SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data

Bulk and single-cell RNA-seq (scRNA-seq) data are being used as alternatives to traditional technology in biology and medicine research. These data are used, for example, for the detection of differentially expressed (DE) genes. Several statistical methods have been developed for the classification of bulk and single-cell RNA-seq data. These feature genes are vitally important for the classification of bulk and single-cell RNA-seq data. The majority of genes are not DE and they are thus irrelevant for class distinction. To improve the classification performance and save the computation time, removal of irrelevant genes is necessary. Removal will aid the detection of the important feature genes. Widely used schemes in the literature, such as the BSS/WSS (BW) method, assume that data are normally distributed and may not be suitable for bulk and single-cell RNA-seq data. In this article, a category encoding (CAEN) method is proposed to select feature genes for bulk and single-cell RNA-seq data classification. This novel method encodes categories by employing the rank of sequence samples for each gene in each class. Correlation coefficients are considered for gene and class with the rank of sample and a new rank of category. The highest gene correlation coefficients are considered feature genes, which are the most effective for classifying bulk and single-cell RNA-seq dataset. The sure screening method was also established for rank consistency properties of the proposed CAEN method. Simulation studies show that the classifier using the proposed CAEN method performs better than, or at least as well as, the existing methods in most settings. Existing real datasets were analyzed, with the results demonstrating superior performance of the proposed method over current competitors. The application has been coded into an R package named "CAEN" to facilitate wide use.

Structure Guided Design of CCL27 CAR T-Cells Against CCR10 for Multiple Myeloma

BackgroundThe ability of cancer-associated fibroblasts (CAFs) to encourage angiogenesis, tumor cell spread, and increase treatment resistance makes them pro-tumorigenic. We aimed to investigate the CAF signature in Bladder urothelial carcinoma (BLCA) and, for clinical application, to build a CAF-based risk signature to decipher the immune landscape and screen for suitable treatment BLCA samples.MethodsCAF-related genes were discovered by superimposing CAF marker genes discovered from single-cell RNA-seq (scRNA-seq) data taken from the GEO database with CAF module genes discovered by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data from TCGA. After identifying prognostic genes related with CAF using univariate Cox regression, Lasso regression was used to build a risk signature. With microarray data from the GEO database, prognostic characteristics were externally verified. For high and low CAF-risk categories, immune cells and immunotherapy responses were analyzed. Finally, a nomogram model based on the risk signature and prospective chemotherapeutic drugs were examined.ResultsCombining scRNA-seq and bulk-seq data analysis yielded a total of 124 CAF-related genes. LRP1, ANXA5, SERPINE2, ECM1, RBP1, GJA1, and FKBP10 were the seven BLCA prognostic genes that remained after univariate Cox regression and LASSO regression analyses. Then, based on these genes, prognostic characteristics were created and validated to predict survival in BLCA patients. Additionally, risk signature had a strong correlation with known CAF scores, stromal scores, and certain immune cells. The CAF-risk signature was identified as an independent prognostic factor for BLCA using multifactorial analysis, and its usefulness in predicting immunotherapy response was confirmed. Based on risk classification, we projected six highly sensitive anticancer medicines for the high-risk group.ConclusionThe prognosis of BLCA may be accurately predicted using CAF-based risk signature. With a thorough understanding of the BLCA CAF-signature, it might be able to explain the BLCA patients’ response to immunotherapy and identify a potential target for BLCA treatment.

