Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

DLK1 Distinguishes Subsets of NF1-Associated Malignant Peripheral Nerve Sheath Tumors with Divergent Molecular Signatures

PURPOSE

Malignant peripheral nerve sheath tumor (MPNST) is the leading cause of premature death among individuals with neurofibromatosis type 1 (NF1), and the transcriptional aberrations that precede malignant transformation and contribute to MPNST tumorigenesis remain poorly defined. Alterations involving CDKN2A and components of PRC2 have been implicated as early drivers of peripheral nerve sheath tumor (PNST) evolution, but these events do not occur in all MPNST. Accordingly, emerging data have begun to highlight the importance of molecular-based stratification to improve outcomes in patients with NF1-PNST.

EXPERIMENTAL DESIGN

In this study, we perform an integrated analysis of multiple, independent datasets obtained from human patients with NF1 to gain critical insights into PNST evolution and MPNST heterogeneity.

RESULTS

We show that delta-like noncanonical Notch ligand 1 (DLK1) is significantly increased in MPNST and provide evidence that DLK1 overexpression may precede histologic changes consistent with malignancy. In complementary analyses, we find that serum levels of DLK1 are significantly higher in both mice and humans harboring MPNST compared with those without malignancy. Importantly, although DLK1 expression is increased in MPNST overall, through the integration of multiple, independent datasets, we demonstrate that divergent levels of DLK1 expression distinguish MPNST subsets characterized by unique molecular programs and potential therapeutic vulnerabilities. Specifically, we show that overexpression of DLK1 is associated with the reactivation of embryonic signatures, an immunosuppressive microenvironment, and a worse overall survival in patients with NF1-MPNST.

CONCLUSIONS

Collectively, our findings provide critical insights into MPNST tumorigenesis and support prospective studies evaluating the utility of DLK1 tissue and serum levels in augmenting diagnosis, risk assessment, and therapeutic stratification in the setting of NF1-PNST.

Cell-type deconvolution methods for spatial transcriptomics

Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.

DECEPTICON: a correlation-based strategy for RNA-seq deconvolution inspired by a variation of the Anna Karenina principle

Accurately deconvoluting cellular composition from bulk RNA-seq data is pivotal for understanding the tumor microenvironment and advancing precision medicine. Existing methods often struggle to consistently and accurately quantify cell types across heterogeneous RNA-seq datasets, particularly when ground truths are unavailable. In this study, we introduce DECEPTICON, a deconvolution strategy inspired by the Anna Karenina principle, which postulates that successful outcomes share common traits, while failures are more varied. DECEPTICON selects top-performing methods by leveraging correlations between different strategies and combines them dynamically to enhance performance. Our approach demonstrates superior accuracy in predicting cell-type proportions across multiple tumor datasets, improving correlation by 23.9% and reducing root mean square error by 73.5% compared to the best of 50 analyzed strategies. Applied to The Cancer Genome Atlas (TCGA) datasets for breast carcinoma, cervical squamous cell carcinoma, and lung adenocarcinoma, DECEPTICON-based predictions showed improved differentiation between patient prognoses. This correlation-based strategy offers a reliable, flexible tool for deconvoluting complex transcriptomic data and highlights its potential in refining prognostic assessments in oncology and advancing cancer biology.

PANDA: PAN Cancer Data Analysis Web Tool

Cancer research faces challenges due to the genetic diversity within tumors and individual variability. Precision medicine aims to identify genomic and molecular factors linked to clinical outcomes, leveraging large datasets for drug discovery and patient stratification. We introduce PANDA (PAN-cancer Data Analysis web tool) (https://panda.bio.uniroma2.it), a web server designed for analyzing TCGA genomic data. A total of 32 tumor types and 10,711 samples were selected for this analysis. PANDA simplifies complex tasks such as differential expression, survival analysis, and patient stratification, incorporating clinical factors like sex, stage, and treatment history. It also enables the exploration of biological pathways and immune cell type proportion, providing insights into tumor progression. PANDA is user-friendly, designed for researchers with limited informatics expertise, and supports diverse analyses to advance cancer research.

Transcriptomic landscape around wound bed defines regenerative versus non-regenerative outcomes in mouse digit amputation

In the mouse distal terminal phalanx (P3), it remains mystery why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (Bmp2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, indicating that the regenerative probability may be "encoded" in the transcriptomic response to amputation.

