MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Topical JAK inhibition ameliorates EGFR inhibitor-induced rash in rodents and humans

Epidermal growth factor receptor inhibitors (EGFRis) are used to treat many cancers, but their use is complicated by the development of a skin rash that may be severe, limiting their use and adversely affecting patient quality of life. Most studies of EGFRi-induced rash have focused on the fully developed stage of this skin disorder, and early pathological changes remain unclear. We analyzed high-throughput transcriptome sequencing of skin samples from rats exposed to the EGFRi afatinib and identified that keratinocyte activation is an early pathological alteration in EGFRi-induced rash. Mechanistically, the induction of S100 calcium-binding protein A9 (S100A9) occurred before skin barrier disruption and led to keratinocyte activation, resulting in expression of specific cytokines, chemokines, and surface molecules such as interleukin 6 (Il6) and C-C motif chemokine ligand 2 (CCL2) to recruit and activate monocytes through activation of the Janus kinase (JAK)-signal transducers and activators of transcription (STAT) pathway, further recruiting more immune cells. Topical JAK inhibition suppressed the recruitment of immune cells and ameliorated the severity of skin rash in afatinib-treated rats and mice with epidermal deletion of EGFR, while having no effect on EGFRi efficacy in tumor-bearing mice. In a pilot clinical trial (NCT05120362), 11 patients with EGFRi-induced rash were treated with delgocitinib ointment, resulting in improvement in rash severity by at least one grade in 10 of them according to the MASCC EGFR inhibitor skin toxicity tool (MESTT) criteria. These findings provide a better understanding of the early pathophysiology of EGFRi-induced rash and suggest a strategy to manage this condition.

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

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

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix

Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn RNA-seq data and bulk RNA-seq data from target samples to enhance the accuracy of CTS inference. We demonstrate through comprehensive simulations across three tissues that EPIC-unmix achieved 4.6% - 109.8% higher accuracy compared to alternative methods. By applying EPIC-unmix to human bulk brain RNA-seq data from the ROSMAP and MSBB cohorts, we identified multiple genes differentially expressed between Alzheimer's disease (AD) cases versus controls in a CTS manner, including 57.4% novel genes not identified using similar sample size sc/snRNA-seq data, indicating the power of our in-silico approach. Among the 6-69% overlapping, 83%-100% are in consistent direction with those from sc/snRNA-seq data, supporting the reliability of our findings. EPIC-unmix inferred CTS expression profiles similarly empowers CTS eQTL analysis. Among the novel eQTLs, we highlight a microglia eQTL for AD risk gene AP3B2, obscured in bulk and missed by sc/snRNA-seq based eQTL analysis. The variant resides in a microglia-specific cCRE, forming chromatin loop with AP3B2 promoter region in microglia. Taken together, we believe EPIC-unmix will be a valuable tool to enable more powerful CTS analysis.

Integrative multi-omic analysis reveals conserved cell-projection deficits in human Down syndrome brains

Down syndrome (DS) is the most common genetic cause of cognitive disability. However, it is largely unclear how triplication of a small gene subset may impinge on diverse aspects of DS brain physiopathology. Here, we took a multi-omic approach and simultaneously analyzed by RNA-seq and proteomics the expression signatures of two diverse regions of human postmortem DS brains. We found that the overexpression of triplicated genes triggered global expression dysregulation, differentially affecting transcripts, miRNAs, and proteins involved in both known and novel biological candidate pathways. Among the latter, we observed an alteration in RNA splicing, specifically modulating the expression of genes involved in cytoskeleton and axonal dynamics in DS brains. Accordingly, we found an alteration in axonal polarization in neurons from DS human iPSCs and mice. Thus, our study provides an integrated multilayer expression database capable of identifying new potential targets to aid in designing future clinical interventions for DS.

