT-seq and Sci-RNA-seq) bypass the need for physical isolation of single cells by using combinatorial barcoding to perform scRNA-seq (5, 6). Although these methods have not been applied yet in the cancer immunology field, they hold great promise for accelerating discovery, given that they can be used with fixed cells. This allows for the contemporaneous processing of samples that are collected longitudinally and mitigates batch effects stemming from serial processing of samples. This feature makes these two methods attractive for the analyses of patient samples.

In addition to the single-cell RNA-seq methods described above, single-nucleus RNA-seq methods have been introduced to overcome technical challenges associated with dissociation of single cells from tissue. Single-nucleus methods profile the mRNA landscape within each nucleus separately and can be performed using both plate-based and droplet microfluidic-based technologies (7–9). Single-nucleus sequencing methods have been repeatedly shown to accurately capture heterogeneity across cells and dynamic cell states, despite profiling the RNA in the nucleus only (10, 11). To date, single-nucleus RNA-seq methods have been used primarily for study of the brain but have also been used to profile tumor cells (11). Although immune cells are difficult to profile with single-nucleus RNA-seq due to their low RNA content, future technical advances could make such methods useful given their applicability to frozen archived samples.

scRNA-seq: Deconvolving the tumor immune microenvironment

The ability to read and annotate transcriptomes at single-cell resolution, coupled with the development of computational methodologies for data analysis (see Box 1 and Fig. 1), has enabled the profiling of the different components of the TME at unprecedented depth: many cells and many transcripts. Naturally, scRNA-seq was quickly leveraged to advance our understanding of the immune component of tumors.

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Figure 1.

Single-cell data analysis methods. Several analysis steps are taken to generate an initial characterization of single-cell data (following or in parallel to normalization of technical noise and artifacts). A, Various linear (e.g., PCA: principal component analysis) and nonlinear (e.g., tSNE; t-distributed stochastic neighborhood embedding and diffusion maps) dimensionality reduction methods can be used for identifying the main discriminants of the data of interest and for visualization. Clustering of cells by their transcriptomes can identify sets of cells that comprise units within the system (diffusion component illustration based on ref. 37). B, Gene sets that covary across the data identify gene modules of interest with respect to heterogeneity and potential functionality of the cell subpopulations within a sample (figure based on ref. 50). C, Integration of additional data types and sources can enable broader insights into the scRNA-seq data set. Shown are two examples. Left, scoring single cells for the extent to which they express predefined gene signatures to infer function and characteristics of populations identified. Right, integration of single-cell TCR information generated in parallel to the scRNA-seq data (figure based on ref. 12).

In breast carcinoma, a large-scale scRNA-seq study of over 45,000 cells identified increased heterogeneity of gene expression in intratumoral lymphoid and myeloid cells compared with cells in normal breast tissue, likely reflecting the responses of intratumoral immune cells to the diverse environmental signals present in tumor tissue (12). Other scRNA-seq studies have uncovered previously unappreciated predictive properties of the immune component within the TME. In malignant glioma, scRNA-seq revealed that preestablished ways of distinguishing across glioma subtypes (IDH-A and IDH-O) are accounted for mainly by differences in the TME rather than the malignant cells themselves and that increased tumor grade was associated with differential expression of macrophage over microglia gene programs (13). In metastatic melanoma, Nirschl and colleagues (14) identified a homeostatic IFNγ-dependent program that is enriched in monocytes and dendritic cells and stratifies survival. Future scRNA-seq studies of the TME will continue to advance our knowledge of the immune component of different tumors and its relationship to disease state.

scRNA-seq: Understanding T-cell states in cancer

ScRNA-seq has led to important insights regarding checkpoint receptor expression in tumor-infiltrating lymphocytes (TIL) and the functional states observed in T cells in different cancers. An scRNA-seq study of human breast tumors revealed that the checkpoint receptors TIGIT and Lag-3 were present at a higher frequency on T cells than PD-1, suggesting that the former molecules may be better targets in breast tumors (15). In a melanoma mouse model, Chihara and colleagues used scRNA-seq and mass spectrometry (CyTOF) to identify a coinhibitory gene module in TILs that contains novel checkpoint receptors and is cooperatively regulated by PRDM1 and c-MAF (16). Also, in a melanoma mouse model, Singer and colleagues (17) showed that checkpoint receptor expression can be uncoupled from dysfunctional CD8⁺ T-cell phenotypes and identified distinct dysfunction and activation gene programs that separated cell populations identified with scRNA-seq. In human melanoma, scRNA-seq was used to identify a gene signature for T-cell dysfunction and inferred cell-to-cell interactions between T cells and cancer-associated fibroblasts (18). Lastly, in non–small cell lung cancer, scRNA-seq of T cells identified “exhausted” and “preexhausted” CD8⁺ T-cell populations and showed that a high ratio of preexhausted to exhausted cells was associated with better prognosis (19).

