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Ma, Shi-Xun, and Su Bin Lim. "Single-Cell RNA Sequencing in Parkinson’s Disease." Biomedicines 9, no. 4 (April 1, 2021): 368. http://dx.doi.org/10.3390/biomedicines9040368.
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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.
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Biancalani, Tommaso, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, et al. "Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram." Nature Methods 18, no. 11 (October 28, 2021): 1352–62. http://dx.doi.org/10.1038/s41592-021-01264-7.
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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.
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Ajani, Jaffer A., Yan Xu, Longfei Huo, Ruiping Wang, Yuan Li, Ying Wang, Melissa Pool Pizzi, et al. "YAP1 mediates gastric adenocarcinoma peritoneal metastases that are attenuated by YAP1 inhibition." Gut 70, no. 1 (April 27, 2020): 55–66. http://dx.doi.org/10.1136/gutjnl-2019-319748.
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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.
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Si, Tong, Zackary Hopkins, John Yanev, Jie Hou, and Haijun Gong. "A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis." PLOS ONE 18, no. 11 (November 10, 2023): e0292792. http://dx.doi.org/10.1371/journal.pone.0292792.
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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.
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Li, Shenghao, Hui Guo, Simai Zhang, Yizhou Li, and Menglong Li. "Attention-based deep clustering method for scRNA-seq cell type identification." PLOS Computational Biology 19, no. 11 (November 10, 2023): e1011641. http://dx.doi.org/10.1371/journal.pcbi.1011641.
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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.
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Lall, Snehalika, Sumanta Ray, and Sanghamitra Bandyopadhyay. "A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data." PLOS Computational Biology 18, no. 3 (March 10, 2022): e1009600. http://dx.doi.org/10.1371/journal.pcbi.1009600.
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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.
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Hanamsagar, Richa, Robert Marcus, Mathew Chamberlain, Emanuele de Rinaldis, and Virginia Savova. "Optimum processing conditions for single cell RNA sequencing on frozen human PBMCs." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 131.15. http://dx.doi.org/10.4049/jimmunol.202.supp.131.15.
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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.
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Hagemann, Tobias, Paul Czechowski, Adhideb Ghosh, Wenfei Sun, Hua Dong, Falko Noé, Christian Wolfrum, Matthias Blüher, and Anne Hoffmann. "Laminin α4 Expression in Human Adipose Tissue Depots and Its Association with Obesity and Obesity Related Traits." Biomedicines 11, no. 10 (October 17, 2023): 2806. http://dx.doi.org/10.3390/biomedicines11102806.
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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.
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Le, Huy, Beverly Peng, Janelle Uy, Daniel Carrillo, Yun Zhang, Brian D. Aevermann, and Richard H. Scheuermann. "Machine learning for cell type classification from single nucleus RNA sequencing data." PLOS ONE 17, no. 9 (September 23, 2022): e0275070. http://dx.doi.org/10.1371/journal.pone.0275070.
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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.
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Lehman, Bettina J., Fernando J. Lopez-Diaz, Thom P. Santisakultarm, Linjing Fang, Maxim N. Shokhirev, Kenneth E. Diffenderfer, Uri Manor, and Beverly M. Emerson. "Dynamic regulation of CTCF stability and sub-nuclear localization in response to stress." PLOS Genetics 17, no. 1 (January 7, 2021): e1009277. http://dx.doi.org/10.1371/journal.pgen.1009277.
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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.
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Ciortan, Madalina, and Matthieu Defrance. "GNN-based embedding for clustering scRNA-seq data." Bioinformatics 38, no. 4 (November 19, 2021): 1037–44. http://dx.doi.org/10.1093/bioinformatics/btab787.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the high sparsity of scRNA-seq data, accentuated by dropout events generates challenges that have motivated the development of numerous dedicated clustering methods. Nevertheless, there is still no consensus on the best performing method. Results graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data. While this work analyzes the performance of clustering the embeddings with various clustering algorithms, other downstream tasks can also be performed. A broad experimental study has been performed on both simulated and scRNA-seq datasets. The results indicate that although there is no consistently best method across all the analyzed datasets, graph-sc compares favorably to competing techniques across all types of datasets. Furthermore, the proposed method is stable across consecutive runs, robust to input down-sampling, generally insensitive to changes in the network architecture or training parameters and more computationally efficient than other competing methods based on neural networks. Modeling the data as a graph provides increased flexibility to define custom features characterizing the genes, the cells and their interactions. Moreover, external data (e.g. gene network) can easily be integrated into the graph and used seamlessly under the same optimization task. Availability and implementation https://github.com/ciortanmadalina/graph-sc. Supplementary information Supplementary data are available at Bioinformatics online.
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Noguchi, Kazuhiro, Yasuhiro Ikawa, Mika Takenaka, Yuta Sakai, Toshihiro Fujiki, and Taizo Wada. "SPI1 Is the Master Regulator of the Small Cell Variant of Anaplastic Large Cell Lymphoma Controlled By Methylation of SPI1 Gene Promoter Region." Blood 142, Supplement 1 (November 28, 2023): 6093. http://dx.doi.org/10.1182/blood-2023-179674.