Predicting drug response through tumor deconvolution by cancer cell lines

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Natural history of Ebola virus disease in rhesus monkeys shows viral variant emergence dynamics and tissue-specific host responses

Ebola virus (EBOV) causes Ebola virus disease (EVD), marked by severe hemorrhagic fever; however, the mechanisms underlying the disease remain unclear. To assess the molecular basis of EVD across time, we performed RNA sequencing on 17 tissues from a natural history study of 21 rhesus monkeys, developing new methods to characterize host-pathogen dynamics. We identified alterations in host gene expression with previously unknown tissue-specific changes, including downregulation of genes related to tissue connectivity. EBOV was widely disseminated throughout the body; using a new, broadly applicable deconvolution method, we found that viral load correlated with increased monocyte presence. Patterns of viral variation between tissues differentiated primary infections from compartmentalized infections, and several variants impacted viral fitness in a EBOV/Kikwit minigenome system, suggesting that functionally significant variants can emerge during early infection. This comprehensive portrait of host-pathogen dynamics in EVD illuminates new features of pathogenesis and establishes resources to study other emerging pathogens.

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.

Incubation with porcine urinary bladder matrix yields a late-stage wound transcriptome in endothelial cells and keratinocytes isolated from both diabetic and non-diabetic subjects

Proper wound closure requires the functional coordination of endothelial cells (ECs) and keratinocytes. In the late stages of wound healing, keratinocytes become activated and ECs promote the maturation of nascent blood vessels. In diabetes mellitus, decreased keratinocyte activation and impaired angiogenic action of ECs delay wound healing. Porcine urinary bladder matrix (UBM) improves the rate of wound healing, but the effect of exposure to UBM under diabetic conditions remains unclear. We hypothesized that keratinocytes and ECs isolated from both diabetic and non-diabetic donors would exhibit a similar transcriptome representative of the later stages of wound healing following incubation with UBM. Human keratinocytes and dermal ECs isolated from non-diabetic and diabetic donors were incubated with and without UBM particulate. RNA-Seq analysis was performed to identify changes in the transcriptome of these cells associated with exposure to UBM. While diabetic and non-diabetic cells exhibited different transcriptomes, these differences were minimized following incubation with UBM. ECs exposed to UBM exhibited changes in the expression of transcripts suggesting an increase in the endothelial-mesenchymal transition (EndoMT) associated with vessel maturation. Keratinocytes incubated with UBM demonstrated an increase in markers of activation. Comparison of the whole transcriptomes with public datasets suggested increased EndoMT and keratinocyte activation following UBM exposure. Both cell types exhibited loss of pro-inflammatory cytokines and adhesion molecules. These data suggest that application of UBM may accelerate healing by promoting a transition to the later stages of wound healing. This healing phenotype is achieved in cells isolated from both diabetic and non-diabetic donors.

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

BACKGROUND

Disruption 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.

METHODS

The 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.

RESULTS

OA 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.

CONCLUSIONS

Our 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.

Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools

Traditional bulk RNA-Seq pipelines do not assess cell-type composition within heterogeneous tissues. Therefore, it is difficult to determine whether conflicting findings among samples or datasets are the result of biological differences or technical differences due to variation in sample collections. This report provides a user-friendly, open source method to assess cell-type composition in bulk RNA-Seq datasets for heterogeneous tissues using published single cell (sc)RNA-Seq data as a reference. As an example, we apply the method to analysis of kidney cortex bulk RNA-Seq data from female (N=8) and male (N=9) baboons to assess whether observed transcriptome sex differences are biological or technical, i.e., variation due to ultrasound guided biopsy collections. We found cell-type composition was not statistically different in female versus male transcriptomes based on expression of 274 kidney cell-type specific transcripts, indicating differences in gene expression are not due to sampling differences. This method of cell-type composition analysis is recommended for providing rigor in analysis of bulk RNA-Seq datasets from complex tissues. It is clear that with reduced costs, more analyses will be done using scRNA-Seq; however, the approach described here is relevant for data mining and meta analyses of the thousands of bulk RNA-Seq data archived in the NCBI GEO public database.