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

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.

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.

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 functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.

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).

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

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Obtain expression and cell proportions from calibrated samples

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DAISM-DNN model training including parameter tuning and data formatting

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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-DNN^XMBD (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.

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We propose a data augmentation method (DAISM) for DNN-based cell type deconvolution

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DAISM-DNN enables accurate cell type deconvolution with dataset-specific training data

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DAISM-DNN is robust to random errors in calibration samples

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Trained DAISM-DNN model is reusable across biomedical experiments following same SOP

Computational cell type deconvolution methods were developed to understand the cellular heterogeneity in disease-related tissues from bulk RNA-seq data. Due to the presence of strong batch effects, the performance of existing methods could fluctuate greatly when applied to different datasets even with the latest development in batch normalization or platform-agnostic signature designs. To tackle this issue, we proposed a DNN-based cell abundance estimation method with dataset-specific training data populated from a certain number of calibrated samples from a target dataset using DAISM, a data augmentation method using an in silico mixing strategy. DAISM-DNN enables accurate cell type proportions prediction and is robust to random errors in the ground truth cell type proportions of calibration samples. Importantly, we showed that with strict SOPs, it is possible to create a “train once, reuse many times” DAISM-DNN model for multiple biomedical experiments without the need for retraining.

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.

Effects of IFIH1 rs1990760 variants on systemic inflammation and outcome in critically ill COVID-19 patients in an observational translational study

Background:

Variants in IFIH1, a gene coding the cytoplasmatic RNA sensor MDA5, regulate the response to viral infections. We hypothesized that IFIH1 rs199076 variants would modulate host response and outcome after severe COVID-19.

Methods:

Patients admitted to an intensive care unit (ICU) with confirmed COVID-19 were prospectively studied and rs1990760 variants determined. Peripheral blood gene expression, cell populations, and immune mediators were measured. Peripheral blood mononuclear cells from healthy volunteers were exposed to an MDA5 agonist and dexamethasone ex-vivo, and changes in gene expression assessed. ICU discharge and hospital death were modeled using rs1990760 variants and dexamethasone as factors in this cohort and in-silico clinical trials.

Results:

About 227 patients were studied. Patients with the IFIH1 rs1990760 TT variant showed a lower expression of inflammation-related pathways, an anti-inflammatory cell profile, and lower concentrations of pro-inflammatory mediators. Cells with TT variant exposed to an MDA5 agonist showed an increase in IL6 expression after dexamethasone treatment. All patients with the TT variant not treated with steroids survived their ICU stay (hazard ratio [HR]: 2.49, 95% confidence interval [CI]: 1.29–4.79). Patients with a TT variant treated with dexamethasone showed an increased hospital mortality (HR: 2.19, 95% CI: 1.01–4.87) and serum IL-6. In-silico clinical trials supported these findings.

Conclusions:

COVID-19 patients with the IFIH1 rs1990760 TT variant show an attenuated inflammatory response and better outcomes. Dexamethasone may reverse this anti-inflammatory phenotype.

Funding:

Centro de Investigación Biomédica en Red (CB17/06/00021), Instituto de Salud Carlos III (PI19/00184 and PI20/01360), and Fundació La Marató de TV3 (413/C/2021).

A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Neutrophil-to-lymphocyte ratio as a potential biomarker in predicting influenza susceptibility

BACKGROUND

Human population exposed to influenza viruses exhibited wide variation in susceptibility. The ratio of neutrophils to lymphocytes (NLR) has been examined to be a marker of systemic inflammation. We sought to investigate the relationship between influenza susceptibility and the NLR taken before influenza virus infection.

METHODS

We investigated blood samples from five independent influenza challenge cohorts prior to influenza inoculation at the cellular level by using digital cytometry. We used multi-cohort gene expression analysis to compare the NLR between the symptomatic infected (SI) and asymptomatic uninfected (AU) subjects. We then used a network analysis approach to identify host factors associated with NLR and influenza susceptibility.

