Identification Of Cell Types Research Articles

Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy.

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell-associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell-associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

Abstract LB253: Crafted experiments to evaluate feature selection methods for single cell RNA-seq data

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized our understanding of cellular compositions and tumor behavior at the single-cell level, and have been widely used in many biomedical applications, such as identifying and characterizing novel cell types/states. While numerous computational methods have been developed for analyzing scRNA-seq data, benchmarking various analytical methods remains a key challenge for two reasons. First, there is a lack of ground truth datasets where unambiguous cell type identities are known using external information. Second, simulation of scRNA-seq data tends to be overly artificial and simplistic, which may not well mimic the underlying biological complexity as well as high level of technical variability. Here, we propose the use of crafted experiments, a new approach based upon perturbing signals in a real dataset for comparing different scRNA-seq analytical methodologies. We demonstrate the effectiveness of crafted experiments in the context of a novel univariate distribution-oriented suite of feature selection methods, called GOF (Goodness of Fit). We show that GOF more frequently selects features that robustly identify crafted artificial clusters in crafted experiments and achieves similar performance compared to the field standard method using real datasets. Importantly, we show the use of crafted experiments for identifying the contexts in which each method performs the best. Crafted experiments offer valuable comparisons of scRNA-seq analysis methods, and the crafted datasets generated from this study provide a useful resource for the single-cell community. Citation Format: Siyao Liu, David Corcoran, Susana Garcia-Recio, Charles Perou, J.S. Marron. Crafted experiments to evaluate feature selection methods for single cell RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB253.

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids.

Over the past decade, single-cell transcriptomics has significantly evolved and become a standard laboratory method for simultaneous analysis of gene expression profiles of individual cells, allowing the capture of cellular diversity. In order to overcome limitations posed by difficult-to-isolate cell types, an alternative approach aiming at recovering single nuclei instead of intact cells can be utilized for sequencing, making transcriptome profiling of individual cells universally applicable. These techniques have become a cornerstone in the study of brain organoids, establishing them as models of the developing human brain. Leveraging the potential of single-cell and single-nucleus transcriptomics in brain organoid research, this protocol presents a step-by-step guide encompassing key procedures such as organoid dissociation, single-cell or nuclei isolation, library preparationand sequencing. By implementing these alternative approaches, researchers can obtain high-quality datasets, enabling the identification of neuronal and non-neuronal cell types, gene expression profiles, and cell lineage trajectories. This facilitates comprehensive investigations into cellular processes and molecular mechanisms shaping brain development.

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.

Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707043 cells and 1154611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.

ScBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.

Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.

ScINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

Single-cell profiling transcriptomic reveals cellular heterogeneity and cellular crosstalk in choroidal neovascularization model

Choroidal neovascularization (CNV) is a hallmark of neovascular age-related macular degeneration (nAMD) and a major contributor to vision loss in nAMD cases. However, the identification of specific cell types associated with nAMD remains challenging. Herein, we performed single-cell sequencing to comprehensively explore the cellular diversity and understand the foundational components of the retinal pigment epithelium (RPE)/choroid complex. We unveiled 10 distinct cell types within the RPE/choroid complex. Notably, we observed significant heterogeneity within endothelial cells (ECs), fibroblasts, and macrophages, underscoring the intricate nature of the cellular composition in the RPE/choroid complex. Within the EC category, four distinct clusters were identified and EC cluster 0 was tightly associated with choroidal neovascularization. We identified five clusters of fibroblasts actively involved in the pathogenesis of nAMD, influencing fibrotic responses, angiogenic effects, and photoreceptor function. Additionally, three clusters of macrophages were identified, suggesting their potential roles in regulating the progression of nAMD through immunomodulation and inflammation regulation. Through CellChat analysis, we constructed a complex cell-cell communication network, revealing the role of EC clusters in interacting with fibroblasts and macrophages in the context of nAMD. These interactions were found to govern angiogenic effects, fibrotic responses, and inflammatory processes. In summary, this study reveals noteworthy cellular heterogeneity in the RPE/choroid complex and provides valuable insights into the pathogenesis of CNV. These findings will open up potential avenues for deep understanding and targeted therapeutic interventions in nAMD.

Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

Tumor cell type and gene marker identification by single layer perceptron neural network on single-cell RNA sequence data

Tumor cell type and gene marker identification by single layer perceptron neural network on single-cell RNA sequence data

Abstract 4308: Same-section spatial multiomic analyses using MICS technology for investigating the dynamics of the tumor microenvironment

Abstract The recent increase in image-based, spatially-resolved technologies enables researchers to profile the tumor microenvironment (TME) by capturing gene expression profiles within tissue sections. However, a significant limitation of these technologies is the lack of ability to resolve protein and RNA information in the same section, as well as conveniently analyze multimodal data sets. Here, we report a spatial RNA detection method, RNAsky, using Miltenyi Biotec’s MACSima™ Platform as an automated, multiomic approach. Our method integrates spatial proteomics and transcriptomics data to provide in-depth profiling with single-cell resolution on the same tissue section. We demonstrate these capabilities by characterizing key immune-oncology markers across normal and diseased tissues using our specialized MACS® iQ View analysis software. MACS iQ View provides fast segmentation and intuitive gating and clustering strategies to simultaneously assess protein and RNA data. We investigated the impact of clustering using protein, RNA, or the combination of both to evaluate the contribution of different information modalities on TME spatial dynamics. This cutting-edge approach will enable the identification of valuable parameters and new cell types, furthering the discovery and development of predictive and prognostic biomarkers. Citation Format: Dongju Park, Emily Neil, Rebecca C. Hennessey, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Julia Femel, Alex Makrigiorgos, Shaina Lu, John Lee, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Telma Santos, Stefan Borbe, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Werner Müller, Robert Pinard, Andreas Bosio. Same-section spatial multiomic analyses using MICS technology for investigating the dynamics of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4308.

Abstract 1541: Heterogeneity of the hormone receptor-positive breast tumor immune microenvironment according to patient’s body mass index

Abstract Background: Obesity is a risk factor for postmenopausal hormone receptor-positive (HR+) breast cancer (BC) and is associated with poorer outcomes. “Obesity-paradox”, a phenomenon where obesity (BMI ≥ 30) is associated with better outcomes in patients treated with immune checkpoint inhibitors (ICI), has been observed in several cancer types. It is however under-explored in BC, especially HR+ BC. Emerging data from recent trials (KEYNOTE-756 and CheckMate 7FL) have shown the potential benefits of ICI for treating early HR+ BC. This prompted the need for better insights into the biological link between obesity and HR+ BC, particularly its tumor immune microenvironment (TIME). Patients &amp; Methods: A monocentric cohort of 33 patients (11 lean, 11 overweight, 11 obese) with early HR+ BC was selected for the study, whose tumor tissues were collected and subjected to snRNA-seq. Unsupervised clustering and cluster annotation were performed on the processed data leading to identification of cell types and subtypes. We then performed differential gene expression (DGEA) and gene set enrichment analyses (GSEA) according to BMI category for each of the immune cell types. Results: We identified a total of 76,773 immune cells belonging to six major cell types, namely B/plasma cell, dendritic cell (DC), mast cell, monocyte/macrophage (mono/macro), T cell, and natural killer (NK) cell. Their proportions varied between patients, however immune cellular composition was not associated with BMI. DGEA and GSEA revealed highly cell type-specific obesity-associated differences in the expression profile of immune cells. Notably, an enrichment of B cell-mediated immune activities, substantiated by the upregulation of Ig genes, was observed in B cells and plasma cells. This was however coupled with an overexpression of the checkpoint BTLA in B cells. CD8+ and CD4+ T cells showed a lower level of activation (higher LEF1, TCF7, lower ANXA1, IL4R), and NK cells a decreased cytokine production (CD96, CD226) and conflicting differential expression of cytotoxic molecules (lower NKG7, higher granzymes). Checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, HAVCR2 did not show significant changes in their expression in T/NK cells. Mast cells displayed a downregulation of antigen presenting genes (CD74, HLA-A/B/E). DC and mono/macro populations also exhibited both up- and down-regulation of genes involved in different immune pathways. Conclusion: Our current data suggested that obesity was associated with multi-directional changes in the immune activities, possibly implying an unresolved inflammation in the HR+ BC tissue. Further investigation incorporating other cell types and their interaction with immune cells in both tumor and matched normal mammary tissues is warranted and on-going, which could provide more understanding of HR+ breast TIME and lead to speculation on therapeutic implications. Citation Format: Ha-Linh Nguyen, Tatjana Geukens, Maxim De Schepper, Karen Van Baelen, Marion Maetens, Gitte Zels, Amena Mahdami, Edoardo Isnaldi, Sigrid Hatse, Joke Verbeke, Christophe Matthys, Bernard Thienpont, Evy Vanderheyden, Thomas Van Brussel, Rogier Schepers, Gino Philips, Bram Boeckx, Diether Lambrechts, Giuseppe Floris, François Richard, Christine Desmedt. Heterogeneity of the hormone receptor-positive breast tumor immune microenvironment according to patient’s body mass index [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1541.

