Data repository for the scMappR manuscript:

Abstract from biorXiv (https://www.biorxiv.org/content/10.1101/2020.08.24.265298v1.full).

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.

We characterized the immune context in HPV+ and HPV− HNSCC by integrating scRNA-seq and bulk RNA-seq data.

Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.

This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows:

matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz)

*The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include:

nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

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

Vampirium setup This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github: Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline. Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology. Slides.zip contains all the slides used in the workshop. raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.

Conventional (bulk) RNA-sequencing was performed on unfractionated cell suspension or snap frozen whole tissue material. Total RNA was isolated with TRIzol reagent followed by purification over PureLink RNA Mini Kit columns (Invitrogen). RNA-seq was performed using a polyA-enriched strand-specific library construction protocol (doi: 10.1016/j.ccell.2016.02.009) and paired-end 75bp sequencing on an Illumina HiSeq 2500 instrument.

Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2.5.2.a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis (doi: 10.12688/f1000research.9005.3). Gene-level read counts were quantified using HTSEQ-count (v0.11.0; intersection-strict, reverse mode) (doi: 10.1093/bioinformatics/btu638). Genes showing low read counts (i.e., genes not showing counts per million (cpm) > 1.0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3.22.2) package (doi: 10.1093/bioinformatics/btp616).

Sample IDs correspond to those referenced in Wang X et al, Nature Communications (2022).

ObjectiveThis study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.MethodsUtilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.ResultsOur analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.ConclusionThis study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Recommended methods for DE analysis of biological replicate RNA-seq data (simulation results).

We hypothesized that exposure to fatty acids in metabolic dysfunction-associated steatohepatitis-like environment will profoundly affect gene expression of hepatocytes. More precisely, we wish to investigate expression of genes related to ferroptosis, i.e. an iron-catalyzed form of cell death through lethal lipid peroxidation. We aimed to study the effect of fatty acid supplementation in a metabolic dysfunction-associated steatohepatitis-like environment on gene expression of HepG2 cells profiled with bulk mRNA-sequencing. HepG2 cells were exposed for 48 hours to oleic acid (100microM) and palmitic acid (50microM), as well as hyperglycemia (4.5 mg/mL), hyperinsulinemia (100 nM), tumor necrosis factor alpha (50 ng/mL), interleukin 1-beta (25 ng/mL) and transforming growth factor-beta (8ng/mL). Control samples were exposed to solvents needed to dissolve oleic acid and palmitic acid in medium.

Single-Nucleus RNA-Seq Market Outlook

According to our latest research, the global Single-Nucleus RNA-Seq market size reached USD 368 million in 2024, reflecting a robust expansion driven by technological advancements and increasing research applications. The market is expected to grow at a CAGR of 17.2% from 2025 to 2033, with the market size projected to reach USD 1.57 billion by 2033. This impressive growth is fueled by the rising adoption of single-nucleus RNA sequencing (snRNA-seq) in various fields such as neuroscience, oncology, and developmental biology, as well as the increasing availability of high-throughput sequencing platforms and advanced bioinformatics tools.

The growth of the Single-Nucleus RNA-Seq market is primarily propelled by the increasing need to understand cellular heterogeneity at a granular level, particularly in complex tissues such as the brain and tumors. The limitations of traditional bulk RNA sequencing, which averages gene expression across heterogeneous cell populations, have underscored the value of single-nucleus approaches. Researchers and clinicians are leveraging snRNA-seq to unravel disease mechanisms, identify novel therapeutic targets, and develop precision medicine strategies. The adoption of this technology is further accelerated by the emergence of automated instruments, improved sample preparation protocols, and the expanding availability of high-quality consumables, which collectively enhance throughput, reproducibility, and scalability of single-nucleus transcriptomic studies.

