scRNA-seq Technology Research Articles

Decoding physiological and pathological roles of innate immune cells in eye diseases: the perspectives from single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has facilitated a deeper comprehension of the molecular mechanisms behind eye diseases and has prompted the selection of precise therapeutic targets by examining the cellular and molecular intricacies at the single-cell level. This review delineates the pivotal role of scRNA-seq in elucidating the functions of innate immune cells within the context of ocular pathologies. Recent advancements in scRNA-seq have revealed that innate immune cells, both from the periphery and resident in the retina, are actively engaged in various stages of multiple eye diseases. Notably, resident microglia and infiltrating neutrophils exhibit swift responses during the initial phase of injury, while peripheral monocyte-derived macrophages exhibit transcriptomic profiles akin to those of activated microglia, suggesting their potential for long-term residence within the retina. The scRNA-seq analyses have underscored the cellular heterogeneity and gene expression alterations within innate immune cells, which, while sharing commonalities, exhibit disease-specific variations. These insights have not only broadened our understanding of the cellular and molecular mechanisms in eye diseases but also paved the way for the identification of candidate targets for targeted therapeutic interventions. The application of scRNA-seq technology has heralded a new era in the study of ocular pathologies, enabling a more detailed appreciation of the roles that innate immune cells play across a spectrum of eye diseases.

Research Progress of Single-Cell Transcriptome Sequencing Technology in Plants

Multicellular organisms exhibit inherent cellular heterogeneity that cannot be captured by traditional high-throughput sequencing techniques, resulting in the unique cellular characteristics of individual cells being neglected. Single-cell transcriptome sequencing (scRNA-seq) technology can be used to determine the gene expression levels of each individual cell, facilitating the study of intercellular expression heterogeneity. This review provides a comprehensive overview of the development and applications of scRNA-seq technology in plant research. We highlight the significance of integrating single-cell multi-omics approaches to achieve a holistic understanding of plant systems. Additionally, we discuss the current challenges and future research directions for scRNA-seq technology in plant studies, aiming to offer valuable insights for its application across various plant species.

A Single-Cell RNA Sequencing Guided Multienzymatic Hydrogel Design for Self-Regenerative Repair in Diabetic Mandibular Defects.

Conventional bone tissue engineering materials struggle to reinstate physiological bone remodeling in a diabetic context, primarily due to the compromised repolarization of proinflammatory macrophages to anti-inflammatory macrophages. Here, leveraging single-cell RNA sequencing (scRNA-seq) technology, the pivotal role of nitric oxide (NO) and reactive oxygen species (ROS) is unveiled in impeding macrophage repolarization during physiological bone remodeling amidst diabetes. Guided by scRNA-seq analysis, we engineer a multienzymatic bone tissue engineering hydrogel scaffold (MEBTHS) composed is engineered of methylpropenylated gelatin hydrogel integrated with ruthenium nanozymes, possessing both Ru0 and Ru4+ components. This design facilitates efficient NO elimination via Ru0 while simultaneously exhibiting ROS scavenging properties through Ru4+. Consequently, MEBTHS orchestrates macrophage reprogramming by neutralizing ROS and reversing NO-mediated mitochondrial metabolism, thereby rejuvenating bone marrow-derived mesenchymal stem cells and endothelial cells within diabetic mandibular defects, producing newly formed bone with quality comparable to that of normal bone. The scRNA-seq guided multienzymatic hydrogel design fosters the restoration of self-regenerative repair, marking a significant advancement in bone tissue engineering.

