Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

The biphasic role of the infrapatellar fat pad in osteoarthritis.

Osteoarthritis (OA) is a progressive degenerative disease resulting in joint deterioration. It is a whole organ disease characterized by cartilage degeneration and varying degrees of synovitis, involving pathological changes in all joint tissues, such as cartilage, subchondral bone, ligaments, meniscus, synovium, and infrapatellar fat pad (IPFP). IPFP is the largest adipose tissue structure in the knee joint and is composed of fat cells, immune cells and blood vessels. Moreover, IPFP is located close to the cartilage and bone surface so that it may reduce the impact of loading and absorb forces generated through the knee joint, and may have a protective role in joint health. IPFP has been shown to release various cytokines and adipokines that play pro-inflammatory and pro-catabolic roles in cartilage, promoting OA progression. Intra-articular injections of IPFP-derived mesenchymal stem cells and exosomes have been shown to reduce pain and prevent OA progression in patients with knee OA. Previous studies have shown that IPFP has a biphasic effect on OA progression. This article reviews the latest research progress of IPFP, discusses the role and mechanism of IPFP in OA, provide new intervention strategies for the treatment of OA. This article will also discuss the handling of IPFP during the procedure of total knee arthroplasty.

Single-cell RNA sequencing explored potential therapeutic targets by revealing the tumor microenvironment of neuroblastoma and its expression in cell death.

BACKGROUND: Neuroblastoma (NB) is the most common extracranial solid tumor in childhood and is closely related to the early development and differentiation of neuroendocrine (NE) cells. The disease is mainly represented by high-risk NB, which has the characteristics of high mortality and difficult treatment. The survival rate of high-risk NB patients is not ideal. In this article, we not only conducted a comprehensive study of NB through single-cell RNA sequencing (scRNA-seq) but also further analyzed cuproptosis, a new cell death pathway, in order to find clinical treatment targets from a new perspective. MATERIALS AND METHODS: The Seurat software was employed to process the scRNA-seq data. This was followed by the utilization of GO enrichment analysis and GSEA to unveil pertinent enriched pathways. The inferCNV software package was harnessed to investigate chromosomal copy number variations. pseudotime analyses involved the use of Monocle 2, CytoTRACE, and Slingshot software. CellChat was employed to analyze the intercellular communication network for NB. Furthermore, PySCENIC was deployed to review the profile of transcription factors. RESULT: Using scRNA-seq, we studied cells from patients with NB. NE cells exhibited superior specificity in contrast to other cell types. Among NE cells, C1 PCLAF + NE cells showed a close correlation with the genesis and advancement of NB. The key marker genes, cognate receptor pairing, developmental trajectories, metabolic pathways, transcription factors, and enrichment pathways in C1 PCLAF + NE cells, as well as the expression of cuproptosis in C1 PCLAF + NE cells, provided new ideas for exploring new therapeutic targets for NB. CONCLUSION: The results revealed the specificity of malignant NE cells in NB, especially the key subset of C1 PCLAF + NE cells, which enhanced our understanding of the key role of the tumor microenvironment in the complexity of cancer progression. Of course, cell death played an important role in the progression of NB, which also promoted our research on new targets. The scrutiny of these findings proved advantageous in uncovering innovative therapeutic targets, thereby bolstering clinical interventions.

Dynamic changes in pyroptosis following spinal cord injury and the identification of crucial molecular signatures through machine learning and single-cell sequencing.

The pathological cascade of spinal cord injury (SCI) is highly intricate. The onset of neuroinflammation can exacerbate the extent of damage. Pyroptosis is a form of inflammation-linked programmed cell death (PCD), the inhibition of pyroptosis can partially mitigate neuroinflammation. It is imperative to delineate the principal cell types susceptible to pyroptosis and concomitantly identify key genes associated with this process. We initially defined the pyroptosis-related genes (PRGs) and analyzed their expression at different time points post SCI. The results demonstrate a substantial upregulation of differentially expressed genes (DEGs) related to pyroptosis on the 7 days post-injury (dpi), these DEGs in the 7 dpi are closely related to the inflammatory response. Subsequently, immune infiltration analysis revealed a predominant presence of inflammatory microglia. Through correlation analysis, we postulated that pyroptosis primarily manifested within the inflammatory microglia. Employing machine learning algorithms, we identified four pyroptosis-related molecular signatures, which were experimentally validated using BV2 cells and spinal cord tissue samples. The robustness of the identified molecular signatures was further confirmed through single-cell sequencing data analysis. Overall, our study elucidates the temporal dynamics of pyroptosis and identifies key molecular signatures following SCI. These findings can provide novel evidence for therapeutic interventions in SCI.