Another significant growth driver for the Single-Nucleus RNA-Seq market is the increasing investment in genomics and transcriptomics research by both public and private sectors. Major funding agencies and governments across North America, Europe, and Asia Pacific are allocating substantial resources to support large-scale single-cell and single-nucleus sequencing projects. Pharmaceutical and biotechnology companies are integrating snRNA-seq into their drug discovery and development pipelines to better understand disease pathogenesis and patient stratification. The proliferation of collaborative initiatives between academic institutions, industry players, and clinical research organizations is also fostering innovation and expanding the application landscape of single-nucleus RNA sequencing across various domains, including immunology and developmental biology.

The market is also benefiting from the rapid evolution of bioinformatics and data analysis tools tailored specifically for single-nucleus RNA-Seq data. The complexity and volume of data generated by snRNA-seq experiments necessitate sophisticated computational pipelines for quality control, normalization, clustering, and downstream analysis. The development of user-friendly software platforms and cloud-based solutions has democratized access to advanced analytics, enabling researchers with varying levels of computational expertise to derive meaningful insights from their data. This trend is expected to continue as more commercial and open-source solutions emerge, further driving the adoption of single-nucleus RNA sequencing technologies in both research and clinical settings.

From a regional perspective, North America currently dominates the Single-Nucleus RNA-Seq market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. This leadership is attributed to the presence of leading genomics research centers, robust funding infrastructure, and early adoption of cutting-edge sequencing technologies. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, supported by increasing investments in life sciences research, expanding biotechnology industry, and growing awareness of precision medicine. Europe is also expected to maintain a significant market share due to strong academic research output and collaborative initiatives in genomics and transcriptomics.

Product Type Analysis

The Single-Nucleus RNA-Seq market by product type is segmented into

There are two main types of root systems in flowering plants, which are taproot systems in dicot and fibrous root systems in monocot. The cellular and molecular mechanism involved in root development are mainly from the study of dicot model Arabidopsis thaliana. However, mechanisms of root development and their conservation and divergence in monocot, which including the major crops, remain largely elusive. Here we profile the transcriptomes of more than 20,000 single cells in the root tips of two rice cultivars, Nipponbare (Nip; Japonica) and 93-11 (Indica). Single-cell analysis coupled with in situ hybridization identify the cell type-specific marker genes and annotate all the clusters. Comparison of single-cell transcriptome and analysis of mark gene expression suggest well-conserved molecular landscape between rice Nip and 93-11. Moreover, our analysis suggests specific functions gene expression patterns for each cell type cluster, including the hormone genes. Comparison to Arabidopsis single-cell RNA-sequencing dataset reveals extensive differences between Arabidopsis and rice cell types, and species-specific features emphasize the importance of directly studying rice root. Our study reveals transcriptome landscape of major cell types of rice root in singe-cell resolution and provides molecular insight of the cell type morphology of cell type evolution in plants. Bulk RNAseq of protoplast from rice root tipsPlease note that each processed data file was generated from both replicates and is linked to the corresponding rep.1 sample records.

This is an RNAseq experiment from healthy donor CD8 T cells isolated from human PBMCs. T cells were engineered to express a CMV-specific TCR and were stimulated with CMV-peptide-loaded antigen presenting cells in the presence or absence of recombinant IL-27. Dataset consists of 6 fastqs.

n this zipped folder are two folders: data and resultsdatadata contains two subfolders: bulk_data single_cell_databulk_data has the processed bulk data fortocilizumab analysis (Rivellese et al. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial) original data can be found here: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-11611)single_cell_data has the processed single-cell data and the generated pseudobulks used by BuDDI. Within this folder,augmented_* contains the pseudobulksresultsThis folder contains several results folders. Each one is specific to an experiment and a model. The naming convention is "model_experiment"buddiM2: the BuDDI modelbp: BayesPrismcibsersort: CIBERSORTx

Tumor Deconvolution from Bulk RNA-Seq Market Outlook

According to our latest research, the global Tumor Deconvolution from Bulk RNA-Seq market size reached USD 385.6 million in 2024, reflecting the expanding adoption of advanced bioinformatics in oncology. The market is experiencing robust momentum, registering a CAGR of 13.2% during the forecast period. By 2033, the market is forecasted to reach USD 1,082.4 million, underpinned by increasing investments in precision medicine, the growing prevalence of cancer, and the continued integration of machine learning in transcriptomics. This surge is primarily attributed to the rising demand for comprehensive tumor microenvironment analysis and the need for effective diagnostic and therapeutic strategies in personalized oncology.

