Applications of single-cell and bulk RNA sequencing in onco-immunology

Functional exploration and drug prediction on programmed cell death-related biomarkers in lung adenocarcinoma

Background

Our study aims to perform functional exploration and drug prediction of programmed cell death (PCD)-related biomarkers in lung adenocarcinoma (LUAD).

Methods

UCSC-Xena obtained LUAD-related genes. DESeq2 screened PCD-specific differentially expressed genes (DEGs), and these DEGs were intersected with genes identified by weighted gene co-expression network analysis (WGCNA) to pinpoint the key genes. KOBAS-i was used for enrichment analysis. String and GeneMania were used to construct protein interaction networks and gene-gene interaction networks, respectively. Using two machine learning algorithms to screen for key genes, and taking the intersection as biomarkers, validating via receiver operating characteristic (ROC) and in vitro experiments. Building a diagnostic model with a nomogram. Construct transcription factor (TF) regulatory network. CIBERSORT was used for immune infiltration analysis. Enrichr predicts targeted drugs and AutodockTools simulates molecular docking.

Results

120 hub genes related to PCD were identified, and an intersection of these genes with DEGs yielded 10 key genes, which were enriched in apoptosis-related pathways. Further machine learning screening of these genes led to the selection of 7 genes, among which 6 genes (FGR, LAPTM5, SIRPA, TLR4, ZEB2, and NLRC4) exhibited significant differences upon ROC validation, ultimately serving as biomarkers, in vitro experiments also confirmed. A nomogram demonstrated their excellent diagnostic performance. These six biomarkers are correlated with the infiltration status of most immune cells, suggesting that they affect LUAD through the immune system. TF regulation analysis identified the upstream miRNAs. Finally, drug prediction yielded three potential drugs: Lenvatinib, methadone, and trimethoprim.

Conclusion

PCD-related biomarkers in LUAD were explored, which may contribute to further understanding on PCD in LUAD.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the "innate counterpart" of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Prediction of Prognosis and Immunotherapy Response with a Novel Natural Killer Cell Marker Genes Signature in Osteosarcoma

Background: Natural killer (NK) cells are characterized by their antitumor efficacy without previous sensitization, which have attracted attention in tumor immunotherapy. The heterogeneity of osteosarcoma (OS) has hindered therapeutic application of NK cell-based immunotherapy. The authors aimed to construct a novel NK cell-based signature to identify certain OS patients more responsive to immunotherapy. Materials and Methods: A total of eight publicly available datasets derived from patients with OS were enrolled in this study. Single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to screen NK cell marker genes. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was used to construct an NK cell-based prognostic signature in the TARGET-OS dataset. The differences in immune cell infiltration, immune system-related metagenes, and immunotherapy response were evaluated among risk subgroups. Furthermore, this prognostic signature was experimentally validated by reverse transcription-quantitative real-time PCR (RT-qPCR). Results: With differentially expressed NK cell marker genes screened out, a five-gene NK cell-based prognostic signature was constructed. The prognostic predictive accuracy of the signature was validated through internal clinical subgroups and external GEO datasets. Low-risk OS patients contained higher abundances of infiltrated immune cells, especially CD8 T cells and naive CD4 T cells, indicating that T cell exhaustion states were present in the high-risk OS patients. As indicated from correlation analysis, immune system-related metagenes displayed a negative correlation with risk scores, suggesting the existence of immunosuppressive microenvironment in OS. In addition, based on responses to immune checkpoint inhibitor therapy in two immunotherapy datasets, the signature helped predict the response of OS patients to anti-programmed cell death protein 1 (PD-1) or anti-programmed cell death ligand 1 (PD-L1) therapy. RT-qPCR results demonstrated the roughly consistent relationship of these five gene expressions with predicting outcomes. Conclusions: The NK cell-based signature is likely to be available for the survival prediction and the evaluation of immunotherapy response of OS patients, which may shed light on subsequent immunotherapy choices for OS patients. In addition, the authors revealed a potential link between immunosuppressive microenvironment and OS.

Exploring the Molecular Mechanism of Skeletal Muscle Development in Ningxiang Pig by Weighted Gene Co-Expression Network Analysis

With the continuous improvement in living standards, people's demand for high-quality meat is increasing. Ningxiang pig has delicious meat of high nutritional value, and is loved by consumers. However, its slow growth and low meat yield seriously restrict its efficient utilization. Gene expression is the internal driving force of life activities, so in order to fundamentally improve its growth rate, it is key to explore the molecular mechanism of skeletal muscle development in Ningxiang pigs. In this paper, Ningxiang boars were selected in four growth stages (30 days: weaning period, 90 days: nursing period, 150 days: early fattening period, and 210 days: late fattening period), and the longissimus dorsi (LD) muscle was taken from three boars in each stage. The fatty acid content, amino acid content, muscle fiber diameter density and type of LD were detected by gas chromatography, acidolysis, hematoxylin eosin (HE) staining and immunofluorescence (IF) staining. After transcription sequencing, weighted gene co-expression network analysis (WGCNA) combined with the phenotype of the LD was used to explore the key genes and signaling pathways affecting muscle development. The results showed that 10 modules were identified by WGCNA, including 5 modules related to muscle development stage, module characteristics of muscle fiber density, 5 modules characteristic of muscle fiber diameter, and a module characteristic of palmitoleic acid (C16:1) and linoleic acid (C18:2n6C). Gene ontology (GO) enrichment analysis found that 52 transcripts relating to muscle development were enriched in these modules, including 44 known genes and 8 novel genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes were enriched in the auxin, estrogen and cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) pathways. Twelve of these genes were transcription factors, there were interactions among 20 genes, and the interactions among 11 proteins in human, pig and mouse were stable. To sum up, through the integrated analysis of phenotype and transcriptome, this paper analyzed the key genes and possible regulatory networks of skeletal muscle development in Ningxiang pigs at various stages, to provide a reference for the in-depth study of skeletal muscle development.

