Unravelling biology and shifting paradigms in cancer with single-cell sequencing

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis

INTRODUCTION

Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data.

RESULTS

scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics.

CONCLUSIONS

The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.

Methods

The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.

Results

424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.

Conclusion

Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Spatial downregulation of CD74 signatures may drive invasive component development in part-solid lung adenocarcinoma

Pulmonary nodules with part-solid imaging features manifest during the progression from preinvasive to invasive lung adenocarcinoma. To define the spatial composition and evolutionary trajectories of early-stage lung adenocarcinoma, we combined spatial transcriptomics (ST) and pathological annotations from 20 part-solid nodules (PSNs), four of which were matched with single-cell RNA sequencing. Two malignant cell populations (MC1 and MC2) were identified, and a linear evolutionary relationship was observed. Compared to MC2, the pre-existing malignant MC1 exhibited a lower metastatic signature, corresponding to the preinvasive component (lepidic) on pathology and the ground glass component on PSN imaging. Higher immune infiltration was observed among MC1 regions in ST profiles, and further analysis revealed that macrophages may be involved in this process through the CD74 axis. This work provides deeper insights into the evolutionary process and spatial immune cell composition behind PSNs and highlights the mechanisms of immune escape behind this adenocarcinoma trajectory.

High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival

Numerous cell states are known to comprise the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME). However, the developmental stemness and co-occurrence of these cell states remain poorly defined. Here, we performed single-cell RNA sequencing (scRNA-seq) on a cohort of treatment-naive PDAC time-of-diagnosis endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) samples (n = 25). We then combined these samples with surgical resection (n = 6) and publicly available samples to increase statistical power (n = 80). Following annotation into 25 distinct cell states, cells were scored for developmental stemness, and a customized version of the Ecotyper tool was used to identify communities of co-occurring cell states in bulk RNA-seq samples (n = 268). We discovered a tumor microenvironmental community comprised of aggressive basal-like malignant cells, tumor-promoting SPP1+ macrophages, and myofibroblastic cancer-associated fibroblasts associated with especially poor prognosis. We also found a developmental stemness continuum with implications for survival that is present in both malignant cells and cancer-associated fibroblasts (CAFs). We further demonstrated that high-dimensional analyses predictive of survival are feasible using standard-of-care, time-of-diagnosis EUS-FNB specimens. In summary, we identified tumor microenvironmental and developmental stemness characteristics from a high-dimensional gene expression analysis of PDAC using human tissue specimens, including time-of-diagnosis EUS-FNB samples. These reveal new connections between tumor microenvironmental composition, CAF and malignant cell stemness, and patient survival that could lead to better upfront risk stratification and more personalized upfront clinical decision-making.

Unveiling the Role of Tumor-Infiltrating T Cells and Immunotherapy in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is a rapidly rising global health concern, ranking as the third-leading cause of cancer-related mortality. Despite medical advancements, the five-year survival rate remains a dismal 18%, with a daunting 70% recurrence rate within a five-year period. Current systematic treatments, including first-line sorafenib, yield an overall response rate (ORR) below 10%. In contrast, immunotherapies have shown promise by improving ORR to approximately 30%. The IMbravel150 clinical trial demonstrates that combining atezolizumab and bevacizumab surpasses sorafenib in terms of median progression-free survival (PFS) and overall survival (OS). However, the therapeutic efficacy for HCC patients remains unsatisfactory, highlighting the urgent need for a comprehensive understanding of antitumor responses and immune evasion mechanisms in HCC. In this context, understanding the immune landscape of HCC is of paramount importance. Tumor-infiltrating T cells, including cytotoxic T cells, regulatory T cells, and natural killer T cells, are key components in the antitumor immune response. This review aims to shed light on their intricate interactions within the immunosuppressive tumor microenvironment and explores potential strategies for revitalizing dysfunctional T cells. Additionally, current immune checkpoint inhibitor (ICI)-based trials, ICI-based combination therapies, and CAR-T- or TCR-T-cell therapies for HCC are summarized, which might further improve OS and transform the management of HCC in the future.

Mapping pediatric brain tumors to their origins in the developing cerebellum

BACKGROUND

Distinguishing the cellular origins of childhood brain tumors is key for understanding tumor initiation and identifying lineage-restricted, tumor-specific therapeutic targets. Previous strategies to map the cell-of-origin typically involved comparing human tumors to murine embryonal tissues, which is potentially limited due to species-specific differences. The aim of this study was to unravel the cellular origins of the 3 most common pediatric brain tumors, ependymoma, pilocytic astrocytoma, and medulloblastoma, using a developing human cerebellar atlas.

METHODS

We used a single-nucleus atlas of the normal developing human cerebellum consisting of 176 645 cells as a reference for an in-depth comparison to 4416 bulk and single-cell transcriptome tumor datasets, using gene set variation analysis, correlation, and single-cell matching techniques.

RESULTS

We find that the astroglial cerebellar lineage is potentially the origin for posterior fossa ependymomas. We propose that infratentorial pilocytic astrocytomas originate from the oligodendrocyte lineage and MHC II genes are specifically enriched in these tumors. We confirm that SHH and Group 3/4 medulloblastomas originate from the granule cell and unipolar brush cell lineages. Radiation-induced gliomas stem from cerebellar glial lineages and demonstrate distinct origins from the primary medulloblastoma. We identify tumor genes that are expressed in the cerebellar lineage of origin, and genes that are tumor specific; both gene sets represent promising therapeutic targets for future study.