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Introduction The small cell variant of anaplastic large cell lymphoma (SC-ALCL) is a subtype of anaplastic lymphoma kinase (ALK)-positive ALCL characterized by chemoresistance and poor prognosis, necessitating novel treatment strategies. Recent advances in targeted therapies have resulted in significant responses in various chemoresistant hematological malignancies. Therefore, understanding the underlying oncogenic mechanisms and identifying potential therapeutic targets are critical for overcoming SC-ALCL. Immunohistochemically, SC-ALCL comprises two distinct tumor cell populations: small tumor cells demonstrating negative to weak ALK and CD30 protein expression, and large tumor cells with robust expression of these proteins. We previously reported that small tumor cells display particular resistance to chemotherapy; however, the mechanisms underlying chemoresistance and the molecular characteristics of small tumor cells remain elusive. This study aimed to elucidate the molecular attributes of small tumor cells and pave the way for the development of innovative treatment approaches. Methods We investigated the characteristics of small tumor cells using patient blood samples diagnosed with SC-ALCL leukemic presentation and publicly available RNA-seq data from eight ALK-positive ALCL cell lines. Small and large tumor cells were separated from blood samples based on CD30 expression using CD30 - a phycoerythrin antibody and a magnetic bead separation kit. The presence of the NPM-ALK fusion gene was confirmed in both populations by PCR analysis. Bulk RNA-seq analysis was performed on four samples: small and large tumor cells at the onset and at the first (mainly occupied by small tumor cells) and second (mainly occupied by large tumor cells) relapses. Gene set enrichment analysis was used to identify gene expression patterns in small tumor cells. Signature genes of small tumor cells were identified by filtering with the following thresholds: false discovery rate-adjusted P-value <0.05, and fold-change >2 compared to large tumor cells. The transcription factors regulating the signature genes of small tumor cells were predicted using the g:Profiler software. Result Bulk RNA sequencing analysis of the four blood samples revealed that the SPI1 gene was significantly upregulated in small tumor cells compared to that in large tumor cells. SPI1 has emerged as the highest-scoring transcription factor, regulating the expression of 570 genes that characterize small tumor cells. Approximately 70% of these signature genes overlapped with the SPI1 target genes. Furthermore, gene set enrichment analysis revealed that small tumor cells display increased anti-apoptotic gene activity and decreased cell proliferation gene activity. These observations were validated by integrated gene expression analysis using publicly available RNA-seq data from eight ALK-positive ALCL cell lines (Figure 1). The study found high expression of SPI1 and SPI1 targeted genes in small tumor cells, accompanied by low expression of anti-apoptotic genes in ALK-positive ALCL cell lines, which were more analogous to large tumor cells than small tumor cells. Single-cell RNA-seq analysis validated the high expression of the SPI1 gene in small tumor cells. To explore the mechanism regulating SPI1 gene expression, we examined DNA methylation patterns in the SPI1 promoter region using conventional bisulfite sequencing. Conventional bisulfite sequencing revealed a hypomethylated SPI1 promoter region in small tumor cells and a hypermethylated region in large tumor cells, potentially accounting for the differential SPI1 expression. Conclusion This study highlights SPI1gene as the master regulator determining the unique characteristics of small tumor cells in SC-ALCL. Furthermore, the regulation of SPI1 gene expression appeared to be mediated by DNA methylation patterns within the SPI1 promoter region. These findings pave the way for innovative treatment strategies for SC-ALCL such as SPI1 targeting therapy and methylation-based therapies, offering promising avenues for treating SC-ALCL.
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Liu, Chuan-He, Yan Liu, Xue-Hua Shao, and Duo Lai. "Comparative Analyses of the Transcriptome and Proteome of Comte de Paris and Smooth Cayenne to Improve the Understanding of Ethephon-Induced Floral Transition in Pineapple." Cellular Physiology and Biochemistry 50, no. 6 (2018): 2139–56. http://dx.doi.org/10.1159/000495057.
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Background/Aims: Ethylene is usually used to induce floral transition in pineapple. However, its successful induction in plants categorized as Cayenne is difficult or completely ineffective, and information concerned is limited. The present study was undertaken to investigate the molecular mechanisms underlying this obstacle. Methods: Transcriptome and proteome comparative analyses were performed to explore the important regulation and pathway variations after ethephon induction in the induction-easy ‘Comte de Paris’ (CP) and induction-hard ‘Smooth Cayenne’ (SC) cultivars via RNA-seq (RNA-sequencing) and iTRAQ (isobaric tags for relative and absolute quantification). Results: CP and SC exhibited basic differences at the transcriptomic and proteomic levels before ethephon treatment, including the expression of genes and proteins related to ethylene signal transduction. After ethephon induction, the expression of genes and proteins involved in plant ethylene signal transduction and carbohydrate metabolism responded more strongly in CP than in SC. The expression of the floral meristem identity (FMI) genes AG, TFL and FT exhibited greater changes in CP, and more transcription factors responded in SC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that many differentially expressed genes (DEGs) in CP were annotated to terms and pathways involved in photoperiodism and shared components involved in carbohydrate metabolism and plant hormone signal transduction. Conclusion: These findings contribute to the understanding of the molecular mechanism underlying the variation between CP and SC in response to ethephon-mediated floral induction.
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Deeke, Julie M., and Johann A. Gagnon-Bartsch. "Stably expressed genes in single-cell RNA sequencing." Journal of Bioinformatics and Computational Biology 18, no. 01 (February 2020): 2040004. http://dx.doi.org/10.1142/s0219720020400041.
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Motivation: In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g. synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single-cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery. Results: We compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final products are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well. Supplementary information: Supplementary data are available through GitHub (johanngb/sc-stable).
