A single-dose MCMV-based vaccine elicits long-lasting immune protection in mice against distinct SARS-CoV-2 variants

Current vaccines against COVID-19 elicit immune responses that are overall strong but wane rapidly. As a consequence, the necessary booster shots have contributed to vaccine fatigue. Hence, vaccines that would provide lasting protection against COVID-19 are needed, but are still unavailable. Cytomegaloviruses (CMVs) elicit lasting and uniquely strong immune responses. Used as vaccine vectors, they may be attractive tools that obviate the need for boosters. Therefore, we tested the murine CMV (MCMV) as a vaccine vector against COVID-19 in relevant preclinical models of immunization and challenge. We have previously developed a recombinant MCMV vaccine vector expressing the spike protein of the ancestral SARS-CoV-2 (MCMVS). In this study, we show that the MCMVS elicits a robust and lasting protection in young and aged mice. Notably, spike-specific humoral and cellular immunity was not only maintained but also even increased over a period of at least 6 months. During that time, antibody avidity continuously increased and expanded in breadth, resulting in neutralization of genetically distant variants, like Omicron BA.1. A single dose of MCMVS conferred rapid virus clearance upon challenge. Moreover, MCMVS vaccination controlled two variants of concern (VOCs), the Beta (B.1.135) and the Omicron (BA.1) variants. Thus, CMV vectors provide unique advantages over other vaccine technologies, eliciting broadly reactive and long-lasting immune responses against COVID-19.

Sex-Differential Gene Expression in Developing Human Cortex and Its Intersection With Autism Risk Pathways

Background

Sex-differential biology may contribute to the consistently male-biased prevalence of autism spectrum disorder (ASD). Gene expression differences between males and females in the brain can indicate possible molecular and cellular mechanisms involved, although transcriptomic sex differences during human prenatal cortical development have been incompletely characterized, primarily due to small sample sizes.

Methods

We performed a meta-analysis of sex-differential expression and co-expression network analysis in 2 independent bulk RNA sequencing datasets generated from cortex of 273 prenatal donors without known neuropsychiatric disorders. To assess the intersection between neurotypical sex differences and neuropsychiatric disorder biology, we tested for enrichment of ASD-associated risk genes and expression changes, neuropsychiatric disorder risk genes, and cell type markers within identified sex-differentially expressed genes (sex-DEGs) and sex-differential co-expression modules.

Results

We identified 101 significant sex-DEGs, including Y-chromosome genes, genes impacted by X-chromosome inactivation, and autosomal genes. Known ASD risk genes, implicated by either common or rare variants, did not preferentially overlap with sex-DEGs. We identified 1 male-specific co-expression module enriched for immune signaling that is unique to 1 input dataset.

Conclusions

Sex-differential gene expression is limited in prenatal human cortex tissue, although meta-analysis of large datasets allows for the identification of sex-DEGs, including autosomal genes that encode proteins involved in neural development. Lack of sex-DEG overlap with ASD risk genes in the prenatal cortex suggests that sex-differential modulation of ASD symptoms may occur in other brain regions, at other developmental stages, or in specific cell types, or may involve mechanisms that act downstream from mutation-carrying genes.

Comparative Gene Signature of Nociceptors Innervating Mouse Molar Teeth, Cranial Meninges, and Cornea

BACKGROUND

The trigeminal ganglion (TG) collects afferent sensory information from various tissues. Recent large-scale RNA sequencing of neurons of the TG and dorsal root ganglion has revealed a variety of functionally distinct neuronal subpopulations, but organ-specific information is lacking.

METHODS

To link transcriptomic and tissue-specific information, we labeled small-diameter neurons of 3 specific subpopulations of the TG by local application of lipophilic carbocyanine dyes to their innervation site in the dental pulp, cornea, and meninges (dura mater). We then collected mRNA-sequencing data from fluorescent neurons. Differentially expressed genes (DEGs) were analyzed and subjected to downstream gene set enrichment analysis (GSEA), and ion channel profiling was performed.