One of the primary growth factors driving the Tumor Deconvolution from Bulk RNA-Seq market is the escalating burden of cancer worldwide. As cancer incidence rates continue to rise, the need for advanced molecular profiling tools that can dissect the complex cellular composition of tumors has become paramount. Bulk RNA sequencing, combined with sophisticated deconvolution algorithms, enables researchers and clinicians to unravel the heterogeneity within tumor samples, thereby facilitating a deeper understanding of tumor biology and the tumor microenvironment. This capability is crucial for identifying novel therapeutic targets, predicting patient response to immunotherapies, and designing personalized treatment regimens. The integration of tumor deconvolution tools into clinical and research workflows is thus becoming increasingly indispensable, propelling market growth.

Another significant growth driver is the rapid advancements in computational biology and artificial intelligence, particularly the application of machine learning in transcriptomic data analysis. The development of robust, user-friendly software platforms and cloud-based solutions has democratized access to tumor deconvolution technologies, making them accessible to a broader spectrum of end-users, including hospitals, research institutes, and pharmaceutical companies. These technological innovations not only enhance the accuracy and scalability of deconvolution analyses but also reduce turnaround times and operational costs. As a result, there is a marked increase in the adoption of these solutions for both research and clinical applications, further amplifying market expansion.

In addition to technological progress, the increasing collaboration between academic institutions, biotechnology firms, and healthcare providers is fostering innovation in the Tumor Deconvolution from Bulk RNA-Seq market. Strategic partnerships are facilitating the development of next-generation deconvolution methodologies, the validation of new biomarkers, and the translation of research findings into clinical practice. Furthermore, supportive government initiatives and funding for cancer genomics research are accelerating the pace of discovery and commercialization. These collaborative efforts are not only enhancing the quality and reliability of tumor deconvolution solutions but also expanding their application across various cancer types and stages, thereby broadening the market’s reach.

From a regional perspective, North America continues to dominate the Tumor Deconvolution from Bulk RNA-Seq market, driven by a well-established healthcare infrastructure, high research funding, and the presence of leading biotechnology companies. However, the Asia Pacific region is emerging as a significant growth frontier, propelled by increasing cancer incidence, improving healthcare access, and rising investments in genomics research. Europe also holds a substantial market share, supported by robust clinical research networks and regulatory support for precision medicine initiatives. Collectively, these regional dynamics are shaping a competitive and innovation-driven global market landscape.

Product Type Analysis

The Tumor Deconvolution from Bulk RNA-Seq market by product typ

To understand how the MSI2-HOXA9 translocation triggers blast crisis CML (bcCML), we compared gene expression patterns in BCR-ABL and BCR-ABL/MSI2-HOXA9-driven disease. RNA-seq analysis was carried out on lineage negative cells from leukemia established with BCR-ABL alone or with a combination of BCR-ABL and MSI2-HOXA9. Network mapping of all differentially expressed genes (q-value <0.05) using non-redundant functional grouping revealed an enrichment of metabolic processes with oncogenic pathways and developmental programs. Programs that were dominantly upregulated by MSI2/HOXA9 were those involved in development, including Aldh1a1, Erbb3, and Kit, consistent with BCR-ABL/MSI2-HOXA9 driving a more undifferentiated disease, known oncogenes, including Frat1, Map7, and Fzd3, and components of the mitochondria including mt-Co2, mt-Atp8, and mt-Nd5. Among the genes most enriched in&nb..., Lin- leukemia cells were sorted from mice transplanted with BCR-ABL/Control or BCR-ABL/MSI2-HOXA9 transduced KLS cells. Total RNA was isolated using the RNAeasy Micro Plus kit (QIAGEN). RNA libraries were generated from 150ng of RNA using Illumina's TruSeq Stranded mRNA Sample PrepKit (Illumina). Libraries were pooled ands single end sequenced (1x75) on the Illumina NextSeq 500 using the High output V2 kit (Illumina). Resultant fastq files were pseudoaligned into transcript-level summaries using Kallisto. Transcript level summaries were processed into gene-level summaries using Sleuth and differential expression analysis was performed using the Wald test. , # Bulk RNA sequencing analysis of Lin- leukemia BCR-ABL and BCR-ABL/MSI2-HOXA9 cells (post-transplantation)