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer.

BACKGROUND: This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs. METHODS: Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group. RESULTS: In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy. CONCLUSION: A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Macrophage manufacturing and engineering with 5'-Cap1 and N1-methylpseudouridine-modified mRNA.

Macrophage-based cell therapeutics is an emerging modality to treat cancer and repair tissue damage. A reproducible manufacturing and engineering process is central to fulfilling their therapeutic potential. Here, we establish a robust macrophage-manufacturing platform (Mo-Mac) and demonstrate that macrophage functionality can be enhanced by N1-methylpseudouridine (m1Ψ)-modified mRNA. Using single-cell transcriptomic analysis as an unbiased approach, we found that >90% cells in the final product were macrophages while the rest primarily comprised T cells, B cells, natural killer cells, promyelocytes, promonocytes, and hematopoietic stem cells. This analysis also guided the development of flow-cytometry strategies to assess cell compositions in the manufactured product to meet requirements by the National Medical Products Administration. To modulate macrophage functionality, as an illustrative example we examined whether the engulfment capability of macrophages could be enhanced by mRNA technology. We found that efferocytosis was increased in vitro when macrophages were electroporated with m1Ψ-modified mRNA encoding CD300LF (CD300LF-mRNA-macrophage). Consistently, in a mouse model of acute liver failure, CD300LF-mRNA-macrophages facilitated organ recovery from acetaminophen-induced hepatotoxicity. These results demonstrate a GMP-compliant macrophage-manufacturing process and indicate that macrophages can be engineered by versatile mRNA technology to achieve therapeutic goals.

ScRNAs reveals high-frequency rTMS-induced pericyte differentiation: Potential implications for vascular regeneration and blood-brain barrier stability in stroke.

Stroke is a major cause of adult disability worldwide, often involving disruption of the blood-brain barrier (BBB). Repairing the BBB is crucial for stroke recovery, and pericytes, essential components of the BBB, are potential intervention targets. Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a treatment for functional impairments after stroke, with potential effects on BBB integrity. However, the underlying mechanisms remain unclear. In this study using a transient middle cerebral artery occlusion (tMCAO) rat model, we investigated the impact of rTMS on post-stroke BBB. Through single-cell sequencing (ScRNAs), we observed developmental relationships among pericytes, endothelial cells, and vascular smooth muscle cells, suggesting the differentiation potential of pericytes. A distinct subcluster of pericytes emerged as a potential therapeutic target for stroke. Additionally, our results revealed enhanced cellular communication among these cell types, enriching signaling pathways such as IGF, TNF, NOTCH, and ICAM. Analysis of differentially expressed genes highlighted processes related to stress, differentiation, and development. Notably, rTMS intervention upregulated Reck in vascular smooth muscle cells, implicating its role in the classical Wnt signaling pathway. Overall, our bioinformatics findings suggest that rTMS may modulate BBB permeability and promote vascular regeneration following stroke. This might happen through 20 Hz rTMS promoting pericyte differentiation into vascular smooth muscle cells, upregulating Reck, then activating the classical Wnt signaling pathway, and facilitating vascular regeneration and BBB stability.

Unraveling the role of bisphenol A in osteosarcoma biology: insights into prognosis and immune microenvironment modulation.