Integrative analyses of bulk and single-cell RNA-seq reveals the correlation between SPP1+ macrophages and resistance to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Neoadjuvant chemoimmunotherapy (NACI) has significant implications for the treatment of esophageal cancer. However, its clinical efficacy varies considerably among patients, necessitating further investigation into the underlying mechanisms. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology facilitates the analysis of patient heterogeneity at the cellular level, particularly regarding treatment outcomes. In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression Omnibus (GEO) database. After performing dimensionality reduction, clustering, and annotation on the scRNA-seq data, we employed CellChat to investigate differences in cell-cell communication among samples from distinct efficacy groups. The results indicated that macrophages in the non-responder exhibited stronger cell communication intensity compared to those in responders, with SPP1 and GALECTIN signals showing the most significant differences between the two groups. This finding underscores the crucial role of macrophages in the efficacy of NACI. Subsequently, reclustering of macrophages revealed that Mac-SPP1 may be primarily responsible for treatment resistance, while Mac-C1QC appears to promote T cell activation. Finally, we conducted transcriptome sequencing on ESCC tissues obtained from 32 patients who underwent surgery following NACI. Utilizing CIBERSORT, CIBERSORTx, and WGCNA, we analyzed the heterogeneity of tumor microenvironment among different efficacy groups and validated the correlation between SPP1+ macrophages and resistance to NACI in ESCC using publicly available transcriptome sequencing datasets. These findings suggest that SPP1+ macrophages may represent a key factor contributing to resistance against NACI in ESCC.

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders

BackgroundSingle-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations.ResultsTo address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction.ConclusionsThese results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

ScCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.

Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement of single-cell RNA sequencing (scRNA-seq) technology has made it possible to address the problem of cancer cell identification at the single-cell level more efficiently with computational methods, as opposed to the time-consuming and less reproducible manual identification methods. However, existing computational methods have shown suboptimal identification performance and a lack of capability to incorporate external reference data as prior information. Here, we propose scCrab, a reference-guided automatic cancer cell identification method, which performs ensemble learning based on a Bayesian neural network (BNN) with multi-head self-attention mechanisms and a linear regression model. Through a series of experiments on various datasets, we systematically validated the superior performance of scCrab in both intra- and inter-dataset predictions. Besides, we demonstrated the robustness of scCrab to dropout rate and sample size, and conducted ablation experiments to investigate the contributions of each component in scCrab. Furthermore, as a dedicated model for cancer cell identification, scCrab effectively captures cancer-related biological significance during the identification process.

Unravelling the Complexity of HNSCC Using Single-Cell Transcriptomics.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and the most common form of head and neck cancer, posing significant challenges for disease management. The objective of this review is to assess the utility of single-cell RNA sequencing (scRNAseq) in addressing these challenges by enabling a detailed characterization of the tumor microenvironment (TME) at the cellular level. This review compiles and analyzes current strategies that utilize scRNAseq and other single-cell technologies in HNSCC research. For HNSCC etiology, scRNAseq allows for the construction of cellular atlases, characterization of different cell types, and investigation of genes and processes involved in cancer initiation, development, and progression within the TME. In terms of HNSCC diagnosis and prognosis, the resolution offered by scRNAseq enables the identification of cell type-specific signatures, enhancing prognostic models and disease stratifiers for patient outcome assessments. Regarding HNSCC treatment, scRNAseq provides insights into cellular responses to various treatments, including radiotherapy, chemotherapy, and immunotherapy, contributing to a better understanding of treatment efficacy and patient outcomes. This review highlights the contributions of scRNAseq to HNSCC research, addressing its cellular and biological complexity, and emphasizes its potential for advancing research and clinical practice in other cancer types.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

ScDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

ScPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Recent progress and applications of single-cell sequencing technology in breast cancer.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Single-Cell Transcriptomics Applied in Plants.

Single-cell RNA sequencing (scRNA-seq) is a high-tech method for characterizing the expression patterns of heterogeneous cells in the same tissue and has changed our evaluation of biological systems by increasing the number of individual cells analyzed. However, the full potential of scRNA-seq, particularly in plant science, has not yet been elucidated. To explore the utilization of scRNA-seq technology in plants, we firstly conducted a comprehensive review of significant scRNA-seq findings in the past few years. Secondly, we introduced the research and applications of scRNA-seq technology to plant tissues in recent years, primarily focusing on model plants, crops, and wood. We then offered five databases that could facilitate the identification of distinct expression marker genes for various cell types. Finally, we analyzed the potential problems, challenges, and directions for applying scRNA-seq in plants, with the aim of providing a theoretical foundation for the better use of this technique in future plant research.