Advances in Melanoma: From Genetic Insights to Therapeutic Innovations

Advances in melanoma research have unveiled critical insights into its genetic and molecular landscape, leading to significant therapeutic innovations. This review explores the intricate interplay between genetic alterations, such as mutations in BRAF, NRAS, and KIT, and melanoma pathogenesis. The MAPK and PI3K/Akt/mTOR signaling pathways are highlighted for their roles in tumor growth and resistance mechanisms. Additionally, this review delves into the impact of epigenetic modifications, including DNA methylation and histone changes, on melanoma progression. The tumor microenvironment, characterized by immune cells, stromal cells, and soluble factors, plays a pivotal role in modulating tumor behavior and treatment responses. Emerging technologies like single-cell sequencing, CRISPR-Cas9, and AI-driven diagnostics are transforming melanoma research, offering precise and personalized approaches to treatment. Immunotherapy, particularly immune checkpoint inhibitors and personalized mRNA vaccines, has revolutionized melanoma therapy by enhancing the body's immune response. Despite these advances, resistance mechanisms remain a challenge, underscoring the need for combined therapies and ongoing research to achieve durable therapeutic responses. This comprehensive overview aims to highlight the current state of melanoma research and the transformative impacts of these advancements on clinical practice.

Single-Cell RNA Sequencing and Its Applications in Pituitary Research

BACKGROUND

Mounting evidence underscores the significance of cellular diversity within the endocrine system and the intricate interplay between different cell types and tissues, essential for preserving physiological balance and influencing disease trajectories. The pituitary gland, a central player in the endocrine orchestra, exemplifies this complexity with its assortment of hormone-secreting and nonsecreting cells.

SUMMARY

The pituitary gland houses several types of cells responsible for hormone production, alongside nonsecretory cells like fibroblasts and endothelial cells, each playing a crucial role in the gland's function and regulatory mechanisms. Despite the acknowledged importance of these cellular interactions, the detailed mechanisms by which they contribute to pituitary gland physiology and pathology remain largely uncharted. The last decade has seen the emergence of groundbreaking technologies such as single-cell RNA sequencing, offering unprecedented insights into cellular heterogeneity and interactions. However, the application of this advanced tool in exploring the pituitary gland's complexities has been scant. This review provides an overview of this methodology, highlighting its strengths and limitations, and discusses future possibilities for employing it to deepen our understanding of the pituitary gland and its dysfunction in disease states.

KEY MESSAGE

Single-cell RNA sequencing technology offers an unprecedented means to study the heterogeneity and interactions of pituitary cells, though its application has been limited thus far. Further utilization of this tool will help uncover the complex physiological and pathological mechanisms of the pituitary, advancing research and treatment of pituitary diseases.

Development and evaluation of a disulfidoptosis-related lncRNA index for prognostication in clear cell renal cell carcinoma

Background

This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC).

Methods

We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy.

Results

The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram's performance.

Conclusions

The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer

In recent years, single-cell analyses have revealed the heterogeneity of the tumour microenvironment (TME) at the genomic, transcriptomic, and proteomic levels, further improving our understanding of the mechanisms of tumour development. Single-cell RNA sequencing (scRNA-seq) technology allow analysis of the transcriptome at the single-cell level and have unprecedented potential for exploration of the characteristics involved in tumour development and progression. These techniques allow analysis of transcript sequences at higher resolution, thereby increasing our understanding of the diversity of cells found in the tumour microenvironment and how these cells interact in complex tumour tissue. Although scRNA-seq has emerged as an important tool for studying the tumour microenvironment in recent years, it cannot be used to analyse spatial information for cells. In this regard, spatial transcriptomics (ST) approaches allow researchers to understand the functions of individual cells in complex multicellular organisms by understanding their physical location in tissue sections. In particular, in related research on tumour heterogeneity, ST is an excellent complementary approach to scRNA-seq, constituting a new method for further exploration of tumour heterogeneity, and this approach can also provide unprecedented insight into the development of treatments for pancreatic cancer (PC). In this review, based on the methods of scRNA-seq and ST analyses, research progress on the tumour microenvironment and treatment of pancreatic cancer is further explained.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Pancreatic cancer environment: from patient-derived models to single-cell omics

Pancreatic cancer (PC) is a highly malignant cancer characterized by poor prognosis, high heterogeneity, and intricate heterocellular systems. Selecting an appropriate experimental model for studying its progression and treatment is crucial. Patient-derived models provide a more accurate representation of tumor heterogeneity and complexity compared to cell line-derived models. This review initially presents relevant patient-derived models, including patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived explants (PDEs), which are essential for studying cell communication and pancreatic cancer progression. We have emphasized the utilization of these models in comprehending intricate intercellular communication, drug responsiveness, mechanisms underlying tumor growth, expediting drug discovery, and enabling personalized medical approaches. Additionally, we have comprehensively summarized single-cell analyses of these models to enhance comprehension of intercellular communication among tumor cells, drug response mechanisms, and individual patient sensitivities.

Hypoxia-related THBD+ macrophages as a prognostic factor in glioma: Construction of a powerful risk model

Glioma is a prevalent malignant tumour characterized by hypoxia as a pivotal factor in its progression. This study aims to investigate the impact of the most severely hypoxic cell subpopulation in glioma. Our findings reveal that the THBD+ macrophage subpopulation is closely associated with hypoxia in glioma, exhibiting significantly higher infiltration in tumours compared to non-tumour tissues. Moreover, a high proportion of THBD+ cells correlates with poor prognosis in glioblastoma (GBM) patients. Notably, THBD+ macrophages exhibit hypoxic characteristics and epithelial-mesenchymal transition features. Silencing THBD expression leads to a notable reduction in the proliferation and metastasis of glioma cells. Furthermore, we developed a THBD+ macrophage-related risk signature (THBDMRS) through machine learning techniques. THBDMRS emerges as an independent prognostic factor for GBM patients with a substantial prognostic impact. By comparing THBDMRS with 119 established prognostic features, we demonstrate the superior prognostic performance of THBDMRS. Additionally, THBDMRS is associated with glioma metastasis and extracellular matrix remodelling. In conclusion, hypoxia-related THBD+ macrophages play a pivotal role in glioma pathogenesis, and THBDMRS emerges as a potent and promising prognostic tool for GBM, contributing to enhanced patient survival outcomes.

Analysis of cancer-associated fibroblasts related genes identifies COL11A1 associated with lung adenocarcinoma prognosis

BACKGROUND

The treatment of lung adenocarcinoma is difficult due to the limited therapeutic options. Cancer-associated fibroblasts play an important role in the development of cancers. This study aimed to identify a promising molecular target associated with cancer-associated fibroblasts for the treatment of lung adenocarcinoma.

