Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy
Military Medical Research volume 9, Article number: 52 (2022)
The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.
Since the beginning of the twenty-first century, tumours have been a great threat to human health. Tumour heterogeneity has a significant impact on cancer prognosis and response to therapies . Traditional genomic and transcriptomic analyses have been widely used to study different cancer types, stratifying tumours into distinct groups. Some previous findings have been translated into clinical practice owing to their potential roles in predicting prognosis and response to different therapies, as well as providing targets for cancer therapy [2,3,4,5,6]. However, previous studies have mainly focused on the genomic and transcriptomic features of malignant cells. In addition to malignant cells, the tumour immune microenvironment (TIME) is an essential component of various tumours. Single-cell profiling of different cancer types indicates that the cellular details of the TIME are primarily shared across multiple cancer types . Currently, the TIME has drawn increasing attention since the discovery of checkpoint inhibitors, and immunotherapy has revolutionized cancer treatment [8, 9]. The TIME is composed of noncellular components (vessels, extracellular matrix, signaling molecules, etc.) and cellular components (T cells, myeloid cells, fibroblasts, etc.) [10,11,12]. Although traditional genomic and transcriptomic analyses, such as CIBERSORTx and DWLS, also emphasize immune-related pathways and computational approaches and have been applied to predict immune cell components, technical limitations have confounded the precise characterization of the TIME [13, 14].
Traditional bulk genomic and transcriptomic analyses average signals from a group of different cells, which obscures the identification of specific cell types and states. In situ hybridization and immunohistochemistry have been used to explore the genomic, transcriptomic, and proteomic characteristics of individual cells, but their outputs are relatively low [15, 16]. Flow cytometry and cytometry by time-of-flight (CyTOF) are capable of analysing thousands or millions of single-cell proteomic profiles ; however, these methods require prior selection of antibodies of interest. With breakthroughs in cell isolation and sequencing technologies, single-cell RNA sequencing (scRNA-seq) has enabled unbiased genome-wide profiling of many cells at the single-cell level in a single run. scRNA-seq has been used to analyse the transcriptomics of individual cells, which helps characterize the cellular heterogeneity in each sample [18,19,20,21].
In this review, we mainly focus on the TIME, which plays an essential role in tumorigenesis, as well as in cancer progression, invasion, and metastasis . The TIME has shown potential in diagnosing, treating, and predicting the prognosis of different types of cancer . Compared with conventional methods, scRNA-seq can be utilized to identify novel cellular types and corresponding cellular states, deepening our understanding of TIME . Furthermore, combining scRNA-seq with other computational methods can reveal dynamic changes in the TIME. Hence, here, we review new findings concerning the TIME discovered through the application of scRNA-seq.
Application of scRNA-seq in TIME and cancer therapy
Technological advances in scRNA-seq
The main scRNA-seq procedure includes separation and extraction of single cells, cDNA synthesis, nucleic acid amplification, sequencing, and data analysis . We depict the major procedures of scRNA-seq in Fig. 1. One challenge of scRNA-seq is the relatively small amount of RNA in individual cells compared with traditional bulk sequencing. Thus, more efficient amplification methods are needed. Researchers have successfully established stable single-cell library construction processes to generate sufficient cDNA for sequencing, including polymerase chain reaction (PCR) amplification, such as SMART-Seq2, and in vitro transcription (IVT) amplification, such as CEL-Seq2 .
Single-cell separation and capture are essential procedures for scRNA-seq in different approaches. Figure 1a concludes the current common approaches for single-cell separation and capture. These procedures fall into four major categories: laser capture microdissection (LCM), micromanipulation, fluorescence-activated cell sorting (FACS), and microfluidics [27, 28]. Fluidigm C1, launched in 2012, was the first commercial platform using microfluidic chips. Taking advantage of Fluidigm C1 lowers the threshold for single-cell sequencing . The Fluidigm C1 microfluidics system allows researchers to obtain the complete transcriptome data of 96 cells with diameters of 5–25 μm in a single run. This approach also provides high-quality gene expression readouts. However, low cell throughput and high cost limit its application. Thus, this approach is mainly used for small-sample studies [30, 31].
The development of microfluidics and reverse emulsion devices allows the isolation of single cells into droplets, which is currently the most widely used method. The approach is exemplified by two academically developed technologies, Drop-seq and inDrop [32, 33]. Compared with Drop-seq, inDrop is easier to set up and produces higher throughput data as it detects more genes. However, inDrop cell barcoding has a much higher error rate than Drop-seq and higher reagent costs . In 2017, the commercial sequencing platform 10 × Genomics was successfully developed based on the above techniques, enabling a significant increase in cell throughput and a considerable reduction in single-cell sequencing costs .
In addition to microfluidic devices trapping cells inside hydrogel droplets, FACS combined with microwell plates is a commonly applied scRNA-seq technique . Various amplification methods can be used within microwell plates, including SMART-seq2, CEL-Seq, MARS-seq, and STRT-seq [37,38,39,40]. These manual approaches are not restricted by the cell size constraints of the Fluidigm system, and the equipment and setup costs are meagre. More importantly, SMART-seq2 and STRT-seq allow for full coverage of cDNA sequencing, facilitating the analysis of alternative splicing patterns . In contrast, Drop-seq and inDrop only provide sequence information at the 3′ or 5′ ends of the cDNA. Figure 1b displays the reverse transcription amplification and generation of cDNA libraries of different approaches. CytoSeq also takes advantage of microwells for cell separation and capture. A bead similar to that in the Drop-seq approach is suspended in each well using CytoSeq. Compared with Drop-seq, CytoSeq does not require a complicated microfluidic system to sort cells, and it is easier to achieve sequencing throughput expansion using plates with more microwells . Derived from CytoSeq and launched in 2018, the BD Rhapsody platform uses a large number of microwells, far exceeding the number of cells for cell capture, to ensure that each cell occupies a single well .
The advent of SPLiT-seq reduced the cost of library construction for sequencing to $0.01 for each cell. SPLiT-seq reduces equipment requirements and the costs for generating cDNA libraries by labelling the cellular origin of RNA through combinatorial barcoding. In addition, the quality of scRNA-seq data obtained was similar to that obtained with Drop-seq and InDrop . The common down-stream data analysis of scRNA-seq is shown in Fig. 1c. Table 1 compares the current major scRNA-seq technologies. The principles, number of cells in each single-run and sensitivity of different approaches are concluded. In addition, we also reviewed the strength and weakness of each approaches, as well as their potential applications.
Future development of scRNA-seq techniques may reduce the costs and increase the cell throughput, making scRNA-seq a standard tool for studying individual cell transcriptomes. Genomes of individual cells can be inferred from computational approaches  or from newly developed single-cell sequencing approaches . In addition, single-cell protein expression and epigenomics can also be analysed to better understand cellular diversity and gene regulatory mechanisms. Recent reviews have concluded that the advances in these technologies [47, 48] still need improvement before wide application. High-throughput single-cell multiomics data might play an essential role in uncovering the features of individual cells at unprecedented resolution in the future.
As scRNA-seq technology becomes widespread, specialized computational methods and tutorials for scRNA-seq data analysis have been put forward . (1) The batch effect is a common issue in data integration. Several batch-effect correction methods have been utilized, such as Harmony , fastMNN , and Scanorama , and a recent study compared the properties of these methods . (2) scRNA-seq data are high dimensional because each cell contains the expression of more than 10,000 genes (variables). Dimensionality reduction is required to improve downstream analysis. Different approaches have been proposed, such as principal component analysis (PCA) , nonnegative matrix factorization (NMF) , and deep neural networks . The detailed features of these methods have been described in other reviews [57, 58]. Zero inflation is another challenge of scRNA-seq data analysis. There are a large number of zero values in the scRNA-seq expression matrix. This is due to stochastic gene expression, the different states of various cells and technical noise, such as RNA capture efficiency and random dropouts during library preparation . Many methods, including MAGIC , CIDR  and scImpute , have been used to impute the zeros in the expression matrix. However, these methods can introduce false-positive results while reducing technical noise. To conclude, due to the complexity of scRNA-seq data, the computational methods still need to be improved.
