PANoptosis, an indicator of COVID-19 severity and outcomes

Coronavirus disease 2019 (COVID-19) has been wreaking havoc for 3 years. PANoptosis, a distinct and physiologically relevant inflammatory programmed cell death, perpetuates cytokine storm and multi-organ injuries in COVID-19. Although PANoptosis performs indispensable roles in host defense, further investigation is needed to elucidate the exact processes through which PANoptosis modulates immunological responses and prognosis in COVID-19. This study conducted a bioinformatics analysis of online single-cell RNA sequence (scRNA-seq) and bulk RNA-seq datasets to explore the potential of PANoptosis as an indicator of COVID-19 severity. The degree of PANoptosis in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) indicated the severity of COVID-19. Single-cell transcriptomics identified pro-inflammatory monocytes as one of the primary sites of PANoptosis in COVID-19. The study subsequently demonstrated the immune and metabolic characteristics of this group of pro-inflammatory monocytes. In addition, the analysis illustrated that dexamethasone was likely to alleviate inflammation in COVID-19 by mitigating PANoptosis. Finally, the study showed that the PANoptosis-related genes could predict the intensive care unit admission (ICU) and outcomes of COVID-19 patients who are hospitalized.

Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing

Macrophages are found in a variety of tumors and play a critical role in shaping the tumor microenvironment, affecting tumor progression, metastasis, and drug resistance. However, the clinical relevance of marker genes associated with macrophage in kidney renal clear cell carcinoma (KIRC) has yet to be documented. In this study, we initiated a thorough examination of single-cell RNA sequencing (scRNA-seq) data for KIRC retrieved from the Gene Expression Omnibus (GEO) database and determined 244 macrophage marker genes (MMGs). Univariate analysis, LASSO regression, and multivariate regression analysis were performed to develop a five-gene prognostic signature in The Cancer Genome Atlas (TCGA) database, which could divide KIRC patients into low-risk (L-R) and high-risk (H-R) groups. Then, a nomogram was constructed to predict the survival rate of KIRC patients at 1, 3, and 5 years, which was well assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA). Functional enrichment analysis showed that immune-related pathways (such as immunoglobulin complex, immunoglobulin receptor binding, and cytokine-cytokine receptor interaction) were mainly enriched in the H-R group. Additionally, in comparison to the L-R cohort, patients belonging to the H-R cohort exhibited increased immune cell infiltration, elevated expression of immune checkpoint genes (ICGs), and a higher tumor immune dysfunction and exclusion (TIDE) score. This means that patients in the H-R group may be less sensitive to immunotherapy than those in the L-R group. Finally, IFI30 was validated to increase the ability of KIRC cells to proliferate, invade and migrate in vitro. In summary, our team has for the first time developed and validated a predictive model based on macrophage marker genes to accurately predict overall survival (OS), immune characteristics, and treatment benefit in KIRC patients.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

Background

Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level.

Methods

Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation.

Results

A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities.

Conclusion

This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future.

Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses

Background

Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated.

Methods

We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes.

Results

Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator-the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines.

Conclusion

This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.

Exploring the role of LIAS-related cuproptosis in systemic lupus erythematosus

BACKGROUND

Cuproptosis is a novel mode of cell death, which is strongly related to energy metabolism in mitochondria and regulated by protein lipoylation. Currently, the molecular mechanisms of cuproptosis-related genes (CRGs) involved in systemic lupus erythematosus (SLE) largely remained unclear, our study is aimed to explore the mechanisms of cuproptosis and CRGs involved in SLE.

METHODS

Bulk RNA-seq datasets were collected to display the expressions of CRGs in peripheral blood mononuclear cells (PBMCs) of SLE and healthy individuals, and then ROC analysis was used to establish the diagnostic models of CRGs. Next, the immune infiltration analyses were applied to reveal the difference of immune cells infiltration in LIAS-low and LIAS-high group. Additionally, WGCNA analysis was performed to find the gene modules significantly correlated with the LIAS expression level. We also performed the functional enrichment analyses for LIAS-related gene modules to determine the potential pathways involved in the development of SLE. Finally, scRNA-seq dataset was used to cluster immune cell subsets, reveal the activated pathways, and study cell-cell interactions in LIAS-low and LIAS-high cells.