BACKGROUND: Bisphenol A (BPA) is a common environmental pollutant, and its specific mechanisms in cancer development and its impact on the tumor immune microenvironment are not yet fully understood. METHODS: Transcriptome data from osteosarcoma (OS) patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. BPA-related genes were identified through the Comparative Toxicogenomics Database (CTD), yielding 177 genes. Differentially expressed genes were analyzed using the GSE162454 dataset from the Tumor Immune Single Cell Hub 2 (TISCH2). We constructed the prognostic model using univariate Cox regression and LASSO analysis. The model was validated using the GSE16091 dataset. GO, KEGG, and GSEA analyses were performed to investigate the mechanisms of BPA-related genes. RESULTS: A total of 15 BPA-related genes were identified as differentially expressed in OS. Univariate Cox regression and LASSO analysis identified four key prognostic genes (FOLR1, MYC, ESRRA, VEGFA). The prognostic model exhibited strong predictive performance with area under the curve (AUC) values of 0.89, 0.6, and 0.79 for predicting 1-, 2-, and 3-year survival, respectively. External validation using the GSE16091 dataset confirmed the model's high accuracy with AUC values exceeding 0.88. Our results indicated that the prognosis of the high-risk population is generally poorer, which may be associated with alterations in the tumor immune microenvironment. In the high-risk group, immune cells showed predominantly low expression levels, while immune checkpoint genes were significantly overexpressed, along with markedly elevated tumor purity. These findings revealed a correlation between upregulation of BPA-related genes and formation of an immunosuppressive microenvironment, leading to unfavorable patient outcomes. CONCLUSION: Our study highlighted the significant association of BPA with OS biology, particularly in its potential role in modulating the tumor immune microenvironment. We offered a fresh insight into the influence of BPA on cancer development, thus providing valuable insights for future clinical interventions and treatment strategies.

Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single­cell sequencing data.

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

Single-cell and spatial sequencing identifies senescent and germinal tumor cells in adamantinomatous craniopharyngiomas.

Adamantinomatous craniopharyngioma (ACP) is a clinically aggressive tumor without effective treatment method. Previous studies proposed a paracrine tumorigenesis model, in which oncogenic ß-catenin induces senescence in pituitary stem cells and the senescent cells lead the formation of paracrine tumors through secretion of pro-tumorigenic factors. However, there lacks characterization on senescent cells in ACPs. Here, we profiled 12 ACPs with single-cell RNA and TCR-sequencing to elucidate the cellular atlas in ACPs and 3 of them were also subject to spatial sequencing to localize different subpopulations of the tumor cells. In total, we obtained the transcriptome profiles of 70,682 cells. Tumor cells, which were unambiguously identified through the cellular mutation status of the driver CTNNB1 mutations, were clustered into 6 subsets. The whorl-like cluster (WC) cells show distinct molecular features from the other tumor cells and the palisading epithelium (PE) cells consists of a proliferating subset. Other than typical PE and WC, we identified two novel subpopulations of the tumor cells. In one subpopulation, the cells express a high level of cytokines, e.g., FDCSP and S100A8/A9, and are enriched with the senescence-associated secretory phenotype (SASP) factors. Hematoxylin and eosin staining reveals that these SASP cells lack an ordered structures and their nuclei are elongated. In the other subpopulation, the cell sizes are small and they are tightly packed together with an unusual high density expressing a high level of mitochondrial genes (median 10.9%). These cells are the origin of the tumor developmental trajectories revealed by RNA velocity and pseudo-time analysis. Single-cell RNA and TCR analysis reveals that some ACPs are infiltrated with clonally expanded cytotoxic T cells. We propose a hypothesis that WC and PE are formed via different negative regulation mechanisms of the overactivated WNT/ß-catenin signaling which provides a new understanding on the tumorigenesis of ACPs. The study lays a foundation for future studies on targeting senescent cells in ACPs with senolytic compounds or other therapeutic agents.

Potential regulation and prognostic model of colorectal cancer with extracellular matrix genes.