RESULTS

A total of 10,903 genes were mapped to the mouse genome (>500 reads). DEG analysis revealed 18 and 81 genes with differential expression (log 2 fold change > 2, Padj < .05) in primary afferent neurons innervating the dental pulp (dental primary afferent neurons [DPAN]) compared to those innervating the meninges (meningeal primary afferent neurons [MPAN]) and the cornea (corneal primary afferent neurons [CPAN]). We found 250 and 292 genes differentially expressed in MPAN as compared to DPAN and to CPAN, and 21 and 12 in CPAN as compared to DPAN and MPAN. Scn2b had the highest log 2 fold change when comparing DPAN versus MPAN and Mmp12 was the most prominent DEG when comparing DPAN versus CPAN and, CPAN versus MPAN. GSEA revealed genes of the immune and mitochondrial oxidative phosphorylation system for the DPAN versus MPAN comparison, cilium- and ribosome-related genes for the CPAN versus DPAN comparison, and respirasome, immune cell- and ribosome-related gene sets for the CPAN versus MPAN comparison. DEG analysis for ion channels revealed no significant differences between the neurons set except for the sodium voltage-gated channel beta subunit 2, Scn2b . However, in each tissue a few ion channels turned up with robust number of reads. In DPAN, these were Cacna1b , Trpv2 , Cnga4 , Hcn1 , and Hcn3 , in CPAN Trpa1 , Trpv1 , Cacna1a , and Kcnk13 and in MPAN Trpv2 and Scn11a .

CONCLUSIONS

Our study uncovers previously unknown differences in gene expression between sensory neuron subpopulations from the dental pulp, cornea, and dura mater and provides the basis for functional studies, including the investigation of ion channel function and their suitability as targets for tissue-specific analgesia.

Divergent landscapes of A-to-I editing in postmortem and living human brain

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Divergent landscapes of A-to-I editing in postmortem and living human brain

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR1 and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries illuminate the nuanced functions and intricate regulatory mechanisms of RNA editing within the human brain.

Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response

Some individuals show a discrepancy between cognition and the amount of neuropathological changes characteristic for Alzheimer's disease (AD). This phenomenon has been referred to as 'resilience'. The molecular and cellular underpinnings of resilience remain poorly understood. To obtain an unbiased understanding of the molecular changes underlying resilience, we investigated global changes in gene expression in the superior frontal gyrus of a cohort of cognitively and pathologically well-defined AD patients, resilient individuals and age-matched controls (n = 11-12 per group). 897 genes were significantly altered between AD and control, 1121 between resilient and control and 6 between resilient and AD. Gene set enrichment analysis (GSEA) revealed that the expression of metallothionein (MT) and of genes related to mitochondrial processes was higher in the resilient donors. Weighted gene co-expression network analysis (WGCNA) identified gene modules related to the unfolded protein response, mitochondrial processes and synaptic signaling to be differentially associated with resilience or dementia. As changes in MT, mitochondria, heat shock proteins and the unfolded protein response (UPR) were the most pronounced changes in the GSEA and/or WGCNA, immunohistochemistry was used to further validate these processes. MT was significantly increased in astrocytes in resilient individuals. A higher proportion of the mitochondrial gene MT-CO1 was detected outside the cell body versus inside the cell body in the resilient compared to the control group and there were higher levels of heat shock protein 70 (HSP70) and X-box-binding protein 1 spliced (XBP1s), two proteins related to heat shock proteins and the UPR, in the AD donors. Finally, we show evidence for putative sex-specific alterations in resilience, including gene expression differences related to autophagy in females compared to males. Taken together, these results show possible mechanisms involving MTs, mitochondrial processes and the UPR by which individuals might maintain cognition despite the presence of AD pathology.

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

Background

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets.

Results

A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements.

Conclusions

Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.

Challenges and perspectives in computational deconvolution of genomics data

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

Profiling of microglia nodules in multiple sclerosis reveals propensity for lesion formation

Microglia nodules (HLA-DR+ cell clusters) are associated with brain pathology. In this post-mortem study, we investigated whether they represent the first stage of multiple sclerosis (MS) lesion formation. We show that microglia nodules are associated with more severe MS pathology. Compared to microglia nodules in stroke, those in MS show enhanced expression of genes previously found upregulated in MS lesions. Furthermore, genes associated with lipid metabolism, presence of T and B cells, production of immunoglobulins and cytokines, activation of the complement cascade, and metabolic stress are upregulated in microglia nodules in MS. Compared to stroke, they more frequently phagocytose oxidized phospholipids and possess a more tubular mitochondrial network. Strikingly, in MS, some microglia nodules encapsulate partially demyelinated axons. Taken together, we propose that activation of microglia nodules in MS by cytokines and immunoglobulins, together with phagocytosis of oxidized phospholipids, may lead to a microglia phenotype prone to MS lesion formation.