CONCLUSION

Based on our results, individual cells within a tumor may resemble different cell types along a restricted developmental lineage. Therefore, we suggest that tumors can arise from multiple cellular states along the cerebellar "lineage of origin."

Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress

Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

The prognostic and biology of tumour-infiltrating lymphocytes in the immunotherapy of cancer

Tumour immunotherapy has achieved remarkable clinical success in many different types of cancer in the past two decades. The outcome of immune checkpoint inhibitors in cancer patients has been linked to the quality and magnitude of T cell, NK cell, and more recently, B cell within the tumour microenvironment, suggesting that the immune landscape of a tumour is highly connected to patient response and prognosis. It is critical to understanding tumour immune microenvironments for identifying immune modifiers of cancer progression and developing cancer immunotherapies. The infiltration of solid tumours by immune cells with anti-tumour activity is both a strong prognostic factor and a therapeutic goal. Recent approaches and applications of new technologies, especially single-cell mRNA analysis in dissecting tumour microenvironments have brought important insights into the biology of tumour-infiltrating immune cells, revealed a remarkable degree of cellular heterogeneity and distinct patterns of immune response. In this review, we will discuss recent advances in the understanding of tumour infiltrated lymphocytes, their prognostic benefit, and predictive value for immunotherapy.

Follicular lymphoma microenvironment: insights provided by single-cell analysis

Follicular lymphoma (FL) is the most frequent indolent lymphoma and is characterized by the abundant infiltration of tumor microenvironment (TME) cells. The activity of TME cells reportedly plays an important role in the biology of FL. TME cells that reside within neoplastic follicles, such as T-follicular helper cells and follicular dendritic cells, have been shown to aid in FL development and progression through interactions with malignant B cells, whereas regulatory T cells have unexpectedly shown an apparently favorable prognostic impact in FL. Unfortunately, the understanding of the FL TME, particularly regarding minor cell subsets, has been hampered by unknown cell heterogeneity. As with other solid and hematologic cancers, novel single-cell analysis technologies have recently been applied to FL research and have uncovered previously unrecognized heterogeneities, not only in malignant B cells but also in TME cells. These reports have greatly increased the resolution of our understanding of the FL TME and, at the same time, raised questions about newly identified TME cells. This review provides an overview of the unique aspects of FL TME cells with a clinical viewpoint and highlights recent discoveries from single-cell analysis, while also suggesting potential future directions.

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

Background

Cuproptosis, a novel mode of cell death associated with the tricarboxylic acid (TCA) cycle, is relevant to the development of cancer. However, the impact of single-cell-based Cuproptosis-associated lncRNAs on the Tumor immune microenvironment (TIME) of Pancreatic adenocarcinoma (PAAD) and its potential value for individualized immunotherapy has not been clarified.

Methods

14 immune-related CRGs were screened by exploring the interaction between differentially expressed Immune-Related Genes (IRGs) and Cuproptosis-Related Genes (CRGs) in PAAD. Next, the expression amount and expression distribution of CRGs in single-cell samples were analyzed by focusing on 7-CRGs with significant expressions. On the one hand, MAP2K2, SOD1, and VEGFA, which were significantly differentially expressed between PAAD sites and normal tissues adjacent to them, were subjected to immunohistochemical validation and immune landscape analysis. On the other hand, from these 7-CRGs, prognostic signatures of lncRNAs were established by co-expression and LASSO-COX regression analysis, and their prognostic value and immune relevance were assessed. In addition, this study not only validated the hub CRGs and the lncRNAs constituting the signature in a PAAD animal model treated with immunotherapy-based combination therapy using immunohistochemistry and qRT-PCR but also explored the potential value of the combination of targeted, chemotherapy and immunotherapy.

Results

Based on the screening of 7-CRGs significantly expressed in a PAAD single-cell cohort and their co-expressed Cuproptosis-Related lncRNAs (CRIs), this study constructed a prognostic signature of 4-CRIs named CIR-score. A Nomogram integrating the CIR-score and clinical risk factors was constructed on this basis to predict the individualized survival of patients. Moreover, high and low-risk groups classified according to the median of signatures exhibited significant differences in clinical prognosis, immune landscape, bioenrichment, tumor burden, and drug sensitivity. And the immunohistochemical and qRT-PCR results of different mouse PAAD treatment strategies were consistent with the trend of inter-group variability in drug sensitivity of hub CRGs and CIR-score. The combination of immunotherapy, targeted therapy, and chemotherapy exhibited a better tumor suppression effect.

Conclusion

CIR-score, as a Cuproptosis-related TIME-specific prognostic signature based on PAAD single cells, not only predicts the prognosis and immune landscape of PAAD patients but also provides a new strategy for individualized immunotherapy-based combination therapy.

Microfluidic Isolation of Disseminated Tumor Cells from the Bone Marrow of Breast Cancer Patients

Disseminated tumor cells (DTCs) in the bone marrow (BM) of breast cancer (BC) patients are putative precursors of metastatic disease, and their presence is associated with an adverse clinical outcome. To achieve the personalization of therapy on a clinical routine level, the characterization of DTCs and in vitro drug testing on DTCs are of great interest. Therefore, biobanking methods, as well as novel approaches to DTC isolation, need to be developed. In this study, we established a protocol for the biobanking of BM samples and evaluated a microfluidic-based separation system (Parsortix®) for the enrichment of cryopreserved DTCs. We were able to successfully isolate viable DTCs after the prior cryopreservation of BM samples. We calculated a significant increase of up to 90-fold in harvested DTCs with the proposed method compared to the current standard techniques, opening up new analysis possibilities for DTCs. Our advanced method further presents options for 3D DTC cultures, enabling the individualized testing of targeted therapies for BC patients. In conclusion, we present a novel approach for DTC enrichment, with possibilities for future clinical implications.

CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server

MOTIVATION

Single-cell sequencing technology has become a routine in studying many biological problems. A core step of analyzing single-cell data is the assignment of cell clusters to specific cell types. Reference-based methods are proposed for predicting cell types for single-cell clusters. However, the scalability and lack of preprocessed reference datasets prevent them from being practical and easy to use.

RESULTS

Here, we introduce a reference-based cell annotation web server, CellAnn, which is super-fast and easy to use. CellAnn contains a comprehensive reference database with 204 human and 191 mouse single-cell datasets. These reference datasets cover 32 organs. Furthermore, we developed a cluster-to-cluster alignment method to transfer cell labels from the reference to the query datasets, which is superior to the existing methods with higher accuracy and higher scalability. Finally, CellAnn is an online tool that integrates all the procedures in cell annotation, including reference searching, transferring cell labels, visualizing results, and harmonizing cell annotation labels. Through the user-friendly interface, users can identify the best annotation by cross-validating with multiple reference datasets. We believe that CellAnn can greatly facilitate single-cell sequencing data analysis.

AVAILABILITY AND IMPLEMENTATION

The web server is available at www.cellann.io, and the source code is available at https://github.com/Pinlyu3/CellAnn_shinyapp.

Automated microarray platform for single-cell sorting and collection of lymphocytes following HIV reactivation

A promising strategy to cure HIV-infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5%, 11.2%, and 12.1% percentage of cells, respectively. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single-cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found.

Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics

Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.

Single-cell analyses implicate ascites in remodeling the ecosystems of primary and metastatic tumors in ovarian cancer

Ovarian cancer (OC) is an aggressive gynecological tumor usually diagnosed with widespread metastases and ascites. Here, we depicted a single-cell landscape of the OC ecosystem with five tumor-relevant sites, including omentum metastasis and malignant ascites. Our data reveal the potential roles of ascites-enriched memory T cells as a pool for tumor-infiltrating exhausted CD8+ T cells and T helper 1-like cells. Moreover, tumor-enriched macrophages exhibited a preference for monocyte-derived ontogeny, whereas macrophages in ascites were more of embryonic origin. Furthermore, we characterized MAIT and dendritic cells in malignant ascites, as well as two endothelial subsets in primary tumors as predictive biomarkers for platinum-based chemotherapy response. Taken together, our study provides a global view of the female malignant ascites ecosystem and offers valuable insights for its connection with tumor tissues and paves the way for potential markers of efficacy evaluation and therapy resistance in OC.

Identification of fibroblast-related genes based on single-cell and machine learning to predict the prognosis and endocrine metabolism of pancreatic cancer

Background

Single-cell sequencing technology has become an indispensable tool in tumor mechanism and heterogeneity studies. Pancreatic adenocarcinoma (PAAD) lacks early specific symptoms, and comprehensive bioinformatics analysis for PAAD contributes to the developmental mechanisms.

Methods

We performed dimensionality reduction analysis on the single-cell sequencing data GSE165399 of PAAD to obtain the specific cell clusters. We then obtained cell cluster-associated gene modules by weighted co-expression network analysis and identified tumorigenesis-associated cell clusters and gene modules in PAAD by trajectory analysis. Tumor-associated genes of PAAD were intersected with cell cluster marker genes and within the signature module to obtain genes associated with PAAD occurrence to construct a prognostic risk assessment tool by the COX model. The performance of the model was assessed by the Kaplan-Meier (K-M) curve and the receiver operating characteristic (ROC) curve. The score of endocrine pathways was assessed by ssGSEA analysis.

Results

The PAAD single-cell dataset GSE165399 was filtered and downscaled, and finally, 17 cell subgroups were filtered and 17 cell clusters were labeled. WGCNA analysis revealed that the brown module was most associated with tumorigenesis. Among them, the brown module was significantly associated with C11 and C14 cell clusters. C11 and C14 cell clusters belonged to fibroblast and circulating fetal cells, respectively, and trajectory analysis showed low heterogeneity for fibroblast and extremely high heterogeneity for circulating fetal cells. Next, through differential analysis, we found that genes within the C11 cluster were highly associated with tumorigenesis. Finally, we constructed the RiskScore system, and K-M curves and ROC curves revealed that RiskScore possessed objective clinical prognostic potential and demonstrated consistent robustness in multiple datasets. The low-risk group presented a higher endocrine metabolism and lower immune infiltrate state.

Conclusion

We identified prognostic models consisting of APOL1, BHLHE40, CLMP, GNG12, LOX, LY6E, MYL12B, RND3, SOX4, and RiskScore showed promising clinical value. RiskScore possibly carries a credible clinical prognostic potential for PAAD.