METHODS

The Cancer Genome Atlas lung adenocarcinoma dataset was used to screen hub genes associated with cancer-associated fibroblasts via the EPIC algorithm and Weighted Gene Co-expression Network Analysis. Multiple databases were used together with our data to verify the differential expression and survival of COL11A1. Functional enrichment analysis and the single-cell TISCH database were used to elucidate the mechanisms underlying COL11A1 expression. The correlation between COL11A1 and immune checkpoint genes in human cancers was also evaluated.

RESULTS

Using the EPIC algorithm and Weighted Gene Co-expression Network Analysis, 13 hub genes associated with cancer-associated fibroblasts in lung adenocarcinoma were screened. Using the GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, GSE32863, and our immunohistochemistry experiment data, we confirmed that COL11A1 overexpresses in lung adenocarcinoma and that high expression of COL11A1 is associated with a poor prognosis. COL11A1 has a genetic alteration frequency of 22% in patients with lung adenocarcinoma. COL11A1 is involved in the extracellular matrix activities of lung adenocarcinoma. Using the TISCH database, we found that COL11A1 is mainly expressed by cancer-associated fibroblasts in the tumor microenvironment rather than by lung adenocarcinoma cells. Finally, we found that COL11A1 is positively correlated with HAVCR2(TIM3), CD274 (PD-L1), CTLA4, and LAG3 in lung adenocarcinoma.

CONCLUSION

COL11A1 may be expressed and secreted by cancer-associated fibroblasts, and a high expression of COL11A1 may result in T cell exhaustion in the tumor microenvironment of lung adenocarcinoma. COL11A1 may serve as an attractive biomarker to provide new insights into cancer therapeutics.

Drug repositioning for immunotherapy in breast cancer using single-cell analysis

Immunomodulatory peptides, while exhibiting potential antimicrobial, antifungal, and/or antiviral properties, can play a role in stimulating or suppressing the immune system, especially in pathological conditions like breast cancer (BC). Thus, deregulation of these peptides may serve as an immunotherapeutic strategy to enhance the immune response. In this meta-analysis, we utilized single-cell RNA sequencing data and known therapeutic peptides to investigate the deregulation of these peptides in malignant versus normal human breast epithelial cells. We corroborated our findings at the chromatin level using ATAC-seq. Additionally, we assessed the protein levels in various BC cell lines. Moreover, our in-house drug repositioning approach was employed to identify potential drugs that could positively impact the relapse-free survival of BC patients. Considering significantly deregulated therapeutic peptides and their role in BC pathology, our approach aims to downregulate B2M and SLPI, while upregulating PIGR, DEFB1, LTF, CLU, S100A7, and SCGB2A1 in BC epithelial cells through our drug repositioning pipeline. Leveraging the LINCS L1000 database, we propose BRD-A06641369 for B2M downregulation and ST-4070043 and BRD-K97926541 for SLPI downregulation without negatively affecting the MHC complex as a significantly correlated pathway with these two genes. Furthermore, we have compiled a comprehensive list of drugs for the upregulation of other selected immunomodulatory peptides. Employing an immunotherapeutic approach by integrating our drug repositioning pipeline with single-cell analysis, we proposed potential drugs and drug targets to fortify the immune system against BC.

Molecular characterization of chronic cutaneous wounds reveals subregion- and wound type-specific differential gene expression

A limited understanding of the pathology underlying chronic wounds has hindered the development of effective diagnostic markers and pharmaceutical interventions. This study aimed to elucidate the molecular composition of various common chronic ulcer types to facilitate drug discovery strategies. We conducted a comprehensive analysis of leg ulcers (LUs), encompassing venous and arterial ulcers, foot ulcers (FUs), pressure ulcers (PUs), and compared them with surgical wound healing complications (WHCs). To explore the pathophysiological mechanisms and identify similarities or differences within wounds, we dissected wounds into distinct subregions, including the wound bed, border, and peri-wound areas, and compared them against intact skin. By correlating histopathology, RNA sequencing (RNA-Seq), and immunohistochemistry (IHC), we identified unique genes, pathways, and cell type abundance patterns in each wound type and subregion. These correlations aim to aid clinicians in selecting targeted treatment options and informing the design of future preclinical and clinical studies in wound healing. Notably, specific genes, such as PITX1 and UPP1, exhibited exclusive upregulation in LUs and FUs, potentially offering significant benefits to specialists in limb preservation and clinical treatment decisions. In contrast, comparisons between different wound subregions, regardless of wound type, revealed distinct expression profiles. The pleiotropic chemokine-like ligand GPR15L (C10orf99) and transmembrane serine proteases TMPRSS11A/D were significantly upregulated in wound border subregions. Interestingly, WHCs exhibited a nearly identical transcriptome to PUs, indicating clinical relevance. Histological examination revealed blood vessel occlusions with impaired angiogenesis in chronic wounds, alongside elevated expression of genes and immunoreactive markers related to blood vessel and lymphatic epithelial cells in wound bed subregions. Additionally, inflammatory and epithelial markers indicated heightened inflammatory responses in wound bed and border subregions and reduced wound bed epithelialization. In summary, chronic wounds from diverse anatomical sites share common aspects of wound pathophysiology but also exhibit distinct molecular differences. These unique molecular characteristics present promising opportunities for drug discovery and treatment, particularly for patients suffering from chronic wounds. The identified diagnostic markers hold the potential to enhance preclinical and clinical trials in the field of wound healing.

Prognostic Risk Models Using Epithelial Cells Identify β-Sitosterol as a Potential Therapeutic Target Against Esophageal Squamous Cell Carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is an aggressive and fatal malignancy that leads to epithelial cancer. The association between epithelial cell heterogeneity, prognosis, and immune response in this cancer remains uncertain. This study aimed to investigate epithelial cell heterogeneity in ESCC and develop a predictive risk model using the identified cell types.

Methods

Single-cell RNA sequencing (scRNA-seq) and differential ESCC gene data were accessed from the Gene Expression Omnibus. Functional enrichment analysis, inferCNV, cell development trajectories, and intercellular communication were analyzed following epithelial cell characterization. Differentially expressed ESCC (n = 773) and epithelial cell marker genes (n = 3407) were intersected to obtain core genes, and epithelial cell-related prognostic genes were identified. LASSO regression analysis was used to construct a prognostic model. The external dataset GSE53624 was used to further validate the stability of the model. Drug sensitivity predictions, and immune cell infiltration were analyzed. Molecular docking clarified the possible therapeutic role of β-sitosterol in ESCC. Finally, wound healing assay, cell colony, and transwell assay were constructed to detect the effects of the core gene PDLIM2 on ESCC cell proliferation, invasion, and migration.