Conventional cellular components in the TIME
The cellular components of the TIME include lymphocytes (T and NK cells), myeloid cells (macrophages and dendritic cells), fibroblasts, and other immune cells. Fibroblasts are traditionally categorized as stromal cells due to their essential roles in constructing the extracellular matrix. Here, we include cancer-associated fibroblasts in the TIME, as they secrete abundant proinflammatory and anti-inflammatory factors to reshape the TIME.
Cytotoxic CD8+ T cells, which recognize specific antigens on tumour cells and subsequently eliminate them, are the most common and effective immune cells in the TIME . The cytotoxic function of CD8+ T cells relies on CD4+ T helper 1 (Th1) cells . Other CD4+ T cells, including T helper 2 (Th2) cells and T helper 17 (Th17) cells, also facilitate immune responses in the tumour microenvironment [65, 66]. In contrast, regulatory T cells (Tregs) inhibit the TIME and exacerbate tumour progression [67, 68]. Natural killer T (NKT) and natural killer (NK) cells are also involved. Their receptors recognize tumour cells, which leads to the activation of other immune cells [69, 70].
As an important constituent of innate immunity, myeloid cells, including tumour-associated macrophages (TAMs) and dendritic cells (DCs), play essential roles in the TIME. Macrophages are conventionally classified into proinflammatory M1 and anti-inflammatory M2 phenotypes. TAMs populations are predominantly composed of M2 macrophages, facilitating tumour growth, tumour survival, and angiogenesis by producing growth factors and cytokines . However, DCs are essential for antigen presentation to T cells, connecting innate and adaptive immunity [72,73,74].
Cancer-associated fibroblasts (CAFs) sustain proliferation and secrete regulatory factors in the TIME and can be divided into inflammatory CAFs (iCAFs) and myofibroblastic CAFs (myCAFs). iCAFs have higher secretion of cytokines and chemokines, while myCAFs highly express contractile proteins . CAFs have conflicting effects on the TIME. Some studies have demonstrated that CAFs recruit M2 macrophages and Tregs, inhibiting immune responses in the tumour microenvironment [76, 77]. CAFs have also been found to support antitumor immunity in some cases .
In addition to secreting antibodies, B cells also participate in cellular immunity through the production of cytokines that interact with T cells . Studies have shown that B cells inhibit cytotoxic T cells  and induce CD4+ T cell differentiation into Tregs [81, 82]. Furthermore, B cells are essential components of recently introduced tertiary lymphoid structures (TLSs). B-cell-rich TLSs are associated with survival and immunotherapy responses in various tumours [83, 84].
Previous studies have emphasized the essential role of cellular components in the TIME. However, the identification of immune cells is based on limited cell markers with the aid of immunohistochemistry. Transcriptomic atlases of individual immune cells are required to explore distinct immune cells and their corresponding functions. In addition, cell evolution is a dynamic process in which cellular alterations gradually accumulate. To understand this evolutionary process and its determinants, it is necessary to observe the transcriptomic fluctuation of every single cell simultaneously, which requires the application of scRNA-seq.
New discoveries concerning the TIME explored by scRNA-seq
Clustering and annotation are essential in interpreting scRNA-seq data and exploring the TIME. The data are partitioned based on cell similarities [85,86,87], and the challenge is to estimate the intrinsic cluster number or density without providing a priori knowledge . A possible solution is to adopt hierarchical clustering methods to reveal the hierarchical structure of cells , which is also consistent with cell ontologies. Given a data partition result produced by clustering methods, cell type annotation is needed to provide biological meanings. The primary challenge of annotation is determining how many cell types are present in each cluster and whether currently undiscovered cell types exist . In practice, researchers typically first identify marker genes of each cluster and then annotate them based on expertise and literature. To avoid subjective annotations, certain tools can automatically annotate cells based on evident or probabilistic similarities, leveraging a wide range of marker repositories as references [91,92,93]. This variant of annotation methods builds on the transfer learning paradigm . Specifically, a cell type classifier is trained based on previously annotated scRNA-seq data, which can directly map gene expression to cell type [95, 96]. A similar approach is to match query cells with annotated ones to establish correspondences [97, 98]. However, these methods might produce suboptimal results when data are heavily confounded with batch effects; thus, preliminary batch integration would be necessary. Here, we mainly focus on studies that annotated clusters by identifying marker genes based on expertise and the literature.
scRNA-seq has enabled researchers to classify immune cells into subpopulations with distinct functions at a higher resolution, which depicts the heterogeneity of conventional subtypes of immune cells. Novel clusters of lymphocytes (T and NK cells), myeloid cells (macrophages and dendritic cells), and fibroblasts discovered with the help of scRNA-seq are concluded in Fig. 2. scRNA-seq of human and mouse samples indicated that CAFs could be categorized as antigen presentation CAFs (apCAFs), iCAFs, or myCAFs. apCAFs uniquely express major histocompatibility complex (MHC) class II genes, including CD74, which activates CD4+ T cells . A similar subpopulation of apCAFs has also been observed in colorectal cancer . scRNA-seq of fibroblasts in a genetically engineered mouse model of breast cancer further identified vascular CAFs (vCAFs), matrix CAFs (mCAFs), developmental CAFs (dCAFs), and cycling CAFs (cCAFs) . vCAFs, mCAFs, and dCAFs seem to originate in a perivascular location when resident fibroblasts and malignant cells undergo epithelial-mesenchymal transition (EMT) . cCAFs are the proliferating portion of the vCAFs population. vCAFs and mCAFs were also found in other mouse models, were conserved in patient breast tumour samples, and were found to increase the metastasis of breast cancer cells . Improving the resolution of CAFs provides biomarkers for the development of drugs that precisely target CAFs. Another scRNA-seq study of breast cancer divided Tregs into five clusters: Tregs coexpressing Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), T cell immunoreceptor with Ig and ITIM domains (TIGIT), and GITR and other Tregs mutually or exclusively expressing the same genes, which presented distinct functions . Patients with different prognoses have different proportions of Treg clusters, providing targets for personalized therapy. More detailed pan-cancer research focusing on the T cells and myeloid cells in the TIME revealed the existence of granzyme K (GZMK)+ Tem, interferon-stimulated genes (ISG)+ T cells, killer cell immunoglobulin like receptor (KIR)+ NKT cells, transcription factor 7 (TCF7)+ CD8+ T cells, ficolin 1 (FCN1)+ conventional DC (cDC)2, secreted phosphoprotein 1 (SPP1)+ TAMs, and folate receptor beta (FOLR2)+ TAMs in the tumour microenvironment [105, 106].
Based on scRNA-seq data, novel subpopulations of immune cells have also been discovered in the TIME. scRNA-seq of uveal melanoma identified previously unrecognized cell types, including CD8+ T cells that predominantly express the checkpoint marker LAG3 instead of programmed death-1 (PD-1) or CTLA-4 . Meanwhile, clonal enrichment of infiltrating exhausted CD8+ T cells and Tregs with high expression of layilin (LAYN) was found in hepatocellular carcinoma . These studies provide novel targets for cancer immunotherapy, as CD8+ T cells are the main constituents involved in elimination of malignant cells. scRNA-seq of colorectal cancer identified C-X-C motif chemokine ligand (CXCL)13+ basic helix-loop-helix family member E40 (BHLHE40)+ Th1-like cells associated with the interferon-γ (IFN-γ)-regulating transcription factor BHLHE40. These cells were found to have a favourable response to immune checkpoint blockade in microsatellite-instable tumours , potentially increasing the efficacy of immunotherapy.
