Using RNA sequencing to unravel molecular changes underlying the defense response in chickpea induced by Phytophthora medicaginis.

Phytophthora root rot (PRR), caused by Phytophthora medicaginis, is a major soil-borne disease of chickpea in Australia. Breeding for PRR resistance is an effective approach to avoid significant yield loss. Genetic resistance has been identified in cultivated chickpea (Cicer arietinum) and in the wild relative C. echinospermum, with previous studies identifying independent genetic loci associated with each of these sources. However, the molecular mechanisms associated with PRR resistance are not known. RNA sequencing analysis employed in this study identified changes in gene expression in roots of three chickpea genotypes grown hydroponically, early post-infection with P. medicaginis zoospores. Analyses of differentially expressed genes (DEG) identified the activation of a higher number of non-specific R-genes in a PRR-susceptible variety than in the resistant genotypes, suggesting a whole plant resistance response occurring in chickpea against the pathogen. Contrasting molecular changes in signaling profiles, proteolysis and transcription factor pathways were observed in the cultivated and wild Cicer-derived resistant genotypes. DEG patterns supported a hypothesis that increased root elongation and reduced adventitious root formation limit the pathogen entry points in the genotype containing the wild Cicer source of PRR resistance. Candidate resistance genes, including an aquaporin and a maltose transporter in the wild Cicer source and GDSL esterases/lipases in the cultivated source of resistance, were oppositely regulated. Increased knowledge of these genes and pathways will improve our understanding of molecular mechanisms controlling PRR resistance in chickpea, and support the development of elite chickpea varieties through molecular breeding approaches.

Identification and experimental validation of cuproptosis regulatory program in a sepsis immune microenvironment through a combination of single-cell and bulk RNA sequencing.

Background: In spite of its high mortality rate and poor prognosis, the pathogenesis of sepsis is still incompletely understood. This study established a cuproptosis-based risk model to diagnose and predict the risk of sepsis. In addition, the cuproptosis-related genes were identified for targeted therapy. Methods: Single-cell sequencing analyses were used to characterize the cuproptosis activity score (CuAS) and intercellular communications in sepsis. Differential cuproptosis-related genes (CRGs) were identified in conjunction with single-cell and bulk RNA sequencing. LASSO and Cox regression analyses were employed to develop a risk model. Three external cohorts were conducted to assess the model's accuracy. Differences in immune infiltration, immune cell subtypes, pathway enrichment, and the expression of immunomodulators were further evaluated in distinct groups. Finally, various in-vitro experiments, such as flow cytometry, Western blot, and ELISA, were used to explore the role of LST1 in sepsis. Results: ScRNA-seq analysis demonstrated that CuAS was highly enriched in monocytes and was closely related to the poor prognosis of sepsis patients. Patients with higher CuAS exhibited prominent strength and numbers of cell-cell interactions. A total of five CRGs were identified based on the LASSO and Cox regression analyses, and a CRG-based risk model was established. The lower riskScore cohort exhibited enhanced immune cell infiltration, elevated immune scores, and increased expression of immune modulators, indicating the activation of an antibacterial response. Ultimately, in-vitro experiments demonstrated that LST1, a key gene in the risk model, was enhanced in the macrophage in response to LPS, which was closely related to the decrease of macrophage survival rate, the enhancement of apoptosis and oxidative stress injury, and the imbalance of the M1/M2 phenotype. Conclusions: This study constructed a cuproptosis-related risk model to accurately predict the prognosis of sepsis. We further characterized the cuproptosis-related gene LST1 to provide a theoretical framework for sepsis therapy.

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma.

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Single-cell analysis of Crohn's disease: Unveiling heterogeneity and evaluating ustekinumab outcomes.

BACKGROUND: The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue. METHODS: We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways. RESULTS: Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level. CONCLUSIONS: Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.

Identification of unique cell type responses in pancreatic islets to stress.

Diabetes involves the death or dysfunction of pancreatic ß-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that ß-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

RNA sequencing reveals the potential mechanism of exercise preconditioning for cerebral ischemia reperfusion injury in rats.