Abstract 4961: Large-scale CyTOF data modeling of leukemia patient cohorts

Abstract Introduction: Cytometry by Time-of-Flight (CyTOF) is a single-cell proteomic assay that uses antibody-lanthanide metal conjugates to tag surface or intracellular proteins, quantified via mass spectrometry. Compared to flow cytometry, CyTOF significantly reduces overlapping signals and increases the maximum protein features from 8 to 50. CyTOF data is commonly analyzed with the ‘gating’ workflow, which is the iterative process of plotting cells by two protein features to select discrete cell populations. While CyTOF can comprehensively profile nuanced cell types, gating does not scale well in the number of features. Biases in gating often arise from the forced discretization of cell populations, subjectivity of gate boundaries, and intrinsic sample-sample variation. Contemporary challenges in CyTOF data analysis can be attributed to data volume and gating bias, which can lead to oversight of disease-related cell populations while hindering reproducibility. Methods: We developed a computational platform that enables the processing and analysis of large-scale CyTOF data. Our workflow includes modular components for data harmonization, normalization, transformation, batch correction, quality control, subsampling, and dimensionality reduction via UMAP of input sample files in Flow Cytometry Standard (FCS) format. Further, additional analytical modules are currently being integrated to deploy large-scale machine learning models for cell type identification. We use the results of these models as input for multi-scale analysis, where we aim to link single-cell CyTOF data with clinical outcomes. We test our workflow on previously published datasets and apply it to new leukemia patient cohorts of blood and bone marrow samples and a variety of marker panels. Results: We tested our workflow on previously published datasets of 4.7 M peripheral blood mononuclear cells from healthy donors (N= 20) and T cells (N = 8). We observed that samples mixed well upon integration and separated into major cell types like monocytes, CD4+/CD8+ T-cells, naïve/mature B-cells, and natural killer T-cells. We then applied it to a bone marrow cohort of healthy donors (N = 10), where we observe good mixing of samples but less clear separation of cell types. Finally, we applied our workflow to 9.3 M cells from 51 chronic lymphocytic leukemia (CLL) patients and 4 healthy donors, using a lymphocytic marker panel. Here, we observed a clear resolution of a major clonal B-cell population and immune T-cells. We are in the process of applying it to Acute Myeloid Leukemia (AML) patient data with myeloid and immune marker panels. Conclusions: We developed a computational platform that facilitates the simultaneous analysis of large-scale CyTOF data, both in the number of samples and cells per sample, a variety of marker panels, while minimizing gating biases common in traditional gating analysis. Citation Format: Garth Kong, Tania Vu, Evan Lind, Olga H. Nikolova. Large-scale CyTOF data modeling of leukemia patient cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4961.