DCs are essential for presenting antigens to activate T cells in the TIME. scRNA-seq of gastric cancer revealed a novel DCs cluster expressing indoleamine 2,3-dioxygenase 1 (IDO1) and the chemokines C–C motif chemokine ligand (CCL)22, CCL17, CCL19, and interleukin (IL)-32, which are involved in the recruitment of T cells . scRNA-seq of pancreatic ductal adenocarcinoma also identified DCs clusters that highly expressed IDO1 in addition to conventional cell markers. IDO1 is essential for catalysing tryptophan depletion and kynurenine production, inhibiting T-cell proliferation and cytotoxicity , which reveals the close interactions between DCs and T cells. Moreover, lysosomal associated membrane protein 3 (LAMP3)+ DCs were identified by scRNA-seq and appeared to be the mature form of classical DCs. LAMP3+ DCs can migrate to lymph nodes and highly express ligands that interact with T cells . The discovery of these novel DCs clusters expressing specific markers provides a new perspective for cancer immunotherapy because of their strong communication with T cells.
Novel signature genes of TAMs, including triggering receptor expressed on myeloid cells 2 (TREM2), CD81, macrophage receptor with collagenous structure (MARCO), and apolipoprotein E (APOE), were discovered in lung adenocarcinoma using scRNA-seq . Furthermore, scRNA-seq of breast cancer indicated that the angiogenesis factors plasminogen activator, urokinase receptor (PLAUR) and IL-8 were expressed in TAMs in addition to M2-type genes, such as CD163, membrane spanning 4-domains A6A (MS4A6A), and transforming growth factor beta-1 (TGF-β1) . These novel gene signature profiles in TAMs are associated with patient survival and provide new potential targets for cancer therapy. scRNA-seq of tumour samples revealed that a subpopulation of TAMs presented with high expression of SPP1, the macrophage scavenger receptor MARCO, and MHC II class genes. MARCO and SPP1 are anti-inflammatory and immune-suppressive signatures in macrophage activation, while MHC II class genes are related to proinflammatory functions [99, 100]. Additional scRNA-seq studies have indicated that TAMs frequently present with both proinflammatory and anti-inflammatory signatures . This phenomenon suggests that macrophage activation in the tumour microenvironment is not consistent with conventional M1/M2 polarization, which is further discussed in the next section.
Evolution of immune cells suggested by single-cell data
Most immune cells are in the process of cellular development. An abundance of immune cells are in transient states of developmental trajectories rather than the discrete states of well-differentiated cells. With the help of scRNA-seq and in-depth analyses, researchers can explore the characteristics of differentiated cells and the transition of a specific cell type and its possible mechanisms.
The most commonly used computational method is the pseudotime trajectory. The pseudotrajectory describes the developmental processes of cells, characterized by cascading changes in gene expression. A branching point represents a significant divergence in cellular differentiation. Various machine learning computational methods have been utilized to construct trajectories, including Monocle3 , DTFLOW , DPT , SCORPIUS , and TSCAN , which have been evaluated and compared in a separate review . As TAMs and T cells represent the most abundant immune cell types in the TIME, we mainly focus on these two cell types.
scRNA-seq of various tumours revealed that TAMs frequently coexpress M1 genes, including TNF-α, and M2 genes, such as IL-10 , and that the differentiation and states of TAMs are directly correlated with their antitumor effects. Pseudotime trajectory analysis confirmed that TAMs transition continuously between M1 and M2 phenotypes (Fig. 3a). The transcription factors IRF2, IRF7, IRF9, STAT2, and IRF8 seem important in determining TAMs differentiation  and could be utilized as epigenetic targets to induce M1 polarization of TAMs, resulting in proinflammatory and antitumor microenvironments.
T-cell phenotypes were determined using environmental stimuli and antigen T-cell receptor (TCR) stimulation. The overlap of TCR repertoires between cells in different states, known as TCR sharing, can also be utilized to study the evolution of T cells. Combining scRNA-seq and TCR tracking in colorectal cancer identified 20 T-cell subsets with distinct functions . An exhaustion signature of 28 genes, including TIGIT, TNFRSF9/4-1BB, and CD27, was identified in exhausted T cells in melanoma tumours and was also found to be upregulated in high-exhaustion cells in most tumours . Another study on T cells further identified other exhaustion markers in CD8+ T cells, such as LAYN, pleckstrin homology like domain family A member 1 (PHLDA1), and Synaptosome associated protein 47 (SNAP47) . Pseudotime trajectory analysis indicated that T cells are in continuous activation and terminal differentiation (exhaustion) states in the TIME (Fig. 3b) . Additional studies have been performed to study the evolution of exhausted T cells and potential targets to reverse T-cell exhaustion. scRNA-seq combined with TCR analysis demonstrated that dysfunctional exhausted T cells and cytotoxic T cells might be developmentally related in the TIME .
Consequently, studies have focused on the transition process of CD8+ T cells from effector to exhausted T cells . Two CD8+ T-cell clusters were identified by scRNA-seq as pre-exhausted T cells in non-small cell lung cancer (NSCLC). The pre-exhausted to exhausted T cell ratio is associated with a better prognosis in lung adenocarcinoma. Thus, interrupting pre-exhausted T cells before exhaustion may be essential for cancer immunotherapy .
Due to the close interactions between immune cells and malignant cells, the evolution of malignant cells also plays a crucial role in immune cell evolution. Pseudotime trajectory analysis has indicated that the trajectory branch of metastatic lung adenocarcinoma is distinct from normal differentiation towards ciliated cells and alveolar-type cells . Affected by the evolution of malignant cells, normal resident myeloid cell populations are replaced with monocyte-derived macrophages and novel dendritic cells (CD163+ CD14+ DCs). T cells have also been found to undergo exhaustion, constructing an immunosuppressive tumour microenvironment . Similarly, another study demonstrated that anaplastic thyroid cancer cells are derived from a subcluster of papillary thyroid cancer cells, where a distinct TIME was constructed, leading to significantly worse prognoses .
Communications between various cells in the TIME
Cell communication in the TIME is related to tumour progression. Ligand-receptor interactions, a vital type of cell communication, are essential for constructing the TIME and identifying potential therapeutic targets . scRNA-seq was conducted on a cellular basis, making it feasible to investigate undiscovered cellular interactions. Many analytical tools for investigating ligand-receptor interactions based on scRNA-seq data have been developed (Fig. 4a), including iTALK , CellTalker , and CellPhoneDB . These tools take advantage of databases of known ligand-receptor pair interactions. Among them, CellTalker utilizes differentially expressed genes, while CellPhoneDB includes the subunit architectures of ligands and receptors. Other tools, such as NicheNet, also consider the alterations in downstream pathways in receiver cells . Other reviews have presented a detailed comparison of different computational approaches [131, 132].