Results

Eight cell clusters were identified, and epithelial cells were categorized into tumor and paratumor groups. The tumor group possessed more chromosomal variants than the paratumor group. Epithelial cells were associated with multiple cell types and significantly correlated with the Wnt, transforming growth factor, and epidermal growth factor signaling pathways. From 231 intersected genes, five core genes were screened for use in the risk model: CTSL, LAPTM4B, MYO10, NCF2, and PDLIM2. These genes may contribute to the cancerous transformation of normal esophageal epithelial cells and thereby act as biomarkers and potential therapeutic targets in patients with ESCC. β-Sitosterol furthermore displayed excellent docking potential with these genes. Meanwhile, further experiments demonstrated that the gene PDLIM2 plays a major role in the progression of oesophageal squamous carcinoma.

Conclusion

We successfully developed a risk model for the prognosis of ESCC based on epithelial cells that addresses the response of ESCC to immunotherapy and offers novel cancer treatment options.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Transcriptomic signatures of chronic active antibody-mediated rejection deciphered by RNA sequencing of human kidney allografts

Natural killer (NK) cells mediate spontaneous cell-mediated cytotoxicity and antibody-dependent cell-mediated cytotoxicity. This dual functionality could enable their participation in chronic active antibody-mediated rejection (CA-ABMR). Earlier microarray profiling studies have not subcategorized antibody-mediated rejection into CA-ABMR and active-ABMR, and the gene expression pattern of CA-ABMR has not been compared with that of T cell-mediated rejection (TCMR). To fill these gaps, we RNA sequenced human kidney allograft biopsies categorized as CA-ABMR, active-ABMR, TCMR, or No Rejection (NR). Among the 15,910 genes identified in the biopsies, 60, 114, and 231 genes were uniquely overexpressed in CA-ABMR, TCMR, and active-ABMR, respectively; compared to NR, 50 genes were shared between CA-ABMR and active-ABMR, and 164 genes between CA-ABMR and TCMR. The overexpressed genes were annotated to NK cells and T cells in CA-ABMR and TCMR, and to neutrophils and monocytes in active-ABMR. The NK cell cytotoxicity and allograft rejection pathways were enriched in CA-ABMR. Genes encoding perforin, granzymes, and death receptor were overexpressed in CA-ABMR versus active-ABMR but not compared to TCMR. NK cell cytotoxicity pathway gene set variation analysis score was higher in CA-ABMR compared to active-ABMR but not in TCMR. Principal component analysis of the deconvolved immune cellular transcriptomes separated CA-ABMR and TCMR from active-ABMR and NR. Immunohistochemistry of kidney allograft biopsies validated a higher proportion of CD56+ NK cells in CA-ABMR than in active-ABMR. Thus, CA-ABMR was exemplified by the overexpression of the NK cell cytotoxicity pathway gene set and, surprisingly, molecularly more like TCMR than active-ABMR.

Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching

Accurately identifies the cellular composition of complex tissues, which is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA-seq) typically rely on matched single-cell RNA sequencing (scRNA-seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single-cell references. Hence, a novel computational method named SCROAM is introduced to address these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. Subsequently, cell-type-specific expression matrices are generated from the scRNA-seq data, facilitating the precise identification of cell types within bulk tissues. The performance of SCROAM is assessed through benchmarking against simulated and real datasets, demonstrating its accuracy and robustness. To further validate SCROAM's performance, single-cell and bulk RNA-seq experiments are conducted on mouse spinal cord tissue, with SCROAM applied to identify cell types in bulk tissue. Results indicate that SCROAM is a highly effective tool for identifying similar cell types. An integrated analysis of liver cancer and primary glioblastoma is then performed. Overall, this research offers a novel perspective for delivering precise insights into disease pathogenesis and potential therapeutic strategies.

Single-cell RNA sequencing reveals immune microenvironment of small cell lung cancer-associated malignant pleural effusion

We used 10 × genomics single-cell transcriptome sequencing technology to reveal the tumor immune microenvironment characteristics of small cell lung cancer (SCLC) in a patient with malignant pleural effusion (MPE). A total of 8008 high-quality cells were finally obtained for subsequent bioinformatic analysis, which were divided into 10 cell clusters further identified as B cells, T cells, myeloid cells, NK cells, and cancer cells. Such SCLC related genes as NOTCH1, MYC, TSC22D1, SOX4, BLNK, YBX3, VIM, CD8A, CD8B, and KLF6 were expressed in different degrees during differentiation of T and B cells. Different ligands and receptors between T, B and tumor cells almost interact through MHC II, IL-16, galectin, and APP signaling pathway.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension

BACKGROUND

Pulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.

METHODS

Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.

RESULTS

Nineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.

CONCLUSION

WDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma

OBJECTIVE

Lung adenocarcinoma (LA) is one of the most common malignancies and is responsible for the greatest number of tumor-related deaths. Our research aimed to explore the molecular subtype signatures of LA to clarify the correlation among the immune microenvironment, clinical outcomes, and therapeutic response.

METHODS

The LA immune cell marker genes (LICMGs) identified by single-cell RNA sequencing (scRNA-seq) analysis were used to discriminate the molecular subtypes and homologous immune and metabolic traits of GSE72094 LA cases. In addition, the model-building genes were identified from 1441 LICMGs by Cox-regression analysis, and a LA immune difference score (LIDscore) was developed to quantify individual differences in each patient, thereby predicting prognosis and susceptibility to immunotherapy and chemotherapy of LA patients.

RESULTS

Patients of the GSE72094 cohort were divided into two distinct molecular subtypes based on LICMGs: immune activating subtype (Cluster-C1) and metabolically activating subtype (cluster-C2). The two molecular subtypes have distinct characteristics regarding prognosis, clinicopathology, genomics, immune microenvironment, and response to immunotherapy. Among the LICMGs, LGR4, GOLM1, CYP24A1, SFTPB, COL1A1, HLA-DQA1, MS4A7, PPARG, and IL7R were enrolled to construct a LIDscore model. Low-LIDscore patients had a higher survival rate due to abundant immune cell infiltration, activated immunity, and lower genetic variation, but probably the higher levels of Treg cells in the immune microenvironment lead to immune cell dysfunction and promote tumor immune escape, thus decreasing the responsiveness to immunotherapy compared with that of the high-LIDscore patients. Overall, high-LIDscore patients had a higher responsiveness to immunotherapy and a higher sensitivity to chemotherapy than the low-LIDscore group.