Single-cell RNA-sequencing analysis of the effects of frozen-thawed embryo transfer on the transcriptome of trophoblasts

Objectives: Frozen-thawed embryo transfer (FET) is widely used in in vitro fertilization (IVF) clinics but is associated with an increased risk of several pregnancy complications, including large-for-gestational age and placenta-related diseases. However, the effects of FET on placentation remain unclear. Therefore, we used single-cell RNA-sequencing (scRNA-seq) technology to investigate the impact of FET on placental gene expression in different subtypes of trophoblasts. Methods: A mouse model of IVF and FET was constructed to collect placenta tissues. scRNA-seq was performed on placentas from two dams undergoing IVF-embryo transfer and two dams undergoing IVF-FET. Differentially expressed gene (DEG) analyses were performed in different subtypes of trophoblasts. Identified DEGs were PCR validated. Results: The fetal weights and placental efficiency were higher in the FET group than those on the IVF group at E18.5, with no significant difference in placental weights. Subsequently, 55,406 placental cells were captured and annotated. Upregulated DEGs in the FET group in syncytiotrophoblasts (SynTs) and sinusoidal trophoblast giant cells (S-TGCs) within the placental labyrinth were enriched in pathways related to vascular development and oxidative stress, respectively. The expression of the imprinted gene Igf2 in SynTs, S-TGCs, and spongiotrophoblasts was significantly increased. In the junctional zone, FET upregulated the expression of prolactin genes such as Prl3b1 in glycogen trophoblasts (GlyTs) while the downregulated expression of GlyT genes following FET was associated with mesenchyme development. Conclusion: This study first identifies DEGs and enriched pathways in different subtypes of trophoblasts following FET. These genes and pathways may contribute to the increased placental efficiency and fetal weights. Future studies are required to confirm these results and further explore the key mechanisms in placental pathologies.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BackgroundWith the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.ResultsWe introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.ConclusionsSINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM.

ScCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.

Medullary thyroid cancer: single-cell transcriptome and tumor evolution

BackgroundMedullary thyroid cancer (MTC) is a rare neuroendocrine tumor that originates from the parafollicular C cells of thyroid gland. Understanding the fundamental pathophysiology of MTC is essential for clinical management. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for identifying distinct cell types, offering a new biological foundation for comprehending the MTC ecosystem and developing precise treatment.MethodsFormalin fixed and paraffin-embedded (FFPE) samples of primary and adjacent non-cancerous tissues of three MTC cases were collected, and single-cell transcriptome data of MTC were obtained by using scRNA-seq technology. Annotated cell subpopulations were categorized and functionally enriched by principal component analysis, differential gene expression, and cell clustering analysis, to explore the biological process of tumor evolution that may be involved in each cell subpopulation. The copy number variation (CNV) profile was used to distinguish the malignancy of parafollicular thyroid cells, and the evolutionary trajectories of normal cells and tumor cells were revealed by the proposed time series analysis. The highly expressed genes in each cell subpopulation were analyzed by the FindAllMarker function of Seurat software, and verified by immunohistochemistry and fluorescence in situ hybridization. The prognostic value of specific cell subtypes was validated using large-scale public datasets.ResultsA total of 32,544 cells were obtained from the MTC tissue samples and 11,751 cells from the adjacent non-cancerous samples, which were classified into 7 heterogenous subpopulations by using R package of Seurat module. Copy number variations (CNVs) were significantly higher in tumor tissues than in adjacent non-tumor samples, predominantly enriched in subtypes C2 and C4. In addition, the pseudo-time for trajectory analysis suggested that the evolution of MTC tumor cells might begin with the C2 subtype, then transition to the early cancer subgroup C3, and further differentiate into four major malignant cell subpopulations C0, C1, C5 and C6. Survival analysis of a thyroid cancer cohort using the TCGA dataset revealed that high expression of genes linked to the C0 subcluster was correlated with poorer overall survival compared to low expression. Immunohistochemical staining showed that MAP3K4 was highly expressed in MTC tissues compared to adjacent non-cancerous tissues. Fluorescence in situ hybridization also confirmed the amplification of these two genes in MTC samples.ConclusionsBy conducting scRNA-seq on FFPE samples, we mapped the single-cell transcriptome of MTC, uncovering the tumor heterogeneity and unique biological features of each cellular subpopulation. The biological roles of identified tumor cell subpopulations such as C0 and C3 subtypes of parafollicular cells suggested the potential to discover new therapeutic targets and biomarkers for MTC, providing valuable insights for future translational and clinical research.