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Tang, Binqing, Yingen Wu, Hong Fang, Yuqin Wu, and Kehua Shi. "Small RNA Sequencing Reveals Exosomal miRNAs Involved in the Treatment of Asthma by Scorpio and Centipede." BioMed Research International 2020 (January 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/1061407.
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Asthma is a common respiratory disease with inflammation in the lungs. Exosomes and microRNAs (miRNAs) play crucial role in inflammation, whereas the role of exosomal miRNA in asthma remains unknown. Here, we aimed to identify the key exosomal miRNAs and their underlying mechanisms involved in scorpio and centipede (SC) treatment in asthma. Eighteen mice were randomly divided into three groups: control group, asthma group, and SC treatment group. Effect of SC was assessed by hematoxylin-eosin staining and real-time PCR. Exosomes from asthma and SC treatment groups were analyzed by small RNA-seq. Results revealed SC significantly alleviated the pathogenesis of asthma and suppressed the release of inflammatory cytokines. A total of 328 exosomal miRNAs were differentially expressed between the exosomes from asthma and SC-treated mice, including 118 up- and 210 downregulated in SC-treated mice. The altered exosomal miRNAs were primarily involved in the function of transcription, apoptotic process, and cell adhesion; and pathway of calcium, Wnt, and MAPK signaling. Real-time PCR verified exosomal miR-147 was downregulated, while miR-98-5p and miR-10a-5p were upregulated in SC-treated mice compared to asthma mice. Moreover, the target genes of miR-147-3p, miR-98-5p, and miR-10a-5p were mainly enriched in Wnt and MAPK inflammatory signaling. miR-10a-5p promoted the proliferation of mouse lung epithelial cells and downregulated the expression of Nfat5 and Map2k6. These data suggest SC-induced exosomal miRNAs might mediate the inflammatory signaling and might be involved in the SC treatment in asthma. The exosomal miRNAs might be promising candidates for the treatment of asthma.
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Velalopoulou, Anastasia, Ilias V. Karagounis, Giorgos Skoufos, Ioannis I. Verginadis, Michele Kim, Khayrullo Shoniyozov, Artemis G. Hatzigeorgiou, et al. "Abstract 3304: Gene expression profiling of full-thickness skin after FLASH proton radiotherapy." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3304. http://dx.doi.org/10.1158/1538-7445.am2022-3304.
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Abstract Purpose: To investigate the transcriptomic changes induced by FLASH proton radiotherapy (F-PRT) that could be responsible for the protection of normal epithelial tissues by radiation-induced toxicities as have been previously shown by us and others. Methods: C57BL/6J mice received 30 Gy of F-PRT or S-PRT to the hind leg at respective dose rates of 69-124 Gy/sec or 0.39-0.65 Gy/sec. RNA sequencing was performed using full-thickness leg skin at 5 days after radiation revealing major pathways regulated by F-PRT and S-PRT. In an endeavor to identify the full repertoire of cells and gene expression profiles that are involved in the sparing effects of FLASH PRT, we expanded our studies to include single-cell RNA sequencing (sc-RNA seq) and examined additional time points such as Day 2 and Day 10 after radiation. Single-cell transcriptome libraries were generated on a 10X Genomics Chromium system. Datasets were acquired from cell samples derived and sequenced from pooled skin samples of three mice per group. Skin from the sequenced mice was also embedded for spatial analysis of gene expression. Results: RNA sequencing revealed that F-PRT uniquely upregulates almost four times more genes compared to S-PRT (F-PRT-uniquely upregulated 489 genes vs S-PRT-uniquely upregulated 129 genes). Also, F-PRT uniquely downregulated 178 genes, compared to the 125 genes uniquely downregulated by S-PRT. GO analysis demonstrates that the keratinization and apoptosis pathways are uniquely upregulated by S-PRT, whereas F-PRT uniquely upregulates genes involved in vascular development pathway. During submission of the abstract, analysis of sc-RNA seq samples was pending. Conclusion: Our comprehensive studies inform on the transcriptomic profiling of skin cell populations that are affected by F-PRT vs S-PRT; this insight will further spur discoveries on the biology of FLASH radiotherapy effects. Citation Format: Anastasia Velalopoulou, Ilias V. Karagounis, Giorgos Skoufos, Ioannis I. Verginadis, Michele Kim, Khayrullo Shoniyozov, Artemis G. Hatzigeorgiou, Eric Diffenderfer, Lei Dong, James Metz, Constantinos Koumenis, Keith A. Cengel, Amit Maity, Theresa M. Busch. Gene expression profiling of full-thickness skin after FLASH proton radiotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3304.
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Grigoryeva, E., L. Tashireva, V. V. Alifanov, M. Zavyalova, M. Menyailo, E. V. Denisov, N. O. Popova, N. Cherdyntseva, and V. Perelmuter. "485P A novel approach to identify subpopulation of CTCs with metastatic potential using sc-RNA-seq." Annals of Oncology 34 (October 2023): S385—S386. http://dx.doi.org/10.1016/j.annonc.2023.09.661.
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Katims, Andrew B., Fengshen Kuo, Peter Reisz, Andrew Tracey, Jasmine Thomas, Wesley Yip, Taha Merghoub, et al. "Characterizing the immune phenotype of FGFR3 mutated upper tract urothelial carcinoma (UTUC) using single-cell (sc)RNA-sequencing (seq)." Journal of Clinical Oncology 41, no. 6_suppl (February 20, 2023): 558. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.558.