CONCLUSIONS

Molecular subtypes based on LICMGs provided a promising strategy for predicting patient prognosis, biological characteristics, and immune microenvironment features. In addition, they helped identify the patients most likely to benefit from immunotherapy and chemotherapy.

Identification of cancer-associated fibroblasts signature for predicting the prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally with poor prognosis. Cancer-associated fibroblasts (CAFs) play multiple functions in the regulation of tumorigenesis, metastasis and therapeutic resistance of cancer. The current study aimed to explore the role of CAFs-related genes in the prognosis and immunotherapy response in HCC. CAFs-related genes were identified by using single-cell RNA-sequencing analysis. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to develop a CAFs-related prognostic signature (FRPS) in TCGA dataset and verified in ICGC, GSE14520 and GSE76427 cohorts. Several tools, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore, and Tumor Mutation Burden (TMB) score were used to evaluate the value of FRPS in predicting immunotherapy benefits. The FRPS constructed based on 10 genes (RGS5, CNN3, PALLD, FLNA, KLHL23, MYC, NDRG2, SERPINE1, CD151 CALU) served as an independent risk factor and showed stable and powerful performance in predicting the overall survival rate of HCC patients with an AUCs of 0. 734, 0.727, and 0.717 in 2-, 3-, and 4-year ROC curve in TCGA cohort. Low risk score indicated a higher abundance of CD8+ T cells and NK, and lower abundance of Treg. Moreover, HCC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, and lower TIDE score. Moreover, high risk score indicated a lower IC50 value of 5-fluorouracil, camptothecin, cisplatin, docetaxel, gemcitabine, paclitaxel, afatinib, crizotinib, dasatinib, erlotinib, erlotinib, gefitinib, lapatinib, and osimertinib in HCC. Our study develops a novel FRPS HCC. The FRPS acts as a risk factor for the prognosis of HCC patients and it can predict the immunotherapy benefits of HCC patients.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood.

METHODS

Multiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds.

RESULTS

We elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis.

CONCLUSION

Our study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

Single-cell analysis reveals the immune heterogeneity and interactions in lungs undergoing hepatic ischemia-reperfusion

Hepatic ischemia-reperfusion IR (HIR) is an unavoidable pathophysiological process during liver transplantation, resulting in systematic sterile inflammation and remote organ injury. Acute lung injury (ALI) is a serious complication after liver transplantation with high postoperative morbidity and mortality. However, the underlying mechanism is still unclear. To assess the phenotype and plasticity of various cell types in the lung tissue microenvironment after HIR at the single-cell level, single-cell RNA sequencing (scRNA-seq) was performed using the lungs from HIR-induced mice. In our results, we identified 23 cell types in the lungs after HIR and found that this highly complex ecosystem was formed by subpopulations of bone marrow-derived cells that signaled each other and mediated inflammatory responses in different states and different intervals. We described the unique transcriptional profiles of lung cell clusters and discovered two novel cell subtypes (Tspo+Endothelial cells and Vcan+ monocytes), as well as the endothelial cell-immune cell and immune cell-T cell clusters interactome. In addition, we found that S100 calcium binding protein (S100a8/a9), specifically and highly expressed in immune cell clusters of lung tissues and exhibited detrimental effects. Finally, the cellular landscape of the lung tissues after HIR was established, highlighting the heterogeneity and cellular interactions between major immune cells in HIR-induced lungs. Our findings provided new insights into the mechanisms of HIR-induced ALI and offered potential therapeutic target to prevent ALI after liver transplantation.

Effect of age on the risk of immune-related adverse events in patients receiving immune checkpoint inhibitors

Identifying patients at increased risk of immune-related adverse events (irAEs) facilitates safe application of immune checkpoint inhibitors (ICIs). This retrospective study aimed to determine the effect of age on the risk of irAEs in patients receiving ICIs and to identify potential mechanisms underlying age-related irAE risk differences. We analyzed reports of FDA Adverse Event Reporting System from July 1, 2014, to September 30, 2021. The information component ratio (ICΔ) was used to compare the irAE risk between older adults (> 65 years) and younger adults (25-65 years), of which the 95% confidential interval lower limit (ICΔ025) exceeding zero indicated significantly increased risk. We found that older adults had a significantly higher overall irAE risk than younger adults (ICΔ025 0.38), which was observed in almost all organ systems. We further analyzed the correlation between age-related irAE risks and age-related transcriptional changes to identify potential genes and pathways underlying age-related irAE risk differences. We found that genes significantly correlated with ICΔ were enriched in processes including extracellular matrix organization, regulation of myeloid leukocyte mediated immunity, and regulation of c-Jun N-terminal kinase (JNK) cascade. In addition, single-cell RNA sequencing analysis confirmed that genes involved in collagen-containing extracellular matrix and JNK cascade were significantly upregulated in myeloid cells from ICI-associated colitis tissues compared with ICI-treated colon tissues without colitis. In conclusion, older adults receiving ICIs have higher irAE risks than younger adults. Upregulation of genes involved in JNK cascade and collagen-containing extracellular matrix in myeloid cells may contribute to increased irAE risks in older adults.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia

Introduction

Acute myeloid leukemia (AML) is a type of blood cancer that is identified by the unrestricted growth of immature myeloid cells within the bone marrow. Despite therapeutic advances, AML prognosis remains highly variable, and there is a lack of biomarkers for customizing treatment. RNA N6-methyladenosine (m6A) modification is a reversible and dynamic process that plays a critical role in cancer progression and drug resistance.

Methods

To investigate the m6A modification patterns in AML and their potential clinical significance, we used the AUCell method to describe the m6A modification activity of cells in AML patients based on 23 m6A modification enzymes and further integrated with bulk RNA-seq data.

Results

We found that m6A modification was more effective in leukemic cells than in immune cells and induced significant changes in gene expression in leukemic cells rather than immune cells. Furthermore, network analysis revealed a correlation between transcription factor activation and the m6A modification status in leukemia cells, while active m6A-modified immune cells exhibited a higher interaction density in their gene regulatory networks. Hierarchical clustering based on m6A-related genes identified three distinct AML subtypes. The immune dysregulation subtype, characterized by RUNX1 mutation and KMT2A copy number variation, was associated with a worse prognosis and exhibited a specific gene expression pattern with high expression level of IGF2BP3 and FMR1, and low expression level of ELAVL1 and YTHDF2. Notably, patients with the immune dysregulation subtype were sensitive to immunotherapy and chemotherapy.