Single-cell transcriptome analysis reveals T population heterogeneity and functions in tumor microenvironment of colorectal cancer metastases

Cell mediated immune escape, a microenvironment factor, induces tumorigenesis and metastasis. The purpose of this study was to display the characteristics of T cell populations in immune microenvironments for colorectal cancer (CRC) metastasis. Unsupervised cluster analysis was conducted to identify functionally distinct T cell clusters from 3,003 cells in peripheral blood and 4,656 cells in tissues. Subsequently, a total of 8 and 4 distinct T cell population clusters were identified from tumor tissue and peripheral blood, respectively. High levels of CD8+TEX, CD4+TRM, TH1-like T cells, CD8+TEM, tumor-Treg from tissues, and CD4+TN from peripheral blood are essential components of immune microenvironment for the prediction of CRC metastasis. Moreover, exhausted T cells are characterized by higher expression of multiple inhibitory receptors, including PDCD1 and LAG3. Some genes such as PFKFB3, GNLY, circDCUN1D4, TXNIP and NR4A2 in T cells of cluster were statistically different between CRC metastasis and non-metastasis. The ligand-receptor interactions identified between different cluster cells and metastases-related DEGs identified from each cluster revealed that the communications of cells, alterations of functions, and numbers of T subsets may contribute to the metastasis of CRC. The mutation frequency of KiAA1551, ATP8B4 and LNPEP in T cells from tissues and SOR1 from peripheral blood were higher in metastatic CRC than that in non-metastatic CRC. In conclusion, the discovery of differential genes in T cells may provide potential targets for immunotherapy of CRC metastasis and relevant insights into the clinical prediction and prognosis of CRC metastasis.

Gene expression networks involved in multiple cellular programs coexist in individual hepatocellular cancer cells

The gene expression networks of a single cell can be used to reveal cell type- and condition-specific patterns that account for cell states, cell identity, and its responses to environmental changes. We applied single cell sequencing datasets to define mRNA patterns and visualized potential cellular capacities among hepatocellular cancer cells. The expressing numbers and levels of genes were highly heterogenous among the cancer cells. The cellular characteristics were dependent strongly on the expressing numbers and levels of genes, especially oncogenes and anti-oncogenes, in an individual cancer cell. The transcriptional activations of oncogenes and anti-oncogenes were strongly linked to inherent multiple cellular programs, some of which oppose and contend against other processes, in a cancer cell. The gene expression networks of multiple cellular programs proliferation, differentiation, apoptosis, autophagy, epithelial-mesenchymal transition, ATP production, and neurogenesis coexisted in an individual cancer cell. The findings give rise a hypothesis that a cancer cell expresses balanced combinations of genes and undergoes a given biological process by rapidly transmuting gene expressing networks.

Technological and computational approaches to detect somatic mosaicism in epilepsy

Lesional epilepsy is a common and severe disease commonly associated with malformations of cortical development, including focal cortical dysplasia and hemimegalencephaly. Recent advances in sequencing and variant calling technologies have identified several genetic causes, including both short/single nucleotide and structural somatic variation. In this review, we aim to provide a comprehensive overview of the methodological advancements in this field while highlighting the unresolved technological and computational challenges that persist, including ultra-low variant allele fractions in bulk tissue, low availability of paired control samples, spatial variability of mutational burden within the lesion, and the issue of false-positive calls and validation procedures. Information from genetic testing in focal epilepsy may be integrated into clinical care to inform histopathological diagnosis, postoperative prognosis, and candidate precision therapies.

Identification and validation of biomarkers for epithelial-mesenchymal transition-related cells to estimate the prognosis and immune microenvironment in primary gastric cancer by the integrated analysis of single-cell and bulk RNA sequencing data

BACKGROUND

The epithelial-mesenchymal transition (EMT) is associated with gastric cancer (GC) progression and immune microenvironment. To better understand the heterogeneity underlying EMT, we integrated single-cell RNA-sequencing (scRNA-seq) data and bulk sequencing data from GC patients to evaluate the prognostic utility of biomarkers for EMT-related cells (ERCs), namely, cancer-associated fibroblasts (CAFs) and epithelial cells (ECs).

METHODS

scRNA-seq data from primary GC tumor samples were obtained from the Gene Expression Omnibus (GEO) database to identify ERC marker genes. Bulk GC datasets from the Cancer Genome Atlas (TCGA) and GEO were used as training and validation sets, respectively. Differentially expressed markers were identified from the TCGA database. Univariate Cox, least-absolute shrinkage, and selection operator regression analyses were performed to identify EMT-related cell-prognostic genes (ERCPGs). Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses were adopted to evaluate the prognostic utility of the ERCPG signature. An ERCPG-based nomogram was constructed by integrating independent prognostic factors. Finally, we evaluated the correlations between the ERCPG signature and immune-cell infiltration and verified the expression of ERCPG prognostic signature genes by in vitro cellular assays.

RESULTS

The ERCPG signature was comprised of seven genes (COL4A1, F2R, MMP11, CAV1, VCAN, FKBP10, and APOD). Patients were divided into high- and low-risk groups based on the ERCPG risk scores. Patients in the high-risk group showed a poor prognosis. ROC and calibration curves suggested that the ERCPG signature and nomogram had a good prognostic utility. An immune cell-infiltration analysis suggested that the abnormal expression of ERCPGs induced the formation of an unfavorable tumor immune microenvironment. In vitro cellular assays showed that ERCPGs were more abundantly expressed in GC cell lines compared to normal gastric tissue cell lines.

CONCLUSIONS

We constructed and validated an ERCPG signature using scRNA-seq and bulk sequencing data from ERCs of GC patients. Our findings support the estimation of patient prognosis and tumor treatment in future clinical practice.