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558 Background: Fibroblast growth factor 3 (FGFR3) is the most common mutation in UTUC and is altered in approximately 75% of tumors. Tumors harboring FGFR3 mutations (FGFR3-M) have a T-cell impaired tumor microenvironment (TME) which may explain the incomplete response to immune checkpoint blockade. We performed scRNA-seq on 8 untreated tumors to further characterize the T-cell immune phenotype of FGFR3-M tumors. Methods: scRNA-seq (10x Genomics platform) was performed on 8 UTUC tissue specimens from 8 different patients who had not received treatment (chemotherapy or immunotherapy) using an established institutional process. We also performed targeted gene sequencing (MSK-IMPACT) on all samples to identify mutational calls. We assessed the phenotype of defined cell clusters and the immune composition of each sample according to known marker gene expression as well as SingleR prediction. We then performed the gene set enrichment analysis over the differentially expressed genes with the Gene Ontology Biologic Process (GO:BP) to identify unique biologic processes and possible functional state of each immune cluster. Results: Among the 8 samples, 4 (50%) had altered FGFR3 (Table). We identified 19 immune cell clusters (8 T-cell clusters) with unique biologic function. Within the CD4 compartment, FGFR3-M was enriched with exhausted/active CD4 cells characterized with Th17 cell differentiation/immune regulatory function (cluster 4) and yet with lower frequency of naive-like CD4 cells possessing alpha-beta T cell activation functions and lower T-cell receptor (TCR) signaling (cluster 2). Regulatory T cells (cluster 5) were less frequently found in FGFR3-M tumors compared to their wild-type counterpart. In the CD8 compartment, FGFR3-M tumors had higher infiltration specifically in cluster 3 which corresponds to a naïve state with lower exhausted/active markers, lower cytotoxic activity, leukocyte apoptotic process, and alpha-beta T cell differentiation regulation. There was also a lower proportion among cluster 9, a mixture of NK and CD8 cytotoxic cells, which is characterized with response to interleukin (IL)-1, tumor necrosis factor (TNF), and NK cell chemotaxis. Additionally, this cluster had high cytotoxic activity and lower exhausted/active markers. Conclusions: FGFR3 mutated patients have a T-cell phenotype with more active/exhausted Th17-like CD4, lower Treg, and more CD8/cytotoxic cells in naïve state with lower response to IL-1 and TNF. scRNA-seq revealed enrichment of different functional states among T-cell compartments which may lead to improved therapeutic decision making in the future. [Table: see text]
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Singh, Komudi, Michelle Baird, Robert Fischer, Vijender Chaitankar, Fayaz Seifuddin, Yun-Ching Chen, Ilker Tunc, Clare M. Waterman, and Mehdi Pirooznia. "Misregulation of ELK1, AP1, and E12 Transcription Factor Networks Is Associated with Melanoma Progression." Cancers 12, no. 2 (February 17, 2020): 458. http://dx.doi.org/10.3390/cancers12020458.
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Melanoma is among the most malignant cutaneous cancers and when metastasized results in dramatically high mortality. Despite advances in high-throughput gene expression profiling in cancer transcriptomic studies, our understanding of mechanisms driving melanoma progression is still limited. We present here an in-depth bioinformatic analysis of the melanoma RNAseq, chromatin immunoprecipitation (ChIP)seq, and single-cell (sc)RNA seq data to understand cancer progression. Specifically, we have performed a consensus network analysis of RNA-seq data from clinically re-grouped melanoma samples to identify gene co-expression networks that are conserved in early (stage 1) and late (stage 4/invasive) stage melanoma. Overlaying the fold-change information on co-expression networks revealed several coordinately up or down-regulated subnetworks that may play a critical role in melanoma progression. Furthermore, by incorporating histone lysine-27 acetylation information and highly expressed genes identified from the single-cell RNA data from melanoma patient samples, we present a comprehensive list of pathways, putative protein-protein interactions (PPIs) and transcription factor (TF) networks that are driving cancer progression. From this analysis, we have identified Elk1, AP1 and E12 TF networks that coordinately change expression in late melanoma when compared to early melanoma, implicating these TFs in melanoma progression. Additionally, the sumoylation-associated interactome is upregulated in invasive melanoma. Together, this bioinformatic analysis potentially implicates a combination of TF networks and PPIs in melanoma progression, which if confirmed in the experimental systems, could be used as targets for drug intervention in melanoma.
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Shimizu, Takuya, Takero Shindo, Akira Watanabe, and Akifumi Takaori-Kondo. "Single-Cell RNA Sequencing Revealed the YY1/EZH2/MLH1 Axis As a Possible Therapeutic Target of Intractable Adult T-Cell Leukemia." Blood 142, Supplement 1 (November 28, 2023): 6084. http://dx.doi.org/10.1182/blood-2023-185712.