Transposable elements (TEs) are an integral part of the host transcriptome. TE-containing noncoding RNAs (ncRNAs) show considerable tissue specificity and play important roles during development, including stem cell maintenance and cell differentiation. Recent advances in single-cell RNA-seq (scRNA-seq) revolutionized cell type–specific gene expression analysis. However, effective scRNA-seq quantification tools tailored for TEs are lacking, limiting our ability to dissect TE expression dynamics at single-cell resolution. To address this issue, we established a TE expression quantification pipeline that is compatible with scRNA-seq data generated across multiple technology platforms. We constructed TE-containing ncRNA references using bulk RNA-seq data and showed that quantifying TE expression at the transcript level effectively reduces noise. As proof of principle, we applied this strategy to mouse embryonic stem cells and successfully captured the expression profile of endogenous retroviruses in single cells. We further expanded our analysis to scRNA-seq data from early stages of mouse embryogenesis. Our results illustrated the dynamic TE expression at preimplantation stages and revealed 146 TE-containing ncRNA transcripts with substantial tissue specificity during gastrulation and early organogenesis.

BackgroundSingle‐cell RNA sequencing (scRNA‐seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA‐seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA‐Seq data on single‐cell data, and some new methods for scRNA‐seq data have also been developed. Bulk and single‐cell RNA‐seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA‐seq data is needed.ResultsIn this study, we conducted a series of experiments on scRNA‐seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA‐seq data and new methods specifically designed for scRNA‐seq data. We obtained observations and recommendations for the methods under different situations.ConclusionsDE analysis methods should be chosen for scRNA‐seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA‐seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA‐seq data.

BackgroundDisruption of N6 methyl adenosine (m6A) modulation hampers gene expression and cellular functions, leading to various illnesses. However, the role of m6A modification in osteoarthritis (OA) synovitis remains unclear. This study aimed to explore the expression patterns of m6A regulators in OA synovial cell clusters and identify key m6A regulators that mediate synovial macrophage phenotypes.MethodsThe expression patterns of m6A regulators in the OA synovium were illustrated by analyzing bulk RNA-seq data. Next, we built an OA LASSO-Cox regression prediction model to identify the core m6A regulators. Potential target genes of these m6A regulators were identified by analyzing data from the RM2target database. A molecular functional network based on core m6A regulators and their target genes was constructed using the STRING database. Single-cell RNA-seq data were collected to verify the effects of m6A regulators on synovial cell clusters. Conjoint analyses of bulk and single-cell RNA-seq data were performed to validate the correlation between m6A regulators, synovial clusters, and disease conditions. After IGF2BP3 was screened as a potential modulator in OA macrophages, the IGF2BP3 expression level was tested in OA synovium and macrophages, and its functions were further tested by overexpression and knockdown in vitro.ResultsOA synovium showed aberrant expression patterns of m6A regulators. Based on these regulators, we constructed a well-fitting OA prediction model comprising six factors (FTO, YTHDC1, METTL5, IGF2BP3, ZC3H13, and HNRNPC). The functional network indicated that these factors were closely associated with OA synovial phenotypic alterations. Among these regulators, the m6A reader IGF2BP3 was identified as a potential macrophage mediator. Finally, IGF2BP3 upregulation was verified in the OA synovium, which promoted macrophage M1 polarization and inflammation.ConclusionsOur findings revealed the functions of m6A regulators in OA synovium and highlighted the association between IGF2BP3 and enhanced M1 polarization and inflammation in OA macrophages, providing novel molecular targets for OA diagnosis and treatment.