CTPredictor: A comprehensive and robust framework for predicting cell types by integrating multi-scale features from single-cell Hi-C data

Single-cell Hi-C (scHi-C) has emerged as a powerful technology for deciphering cell-to-cell variability in three-dimensional (3D) chromatin organization, providing insights into genome-wide chromatin interactions and their correlation with cellular functions. Nevertheless, the accurate identification of cell types across different datasets remains a formidable challenge, hindering comprehensive investigations into genome structure. In response, we introduce CTPredictor, an innovative computational method that integrates multi-scale features to accurately predict cell types in various datasets. CTPredictor strategically incorporates three distinct feature sets, namely, small intra-domain contact probability (SICP), smoothed small intra-domain contact probability (SSICP), and smoothed bin contact probability (SBCP). The resulting fusion classification model significantly enhances the accuracy of cell type prediction based on single-cell Hi-C data (scHi-C). Rigorous benchmarking against established methods and three conventional machine learning approaches demonstrates the robust performance of CTPredictor, positioning it as an advanced tool for cell type prediction within scHi-C data. Beyond its prediction capabilities, CTPredictor holds promise in illuminating 3D genome structures and their functional significance across a wide array of biological processes.

Identification of red blood cell distribution width as a prognostic factor in acute myeloid leukemia

Many prognostic factors have been identified in acute myeloid leukemia (AML). In this study, we investigated novel prognostic biomarkers using machine learning and Cox regression models in a prospective cohort of 591 patients with AML and tried to identify potential therapeutic targets based on transcriptomic data. We found that elevated red blood cell distribution width (RDW) at diagnosis was an adverse prognostic factor for AML, independent of the 2022 European LeukemiaNet (ELN2022) genetic risk. As a continuous variable, higher RDW was associated with shorter overall survival (OS) (hazard ratio [HR] 1.087, 95% confidence interval [CI] 1.036-1.139, p < 0.001) and event-free survival (EFS) (HR 1.078, 95% CI 1.033-1.124, p < 0.001). Elevated RDW returned to normal after consolidation therapy, which indicated that leukemia cells resulted in abnormal RDW. We further investigated the relationship between RDW and transcriptome in another cohort of 191 patients with AML and public datasets using gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). We found that patients in the high-RDW group were significantly enriched in the positive regulation of erythroid differentiation and inflammation-related pathways. Finally, we identified the inflammation-associated gene IL12RB2 and verified its prognostic relevance with patients with AML in public databases, suggesting it as a potential therapy target.

Spatially organized cellular communities form the developing human heart

The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell–cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.

Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

ABSTRACT This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1‘s pivotal role validated. FDX1‘s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1‘s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Refining the identity of mesenchymal cell types associated with murine periosteal and endosteal bone

Single-cell RNA sequencing has led to novel designations for mesenchymal cells associated with bone as well as multiple designations for what appear to be the same cell type. The main goals of this study were to increase the amount of single cell RNA sequence data for osteoblasts and osteocytes, to compare cells from the periosteum to those inside bone, and to clarify the major categories of cell types associated with murine bone. We created an atlas of murine bone-associated cells by harmonizing published datasets with in-house data from cells targeted by Osx1-Cre and Dmp1-Cre driver strains. Cells from periosteal bone were analyzed separately from those isolated from the endosteum and trabecular bone. Over 100,000 mesenchymal cells were mapped to reveal 11 major clusters designated fibro-1, fibro-2, chondrocytes, articular chondrocytes, tenocytes, adipo-CAR, osteo-CAR, pre-osteoblasts, osteoblasts, osteocytes, and osteo-X, the latter defined in part by Postn expression. Osteo-X, osteo-CAR, and pre-osteoblasts were closely associated with osteoblasts at the trabecular bone surface. Wnt16 was expressed in multiple cell types from the periosteum but not in cells from endocortical or cancellous bone. Fibro-2 cells, which express markers of stem cells, localized to the periosteum but not trabecular bone in adult mice. Suppressing bone remodeling eliminated osteoblasts and altered gene expression in pre-osteoblasts but did not change the abundance or location of osteo-X or osteo-CAR cells. These results provide a framework for identifying bone cell types in murine single cell RNA sequencing datasets and suggest that osteoblast progenitors reside near the surface of remodeling bone.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Immunogenic cell death related mRNAs associated signature to predict immunotherapeutic response in osteosarcoma