Discussion

Collectively, our findings suggest that m6A modification could be a potential therapeutic target for AML, and the identified subtypes could guide personalized therapy.

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.

Candidate regulators of drought stress in tomato revealed by comparative transcriptomic and proteomic analyses

Drought is among the most common abiotic constraints of crop growth, development, and productivity. Integrating different omics approaches offers a possibility for deciphering the metabolic pathways and fundamental mechanisms involved in abiotic stress tolerance. Here, we explored the transcriptional and post-transcriptional changes in drought-stressed tomato plants using transcriptomic and proteomic profiles to determine the molecular dynamics of tomato drought stress responses. We identified 22467 genes and 5507 proteins, among which the expression of 3765 genes and 294 proteins was significantly changed under drought stress. Furthermore, the differentially expressed genes (DEGs) and differentially abundant proteins (DAPs) showed a good correlation (0.743). The results indicated that integrating different omics approaches is promising in exploring the multilayered regulatory mechanisms of plant drought resistance. Gene ontology (GO) and pathway analysis identified several GO terms and pathways related to stress resistance, including response to stress, abiotic stimulus, and oxidative stress. The plant hormone abscisic acid (ABA) plays pivotal roles in response to drought stress, ABA-response element binding factor (AREB) is a key positive regulator of ABA signaling. Moreover, our analysis indicated that drought stress increased the abscisic acid (ABA) content, which activated AREB1 expression to regulate the expression of TAS14, GSH-Px-1, and Hsp, ultimately improving tomato drought resistance. In addition, the yeast one-hybrid assay demonstrated that the AREB1 could bind the Hsp promoter to activate Hsp expression. Thus, this study involved a full-scale analysis of gene and protein expression in drought-stressed tomato, deepening the understanding of the regulatory mechanisms of the essential drought-tolerance genes in tomato.

Beneficial effects of ginkgetin on improving nonalcoholic steatohepatitis characterized by bulk and single-cell RNA sequencing analysis

Background and aims: Nonalcoholic steatohepatitis (NASH) has become one of the major causes of cirrhosis and liver failure. However, there are currently no approved medications for managing NASH. Our study was designed to assess the effects of ginkgetin on NASH and the involved mechanisms. Methods: We constructed a mouse model of NASH by high-fat diet for 24 weeks. The effects of ginkgetin on NASH were evaluated by histological study, Western blot, and biochemical analysis. RNA Sequencing (RNA-Seq) analysis was used to investigate the alteration in gene expression and signaling pathways at bulk and single-cell levels. Results: Administration of ginkgetin resulted in a marked improvement in hepatic lipid accumulation, inflammation, and fibrosis in the NASH model. And these results were supported by bulk RNA-Seq analysis, in which the related signaling pathways and gene expression were markedly downregulated. Furthermore, single-cell RNA-Seq (scRNA-Seq) analysis revealed that the effects of ginkgetin on NASH were associated with the reprogramming of macrophages, hepatic stellate cells, and endothelial cells. Especially, ginkgetin induced a marked decrease in macrophages and a shift from pro-inflammatory to anti-inflammatory phenotype in NASH mice. And the NASH-associated macrophages (NAMs), which emerge during NASH, were also significantly downregulated by ginkgetin. Conclusion: Ginkgetin exhibits beneficial effects on improving NASH, supported by bulk and single-cell RNA-Seq. Our study may promote pharmacological therapy for NASH and raise the existent understanding of NASH.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

Decoding Oncofusions: Unveiling Mechanisms, Clinical Impact, and Prospects for Personalized Cancer Therapies

Cancer-associated gene fusions, also known as oncofusions, have emerged as influential drivers of oncogenesis across a diverse range of cancer types. These genetic events occur via chromosomal translocations, deletions, and inversions, leading to the fusion of previously separate genes. Due to the drastic nature of these mutations, they often result in profound alterations of cellular behavior. The identification of oncofusions has revolutionized cancer research, with advancements in sequencing technologies facilitating the discovery of novel fusion events at an accelerated pace. Oncofusions exert their effects through the manipulation of critical cellular signaling pathways that regulate processes such as proliferation, differentiation, and survival. Extensive investigations have been conducted to understand the roles of oncofusions in solid tumors, leukemias, and lymphomas. Large-scale initiatives, including the Cancer Genome Atlas, have played a pivotal role in unraveling the landscape of oncofusions by characterizing a vast number of cancer samples across different tumor types. While validating the functional relevance of oncofusions remains a challenge, even non-driver mutations can hold significance in cancer treatment. Oncofusions have demonstrated potential value in the context of immunotherapy through the production of neoantigens. Their clinical importance has been observed in both treatment and diagnostic settings, with specific fusion events serving as therapeutic targets or diagnostic markers. However, despite the progress made, there is still considerable untapped potential within the field of oncofusions. Further research and validation efforts are necessary to understand their effects on a functional basis and to exploit the new targeted treatment avenues offered by oncofusions. Through further functional and clinical studies, oncofusions will enable the advancement of precision medicine and the drive towards more effective and specific treatments for cancer patients.

TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs

Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer

BACKGROUND

Triple negative breast cancer (TNBC) is an aggressive and fatal malignancy. The current success of tumor immunotherapy has focused attention on intermediate T-cell subsets and the tumor microenvironment, which are essential for activation of the anti-tumor response. Therefore, both areas require further research to accelerate progress in developing tailored immunotherapeutic approaches for patients with TNBC.

METHODS

We obtained scRNA-seq data of TNBC from the GEO database. A multiplex strategy was used to analyze and identify the T-cell heterogeneity of TNBC. By combining the METABRIC and GEO databases, a prognostic risk model for T-cell marker genes was constructed and validated. In addition, the immune-infiltrating cells of TNBC was analyzed using CIBERSORT, and the association between the risk model and response to immunotherapy was investigated.

RESULTS

Based on scRNA-seq data, 25,932 cells were identified for multiple analyzes. T cells were studied with a focus on 2 subtypes, including CD8⁺ and CD4⁺. There were also communication relationships between T cells and multiple cell types. The results of the enrichment analysis showed that the T-cell marker genes were focused in pathways related to the immune system. In addition, OPTN, TMEM176A, PKM and HES1 deserve attention as prognostic markers in TNBC. The immune infiltration results showed that the high-risk group had significant immune cell infiltration and immunosuppression status.