Single-Cell Transcriptional Response of the Placenta to the Ablation of Caveolin-1: Insights into the Adaptive Regulation of Brain-Placental Axis in Mice

Caveolin-1 (Cav1) is a major plasma membrane protein that plays important functions in cellular metabolism, proliferation, and senescence. Mice lacking Cav1 show abnormal gene expression in the fetal brain. Though evidence for placental influence on brain development is emerging, whether the ablation of Cav1 affects the regulation of the brain-placental axis remains unexamined. The current study tests the hypothesis that gene expression changes in specific cells of the placenta and the fetal brain are linked to the deregulation of the brain-placental axis in Cav1-null mice. By performing single-nuclei RNA sequencing (snRNA-seq) analyses, we show that the abundance of the extravillious trophoblast (EVT) and stromal cells, but not the cytotrophoblast (CTB) or syncytiotrophoblast (STB), are significantly impacted due to Cav1 ablation in mice. Interestingly, specific genes related to brain development and neurogenesis were significantly differentially expressed in trophoblast cells due to Cav1 deletion. Comparison of single-cell gene expression between the placenta and the fetal brain further showed that specific genes such as plexin A1 (Plxna1), phosphatase and actin regulator 1 (Phactr1) and amyloid precursor-like protein 2 (Aplp2) were differentially expressed between the EVT and STB cells of the placenta, and also, between the radial glia and ependymal cells of the fetal brain. Bulk RNA-seq analysis of the whole placenta and the fetal brain further identified genes differentially expressed in a similar manner between the placenta and the fetal brain due to the absence of Cav1. The deconvolution of reference cell types from the bulk RNA-seq data further showed that the loss of Cav1 impacted the abundance of EVT cells relative to the stromal cells in the placenta, and that of the glia cells relative to the neuronal cells in the fetal brain. Together, the results of this study suggest that the ablation of Cav1 causes deregulated gene expression in specific cell types of the placenta and the fetal brain in mice.

Transcriptomics and Spatial Proteomics for Discovery and Validation of Missing Proteins in the Human Ovary

Efforts to understand the complexities of human biology encompass multidimensional aspects, with proteins emerging as crucial components. However, studying the human ovary introduces unique challenges due to its complex dynamics and changes over a lifetime, varied cellular composition, and limited sample access. Here, four new RNA-seq samples of ovarian cortex spanning ages of 7 to 32 were sequenced and added to the existing data in the Human Protein Atlas (HPA) database www.proteinatlas.org, opening the doors to unique possibilities for exploration of oocyte-specific proteins. Based on transcriptomics analysis of the four new tissue samples representing both prepubertal girls and women of fertile age, we selected 20 protein candidates that lacked previous evidence at the protein level, so-called "missing proteins" (MPs). The proteins were validated using high-resolution antibody-based profiling and single-cell transcriptomics. Fourteen proteins exhibited consistent single-cell expression patterns in oocytes and granulosa cells, confirming their presence in the ovary and suggesting that these proteins play important roles in ovarian function, thus proposing that these 14 proteins should no longer be classified as MPs. This research significantly advances the understanding of MPs, unearthing fresh avenues for prospective exploration. By integrating innovative methodologies and leveraging the wealth of data in the HPA database, these insights contribute to refining our understanding of protein roles within the human ovary and opening the doors for further investigations into missing proteins and human reproduction.

scMD facilitates cell type deconvolution using single-cell DNA methylation references

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

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

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

Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome

The human brain is a complex organ comprised of distinct cell types, and the contribution of the 3D genome to lineage specific gene expression remains poorly understood. To decipher cell type specific genome architecture, and characterize fine scale changes in the chromatin interactome across neural development, we compared the 3D genome of the human fetal cortical plate to that of neurons and glia isolated from the adult prefrontal cortex. We found that neurons have weaker genome compartmentalization compared to glia, but stronger TADs, which emerge during fetal development. Furthermore, relative to glia, the neuronal genome shifts more strongly towards repressive compartments. Neurons have differential TAD boundaries that are proximal to active promoters involved in neurodevelopmental processes. CRISPRi on CNTNAP2 in hIPSC-derived neurons reveals that transcriptional inactivation correlates with loss of insulation at the differential boundary. Finally, re-wiring of chromatin loops during neural development is associated with transcriptional and functional changes. Importantly, differential loops in the fetal cortex are associated with autism GWAS loci, suggesting a neuropsychiatric disease mechanism affecting the chromatin interactome. Furthermore, neural development involves gaining enhancer-promoter loops that upregulate genes that control synaptic activity. Altogether, our study provides multi-scale insights on the 3D genome in the human brain.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues

Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures

Focal cortical dysplasia (FCD) is a brain malformation that causes medically refractory epilepsy. FCD is classified into three categories based on structural and cellular abnormalities, with FCD type II being the most common and characterized by disrupted organization of the cortex and abnormal neuronal development. In this study, we employed cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq from multiple transcriptomic studies, aiming to characterize the cellular composition of brain lesions in patients with FCD IIa and IIb subtypes. Our deconvolution analyses revealed specific cellular changes in FCD IIb, including neuronal loss and an increase in reactive astrocytes (astrogliosis) when compared to FCD IIa. Astrogliosis in FCD IIb was further supported by a gene signature analysis and histologically confirmed by glial fibrillary acidic protein (GFAP) immunostaining. Overall, our findings demonstrate that FCD II subtypes exhibit differential neuronal and glial compositions, with astrogliosis emerging as a hallmark of FCD IIb. These observations, validated in independent patient cohorts and confirmed using immunohistochemistry, offer novel insights into the involvement of glial cells in FCD type II pathophysiology and may contribute to the development of targeted therapies for this condition.

Fine-grained cell-type specific association studies with human bulk brain data using a large single-nucleus RNA sequencing based reference panel

Brain disorders are leading causes of disability worldwide. Gene expression studies provide promising opportunities to better understand their etiology but it is critical that expression is studied on a cell-type level. Cell-type specific association studies can be performed with bulk expression data using statistical methods that capitalize on cell-type proportions estimated with the help of a reference panel. To create a fine-grained reference panel for the human prefrontal cortex, we performed an integrated analysis of the seven largest single nucleus RNA-seq studies. Our panel included 17 cell-types that were robustly detected across all studies, subregions of the prefrontal cortex, and sex and age groups. To estimate the cell-type proportions, we used an empirical Bayes estimator that substantially outperformed three estimators recommended previously after a comprehensive evaluation of methods to estimate cell-type proportions from brain transcriptome data. This is important as being able to precisely estimate the cell-type proportions may avoid unreliable results in downstream analyses particularly for the multiple cell-types that had low abundances. Transcriptome-wide association studies performed with permuted bulk expression data showed that it is possible to perform transcriptome-wide association studies for even the rarest cell-types without an increased risk of false positives.

scMD: cell type deconvolution using single-cell DNA methylation references

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

A study of gene expression in the living human brain

A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").

Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients

BACKGROUND

Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression.

RESULTS

A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes.

CONCLUSIONS

HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.

Multicellular communities are perturbed in the aging human brain and Alzheimer's disease

The role of different cell types and their interactions in Alzheimer's disease (AD) is a complex and open question. Here, we pursued this question by assembling a high-resolution cellular map of the aging frontal cortex using single-nucleus RNA sequencing of 24 individuals with a range of clinicopathologic characteristics. We used this map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA sequencing, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including a somatostatin inhibitory neuronal subtype and oligodendroglial states. We further identified a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and we found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.

Spatial Transcriptional Mapping Reveals Site-Specific Pathways Underlying Human Atherosclerotic Plaque Rupture

BACKGROUND

Atherosclerotic plaque ruptures, triggered by blood flow-associated biomechanical forces, cause most myocardial infarctions and strokes.

OBJECTIVES

This study aims to investigate the exact location and underlying mechanisms of atherosclerotic plaque ruptures, identifying therapeutic targets against cardiovascular events.

METHODS

Histology, electron microscopy, bulk and spatial RNA sequencing on human carotid plaques were studied in proximal, most stenotic, and distal regions along the longitudinal blood flow direction. Genome-wide association studies were used to examine heritability enrichment and causal relationships of atherosclerosis and stroke. Associations between top differentially expressed genes (DEGs) and preoperative and postoperative cardiovascular events were examined in a validation cohort.