RESULT

We found CDKN2A was significantly increased and LIAS was significantly decreased in SLE patients compared with healthy individuals. The AUC score showed that LIAS had a great diagnostic value than other CRGs. Additionally, the results of immune infiltration analyses showed that immune cells proportion were diverse in LIAS-low and LIAS-high samples. The gene sets related to LIAS expression level were involved in dephosphorylation of JAK1 by SHP1, phosphorylation of STAT2, cytokine signaling in immune system, expression of interferon-alpha and beta, inhibition of JAK kinase activity by SOCS1/3, and so on. Finally, the results of cell-cell communication showed that CCL- (CCL5 + CCR1) and ANNEXIN- (ANXA1 + FPR1) might play an essential role in the communication network between LIAS-low and LIAS-high cells.

CONCLUSION

Above findings inferred that LIAS-mediated cuproptosis might involve in a comprehensive cellular and molecular mechanism to cause the occurrence and development of SLE.

Integrating Bulk and Single-Cell RNA Sequencing Reveals Heterogeneity, Tumor Microenvironment, and Immunotherapeutic Efficacy Based on Sialylation-Related Genes in Bladder Cancer

Background

As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.

Methods

Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package "Seurat" to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package "ConsensusClusterPlus" in the bulk RNA-seq data. Signature genes were identified by the R package "limma" and univariate regression analysis to calculate risk scores using the R package "GSVA" and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.

Results

5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.

Conclusion

We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.

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.

Gene representation in scRNA-seq is correlated with common motifs at the 3' end of transcripts

One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros. The sparsity has motivated widespread discussions on dropouts and missing data, as well as imputation algorithms of scRNA-seq analysis. Here, we aim to investigate whether there exist genes that are more prone to be under-detected in scRNA-seq, and if yes, what commonalities those genes may share. From public data sources, we gathered paired bulk RNA-seq and scRNA-seq data from 53 human samples, which were generated in diverse biological contexts. We derived pseudo-bulk gene expression by averaging the scRNA-seq data across cells. Comparisons of the paired bulk and pseudo-bulk gene expression profiles revealed that there indeed exists a collection of genes that are frequently under-detected in scRNA-seq compared to bulk RNA-seq. This result was robust to randomization when unpaired bulk and pseudo-bulk gene expression profiles were compared. We performed motif search to the last 350 bp of the identified genes, and observed an enrichment of poly(T) motif. The poly(T) motif toward the tails of those genes may be able to form hairpin structures with the poly(A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during scRNA-seq library preparation, which is a mechanistic conjecture of why certain genes may be more prone to be under-detected in scRNA-seq.

Dysregulation of Iron Metabolism-Linked Genes at Myocardial Tissue and Cell Levels in Dilated Cardiomyopathy

In heart failure, the biological and clinical connection between abnormal iron homeostasis, myocardial function, and prognosis is known; however, the expression profiles of iron-linked genes both at myocardial tissue and single-cell level are not well defined. Through publicly available bulk and single-nucleus RNA sequencing (RNA-seq) datasets of left ventricle samples from adult non-failed (NF) and dilated cardiomyopathy (DCM) subjects, we aim to evaluate the altered iron metabolism in a diseased condition, at the whole cardiac tissue and single-cell level. From the bulk RNA-seq data, we found 223 iron-linked genes expressed at the myocardial tissue level and 44 differentially expressed between DCM and NF subjects. At the single-cell level, at least 18 iron-linked expressed genes were significantly regulated in DCM when compared to NF subjects. Specifically, the iron metabolism in DCM cardiomyocytes is altered at several levels, including: (1) imbalance of Fe³⁺ internalization (SCARA5 down-regulation) and reduction of internal conversion from Fe³⁺ to Fe²⁺ (STEAP3 down-regulation), (2) increase of iron consumption to produce hemoglobin (HBA1/2 up-regulation), (3) higher heme synthesis and externalization (ALAS2 and ABCG2 up-regulation), (4) lower cleavage of heme to Fe²⁺, biliverdin and carbon monoxide (HMOX2 down-regulation), and (5) positive regulation of hepcidin (BMP6 up-regulation).

Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data

Single-cell RNA-sequencing technology (scRNA-seq) brings research to single-cell resolution. However, a major drawback of scRNA-seq is large sparsity, i.e. expressed genes with no reads due to technical noise or limited sequence depth during the scRNA-seq protocol. This phenomenon is also called 'dropout' events, which likely affect downstream analyses such as differential expression analysis, the clustering and visualization of cell subpopulations, cellular trajectory inference, etc. Therefore, there is a need to develop a method to identify and impute these dropout events. We propose Bubble, which first identifies dropout events from all zeros based on expression rate and coefficient of variation of genes within cell subpopulation, and then leverages an autoencoder constrained by bulk RNA-seq data to only impute those values. Unlike other deep learning-based imputation methods, Bubble fuses the matched bulk RNA-seq data as a constraint to reduce the introduction of false positive signals. Using simulated and several real scRNA-seq datasets, we demonstrate that Bubble enhances the recovery of missing values, gene-to-gene and cell-to-cell correlations, and reduces the introduction of false positive signals. Regarding some crucial downstream analyses of scRNA-seq data, Bubble facilitates the identification of differentially expressed genes, improves the performance of clustering and visualization, and aids the construction of cellular trajectory. More importantly, Bubble provides fast and scalable imputation with minimal memory usage.

Effects of Dlx2 overexpression on the genes associated with the maxillary process in the early mouse embryo

The transcription factor Dlx2 plays an important role in craniomaxillofacial development. Overexpression or null mutations of Dlx2 can lead to craniomaxillofacial malformation in mice. However, the transcriptional regulatory effects of Dlx2 during craniomaxillofacial development remain to be elucidated. Using a mouse model that stably overexpresses Dlx2 in neural crest cells, we comprehensively characterized the effects of Dlx2 overexpression on the early development of maxillary processes in mice by conducting bulk RNA-Seq, scRNA-Seq and CUT&Tag analyses. Bulk RNA-Seq results showed that the overexpression of Dlx2 resulted in substantial transcriptome changes in E10.5 maxillary prominences, with genes involved in RNA metabolism and neuronal development most significantly affected. The scRNA-Seq analysis suggests that overexpression of Dlx2 did not change the differentiation trajectory of mesenchymal cells during this development process. Rather, it restricted cell proliferation and caused precocious differentiation, which may contribute to the defects in craniomaxillofacial development. Moreover, the CUT&Tag analysis using DLX2 antibody revealed enrichment of MNT and Runx2 motifs at the putative DLX2 binding sites, suggesting they may play critical roles in mediating the transcriptional regulatory effects of Dlx2. Together, these results provide important insights for understanding the transcriptional regulatory network of Dlx2 during craniofacial development.

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.