Distinct immune microenvironment of lung adenocarcinoma in never-smokers from smokers

Lung cancer in never-smokers (LCINS) presents clinicopathological and molecular features distinct from that in smokers. Tumor microenvironment (TME) plays important roles in cancer progression and therapeutic response. To decipher the difference in TME between never-smoker and smoker lung cancers, we conduct single-cell RNA sequencing on 165,753 cells from 22 treatment-naive lung adenocarcinoma (LUAD) patients. We find that the dysfunction of alveolar cells induced by cigarette smoking contributes more to the aggressiveness of smoker LUADs, while the immunosuppressive microenvironment exerts more effects on never-smoker LUADs' aggressiveness. Moreover, the SPP1^hi pro macrophage is identified to be another independent source of monocyte-derived macrophage. Importantly, higher expression of immune checkpoint CD47 and lower expression of major histocompatibility complex (MHC)-I in cancer cells of never-smoker LUADs imply that CD47 may be a better immunotherapy target for LCINS. Therefore, this study reveals the difference of tumorigenesis between never-smoker and smoker LUADs and provides a potential immunotherapy strategy for LCINS.

Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects

Gastric cancer is one of the most serious malignant tumor and threatens the health of people worldwide. Its heterogeneity leaves many clinical problems unsolved. To treat it effectively, we need to explore its heterogeneity. Single-cell transcriptome sequencing, or single-cell RNA sequencing (scRNA-seq), reveals the complex biological composition and molecular characteristics of gastric cancer at the level of individual cells, which provides a new perspective for understanding the heterogeneity of gastric cancer. In this review, we first introduce the current procedure of scRNA-seq, and discuss the advantages and limitations of scRNA-seq. We then elaborate on the research carried out with scRNA-seq in gastric cancer in recent years, and describe how it reveals cell heterogeneity, the tumor microenvironment, oncogenesis and metastasis, as well as drug response in to gastric cancer, to facilitate early diagnosis, individualized therapy, and prognosis evaluation.

Single-Cell Next-Generation Sequencing to Monitor Hematopoietic Stem-Cell Transplantation: Current Applications and Future Perspectives

Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are genetically complex and diverse diseases. Such complexity makes challenging the monitoring of response to treatment. Measurable residual disease (MRD) assessment is a powerful tool for monitoring response and guiding therapeutic interventions. This is accomplished through targeted next-generation sequencing (NGS), as well as polymerase chain reaction and multiparameter flow cytometry, to detect genomic aberrations at a previously challenging leukemic cell concentration. A major shortcoming of NGS techniques is the inability to discriminate nonleukemic clonal hematopoiesis. In addition, risk assessment and prognostication become more complicated after hematopoietic stem-cell transplantation (HSCT) due to genotypic drift. To address this, newer sequencing techniques have been developed, leading to more prospective and randomized clinical trials aiming to demonstrate the prognostic utility of single-cell next-generation sequencing in predicting patient outcomes following HSCT. This review discusses the use of single-cell DNA genomics in MRD assessment for AML/MDS, with an emphasis on the HSCT time period, including the challenges with current technologies. We also touch on the potential benefits of single-cell RNA sequencing and analysis of accessible chromatin, which generate high-dimensional data at the cellular resolution for investigational purposes, but not currently used in the clinical setting.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

Tumour Heterogeneity and the Consequent Practical Challenges in the Management of Gastroenteropancreatic Neuroendocrine Neoplasms

Tumour heterogeneity is a common phenomenon in neuroendocrine neoplasms (NENs) and a significant cause of treatment failure and disease progression. Genetic and epigenetic instability, along with proliferation of cancer stem cells and alterations in the tumour microenvironment, manifest as intra-tumoural variability in tumour biology in primary tumours and metastases. This may change over time, especially under selective pressure during treatment. The gastroenteropancreatic (GEP) tract is the most common site for NENs, and their diagnosis and treatment depends on the specific characteristics of the disease, in particular proliferation activity, expression of somatostatin receptors and grading. Somatostatin receptor expression has a major role in the diagnosis and treatment of GEP-NENs, while Ki-67 is also a valuable prognostic marker. Intra- and inter-tumour heterogeneity in GEP-NENS, however, may lead to inaccurate assessment of the disease and affect the reliability of the available diagnostic, prognostic and predictive tests. In this review, we summarise the current available evidence of the impact of tumour heterogeneity on tumour diagnosis and treatment of GEP-NENs. Understanding and accurately measuring tumour heterogeneity could better inform clinical decision making in NENs.

Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity

There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Differentiation-related genes in tumor-associated macrophages as potential prognostic biomarkers in non-small cell lung cancer

Background

The purpose of this study was to evaluate the role of differentiation-related genes (DRGs) in tumor-associated macrophages (TAMs) in non-small cell lung cancer (NSCLC).

Methods

Single cell RNA-seq (scRNA-seq) data from GEO and bulk RNA-seq data from TCGA were analyzed to identify DRGs using trajectory method. Functional gene analysis was carried out by GO/KEGG enrichment analysis. The mRNA and protein expression in human tissue were analyzed by HPA and GEPIA databases. To investigate the prognostic value of these genes, three risk score (RS) models in different pathological types of NSCLC were generated and predicted NSCLC prognosis in datasets from TCGA, UCSC and GEO databases.