RESULTS

In human carotid atherosclerotic plaques, ruptures predominantly occurred in the proximal and most stenotic regions but not in the distal region. Histologic and electron microscopic examination showed that proximal and most stenotic regions exhibited features of plaque vulnerability and thrombosis. RNA sequencing identified DEGs distinguishing the proximal and most stenotic regions from the distal region which were deemed as most relevant to atherosclerosis-associated diseases as shown by heritability enrichment analyses. The identified pathways associated with the proximal rupture-prone regions were validated by spatial transcriptomics, firstly in human atherosclerosis. Of the 3 top DEGs, matrix metallopeptidase 9 emerged particularly because Mendelian randomization suggested that its high circulating levels were causally associated with atherosclerosis risk.

CONCLUSIONS

Our findings show plaque site-specific transcriptional signatures associated with proximal rupture-prone regions of carotid atherosclerotic plaques. This led to the geographical mapping of novel therapeutic targets, such as matrix metallopeptidase 9, against plaque rupture.

Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data

BACKGROUND

Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed.

METHODS

We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls.

RESULTS

We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition.

CONCLUSIONS

Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease

Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.

Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses

Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.

A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences

The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

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.

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.

Increased NOS coupling by the metabolite tetrahydrobiopterin (BH4) reduces preeclampsia/IUGR consequences

Preeclampsia (PE) is a high-prevalence pregnancy disease characterized by placental insufficiency, gestational hypertension, and proteinuria. Overexpression of the A isoform of the STOX1 transcription factor (STOX1A) recapitulates PE in mice, and STOX1A overexpressing trophoblasts recapitulate PE patients hallmarks in terms of gene expression and pathophysiology. STOX1 overexpression induces nitroso-redox imbalance and mitochondrial hyper-activation. Here, by a thorough analysis on cell models, we show that STOX1 overexpression in trophoblasts alters inducible nitric oxide synthase (iNOS), nitric oxide (NO) content, the nitroso-redox balance, the antioxidant defense, and mitochondrial function. This is accompanied by specific alterations of the Krebs cycle leading to reduced l-malate content. By increasing NOS coupling using the metabolite tetrahydrobiopterin (BH4) we restore this multi-step pathway in vitro. Moving in vivo on two different rodent models (STOX1 mice and RUPP rats, alike early onset and late onset preeclampsia, respectively), we show by transcriptomics that BH4 directly reverts STOX1-deregulated gene expression including glutathione metabolism, oxidative phosphorylation, cholesterol metabolism, inflammation, lipoprotein metabolism and platelet activation, successfully treating placental hypotrophy, gestational hypertension, proteinuria and heart hypertrophy. In the RUPP rats we show that the major fetal issue of preeclampsia, Intra Uterine Growth Restriction (IUGR), is efficiently corrected. Our work posits on solid bases BH4 as a novel potential therapy for preeclampsia.

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.

Blood transcriptomics identifies immune signatures indicative of infectious complications in childhood cancer patients with febrile neutropenia

Objectives

Febrile neutropenia (FN) is a major cause of treatment disruption and unplanned hospitalization in childhood cancer patients. This study investigated the transcriptome of peripheral blood mononuclear cells (PBMCs) in children with cancer and FN to identify potential predictors of serious infection.

Methods

Whole-genome transcriptional profiling was conducted on PBMCs collected during episodes of FN in children with cancer at presentation to the hospital (Day 1; n = 73) and within 8-24 h (Day 2; n = 28) after admission. Differentially expressed genes as well as gene pathways that correlated with clinical outcomes were defined for different infectious outcomes.

Results

Global differences in gene expression associated with specific immune responses in children with FN and documented infection, compared to episodes without documented infection, were identified at admission. These differences resolved over the subsequent 8-24 h. Distinct gene signatures specific for bacteraemia were identified both at admission and on Day 2. Differences in gene signatures between episodes with bacteraemia and episodes with bacterial infection, viral infection and clinically defined infection were also observed. Only subtle differences in gene expression profiles between non-bloodstream bacterial and viral infections were identified.

Conclusion

Blood transcriptome immune profiling analysis during FN episodes may inform monitoring and aid in defining adequate treatment for different infectious aetiologies in children with cancer.