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Background: Due to clinical heterogeneity of adult T-cell leukemia/lymphoma (ATL) and its diverse genetic abnormality, common therapeutic targets of ATL remains unclear. Whereas the YY1/EZH2 axis regulates global epigenetic modification of genes in ATL, its downstream target genes have not been fully elucidated. Meanwhile, DNA mismatch repair proteins may be targeted in ATL. Here we explored underlying molecular axes for ATL progression through comprehensive single-cell RNA-sequencing (sc-RNA-seq). Methods: Peripheral blood of 42 ATL patients and asymptomatic HTLV-1 carriers in addition to two cell lines of ATL43T and ED+ were subjected to the following analyses. Molecular markers associated with their clinical phenotypes were extracted through sc-RNA-seq analysis. Expression levels of DNA mismatch repair proteins (MLH1, MSH2) and target molecules were validated by flow cytometry. Finally, their function was verified through lentiviral shRNA-dependent knockdown and molecular-targeting reagents. Results: While the frequencies of CADM1+ cells among CD4+ T cells were generally correlated with HTLV-1 proviral loads (R = 0.76, p < 0.001), substantial CADM1+ cells were detected but did not increase in some long-remitted cases; thus, CADM1+ cells are biologically heterogeneous. sc-RNA-seq of CADM1+ cells of four ATL cases identified a cluster of CD48-deficient cells. in silico promoter analysis of the cluster extracted a transcription factor YY1 as a candidate for common master regulators. EZH2 and the putative driver genes (CELF2, PTPRC, CBLB, ATXN1, IKZF2) were identified as differentially expressed genes (DEGs) in the cluster. The EZH2-overexpressed cluster was increased in acute-type ATL compared with remitted ATL and smoldering-type ATL. Interestingly, the cluster increased moderately in the other smoldering-type ATL, which required UVB irradiation due to intractable skin lesion. Given that YY1 and EZH2 were up-regulated in acute-type ATL, the cluster may represent tumor aggressiveness. Furthermore, biphasic expression of YY1/EZH2 was observed in 2 acute-type ATL, which indicates that the EZH2 high ATL cells confer malignant signature. In-house analysis on microarray dataset GSE55851 specified the MLH1 associated with YY1/EZH2. MLH1 within CADM1+ fraction was down-regulated in ATL patients with remission (n = 7) but maintained in progressive ATL (n = 6) (p < 0.05). Lentiviral shYY1 knockdown resulted in down-regulation of MLH1 in both ATL43T and ED+ (p < 0.01). The EZH1/2 inhibitor valemetostat down-regulated EZH2 (p = 0.018) and MLH1 (p = 0.014) in primary ATL cells. Finally, knockdown of either YY1 or MLH1 suppressed proliferation of ATL43T and ED+ (p < 0.0001). Conclusions: Our findings indicate that down-regulation of MLH1 through YY1/EZH2 inhibition plays a key role in the treatment of aggressive ATL.
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Yoo, Yun Jae, Ki H. Oh, Luke A. Torre-Healy, and Richard A. Moffitt. "Abstract A058: Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity." Cancer Research 82, no. 22_Supplement (November 15, 2022): A058. http://dx.doi.org/10.1158/1538-7445.panca22-a058.
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Abstract Background: Genetically engineered mouse models (GEMMs) are widely used in the study of pancreatic ductal adenocarcinoma (PDAC) because of their immune-competent tumor microenvironment (TME); however, the extent to which particular GEMMs recapitulate the tumor and TME observed in the patient population has not been systematically evaluated. In this study, we integrate single-cell RNA sequencing (sc-RNA-seq) data from multiple studies and multiple GEMM backgrounds to identify differences in the cellular compositions of popular PDAC GEMMs. Methods: A total of 49,191 cells were used from three studies, including normal mouse pancreas (N=2) and five different GEMM backgrounds (N=16). Data curation, integration, and analysis were based on the Seurat pipeline in R. SingelCellNet was used to train a random forest model on manually labeled human sc-RNA-seq data from 20 patients. To enable cross-species use, the classifier was trained using only genes with both human and mouse homologues. Cells classified as neoplastic were further clustered to quantify the number of classical and basal-like cells based on signature gene expression levels. The ratio of these subtypes in each GEMM and the relationship between the modified genes in each model were examined. Results: Ad-hoc clustering and a human-cell-trained single-cell classifier showed 79% agreement in an integrated data set of PDAC GEMMs. Cells identified by both methods as tumor (8,303 cells, 17% of total) were assessed for PDAC tumor subtype via subsequent clustering analysis (basal-like or classical). When comparing the ratio of differently subtyped tumor cells, we identified stark differences between GEMM genetic backgrounds. Among five different models, KIC (KrasLSL−G12D/+Ink4a/Arffl/flPtf1aCre/+), KPPCN (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+Nsdhlfl/fl), and pdx1-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Pdx1Cre/+) exhibited a higher proportion of basal-like PDAC cells compared to KPfC/KPPC (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+) and ptf1a-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Ptf1aCre/+). Interestingly, in the KIC model, which was harvested at early and late time points (40 or 60 days), classical PDAC was overrepresented in early models, and basal-like PDAC was more prevalent in the older tumors. While sample sizes are limited in this study, in Pdx1 driven models, we observed a bias towards basal-like phenotype in GEMMs using the Trp53LSL-R172H/+ method compared to those with Trp53fl/fl. Conclusions: In a comparison of publicly available sc-RNA-seq, we highlight potential biases in the molecular subtypes that arise from specific PDAC GEMMs. Because of the known link between tumor subtype and therapeutic response, these results suggest translational work may benefit from GEMM selection that considers transcriptomic diversity. Citation Format: Yun Jae Yoo, Ki H Oh, Luke A. Torre-Healy, Richard A. Moffitt. Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A058.
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Rehn, Jacqueline, Chelsea Mayoh, Susan L. Heatley, Barbara J. McClure, Laura N. Eadie, Caitlin Schutz, David T. Yeung, Mark J. Cowley, James Breen, and Deborah L. White. "Rascall: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL)." PLOS Genetics 18, no. 10 (October 17, 2022): e1010300. http://dx.doi.org/10.1371/journal.pgen.1010300.
