Auswahl der wissenschaftlichen Literatur zum Thema „Sc-RNA seq“

Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson’s disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.

AbstractCharting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

ObjectivePeritoneal carcinomatosis (PC; malignant ascites or implants) occurs in approximately 45% of advanced gastric adenocarcinoma (GAC) patients and associated with a poor survival. The molecular events leading to PC are unknown. The yes-associated protein 1 (YAP1) oncogene has emerged in many tumour types, but its clinical significance in PC is unclear. Here, we investigated the role of YAP1 in PC and its potential as a therapeutic target.MethodsPatient-derived PC cells, patient-derived xenograft (PDX) and patient-derived orthotopic (PDO) models were used to study the function of YAP1 in vitro and in vivo. Immunofluorescence and immunohistochemical staining, RNA sequencing (RNA-Seq) and single-cell RNA-Seq (sc-RNA-Seq) were used to elucidate the expression of YAP1 and PC cell heterogeneity. LentiCRISPR/Cas9 knockout of YAP1 and a YAP1 inhibitor were used to dissect its role in PC metastases.ResultsYAP1 was highly upregulated in PC tumour cells, conferred cancer stem cell (CSC) properties and appeared to be a metastatic driver. Dual staining of YAP1/EpCAM and sc-RNA-Seq revealed that PC tumour cells were highly heterogeneous, YAP1high PC cells had CSC-like properties and easily formed PDX/PDO tumours but also formed PC in mice, while genetic knockout YAP1 significantly slowed tumour growth and eliminated PC in PDO model. Additionally, pharmacologic inhibition of YAP1 specifically reduced CSC-like properties and suppressed tumour growth in YAP1high PC cells especially in combination with cytotoxics in vivo PDX model.ConclusionsYAP1 is essential for PC that is attenuated by YAP1 inhibition. Our data provide a strong rationale to target YAP1 in clinic for GAC patients with PC.

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell–cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space.

Abstract The field of single cell RNA sequencing (sc-SEQ) has exploded in the past few years. From picking up single cells manually under a microscope, to droplet-based encapsulation of cells using microfluidics – this technology has improved in leaps and bounds. Common droplet-based technologies include inDrop, Drop-seq and 10X Genomics Chromium. All three technologies utilize microfluidics for encapsulating single cells & uniquely barcoded beads within an oil droplet. They differ in their bead material/manufacturing, barcode design and the range to which their operation can be customized by the end user. However, the performance of each sc-SEQ each technology is dependent on factors such as ability to obtain pure, viable single-cell suspension, and ability to accurately quantify the number of cells before running them through the machine. Here, we compare and contrast different conditions for cell processing that can affect single-cell sequencing results – including cell counting and purifying methods, as well as cell subtype enrichment kits; followed by single cell encapsulation, library preparation and analysis using 10X Genomics Chromium workflow.

Laminin α4 (LAMA4) is one of the main structural adipocyte basement membrane (BM) components that is upregulated during adipogenesis and related to obesity in mice and humans. We conducted RNA-seq-based gene expression analysis of LAMA4 in abdominal subcutaneous (SC) and visceral (VIS) adipose tissue (AT) depots across three human sub-cohorts of the Leipzig Obesity BioBank (LOBB) to explore the relationship between LAMA4 expression and obesity (N = 1479) in the context of weight loss (N = 65) and metabolic health (N = 42). We found significant associations of LAMA4 with body fat mass (p < 0.001) in VIS AT; higher expression in VIS AT compared to SC AT; and significant relation to metabolic health parameters e.g., body fat in VIS AT, waist (p = 0.009) and interleukin 6 (p = 0.002) in male VIS AT, and hemoglobin A1c (p = 0.008) in male SC AT. AT LAMA4 expression was not significantly different between subjects with or without obesity, metabolically healthy versus unhealthy, and obesity before versus after short-term weight loss. Our results support significant associations between obesity related clinical parameters and elevated LAMA4 expression in humans. Our work offers one of the first references for understanding the meaning of LAMA4 expression specifically in relation to obesity based on large-scale RNA-seq data.

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods—logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)–as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.

