Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

Integrated transcriptomics, proteomics, and functional analysis to characterize the tissue-specific small extracellular vesicle network of breast cancer

Small extracellular vesicles (sEVs) are essential mediators of intercellular communication within the tumor microenvironment (TME). Although the biological features of sEVs have been characterized based on in vitro culture models, recent evidence indicates significant differences between sEVs derived from tissue and those derived from in vitro models in terms of both content and biological function. However, comprehensive comparisons and functional analyses are still limited. Here, we collected sEVs from breast cancer tissues (T-sEVs), paired normal tissues (N-sEVs), corresponding plasma (B-sEVs), and tumor organoids (O-sEVs) to characterize their transcriptomic and proteomic profiles. We identified the actual cancer-specific sEV signatures characterized by enriched cell adhesion and immunomodulatory molecules. Furthermore, we revealed the significant contribution of cancer-associated fibroblasts in the sEV network within the TME. In vitro model-derived sEVs did not entirely inherit the extracellular matrix- and immunity regulation-related features of T-sEVs. Also, we demonstrated the greater immunostimulatory ability of T-sEVs on macrophages and CD8+ T cells compared to O-sEVs. Moreover, certain sEV biomarkers derived from noncancer cells in the circulation exhibited promising diagnostic potential. This study provides valuable insights into the functional characteristics of tumor tissue-derived sEVs, highlighting their potential as diagnostic markers and therapeutic agents for breast cancer.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Construction of EMT related prognostic signature for kidney renal clear cell carcinoma, through integrating bulk and single-cell gene expression profiles

Introduction: Kidney renal clear cell carcinoma (KIRC), as a main type of malignant kidney cancers, has a poor prognosis. Epithelial-mesenchymal transformation (EMT) exerts indispensable role in tumor progression and metastasis, including in KIRC. This study aimed to mine more EMT related details and build prognostic signature for KIRC. Methods: The KIRC scRNA-seq data and bulk data were downloaded from GEO and TCGA databases, respectively. The cell composition in KIRC was calculated using CIBERSORT. Univariate Cox regression analysis and LASSO Cox regression analysis were combined to determine the prognostic genes. Gene set variation analysis and cell-cell communication analysis were conducted to obtain more functional information. Additionally, functional analyses were conducted to determine the biological roles of si-LGALS1 in vitro. Results: We totally identified 2,249 significant differentially expressed genes (DEGs) in KIRC samples, meanwhile a significant distinct expression pattern was found in KIRC, involving Epithelial Mesenchymal Transition pathway. Among all cell types, significantly higher proportion of epithelial cells were observed in KIRC, and 289 DEGs were identified in epithelial cells. After cross analysis of all DEGs and 970 EMT related genes, SPARC, TMSB10, LGALS1, and VEGFA were optimal to build prognostic model. Our EMT related showed good predictive performance in KIRC. Remarkably, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process. Conclusion: A novel powerful EMT related prognostic signature was built for KIRC patients, based on SPARC, TMSB10, LGALS1, and VEGFA. Of which, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process.

Conserved transcriptional programming across sex and species after peripheral nerve injury predicts treatments for neuropathic pain

BACKGROUND AND PURPOSE

Chronic pain is a devastating problem affecting one in five individuals around the globe, with neuropathic pain the most debilitating and poorly treated type of chronic pain. Advances in transcriptomics have contributed to cataloguing diverse cellular pathways and transcriptomic alterations in response to peripheral nerve injury but have focused on phenomenology and classifying transcriptomic responses.

EXPERIMENTAL APPROACH

To identifying new types of pain-relieving agents, we compared transcriptional reprogramming changes in the dorsal spinal cord after peripheral nerve injury cross-sex and cross-species, and imputed commonalities, as well as differences in cellular pathways and gene regulation.

KEY RESULTS

We identified 93 transcripts in the dorsal horn that were increased by peripheral nerve injury in male and female mice and rats. Following gene ontology and transcription factor analyses, we constructed a pain interactome for the proteins encoded by the differentially expressed genes, discovering new, conserved signalling nodes. We investigated the interactome with the Drug-Gene database to predict FDA-approved medications that may modulate key nodes within the network. The top hit from the analysis was fostamatinib, the molecular target of which is the non-receptor spleen associated tyrosine kinase (Syk), which our analysis had identified as a key node in the interactome. We found that intrathecally administrating the active metabolite of fostamatinib, R406 and another Syk inhibitor P505-15, significantly reversed pain hypersensitivity in both sexes.

CONCLUSIONS AND IMPLICATIONS

Thus, we have identified and shown the efficacy of an agent that could not have been previously predicted to have analgesic properties.

Transcriptomic effects of the foraging gene shed light on pathways of pleiotropy and plasticity

Genes are often pleiotropic and plastic in their expression, features which increase and diversify the functionality of the genome. The foraging (for) gene in Drosophila melanogaster is highly pleiotropic and a long-standing model for studying individual differences in behavior and plasticity from ethological, evolutionary, and genetic perspectives. Its pleiotropy is known to be linked to its complex molecular structure; however, the downstream pathways and interactors remain mostly elusive. To uncover these pathways and interactors and gain a better understanding of how pleiotropy and plasticity are achieved at the molecular level, we explore the effects of different for alleles on gene expression at baseline and in response to 4 h of food deprivation, using RNA sequencing analysis in different Drosophila larval tissues. The results show tissue-specific transcriptomic dynamics influenced by for allelic variation and food deprivation, as well as genotype by treatment interactions. Differentially expressed genes yielded pathways linked to previously described for phenotypes and several potentially novel phenotypes. Together, these findings provide putative genes and pathways through which for might regulate its varied phenotypes in a pleiotropic, plastic, and gene-structure-dependent manner.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female's response to sex peptide

Sex peptide (SP), a seminal fluid protein of Drosophila melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses including increased egg laying, activity, and food intake and a decreased remating rate. While it is known that SP achieves this, at least in part, by altering neuronal signaling in females, the genetic architecture and temporal dynamics of the female's response to SP remain elusive. We used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 h after mating to learn about the genetic architecture, at the gene and exon levels, of the female's response to SP. We find that SP is not essential to trigger early aspects of a virgin-to-mated transcriptional switch, which includes changes in a metabolic gene regulatory network. However, SP is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that SP alters rhythmic gene expression in females and suggests that SP's disruption of the female's circadian rhythm might be key to its widespread effects.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user's gene lists, aiding in hypothesis generation and data-driven literature review.