CONCLUSION

This study provides a resource for understanding T-cell heterogeneity and the associated prognostic risk model for TNBC. The results show that the model helps predict prognosis and response to treatment in breast cancer.

Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM

Background

Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations.

Results

An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM.

Conclusion

MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.

Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing

Background

Cancer stem cells (CSCs) play vital roles in lung adenocarcinoma (LUAD) recurrence, metastasis, and drug resistance. Cuproptosis has provided a novel insight into the treatment of lung CSCs. However, there is a lack of knowledge regarding the cuproptosis-related genes combined with the stemness signature and their roles in the prognosis and immune landscape of LUAD.

Methods

Cuproptosis-related stemness genes (CRSGs) were identified by integrating single-cell and bulk RNA-sequencing data in LUAD patients. Subsequently, cuproptosis-related stemness subtypes were classified using consensus clustering analysis, and a prognostic signature was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression. The association between signature with immune infiltration, immunotherapy, and stemness features was also investigated. Finally, the expression of CRSGs and the functional roles of target gene were validated in vitro.

Results

We identified six CRSGs that were mainly expressed in epithelial and myeloid cells. Three distinct cuproptosis-related stemness subtypes were identified and associated with the immune infiltration and immunotherapy response. Furthermore, a prognostic signature was constructed to predict the overall survival (OS) of LUAD patients based on eight differently expressed genes (DEGs) with cuproptosis-related stemness signature (KLF4, SCGB3A1, COL1A1, SPP1, C4BPA, TSPAN7, CAV2, and CTHRC1) and confirmed in validation cohorts. We also developed an accurate nomogram to improve clinical applicability. Patients in the high-risk group showed worse OS with lower levels of immune cell infiltration and higher stemness features. Ultimately, further cellular experiments were performed to verify the expression of CRSGs and prognostic DEGs and demonstrate that SPP1 could affect the proliferation, migration, and stemness of LUAD cells.

Conclusion

This study developed a novel cuproptosis-related stemness signature that can be used to predict the prognosis and immune landscape of LUAD patients, and provided potential therapeutic targets for lung CSCs in the future.

Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus

Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides in treatment options have been made in the past 15 years, with the US Food and Drug Administration approval of belimumab in 2011, there are still many patients who have inadequate responses to therapy. A better understanding of underlying disease mechanisms with a holistic and multiparametric approach is required to improve clinical assessment and treatment. This Review discusses the evolution of genomics, epigenomics, transcriptomics, and proteomics in the study of systemic lupus erythematosus and ways to amalgamate these silos of data with a systems-based approach while also discussing ways to strengthen the overall process. These mechanistic insights will facilitate the discovery of functionally relevant biomarkers to guide rational therapeutic selection to improve patient outcomes.

Improving head and neck cancer therapies by immunomodulation of the tumour microenvironment

Targeted immunotherapy has improved patient survival in head and neck squamous cell carcinoma (HNSCC), but less than 20% of patients produce a durable response to these treatments. Thus, new immunotherapies that consider all key players of the complex HNSCC tumour microenvironment (TME) are necessary to further enhance tumour-specific T cell responses in patients. HNSCC is an ideal tumour type in which to evaluate immune and non-immune cell differences because of two distinct TME aetiologies (human papillomavirus (HPV)-positive and HPV-negative disease), multiple anatomic sites for tumour growth, and clear distinctions between patients with locally advanced disease and those with recurrent and/or metastatic disease. Recent technological and scientific advancements have provided a more complete picture of all cellular constituents within this complex TME and have evaluated the interplay of both immune and non-immune cells within HNSCC. Here, we include a comprehensive analysis of the complete ecosystem of the HNSCC TME, performed utilizing data-rich resources such as The Cancer Genome Atlas, and cutting-edge techniques, such as single-cell RNA sequencing, high-dimensional flow cytometry and spatial multispectral imaging, to generate improved treatment strategies for this diverse disease.

RNA-Seq Analysis of Clinical Samples from TCGA Reveal Molecular Signatures for Ovarian Cancer

Identifying differentially expressed genes and co-expression modules lead to novel biomarkers. GO, pathway enrichment, network, and tumor stage analysis of 318 ovarian cancer samples from TCGA, categorised into primary and recurrent, pre-menopause and post-menopause, and early and late stage tumors was performed. Upregulated and downregulated genes in primary vs recurrent, early stage vs late-stage and pre-menopause vs post-menopause tumors were 84 and 62, 84 and 35, and 88 and 14, respectively. IRAK2 and CXCL8 had higher expression in recurrent tumors while REG1A had higher expression in post-menopause samples. In late stage tumors constant expression of IRAK2 and REG1A was observed, while that of CXCL8 and EGF decreased. These genes may be potential biomarkers for the diagnosis of the disease.

Immunotherapeutic Approaches in Ovarian Cancer

Ovarian cancer (OC) is gynecological cancer, and diagnosis and treatment are continuously advancing. Next-generation sequencing (NGS)-based diagnoses have emerged as novel methods for identifying molecules and pathways in cancer research. The NGS-based applications have expanded in OC research for early detection and identification of aberrant genes and dysregulation pathways, demonstrating comprehensive views of the entire transcriptome, such as fusion genes, genetic mutations, and gene expression profiling. Coinciding with advances in NGS-based diagnosis, treatment strategies for OC, such as molecular targeted therapy and immunotherapy, have also advanced. Immunotherapy is effective against many other cancers, and its efficacy against OC has also been demonstrated at the clinical phase. In this review, we describe several NGS-based applications for therapeutic targets of OC, and introduce current immunotherapeutic strategies, including vaccines, checkpoint inhibitors, and chimeric antigen receptor (CAR)-T cell transplantation, for effective diagnosis and treatment of OC.

CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer

Introduction

It is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC.

Methods

We obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts.

Results

High riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy.

Discussion

Collectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.