Results

1,738 DRGs were identified through trajectory analysis. GO/KEGG analysis showed that these genes were predominantly related to myeloid leukocyte activation and leukocyte migration. 13 DRGs (C1QB, CCL4, CD14, CD84, FGL2, MS4A6A, NLRP3, PLEK, RNASE6, SAMSN1, SPN, TMEM176B, ZEB2) related to prognosis were obtained through univariate Cox analysis and Lasso regression. C1QB, CD84, FGL2, MS4A6A, NLRP3, PLEK, SAMSN1, SPN, and ZEB2 were downregulated in NSCLC compared to non-cancer tissue. The mRNA of 13 genes were significantly expressed in pulmonary macrophages with strong cell specificity. Meanwhile, immunohistochemical staining showed that C1QB, CCL4, SPN, CD14, NLRP3, SAMSN1, MS4A6A, TMEM176B were expressed in different degrees in lung cancer tissues. ZEB2 (HR=1.4, P<0.05) and CD14 (HR=1.6, P<0.05) expression were associated with a worse prognosis in lung squamous cell carcinoma; ZEB2 (HR=0.64, P<0.05), CD84 (HR=0.65, P<0.05), PLEK (HR=0.71, P<0.05) and FGL2 (HR=0.61, P<0.05) expression were associated with a better prognosis in lung adenocarcinoma. Three RS models based on 13 DRGs both showed that the high RS was significantly associated with poor prognosis in different pathological types of NSCLC.

Conclusions

This study highlights the prognostic value of DRGs in TAMs in NSCLC patients, providing novel insights for the development of therapeutic and prognostic targets based on TAM functional differences.

Fat Loss in Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors

The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month). SF loss was defined as ΔSF < -5%/month. Survival analyses for overall survival (OS) and progression-free survival (PFS) were performed. Patients with SF loss had shorter OS (median, 9.5 months vs. not reached; p < 0.001) and PFS (median, 2.6 months vs. 33.5 months; p < 0.001) than patients without SF loss. ΔSF was independently associated with OS (adjusted hazard ratio (HR), 1.49; 95% confidence interval (CI), 1.07-2.07; p = 0.020) and PFS (adjusted HR, 1.57; 95% CI, 1.17-2.12; p = 0.003), with a 5%/month decrease in SF increasing the risk of death and progression by 49% and 57%, respectively. In conclusion, Loss of SF after treatment initiation is a significant and independent poor prognostic factor for OS and PFS in patients with metastatic ccRCC who receive ICI therapy.

Recent Advances in Methods for Circulating Tumor Cell Detection

Circulating tumor cells (CTCs) are released from primary tumors and transported through the body via blood or lymphatic vessels before settling to form micrometastases under suitable conditions. Accordingly, several studies have identified CTCs as a negative prognostic factor for survival in many types of cancer. CTCs also reflect the current heterogeneity and genetic and biological state of tumors; so, their study can provide valuable insights into tumor progression, cell senescence, and cancer dormancy. Diverse methods with differing specificity, utility, costs, and sensitivity have been developed for isolating and characterizing CTCs. Additionally, novel techniques with the potential to overcome the limitations of existing ones are being developed. This primary literature review describes the current and emerging methods for enriching, detecting, isolating, and characterizing CTCs.

Epstein-Barr virus DNA seropositivity links distinct tumoral heterogeneity and immune landscape in nasopharyngeal carcinoma

Background

Epstein-Barr virus (EBV) DNA seronegative (Sero-) and seropositive (Sero+) nasopharyngeal carcinoma (NPC) are distinctly different disease subtypes. Patients with higher baseline EBV DNA titers seem to benefit less from anti-PD1 immunotherapy, but underlying mechanisms remain unclear. Tumor microenvironment (TME) characteristics could be the important factor affecting the efficacy of immunotherapy. Here, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from cellular compositional and functional perspectives at single-cell resolution.

Method

We performed single-cell RNA sequencing analyses of 28,423 cells from ten NPC samples and one non-tumor nasopharyngeal tissue. The markers, function, and dynamics of related cells were analyzed.

Results

We found that tumor cells from EBV DNA Sero+ samples exhibit low-differentiation potential, stronger stemness signature, and upregulated signaling pathways associated with cancer hallmarks than that of EBV DNA Sero- samples. Transcriptional heterogeneity and dynamics in T cells were associated with EBV DNA seropositivity status, indicating different immunoinhibitory mechanisms employed by malignant cells depending on EBV DNA seropositivity status. The low expression of classical immune checkpoints, early-triggered cytotoxic T-lymphocyte response, global activation of IFN-mediated signatures, and enhanced cell-cell interplays cooperatively tend to form a specific immune context in EBV DNA Sero+ NPC.

Conclusions

Collectively, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from single-cell perspective. Our study provides insights into the altered tumor microenvironment of NPC associated with EBV DNA seropositivity, which will help direct the development of rational immunotherapy strategies.

Automated microarray for single-cell sorting and collection of lymphocytes following HIV reactivation

A promising strategy to cure HIV infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of various LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5 %, 11.2 %, and 12.1 % percentage of cells respectively, reactivated similar to that observed in other experimental systems. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found. These results advance our conceptual understanding of HIV reactivation dynamics at the single-cell level toward a cure for HIV.

Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution

The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.