MYC networks associate with decreased CD8 T-cell presence in diffuse large B-cell lymphoma and may be addressed by the synergistic combination of AZD4573 and Selinexor - a preliminary analysis

Diffuse Large B-cell Lymphoma (DLBCL) is a genomically-heterogenous disease affecting over 70,000 patients per year that presents a clinical challenge despite the success of frontline regimens and second-line Chimeric Antigen receptor T-cell (CAR-T) therapy. Recently, genomic alterations and tumor microenvironment features associated with poor CAR-T response have been identified, with MYC amplification emerging in new analyses. This retrospective analysis aimed to integrate various data to identify genomic partnerships capable of providing added clarity and actionable treatment targets within this population. Publicly-available data were analyzed for differential expression based on MYC, 24-month event-free survival (EFS24) status, and CAR-T response. Notable T-cell partner genes such as IL7R (FDR = 0.00150) and CD58 (FDR = 5.375E-06) and cell death mediators such as PDCD1LG2 (FDR = 4.061E-06) were significantly lost in patients with High/Altered MYC that also failed EFS24. CD8 T-cell presence was also significantly lower in High/Altered MYC de-novo patients (p = 0.00112) and CAR-T non-responders (p = 0.00835). De-novo patients with both High/Altered MYC and CD8 T-cell absence faced a significantly inferior survival compared to counterparts with only one factor or neither (p = 0.0226). rrDLBCL patients reflected similar oncogenic pathways associated with greater scRNA MYC expression. In vitro application of the CDK9 inhibitor AZD4573 and XPO1 inhibitor Selinexor significantly reduced DLBCL cell line viability as single agents and produced synergistic results when applied in combination. Our analysis presents key associations between the MYC oncogene and depleted TME presence capable of providing clarity within the evolving precision CAR-T treatment landscape.

Multi-modal analysis reveals tumor and immune features distinguishing EBV-positive and EBV-negative post-transplant lymphoproliferative disorders

The oncogenic Epstein-Barr virus (EBV) can drive tumorigenesis with disrupted host immunity, causing malignancies including post-transplant lymphoproliferative disorders (PTLDs). PTLD can also arise in the absence of EBV, but the biological differences underlying EBV(+) and EBV(-) B cell PTLD and the associated host-EBV-tumor interactions remain poorly understood. Here, we reveal the core differences between EBV(+) and EBV(-) PTLD, characterized by increased expression of genes related to immune processes or DNA interactions, respectively, and the augmented ability of EBV(+) PTLD B cells to modulate the tumor microenvironment through elaboration of monocyte-attracting cytokines/chemokines. We create a reference resource of proteins distinguishing EBV(+) B lymphoma cells from EBV(-) B lymphoma including the immunomodulatory molecules CD300a and CD24, respectively. Moreover, we show that CD300a is essential for maximal survival of EBV(+) PTLD B lymphoma cells. Our comprehensive multi-modal analyses uncover the biological underpinnings of PTLD and offer opportunities for precision therapies.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.

Identification of host endotypes using peripheral blood transcriptomics in a prospective cohort of patients with endocarditis

OBJECTIVES

Host responses to infection are a major determinant of outcome. However, the existence of different response profiles in patients with endocarditis has not been addressed. Our objective was to apply transcriptomics to identify endotypes in patients with infective endocarditis.

METHODS

A total of 32 patients with infective endocarditis were studied. Clinical data and blood samples were collected at diagnosis and RNA sequenced. Gene expression was used to identify two clusters (endocarditis endotype 1 [EE1] and endocarditis endotype 2 [EE2]). RNA sequencing was repeated after surgery. Transcriptionally active cell populations were identified by deconvolution. Differences between endotypes in clinical data, survival, gene expression, and molecular pathways involved were assessed. The identified endotypes were recapitulated in a cohort of COVID-19 patients.

RESULTS

A total of 18 and 14 patients were assigned to EE1 and EE2, respectively, with no differences in clinical data. Patients assigned to EE2 showed an enrichment in genes related to T-cell maturation and a decrease in the activation of the signal transducer and activator of transcription protein family pathway, with higher counts of active T cells and lower counts of neutrophils. A total of 14 patients (nine in EE1 and five in EE2) were submitted to surgery. Surgery in EE2 patients shifted gene expression toward a EE1-like profile. In-hospital mortality was higher in EE1 (56% vs 14%, P = 0.027), with an adjusted hazard ratio of 12.987 (95% confidence interval 3.356-50). Translation of these endotypes to COVID-19 and non-COVID-19 septic patients yielded similar results in cell populations and outcome.

CONCLUSIONS

Gene expression reveals two endotypes in patients with acute endocarditis, with different underlying pathogenetic mechanisms, responses to surgery, and outcomes.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities such as bulk ATAC-seq data.

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data

BACKGROUND

The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential.

RESULTS

We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies.

CONCLUSIONS

DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.

Maturation of germinal center B cells after influenza virus vaccination in humans

Germinal centers (GC) are microanatomical lymphoid structures where affinity-matured memory B cells and long-lived bone marrow plasma cells are primarily generated. It is unclear how the maturation of B cells within the GC impacts the breadth and durability of B cell responses to influenza vaccination in humans. We used fine needle aspiration of draining lymph nodes to longitudinally track antigen-specific GC B cell responses to seasonal influenza vaccination. Antigen-specific GC B cells persisted for at least 13 wk after vaccination in two out of seven individuals. Monoclonal antibodies (mAbs) derived from persisting GC B cell clones exhibit enhanced binding affinity and breadth to influenza hemagglutinin (HA) antigens compared with related GC clonotypes isolated earlier in the response. Structural studies of early and late GC-derived mAbs from one clonal lineage in complex with H1 and H5 HAs revealed an altered binding footprint. Our study shows that inducing sustained GC reactions after influenza vaccination in humans supports the maturation of responding B cells.

