Investigation of human aging at the single-cell level

Giant Cell Tumor of Bone (GCTB) is a common intermediate tumor, and the specific molecular mechanisms of this disease have not been fully elucidated. It exhibits strong heterogeneity in terms of targets, regulatory mechanisms, cell types, states, and subset distributions in the immune microenvironment. Traditional collective-level analyses cannot accurately reveal these differences. Single-cell sequencing technology is a technique that sequences the genome, transcriptome, and epigenome of diseases at the single-cell level. Single-cell sequencing can utilize a higher pixel resolution to reveal characteristic states of different cell subpopulations, contributing to broadening new perspectives for the study of GCTB heterogeneity. It holds significant value for the precise diagnosis of GCTB, identification of potential immunotherapy targets, and prognosis assessment. This study primarily reviews research related to GCTB, single-cell high-throughput sequencing technology, GCTB immune microenvironment, GCTB heterogeneity, and the construction of GCTB cell maps. It aims to provide theoretical reference for research on single-cell sequencing technology in GCTB and offers a theoretical basis for in-depth exploration of the mechanisms and treatment of GCTB.

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Multiple myeloma (MM) is a hematological malignancy of aberrant clonal plasma cells that reside within the bone marrow (BM).1 The disease course differs from other BM malignancies essentially in 2 features. First, there are 2 precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM), that can transition into MM over time.2 Second, once clinical MM develops, the disease remains largely incurable, and despite significant therapeutic improvements, relapse and refractoriness usually cannot be prevented. The interactions between MM cells and the BM microenvironment (BM-ME) are an area of particularly intense research, as tumor evasion and suppression of the host immune system constellate main factors of MM progression.3 Single-cell sequencing technologies that have emerged over the last years have the potential to significantly advance the field because they enable the evaluation of alterations in cell numbers and states as well as interactions between MM cells and the BM-ME. Recent studies have applied single-cell techniques at different precursor and MM stages to determine the comprehensive changes in the BM-ME and to identify mechanisms that foster oncogenesis.4–11 This article briefly summarizes these studies and proposes how the dissection of the BM-ME on a single-cell level can improve our understanding of MM pathogenesis, thereby advancing prognostication and the therapeutic landscape. We would like to emphasize that single-cell analyses are a rapidly expanding field and the number of published studies investigating the BM-ME in MM and its precursors is likely to grow exponentially in the next few years given the increased interest in and access to single-cell technologies. Thus, this perspective does not cover all available articles. THE IMMUNE MICROENVIRONMENT IN THE EARLY EVOLUTION OF MYELOMA Progression from precursor stages to clinical MM is highly heterogeneous, with some patients progressing quickly, while the others remain stable for decades.2 Treatment is hence not justified solely on the premise of a precursor diagnosis. Hence, recent research has focused on identifying SMM patients, who would benefit from early therapeutic interventions. Current risk classifiers are mainly based on clinical parameters and tumor aberrations (translocation t[4;14], t[14;16], gain1q and/or del13q), yet are only able to capture up to ~60% of patients who progress to MM within 2 years.12 Further molecular characterization of the BM-ME could improve the accuracy of prognostication and also pave the way for better therapeutic avenues. In that regard, Zavidij et al8 performed single-cell RNA sequencing (scRNA-seq) of the immune-ME from 5 MGUS, 11 SMM and 7 MM patients compared with 9 healthy individuals. The authors show that substantial alterations of the immune-ME are already present at the MGUS stage. These include increased populations of natural killer (NK) cells, T cells, particularly Tregs, and nonclassical CD16+ monocytes. CD14+ monocytes showed dysregulated expression of major histocompatibility complex (MHC) type II genes, which resulted in T-cell suppression in in vitro cultures. The progression from MGUS to SMM was associated with the loss of granzyme K+ memory cytotoxic T cells