Background: The tumor microenvironment (TME) of colorectal cancer (CRC) mainly comprises immune cells, stromal cells, tumor cells, as well as the extracellular matrix (ECM), which holds a pivotal position. The ECM affects cancer progression, but its regulatory roles and predictive potential in CRC are not fully understood. Methods: We analyzed transcriptomes from CRC tumors and paired normal tissues to study ECM features. Up-regulated ECM components were examined through functional enrichment analysis, and single-cell sequencing identified cell types producing collagen, regulators, and secreted factors. Transcription factor analysis and cell-cell interaction studies were conducted to identify potential regulators of ECM changes. Additionally, a prognostic model was developed using TCGA-CRC cohort data, focusing on up-regulated core ECM components. Results: Bulk RNA-seq analysis revealed a unique ECM pattern in tumors, with ECM abundance and composition significantly related to patient survival. Up-regulated ECM components were linked to various cancer-related pathways. Fibroblasts and non-fibroblasts interactions were crucial in forming the TME. Key potential regulators identified included ZNF469, PRRX2, TWIST1, and AEBP1. A prognostic model based on five ECM genes (THBS3, LAMB3, ESM1, SPRX, COL9A3) demonstrated strong associations with immune suppression and tumor angiogenesis. Conclusions: The ECM components were involved in various cell-cell interactions and correlated with tumor development and poor survival outcomes. The ECM prognostic model components could be potential targets for novel therapeutic interventions in colorectal cancer.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals MMP mediated expression patterns by distinct tumor microenvironment immune profiles in cervical cancer.

Background: Few studies have analyzed the effect of matrix metalloproteinase (MMP) expression patterns on the tumor microenvironment (TME) during development of cervical cancer (CC). Methods: We elucidated the landscape and score of MMP expression in CC using single-cell RNA sequencing (scRNA-seq) and RNA sequencing datasets. Further, we aimed the MMPscore to probe the infiltration of immune cells. Further, MMP expression was measured by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Results: We found MMPs were cell-type specific expressed in diverse types of CC cells, regulating the relative pathways of CC progression. Two distinct MMP expression patterns that associated infiltrated tumor microenvironment (TME) were identified. We discovered MMP expression patterns can predict the stage of tumor, subtype, stromal activity in the TME, genetic variation, and patient outcome. Patients with high MMPscore benefited from significantly better treatment and clinical outcomes. Conclusion: These results indicate high MMPscore in diverse cell types may regulate immune response and improve the survival of patients with CC, which assist in developing more effective immunization strategies.

Single-cell landscape revealed immune characteristics associated with disease phases in brucellosis patients.

A comprehensive immune landscape for Brucella infection is crucial for developing new treatments for brucellosis. Here, we utilized single-cell RNA sequencing (scRNA-seq) of 290,369 cells from 35 individuals, including 29 brucellosis patients from acute (n = 10), sub-acute (n = 9), and chronic (n = 10) phases as well as six healthy donors. Enzyme-linked immunosorbent assays were applied for validation within this cohort. Brucella infection caused a significant change in the composition of peripheral immune cells and inflammation was a key feature of brucellosis. Acute patients are characterized by potential cytokine storms resulting from systemic upregulation of S100A8/A9, primarily due to classical monocytes. Cytokine storm may be mediated by activating S100A8/A9-TLR4-MyD88 signaling pathway. Moreover, monocytic myeloid-derived suppressor cells were the probable contributors to immune paralysis in acute patients. Chronic patients are characterized by a dysregulated Th1 response, marked by reduced expression of IFN-γ and Th1 signatures as well as a high exhausted state. Additionally, Brucella infection can suppress apoptosis in myeloid cells (e.g., mDCs, classical monocytes), inhibit antigen presentation in professional antigen-presenting cells (APCs; e.g., mDC) and nonprofessional APCs (e.g., monocytes), and induce exhaustion in CD8+ T/NK cells, potentially resulting in the establishment of chronic infection. Overall, our study systemically deciphered the coordinated immune responses of Brucella at different phases of the infection, which facilitated a full understanding of the immunopathogenesis of brucellosis and may aid the development of new effective therapeutic strategies, especially for those with chronic infection.

Definition of a Multi-Omics Signature for Esophageal Adenocarcinoma Prognosis Prediction.