Upregulated FKBP1A Suppresses Glioblastoma Cell Growth via Apoptosis Pathway

Glioblastoma (GBM), the most deadly primary brain tumor, presents a major medical difficulty. The need for better therapeutic targets in GBM is therefore urgent. A growing body of evidence suggests that the gene FKBP1A plays an important role in tumor progression and may be therapeutically useful. However, the role of FKBP1A in glioblastoma and the underlying biologic mechanism remain unclear. The purpose of this study was to identify the role of FKBP1A in GBM and its molecular mechanism. We demonstrated that FKBP1A was the hub gene in GBM via a weighted correlation network analysis (WGCNA) and differentially expressed genes (DEGs) analysis based on the bulk RNA-seq data from TCGA and GTEx. Afterwards, we proved that the upregulated FKBP1A protein could promote GBM cell death by CCK-8 assays in U87MG and t98g GBM cell lines. We further demonstrated two key pathways of FKBP1A in GBM by bioinformatics methods: 'Apoptosis' and 'mTOR signaling pathway'. Subsequently, the key pathways were verified by flow cytometry and Western blot. We identified that upregulated FKBP1A could inhibit GBM growth via the apoptosis pathway. Together, these findings may contribute to future GBM treatment.

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.

Identification of MRAP protein family as broad-spectrum GPCR modulators

BACKGROUND

The melanocortin receptor accessory proteins (MRAP1 and MRAP2) are well-known endocrine regulators for the trafficking and signalling of all five melanocortin receptors (MC1R-MC5R). The observation of MRAP2 on regulating several non-melanocortin G protein-coupled receptors (GPCRs) has been sporadically reported, whereas other endogenous GPCR partners of the MRAP protein family are largely unknown.

METHODS

Here, we performed single-cell transcriptome analysis and drew a fine GPCR blueprint and MRAPs-associated network of two major endocrine organs, the hypothalamus and adrenal gland at single-cell resolution. We also integrated multiple bulk RNA-seq profiles and single-cell datasets of human and mouse tissues, and narrowed down a list of 48 GPCRs with strong endogenous co-expression correlation with MRAPs.

RESULTS

36 and 46 metabolic-related GPCRs were consequently identified as novel interacting partners of MRAP1 or MRAP2, respectively. MRAPs exhibited protein-protein interactions and varying pharmacological properties on the surface translocation, constitutive activities and ligand-stimulated downstream signalling of these GPCRs. Knockdown of MRAP2 expression by hypothalamic administration of adeno-associated virus (AAV) packed shRNA stimulated body weight gain in mouse model. Co-injection of corticotropinreleasing factor (CRF), the agonist of corticotropin releasing hormone receptor 1 (CRHR1), suppressed feeding behaviour in a MRAP2-dependent manner.

CONCLUSIONS

Collectively, our study has comprehensively elucidated the complex GPCR networks in two major endocrine organs and redefined the MRAP protein family as broad-spectrum GPCR modulators. MRAP proteins not only serve as a vital endocrine pivot on the regulation of global GPCR activities in vivo that could explain the composite physiological phenotypes of the MRAP2 null murine model but also provide us with new insights of the phenotyping investigation of GPCR-MRAP functional complexes.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.

METHODS

We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.

RESULTS

We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.

CONCLUSION

We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Single-cell RNA-seq analysis and cell-cluster deconvolution of the human preovulatory follicular fluid cells provide insights into the pathophysiology of ovarian hyporesponse

Reduction in responsiveness to gonadotropins or hyporesponsiveness may lead to the failure of in vitro fertilization (IVF), due to a low number of retrieved oocytes. The ovarian sensitivity index (OSI) is used to reflect the ovarian responsiveness to gonadotropin stimulation before IVF. Although introduced to clinical practice already years ago, its usefulness to predict clinical outcomes requires further research. Nevertheless, pathophysiological mechanisms of ovarian hyporesponse, along with advanced maternal age and in younger women, have not been fully elucidated. Follicles consist of multiple cell types responsible for a repertoire of biological processes including responding to pituitary gonadotropins necessary for follicle growth and oocyte maturation as well as ovulation. Encouraging evidence suggests that hyporesponse could be influenced by many contributing factors, therefore, investigating the variability of ovarian follicular cell types and their gene expression in hyporesponders is highly informative for increasing their prognosis for IVF live birth. Due to advancements in single-cell analysis technologies, the role of somatic cell populations in the development of infertility of ovarian etiology can be clarified. Here, somatic cells were collected from the fluid of preovulatory ovarian follicles of patients undergoing IVF, and RNA-seq was performed to study the associations between OSI and gene expression. We identified 12 molecular pathways differentially regulated between hypo- and normoresponder patient groups (FDR<0.05) from which extracellular matrix organization, post-translational protein phosphorylation, and regulation of Insulin-like Growth Factor (IGF) transport and uptake by IGF Binding Proteins were regulated age-independently. We then generated single-cell RNA-seq data from matching follicles revealing 14 distinct cell clusters. Using cell cluster-specific deconvolution from the bulk RNA-seq data of 18 IVF patients we integrated the datasets as a novel approach and discovered that the abundance of three cell clusters significantly varied between hypo- and normoresponder groups suggesting their role in contributing to the deviations from normal ovarian response to gonadotropin stimulation. Our work uncovers new information regarding the differences in the follicular gene expression between hypo- and normoresponders. In addition, the current study fills the gap in understanding the inter-patient variability of cell types in human preovulatory follicles, as revealed by single-cell analysis of follicular fluid cells.