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes

The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk RNA sequencing data to identify cell types from single-cell transcriptomes. An anchor graph is constructed to depict the relationship between reference samples and cells. By applying a connectivity constraint to the learned graph, LFSC enables the preservation of the underlying cluster structure. Moreover, the overall complexity of LFSC is linear to the size of the data, which greatly improves effectiveness and efficiency. By applying LFSC to real single-cell RNA sequencing datasets, we discovered that it has superior performance over existing baseline methods in clustering accuracy and robustness. An application using infiltrating T cells in liver cancer demonstrates that LFSC can successfully find new cell types, discover differently expressed genes, and explore new cancer-associated biomarkers.

Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland

BACKGROUND

The pituitary gland regulates essential physiological processes such as growth, pubertal onset, stress response, metabolism, reproduction, and lactation. While sex biases in these functions and hormone production have been described, the underlying identity, temporal deployment, and cell-type specificity of sex-biased pituitary gene regulatory networks are not fully understood.

METHODS

To capture sex differences in pituitary gene regulation dynamics during postnatal development, we performed 3' untranslated region sequencing and small RNA sequencing to ascertain gene and microRNA expression, respectively, across five postnatal ages (postnatal days 12, 22, 27, 32, 37) that span the pubertal transition in female and male C57BL/6J mouse pituitaries (n = 5-6 biological replicates for each sex at each age).

RESULTS

We observed over 900 instances of sex-biased gene expression and 17 sex-biased microRNAs, with the majority of sex differences occurring with puberty. Using miRNA-gene target interaction databases, we identified 18 sex-biased genes that were putative targets of 5 sex-biased microRNAs. In addition, by combining our bulk RNA-seq with publicly available male and female mouse pituitary single-nuclei RNA-seq data, we obtained evidence that cell-type proportion sex differences exist prior to puberty and persist post-puberty for three major hormone-producing cell types: somatotropes, lactotropes, and gonadotropes. Finally, we identified sex-biased genes in these three pituitary cell types after accounting for cell-type proportion differences between sexes.

CONCLUSION

Our study reveals the identity and postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary. This work also highlights the importance of considering sex biases in cell-type composition when understanding sex differences in the processes regulated by the pituitary gland.

A single-cell atlas of the normal and malformed human brain vasculature

Cerebrovascular diseases are a leading cause of death and neurologic disability. Further understanding of disease mechanisms and therapeutic strategies requires a deeper knowledge of cerebrovascular cells in humans. We profiled transcriptomes of 181,388 cells to define a cell atlas of the adult human cerebrovasculature, including endothelial cell molecular signatures with arteriovenous segmentation and expanded perivascular cell diversity. By leveraging this reference, we investigated cellular and molecular perturbations in brain arteriovenous malformations, which are a leading cause of stroke in young people, and identified pathologic endothelial transformations with abnormal vascular patterning and the ontology of vascularly derived inflammation. We illustrate the interplay between vascular and immune cells that contributes to brain hemorrhage and catalog opportunities for targeting angiogenic and inflammatory programs in vascular malformations.

The genetic source tracking of human urinary exosomes

The genetic origins of nanoscale extracellular vesicles in our body fluids remains unclear. Here, we perform a tracking analysis of urinary exosomes via RNA sequencing, revealing that urine exosomes mostly express tissue-specific genes for the bladder and have close cell-genetic relationships to the endothelial cell, basal cell, monocyte, and dendritic cell. Tracking the differentially expressed genes of cancers and corresponding enrichment analysis show urine exosomes are intensively involved in immune activities, indicating that they may be harnessed as reliable biomarkers of noninvasive liquid biopsy in cancer genomic diagnostics and precision medicine.

Integrative transcriptomic profiling of a mouse model of hypertension-accelerated diabetic kidney disease

The current understanding of molecular mechanisms driving diabetic kidney disease (DKD) is limited, partly due to the complex structure of the kidney. To identify genes and signalling pathways involved in the progression of DKD, we compared kidney cortical versus glomerular transcriptome profiles in uninephrectomized (UNx) db/db mouse models of early-stage (UNx only) and advanced [UNxplus adeno-associated virus-mediated renin-1 overexpression (UNx-Renin)] DKD using RNAseq. Compared to normoglycemic db/m mice, db/db UNx and db/db UNx-Renin mice showed marked changes in their kidney cortical and glomerular gene expression profiles. UNx-Renin mice displayed more marked perturbations in gene components associated with the activation of the immune system and enhanced extracellular matrix remodelling, supporting histological hallmarks of progressive DKD in this model. Single-nucleus RNAseq enabled the linking of transcriptome profiles to specific kidney cell types. In conclusion, integration of RNAseq at the cortical, glomerular and single-nucleus level provides an enhanced resolution of molecular signalling pathways associated with disease progression in preclinical models of DKD, and may thus be advantageous for identifying novel therapeutic targets in DKD.

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.