Identification of tumour-infiltrating myeloid subsets associated with overall survival in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a primary subtype of lung cancer with limited therapeutic options and poor prognosis, and tumour-infiltrating myeloid cells (TIMs) are key regulators of LUSC. However, the correlation between the abundance of TIM subtypes and clinical outcomes of LUSC remains unexplored. This study aimed to develop and validate a prognostic model for low- and high-risk patients with LUSC based on myeloid cell microenvironments. TIM markers in the tumoural (T) and stromal (S) regions were quantified using immunohistochemistry for 502 LUSC patients. L1-penalized Cox regression was used to develop a myeloid survival score (MSS) model based on the training cohort, followed by validation in distinct cohorts from multiple centres. RNA sequencing and immunostaining were used to examine the mechanisms of myeloid cells in LUSC progression and predict potential drug targets and therapeutic agents. Of the 12 myeloid markers, CD163T, CD163S, and S100A12T were highly associated with overall survival (OS) in LUSC patients. The MSS of the three myeloid signatures accurately categorized LUSC patients into risk categories, with an observable difference in OS between the training and validation cohorts. Tumours with high MSS were associated with enhanced antioxidative ability and hedgehog signalling and a shift to a more pro-tumorigenic microenvironment, accompanied by a reduced tumour cell immunogenicity and increased CD8⁺ T cell exhaustion patterns. Additionally, in high-risk patients, potential drug targets and compounds regulating hedgehog signalling were identified. Our study provides the first prognostic myeloid signature for LUSC, which may help advance precision medicine. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma

Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients. Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram. Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level. Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.

A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data

Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterogeneity. In this study, a framework of three subnetwork-based strategies is presented, incorporating hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes. By applying the proposed approaches to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, we successfully identified multiple HNSCC-specific gene modules with improved prognostic values and mechanism information compared with the standard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data. Overall, the study demonstrates and supports the use of the subnetwork-based approach for distilling reliable and biologically meaningful prognostic factors.

Identification of therapeutic targets and prognostic biomarkers in cholangiocarcinoma via WGCNA

Background

Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor for which limited treatment methods and prognostic signatures are available. This study aims to identify potential therapeutic targets and prognostic biomarkers for CCA.

Methods

Based on differentially expressed genes (DEGs) identified from The Cancer Genome Atlas (TCGA) data, our study identified key gene modules correlated with CCA patient survival by weighted gene coexpression network analysis (WGCNA). Cox regression analysis identified survival-related genes in the key gene modules. The biological properties of the survival-related genes were evaluated by CCK-8 and transwell assays. Then, these genes were used to construct a prognostic signature that was internally and externally validated. Additionally, by combining clinical characteristics with the gene-based prognostic signature, a nomogram for survival prediction was built.

Results

WGCNA divided the 1531 DEGs into four gene modules, and the yellow gene module was significantly associated with overall survival (OS) and histologic neoplasm grade. Our study identified the lncRNA AGAP2-AS1 and a novel gene, GOLGA7B, that are closely related to survival. GOLGA7B downregulation promoted the invasion, migration and proliferation of CCA cells, but AGAP2-AS1 had the opposite effect. AGAP2-AS1 and GOLGA7B were integrated into a gene-based prognostic signature, and both internal and external validation studies confirmed that this two-gene prognostic signature and nomogram could accurately predict CCA patient prognosis.

Conclusion

AGAP2-AS1 and GOLGA7B are potential therapeutic targets and prognostic biomarkers for CCA.

Single-Cell Transcriptome Analysis Reveals Paraspeckles Expression in Osteosarcoma Tissues

Nuclear paraspeckles are subnuclear bodies contracted by nuclear-enriched abundant transcript 1 (NEAT1) long non-coding RNA, localised in the interchromatin space of mammalian cell nuclei. Paraspeckles have been critically involved in tumour progression, metastasis and chemoresistance. To this date, there are limited findings to suggest that paraspeckles, NEAT1 and heterogeneous nuclear ribonucleoproteins (hnRNPs) directly or indirectly play roles in osteosarcoma progression. Herein, we analysed NEAT1, paraspeckle proteins (SFPQ, PSPC1 and NONO) and hnRNP members (HNRNPK, HNRNPM, HNRNPR and HNRNPD) gene expression in 6 osteosarcoma tumour tissues using the single-cell RNA-sequencing method. The normalised data highlighted that the paraspeckles transcripts were highly abundant in osteoblastic OS cells, except NEAT1, which was highly expressed in myeloid cell 1 and 2 subpopulations.

Single-cell RNA-seq analysis and cell-cluster deconvolution of the human preovulatory follicular fluid cells provide insights into the pathophysiology of ovarian hyporesponse

Reduction in responsiveness to gonadotropins or hyporesponsiveness may lead to the failure of in vitro fertilization (IVF), due to a low number of retrieved oocytes. The ovarian sensitivity index (OSI) is used to reflect the ovarian responsiveness to gonadotropin stimulation before IVF. Although introduced to clinical practice already years ago, its usefulness to predict clinical outcomes requires further research. Nevertheless, pathophysiological mechanisms of ovarian hyporesponse, along with advanced maternal age and in younger women, have not been fully elucidated. Follicles consist of multiple cell types responsible for a repertoire of biological processes including responding to pituitary gonadotropins necessary for follicle growth and oocyte maturation as well as ovulation. Encouraging evidence suggests that hyporesponse could be influenced by many contributing factors, therefore, investigating the variability of ovarian follicular cell types and their gene expression in hyporesponders is highly informative for increasing their prognosis for IVF live birth. Due to advancements in single-cell analysis technologies, the role of somatic cell populations in the development of infertility of ovarian etiology can be clarified. Here, somatic cells were collected from the fluid of preovulatory ovarian follicles of patients undergoing IVF, and RNA-seq was performed to study the associations between OSI and gene expression. We identified 12 molecular pathways differentially regulated between hypo- and normoresponder patient groups (FDR<0.05) from which extracellular matrix organization, post-translational protein phosphorylation, and regulation of Insulin-like Growth Factor (IGF) transport and uptake by IGF Binding Proteins were regulated age-independently. We then generated single-cell RNA-seq data from matching follicles revealing 14 distinct cell clusters. Using cell cluster-specific deconvolution from the bulk RNA-seq data of 18 IVF patients we integrated the datasets as a novel approach and discovered that the abundance of three cell clusters significantly varied between hypo- and normoresponder groups suggesting their role in contributing to the deviations from normal ovarian response to gonadotropin stimulation. Our work uncovers new information regarding the differences in the follicular gene expression between hypo- and normoresponders. In addition, the current study fills the gap in understanding the inter-patient variability of cell types in human preovulatory follicles, as revealed by single-cell analysis of follicular fluid cells.

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors-Dependencies and Novel Pitfalls

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.