Dataset DOI: 10.5061/dryad.sbcc2frm6

Description of the data and file structure

To understand how the MSI2-HOXA9 translocation triggers bcCML, we compared gene expression patterns in BCR-ABL and BCR-ABL/MSI2-HOXA9-driven leukemia. To this end, RNA-seq analysis was carried out on lineage negative cells from leukemia established with BCR-ABL alone or the combination of BCR-ABL and MSI2-HOXA9.Â

Lin- leukemia cells were sorted from mice transplanted with BCR-ABL/Control or BCR-ABL/MSI2-HOXA9 transduced KLS cells. Total RNA was isolated using the RNAeasy Micro Plus kit (QIAGEN). RNA libraries were generated from 150ng of RNA using Illumina's TruSeq Stranded mRNA Sample PrepKit (Illumina). Libraries were pooled ands single end sequenced (1x75) on the Illumina NextSeq 500 using the High output V2 kit (Illumina). Resultant fastq files were...,

BackgroundConsiderable evidence suggests that the heterogeneity of ovarian cancer (OC) is a major cause of treatment failure. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to analyse the heterogeneity of the tumour at the single-cell level, leading to a better understanding of cell function at the genetic and cellular levels.MethodsOC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database and the FindCluster () package used for cell cluster analysis. The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score and bulk RNA-seq data were used to analyse the key genes of OC-associated immune cell subsets. CIBERSORT was used to identify immune scores of cells and the “WGCNA” package for the weighted correlation network analysis (WGCNA). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of subtype groups were performed by GSEA. Then, univariate Cox and lasso regression were performed to further establish a signature. Finally, qPCR and immunohistochemistry staining were used to evaluate the expression of signature genes in OC.ResultsTwo scRNA-seq (GSE154600 and GES158937) datasets were integrated to obtain 20 cell clusters. T cells or NK cells (cluster 5, 6, 7, 11), B cells (cluster 16, 19, 20) and myeloid cells (cluster 4, 9, 10) were clustered according to immune cell markers. The ssGSEA revealed that M1- and M2-like myeloid cell-related genes were significantly upregulated in P3 and P4 patients in the GSE154600 data. Immune cell analysis in TCGA-OC showed that a high abundance of M1-like tumour-associated macrophages (TAMS) predicts better survival. WGCNA, univariate Cox and lasso Cox regression established a two-gene signature (RiskScore=-0.059*CXCL13-0.034*IL26). Next, the TCGA-test and TCGA-OC were used to test the risk prediction ability of the signature, showing a good effect in the datasets. Moreover, the qPCR and immunohistochemistry staining revealed that the expression of CXCL13 and IL26 was reduced in OC tissues.ConclusionA two-gene signature prognostic stratification system (CXCL13 and IL26) was developed based on the heterogeneity of OC immune cells to accurately evaluate the prognostic risk.

Evidence before this study

We conducted an extensive literature search using Google Scholar without language restrictions, employing search terms such as “(Predicting OR Classifying OR Annotating) and (cancer hallmarks) AND (Deep OR Machine Learning) OR (Artificial Intelligence OR AI).†Despite notable advances in molecular oncology and computational methodologies, a critical gap remains: no existing machine learning or deep learning framework comprehensively predicts cancer hallmarks from tumor biopsy samples. Current research primarily targets specific molecular pathways associated with individual hallmarks, leaving clinicians without an integrated model to interpret hallmark activity at the level of an individual tumor. Moreover, the absence of wet-lab techniques capable of annotating all cancer hallmarks in biopsy samples has further impeded progress, limiting the clinical utility of hallmark-related insights for precision oncology.