During tumour progression, malignant cells lead to the recruitment and dysfunction of immune cells, which reciprocally influence tumorigenesis and the evolution of malignant cells , forming a vicious cycle (Fig. 4b). TAMs were found to interact with malignant cells via the epidermal growth factor receptor (EGFR)-amphiregulin (AREG) ligand-receptor pair. Modulation of AREG in a basal-like breast cancer cell line led to recruitment of anti-inflammatory TAMs . Meanwhile, an EGFR-associated feedback loop was discovered to promote pancreatic adenosquamous carcinoma progression based on single-cell transcriptomics . Oncostatin M (OSM) derived from TAMs also interacts with its receptor on malignant cells to activate signal transducer and activator of transcription 3 (STAT3) . Researchers discovered communications between CAFs and gastric cancer cells via integrin receptor interactions with collagen, fibronectin, thrombospondin 1 (THBS1) ligands, and leucine rich repeat containing G protein-coupled receptor 4 (LGR4)- R-spondin 3 (RSPO3), which regulate stemness . In addition, scRNA-seq of pancreatic ductal adenocarcinoma revealed interactions between TIGIT and hepatitis A virus cellular receptor 2 (HAVCR2) in T and NK cells, as well as their corresponding ligands PVR and LGALS9 in malignant cells, resulting in immune cell dysfunction and pancreatic cancer progression . Hence, exploring cellular interactions between immune cells and malignant cells based on single-cell data offers possible therapeutic targets to disrupt the vicious cycle of tumour progression.
In addition to malignant cells, scRNA-seq and subsequent analysis also predicted the interactions between immune cells in the TIME, which presented opposite functions (Fig. 4b). For instance, TAMs were found to have decreased CXCL12- C-X-C motif chemokine receptor (CXCR)3 and CXCL12-CXCR4 interactions and enhanced CD86-CTLA-4 interactions between cytotoxic T cells and Tregs in nasopharyngeal carcinoma, resulting in a TIME that aggravates cancer progression . In addition, CAFs recruit Tregs by secreting CXCL12 and are correlated with M2 macrophages via periostin . In murine melanoma, researchers found that myeloid populations displayed the function of T cells recruitment via cytokine-receptor signals, including CXCL10, CCL22, and CCL5, and suppressed T-cell function via the programmed death-ligand 1 (PD-L1)-PD-1 axis . On the other hand, some interactions between immune cells can induce an antitumor TIME. NK cells that recruit cDC1 cells via the chemokine receptor X-C motif chemokine receptor 1 (XCR1) were identified and found to promote cancer immune control [140, 141]. Thus, perturbation of interactions between immune cells might reconstruct the TIME, possibly slowing tumour growth.
The spatial location of cells is vital for cell communication. The application of scRNA-seq in studying physical interactions is limited because of the destructive process of tissue dissociation. Computational tools involving Cellular Spatial Organization mapper (CSOmap) have been presented to recapitulate the spatial organization of cells in the TIME . Sequencing of physically interacting cells (PIC-seq) also helped in better depicting cell interactions. With the help of PIC-seq, Tregs have been discovered as a major T-cell type that interacts with DCs, suggesting that Treg-DCs interactions are important for sustaining an immune tolerance environment . With the development of spatial transcriptomics techniques , the data of transcriptomics and spatial locations of cells are combined. Integrating single-cell and spatial transcriptomics helps us explore cell communications at a new level [145, 146]. However, spatial transcriptomics cannot reach the single-cell level. In situ transcriptomics at single-cell resolution might become possible in the future. Although commercially available methods, including Visium, cannot achieve the single-cell level, recent technologies enable transcriptomics research at single-cell or even subcellular resolution [147, 148]. Additionally, cell interactions occur at the protein level. scRNA-seq-based interaction predictions may not be mirrored accurately because scRNA-seq cannot directly reflect protein levels. Fortunately, single-cell proteomics and multiomics techniques have advanced dramatically [149, 150], which helps researchers better characterize cell communications.
Clinical application and potential targets in the TIME based on scRNA-seq
Quantification of the TIME has been adopted in clinical practice for decades to predict patient survival and response to treatments. The immunohistochemistry-based immune score, which quantifies in situ immune cell infiltration in tumours, was proposed before scRNA-seq. The immune score is a better prognostic factor than the TNM classification in colorectal cancer . The immuno-score is also associated with responses to different treatments , which emphasizes the essential role of the TIME in clinical applications.
Compared with the traditional immuno-score, scRNA-seq provides an unprecedented resolution of infiltrated immune cells in the TIME. Novel immune cell clusters related to prognosis have been identified. For instance, a distinct phenotype of low cytotoxic innate-like CD8+ T cells has been identified in early relapse hepatocellular carcinoma. These T cells overexpress KLRB1 while downregulating costimulatory and exhaustion-related molecules, including tumor necrosis factor receptor superfamily, member 9 (TNFRFS9), CD28, inducible T cell co-stimulator (ICOS), TIGIT, CTLA-4, and HAVCR2. The infiltration of this cluster of T cells is correlated with a poor prognosis in liver cancer . In addition, scRNA-seq-based cellular interactions were also counted in the prediction model. Machine learning models have been constructed based on intercellular communication-associated genes (ICAGs) to predict the recurrence of lung adenocarcinoma. Combining eight ICAGs and patients' clinical information achieved an area under the receiver operator characteristic (ROC) curve of 0.841 . In addition to prognosis prediction, unique cellular interactions in the TIME are also related to responses to immunotherapy. scRNA-seq analysis found distinct cell–cell communication networks between responders and non-responders to anti-PD-1 therapy, potentially predicting patient response to anti-PD-1 therapy . As a result, patient prognosis and responses to immunotherapy were more precisely predicted with the help of scRNA-seq.
Taking advantage of scRNA-seq is inspiring in precision medicine, such as assisting targeted therapy to overcome drug resistance. For instance, physicians applied scRNA-seq of patient-derived xenografts (PDXs) before and after treatment in non-CR muscle-invasive bladder cancer patients treated with tipifarnib. Upregulation of PD-L1 was found in post-treatment PDXs and reduced the antitumor effects of immune cells. Accordingly, additional treatment with a PD-L1 inhibitor (atezolizumab) was chosen. Subsequently, patients achieved favourable responses . In addition, novel immune subtypes were identified via scRNA-seq in monotherapy-resistant tumours. Blocking TAMs with anti-colony stimulating factor 1 receptor (CSF1R) failed to decrease tumour progression in cholangiocarcinoma. scRNA-seq identified compensatory enrichment of granulocytic myeloid-derived suppressor cells expressing APOE, which mediated T-cell inhibition. Dual inhibition of TAMs and granulocytic-Myeloid-derived suppressor cells (G-MDSCs) combined with anti-CSF1R and anti-lymphocyte antigen 6 complex, locus G (Ly6G) therapy augmented immune checkpoint blockade efficacy in a murine model, which is promising for clinical practice .
In addition to treating drug-resistant tumours, the application of scRNA-seq in the TIME has also highlighted potential novel targets that require further investigation. T cells are the most essential immune cells for removing malignant cells in the TIME. However, exhausted CD8+ T cells contribute to an unfavourable prognosis in different tumours. In addition to well-known immunosuppressive checkpoints, scRNA-seq identified exhausted CD8+ T cells highly expressing premelanosome protein (PMEL), tyrosinase related protein 1 (TYRP1), and endothelin receptor type B (EDNRB), which could serve as novel potential targets . Myeloid cells are essential for recruiting immune cells in the TIME . TREM2/APOE/complement component 1, q subcomponent (C1Q)-positive macrophage infiltration was identified by scRNA-seq as a prognostic biomarker for clear cell renal carcinoma recurrence . Another study confirmed that antibodies targeting TREM2 in mice were associated with scant MRC1+ and CX3CR1+ macrophages and an expansion of myeloid clusters expressing immunostimulatory molecules, which promoted T-cell responses and led to a better prognosis . Cellular interactions can also be used as therapeutic targets. scRNA-seq of intrahepatic cholangiocarcinoma revealed crosstalk between vCAFs and intrahepatic cholangiocarcinoma (ICC) cells. IL-6 secreted by vCAFs induces epigenetic alterations in ICC cells, which enhance malignancy . Hence, the interruption of IL-6 signaling in ICC has become quite intriguing. Potential targets for cancer therapy as indicated by scRNA-seq are summarized in Table 2.