leading to reduced immunosurveillance in in vivo models. Symptomatic MM was characterized by an increased INF-alpha response across all immune cell types, which has been shown to promote immunosuppression, favoring expansion of MM cells. Similarly, using scRNA-seq and mass cytometry, Bailur et al11 showed that precursor stages already harbor BM-ME alterations compared with healthy donors, including early changes in NK and myeloid cells as well as increased terminal effector differentiation and enrichment of stem-like T cells in MGUS. Another scRNA-seq study with 8 MGUS, 7 SMM, and 10 MM patients showed substantial alterations in the immune-ME of MM compared with its precursor stages.5 Symptomatic MM was enriched for CD14+ and CD16+ monocyte populations, memory B and CD8 effector cells compared with early stages with a transitional decrease of CD4-positive T cells and MAIT cells. However, there was no differential expression of exhaustion markers such as PD1, TIGIT, and LAG-3 when comparing stages. Importantly, cell proportions were heterogeneous even across the same disease stage, emphasizing the need for larger datasets and also underscoring the existence of an individual immune phenotype, which is likely influenced by other factors than the aberrant plasma cells. THE IMMUNE MICROENVIRONMENT DURING MYELOMA THERAPY AND IN RELAPSING DISEASE Established therapeutic strategies yield high complete remission rates, but disease relapse will ensue in the vast majority of patients. Yet, the disease course remains very heterogeneous with some patients relapsing within months of treatment initiation and others experiencing deep remission for >10 years. This observation has led to the hypothesis that MM cells can enter a dormant state with regrowth over time when conditions become permissive, particularly when immune surveillance falters.13 Although many MM therapies try to stimulate the immune-ME to fight the disease, it has been also shown that therapy causes immunosuppression with ensuing cytopenias and hypogammaglobulinemia, emphasizing that the balance of a stimulated or dysfunctioning immune-ME is very delicate. Deciphering the alterations within the immune-ME during therapy will hence be challenging. Tirier et al6 investigated 20 relapsed/refractory MM patients prior and post salvage therapy and compared them to 8 healthy donors. Applying scRNA-seq to the immune-ME and plasma cells, the authors analyzed both compositional changes and alterations in cell–cell interactions. In this population, which was heavily enriched for gain1q, the investigators found depletion of CD4+/CD8+ naïve and CD4+ memory cells with enrichment of CD14+/CD16+ monocytes, effector T-cell populations (CD8+ memory and cytotoxic cells) as well as gamma/delta T cells with an increase of inflammatory markers in the BM that were primarily secreted by CD14+ monocytes and MM cells. In a similar attempt to understand co-evolution of MM cells and their immune-ME, Liu et al4 described longitudinal alterations during disease progression in 14 MM patients. They report that genetic alterations tend to be associated with distinct immune cells clusters, for example, patients with translocation t(11;14) showed separate T-cell clusters with upregulation of lysine methyltransferase genes KMT2A and KMT2C in CD8+ T cells. Other studies addressed the role of the immune-ME in early relapse patients. Pilcher et al7 performed scRNA-seq on the immune-ME in patients with rapidly progressing disease (<18 months) compared with those without progression within 4 years of follow-up. Early relapse patients had significantly higher numbers of exhausted CD8+ T cells (GZMK+ and TIGIT) with decreased expression of cytotoxic markers (PRF1, GZMB, and GNLY). Similar to the study by Tirier et al, there were also alterations within the monocyte/macrophage compartment with an increase of M2 macrophages in rapid progressors. Yao et al combined scRNA-seq with an additional capture of surface markers (Cellular Indexing of Transcriptomes and Epitopes by Sequencing, CITE-Seq) and the mass cytometry approach cytometry by time of flight (CyTOF) to elucidate the biology of early relapse.9 Stratifying 18 MM patients by international staging system (ISS) and by time to progression (<6 months for rapid progressors or 6 months to 5 years for slow progressors), they found aggressive disease (ISS 3) to be associated with a decrease in CD4+ T cells and the CD4/CD8 ratio. Last but not least, recent studies investigated alterations of the BM-ME during immunotherapies, as those heavily rely on a functional immune