Comparative Toxicotranscriptomics of Single Cell RNA-Seq and Conventional RNA-Seq in TCDD-Exposed Testicular Tissue

In this report, we compare the outcomes and limitations of two methods of transcriptomic inquiry on adult zebrafish testes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) during sexual differentiation: conventional or bulk RNA-seq (bulk-seq) and single cell RNA sequencing (scRNA-seq) data. scRNA-seq has emerged as a valuable tool for uncovering cell type-specific transcriptome dynamics which exist in heterogeneous tissue. Our lab previously showed the toxicological value of the scRNA-seq pipeline to characterize the sequelae of TCDD exposure in testes, demonstrating that loss of spermatids and spermatozoa, but not other cell types, contributed to the pathology of infertility in adult male zebrafish exposed during sexual differentiation. To investigate the potential for technical artifacts in scRNA-seq such as cell dissociation effects and reduced transcriptome coverage, we compared bulk-sequenced and scRNA-seq-paired samples from control and TCDD-exposed samples to understand what is gained and lost in scRNA-seq vs bulk-seq, both transcriptomically and toxicologically. We hypothesized that the testes may be sensitive to tissue disruption as they contain multiple cell types under constant division and/or maturation, and that TCDD exposure may mediate the extent of sensitivity. Thus, we sought to understand the extent to which this dissociation impacts the toxicological value of data returned from scRNA-seq. We confirm that the required dissociation of individual cells from intact tissue has a significant impact on gene expression, affecting gene pathways with the potential to confound toxicogenomics studies on exposures if findings are not well-controlled and well-situated in context. Additionally, a common scRNA-seq method using cDNA amplified from the 3’ end of mRNA under-detects low-expressing transcripts including transcription factors. We confirm this, and show TCDD-related genes may be overlooked by scRNA-seq, however, this under-detection effect is not mediated by TCDD exposure. Even so, scRNA-seq generally extracted toxicologically relevant information better than the bulk-seq method in the present study. This report aims to inform future experimental design for transcriptomic investigation in the growing field of toxicogenomics by demonstrating the differential information extracted from sequencing cells—despite being from the same tissue and exposure scheme—is influenced by the specific protocol used, with implications for the interpretation of exposure-induced risk.

Molecular profiling reveals features of clinical immunity and immunosuppression in asymptomatic P. falciparum malaria

Clinical immunity to P. falciparum malaria is non-sterilizing, with adults often experiencing asymptomatic infection. Historically, asymptomatic malaria has been viewed as beneficial and required to help maintain clinical immunity. Emerging views suggest that these infections are detrimental and constitute a parasite reservoir that perpetuates transmission. To define the impact of asymptomatic malaria, we pursued a systems approach integrating antibody responses, mass cytometry, and transcriptional profiling of individuals experiencing symptomatic and asymptomatic P. falciparum infection. Defined populations of classical and atypical memory B cells and a T_H2 cell bias were associated with reduced risk of clinical malaria. Despite these protective responses, asymptomatic malaria featured an immunosuppressive transcriptional signature with upregulation of pathways involved in the inhibition of T-cell function, and CTLA-4 as a predicted regulator in these processes. As proof of concept, we demonstrated a role for CTLA-4 in the development of asymptomatic parasitemia in infection models. The results suggest that asymptomatic malaria is not innocuous and might not support the induction of immune processes to fully control parasitemia or efficiently respond to malaria vaccines.

Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.

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.

TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A

Variants of UNC13A, a critical gene for synapse function, increase the risk of amyotrophic lateral sclerosis and frontotemporal dementia1-3, two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-434,5. Here we show that TDP-43 depletion induces robust inclusion of a cryptic exon in UNC13A, resulting in nonsense-mediated decay and loss of UNC13A protein. Two common intronic UNC13A polymorphisms strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia risk overlap with TDP-43 binding sites. These polymorphisms potentiate cryptic exon inclusion, both in cultured cells and in brains and spinal cords from patients with these conditions. Our findings, which demonstrate a genetic link between loss of nuclear TDP-43 function and disease, reveal the mechanism by which UNC13A variants exacerbate the effects of decreased TDP-43 function. They further provide a promising therapeutic target for TDP-43 proteinopathies.

Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions

Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared postmortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex, and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia

BACKGROUND

While schizophrenia differs between males and females in the age of onset, symptomatology, and disease course, the molecular mechanisms underlying these differences remain uncharacterized.

METHODS

To address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA sequencing data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium.

RESULTS

Analysis across the cohorts identified a reproducible gene expression signature of schizophrenia that was highly concordant with previous work. Differential expression across sex was reproducible across cohorts and identified X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature was also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality from thousands of genes to dozens of modules and elucidate interactions among genes. We found enrichment of coexpression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involved a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation.

CONCLUSIONS

Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.