Analysis of TCR sequences in single cells is further shedding light on T-cell behavior in tumors. Paired scRNA-seq and TCR sequencing in breast carcinoma showed that different T-cell clones vary in their extent of activation, suggesting the presence of a continuous spectrum of T-cell activation states that is shaped by TCR usage (12). ScRNA-seq and TCR sequence analysis of peripheral blood, tumor, and normal tissue from hepatocellular carcinoma (HCC) patients identified that exhausted CD8⁺ T cells and regulatory T cells (Treg) are enriched and clonally expanded in HCC compared with normal tissues (19). The development of TCR sequencing protocols compatible with droplet technology will further accelerate the current understanding of the relationship of T-cell clonality to functional T-cell states across different tumor types.

Epigenetics: Understanding the chromatin landscape of CD8⁺ T cells in cancer

Coupling NGS with chromatin accessibility assays enables determination of the epigenetic and regulatory landscape of cells. Methods such as DNase-seq, Mnase-seq, and FAIRE-seq enable a genome-wide view of the epigenetic landscape but require laborious protocols and large cell counts (100K–1M cells), thus limiting their application in cancer immunology. The introduction of ATAC-seq (20), a method that detects open chromatin by sequencing transposase-accessible regions and enables mapping of transcription factor occupancy for small cell counts and even single cells, has opened the door to the study of TILs from the epigenetic perspective.

Gaining an understanding of the epigenetic landscape of TILs that exhibit different functional states is important for understanding the underlying mechanisms that govern transition between cellular states and the reprogramming potential of TILs. Philip and colleagues (21) used ATAC-seq to study the epigenetic landscape of CD8⁺ TILs and identified two distinct CD8⁺ T-cell states in a murine tumor model—one that can be reversed upon in vitro activation and one that cannot. Coupling such analyses with TCR sequence data will assist in determining T-cell differentiation trajectories in the TME and how these may change upon therapeutic modulation.

NGS has been applied to analyze the spatial organization of chromatin using methods such as Hi-C. Hi-C has been used to determine the chromosomal abnormalities present in tumor cells (22, 23). This method can also be applied to study long-range DNA–DNA interactions. Chen and colleagues used Hi-C to identify and validate an enhancer 140 kb downstream of PD-L1, which is active in tumor cells (24). Future studies will likely apply Hi-C to study DNA organization and gene regulation in TILs.

Protein and space: The next frontiers

Two novel technologies leverage NGS to expand the dimensions of data obtained. Cellular indexing of transcriptomes and epitopes or CITE-seq and RNA expression and protein sequencing or REAP-seq integrate single-cell transcriptomics with limited scale proteomics (25, 26). Both of these utilize droplet microfluidics and DNA oligo-tagged antibodies to read out protein and RNA expression profiles in a single workflow. These technologies are useful for assessing transcript to protein relationships but are limited to the examination of surface protein expression only. These technologies are anticipated to be integrated into the cancer immunology field in coming years.

Technologies that allow for high-throughput transcriptomic measurements, while observing the cells' spatial location within tissue, have also been developed. Spatial transcriptomics (ST; ref. 27) allows for the measurement of transcriptomes within spatially resolved areas in tissue sections. This is achieved by positioning frozen tissue sections on a glass slide containing a grid with unique positional barcodes. ST can be paired with multiplex imaging and RNA-seq to map cell types and their niches in situ. Although ST does not measure transcriptomes at the single-cell level, paired scRNA-seq data from the same tissue can be used to computationally deconvolve the composition of each ST location (28). In pancreatic cancer, ST revealed that cancer cells and stromal cells colocalize in regions disparate from pancreatic ductal and acinar cells (28). Although this study did not map TILs, we expect ST to be used for such purposes in the future.

NICHE-seq is a different technology for adding spatial context to transcriptomic data. This technique combines two-photon microscopy, photoactivatable fluorescence, and RNA-seq to annotate single-cell transcriptomes in discrete tissue locations (29). An attractive feature of NICHE-seq is the ability to control the timing of fluorescence photo-activation, which allows for kinetic analyses. However, a limitation of this technique is that it can only be applied to tumors that are engineered to express photoactivatable green-fluorescent protein (PA-GFP).