Added value of this study

Thi..., Dataset Collection and Processing

We utilized a large-scale dataset comprising 2.7 million single-cell transcriptomes derived from 14 tumor types, collected from 922 patients across 51 independent studies conducted globally. This dataset was sourced from the Weizmann Institute's 3CA repository. Quality Control

Before generating synthetic datasets for model training, the raw single-cell transcriptomic data underwent a rigorous quality control (QC) process. Cells with over 15% mitochondrial transcript content, fewer than 200, or more than 6,000 expressed mRNA transcripts were excluded to ensure data reliability.

Gene Set Curation

Gene sets representing cancer hallmarks were compiled from multiple databases, retaining only genes identified in at least two independent sources. This selection was refined through manual literature reviews to exclude genes without direct or indirect roles in hallmark-related pathways.

Digital Scoring

Using the curated ..., , # Synthetic bulk RNA-Seq transcriptomic profiles representing 10 Cancer hallmarks

https://doi.org/10.5061/dryad.zw3r228jc

Description of the data and file structure

Data Description: Experimental Efforts

This dataset comprises single-cell transcriptomic data from the Weizmann 3CA repository, encompassing 2.7 million single-cell transcriptomes from 14 tumor types, collected from 922 patients across 51 global studies. The primary objective of the experimental efforts was to generate synthetic datasets for training and validating computational models to identify and analyze cancer hallmarks at the single-cell resolution.

Single-cell RNA sequencing (scRNA-seq) data underwent a rigorous quality control process to ensure reliability and biological relevance. This included exclusion criteria based on mitochondrial transcript content (>15%) and mRNA transcript counts (<200 or >6,000 transcripts). Gene sets corresponding to 10 established ca...,

Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 7..., Total RNA was extracted as described above from 3 mice/treatment group. In a pilot study, we found that 3 mice were sufficient to identify a significant difference in Ptgs2 gene expression by qPCR at α = 0.05 and power = 80%. RNA integrity numbers (RINs) were confirmed to be ≥ 8.8 using a 2100 Bioanalyzer Instrument (Agilent, Santa Clara, CA). cDNA libraries were prepared using mouse TruSeq® Stranded Total RNA Library Prep kit (illumina, San Diego, CA) and quantified using a KAPA Library Quantification kit (Roche, Pleasanton, CA). cDNA libraries were sequenced (75 bp single-ended, ~35-44M reads per sample) on an Illumina NextSeq instrument. Raw reads in FastQ files were trimmed using Trimmomatic-0.39 (Bolger et al. 2014) and quality control of trimmed files performed using FastQC. Salmon was used to align reads in mapping-based mode with selective alignment against a decoy-aware transcriptome generated from mouse transcriptome GENCODE Release M23 (GRCm38.p6). Estimated counts per transc..., , # Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation

https://doi.org/10.5061/dryad.b8gtht7mq

We analyzed gene expression profiles in bronchoalveolar lavage cells (>95% macrophages) isolated from adult female mice exposed to ozone using bulk RNA-sequencing. Mice were sampled 24 and 72 hr after exposure. Specific analysis details are available in the associated manuscript.

Description of the data and file structure

Counts data were analyzed using DESeq2 which resulted in multiple results files:

• File "Supplementary File 1_24 hr DEGs" contains differential expression data generated from DESeq2 comparing counts at 24 hr to counts in air controls.
• File "Supplementary File 2_72 hr DEGs"Â contains differential expression data generated from DESeq2 comparing counts at 72 hr to counts in air controls.
• File "Supplementary File 3_72 hr_vs_24 hr D...

We applied BSCET to the Fadista pancreatic islets bulk RNA-seq data [15] using the MuSiC [14] proportion estimates obtained from the Segerstolpe single-cell reference [16]. We detected 8 SNPs in 8 genes with cell-type-specific AEI significantly associated with HbA1c (FDR adjusted P-value < 0.05), with the direction of the association indicated in column ‘Correlation’. (XLSX)