Application of scRNA-seq in the future
Even though the application of scRNA-seq has made great progressions in TIME studies, the promotion and popularization of single-cell sequencing technology are limited by high sample quality requirements, limited throughput, inevitable technical errors and high costs . It is hoped that with the development of scRNA-seq technology, the threshold will become lower, accelerating the widespread application of scRNA-seq. Currently, single-cell analysis has been extended beyond transcriptomics to genomics, proteomics, and epigenetics [149, 174, 175]. The actual spatial structure of cells in tumour tissues can be reconstructed . Moreover, frozen specimens and paraffin-embedded tissues can be analysed in addition to fresh tissues [177, 178]. These technique improvements shed light on construction of large single-cell datasets with high resolution.
The application of single-cell analysis to precision medicine is promising. Several studies have revealed the substantial value of using scRNA-seq in clinical practice. scRNA-seq of the skin and blood of a patient with drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms (DiHS/DRESS) identified central memory CD4+ T cells enriched in human herpesvirus 6b DNA and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway as potential targets. Subsequent treatment with tofacitinib and antiviral agents was successful in this individual patient . scRNA-seq also provides potential targets for cancer therapy. Increased intratumoral heterogeneity was discovered in therapy-resistant small-cell lung cancer, emphasizing the importance of combination therapies for treatment-naïve tumours .
The unprecedented power of scRNA-seq has started a new era in TIME research. A comprehensive cellular atlas of the TIME has been drawn, providing a novel perspective for clinical application in various tumours. In addition, cellular compositions and communications in the TIME provide potential targets for cancer therapies and contribute to the development of precision medicine. We believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy and that scRNA-seq can subsequently be implemented more frequently in clinical practice.
Availability of data and materials
Adenosine deaminase RNA specific 1
β-Cell CLL/lymphoma 3
Basic helix-loop-helix family member E40
Complement component 1, q subcomponent
Complement C3a receptor
C–C motif chemokine ligand
Colony stimulating factor 1 receptor
Cell surface glycoprotein
Cellular Spatial Organization mapper
Cytotoxic T lymphocyte-associated antigen-4
C-X-C motif chemokine ligand
C-X-C motif chemokine receptor
Drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms
Endothelin receptor type B
Epidermal growth factor receptor
Fluorescence-activated cell sorting
Folate receptor beta
Forkhead box O1
Granulocytic-Myeloid-derived suppressor cells
Hepatitis A virus cellular receptor 2
Hypoxia induced factor-1α
Intercellular communication-associated genes
Inducible T cell co-stimulator
Inducible T-cell co-stimulator ligand
Indoleamine 2,3-dioxygenase 1
IKAROS family zinc finger 2
In vitro transcription
Lysine demethylase 5B
Killer cell immunoglobulin like receptor
Lymphocyte activating 3
Lysosomal associated membrane protein 3
Laser capture microdissection
Leucine rich repeat containing G protein-coupled receptor 4
Lymphocyte antigen 6 complex, locus G
Macrophage activating factor
Macrophage receptor with collagenous structure
Major histocompatibility complex
Membrane spanning 4-domains A6A
Mucin 1 C-terminal subunit
Natural killer T
Non-small cell lung cancer
Polymerase chain reaction
Programmed death 1
Protein disulfide isomerase family A member 5
Programmed death-ligand 1
Pleckstrin homology like domain family A member 1
Sequencing of physically interacting cells
Plasminogen activator, urokinase receptor
Plasmalemma vesicle associated protein
Receiver operator characteristic
Single-cell RNA sequencing
Synaptosome associated protein 47
SRY-box transcription factor 4
Secreted phosphoprotein 1
Signal transducer and activator of transcription
Transcription factor 7
Transforming growth factor beta
Transforming growth factor beta receptor
T helper 1
T helper 17
T helper 2
T cell immunoreceptor with Ig and ITIM domains
Tumour immune microenvironment
Tertiary lymphoid structures
TNF receptor superfamily member 18
TNF receptor superfamily member 9
Tumor necrosis factor-α
Regulatory T cells
Triggering receptor expressed on myeloid cells 2
Tyrosinase related protein 1
Unique molecular identifier
Vascular endothelial growth factor
Vascular endothelial growth factor receptor 2
X-C motif chemokine receptor 1
Vitale I, Shema E, Loi S, Galluzzi L. Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nat Med. 2021;27(2):212–24.
Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.
Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017;171(3):540–56.
Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25(2):152–65.
Choi W, Ochoa A, McConkey DJ, Aine M, Hoglund M, Kim WY, et al. Genetic alterations in the molecular subtypes of bladder cancer: illustration in the cancer genome atlas dataset. Eur Urol. 2017;72(3):354–65.
Zhang L, Ren Z, Su Z, Liu Y, Yang T, Cao M, et al. Novel recurrent altered genes in Chinese patients with anaplastic thyroid cancer. J Clin Endocrinol Metab. 2021;106(4):988–98.
Qian J, Olbrecht S, Boeckx B, Vos H, Laoui D, Etlioglu E, et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 2020;30(9):745–62.
Boussiotis VA. Molecular and biochemical aspects of the PD-1 checkpoint pathway. N Engl J Med. 2016;375(18):1767–78.
Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366(26):2443–54.
Kechagia JZ, Ivaska J, Roca-Cusachs P. Integrins as biomechanical sensors of the microenvironment. Nat Rev Mol Cell Biol. 2019;20(8):457–73.
Bolouri H. Network dynamics in the tumor microenvironment. Semin Cancer Biol. 2015;30:52–9.
Shi Y, Du L, Lin L, Wang Y. Tumour-associated mesenchymal stem/stromal cells: emerging therapeutic targets. Nat Rev Drug Discov. 2017;16(1):35–52.
Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–82.
Tsoucas D, Dong R, Chen H, Zhu Q, Guo G, Yuan GC. Accurate estimation of cell-type composition from gene expression data. Nat Commun. 2019;10(1):2975.
Chrzanowska NM, Kowalewski J, Lewandowska MA. Use of fluorescence in situ hybridization (FISH) in diagnosis and tailored therapies in solid tumors. Molecules. 2020;25(8):1864.
Lin X, Wang F, Chen J, Liu J, Lin YB, Li L, et al. N(6)-methyladenosine modification of CENPK mRNA by ZC3H13 promotes cervical cancer stemness and chemoresistance. Mil Med Res. 2022;9(1):19.
Finotello F, Rieder D, Hackl H, Trajanoski Z. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet. 2019;20(12):724–46.
Levitin HM, Yuan J, Sims PA. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer. 2018;4(4):264–8.
González-Silva L, Quevedo L, Varela I. Tumor functional heterogeneity unraveled by scRNA-seq technologies. Trends Cancer. 2020;6(1):13–9.
Ji JJ, Fan J. Discovering myeloid cell heterogeneity in the lung by means of next generation sequencing. Mil Med Res. 2019;6(1):33.
Wang Y, Wang JY, Schnieke A, Fischer K. Advances in single-cell sequencing: insights from organ transplantation. Mil Med Res. 2021;8(1):45.
Greten FR, Grivennikov SI. Inflammation and cancer: triggers, mechanisms, and consequences. Immunity. 2019;51(1):27–41.
Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541–50.
Lim B, Lin Y, Navin N. Advancing cancer research and medicine with single-cell genomics. Cancer Cell. 2020;37(4):456–70.
Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;17(3):175–88.