Single-cell profiling reveals differences between human classical adenocarcinoma and mucinous adenocarcinoma

Colorectal cancer is a highly heterogeneous disease. Most colorectal cancers are classical adenocarcinoma, and mucinous adenocarcinoma is a unique histological subtype that is known to respond poorly to chemoradiotherapy. The difference in prognosis between mucinous adenocarcinoma and classical adenocarcinoma is controversial. Here, to gain insight into the differences between classical adenocarcinoma and mucinous adenocarcinoma, we analyse 7 surgical tumour samples from 4 classical adenocarcinoma and 3 mucinous adenocarcinoma patients by single-cell RNA sequencing. Our results indicate that mucinous adenocarcinoma cancer cells have goblet cell-like properties, and express high levels of goblet cell markers (REG4, SPINK4, FCGBP and MUC2) compared to classical adenocarcinoma cancer cells. TFF3 is essential for the transcriptional regulation of these molecules, and may cooperate with RPS4X to eventually lead to the mucinous adenocarcinoma mucus phenotype. The observed molecular characteristics may be critical in the specific biological behavior of mucinous adenocarcinoma.

Single-cell RNA binding protein regulatory network analyses reveal oncogenic HNRNPK-MYC signalling pathway in cancer

RNA-binding proteins (RBPs) are key players of gene expression and perturbations of RBP-RNA regulatory network have been observed in various cancer types. Here, we propose a computational method, RBPreg, to identify the RBP regulators by integration of single cell RNA-Seq (N = 233,591) and RBP binding data. Pan-cancer analyses suggest that RBP regulators exhibit cancer and cell specificity and perturbations of RBP regulatory network are involved in cancer hallmark-related functions. We prioritize an oncogenic RBP-HNRNPK, which is highly expressed in tumors and associated with poor prognosis of patients. Functional assays performed in cancer cells reveal that HNRNPK promotes cancer cell proliferation, migration, and invasion in vitro and in vivo. Mechanistic investigations further demonstrate that HNRNPK promotes tumorigenesis and progression by directly binding to MYC and perturbed the MYC targets pathway in lung cancer. Our results provide a valuable resource for characterizing RBP regulatory networks in cancer, yielding potential biomarkers for precision medicine.

Single cell RNA sequencing research in maternal fetal interface

The maternal-fetal interface is an essential environment for embryonic growth and development, and a successful pregnancy depends on the dynamic balance of the microenvironment at the maternal-fetal interface. Single-cell sequencing, which unlike bulk sequencing that provides averaged data, is a robust method for interpreting the cellular and molecular landscape at single-cell resolution. With the support of single-cell sequencing, the issue of maternal-fetal interface heterogeneity during pregnancy has been more deeply elaborated and understood, which is important for a deeper understanding of physiological and pathological pregnancy. In this paper, we analyze the recent studies of single-cell transcriptomics in the maternal-fetal interface, and provide new directions for understanding and treating various pathological pregnancies.

Three-dimensional visualization of human brain tumors using the CUBIC technique

Application of tissue clearing techniques on human brain tumors is still limited. This study was to investigate the application of CUBIC on 3D pathological studies of human brain tumors. Brain tumor specimens derived from 21 patients were cleared with CUBIC. Immunostaining was conducted on cleared specimens to label astrocytes, microglia and microvessels, respectively. All tumor specimens achieved transparency after clearing. Immunostaining and CUBIC are well compatible in a variety of human brain tumors. Spatial morphologies of microvessels, astrocytes and microglia of tumors were clearly visualized in 3D, and their 3D morphological parameters were easily quantified. By comparing the quantitative morphological parameters of microvessels among brain tumors of different malignancy, we found that mean vascular diameter was positively correlated with tumor malignancy. Our study demonstrates that CUBIC can be successfully applied to 3D pathological studies of various human brain tumors, and 3D studies of human brain tumors hold great promise in helping us better understand brain tumor pathology in the future.

Single-cell spatial immune landscapes of primary and metastatic brain tumours

Single-cell technologies have enabled the characterization of the tumour microenvironment at unprecedented depth and have revealed vast cellular diversity among tumour cells and their niche. Anti-tumour immunity relies on cell-cell relationships within the tumour microenvironment^1,2, yet many single-cell studies lack spatial context and rely on dissociated tissues³. Here we applied imaging mass cytometry to characterize the immunological landscape of 139 high-grade glioma and 46 brain metastasis tumours from patients. Single-cell analysis of more than 1.1 million cells across 389 high-dimensional histopathology images enabled the spatial resolution of immune lineages and activation states, revealing differences in immune landscapes between primary tumours and brain metastases from diverse solid cancers. These analyses revealed cellular neighbourhoods associated with survival in patients with glioblastoma, which we leveraged to identify a unique population of myeloperoxidase (MPO)-positive macrophages associated with long-term survival. Our findings provide insight into the biology of primary and metastatic brain tumours, reinforcing the value of integrating spatial resolution to single-cell datasets to dissect the microenvironmental contexture of cancer.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis

Background

There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies.

Methods

Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays.

Result

Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis.

Conclusion

Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Learning cell identity in immunology, neuroscience, and cancer

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Advances in Single-Cell Sequencing Technology and Its Applications in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and is mainly treated with chemotherapy-based combination therapy. In recent years, with the increasing development of global precision medicine, single-cell sequencing (SCS) has become one of the most promising technologies in the field of biotechnology. Moreover, the related application of this technology in TNBC has been applied and developed. By using SCS to study the heterogeneity of TNBC tumor cells, metastasis, drug resistance mechanisms, mutations, and cloning; it can further guide clinical chemotherapy, targeted therapy, and immunotherapy. To further reflect the importance of SCS in TNBC, this paper elaborated on and summarized the research and application progress of SCS in TNBC.