Kawasaki Disease (KD) affects young children less than five years old with severe blood vessel inflammation. Despite being treatable, the causes and mechanisms remain elusive. This study conducted a meta-analysis of RNA sequencing (RNA-seq) data from human and animal models to explore KD's transcriptomic profile and evaluate animal models. We retrieved bulk and single-cell RNA-seq data from Gene Expression Omnibus, with blood and coronary artery samples from KD patients, aorta samples from KD mouse models (Lactobacillus casei cell wall extract-injected mice), and their controls. Upon consistent quality control, we applied Fisher's exact test to assess differential gene expression, followed by an enrichment analysis of overlapping genes. These studies identified 400 differentially expressed genes in blood samples of KD patients compared to controls and 413 genes in coronary artery samples. The data from KD blood and KD coronary artery samples shared only 16 differentially expressed genes. Eighty-one genes overlapped between KD human coronary arteries and KD mouse aortas, and 67 of these 81 genes were regulated in parallel in both humans and mice: 30 genes were up-regulated, and 37 were down-regulated. These included previously identified KD-upregulated genes: CD74, SFRP4, ITGA4, and IKZF1. Gene enrichment analysis revealed significant alterations in the cardiomyopathy pathway. Single-cell RNAseq showed a few significant markers, with known KD markers like S100A9, S100A8, CD74, CD14, IFITM2, and IFITM3, being overexpressed in KD cohorts. Gene profiles obtained from KD human coronary artery are more compatible with data from aorta samples of KD mice than blood samples of KD humans, validating KD animal models for identifying therapeutic targets. Although blood samples can be utilized to discover novel biomarkers, more comprehensive single-cell sequencing is required to detail gene expression in different blood cell populations. This study identifies critical genes from human and mouse tissues to help develop new treatment strategies for KD.

Single-cell sequencing technologies are widely used to discover the evolutionary relationships and the differences in cells. Since dropout events may frustrate the analysis, many imputation approaches for single-cell RNA-seq data have appeared in previous attempts. However, previous imputation attempts usually suffer from the over-smooth problem, which may bring limited improvement or negative effect for the downstream analysis of single-cell RNA-seq data. To solve this difficulty, we propose a novel two-stage diffusion-denoising method called SCDD for large-scale single-cell RNA-seq imputation in this paper. We introduce the diffusion i.e. a direct imputation strategy using the expression of similar cells for potential dropout sites, to perform the initial imputation at first. After the diffusion, a joint model integrated with graph convolutional neural network and contractive autoencoder is developed to generate superposition states of similar cells, from which we restore the original states and remove the noise introduced by the diffusion. The final experimental results indicate that SCDD could effectively suppress the over-smooth problem and remarkably improve the effect of single-cell RNA-seq downstream analysis, including clustering and trajectory analysis.

Background: Neoadjuvant treatment of locally advanced breast cancer (LABC) is now well-established and considerable progress has been made concerning drug development for subgroups of patients. However, reliable predictive markers are still lacking for many patients. Thus, radiological measures like repeated MRI of the breast and axilla, after 3 and 6 months of therapy, are used to follow the patients during neoadjuvant therapy to confirm successful downstaging. Although not established in clinical routine, dynamic MRI contains quantitative information that can provide valuable insight into the tumour microenvironment and, when combined with clinical and molecular data, has the potential to enhance the prediction of tumor responses. Here, we analyze split-dynamic MRI data in relation to clinicopathological and molecular data to further identify disease heterogeneity that can be used to improve prognostic accuracy and inform treatment decisions in breast cancer patients. Methods: This study reports results from eligible patients with ER pos., HER2 neg. LABC that were enrolled in the NEOLETEXE-trial (1). Clinicopathological, molecular, and advanced MRI data were acquired from this trial, collected before, during, and after neoadjuvant endocrine therapy (NET) with aromatase inhibitors given in sequence. MR images were generated using a split-dynamic technique that simultaneously acquires high spatial resolution and high temporal resolution images, providing information on tissue permeability and vascularity. The images underwent post-processing on the nordicICE software where parametric values were extracted based on whole-tumour volumes of interest. These parametric values were analysed in correlation with clinico-pathological data and with molecular findings from bulk RNA-seq and single cell data from a subset of the same patients. Results: From the kinetic MRI data, the volume transfer constant (Ktrans), a parameter reflecting capillary permeability, was significantly reduced after NET (p = 0.005). Bulk RNA-seq data was analysed for gene expression, pathway involvement, and cell type abundance before, during, and after treatment. Gene expression analysis revealed significant dysregulation of genes related to cell proliferation and cell cycle regulation following treatment. Biological pathway analysis revealed significant down-regulation of hemostatic activity and upregulation of cell cycle and interleukin signaling following treatment. Cell type analysis revealed a higher abundance of CD4+ T-cells and T-helper cells following treatment. These results will be further correlated with single cell data as well as measures of treatment response and clinical outcome. Conclusions: Our findings suggest that the addition of MRI data to clinicopathological and molecular data enhances the characterization of tumor responses during NET for locally advanced breast cancer. 1. The NEOLETEXE trial: a neoadjuvant cross-over study exploring the lack of cross resistance between aromatase inhibitors. Bahrami N. et al., Future Oncoloy 15 (32), 3675-3682, 2019. Citation Format: Stephanie Lee, Andliena Tahiri, Kjell-Inge Gjesdal, Torben Lüders, Laurens Reitsma, Marianne Lyngra, Alvaro Köhn-Luque, Vessela Kristensen, Jürgen Geisler, Jonn Terje Geitung. Radiogenomics during neoadjuvant therapy with aromatase inhibitors: integrating dynamic magnetic resonance data and molecular biology [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-01-07.