ΔNp63-restricted viral mimicry response impedes cancer cell viability and remodels tumor microenvironment in esophageal squamous cell carcinoma

Tumor protein p63 isoform ΔNp63 plays roles in the squamous epithelium and squamous cell carcinomas (SCCs), including esophageal SCC (ESCC). By integrating data from cell lines and our latest patient-derived organoid cultures, derived xenograft models, and clinical sample transcriptomic analyses, we identified a novel and robust oncogenic role of ΔNp63 in ESCC. We showed that ΔNp63 maintains the repression of cancer cell endogenous retrotransposon expression and cellular double-stranded RNA sensing. These subsequently lead to a restricted cancer cell viral mimicry response and suppressed induction of tumor-suppressive type I interferon (IFN-I) signaling through the regulations of Signal transducer and activator of transcription 1, Interferon regulatory factor 1, and cGAS-STING pathway. The cancer cell ΔNp63/IFN-I signaling axis affects both the cancer cell and tumor-infiltrating immune cell (TIIC) compartments. In cancer cells, depletion of ΔNp63 resulted in reduced cell viability. ΔNp63 expression is negatively associated with the anticancer responses to viral mimicry booster treatments targeting cancer cells. In the tumor microenvironment, cancer cell TP63 expression negatively correlates with multiple TIIC signatures in ESCC clinical samples. ΔNp63 depletion leads to increased cancer cell antigen presentation molecule expression and enhanced recruitment and reprogramming of tumor-infiltrating myeloid cells. Similar IFN-I signaling and TIIC signature association with ΔNp63 were also observed in lung SCC. These results support the potential application of ΔNp63 as a therapeutic target and a biomarker to guide candidate anticancer treatments exploring viral mimicry responses.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection

BACKGROUND

Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management.

METHODS

We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways.

RESULTS

T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR.

CONCLUSIONS

Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.

Impaired innate and adaptive immune responses to BNT162b2 SARS-CoV-2 vaccination in systemic lupus erythematosus

Understanding the immune responses to SARS-CoV-2 vaccination is critical to optimizing vaccination strategies for individuals with autoimmune diseases, such as systemic lupus erythematosus (SLE). Here, we comprehensively analyzed innate and adaptive immune responses in 19 patients with SLE receiving a complete 2-dose Pfizer-BioNTech mRNA vaccine (BNT162b2) regimen compared with a control cohort of 56 healthy control (HC) volunteers. Patients with SLE exhibited impaired neutralizing antibody production and antigen-specific CD4+ and CD8+ T cell responses relative to HC. Interestingly, antibody responses were only altered in patients with SLE treated with immunosuppressive therapies, whereas impairment of antigen-specific CD4+ and CD8+ T cell numbers was independent of medication. Patients with SLE also displayed reduced levels of circulating CXC motif chemokine ligands, CXCL9, CXCL10, CXCL11, and IFN-γ after secondary vaccination as well as downregulation of gene expression pathways indicative of compromised innate immune responses. Single-cell RNA-Seq analysis reveals that patients with SLE showed reduced levels of a vaccine-inducible monocyte population characterized by overexpression of IFN-response transcription factors. Thus, although 2 doses of BNT162b2 induced relatively robust immune responses in patients with SLE, our data demonstrate impairment of both innate and adaptive immune responses relative to HC, highlighting a need for population-specific vaccination studies.

Discerning Endoscopic Severity of Inflammatory Bowel Disease by Scoping the Peripheral Blood Transcriptome

Background and Aims

Ulcerative colitis (UC) and Crohn's disease (CD) are chronic inflammatory bowel diseases (IBDs) with an incompletely understood etiology and pathogenesis. Identification of suitable drug targets and assessment of disease severity are crucial for optimal management.

Methods

Using RNA sequencing, we investigated differential gene expression in peripheral blood samples from IBD patients and non-inflamed controls, analyzed pathway enrichment, and identified genes whose expression correlated with endoscopic disease severity.

Results

Neutrophil degranulation emerged as the most significant pathway across all IBD sample types. Signaling by interleukins was prominent in patients with active intestinal inflammation but also enriched in CD and UC patients without intestinal inflammation. Nevertheless, genes correlated to endoscopic disease severity implicated the primary cilium in CD patients and translation and focal adhesion in UC patients. Moreover, several of these genes were located in genome-wide associated loci linked to IBD, cholesterol levels, blood cell counts, and levels of markers assessing liver and kidney function. These genes also suggested connections to intestinal epithelial barrier dysfunction, contemporary IBD drug treatment, and new actionable drug targets. A large number of genes associated with endoscopic disease severity corresponded to noncoding RNAs.

Conclusion

This study revealed biological pathways associated with IBD disease state and endoscopic disease severity, thus providing insights into the underlying mechanisms of IBD pathogenesis as well as identifying potential biomarkers and therapies. Peripheral blood might constitute a suitable noninvasive diagnostic sample type, in which gene expression profiles might serve as indicators of ongoing mucosal inflammation, and thus guide personalized treatment decisions.

Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching

Accurately identifies the cellular composition of complex tissues, which is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA-seq) typically rely on matched single-cell RNA sequencing (scRNA-seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single-cell references. Hence, a novel computational method named SCROAM is introduced to address these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. Subsequently, cell-type-specific expression matrices are generated from the scRNA-seq data, facilitating the precise identification of cell types within bulk tissues. The performance of SCROAM is assessed through benchmarking against simulated and real datasets, demonstrating its accuracy and robustness. To further validate SCROAM's performance, single-cell and bulk RNA-seq experiments are conducted on mouse spinal cord tissue, with SCROAM applied to identify cell types in bulk tissue. Results indicate that SCROAM is a highly effective tool for identifying similar cell types. An integrated analysis of liver cancer and primary glioblastoma is then performed. Overall, this research offers a novel perspective for delivering precise insights into disease pathogenesis and potential therapeutic strategies.

Bone marrow macrophages are involved in the ineffective hematopoiesis of myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are a group of heterogeneous myeloid clonal disorders characterized by ineffective hematopoiesis. Accumulating evidence has shown that macrophages (MΦs) are important components in the regulation of tumor progression and hematopoietic stem cells (HSCs). However, the roles of bone marrow (BM) MΦs in regulating normal and malignant hematopoiesis in different clinical stages of MDS are largely unknown. Age-paired patients with lower-risk MDS (N = 15), higher-risk MDS (N = 15), de novo acute myeloid leukemia (AML) (N = 15), and healthy donors (HDs) (N = 15) were enrolled. Flow cytometry analysis showed increased pro-inflammatory monocyte subsets and a decreased classically activated (M1) MΦs/alternatively activated (M2) MΦs ratio in the BM of patients with higher-risk MDS compared to lower-risk MDS. BM MФs from patients with higher-risk MDS and AML showed impaired phagocytosis activity but increased migration compared with lower-risk MDS group. AML BM MΦs showed markedly higher S100A8/A9 levels than lower-risk MDS BM MΦs. More importantly, coculture experiments suggested that the HSC supporting abilities of BM MΦs from patients with higher-risk MDS decreased, whereas the malignant cell supporting abilities increased compared with lower-risk MDS. Gene Ontology enrichment comparing BM MΦs from lower-risk MDS and higher-risk MDS for genes was involved in hematopoiesis- and immunity-related pathways. Our results suggest that BM MΦs are involved in ineffective hematopoiesis in patients with MDS, which indicates that repairing aberrant BM MΦs may represent a promising therapeutic approach for patients with MDS.

Integrative analyses reveal outcome-associated and targetable molecular partnerships between TP53, BRD4, TNFRSF10B, and CDKN1A in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a common, genomically heterogenous disease that presents a clinical challenge despite the success of frontline regimens and second-line chimeric antigen receptor T-cell (CAR-T) therapy. Recently, genomic alterations and tumor microenvironment features associated with poor CAR-T response have been identified, namely those to the TP53 tumor suppressor gene. This retrospective analysis aimed to integrate various data to identify genomic partnerships capable of providing further clarity and actionable treatment targets within this population. Publicly available data were analyzed for differential expression based on TP53 and 24-month event-free survival (EFS24) status, revealing enrichments of the BRD4 bromodomain oncogene (p < 0.0001, p = 0.001). High-BRD4 and TP53 alterations were significantly associated with lower CDKN1A (p21) and TNFRSF10B (TRAIL-R2), a key tumor suppressor and CAR-T modulator, respectively. Significant loss of CD8 T-cell presence within low-TNFRSF0B (p = 0.0042) and altered-TP53 (p = 0.0424) patients showcased relevant outcome-associated tumor microenvironment features. Furthermore, reduced expression of CDKN1A was associated with low TNFRSF10B (FDR < 0.0001) and increased BRD4 interactant genes (FDR < 0.0001). Promisingly, in vitro MDM2 inhibition with Idasnutlin and TP53 reactivation via Eprenetapopt was able to renew TNFRSF10B protein expression. Additionally, applying the BRD4-degrading PROTAC ARV-825 and the CDK4/6 inhibitor Abemaciclib as single-agents and in synergistic combination significantly reduced TP53-altered DLBCL cell line viability. Our analysis presents key associations within a genomic network of actionable targets capable of providing clarity within the evolving precision CAR-T treatment landscape.