RESULTS

The baseline NLR was significantly higher in the SI group in both discovery and validation cohorts. The NLR achieved an AUC of 0.724 on the H3N2 data, and 0.736 on the H1N1 data in predicting influenza susceptibility. We identified four key modules that were not only significantly correlated with the baseline NLR, but also differentially expressed between the SI and AU groups. Genes within these four modules were enriched in pathways involved in B cell-mediated immune responses, cellular metabolism, cell cycle, and signal transduction, respectively.

CONCLUSIONS

This study identified the NLR as a potential biomarker for predicting disease susceptibility to symptomatic influenza. An elevated NLR was detected in susceptible hosts, who may have defects in B cell-mediated immunity or impaired function in cellular metabolism, cell cycle or signal transduction. Our work can serve as a comparative model to provide insights into the COVID-19 susceptibility.

A four immune-related long noncoding RNAs signature as predictors for cervical cancer

The progression, metastasis, and prognosis of cervical cancer (CC) is influenced by the tumor immune microenvironment. Studies proved that long non-coding RNAs (lncRNAs) to engage in cervical cancer development, especially immune-related lncRNAs, have emerged crucial in the tumor immune process. This study was set out to identify an immune-related lncRNA signature. In total, 13,838 lncRNA expression profiles and 328 immune genes were acquired from the clnical data of 306 CC tissues and 3 non-CC tissues. From the 433 identified immune-related lncRNAs, 4 candidate immune-related lncRNAs (SOX21-AS1, AC005332.4, NCK1-DT, LINC01871) were considered independent indicators of cervical cancer prognosis through the univariate and multivariate Cox regression analysis, and they were used to construct a prognostic and survival lncRNA signature model followed by the bootstrap method for further verification. Kaplan-Meier curves illustrated that cervical cancer patients could be divided into high-risk and low-risk groups with significant differences (P = 2.052e - 05), and the discrepancy of immune profiles between these two risk groups was illustrated by principal components analysis. Taken together, the novel survival predictive model created by the four immune-related lncRNAs showed promising clinical prediction value in cervical cancer.

Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients

Background

Ovarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.

Methods

We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.

Results

Patients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).

Conclusion

ICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer

Background

Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.

Results

To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10-33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10-3, n = 348, IMvigor210 cohort) in the tumor patients.

Conclusions

Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

Cardiomyocytes recruit monocytes upon SARS-CoV-2 infection by secreting CCL2

Heart injury has been reported in up to 20% of COVID-19 patients, yet the cause of myocardial histopathology remains unknown. Here, using an established in vivo hamster model, we demonstrate that SARS-CoV-2 can be detected in cardiomyocytes of infected animals. Furthermore, we found damaged cardiomyocytes in hamsters and COVID-19 autopsy samples. To explore the mechanism, we show that both human pluripotent stem cell-derived cardiomyocytes (hPSC-derived CMs) and adult cardiomyocytes (CMs) can be productively infected by SARS-CoV-2, leading to secretion of the monocyte chemoattractant cytokine CCL2 and subsequent monocyte recruitment. Increased CCL2 expression and monocyte infiltration was also observed in the hearts of infected hamsters. Although infected CMs suffer damage, we find that the presence of macrophages significantly reduces SARS-CoV-2-infected CMs. Overall, our study provides direct evidence that SARS-CoV-2 infects CMs in vivo and suggests a mechanism of immune cell infiltration and histopathology in heart tissues of COVID-19 patients.

Macrophage-derived IL-6 trans-signaling as a novel target in the pathogenesis of bronchopulmonary dysplasia

Rationale

Premature infants exposed to oxygen are at risk for bronchopulmonary dysplasia (BPD), which is characterised by lung growth arrest. Inflammation is important, but the mechanisms remain elusive. Here, we investigated inflammatory pathways and therapeutic targets in severe clinical and experimental BPD.