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RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.
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Vukojicic, Nevena, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, and Brandi Davis-Dusenbery. "Abstract 2075: Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC." Cancer Research 83, no. 7_Supplement (April 4, 2023): 2075. http://dx.doi.org/10.1158/1538-7445.am2023-2075.
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Abstract Single-cell (sc) transcriptomics has revolutionized our understanding of the biological characteristics and dynamics of cancer development. It can help us identify rare cell subpopulations and understand mechanisms associated with tumor genesis, progression, and response to therapy. The most important step in the analyses of any scRNA-seq dataset is subpopulation identification, usually performed via unsupervised clustering, followed by gene marker identification. We created a highly customizable workflow for sc data analysis, implemented in Common Workflow Language (CWL) on the Cancer Genomics Cloud (CGC) platform. The NCI-funded CGC platform, powered by Seven Bridges, provides a collaborative cloud base computation infrastructure that collocates computation, over 750 bioinformatics workflows, and 3+ PB data to researchers, making the analysis of large datasets accessible from any environment. The “Multi-Sample Clustering and Gene Marker Identification with Seurat 4.1.0” workflow comprises the following steps: Loading scRNA-seq Expression Datasets, Quality Control and Preprocessing, and Clustering and Identification of Gene Markers. Our solution supports gene-cell count matrices generated by several commonly used quantifiers (for example, Cell Ranger counts, Salmon Alevin, Kallisto BUStools, STAR) from single or multiple sc datasets from different batches, as well as single or multiple single-cell samples combined in a single SingleCellExperiment object. The versatility of the pipeline is obtained using several implemented options in each of the steps. Quality control can be performed manually or automatically using several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm) and for batch effect correction (Seurat and Harmony). For clustering, the pipeline uses Seurat's graph-based approach, with options for different clustering resolutions. After performing identification of gene markers for each cluster, a researcher can test differential expression using various packages including wilcox, bimod, roc, and DESeq2. Here, we demonstrate the application of this workflow to a typical sc analysis, by processing an open access dataset of 61k cells isolated from embryonal mouse pons and forebrain, two major brain tumor locations. We used different clustering resolutions to achieve different degrees of granularity and identified cluster-specific marker genes used to identify vulnerable cell populations. To enable researchers to use this analysis as a guideline, we made this analysis available as a public project. Further development of single-cell sequencing techniques will undoubtedly improve our understanding of tumor biology and highlight promising drug targets. CGC’s cloud base computation infrastructure, along with numerous available cancer datasets and easy-to-use single-cell data processing workflows, among others, will be instrumental in this process. Citation Format: Nevena Vukojicic, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, Brandi Davis-Dusenbery. Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2075.
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Gupta, Pravesh, Minghao Dang, Dapeng Hao Hao, Krishna Bojja, Tuan M. Tran, Huma Shehwana, Carlos Kamiya-Matsuoka, et al. "IMMU-43. IMMUNE CONTEXTURE OF ISOCITRATE DEHYDROGENASE STRATIFIED HUMAN GLIOMAS REVEALED BY SINGLE-CELL TRANSCRIPTOMICS AND ACCESSIBLE CHROMATIN." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi102. http://dx.doi.org/10.1093/neuonc/noab196.402.
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Abstract The immune cell composition of isocitrate dehydrogenase wild type (IDH-wt) glioma patients significantly differs compared to IDH-mutant (IDH-mut) yet a detailed and unbiased understanding of their transcriptomic and epigenetic landscapes remains elusive. To this end, we performed single-cell RNA-sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (sc-ATAC-seq) on ~100,000 tumor-associated immune cells from seventeen IDH mutation classified primary and recurrent human gliomas and non-glioma brains (NGBs). Our analyses revealed sixty-two transcriptionally distinct myeloid and lymphoid cell states within and across glioma subtypes and we noted microglial attrition with increasing disease severity concomitant with invading monocyte-derived cells (MDCs) and lymphocytes. Specifically, certain microglial and monocyte-derived subpopulations were associated with antigen presentation gene modules, akin to cross-presenting dendritic cells. As tissue macrophages exhibit multifaceted polarization in response to microenvironmental cues, we clarify the existence of microglia/macrophage functional states beyond M1/M2 paradigms exemplified by the presence of palmitic-, oleic- acid, and glucocorticoid responsive polarized states. We identified cytotoxic T cells with poly-functional cytolytic states mostly in recurrent IDH-wt gliomas. Furthermore, ligand-receptor interactome analyses showed a preponderance of antigen presentation/phagocytosis over the checkpoint axis in IDH-wt compared to IDH-mut gliomas. Additionally, our sc-ATAC-seq analyses revealed differences in regulatory networks in NGBs, IDH-mut, and IDH-wt glioma-associated immune cells. In particular, we noted reduced microglial usage of an iron recycling SPIC transcription factor and Interferon Regulatory Factors (IRFs); IRF1 and IRF2 in IDH-wt relative to IDH-mut and NGBs. Unique features such as amplification of 11-Zinc Finger Protein accessibility were restricted to MDCs. Finally, sc-ATAC-seq profiles of CD8+ exhausted T cells from IDH-wt showed strong enhancer accessibility on CTLA-4, Layilin, and TIM-3 but no enrichment on PD1 was seen. In summary, our study provides unprecedented granular detail of transcriptionally and epigenomically defined glioma-specific immune contexture that can be exploited for immunotherapy applications.