Picelli S. Single-cell RNA-sequencing: the future of genome biology is now. RNA Biol. 2017;14(5):637–50.
Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol. 2018;14(8):479–92.
Zhang X, Marjani SL, Hu Z, Weissman SM, Pan X, Wu S. Single-cell sequencing for precise cancer research: progress and prospects. Cancer Res. 2016;76(6):1305–12.
Xin Y, Kim J, Ni M, Wei Y, Okamoto H, Lee J, et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci USA. 2016;113(12):3293–8.
See P, Lum J, Chen J, Ginhoux F. A single-cell sequencing guide for immunologists. Front Immunol. 2018;9:2425.
DeLaughter DM. The use of the Fluidigm C1 for RNA expression analyses of single cells. Curr Protoc Mol Biol. 2018;122(1):e55.
Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187–201.
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–14.
Zhang X, Li T, Liu F, Chen Y, Yao J, Li Z, et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol Cell. 2019;73(1):130–42.
Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.
Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18(1):35–45.
Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.
Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2(3):666–73.
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–9.
Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11(2):163–6.
Hochgerner H, Lönnerberg P, Hodge R, Mikes J, Heskol A, Hubschle H, et al. STRT-seq-2i: dual-index 5’ single cell and nucleus RNA-seq on an addressable microwell array. Sci Rep. 2017;7(1):16327.
Fan HC, Fu GK, Fodor SP. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science. 2015;347(6222):1258367.
Shum EY, Walczak EM, Chang C, Fan HC. Quantitation of mRNA transcripts and proteins using the BD Rhapsody™ single-cell analysis system. Adv Exp Med Biol. 2019;1129:63–79.
Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176–82.
Fan J, Lee HO, Lee S, Ryu DE, Lee S, Xue C, et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 2018;28(8):1217–27.
Laks E, McPherson A, Zahn H, Lai D, Steif A, Brimhall J, et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell. 2019;179(5):1207-21.e22.
Liu L, Chen D, Wang J, Chen J. Advances of single-cell protein analysis. Cells. 2020;9(5):1271.
Shema E, Bernstein BE, Buenrostro JD. Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution. Nat Genet. 2019;51(1):19–25.
Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc. 2021;16(1):1–9.
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96.
Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36(5):421–7.
Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 2019;37(6):685–91.
Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12.
Ringnér M. What is principal component analysis? Nat Biotechnol. 2008;26(3):303–4.
Lin X, Boutros PC. Optimization and expansion of non-negative matrix factorization. BMC Bioinform. 2020;21(1):7.
Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20(7):389–403.
Koch FC, Sutton GJ, Voineagu I, Vafaee F. Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data. Brief Bioinform. 2021;22(6):bbab304.
Wu Y, Zhang K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol. 2020;16(7):408–21.
Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol. 2022;23(1):31.
van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, et al. Recovering gene interactions from single-cell data using data diffusion. Cell. 2018;174(3):716-29.e27.
Lin P, Troup M, Ho JW. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 2017;18(1):59.
Li WV, Li JJ. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun. 2018;9(1):997.
St Paul M, Ohashi PS. The roles of CD8+ T cell subsets in antitumor immunity. Trends Cell Biol. 2020;30(9):695–704.
Moeller M, Kershaw MH, Cameron R, Westwood JA, Trapani JA, Smyth MJ, et al. Sustained antigen-specific antitumor recall response mediated by gene-modified CD4+ T helper-1 and CD8+ T cells. Cancer Res. 2007;67(23):11428–37.
Yamanaka K, Clark R, Dowgiert R, Hurwitz D, Shibata M, Rich BE, et al. Expression of interleukin-18 and caspase-1 in cutaneous T-cell lymphoma. Clin Cancer Res. 2006;12(2):376–82.
Lv L, Pan K, Li XD, She KL, Zhao JJ, Wang W, et al. The accumulation and prognosis value of tumor infiltrating IL-17 producing cells in esophageal squamous cell carcinoma. PLoS ONE. 2011;6(3):e18219.
Lai C, August S, Behar R, Polak M, Ardern-Jones M, Theaker J, et al. Characteristics of immunosuppressive regulatory T cells in cutaneous squamous cell carcinomas and role in metastasis. Lancet. 2015;385(Suppl 1):S59.
Liénart S, Merceron R, Vanderaa C, Lambert F, Colau D, Stockis J, et al. Structural basis of latent TGF-β1 presentation and activation by GARP on human regulatory T cells. Science. 2018;362(6417):952–6.
Vivier E, Ugolini S, Blaise D, Chabannon C, Brossay L. Targeting natural killer cells and natural killer T cells in cancer. Nat Rev Immunol. 2012;12(4):239–52.
Liu X, Li L, Si F, Huang L, Zhao Y, Zhang C, et al. NK and NKT cells have distinct properties and functions in cancer. Oncogene. 2021;40(27):4521–37.
Munir MT, Kay MK, Kang MH, Rahman MM, Al-Harrasi A, Choudhury M, et al. Tumor-associated macrophages as multifaceted regulators of breast tumor growth. Int J Mol Sci. 2021;22(12):6526.
Gabrilovich DI, Ostrand-Rosenberg S, Bronte V. Coordinated regulation of myeloid cells by tumours. Nat Rev Immunol. 2012;12(4):253–68.
Fricke I, Gabrilovich DI. Dendritic cells and tumor microenvironment: a dangerous liaison. Immunol Invest. 2006;35(3–4):459–83.
Audsley KM, McDonnell AM, Waithman J. Cross-presenting XCR1+ dendritic cells as targets for cancer immunotherapy. Cells. 2020;9(3):565.
Chen X, Song E. Turning foes to friends: targeting cancer-associated fibroblasts. Nat Rev Drug Discov. 2019;18(2):99–115.
Bu L, Baba H, Yoshida N, Miyake K, Yasuda T, Uchihara T, et al. Biological heterogeneity and versatility of cancer-associated fibroblasts in the tumor microenvironment. Oncogene. 2019;38(25):4887–901.
Li X, Bu W, Meng L, Liu X, Wang S, Jiang L, et al. CXCL12/CXCR4 pathway orchestrates CSC-like properties by CAF recruited tumor associated macrophage in OSCC. Exp Cell Res. 2019;378(2):131–8.
Hutton C, Heider F, Blanco-Gomez A, Banyard A, Kononov A, Zhang X, et al. Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity. Cancer Cell. 2021;39(9):1227-44.e20.
Shen P, Fillatreau S. Antibody-independent functions of B cells: a focus on cytokines. Nat Rev Immunol. 2015;15(7):441–51.
Shah S, Divekar AA, Hilchey SP, Cho HM, Newman CL, Shin SU, et al. Increased rejection of primary tumors in mice lacking B cells: inhibition of anti-tumor CTL and TH1 cytokine responses by B cells. Int J Cancer. 2005;117(4):574–86.
Schioppa T, Moore R, Thompson RG, Rosser EC, Kulbe H, Nedospasov S, et al. B regulatory cells and the tumor-promoting actions of TNF-α during squamous carcinogenesis. Proc Natl Acad Sci USA. 2011;108(26):10662–7.
Olkhanud PB, Damdinsuren B, Bodogai M, Gress RE, Sen R, Wejksza K, et al. Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4+ T cells to T-regulatory cells. Cancer Res. 2011;71(10):3505–15.
Petitprez F, de Reyniès A, Keung EZ, Chen TW, Sun CM, Calderaro J, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature. 2020;577(7791):556–60.
Cabrita R, Lauss M, Sanna A, Donia M, Skaarup Larsen M, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561–5.
Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods. 2017;14(4):414–6.
Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14(5):483–6.
Zhu L, Lei J, Klei L, Devlin B, Roeder K. Semisoft clustering of single-cell data. Proc Natl Acad Sci USA. 2019;116(2):466–71.