Measurable residual disease by flow cytometry in acute myeloid leukemia is prognostic, independent of genomic profiling

Measurable residual disease (MRD) assessment provides a potent indicator of the efficacy of anti-leukemic therapy. It is unknown, however, whether integrating MRD with molecular profiling better identifies patients at risk of relapse. To investigate the clinical relevance of MRD in relation to a molecular-based prognostic schema, we measured MRD by flow cytometry in 189 AML patients enrolled in ECOG-ACRIN E1900 trial (NCT00049517) in morphologic complete remission (CR) (28.8 % of the original cohort) representing 44.4 % of CR patients. MRD positivity was defined as ≥ 0.1 % of leukemic bone marrow cells. Risk classification was based on standard cytogenetics, fluorescence-in-situ-hybridization, somatic gene analysis, and sparse whole genome sequencing for copy number ascertainment. At 84.6 months median follow-up of patients still alive at the time of analysis (range 47.0-120 months), multivariate analysis demonstrated that MRD status at CR (p = 0.001) and integrated molecular risk (p = 0.0004) independently predicted overall survival (OS). Among risk classes, MRD status significantly affected OS only in the favorable risk group (p = 0.002). Expression of CD25 (α-chain of the interleukin-2 receptor) by leukemic myeloblasts at diagnosis negatively affected OS independent of post-treatment MRD levels. These data suggest that integrating MRD with genetic profiling and pre-treatment CD25 expression may improve prognostication in AML.

Single-Cell Sequencing in Rheumatic Diseases: New Insights from the Perspective of the Cell Type

Rheumatic diseases are a group of highly heterogeneous autoimmune and inflammatory disorders involving multiple systems. Dysfunction of immune and non-immune cells participates in the complex pathogenesis of rheumatic diseases. Therefore, studies on the abnormal activation of cell subtypes provided a specific basis for understanding the pathogenesis of rheumatic diseases, which promoted the accuracy of disease diagnosis and the effectiveness of various treatments. However, there was still a far way to achieve individualized precision medicine as the result of heterogeneity among cell subtypes. To obtain the biological information of cell subtypes, single-cell sequencing, a cutting-edge technology, is used for analyzing their genomes, transcriptomes, epigenetics, and proteomics. Novel results identified multiple cell subtypes in tissues of patients with rheumatic diseases by single-cell sequencing. Consequently, we provide an overview of recent applications of single-cell sequencing in rheumatic disease and cross-tissue to understand the cell subtypes and functions.

Rethinking cancer targeting strategies in the era of smart cell therapeutics

In the past several decades, the development of cancer therapeutics has largely focused on precision targeting of single cancer-associated molecules. Despite great advances, such targeted therapies still show incomplete precision and the eventual development of resistance due to target heterogeneity or mutation. However, the recent development of cell-based therapies such as chimeric antigen receptor (CAR) T cells presents a revolutionary opportunity to reframe strategies for targeting cancers. Immune cells equipped with synthetic circuits are essentially living computers that can be programmed to recognize tumours based on multiple signals, including both tumour cell-intrinsic and microenvironmental. Moreover, cells can be programmed to launch broad but highly localized therapeutic responses that can limit the potential for escape while still maintaining high precision. Although these emerging smart cell engineering capabilities have yet to be fully implemented in the clinic, we argue here that they will become much more powerful when combined with machine learning analysis of genomic data, which can guide the design of therapeutic recognition programs that are the most discriminatory and actionable. The merging of cancer analytics and synthetic biology could lead to nuanced paradigms of tumour recognition, more akin to facial recognition, that have the ability to more effectively address the complex challenges of treating cancer.

Single-cell analysis of multiple myelomas refines the molecular features of bortezomib treatment responsiveness

Both the tumor and tumor microenvironment (TME) are crucial for pathogenesis and chemotherapy resistance in multiple myeloma (MM). Bortezomib, commonly used for MM treatment, works on both MM and TME cells, but innate and acquired resistance easily develop. By single-cell RNA sequencing (scRNA-seq), we investigated bone marrow aspirates of 18 treatment-naïve MM patients who later received bortezomib-based treatments. Twelve plasma and TME cell types and their subsets were identified. Suboptimal responders (SORs) to bortezomib exhibited higher copy number alteration burdens than optimal responders (ORs). Forty-four differentially expressed genes for SORs based on scRNA-seq data were further analyzed in an independent cohort of 90 treatment-naïve MMs, where 24 genes were validated. A combined model of three clinical variables (older age, low absolute lymphocyte count, and no autologous stem cell transplantation) and 24 genes was associated with bortezomib responsiveness and poor prognosis. In T cells, cytotoxic memory, proliferating, and dysfunctional subsets were significantly enriched in SORs. Moreover, we identified three monocyte subsets associated with bortezomib responsiveness and an MM-specific NK cell trajectory that ended with an MM-specific subset. scRNA-seq predicted the interaction of the GAS6-MERTK, ALCAM-CD6, and BAG6-NCR gene networks. Of note, tumor cells from ORs and SORs were the most prominent sources of ALCAM on effector T cells and BAG6 on NK cells, respectively. Our results indicate that the complicated compositional and molecular changes of both tumor and immune cells in the bone marrow (BM) milieu are important in the development and acquisition of resistance to bortezomib-based treatment of MM.