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Zeng, Andy G. X., Ilaria Iacobucci, Sayyam Shah, Gordon Wong, Amanda Mitchell, Qingsong Gao, Hyerin Kim, et al. "Precise Single-Cell Transcriptomic Mapping of Leukemia Cell States Reveals Unconventional Lineage Priming in Acute Myeloid Leukemia." Blood 142, Supplement 1 (November 28, 2023): 1593. http://dx.doi.org/10.1182/blood-2023-189697.
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Initial disease classification within acute leukemia relies on identifying morphological and immunophenotypic features of hematopoietic differentiation retained by leukemic blasts. While existing approaches can classify leukemic cells into broad lineages, they lack precision in discerning between specific cell states, particularly at the level of immature blasts. Single-cell (sc) RNA-sequencing (RNA-seq) provides thousands of new markers to enable precise determination of leukemia cell state and provides an opportunity to refine our classification of acute leukemia. To extend our ability to identify leukemia cell states by scRNA-seq, we developed a comprehensive reference map of human bone marrow hematopoiesis. Specifically, we integrated three unsorted bone marrow datasets and three CD34+ hematopoietic stem and progenitor (HSPC) purified datasets to ensure balanced representation of early HSPCs and terminally differentiated immune cells. This culminated in a curated atlas of 263,159 high-quality sc transcriptomes, representing 55 cellular states spanning 45 healthy donors (Fig 1A). We validated our cell state annotations to ensure concordance between transcriptional states and functional populations through reference mapping of bulk and sc transcriptomes from purified HSPC subsets. For example, mapping of scRNA-seq profiles from Lin-CD34+38-45RA-90+49f+ long-term (LT) HSCs revealed 91% concordance between transcriptional HSC and functional LT-HSC. We next performed scRNA-seq profiling on 12 AML patient samples harbouring either myelodysplasia-related genetic features or classical alterations involving NPM1 or KMT2A. By mapping leukemia cells onto our reference atlas, we identified pronounced involvement of early erythroid-like blasts in a subset of AML samples. To further investigate unconventional lineage priming in AML, we processed and mapped scRNA-seq profiles from eleven studies comprising 166 additional AML samples and 9 mixed phenotype acute leukemia (MPAL) samples (Fig 1B). After quality control and exclusion of mature lymphoid cells, we performed composition analysis utilizing 600,570 leukemia cells mapped to 38 cell states to identify patterns of variation among leukemia cell states. Immature leukemia cells map to a range of cellular states spanning HSC/MPP-like, LMPP-like, MLP-like, GMP-like, MEP-like, and EarlyEry-like, among others. Notably, we identified a subset of AMLs with high MLP-like enrichment that co-cluster with MPALs as well as a subset with high EarlyEry-like involvement that co-cluster with acute erythroid leukemias (AEL), thus highlighting the biological continuum between these diseases. We next identified marker genes from each leukemia cell state and trained sparse regression models to estimate their relative abundance within bulk RNA-seq cohorts. Through bulk analysis, we confirmed that MPAL is highly enriched for MLP-like cells compared to AML (p=2.5e-12) and that M6 AELs are highly enriched for EarlyEry-like cells compared to M0-M5 AMLs (p=0.00067). Among 864 AML patients, we found that high MLP-like abundance was associated with NUP98-NSD1 fusions (p=0.0011) and RUNX1 mutations (p=8.0e-10) while high EarlyEry-like abundance was associated with Complex Cytogenetics (p=8e-15) and TP53 mutations (p=2.3e-25). Cellular states of immature leukemic cells captured inter-patient heterogeneity within 136 AEL samples, wherein high EarlyEry-like abundance was associated with TP53 mutations (p=3.6e-9) and Poor cytogenetic risk (p=1.1e-9) while high GMP-like abundance was associated with KMT2A alterations (p=0.00046) and Good/Intermediate risk (p=0.00032). Finally, through integration of sc transcriptomics and targeted DNA profiling, we found examples of genetic subclones impacting the cellular hierarchies of individual patients through induction of early differentiation blocks, skewing of lineage output from myeloid to erythroid, or potentiation of transcriptional self-renewal programs within mature myeloid cells. Together, our high-resolution mapping approach highlights the cellular state continuum between acute leukemias and enumerates the impact of genetic drivers on leukemia cell hierarchies. Precise definitions of cellular states, coupled with the presence or absence of genetic alterations, may help to refine future disease classification, prognostication, and therapy development.
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Guo, Shuai, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, et al. "Abstract 4273: Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution." Cancer Research 83, no. 7_Supplement (April 4, 2023): 4273. http://dx.doi.org/10.1158/1538-7445.am2023-4273.
