Immune cells have many functions, including killing infected and mutated cells, priming the adaptive immune response, and causing chronic inflammation ^[1][33]. However, immune cells’ functions are complicated. First, the same immune cells may perform different functions, sometimes even opposite functions. Second, the same T cell subset may serve dual roles. What causes the same immune cells/subsets to serve different or opposite functions remains unclear, hindering the development of successful immunotherapy. One possibility is that several distinct cell subsets with different phenotypes and functions coexist in the tumor microenvironment (TME). Previously, the identification of immune cells depended on the knowledge of surface markers. However, single-cell RNA sequencing (scRNA-seq)cRNA-seq can define cell types using transcriptome analysis, which enables the identification of novel cell subsets and their marker genes using DEGman ^[2][34]. Data analysis from scRNA-seq using novel bioinformatics methods might accurately identify this heterogeneity and new subsets of immune cells.

Reliable markers are critical for the early diagnosis of some diseases and can lead to better treatments and outcomes. Complement-secreting CAFs (csCAFs) were discovered using scRNA-seq. In addition, several new markers of regulatory and exhausted T cells, such as DUSP4, FANK1, LAIR2, and DUSP4, are new signature genes for regulatory T cells (Treg), which can serve as prognosis markers and potential therapeutic targets ^[3][5]. scRNA-seq has been employed to compare the transcriptome data between primary tumors and their metastatic counterparts in pancreatic neuroendocrine tumors. A novel gene signature (PCSK1 and SMOC1) defining the metastatic potential of the tumor and its prognostic value has been identified and validated ^[4][35]. These studies demonstrated that scRNA-seq could identify novel immunocyte cell populations, subpopulations, and novel marker genes; it could identify disease-specific cells and act as prognosis markers.

The heterogeneity in immunity has attracted substantial attention ^{[5][6][7][8][9]}[36,37,38,39,40]. The immune system is a complex network in which the types, states, and locations of immune cells are diverse in healthy individuals and patients ^[10][32]. Active research areas include the heterogeneity of immune systems involving immune cell heterogeneity, immune responses, and the immune microenvironment. scRNA-seq can sequence the smallest independent genetic units in life to address problems derived from heterogeneity that cannot be resolved using bulk RNA sequencing ^[11][12][41,42]. scRNA-seq holds substantial potential for revealing immunological mechanisms regarding tissue composition, transcription dynamics, the regulatory relationships between immune genes, and the heterogeneity of the traditional “same” immune cell types.

When fighting pathogens and diseases, immune cells are highly heterogeneous ^[13][43]. Heterogeneity is usually defined by surface markers that can be measured using flow cytometry or the giant cell technique. However, these methods are limited by the number of surface markers. A more robust method of cell typing is based on scRNA-seq, which facilitates elucidating heterogeneity without understanding gene functions. Although PD-LI/PD-1 can be treated as promising targets for immunotherapy and have been applied to cancers such as lung, breast, and hematologic cancers, they are ineffective for other cancers. The reason may be the intratumoral heterogeneity of immune cells, which complicates the identification of effective immunotherapeutic targets. By applying scRNA-seq to analyze the heterogeneity of immune cells in ovarian cancer, researchers demonstrated that tumor-infiltrated myeloid cells are heterogenous across patients ^[14][44]. Thus, scRNA-seq should help develop specific immunotherapy for cancers by analyzing immune cell heterogeneity.

The response may differ by using the same immunotherapy to treat patients with the same malignant disease because immune response heterogeneity is common. The reason behind this may be related to the heterogeneity of the TME. scRNA-seq has been used to investigate whether the pancreatic ductal adenocarcinoma (PDAC) response to an immune checkpoint inhibitor is enhanced by changing the TME via targeting CD47 with a monoclonal antibody. Various PDAC mouse models displayed varying responses to anti-CD47 and anti-PD-L1 blockade because the TME established by different PDAC cell lines varied across these models. These findings suggest that targeting CD47 may alter the TME and alter the response of PDAC to immune checkpoint inhibitors ^[15][45].

Tumor metabolism is dynamic, and the components of TME change constantly. A study analyzed the dynamics of the transcriptome profile of immune cells at the single cell level from the primary state to metastasis of a pancreatic neuroendocrine tumor and found that the immune microenvironment of the primary lesions was distinct from the metastatic ones, revealing the intra- and inter-humoral heterogeneity of cell populations ^[4][35]. Because the immune system is a complex network for defending against various diseases, heterogeneity in immune cells and immune responses is common. The heterogeneity of immune cells also contributes to immune tolerance ^[16][46]. The heterogeneity and dynamics of cellular components in the TME at various stages of cancer development should be characterized before performing immunotherapy.

scRNA-seq can also be used to detect immune cell development, including differentiation ^[17][18][19][47,48,49], maturation ^[20][50], responses to stimulation, and activation ^[21][51]. Reconstructing the trajectory of immune cell development and differentiation at the single cell level can provide valuable insight into the immune microenvironment and locate immune cells’ origin and critical events in disease progression ^[22][52]. The immune microenvironment is complicated and dynamic. Understanding the dynamics of immune/inflammatory cells can deepen the insight into TME.