system for optimal efficacy. Adams III et al determined the effect of the CD38-antibody Daratumumab on immune cells in the BM and peripheral blood (PB) in relapsed MM patients.10 Daratumumab depleted NK cells and reduced the amount of CD38+ basophils. As seen in the previous studies, immune profiling differed between responders and nonresponders with higher granzyme B expression in CD8+ T cells of responders, suggesting treatment induced T-cell activation with increased killing capacity. Friedrich et al determined the immunological single-cell mechanisms of resistance to bispecific BCMAxCD3 T-cell engager (TCE) therapy, a novel immunotherapy leading to unprecedented response rates in refractory MM.14 They show that TCEs lead to clonal expansion of immune cell subsets, particularly effector CD8+ T cells and that primary or acquired failure to TCE therapy is associated with exhaustion of CD8+ T cells. Importantly, treatment failure or loss of response was not solely associated with immune-ME alterations but also MM intrinsic adaptations, such as loss of the target epitope and MHC class I protein. Taken together, current research is rapidly progressing to identifying alterations within the immune-ME of particularly aggressive disease, as these patients are in most need of novel therapeutic approaches. Single-cell sequencing studies so far appear to uniformly show dysregulation of T cells, particularly cytotoxic/effector T cells with increase of exhaustion markers in progressing patients. Furthermore, there appear to be alterations leading to a dysfunctional state in the monocytes/macrophages compartment in more aggressive MM. CHALLENGES AND FUTURE PERSPECTIVES Single-cell techniques have the potential to comprehensively dissect disease mechanisms within the immune-ME and their relationship with MM cells, thereby offering a path to truly individualized medicine leading to improved prognostic measure and therapeutic avenues. Challenges remain in unifying cell subset annotations to better compare studies. The encountered heterogeneity between patients could be accounted for by increasing sample sizes and by stratifying patients based on the parameters such as age, genetic profiles of MM, and treatment received. Age has shown to have a significant impact on immune function and is manifested at multiple levels including reduced production of B and T cells and diminished function of mature lymphocytes.15 It is hence not surprising that increased age has repeatedly shown to be an adverse risk factor for clinical outcome in MM, which is likely not only due to the higher prevalence of comorbidities in this population.16 The notion that molecular subgroups of MM can differ in the surrounding BM-ME has been reported long before the onset of single-cell technologies. For example, patients of the low bone disease molecular subgroup barely show osteolytic lesions, suggesting that this subset spares the stimulation of osteoclasts and the resulting bone resorption.17 The recently discovered associations of an immune-ME signature and +1q MM and the distinct clustering of immune cells in patients with translocation t(11;14) further corroborate the hypothesis that the MM genotype and phenotype influences its surrounding or vice versa. Hence, analyzing MM in its BM-ME on a single-cell level will offer the unique opportunity to associate alterations in the ME to intrinsic MM cell features, which could revolutionize our understanding of MM biology. Treatment has a significant effect on the immune-ME, and can lead to alterations in cell populations and expression patterns, which will need to be taken into account when analyzing single-cell data.10,18,19 These alterations are associated with immune cell cytotoxicity, exhaustion, and senescence, which can influence the further disease course.20 Conversely, comprehensive immune profiling before therapy could determine which treatment modalities would yield best responses. The use of single-cell techniques before and during therapy would enable us to dissect mechanisms of response versus resistance and could tailor therapeutic approaches based on the patients' individualized immune profile. Additional challenges to single-cell technologies are based on the current limitations of these techniques and are unlikely to be overcome by merely increasing the number of samples. This includes the limited number of analyzed cells due to the high costs. As an alternative or complementary approach, multidimensional flow cytometry allows for a cost-effective assessment of immune-phenotypes in the BM-ME and yields prognostic