Nadif M, Role F. Unsupervised and self-supervised deep learning approaches for biomedical text mining. Brief Bioinform. 2021;22(2):1592–603.
Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015;347(6226):1138–42.
Kiselev VY, Andrews TS, Hemberg M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet. 2019;20(5):273–82.
Shao X, Liao J, Lu X, Xue R, Ai N, Fan X. scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience. 2020;23(3):100882.
Zhang AW, O’Flanagan C, Chavez EA, Lim JLP, Ceglia N, McPherson A, et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019;16(10):1007–15.
Ekiz HA, Conley CJ, Stephens WZ, O’Connell RM. CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments. BMC Bioinform. 2020;21(1):191.
Shao L, Zhu F, Li X. Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst. 2015;26(5):1019–34.
Cahan P, Li H, Morris SA, da Rocha EL, Daley GQ, Collins JJ. Cell Net: network biology applied to stem cell engineering. Cell. 2014;158(4):903–15.
Pliner HA, Shendure J, Trapnell C. Supervised classification enables rapid annotation of cell atlases. Nat Methods. 2019;16(10):983–6.
Kiselev VY, Yiu A, Hemberg M. Scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. 2018;15(5):359–62.
Hu J, Li X, Hu G, Lyu Y, Susztak K, Li M. Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. Nat Mach Intell. 2020;2(10):607–18.
Elyada E, Bolisetty M, Laise P, Flynn WF, Courtois ET, Burkhart RA, et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 2019;9(8):1102–23.
Mei Y, Xiao W, Hu H, Lu G, Chen L, Sun Z, et al. Single-cell analyses reveal suppressive tumor microenvironment of human colorectal cancer. Clin Transl Med. 2021;11(6):e422.
Sebastian A, Hum NR, Martin KA, Gilmore SF, Peran I, Byers SW, et al. Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer. Cancers. 2020;12(5):1307.
Li H, Courtois ET, Sengupta D, Tan Y, Chen KH, Goh JJL, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet. 2017;49(5):708–18.
Bartoschek M, Oskolkov N, Bocci M, Lövrot J, Larsson C, Sommarin M, et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nat Commun. 2018;9(1):5150.
Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. 2018;174(5):1293-308.e36.
Cheng S, Li Z, Gao R, Xing B, Gao Y, Yang Y, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell. 2021;184(3):792–809.
Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science. 2021;374(6574):abe6474.
Durante MA, Rodriguez DA, Kurtenbach S, Kuznetsov JN, Sanchez MI, Decatur CL, et al. Single-cell analysis reveals new evolutionary complexity in uveal melanoma. Nat Commun. 2020;11(1):496.
Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017;169(7):1342-56.e16.
Zhang L, Yu X, Zheng L, Zhang Y, Li Y, Fang Q, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018;564(7735):268–72.
Sathe A, Grimes SM, Lau BT, Chen J, Suarez C, Huang RJ, et al. Single-cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment. Clin Cancer Res. 2020;26(11):2640–53.
Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, et al. Landscape and fynamics of single immune cells in hepatocellular carcinoma. Cell. 2019;179(4):829-45.e20.
Lavin Y, Kobayashi S, Leader A, Amir ED, Elefant N, Bigenwald C, et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell. 2017;169(4):750-65.e17.
Chung W, Eum HH, Lee HO, Lee KM, Lee HB, Kim KT, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8:15081.
Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496–502.
Wei J, Zhou T, Zhang X, Tian T. DTFLOW: inference and visualization of single-cell pseudotime trajectory using diffusion propagation. Genom Proteom Bioinform. 2021;19:306–18.
Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016;13(10):845–8.
Cannoodt R, Saelens W, Sichien D, Tavernier S, Janssens S, Guilliams M, et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. 2016. https://doi.org/10.1101/079509.
Ji Z, Ji H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016;44(13):e117.
Chen J, Rénia L, Ginhoux F. Constructing cell lineages from single-cell transcriptomes. Mol Aspects Med. 2018;59:95–113.
Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med. 2018;24(8):1277–89.
Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–96.
van der Leun AM, Thommen DS, Schumacher TN. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat Rev Cancer. 2020;20(4):218–32.
Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med. 2018;24(7):978–85.
Kim N, Kim HK, Lee K, Hong Y, Cho JH, Choi JW, et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun. 2020;11(1):2285.
Luo H, Xia X, Kim GD, Liu Y, Xue Z, Zhang L, et al. Characterizing dedifferentiation of thyroid cancer by integrated analysis. Sci Adv. 2021;7(31):eabf3657.
Finlay DB, Duffull SB, Glass M. 100 years of modelling ligand-receptor binding and response: a focus on GPCRs. Br J Pharmacol. 2020;177(7):1472–84.
Wang Y, Wang R, Zhang S, Song S, Wang L. iTALK: an R package to characterize and illustrate intercellular communication. 2019. https://doi.org/10.1101/507871.
Cillo AR, Kürten CHL, Tabib T, Qi Z, Onkar S, Wang T, et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity. 2020;52(1):183-99.e9.
Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. Cell PhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15(4):1484–506.
Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods. 2020;17(2):159–62.
Ma F, Zhang S, Song L, Wang B, Wei L, Zhang F. Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level. Cell Biosci. 2021;11(1):121.
Shao X, Lu X, Liao J, Chen H, Fan X. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell. 2020;11(12):866–80.
Galon J, Bruni D. Tumor immunology and tumor evolution: intertwined histories. Immunity. 2020;52(1):55–81.
Nickerson NK, Mill CP, Wu HJ, Riese DJ 2nd, Foley J. Autocrine-derived epidermal growth factor receptor ligands contribute to recruitment of tumor-associated macrophage and growth of basal breast cancer cells in vivo. Oncol Res. 2013;20(7):303–17.
Zhao X, Li H, Lyu S, Zhai J, Ji Z, Zhang Z, et al. Single-cell transcriptomics reveals heterogeneous progression and EGFR activation in pancreatic adenosquamous carcinoma. Int J Biol Sci. 2021;17(10):2590–605.
Hara T, Chanoch-Myers R, Mathewson ND, Myskiw C, Atta L, Bussema L, et al. Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma. Cancer Cell. 2021;39(6):779-92.e11.
Steele NG, Carpenter ES, Kemp SB, Sirihorachai V, The S, Delrosario L, et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nat Cancer. 2020;1(11):1097–112.
Jin S, Li R, Chen MY, Yu C, Tang LQ, Liu YM, et al. Single-cell transcriptomic analysis defines the interplay between tumor cells, viral infection, and the microenvironment in nasopharyngeal carcinoma. Cell Res. 2020;30(11):950–65.
Li X, Sun Z, Peng G, Xiao Y, Guo J, Wu B, et al. Single-cell RNA sequencing reveals a pro-invasive cancer-associated fibroblast subgroup associated with poor clinical outcomes in patients with gastric cancer. Theranostics. 2022;12(2):620–38.
Chen YP, Yin JH, Li WF, Li HJ, Chen DP, Zhang CJ, et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 2020;30(11):1024–42.
Böttcher JP, Bonavita E, Chakravarty P, Blees H, Cabeza-Cabrerizo M, Sammicheli S, et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell. 2018;172(5):1022-37.e14.
Ren X, Zhong G, Zhang Q, Zhang L, Sun Y, Zhang Z. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Cell Res. 2020;30(9):763–78.
Giladi A, Cohen M, Medaglia C, Baran Y, Li B, Zada M, et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat Biotechnol. 2020;38(5):629–37.
Rao A, Barkley D, Franca GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021;596(7871):211–20.