Methods and results

First, transcriptomic analysis with in silico cellular deconvolution identified a lung-intrinsic M1-like-driven cytokine pattern in newborn mice after hyperoxia. These findings were confirmed by gene expression of macrophage-regulating chemokines (Ccl2, Ccl7, Cxcl5) and markers (Il6, Il17A, Mmp12). Secondly, hyperoxia-activated interleukin 6 (IL-6)/signal transducer and activator of transcription 3 (STAT3) signalling was measured in vivo and related to loss of alveolar epithelial type II cells (ATII) as well as increased mesenchymal marker. Il6 null mice exhibited preserved ATII survival, reduced myofibroblasts and improved elastic fibre assembly, thus enabling lung growth and protecting lung function. Pharmacological inhibition of global IL-6 signalling and IL-6 trans-signalling promoted alveolarisation and ATII survival after hyperoxia. Third, hyperoxia triggered M1-like polarisation, possibly via Krüppel-like factor 4; hyperoxia-conditioned medium of macrophages and IL-6-impaired ATII proliferation. Finally, clinical data demonstrated elevated macrophage-related plasma cytokines as potential biomarkers that identify infants receiving oxygen at increased risk of developing BPD. Moreover, macrophage-derived IL6 and active STAT3 were related to loss of epithelial cells in BPD lungs.

Conclusion

We present a novel IL-6-mediated mechanism by which hyperoxia activates macrophages in immature lungs, impairs ATII homeostasis and disrupts elastic fibre formation, thereby inhibiting lung growth. The data provide evidence that IL-6 trans-signalling could offer an innovative pharmacological target to enable lung growth in severe neonatal chronic lung disease.

M1-like macrophage activation is linked to IL-6/STAT3 axis in clinical and experimental BPD. Inhibition of macrophage-related IL-6 trans-signalling promotes ATII survival and lung growth in experimental BPD as a new therapy for preterm infants.https://bit.ly/3AhF7GP

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.

Monocyte metabolic transcriptional programs associate with resistance to tuberculin skin test/interferon-γ release assay conversion

After extensive exposure to Mycobacterium tuberculosis (Mtb), most individuals acquire latent Mtb infection (LTBI) defined by a positive tuberculin skin test (TST) or interferon-γ release assay (IGRA). To identify mechanisms of resistance to Mtb infection, we compared transcriptional profiles from highly exposed contacts who resist TST/IGRA conversion (resisters, RSTRs) and controls with LTBI using RNAseq. Gene sets related to carbon metabolism and free fatty acid (FFA) transcriptional responses enriched across 2 independent cohorts suggesting RSTR and LTBI monocytes have distinct activation states. We compared intracellular Mtb replication in macrophages treated with FFAs and found that palmitic acid (PA), but not oleic acid (OA), enhanced Mtb intracellular growth. This PA activity correlated with its inhibition of proinflammatory cytokines in Mtb-infected cells. Mtb growth restriction in PA-treated macrophages was restored by activation of AMP kinase (AMPK), a central host metabolic regulator known to be inhibited by PA. Finally, we genotyped AMPK variants and found 7 SNPs in PRKAG2, which encodes the AMPK-γ subunit, that strongly associated with RSTR status. Taken together, RSTR and LTBI phenotypes are distinguished by FFA transcriptional programs and by genetic variation in a central metabolic regulator, which suggests immunometabolic pathways regulate TST/IGRA conversion.

Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. The molecular profiles of immune components and related genes are of tremendous value for the study of primary resistance to immune checkpoint blockers (ICBs) for gastric cancer (GC) and serve as prognostic biomarkers to predict GC survival. Recent studies have revealed that tumor immune cell infiltration (ICI) is an indicator of the survival and responsiveness to chemotherapy in GC patients. Here, we describe the immune cell landscape based on the ESTIMATE and CIBERSORT algorithms to help separate GC into 3 ICI clusters using the unsupervised clustering method. Further in-depth analyses, such as differential expression gene (DEG) analysis and principal component analysis (PCA), help to establish an ICI scoring system. A low ICI score is characterized by an increased tumor mutation burden (TMB). The combination of the ICI score and TMB score better predicts the survival of GC patients. Analyses based on public and our own database revealed that the ICI scoring system could also help predict the survival and chemotherapy responsiveness of GC patients. The present study demonstrated that the ICI score may be an effective prognostic biomarker and predictive indicator for chemotherapy and immunotherapy.