information in MM and its precursor stages.19,21,22 A particular hindrance to single-cell technologies is the lack of spatial resolution as they are performed on BM aspirates or PB. Although the current algorithms can predict interactions between cell populations based on the surface markers, the BM-ME architecture remains elusive. Furthermore, aspirates contain very small numbers of adherent cells, such as stromal cells, osteoblasts, and osteoclasts. Hence, to determine the structure and composition of the BM and its relationship with MM in great detail, it will be important to combine single-cell technologies with multiplex immunohistochemistry studies.23 Of note, BM-ME single-cell data are usually derived from a random BM site and might not be representative of the whole disease process. This is particularly important for MM, as the vast majority of patients present with focal lesions where genetic profiles of MM cells have been shown to be distinct from the random BM site.24,25 First reports suggest that this also holds true for the BM-ME at these sites.26,27 There is growing awareness that MM cell heterogeneity is, to at least some degree, reflected by circulating tumor cells, which are thought to originate from all affected MM sites and have prognostic impact.22 Thus, paired profiling of the BM and PB could unravel alterations that are not evident at the random BM site. A summary of the anticipated comprehensive analysis of MM and its BM-ME in the future is presented in Figure 1.Figure 1.: Summary of the anticipated future comprehensive analysis of myeloma and its microenvironment at a single-cell level.In conclusion, single-cell technologies have already influenced the MM field by identifying mechanisms underlying treatment resistance and they have the potential to further advance our current understanding of risk classification, prognostication, and individualized treatment approaches. While high costs currently limit their application, it is anticipated that technical advances and broader use will significantly reduce the costs and allow for larger sample collections. Hence, comprehensive and longitudinal profiling of the immune-ME using multiomic single-cell approaches with integration of the spatial BM architecture and genomic profiling of MM cells could soon be a reality for our MM patients and will eventually lead to improved clinical outcomes and cure. ACKNOWLEDGMENTS As a result of the space constraints, we apologize that we were unable to cite all our colleagues that have made an impact in defining our current knowledge on this topic. DISCLOSURES NW was supported by the Dietmar-Hopp Foundation. The authors declare no conflicts of interest. SOURCES OF FUNDING NW was supported by the Dietmar-Hopp Foundation.

Single cell sequencing (SCS) was chosen as Method of the Year in 2013 by Nature Methods. It refers to the deep sequencing of DNA or RNA molecules of single cells for the study of genome, transcriptome, noncoding RNA, and protein sequences encompassed within the single cells of interest. SCS possesses a strong potential to exert a great impact on biological research due to its unprecedented opportunity and capability of resolving sequence composition and genetic variations on single cell basis, which can be further integrated to explain the biological phenomena at the population or organismal level. During the past few years, SCS has produced promising results in a number of fields, especially cancer research. Prior to SCS, tissue sequencing could only analyze a tumor mass or a cancer cell population as a whole. With methods for single cell isolation, further powered by molecular level resolution made possible by minute DNA amplification and nucleotide level resolution made possible by next-generation sequencing, SCS technologies are becoming robust approaches for the study of cancer genomics and transcriptomics, and the cancer–normal cell interactions in the microenvironment. SCS analyses of cancer evolution, for example, have shown the capability of being able to empirically infer the driver mutations and map the sequential mutation events during cancer development. Undoubtedly, these novel approaches will produce profound health benefits. SCS will continue to attract more attention, and further expansion of its applications in many biological fields is inevitable. This chapter focuses on SCS methodologies, its existing applications, especially its capability in reconstructing the evolutionary history of cancer progression and in profiling cancer transcriptome, and the potential applications expected to come.