INTRODUCTION: Cerebral ischemia reperfusion injury (CIRI) often leads to deleterious complications after stroke patients receive reperfusion therapy. Exercise preconditioning (EP) has been reported to facilitate brain function recovery. We aim to explore the specific mechanism of EP in CIRI. METHODS: Sprague-Dawley rats were randomized into Sham, middle cerebral artery occlusion (MCAO), and EP groups (n = 11). The rats in the EP group received adaptive training for 3 days (10 m/min, 20 min/day, with a 0° incline) and formal training for 3 weeks (6 days/week, 25 m/min, 30 min/day, with a 0° incline). Then, rats underwent MCAO surgery to establish CIRI models. After 48 h, neurological deficits and cerebral infarction of the rats were measured. Neuronal death and apoptosis in the cerebral cortices were detected. Furthermore, RNA sequencing was conducted to investigate the specific mechanism of EP on CIRI, and qPCR and Western blotting were further applied to confirm RNA sequencing results. RESULTS: EP improved neurological deficit scores and reduced cerebral infarction in MCAO rats. Additionally, pre-ischemic exercise also alleviated neuronal death and apoptosis of the cerebral cortices in MCAO rats. Importantly, 17 differentially expressed genes (DEGs) were identified through RNA sequencing, and these DEGs were mainly enriched in the HIF-1 pathway, cellular senescence, proteoglycans in cancer, and so on. qPCR and Western blotting further confirmed that EP could suppress TIMP1, SOCS3, ANGPTL4, CDO1, and SERPINE1 expressions in MCAO rats. CONCLUSION: EP can improve CIRI in vivo, the mechanism may relate to TIMP1 expression and HIF-1 pathway, which provided novel targets for CIRI treatment.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy.

BACKGROUND: Understanding the mechanism behind sepsis-associated encephalopathy (SAE) remains a formidable task. This study endeavors to shed light on the complex cellular and molecular alterations that occur in the brains of a mouse model with SAE, ultimately unraveling the underlying mechanisms of this condition. METHODS: We established a murine model using intraperitoneal injection of lipopolysaccharide (LPS) in wild type and Anxa1-/- mice and collected brain tissues for analysis at 0-hour, 12-hour, 24-hour, and 72-hour post-injection. Utilizing advanced techniques such as single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq, we conducted a comprehensive characterization of the cellular responses and molecular patterns within the brain. RESULTS: Our study uncovered notable temporal differences in the response to LPS challenge between Anxa1-/- (annexin A1 knockout) and wild type mice, specifically at the 12-hour and 24-hour time points following injection. We observed a significant increase in the proportion of Astro-2 and Micro-2 cells in these mice. These cells exhibited a colocalization pattern with the vascular subtype Vas-1, forming a distinct region known as V1A2M2, where Astro-2 and Micro-2 cells surrounded Vas-1. Moreover, through further analysis, we discovered significant upregulation of ligands and receptors such as Timp1-Cd63, Timp1-Itgb1, Timp1-Lrp1, as well as Ccl2-Ackr1 and Cxcl2-Ackr1 within this region. In addition, we observed a notable increase in the expression of Cd14-Itgb1, Cd14-Tlr2, and Cd14-C3ar1 in regions enriched with Micro-2 cells. Additionally, Cxcl10-Sdc4 showed broad upregulation in brain regions containing both Micro-2 and Astro-2 cells. Notably, upon LPS challenge, there was an observed increase in Anxa1 expression in the mouse brain. Furthermore, our study revealed a noteworthy increase in mortality rates following Anxa1 knockdown. However, we did not observe substantial differences in the types, numbers, or distribution of other brain cells between Anxa1-/- and wildtype mice over time. Nevertheless, when comparing the 24-hour post LPS injection time point, we observed a significant decrease in the proportion and distribution of Micro-2 and Astro-2 cells in the vicinity of blood vessels in Anxa1-/- mice. Additionally, we noted reduced expression levels of several ligand-receptor pairs including Cd14-Tlr2, Cd14-C3ar1, Cd14-Itgb1, Cxcl10-Sdc4, Ccl2-Ackr1, and Cxcl2-Ackr1. CONCLUSIONS: By combining snRNA-seq and Stereo-seq techniques, our study successfully identified a distinctive cellular colocalization, referred to as a special pathological niche, comprising Astro-2, Micro-2, and Vas-1 cells. Furthermore, we observed an upregulation of ligand-receptor pairs within this niche. These findings suggest a potential association between this cellular arrangement and the underlying mechanisms contributing to SAE or the increased mortality observed in Anxa1 knockdown mice.

Single cell RNA-sequencing in uveal melanoma: advances in heterogeneity, tumor microenvironment and immunotherapy.