A modified immune cell infiltration score achieves ideal stratification for CD8+ T cell efficacy and immunotherapy benefit in hepatocellular carcinoma

Immunotherapy, which aims to enhance the function of T cells, has emerged as a novel therapeutic approach for hepatocellular carcinoma (HCC). Nevertheless, the clinical utility of using flow cytometry to assess immune cell infiltration (ICI) is hindered by its cumbersome procedures, prompting the need for more accessible methods. Here, we acquired gene expression profiles and survival data of HCC from TCGA and GSE10186 datasets. The patients were categorized into two clusters of ICI, and a set of 11 characteristic genes responsible for the differentiation performance of these ICI clusters were identified. Subsequently, we successfully developed a modified ICI score (mICIS) by utilizing the expression levels of these genes. The efficacy of our mICIS was confirmed via mass cytometry, flow cytometry, and immunohistochemistry. Our research indicated that the favorable overall survival (OS) rate could be attributed to the improved function of anti-tumor leukocytes rather than their infiltration. Furthermore, we observed that the low score group exhibited lower expression levels of T-cell exhaustion-associated genes, which was confirmed in both HCC tissues from patients and mice, which demonstrated that the benefits of the low scores were due to enhanced active/cytotoxic CD8+ T cells and reduced exhausted CD8+ T cells. Additionally, our mICIS stratified the benefits derived from immunotherapies. Lastly, we observed a misalignment between CD8+ T-cell infiltration and function in HCC. In summary, our mICIS demonstrated proficiency in assessing the OS rate of HCC and offering significant stratified data pertaining to distinct responses to immunotherapy.

HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD)

BACKGROUND

Cell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.

METHODS

We developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.

RESULTS

HASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients' overall survival.

CONCLUSION

HASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD .

Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)

Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful. Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene's expression that was attributable to the tumor compartment. A machine learning algorithm designed to identify features most closely associated with survival outcomes was used to identify the most clinically impactful metabolic genes. Results: Two metabolic subtypes (M1 and M2) were identified, based on the pattern of expression of the 26 most important metabolic genes. The M2 phenotype had a significantly worse survival, which was replicated in three external PDAC cohorts. This PDAC subtype was characterized by net glycogen catabolism, accelerated glycolysis, and increased proliferation and cellular migration. Single cell data demonstrated substantial intercellular heterogeneity in the metabolic features that typified this aggressive phenotype. Conclusion: By focusing on features within the tumor compartment, two novel and clinically impactful metabolic subtypes of PDAC were identified. Our study emphasizes the challenges of defining tumor phenotypes in the face of the significant intratumoral heterogeneity that typifies PDAC. Further studies are required to understand the microenvironmental factors that drive the appearance of the metabolic features characteristic of the aggressive M2 PDAC phenotype.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

BACKGROUND

Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.

RESULTS

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.

CONCLUSIONS

Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

Emerging Insights into Molecular Mechanisms of Inflammation in Myelodysplastic Syndromes

Inflammation impacts human hematopoiesis across physiologic and pathologic conditions, as signals derived from the bone marrow microenvironment, such as pro-inflammatory cytokines and chemokines, have been shown to alter hematopoietic stem cell (HSCs) homeostasis. Dysregulated inflammation can skew HSC fate-related decisions, leading to aberrant hematopoiesis and potentially contributing to the pathogenesis of hematological disorders such as myelodysplastic syndromes (MDS). Recently, emerging studies have used single-cell sequencing and muti-omic approaches to investigate HSC cellular heterogeneity and gene expression in normal hematopoiesis as well as in myeloid malignancies. This review summarizes recent reports mechanistically dissecting the role of inflammatory signaling and innate immune response activation due to MDS progression. Furthermore, we highlight the growing importance of using multi-omic techniques, such as single-cell profiling and deconvolution methods, to unravel MDSs' heterogeneity. These approaches have provided valuable insights into the patterns of clonal evolution that drive MDS progression and have elucidated the impact of inflammation on the composition of the bone marrow immune microenvironment in MDS.

IL17A Blockade with Ixekizumab Suppresses MuvB Signaling in Clinical Psoriasis

Unbiased informatics approaches have the potential to generate insights into uncharacterized signaling pathways in human disease. In this study, we generated longitudinal transcriptomic profiles of plaque psoriasis lesions from patients enrolled in a clinical trial of the anti-IL17A antibody ixekizumab (IXE). This dataset was then computed against a curated matrix of over 700 million data points derived from published psoriasis and signaling node perturbation transcriptomic and chromatin immunoprecipitation-sequencing datasets. We observed substantive enrichment within both psoriasis-induced and IXE-repressed gene sets of transcriptional targets of members of the MuvB complex, a master regulator of the mitotic cell cycle. These gene sets were similarly enriched for pathways involved in the regulation of the G2/M transition of the cell cycle. Moreover, transcriptional targets for MuvB nodes were strongly enriched within IXE-repressed genes whose expression levels correlated strongly with the extent and severity of the psoriatic disease. In models of human keratinocyte proliferation, genes encoding MuvB nodes were transcriptionally repressed by IXE, and depletion of MuvB nodes reduced cell proliferation. Finally, we made the expression and regulatory networks that supported this study available as a freely accessible, cloud-based hypothesis generation platform. Our study positions inhibition of MuvB signaling as an important determinant of the therapeutic impact of IXE in psoriasis.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort

The molecular mechanisms underlying posttraumatic stress disorder (PTSD) are yet to be fully elucidated, especially in underrepresented population groups. Expression quantitative trait loci (eQTLs) are DNA sequence variants that influence gene expression, in a local (cis-) or distal (trans-) manner, and subsequently impact cellular, tissue, and system physiology. This study aims to identify genetic loci associated with gene expression changes in a South African PTSD cohort. Genome-wide genotype and RNA-sequencing data were obtained from 32 trauma-exposed controls and 35 PTSD cases of mixed-ancestry, as part of the SHARED ROOTS project. The first approach utilised 108 937 single-nucleotide polymorphisms (SNPs) (MAF > 10%) and 11 312 genes with Matrix eQTL to map potential eQTLs, while controlling for covariates as appropriate. The second analysis was focused on 5638 SNPs related to a previously calculated PTSD polygenic risk score for this cohort. SNP-gene pairs were considered eQTLs if they surpassed Bonferroni correction and had a false discovery rate <0.05. We did not identify eQTLs that significantly influenced gene expression in a PTSD-dependent manner. However, several known cis-eQTLs, independent of PTSD diagnosis, were observed. rs8521 (C > T) was associated with TAGLN and SIDT2 expression, and rs11085906 (C > T) was associated with ZNF333 expression. This exploratory study provides insight into the molecular mechanisms associated with PTSD in a non-European, admixed sample population. This study was limited by the cross-sectional design and insufficient statistical power. Overall, this study should encourage further multi-omics approaches towards investigating PTSD in diverse populations.

Repair of Acute Respiratory Distress Syndrome in COVID-19 by Stromal Cells (REALIST-COVID Trial): A Multicenter, Randomized, Controlled Clinical Trial

Rationale: Mesenchymal stromal cells (MSCs) may modulate inflammation, promoting repair in coronavirus disease (COVID-19)-related acute respiratory distress syndrome (ARDS). Objectives: We investigated the safety and efficacy of ORBCEL-C (CD362 [cluster of differentiation 362]-enriched, umbilical cord-derived MSCs) in COVID-19-related ARDS. Methods: In this multicenter, randomized, double-blind, allocation-concealed, placebo-controlled trial (NCT03042143), patients with moderate to severe COVID-19-related ARDS were randomized to receive ORBCEL-C (400 million cells) or placebo (Plasma-Lyte 148). The primary safety and efficacy outcomes were the incidence of serious adverse events and oxygenation index at Day 7, respectively. Secondary outcomes included respiratory compliance, driving pressure, PaO2:FiO2 ratio, and Sequential Organ Failure Assessment score. Clinical outcomes relating to duration of ventilation, lengths of ICU and hospital stays, and mortality were collected. Long-term follow-up included diagnosis of interstitial lung disease at 1 year and significant medical events and mortality at 2 years. Transcriptomic analysis was performed on whole blood at Days 0, 4, and 7. Measurements and Main Results: Sixty participants were recruited (final analysis: n = 30 received ORBCEL-C, n = 29 received placebo; 1 participant in the placebo group withdrew consent). Six serious adverse events occurred in the ORBCEL-C group and three in the placebo group (risk ratio, 2.9 [95% confidence interval, 0.6-13.2]; P = 0.25). Day 7 mean (SD) oxygenation index did not differ (ORBCEL-C, 98.3 [57.2] cm H2O/kPa; placebo, 96.6 [67.3] cm H2O/kPa). There were no differences in secondary surrogate outcomes or in mortality at Day 28, Day 90, 1 year, or 2 years. There was no difference in the prevalence of interstitial lung disease at 1 year or significant medical events up to 2 years. ORBCEL-C modulated the peripheral blood transcriptome. Conclusion: ORBCEL-C MSCs were safe in subjects with moderate to severe COVID-19-related ARDS but did not improve surrogates of pulmonary organ dysfunction.

Distinct immune responses associated with vaccination status and protection outcomes after malaria challenge

Understanding immune mechanisms that mediate malaria protection is critical for improving vaccine development. Vaccination with radiation-attenuated Plasmodium falciparum sporozoites (PfRAS) induces high level of sterilizing immunity against malaria and serves as a valuable tool for the study of protective mechanisms. To identify vaccine-induced and protection-associated responses during malarial infection, we performed transcriptome profiling of whole blood and in-depth cellular profiling of PBMCs from volunteers who received either PfRAS or noninfectious mosquito bites, followed by controlled human malaria infection (CHMI) challenge. In-depth single-cell profiling of cell subsets that respond to CHMI in mock-vaccinated individuals showed a predominantly inflammatory transcriptome response. Whole blood transcriptome analysis revealed that gene sets associated with type I and II interferon and NK cell responses were increased in prior to CHMI while T and B cell signatures were decreased as early as one day following CHMI in protected vaccinees. In contrast, non-protected vaccinees and mock-vaccinated individuals exhibited shared transcriptome changes after CHMI characterized by decreased innate cell signatures and inflammatory responses. Additionally, immunophenotyping data showed different induction profiles of vδ2+ γδ T cells, CD56+ CD8+ T effector memory (Tem) cells, and non-classical monocytes between protected vaccinees and individuals developing blood-stage parasitemia, following treatment and resolution of infection. Our data provide key insights in understanding immune mechanistic pathways of PfRAS-induced protection and infective CHMI. We demonstrate that vaccine-induced immune response is heterogenous between protected and non-protected vaccinees and that inducted-malaria protection by PfRAS is associated with early and rapid changes in interferon, NK cell and adaptive immune responses. Trial Registration: ClinicalTrials.gov NCT01994525.