Background: Effective cancer immunotherapy is significantly affected by intratumoral heterogeneity, not only in different cell types and functions but also in the spatial pattern of immune cells within the tumor tissue. While most single-cell RNA sequencing technologies could enable the exploration of gene expression heterogeneity at the single-cell level, they cannot reveal spatial information within the tumor for us to map the whole transcriptome with morphological context. Spatial transcriptomics is an in situ capturing technique, which profiles gene expression at the RNA level, whilst preserving the spatial information of histological tissue sections. Thus we try to develop our spatial transcriptomics assay through a serial of validation experiments and to explore the mechanism of immune therapy against tumors. Material and Method: Firstly, the tumor was frozen and sections were cryosectioned at 10 μm thickness. Sections processing including H&amp;E staining, tissue optimization, permeabilization, reverse transcription, and cDNA library preparation was carried out following the user guide of Visium Spatial Gene Expression Reagent Kits (10X Genomics). And the final cDNA libraries were sequenced using the Illumina Novaseq instrument. Using gene expression matrix and aligned H&amp;E image processed by Cell Ranger and Loupe Browser with default parameters, we performed data QC, normalization, dimensionality reduction, and clustering by Seurat and cell components identification in tumor microenvironment based on a multistep approach. Differential gene expression and spatial heterogeneity were analyzed in each cluster. Results: Here we established a reliable system for spatial transcriptomics assay to elucidate the mechanism of anti-PD1 treatment in MC38 syngeneic colorectal tumor. Using this system, we constructed the gene expression spatial patterns of tumor cells and immune cells, including periphery and center of tumor cell, immune-cell enrichment area, and tumor cell surrounding the immune cell. Furthermore, we explored the cell-cell interactions in which the tumor cells directly interact with adjacent non-tumor cells, for example, macrophages. Using this system, we tried to investigate the mechanism of anti-PD-1 therapy against the tumors by comparing the spatial transcriptome profiles after the treatment with anti-PD-1 or isotype control in colorectal cancer models. Interestingly, changes in both gene-level and spatial levels were observed, and it will help reveal how the immune therapy may shake up the complex ecosystem. Summary: We established a reliable system for spatial transcriptomics analysis and explored the mechanism of anti-PD-1 treatment against the colorectal cancer by using this system. Citation Format: Panpan Wang, Huacheng He, Qiyao Zhang, Jingjing Wang, Dan Lu, Fei Duan, Qiangqiang Fan, Qingyang Gu, Qunsheng Ji. Establishment of spatial transcriptomics assay to study the mechanism of immune therapy against tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6128.

BackgroundRenal fibrosis, characterized by the abnormal accumulation of extracellular matrix in renal tissue and progressive loss of kidney function, is posing a significant challenge in clinical treatment. While several therapeutic options exist, effective treatments remain limited. Inonotus obliquus (Chaga), a traditional medicinal mushroom, has shown promising effects in chronic kidney disease (CKD), yet its cellular and molecular mechanisms remain largely unexplored.MethodsWe analysed the chemical composition of Chaga using UPLC-MS and predicted its biological targets using PubChem and Swiss Target Prediction. We used single-cell RNA sequencing to study cellular responses in a mouse model of folic acid-induced renal fibrosis, complemented by spatial transcriptomics to map cellular location patterns. Histological assessment was performed using H&E and Masson trichrome staining.ResultsFor the first time, we employed single-cell RNA sequencing technology to investigate Chaga treatment in renal fibrosis. Histological analysis revealed that Chaga treatment significantly reduced renal tubular damage scores [from 5.00 (5.00, 5.00) to 2.00 (2.00, 2.00), p < 0.05] and decreased collagen deposition area (from 11.40% ± 3.01% to 4.06% ± 0.45%, p < 0.05) at day 14. Through analysis of 82,496 kidney cells, we identified 30 distinct cell clusters classified into eight cell types. Key findings include the downregulation of pro-inflammatory M1 macrophages and upregulation of anti-inflammatory M2 macrophages, alongside decreased T cell responses. Single-cell sequencing revealed differential gene expression in proximal tubular subpopulations associated with reduced fibrosis. Pathway and network pharmacology analyses of 60 identified compounds in Chaga and their 675 predicted targets suggested potential effects on immune and fibrotic pathways, particularly affecting Tregs and NKT cells. Cell-to-cell communication analyses revealed potential interactions between proximal tubular cells, macrophages, and T Cells, providing insights into possible mechanisms by which Chaga may ameliorate renal fibrosis.ConclusionOur study provided new insights into the potential therapeutic effects of Chaga in renal fibrosis through single-cell sequencing analysis. Our findings suggest that Chaga may represent a promising candidate for renal fibrosis treatment, though further experimental validation is needed to establish its clinical application.