Uveal melanoma (UM) is a highly aggressive and fatal tumor in the eye, and due the special biology of UM, immunotherapy showed little effect in UM patients. To improve the efficacy of immunotherapy for UM patients is of great clinical importance. Single-cell RNA sequencing(scRNA-seq) provides a critical perspective for deciphering the complexity of intratumor heterogeneity and tumor microenvironment(TME). Combing the bioinformatics analysis, scRNA-seq could help to find prognosis-related molecular indicators, develop new therapeutic targets especially for immunotherapy, and finally to guide the clinical treatment options.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Single-cell RNA sequencing reveals that NRF2 regulates vascular smooth muscle cell phenotypic switching in abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a life-threatening disease characterized by extensive membrane destruction in the vascular wall that is closely associated with vascular smooth muscle cell (VSMC) phenotypic switching. A thorough understanding of the changes in regulatory factors during VSMC phenotypic switching is essential for managing AAA therapy. In this study, we revealed the impact of NRF2 on the modulation of VSMC phenotype and the development of AAA based on single-cell RNA sequencing analysis. By utilizing a murine model of VSMC-specific knockout of nuclear factor E2-related factor 2 (NRF2), we observed that the absence of NRF2 in VSMCs exacerbated AAA formation in an angiotensin II-induced AAA model. The downregulation of NRF2 promoted VSMC phenotypic switching, leading to an enhanced inflammatory response. Through genome-wide transcriptome analysis and loss- or gain-of-function experiments, we discovered that NRF2 upregulated the expression of VSMC contractile phenotype-specific genes by facilitating microRNA-145 (miR-145) expression. Our data identified NRF2 as a novel regulator involved in maintaining the VSMC contractile phenotype while also influencing AAA formation through an miR-145-dependent regulatory mechanism.

Scanpro is a tool for robust proportion analysis of single-cell resolution data.

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Detecting haplotype-specific transcript variation in long reads with FLAIR2.

BACKGROUND: RNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community's ability to interrogate both forms of RNA variation simultaneously. RESULTS: We employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis. CONCLUSIONS: Ultimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.

SimSpliceEvol2: alternative splicing-aware simulation of biological sequence evolution and transcript phylogenies.

BACKGROUND: SimSpliceEvol is a tool for simulating the evolution of eukaryotic gene sequences that integrates exon-intron structure evolution as well as the evolution of the sets of transcripts produced from genes. It takes a guide gene tree as input and generates a gene sequence with its transcripts for each node of the tree, from the root to the leaves. However, the sets of transcripts simulated at different nodes of the guide gene tree lack evolutionary connections. Consequently, SimSpliceEvol is not suitable for evaluating methods for transcript phylogeny inference or gene phylogeny inference that rely on transcript conservation. RESULTS: Here, we introduce SimSpliceEvol2, which, compared to the first version, incorporates an explicit model of transcript evolution for simulating alternative transcripts along the branches of a guide gene tree, as well as the transcript phylogenies inferred. We offer a comprehensive software with a graphical user interface and an updated version of the web server, ensuring easy and user-friendly access to the tool. CONCLUSION: SimSpliceEvol2 generates synthetic datasets that are useful for evaluating methods and tools for spliced RNA sequence analysis, such as spliced alignment methods, methods for identifying conserved transcripts, and transcript phylogeny reconstruction methods. The web server is accessible at https://simspliceevol.cobius.usherbrooke.ca , where you can also download the standalone software. Comprehensive documentation for the software is available at the same address. For developers interested in the source code, which requires the installation of all prerequisites to run, it is provided at  https://github.com/UdeS-CoBIUS/SimSpliceEvol .

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Summary data-based Mendelian randomization and single-cell RNA sequencing analyses identify immune associations with low-level LGALS9 in sepsis.

Sepsis is one of the major challenges in intensive care units, characterized by the complexity of the host immune status. To gain a deeper understanding of the pathogenesis of sepsis, it is crucial to study the phenotypic changes in immune cells and their underlying molecular mechanisms. We conducted Summary data-based Mendelian randomization analysis by integrating genome-wide association studies data for sepsis with expression quantitative trait locus data, revealing a significant decrease in the expression levels of 17 biomarkers in sepsis patients. Furthermore, based on single-cell RNA sequencing data, we elucidated potential molecular mechanisms at single-cell resolution and identified that LGALS9 inhibition in sepsis patients leads to the activation and differentiation of monocyte and T-cell subtypes. These findings are expected to assist researchers in gaining a more in-depth understanding of the immune dysregulation in sepsis.

A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.

Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.

Single-nucleus RNA sequencing reveals glial cell type-specific responses to ischemic stroke in male rodents.

Neuroglia critically shape the brain´s response to ischemic stroke. However, their phenotypic heterogeneity impedes a holistic understanding of the cellular composition of the early ischemic lesion. Here we present a single cell resolution transcriptomics dataset of the brain´s acute response to infarction. Oligodendrocyte lineage cells and astrocytes range among the most transcriptionally perturbed populations and exhibit infarction- and subtype-specific molecular signatures. Specifically, we find infarction restricted proliferating oligodendrocyte precursor cells (OPCs), mature oligodendrocytes and reactive astrocytes, exhibiting transcriptional commonalities in response to ischemic injury. OPCs and reactive astrocytes are involved in a shared immuno-glial cross talk with stroke-specific myeloid cells. Within the perilesional zone, osteopontin positive myeloid cells accumulate in close proximity to CD44+ proliferating OPCs and reactive astrocytes. In vitro, osteopontin increases the migratory capacity of OPCs. Collectively, our study highlights molecular cross talk events which might govern the cellular composition of acutely infarcted brain tissue.