By comparing samples with different stages of PDAC based on scRNA-seq, the proportions of classical CAFs (cCAF), csCAFs, and pancreatic stellate cells (PSCs) experience significant changes from early PDAC to late PDAC. Investigation into the developmental trajectories of these subpopulations demonstrated that csCAFs suppress tumors in the TME of PDAC and decrease during progression; the TME of late PDAC contains only PSCs but not cCAFs or csCAFs. The evolution of cCAFs and csCAFs towards PSCs may be a strategy to convert anti-tumor CAFs into pro-tumor CAFs ^[3][5]. During PDAC progression, an anti-tumor immune response occurs but is disabled by negative regulation from accumulated Tregs, exhausted T cells, and tumor-associated macrophages (TAMs).

Trajectory analysis helps investigate the origins of immune cells and neoplastic cells. An analysis was performed between two subgroups of ductal cells and acinar cells, revealing that acinar cells could become type 1 ductal cells and then transform into type 2 ductal cells in PDAC ^[23][53]. Another study described the evolutionary trajectory of immune cells using scRNA-seq, and critical molecular events were identified in the progression from primary lesions to metastases ^[4][35]. Metabolic reprogramming develops at the “mid-late” stage, supporting the notion that tumor metabolism is a dynamic process and is adapted to the TME. scRNA-seq was used to study bladder urothelial carcinoma and revealed that monocytes undergo M2 polarization in the tumor region and differentiate into TAMs, while the LAMP3-positive dendritic cells (DCs) recruit Tregs. These immune cells with inflammatory CAFs (iCAFs) potentially take part in the formation of an immunosuppressive TME, therefore playing a role in tumor progression ^[24][54]. These representative studies provide insights into cancer immunology and provide an essential resource for immunotherapy and drug discovery.

Thanks to the work of the innate immune system and the adaptive immune system, health can be maintained. Chronic inflammation occurs because of abnormalities in the innate immune system, whereas exercise reduces chronic inflammation involving immunology and oxidative stress ^[25][55]. A deep understanding of the immune mechanisms of chronic inflammation and immune escape is critical, focused on how the immune mechanism protects the host and on why the immune mechanism loses its ability to protect the host. scRNA-seq can uncover these immune mechanisms. Immune escape (i.e., when the host loses the ability to defend itself against the foreign invaders) leads to disease progression. Immune surveillance evasion is the hallmark of tumor progression ^[26][56]. By analyzing the scRNA-seq data from a patient with multidrug-resistant Mantle cell lymphoma, several potential immune escape mechanisms of malignant cells were identified, including the anti-perforin pathway and the low tumor cell immunogenicity pathway ^[27][57]. Similarly, scRNA-seq in bladder urothelial carcinoma suggests that bladder cancer cells may evade immune detection by down-regulating immunogenicity ^[24][54].

The immune microenvironment is involved in osteosarcoma tumorigenesis and progression. However, the distribution and dynamics of immune cells in osteosarcoma are not well understood. scRNA-seq data in osteosarcoma suggests that a cluster of regulatory DCs forms the immunosuppressive TME by recruiting Treg cells. The major histocompatibility complex class I is downregulated in osteosarcoma ^[28][58]. These findings suggest that decreased tumor immunogenicity may be the potential mechanism of immune escape.

Lineage plasticity and stemness contribute to drug resistance in cancer therapy because these flexible states allow cancer cells to dedifferentiate into stem-like cells. Understanding how the immune system works in drug resistance may provide insights into immune therapy against cancer. A study reported scRNA-seq in malignant and microenvironment cells in patients with relapsed/refractory early T-cell progenitor acute lymphoblastic leukemia carrying activating NOTCH1 mutations; the investigators detected two functionally different stem-like states, irrespective of cell cycle or oncogenic signaling. More importantly, these two stem-like states differentiated into mature leukemia states, suggesting that they have prominent immunomodulatory functions, promoting an immunosuppressive leukemia ecosystem with the clonal accumulation of dysfunctional CD8+ T cells that expressed HAVC ^[29][59]. This finding suggests therapeutic targets based on cellular states might limit cancer cells’ molecular escape.

Colorectal cancer (CRC) is a typical “cold” cancer characterized by insufficient immune cell infiltration. Overcoming the deficiencies in CRC treatment might depend on improving the understanding of TME. scRNA-seq data from ten human CRC samples from the Gene Expression Omnibus (GEO) database revealed that differentiation is accompanied by remodeling of lipid metabolism and suppression of immune function, suggesting that lipid remodeling may be a fundamental cause of immunosuppression ^[30][60]. Thus, scRNA-seq could uncover immune mechanisms, enriching our understanding of cancer immunology and leading to the development of specific cancer immunotherapies.