Acute lung injury (ALI) is the most common type of organ injury in sepsis, with high morbidity and mortality. Sepsis is characterized by an inappropriate inflammatory response while neutrophils exert an important role in the excessive inflammatory response. The discovery of specialized pro-resolving mediators (SPMs) provides a new direction for the treatment of a series of inflammatory-related diseases including sepsis. Among them, the regulation of Maresin1 on immune cells was widely demonstrated. However, current research on the regulatory effects of Maresin1 on immune cells has remained at the level of certain cell types. Under inflammatory conditions, the immune environment is complex and immune cells exhibit obvious heterogeneity. Neutrophils play a key role in the occurrence and development of septic lung injury. Whether there is a subpopulation bias in the regulation of neutrophils by Maresin1 has not been elucidated. Therefore, with the well-established cecal ligation and puncture (CLP) model and single-cell sequencing technology, our study reveals for the first time the regulatory mechanism of Maresin1 on neutrophils at the single-cell level. Our study suggested that Maresin1 can significantly reduce neutrophil infiltration in septic lung injury and that this regulatory effect is more concentrated in the Neutrophil-Cxcl3 subpopulation. Maresin1 can significantly reduce the infiltration of the Neutrophil-Cxcl3 subpopulation and inhibit the expression of related inflammatory genes and key transcription factors in the Neutrophil-Cxcl3 subpopulation. Our study provided new possibilities for specific modulation of neutrophil function in septic lung injury.

Breast cancer originates from ducts and epithelial cells, and gradually develops from hyperplasia to atypical hyperplasia, in situ (adeno) carcinoma, to early and advanced invasive carcinoma. Traditional high-throughput sequencing mainly aims to identify candidate 'driver genes' attributable to development and progression of breast cancer, which has deficiencies in characterizing genomic structure alteration and subclone evolution, and thus ignores intratumoral, intertumoral or interpatient heterogeneity. The single-cell sequencing technology analyzes transcriptome (e.g., gene copy number and gene expression), explores cellular composition, differentiation and fate, fine-maps the tumor microenvironment, and provides supporting evidence for accurate stratification as well as personalized, precise therapy. At the same time, a complex relationship between breast cancer cells and T cells, macrophages and other immune cells can be revealed, thus facilitating discovery of new therapeutic targets and immune checkpoints. Here, we review state-of-the-art single-cell sequencing technologies and its application in breast cancer, in order to decipher multi-faceted alterations in the crosstalk/interactions between tumors and its microenvironments at the single-cell level, and provide a basis for better understanding of complicated pathogenesis and new avenues for immunotherapy.

Cancer is one of the leading causes of death worldwide and well known for its complexity. Cancer cells within the same tumor or from different tumors are highly heterogeneous. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in how tumors progress and respond to different treatments. Recent advances in single cell technologies, especially massively parallel single cell sequencing, have made it possible to analyze cancer cells and cells in its tumor microenvironment in parallel with unprecedented high resolution. In this chapter, we will review recent developments in single cell sequencing technologies and their applications in cancer research. We will also explain how insights generated from single cell sequencing can be used to develop novel diagnostic and therapeutic approaches to conquer cancer.