Transcriptome Analysis of Microglia Reveals That the TLR2/IRF7 Signaling Axis Mediates Neuroinflammation After Subarachnoid Hemorrhage

Microglia-mediated neuroinflammatory response in the early brain injury after subarachnoid hemorrhage (SAH) has been reported to have an impact on progress, and the mechanism is not completely understood. Here, we performed genome-wide transcriptome analysis of microglia purified from damaged hemisphere of adult mice at 3 days after SAH or sham operation. Robust transcriptional changes were observed between SAH-induced and healthy microglia, indicating rapid activation of microglia after suffering from SAH. We identified 1576 differentially expressed genes (DEGs; 928 upregulated and 648 downregulated) in SAH-induced microglia compared with sham microglia, representing a strong alteration of the genome (6.85% of total ∼23,000 genes). Functional enrichment of these DEGs indicated that cell division, inflammatory response, cytokine production, and leukocyte chemotaxis were strongly activated in SAH-induced microglia. Moreover, we identified and proved that the TLR2/IRF7 signaling axis was involved in the regulation of this microglia-mediated inflammation in SAH mice by performing flow cytometry and immunofluorescence. Together, these results provided a perspective of microglia-mediated neuroinflammatory response in the early stage of SAH and might give a new therapeutic target for SAH.

Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions

In vitro differentiation or expansion of stem and progenitor cells under chemical stimulation or genetic manipulation is used for understanding the molecular mechanisms of cell differentiation and self-renewal. However, concerns around the cell identity of in vitro-cultured cells exist. Bioinformatics methods, which rely heavily on signatures of cell types, have been developed to estimate cell types in bulk samples. The Tabula Muris Senis project provides an important basis for the comprehensive identification of signatures for different cell types. Here, we identified 46 cell type-specific (CTS) gene clusters for 83 mouse cell types. We conducted Gene Ontology term enrichment analysis on the gene clusters and revealed the specific functions of the relevant cell types. Next, we proposed a simple method, named CTSFinder, to identify different cell types between bulk RNA-Seq samples using the 46 CTS gene clusters. We applied CTSFinder on bulk RNA-Seq data from 17 organs and from developing mouse liver over different stages. We successfully identified the specific cell types between organs and captured the dynamics of different cell types during liver development. We applied CTSFinder with bulk RNA-Seq data from a growth factor-induced neural progenitor cell culture system and identified the dynamics of brain immune cells and nonimmune cells during the long-time cell culture. We also applied CTSFinder with bulk RNA-Seq data from reprogramming induced pluripotent stem cells and identified the stage when those cells were massively induced. Finally, we applied CTSFinder with bulk RNA-Seq data from in vivo and in vitro developing mouse retina and captured the dynamics of different cell types in the two development systems. The CTS gene clusters and CTSFinder method could thus serve as promising toolkits for assessing the cell identity of in vitro culture systems.

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.