Single-cell RNA sequencing in endometrial cancer: exploring the epithelial cells and the microenvironment landscape.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for dissecting cellular heterogeneity and understanding the intricate biology of diseases, including cancer. Endometrial cancer (EC) stands out as the most prevalent gynecological malignancy in Europe and the second most diagnosed worldwide, yet its cellular complexity remains poorly understood. In this review, we explore the contributions of scRNA-seq studies to shed light on the tumor cells and cellular landscape of EC. We discuss the diverse tumoral and microenvironmental populations identified through scRNA-seq, highlighting the implications for understanding disease progression. Furthermore, we address potential limitations inherent in scRNA-seq studies, such as technical biases and sample size constraints, emphasizing the need for larger-scale research encompassing a broader spectrum of EC histological subtypes. Notably, a significant proportion of scRNA-seq analyses have focused on primary endometrioid carcinoma tumors, underscoring the need to incorporate additional histological and aggressive types to comprehensively capture the heterogeneity of EC. By critically evaluating the current state of scRNA-seq research in EC, this review underscores the importance of advancing towards more comprehensive studies to accelerate our understanding of this complex disease.

Research on single-cell transcriptomics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent malignant tumors of the digestive system globally, with a particularly high incidence recorded in China due to the prevalence of chronic hepatitis virus infections. Recent developments in single-cell RNA sequencing (scRNA-seq) technology have provided new perspectives and approaches for cancer research, particularly showing great potential in understanding the heterogeneity of HCC. scRNA-seq technology, through detailed gene expression analysis at the single-cell level, reveals the cellular heterogeneity of hepatocellular carcinoma, identify key drivers of tumor progression, and elucidates the complex features of the tumor microenvironment. Such insights are pivotal for decoding the underlying mechanisms of hepatocellular carcinoma, thereby guiding the development of precise therapeutic strategies and personalized treatments. Furthermore, identifying key drivers of tumor progression and analyzing the gene expression characteristics of the surrounding microenvironment at single-cell resolution is expected to provide clues for developing new therapeutic strategies. Therefore, this article aims to provide a systematic overview of the fundamental principles of scRNA-seq, review its progress in HCC research, and explore the challenges and future directions in this field to offer researchers a comprehensive perspective.

Deciphering single-cell transcriptomic landscape of tumors in colorectal cancer treated with photothermal semiconducting polymers to design the combination therapy with checkpoint blockades for inhibition of tumor progression and metastasis

Immunotherapy is often less effective for tumors with “immune-excluded,” or “immune-desert” microenvironment. To circumvent this hurdle, new therapeutic strategies need to be designed to reprogram the tumor microenvironment (TME). It is well known that photothermal therapy (PTT) can prime the TME and provoke the systemic anti-tumor immunity. But few studies perform extensive exploration in depth to decipher the changes in TME after PTT with cutting-edge technology besides conventional flow cytometry analysis. In this study, we designed a nanosystem consisting of organic semiconductor polymer liposome nanoparticles (Y8 NPs) as a multimodal imaging and photothermal agent to image and treat tumors. Upon laser irradiation, Y8 NPs in tumors could rapidly increase the temperature, induce massive cancer cell apoptosis and also promote the immunogenic cell death (ICD), ultimately remodulating the TME. We employed single cell RNA sequencing (scRNA-seq) technology to comprehensively explore transcriptomic profiling of individual cells constituting TME, demonstrating heterogenous alterations in the subtypes of immune cells, even tumor cells. Considering the findings by scRNA-seq and TME remodulation, the combinational strategy with checkpoint blockade PD-1 antibody was designed to treat the tumors, demonstrating that the combination not only impedes primary tumor progression but also produce profound abscopal anti-tumor effect. In conclusion, we provide a simple strategy for the remodulation of TME with a photothermal nanosystem to achieve activation of anti-tumor immunity, and suppress primary and metastatic tumor progression.