Variation and heterogeneity between cells are the basic characteristics of stem cells. Traditional sequencing analysis methods often cover up this difference. Single-cell sequencing technology refers to the technology of high-throughput sequencing analysis of genomes at the single-cell level. It can effectively analyze cell heterogeneity and identify a small number of cell populations. With the continuous progress of cell sorting, nucleic acid extraction and other technologies, single-cell sequencing technology has also made great progress. Encouraging new discoveries have been made in stem cell research, including pluripotent stem cells, tissue-specific stem cells and cancer stem cells. In this review, we discuss the latest progress and future prospects of single-cell sequencing technology in the field of stem cells.

IntroductionIn recent decades, single-cell sequencing technology has developed rapidly and used widely in various fields of life sciences, especially for the detection of immune cells. A bibliometric analysis of single-cell sequencing research work on immune cells published during the 2011-2021 period should provide new insight on the use of single-cell sequencing.MethodsWe screened 1,460 publications on single-cell sequencing on immune cells according to the publication date, article type, language, and country.ReultsThe United States published the first and largest number of articles, while China’s research started relatively late, but ranked second in the number of publications. T cells were the most commonly studied immune cells by single-cell sequencing, followed by mononuclear macrophages. Cancer biology was the most common field of immune cell research by single-cell sequencing. Single-cell sequencing studies using γδ T cells were mainly in the fields of cancer biology and cell development, and focused over time from cell surface receptor to cell function. Through in-depth analysis of the articles on single-cell sequencing of T cells in the oncology field, our analysis found that immunotherapy and tumor microenvironment were the most popular research directions in recent years.DiscussionThe combination of DNA damage repair and immunotherapy seems to provide a new strategy for cancer therapy.

Colorectal cancer (CRC) stands as the most common gastrointestinal malignancy, with the Follistatin-like 1 (FSTL1) gene associated with unfavorable outcomes in diverse cancer forms. Nonetheless, the precise mechanisms through which FSTL1 influences CRC pathogenesis and its associated multi-omics characteristics remain unclear. This study leveraged RNA sequencing and single-cell RNA sequencing (scRNAseq) data from the Gene Expression Omnibus (GEO) database to scrutinize the expression profile, prognostic significance, and clinicopathological relevance of FSTL1 in colorectal cancer. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis were conducted to delve into the potential biological functions of FSTL1 in colorectal cancer and its associated pathways. The ESTIM algorithm and Tumor Immune Estimation Resource (TIDE) database were utilized to investigate FSTL1 expression in immune cell infiltration and immune checkpoints. Mutation characteristics of FSTL1 were elucidated using the cBioPortal database. The pRRophetic package was employed to identify potential chemotherapeutic drugs targeting FSTL1. Expression levels of FSTL1 in colon cancer and adjacent normal tissues were evaluated using the Human Protein Atlas (HPA) database. Furthermore, single-cell sequencing technology and cell communication were employed to assess the immunogenomic features of FSTL1 in the tumor microenvironment. In vitro experiments were conducted to validate the efficacy of FSTL1. FSTL1 levels were markedly higher in colorectal cancer tissues compared to healthy tissues, correlating significantly with unfavorable patient outcomes. Elevated FSTL1 expression was closely linked to key biological processes including the PI3K pathway, cell adhesion, proliferation, migration, and extracellular matrix remodeling. Furthermore, FSTL1 expression exhibited strong associations with immune cell levels and the tumor immune microenvironment in colorectal cancer. Notably, FSTL1 mutations predominantly comprised missense mutations and displayed significant correlations with various immune checkpoints and low methylation levels. Axitinib emerged as a promising targeted therapeutic option for patients with high FSTL1 expression. Analysis from the HPA database confirmed elevated FSTL1 expression in colorectal tissues compared to adjacent normal tissues. Single-cell sequencing analysis identified stromal cells as the primary source of abundant FSTL1 expression, potentially influencing tumor microenvironment remodeling by myeloid and B cells via the APP-CD74 pathway. Downregulating FSTL1 expression in colon cancer cells suppresses the proliferation, motility, and invasiveness of SW480 and HCT116 colon cancer cells. In summary, the findings of the study indicate that elevated FSTL1 expression could serve as a prognostic biomarker for unfavorable outcomes in colorectal cancer (CRC) diagnosis and may also identify potential targets for immunotherapy in CRC.