Esophageal cancer is a highly lethal malignancy, representing 5% of all cancer-related deaths. The two main subtypes are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most research has focused on ESCC, few studies have analyzed EAC for transcriptional signatures linked to diagnosis or prognosis. In this study, we utilized single-cell RNA sequencing and bulk RNA sequencing to identify specific immune cell types that contribute to anti-tumor responses, as well as differentially expressed genes (DEGs). We have characterized transcriptional signatures, validated against a wide cohort of TCGA patients, that are capable of predicting clinical outcomes and the prognosis of EAC post-surgery with efficacy comparable to the currently accepted prognostic factors. In conclusion, our findings provide insights into the immune landscape and therapeutic targets of EAC, proposing novel immunological biomarkers for predicting prognosis, aiding in patient stratification for post-surgical outcomes, follow-up, and personalized adjuvant therapy decisions.

Single-cell sequencing analysis revealed that WDR72 was a novel cancer stem cells related gene in gastric cancer.

Background: Cancer stem cells (CSCs) are pivotal in tumor resistance to chemotherapy and gastric cancer's rapid proliferation and metastasis. We aimed to explore the CSCs-related genes in gastric cancer epithelial cells. Methods: The mRNA expression profile and single-cell sequencing data of gastric cancer were downloaded from the public database. Results: We identified WDR72 as a CSCs-related gene in gastric cancer epithelial cells. WDR72 was highly expressed in gastric cancer tissues, and high expression of WDR72 was associated with inferior prognosis of patients. WDR72 expression had a significant negative correlation with the infiltration of CD8 + T cells and activated memory CD4 + T cells. PD-L1 expression was significantly reduced in gastric cancer patients with high WDR72 expression. WDR72 was correlated with IC50 of multiple small-molecule drugs. Conclusion: We identified a novel CSCs-related gene in gastric cancer epithelial cells, WDR72, which was highly expressed in patients with high stemness scores.

Identification of the novel exhausted T cell CD8 + markers in breast cancer.

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

Deciphering the immune-metabolic nexus in sepsis: a single-cell sequencing analysis of neutrophil heterogeneity and risk stratification.

Background: Metabolic dysregulation following sepsis can significantly compromise patient prognosis by altering immune-inflammatory responses. Despite its clinical relevance, the exact mechanisms of this perturbation are not yet fully understood. Methods: Single-cell RNA sequencing (scRNA-seq) was utilized to map the immune cell landscape and its association with metabolic pathways during sepsis. This study employed cell-cell interaction and phenotype profiling from scRNA-seq data, along with pseudotime trajectory analysis, to investigate neutrophil differentiation and heterogeneity. By integrating scRNA-seq with Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, key genes were identified. These genes were used to develop and validate a risk score model and nomogram, with their efficacy confirmed through Receiver Operating Characteristic (ROC) curve analysis. The model's practicality was further reinforced through enrichment and immune characteristic studies based on the risk score and in vivo validation of a critical gene associated with sepsis. Results: The complex immune landscape and neutrophil roles in metabolic disturbances during sepsis were elucidated by our in-depth scRNA-seq analysis. Pronounced neutrophil interactions with diverse cell types were revealed in the analysis of intercellular communication, highlighting pathways that differentiate between proximal and core regions within atherosclerotic plaques. Insight into the evolution of neutrophil subpopulations and their differentiation within the plaque milieu was provided by pseudotime trajectory mappings. Diagnostic markers were identified with the assistance of machine learning, resulting in the discovery of PIM1, HIST1H1C, and IGSF6. The identification of these markers culminated in the development of the risk score model, which demonstrated remarkable precision in sepsis prognosis. The model's capability to categorize patient profiles based on immune characteristics was confirmed, particularly in identifying individuals at high risk with suppressed immune cell activity and inflammatory responses. The role of PIM1 in modulating the immune-inflammatory response during sepsis was further confirmed through experimental validation, suggesting its potential as a therapeutic target. Conclusion: The understanding of sepsis immunopathology is improved by this research, and new avenues are opened for novel prognostic and therapeutic approaches.

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses to estimate the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data.

BACKGROUND: As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users. RESULTS: We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells. CONCLUSIONS: TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.