The nuclear protein CCCTC-binding factor (CTCF) has diverse roles in chromatin architecture and gene regulation. Functionally, CTCF associates with thousands of genomic sites and interacts with proteins, such as cohesin, or non-coding RNAs to facilitate specific transcriptional programming. In this study, we examined CTCF during the cellular stress response in human primary cells using immune-blotting, quantitative real time-PCR, chromatin immunoprecipitation-sequence (ChIP-seq) analysis, mass spectrometry, RNA immunoprecipitation-sequence analysis (RIP-seq), and Airyscan confocal microscopy. Unexpectedly, we found that CTCF is exquisitely sensitive to diverse forms of stress in normal patient-derived human mammary epithelial cells (HMECs). In HMECs, a subset of CTCF protein forms complexes that localize to Serine/arginine-rich splicing factor (SC-35)-containing nuclear speckles. Upon stress, this species of CTCF protein is rapidly downregulated by changes in protein stability, resulting in loss of CTCF from SC-35 nuclear speckles and changes in CTCF-RNA interactions. Our ChIP-seq analysis indicated that CTCF binding to genomic DNA is largely unchanged. Restoration of the stress-sensitive pool of CTCF protein abundance and re-localization to nuclear speckles can be achieved by inhibition of proteasome-mediated degradation. Surprisingly, we observed the same characteristics of the stress response during neuronal differentiation of human pluripotent stem cells (hPSCs). CTCF forms stress-sensitive complexes that localize to SC-35 nuclear speckles during a specific stage of neuronal commitment/development but not in differentiated neurons. We speculate that these particular CTCF complexes serve a role in RNA processing that may be intimately linked with specific genes in the vicinity of nuclear speckles, potentially to maintain cells in a certain differentiation state, that is dynamically regulated by environmental signals. The stress-regulated activity of CTCF is uncoupled in persistently stressed, epigenetically re-programmed “variant” HMECs and certain cancer cell lines. These results reveal new insights into CTCF function in cell differentiation and the stress-response with implications for oxidative damage-induced cancer initiation and neuro-degenerative diseases.

L'interleukine-26 (IL-26) a été identifiée comme un facteur de risque pour les maladies inflammatoires chroniques de l'intestin (MICI). Cependant, les fonctions de l'IL-26 ne sont pas bien comprises en raison de son absence chez les rongeurs. Comme le poisson zèbre possède un orthologue de cette cytokine, nous utilisons ce modèle pour étudier son rôle dans l'homéostasie intestinale.Nous avons généré le premier mutant de cette cytokine in vivo, et nous avons découvert que le microbiote intestinal module l'expression de l'IL-26 dans l'intestin. Nous avons révélé que l'IL-26 module la cycle cellulaire, la réplication de l'ADN et la réparation de l'ADN. Nous avons confirmé que cette cytokine inhibe la prolifération cellulaire dans l'intestin. Nous prévoyons ensuite d'identifier les cellules cibles de l'IL-26 et d'explorer la conservation de ces fonctions chez les mammifères.Afin de comprendre la fonction de l'IL-26 au cours d'une inflammation intestinale, nous avons injecté un extrait bactérien dans l'intestin de larves contrôles et observé que l'IL-26 est transitoirement mais fortement induite. Nous planifions d'étudier les conséquences de cette induction, notamment ses effets sur la prolifération et les dommages à l'ADN des progéniteurs épithéliaux intestinaux et determiner si cette cytokine est exprimée par les cellules lymphoïdes années.Nous avons confirmé la conservation de l'activité bactéricide intrinsèque de l'IL-26 chez le poisson zèbre, et nous explorerons l'impact in vivo de cette activité.En résumé, ce projet pourrait aider à démêler un circuit entre le microbiote, les cellules lymphoïdes innées et les cellules épithéliales intestinales pour préserver l'homéostasie dans l'intestin grâce à l'IL-26. Une meilleure caractérisation du rôle de l'IL-26 dans le maintien de l'homéostasie intestinale est essentielle pour comprendre l'étiologie des MICI et pourrait aider au développement de cibles thérapeutiques afin de soigner ces maladies
Interleukin-26 (IL-26) was identified as a risk factor for inflammatory bowel disease (IBD) in humans and was shown to be overexpressed in IBD lesions. However, the in vivo functions of IL-26 are not fully understood due to its absence in rodents. Since the zebrafish has an orthologue of IL-26, we are utilizing this model to study IL-26 role in gut homeostasis.We generated the first in vivo loss-of-function model to study IL-26, and found that the gut microbiota modulates IL-26 expression in the larval gut. By performing RNA-seq on dissected guts, we revealed that IL-26 modulates pathways related to cell cycle, DNA replication, and DNA repair. We confirmed that IL-26 inhibits cell proliferation in the gut. Next, we plan to identify the cell types targeted by the antiproliferative function of IL-26 and to explore the conservation of this function in mammals.In order to understand the function of IL-26 in gut inflammation, we injected a bacterial extract into the gut of WT larvae and observed that IL-26 is transiently but highly induced post-injection. We further plan to investigate the consequences of this induction, including its effects on proliferation and DNA damage in gut epithelial progenitors, as well as the role of innate lymphoid cells as the cell source of IL-26.In addition, we confirmed the conservation of IL-26 intrinsic bactericidal activity in zebrafish, and will explore the in vivo impact of this activity using IL-26 receptor knockout.In summary, this project exploits the zebrafish to address questions about the functions of IL-26 that are not possible to answer using other animal models. This study could help unravel a circuit between microbiota, Innate lymphoid cells, and intestinal epithelial cells to preserve homeostasis in the gut through IL-26. A better characterization of the role of IL-26 in maintaining gut homeostasis is critical for understanding the aetiology of IBD and may aid in the development of therapeutic targets for this disorder