Over the past decade, there have been remarkable improvements in the treatment and survival rates of melanoma patients. Treatment resistance remains a persistent challenge, however, and is partly attributable to intratumoural heterogeneity. Melanoma cells can transition through a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The diverse stromal and immune contexture of the tumour microenvironment also contributes to intratumoural heterogeneity and disparities in treatment response in melanoma patients. Recent advances in single-cell sequencing technologies have enabled a more detailed understanding of melanoma heterogeneity and the underlying transcriptional programs that regulate melanoma cell diversity and behaviour. In this review, we examine the concept of intratumoural heterogeneity and the challenges it poses to achieving long-lasting treatment responses. We focus on the significance of next generation single-cell sequencing in advancing our understanding of melanoma diversity and the unique insights gained from single-cell studies.

Recently, immunotherapy has gained increasing popularity in oncology. Several immunotherapies obtained remarkable clinical effects, but the efficacy varied, and only subsets of cancer patients benefited. Breaking the constraints and improving immunotherapy efficacy is extremely important in precision medicine. Whereas traditional sequencing approaches mask the characteristics of individual cells, single-cell sequencing provides multiple dimensions of cellular characterization at the single-cell level, including genomic, transcriptomic, epigenomic, proteomic, and multi-omics. Hence, the complexity of the tumor microenvironment, the universality of tumor heterogeneity, cell composition and cell-cell interactions, cell lineage tracking, and tumor drug resistance mechanisms are revealed in-depth. However, the clinical transformation of single-cell technology is not to the point of in-depth study, especially in the application of immunotherapy. The newly discovered vital cells and tremendous biomarkers facilitate the development of more efficient individualized therapeutic regimens to guide clinical treatment and predict prognosis. This review provided an overview of the progress in distinct single-cell sequencing methods and emerging strategies. For perspective, the expanding utility of combining single-cell sequencing and other technologies was discussed.

Tumor heterogeneity is a fundamental driver of therapeutic resistance across solid malignancies, arising from genetic, epigenetic, phenotypic, spatial, temporal, and microenvironmental diversity. In tumors developing at mucosal barrier sites, these heterogeneous features are further shaped by the unique immunological context of mucosal tissues, where immune tolerance, chronic inflammation, and continuous antigen exposure create permissive environments for immune escape and adaptive resistance. Accumulating evidence indicates that myeloid cell plasticity, including functional diversification of granulocytes, macrophages, monocytes, and dendritic cells, represents a critical interface between tumor-intrinsic heterogeneity and mucosal immune regulation. These myeloid populations contribute to spatially organized immunosuppressive niches, altered antigen processing and presentation, and therapy-induced immune remodeling, collectively influencing responses to chemotherapy, targeted therapy, and immunotherapy. Advances in single-cell sequencing, spatial transcriptomics, multiplex imaging, and liquid biopsy technologies, coupled with artificial intelligence-enabled analytics, have enabled high-resolution mapping of heterogeneous tumor immune landscapes and revealed convergent resistance mechanisms driven by clonal selection, phenotypic plasticity, microenvironmental buffering, and myeloid-mediated immune suppression. In this review, we synthesize mechanistic and clinical evidence across major cancer types, including colorectal and lung cancers as archetypal mucosal tumors, along with broader examples from breast cancer, melanoma, and immunotherapy-treated malignancies. We highlight how heterogeneous cellular states and immune niches influence clinical outcomes. Finally, we discuss emerging translational strategies to overcome resistance, including rational combination regimens, epigenetic and metabolic targeting, adaptive therapy, myeloid reprogramming approaches, and real-time biomarker monitoring. These approaches aim to restore effective anti-tumor immunity while accounting for the unique constraints of mucosal barrier tissue.