Differences in Whole-Blood Transcriptional Profiles in Inflammatory Bowel Disease Patients Responding to Vedolizumab Compared with Non-Responders

Vedolizumab is efficacious in the treatment of Crohn's disease (CD) and ulcerative colitis (UC). However, a significant proportion of patients present with a non-response. To investigate whether differences in the clinical response to vedolizumab is reflected in changes in gene expression levels in whole blood, samples were collected at baseline before treatment, and at follow-up after 10-12 weeks. Whole genome transcriptional profiles were established by RNA sequencing. Before treatment, no differentially expressed genes were noted between responders (n = 9, UC 4, CD 5) and non-responders (n = 11, UC 3, CD 8). At follow-up, compared with baseline, responders displayed 201 differentially expressed genes, and 51 upregulated (e.g., translation initiation, mitochondrial translation, and peroxisomal membrane protein import) and 221 downregulated (e.g., Toll-like receptor activating cascades, and phagocytosis related) pathways. Twenty-two of the upregulated pathways in responders were instead downregulated in non-responders. The results correspond with a dampening of inflammatory activity in responders. Although considered a gut-specific drug, our study shows a considerable gene regulation in the blood of patients responding to vedolizumab. It also suggests that whole blood is not optimal for identifying predictive pre-treatment biomarkers based on individual genes. However, treatment outcomes may depend on several interacting genes, and our results indicate a possible potential of pathway analysis in predicting response to treatment, which merits further investigation.

Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.

Transcriptomic clustering of critically ill COVID-19 patients

BACKGROUND

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit.

METHODS

In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings.

RESULTS

We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results.

CONCLUSIONS

These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

Mass-cytometry-based quantitation of global histone post-translational modifications at single-cell resolution across peripheral immune cells in IBD

BACKGROUND AND AIMS

Current understanding of histone post-translational modifications (histone modifications) across immune cell types in patients with inflammatory bowel disease (IBD) during remission and flare is limited. The study aimed to quantify histone modifications at a single-cell resolution in IBD patients during remission and flare and how they differ compared to healthy controls.

METHODS

We performed a case-control study of 94 subjects (83 IBD patients and 11 healthy controls). IBD patients had either UC (n=38) or CD (n=45) in clinical remission or flare. We used epigenetic profiling by time-of-flight (EpiTOF) to investigate changes in histone modifications within peripheral blood mononuclear cells from IBD patients.

RESULTS

We discovered substantial heterogeneity in histone modifications across multiple immune cell types in IBD patients. They had a higher proportion of less differentiated CD34 + hematopoietic progenitors, and a subset of CD56 bright NK cells and γδ T cells characterized by distinct histone modifications associated with the gene transcription. The subset of CD56 bright NK cells had increased several histone acetylations. An epigenetically defined subset of NK was associated with higher levels of CRP in peripheral blood. CD14+ monocytes from IBD patients had significantly decreased cleaved H3T22, suggesting they were epigenetically primed for macrophage differentiation.

CONCLUSION

We describe the first systems-level quantification of histone modifications across immune cells from IBD patients at a single-cell resolution revealing the increased epigenetic heterogeneity that is not possible with traditional ChIP-seq profiling. Our data open new directions in investigating the association between histone modifications and IBD pathology using other epigenomic tools.

Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNNXMBD

Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-DNN protocol to achieve robust cell type proportion estimation on the target dataset. We describe the preparation of calibrated samples from human blood samples. We then detail steps to train a dataset-specific deep neural network (DNN) model and cell type proportion estimation using the trained model.

For complete details on the use and execution of this protocol, please refer to Lin et al. (2022).

•

A protocol for accurate cell type deconvolution with data-driven DNN-based approach

•

Obtain expression and cell proportions from calibrated samples

•

DAISM-DNN model training including parameter tuning and data formatting

•

Trained model can be applied to other biomedical experiments under the same conditions

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Comprehensive Analysis of the Significance of Ferroptosis-Related Genes in the Prognosis and Immunotherapy of Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a life-threatening disease, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier and receiver operating characteristic (ROC) methods were used to test the effectiveness of the signature, followed by the expression analysis of human leukocyte antigen (HLA) and immune checkpoints. The Cox regression analysis identified 4 hubs from 103 FRGs expressed in OSCC that were associated with overall survival (OS). A risk model based on the 4 FRGs was established to classify patients into high-risk and low-risk groups. Compared with the low-risk group, the survival time of the high-risk group was significantly reduced. According to the multivariate Cox regression analysis, the risk score acted as an independent predictor for OS. The accuracy of the 4 FRGs risk predictive model was confirmed by ROC curve analysis. Moreover, the low-risk group had the characteristics of higher expression of HLA and immune checkpoints, a lower tumor purity, and a higher immune infiltration, indicating a more sensitive response to immunotherapy. The novel FRGs-OSCC risk score system can be used to predict the prognosis of OSCC patients and their response to immunotherapy.

SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure

A challenge in bulk gene differential expression analysis is to differentiate changes due to cell type-specific gene expression and cell type proportions. SCADIE is an iterative algorithm that simultaneously estimates cell type-specific gene expression profiles and cell type proportions, and performs cell type-specific differential expression analysis at the group level. Through its unique penalty and objective function, SCADIE more accurately identifies cell type-specific differentially expressed genes than existing methods, including those that may be missed from single cell RNA-Seq data. SCADIE has robust performance with respect to the choice of deconvolution methods and the sources and quality of input data.

Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing

Background: Understanding the proportion of cell types in heterogeneous tissue samples is important in bioinformatics. It is a challenge to infer the proportion of tissues using bulk RNA sequencing data in bioinformatics because most traditional algorithms for predicting tissue cell ratios heavily rely on standardized specific cell-type gene expression profiles, and do not consider tissue heterogeneity. The prediction accuracy of algorithms is limited, and robustness is lacking. This means that new approaches are needed urgently. Methods: In this study, we introduced an algorithm that automatically predicts tissue cell ratios named Autoptcr. The algorithm uses the data simulated by single-cell RNA sequencing (ScRNA-Seq) for model training, using convolutional neural networks (CNNs) to extract intrinsic relationships between genes and predict the cell proportions of tissues. Results: We trained the algorithm using simulated bulk samples and made predictions using real bulk PBMC data. Comparing Autoptcr with existing advanced algorithms, the Pearson correlation coefficient between the actual value of Autoptcr and the predicted value was the highest, reaching 0.903. Tested on a bulk sample, the correlation coefficient of Lin was 41% higher than that of CSx. The algorithm can infer tissue cell proportions directly from tissue gene expression data. Conclusions: The Autoptcr algorithm uses simulated ScRNA-Seq data for training to solve the problem of specific cell-type gene expression profiles. It also has high prediction accuracy and strong noise resistance for the tissue cell ratio. This work is expected to provide new research ideas for the prediction of tissue cell proportions.

Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution

MOTIVATION

Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods.

RESULTS

To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data.

AVAILABILITY AND IMPLEMENTATION

EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Characterization of the Different Subtypes of Immune Cell Infiltration to Aid Immunotherapy

Background?PD-1 ablation or PD-L1 specific monoclonal antibody against PD-1 can recruit the accumulation of functional T cells, leading to tumor rejection in the microenvironment and significantly improving the prognosis of various cancers. Despite these unprecedented clinical successes, intervention remission rates remain low after treatment, rarely exceeding 40%. The observation of PD-1/L1 blocking in patients is undoubtedly multifactorial, but the infiltrating degree of CD8+T cell may be an important factor for immunotherapeutic resistance.

Methods:We proposed two computational algorithms to reveal the immune cell infiltration (ICI) landscape of 1646 lung adenocarcinoma patients. Three immune cell infiltration patterns were defined and the relative ICI scoring depended on principal-component analysis.

Results:A high ICI score was associated with the increased tumor mutation burden and cell proliferation-related signaling pathways. Different cellular signaling pathways were observed in low ICI score subtypes, indicating active cell proliferation, and may be associated with poor prognosis.

Conclusion:Our research identified that the ICI scores worked as an effective immunotherapy index, which may provide promising therapeutic strategies on immune therapeutics for lung adenocarcinoma.

Comprehensive evaluation of deconvolution methods for human brain gene expression

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.

DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks

Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNNXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (denoted as DAISM-DNN) pipeline that trains a deep neural network (DNN) with dataset-specific training data populated from a certain amount of calibrated samples using DAISM, a novel data augmentation method with an in silico mixing strategy. The evaluation results demonstrate that the DAISM-DNN pipeline outperforms other existing methods consistently and substantially for all the cell types under evaluation in real-world datasets.

RNA-seq analysis of gene expression profiles in posttraumatic stress disorder, Parkinson's disease and schizophrenia identifies roles for common and distinct biological pathways

Evidence suggests that shared pathophysiological mechanisms in neuropsychiatric disorders (NPDs) may contribute to risk and resilience. We used single-gene and network-level transcriptomic approaches to investigate shared and disorder-specific processes underlying posttraumatic stress disorder (PTSD), Parkinson's disease (PD) and schizophrenia in a South African sample. RNA-seq was performed on blood obtained from cases and controls from each cohort. Gene expression and weighted gene correlation network analyses (WGCNA) were performed using DESeq2 and CEMiTool, respectively. Significant differences in gene expression were limited to the PTSD cohort. However, WGCNA implicated, amongst others, ribosomal expression, inflammation and ubiquitination as key players in the NPDs under investigation. Differential expression in ribosomal-related pathways was observed in the PTSD and PD cohorts, and focal adhesion and extracellular matrix pathways were implicated in PD and schizophrenia. We propose that, despite different phenotypic presentations, core transdiagnostic mechanisms may play important roles in the molecular aetiology of NPDs.

Beyond Experiments

It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.