BackgroundImmunogenic cell death (ICD) is related to cancer prognosis, which has a synergic effect in combination with chemotherapy or immunotherapy. Yet, the relationship between ICD and osteosarcoma remained unclear. Materials and methodsThree osteosarcoma datasets including therapeutically applicable research to generate effective treatments (TARGET), GSE126209 and GSE21257 datasets were included. A protein-protein interaction network was constructed based on ICD-related genes. We performed unsupervised consensus clustering to classify molecular subtypes (clusters). Survival analysis, Estimation of stromal and immune cells in malignant tumour tissues using expression data (ESTIMATE), Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and differential analysis were employed to characterize the molecular differences between different clusters. Univariate Cox regression analysis was conducted to confirm prognostic genes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to demonstrate the aberrant expression of ICD-correlated signature genes in osteosarcoma. A series of cellular experiments, including cell counting kit-8 (CCK-8), transwell, and flow cytometry, were used to demonstrate the regulatory role of key genes in the ICD model on the malignant phenotype of osteosarcoma. ResultsThree clusters (cluster1, 2, 3) were constructed and they showed distinct overall survival and immune infiltration. ICD-related genes were highly expressed in cluster1. Moreover, Cluster1 had the best prognosis, high immune score and high expression of human leukocyte antigen (HLA)-related genes. TLR4, LY96, IFNGR1, CD4, and CASP1 were identified as prognostic genes for establishing an ICD-related risk signature. According to the risk signature, two risk groups (high and low risks) showing differential prognosis and response to immunotherapy. The low risks group had a better prognosis but was not sensitive to immunotherapy. Molecular assays verified that prognostic genes were abnormally under-expressed in osteosarcoma. Cellular assays demonstrated that LY96, the most significantly down-regulated gene in osteosarcoma, inhibited the migration, invasion, and proliferation phenotypes of osteosarcoma cells and prolonged the cell cycle. Analysis of oxidative stress related pathway enrichment in tumor microenvironment was conducted by single-sample gene set enrichment analysis (ssGSEA). ConclusionsThis study demonstrated the prognostic significance of ICD-correlated genes in osteosarcoma patients. The five-gene risk signature facilitate prognostic evaluation and prediction of osteosarcoma patients’ response to immunotherapy. The risk signature also offered a possibility for the exploit of novel ICD-related treatment.

CCND2 is a prognostic biomarker and correlates with immune infiltration in lung adenocarcinoma.

CCND2 expression influences the growth and proliferation of cancer cells and plays a crucial role in immune response of tumor. However, few studies focused on the correlation between CCND2 and lung adenocarcinoma (LUAD) in terms of prognosis and tumor immune infiltration. Original LUAD case data were screened from The Cancer Genome Atlas (TCGA) database. Using R software, we analyzed differently expressed CCND2 between LUAD and adjacent normal tissues. Kaplan-Meier analysis was conducted to determine the relationship between CCND2 expression and the overall survival of LUAD patients, and Cox regression analysis was performed to identify the independently prognostic risk factors for LUAD. Using TIMER (Tumor Immune Estimation Resource) and CIBERSORTx (Cell-type Identification by Estimating Relative Subsets of known RNA Transcripts) databases, the connection between CCND2 expression and LUAD immune infiltration was investigated. The level of CCND2 was significantly lower in LUAD than in adjacent normal tissues [adjusted P<0.05 and log2 fold change (FC) =-1.33]. LUAD patients who expressed lower CCND2 had a shorter overall survival (P=0.046) and CCND2 was an independently prognostic risk factor for LUAD [hazard ratio (HR): 0.77, P=0.049]. In LUAD patients, CCND2 expression was positively associated with the levels of B cells (r=0.159, P=4.00e-04), CD8+ T cells (r=0.287, P=7.88e-11), CD4+ T cells (r=0.301, P=8.14e-12), macrophages (r=0.128, P=4.57e-03), neutrophils (r=0.373, P=1.07e-17), and myeloid dendritic cells (r=0.284, P=1.43e-10). The levels of B cells and macrophages had significantly association with the overall survival of LUAD patients. CIBERSORTx showed that the proportions of naive B cells, resting dendritic cells, and macrophages M1 were higher in the low CCND2 expression group (P<0.05); whereas macrophages M1, activated natural killer (NK) cells, and resting CD4+ memory cells were lower (P<0.05). CCND2 can be exploited as a novel prognostic biomarker involved in immune infiltration of LUAD, hence providing new preventative and therapeutic options for LUAD.