Cell communication pathway prognostic model identified detrimental neurodevelopmental pathways in neuroblastoma

Introduction ccRCC exhibits diverse tumor microenvironments, and associations with ICI response have been demonstrated using bulk RNA sequencing and molecular subtype analyses. However, the factors that drive this microenvironmental heterogeneity remain unclear. This study aimed to analyze large-scale single-cell data to identify multicellular co-occurrence patterns within the tumor microenvironment and the tumor-intrinsic programs that shape these ecosystems. Methods We analyzed single-cell RNA-seq data from 58 ccRCC cases (224,018 cells), including 6 untreated, 31 pre-ICI, 14 post-ICI, 3 pre-TKI, and 4 post-TKI (Kashima, ASCO 2024). We performed high-resolution clustering for each non-tumor cell lineage (CD8+ T, CD4+ T, regulatory T cells, myeloid cells, endothelial cells, and fibroblasts). Pairwise correlations of cluster frequencies across samples were computed to detect “modules” representing co-existing immune and stromal populations. These modules were validated using an independent single-cell RNA-seq dataset (HCRN GU16-260, N = 16; Hugaboom, Cancer Discov. 2025). Tumor cells (50,346 cells) were separately analyzed using consensus NMF to identify shared gene programs. We compared the prevalence of modules in responders (R) (n = 10; complete or partial response) with non-responders (NR) (n = 8; progressive disease) to ICI therapies. Results We identified 78 fine clusters and 21 co-existence modules. These clusters and modules captured features not fully explained by bulk RNA-seq or small-scale single-cell data. Most modules consisted of multiple cell types rather than a single lineage, i.e., a CXCL13+ CD4+ T cell–memory B cell module. Module structure was supported in the HCRN cohort (Mantel r = 0.3, P < 0.001). Patients exhibited highly heterogeneous combinations of these modules. Several modules were associated with favorable ICI responses, including exhausted CD8+ T cells–MACRO+ macrophages (Module R1) and cDC–iCAF (Module R2). Conversely, modules such as Th17 CD4+ T cells–GNLY+ ILCs–pDCs (Module NR1) and GZMA+ CD4+ T cells–Trm CD8+ T cells–IFNγ+ ILCs (Module NR2) were associated with poor ICI response. Consensus NMF of tumor cells revealed six interpretable gene programs shared across samples. Notably, the tumor-intrinsic IL6 and VEGF programs were correlated with Module NR1, while Module NR2 was mainly associated with the MYC program. Modules R1 and R2 also demonstrated distinct associations with specific tumor gene programs. These findings suggest that patient-specific, multiple biological pathways may contribute to both resistance and responsiveness to ICI therapy. Conclusion Our large-scale single-cell analysis identifies reproducible multicellular modules within the ccRCC tumor microenvironment, several of which are positively or negatively associated with ICI response. The observed relationships between non-tumor cell modules and tumor-intrinsic gene programs indicate that tumor cell states and microenvironmental composition are interdependent, highlighting potential cellular crosstalk that may influence immunotherapy efficacy. Citation Format: Katsuhiro Ito, Soki Kashima, Rishabh Rout, Zhaochen Ye, Nicholas R. Schindler, David A. Braun. Multicellular modules and tumor programs shape the tumor microenvironment and predict immune checkpoint inhibitor (ICI) response in clear cell renal cell carcinoma (ccRCC) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Kidney Cancer Research: From Molecular Insights to Therapeutic Breakthroughs; 2026 Mar 13-16; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(5_Suppl_2):Abstract nr A009.