Saviano A, Henderson NC, Baumert TF. Single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology. J Hepatol. 2020;73(5):1219–30.
Longo SK, Guo MG, Ji AL, Khavari PA. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet. 2021;22(10):627–44.
Stickels RR, Murray E, Kumar P, Li J, Marshall JL, di Bella DJ, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2021;39(3):313–9.
Cho CS, Xi J, Si Y, Park SR, Hsu JE, Kim M, et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell. 2021;184(13):3559-72.e22.
Vistain LF, Tay S. Single-cell proteomics. Trends Biochem Sci. 2021;46(8):661–72.
Nam AS, Chaligne R, Landau DA. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet. 2021;22(1):3–18.
Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, et al. Towards the introduction of the “Immunoscore” in the classification of malignant tumours. J Pathol. 2014;232(2):199–209.
Jiang Y, Zhang Q, Hu Y, Li T, Yu J, Zhao L, et al. ImmunoScore signature: a prognostic and predictive tool in gastric cancer. Ann Surg. 2018;267(3):504–13.
Sun Y, Wu L, Zhong Y, Zhou K, Hou Y, Wang Z, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell. 2021;184(2):404–21.
Zhao QY, Liu LP, Lu L, Gui R, Luo YW. A novel intercellular communication-associated gene signature for prognostic prediction and clinical value in patients with lung adenocarcinoma. Front Genet. 2021;12:702424.
Jiang YQ, Wang ZX, Zhong M, Shen LJ, Han X, Zou X, et al. Investigating mechanisms of response or resistance to immune checkpoint inhibitors by analyzing cell-cell communications in tumors before and after programmed cell death-1 (PD-1) targeted therapy: an integrative analysis using single-cell RNA and bulk-RNA sequencing data. Oncoimmunology. 2021;10(1):1908010.
Lee HW, Chung W, Lee HO, Jeong DE, Jo A, Lim JE, et al. Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient. Genome Med. 2020;12(1):47.
Loeuillard E, Yang J, Buckarma E, Wang J, Liu Y, Conboy C, et al. Targeting tumor-associated macrophages and granulocytic myeloid-derived suppressor cells augments PD-1 blockade in cholangiocarcinoma. J Clin Invest. 2020;130(10):5380–96.
Deng W, Ma Y, Su Z, Liu Y, Liang P, Huang C, et al. Single-cell RNA-sequencing analyses identify heterogeneity of CD8+ T cell subpopulations and novel therapy targets in melanoma. Mol Ther Oncolytics. 2021;20:105–18.
Mao X, Yang X, Chen X, Yu S, Yu S, Zhang B, et al. Single-cell transcriptome analysis revealed the heterogeneity and microenvironment of gastrointestinal stromal tumors. Cancer Sci. 2021;112(3):1262–74.
Obradovic A, Chowdhury N, Haake SM, Ager C, Wang V, Vlahos L, et al. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages. Cell. 2021;184(11):2988-3005.e16.
Molgora M, Esaulova E, Vermi W, Hou J, Chen Y, Luo J, et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy. Cell. 2020;182(4):886–900.
Zhang M, Yang H, Wan L, Wang Z, Wang H, Ge C, et al. Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma. J Hepatol. 2020;73(5):1118–30.
Ishizuka JJ, Manguso RT, Cheruiyot CK, Bi K, Panda A, Iracheta-Vellve A, et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature. 2019;565(7737):43–8.
Yamashita N, Long M, Fushimi A, Yamamoto M, Hata T, Hagiwara M, et al. MUC1-C integrates activation of the IFN-γ pathway with suppression of the tumor immune microenvironment in triple-negative breast cancer. J Immunother Cancer. 2021;9(1):e002115.
Zhang H, He J, Dai Z, Wang Z, Liang X, He F, et al. PDIA5 is correlated with immune infiltration and predicts poor prognosis in gliomas. Front Immunol. 2021;12:628966.
Lu Y, Zhao Q, Liao JY, Song E, Xia Q, Pan J, et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell. 2020;180(6):1081-97.e24.
Ni J, Wang X, Stojanovic A, Zhang Q, Wincher M, Bühler L, et al. Single-cell RNA sequencing of tumor-infiltrating NK cells reveals that inhibition of transcription factor HIF-1α unleashes NK cell activity. Immunity. 2020;52(6):1075-87.e8.
Wang Q, Guldner IH, Golomb SM, Sun L, Harris JA, Lu X, et al. Single-cell profiling guided combinatorial immunotherapy for fast-evolving CDK4/6 inhibitor-resistant HER2-positive breast cancer. Nat Commun. 2019;10(1):3817.
Lee JJ, Bernard V, Semaan A, Monberg ME, Huang J, Stephens BM, et al. Elucidation of tumor-stromal heterogeneity and the ligand-receptor interactome by single-cell transcriptomics in real-world pancreatic cancer biopsies. Clin Cancer Res. 2021;27(21):5912–21.
Sharma A, Seow JJW, Dutertre CA, Pai R, Bleriot C, Mishra A, et al. Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in hepatocellular carcinoma. Cell. 2020;183(2):377–94.
Nagaoka K, Shirai M, Taniguchi K, Hosoi A, Sun C, Kobayashi Y, et al. Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy. J Immunother Cancer. 2020;8(2):e001358.
Aoki T, Chong LC, Takata K, Milne K, Marshall A, Chavez EA, et al. Single-cell profiling reveals the importance of CXCL13/CXCR5 axis biology in lymphocyte-rich classic Hodgkin lymphoma. Proc Natl Acad Sci U S A. 2021;118(41):e2105822118.
Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21(1):31.
Camp JG, Platt R, Treutlein B. Mapping human cell phenotypes to genotypes with single-cell genomics. Science. 2019;365(6460):1401–5.
Kelsey G, Stegle O, Reik W. Single-cell epigenomics: recording the past and predicting the future. Science. 2017;358(6359):69–75.
Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol. 2021;39(1):43–58.
Guillaumet-Adkins A, Rodríguez-Esteban G, Mereu E, Mendez-Lago M, Jaitin DA, Villanueva A, et al. Single-cell transcriptome conservation in cryopreserved cells and tissues. Genome Biol. 2017;18(1):45.
Martelotto LG, Baslan T, Kendall J, Geyer FC, Burke KA, Spraggon L, et al. Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples. Nat Med. 2017;23(3):376–85.
Kim D, Kobayashi T, Voisin B, Jo JH, Sakamoto K, Jin SP, et al. Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: a case report. Nat Med. 2020;26(2):236–43.
Stewart CA, Gay CM, Xi Y, Sivajothi S, Sivakamasundari V, Fujimoto J, et al. Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer. Nat Cancer. 2020;1:423–36.
This work was supported by the National Key Research Development Program of China (2021YFA1301203); the National Natural Science Foundation of China (82103031, 82103918, 81973408); the Clinical Research Incubation Project, West China Hospital, Sichuan University (22HXFH019); the China Postdoctoral Science Foundation (2019 M653416); the International Cooperation Project of Chengdu Municipal Science and Technology Bureau (2020-GH02-00017-HZ); the “1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University” (ZYJC18035, ZYJC18025, ZYYC20003, ZYJC18003); the GIST Research Institute (GRI) IIBR grants funded by the GIST; the National Research Foundation of Korea funded by the Korean government (MSIP) (2019R1C1C1005403, 2019R1A4A1028802 and 2021M3H9A2097520); and the Post-Doctor Research Project, West China Hospital, Sichuan University (2021HXBH054).
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
About this article
Cite this article
Li, PH., Kong, XY., He, YZ. et al. Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy. Military Med Res 9, 52 (2022). https://doi.org/10.1186/s40779-022-00414-y