The excessive amount of zeros in single-cell RNA-seq (scRNA-seq) data includes 'real' zeros due to the on-off nature of gene transcription in single cells and 'dropout' zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect three types of DE genes in scRNA-seq data with higher accuracy. The R package DEsingle is freely available at Bioconductor (https://bioconductor.org/packages/DEsingle). Supplementary data are available at Bioinformatics online.

While recent studies have highlighted oxidative stress (OS) as a pivotal factor influencing tumor dynamics, its specific interactions within the tumor microenvironment (TME) of colorectal cancer (CRC) remain elusive. This study seeks to unveil the impact of OS on the CRC TME and to develop an advanced OS-related risk signature (OSRRS) model. We analyzed OS-related pathway activities using both single-cell and bulk RNA-seq data. An unsupervised clustering algorithm was utilized to identify OS-related subtypes. Based on genes associated with the OS pathways, we constructed an OSRRS model employing the LASSO Cox analysis. For validation, we employed quantitative real-time polymerase chain reaction (qRT-PCR) coupled with immunohistochemical (IHC) analyses on tissue microarrays (TMA) to confirm the expression of the identified gene. Examination of the single-cell RNA-seq GSE132465 dataset revealed a universal elevation in OS-associated pathway activities within malignant cells. By integrating this with the bulk RNA-seq TCGA-CRC dataset, we identified two unique OS-specific clusters. This led to the establishment of a 12-gene OSRRS using the LASSO Cox method. The robustness of our model was further verified using the GSE39582 and GSE17538 cohorts. Notably, increased expression of the UCN gene was observed in CRC specimens, as confirmed by qRT-PCR and IHC assays on TMA. In this research, we delineated two distinct subtypes of CRC associated with OS. The developed OSRRS holds promise as a candidate prognostic and stratification tool for CRC management. Collectively, these results shed light on the intricate role of OS in CRC pathology.

Gene expression varies stochastically across genders, racial groups, and health statuses. Deciphering these patterns is crucial for identifying informative genes, classifying samples, and understanding diseases like cancer. This study analyzes 11,252 bulk RNA-seq samples to explore expression patterns of 19,156 genes, including 10,512 cancer tissue samples and 740 normal samples. Additionally, 4,884 single-cell RNA-seq samples are examined. Statistical analysis using 16 probability distributions shows that normal samples display a wider range of distributions compared to cancer samples. Cancer samples tend to favor asymmetric distributions such as generalized extreme value, logarithmic normal, and Gaussian mixture distributions. In contrast, certain genes in normal samples exhibit symmetric distributions. Remarkably, more than 95.5% of genes exhibit non-normal distributions, which challenges traditional assumptions. Furthermore, distributions differ significantly between bulk and single-cell RNA-seq data. Many cancer driver genes exhibit distinct distribution patterns across sample types, suggesting potential for gene selection and classification based on distribution characteristics. A novel skewness-based metric is proposed to quantify distribution variation across datasets, showing genes with significant skewness differences have biological relevance. Finally, an improved naïve Bayes method incorporating gene-specific distributions demonstrates superior performance in simulations over traditional methods. This work enhances understanding of gene expression and its application in omics-based gene selection and sample classification.

Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.