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Abstract The accuracy of current deconvolution methods largely relies on the quality of cell-type expression references. However, single-cell (sc) and single-nuclei (sn) RNA-seq data used for building the reference are usually generated from independent studies that are distinct from the bulk RNA-seq data to be deconvolved. This study design inherently introduces technical confounding factors as unwanted variations, which is not fully addressed by current methods. To evaluate the impact of this variation on deconvolution accuracy, we generated a benchmark dataset where bulk and snRNA-seq profiling were performed from the same aliquot of single-nuclei that were extracted from 24 healthy retina samples. All donor eye samples were collected within six hours post-mortem and were absent of any disease. This study design guarantees the matched sequencing data to present the same cell-type compositions, so that cross-platform technical artifacts become the remaining confounding factor. We used the benchmark dataset to evaluate the performance of seven current deconvolution methods and found they performed much worse in matched real-bulk data than in matched pseudo-bulks that were summations of the single-cell data. This finding suggests that none of these methods have fully addressed the major technical artifacts between bulk and single-cell sequencing platforms. We therefore propose DeMix.SC, a new deconvolution framework that optimizes deconvolution parameters using a small set of matched bulk and sc/snRNA-seq data from the same tissue type. DeMix.SC includes two major steps. First, we measure the technical variations across genes and across platforms using the benchmark data. Second, we introduce a new weight function for each gene that produces a ranking order that accounts for both the platform-specific technical variations and cell-type specific expressions at gene level. Using the benchmark data for retina, we applied DeMix.SC to previously published human retinal RNA-seq data from 523 individuals with different stages of age-related macular degeneration (AMD). We observed that DeMix.SC can accurately capture the cell-type composition shifts in the AMD retina. DeMix.SC revealed a significant drop of rod cells as well as increased astrocytes, bipolar cells, and Müller cells in the AMD retina compared to the non-AMD group. The proportion changes of the later three minor cell types were not identified by other methods, while DeMix.SC could reveal such tendency. In summary, DeMix.SC integrates benchmark data to improve the deconvolution accuracy in retina samples. Our method is generic and can be applied to other disease conditions, such as deciphering the cell-type heterogeneity in cancer. We expect DeMix.SC will help revolutionize the downstream cell-type specific analysis of bulk RNA-seq data and identify cellular targets of human diseases. Citation Format: Shuai Guo, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, Ivana K. Kim, John Weinstein, Scott Kopetz, John Paul Shen, Margaret M. DeAngelis, Rui Chen, Wenyi Wang. Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4273.
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Sehgal, Kartik, Andrew Portell, Elena Ivanova, Patrick Lizotte, Navin Mahadevan, Jonathan Greene, Amir Vadji, et al. "248 Immunotherapy persister cells uncovered by dynamic single-cell RNA-sequencing." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A268—A269. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0248.
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BackgroundTo understand fundamental mechanisms of immune escape, we leveraged our functional ex vivo platform of murine derived organotypic tumor spheroids (DOTS)1 to determine if drug-tolerant persister cells analogous to oncogene targeted therapies limit efficacy of programmed death (PD)-1 blockade, and to identify therapeutic vulnerabilities to overcome anti-PD-1 (αPD-1) resistance.MethodsMurine syngeneic cancer models with well-characterized response to αPD-1 therapy were chosen: MC38 (sensitive) and CT26 (partially resistant). Bulk and single-cell (sc) RNA-sequencing (RNA-seq) were performed on αPD-1 treated DOTS. In vitro culture studies were conducted with or without cytokines (100 ng/ml) or drugs (500 nM). In vivo studies in mice bearing MC38 or CT26 tumors evaluated the combinatorial strategy with PD-1 blockade. We further evaluated our findings in scRNA-seq of an αPD-1 refractory colorectal cancer (CRC) patient tumor.2ResultsBulk RNA-seq of αPD-1 treated DOTS revealed a mesenchymal resistant phenotype with upregulated TNF-α/NFκB signaling (figure 1). scRNA-seq further identified a discrete sub-population of immunotherapy persister cells (IPCs). These cells expressed a stem-like phenotype including downregulation of E2F targets indicative of quiescence, suppression of interferon-γ response genes, induction of hybrid epithelial-to-mesenchymal state, and active IL-6 signaling (figure 1). Ly6a/stem cell antigen-1 (Sca-1) and Snai1 were found to be differentially upregulated in IPCs resistant to PD-1 blockade (not shown). Sca-1 positivity was confirmed in pre-existing tumor populations in vitro (figure 2). When enriched via sorting, these cells remained more persistently Sca-1+ at 96 hours in culture of CT26 compared to MC38 cells, related to increased autocrine IL-6 production by CT26 Sca-1+ cells. Indeed, IL-6 supplementation was capable of expanding Sca-1+ cells in culture (figure 2). Sca-1+ cells expressing ovalbumin peptide were refractory to OT-1 T cell mediated killing and failed to upregulate MHC class-1 antigen presentation (H-2Kb) in response to IL-6, in contrast to interferon-γ (not shown). Analysis of RNA-seq data further identified Birc2/3 as potential targets limiting TNF-mediated apoptosis of these cells (not shown). Notably, Birc2/3 antagonism depleted Sca-1+ IPCs in vitro and significantly potentiated the impact of PD-1 blockade in vivo in MC38, and less robustly in CT26 (figure 3). Evaluation in a microsatellite-instability high CRC patient identified a pre-existent IPC subpopulation within the αPD-1 refractory pre-treatment tumor, with high SNAI1 expression compared to CRC samples in TCGA (figure 4).Abstract 248 Figure 1Bulk and single-cell (sc) RNA-sequencing (RNA-seq) of MDOTS identifies an anti-PD-1 (αPD-1) resistant subpopulation of persister cells. IgG= isotype controlAbstract 248 Figure 2Pre-existent population of stem cell antigen-1 (Sca-1)+ cells expands in response to interleukin-6 (IL-6), as characterized by flow cytometry evaluation in murine syngeneic cancer models at baseline and after purification by fluorescence-activated cell sorting (FACS). H = hoursAbstract 248 Figure 3Combination of anti-PD-1 therapy with Birc2/3 antagonism increases tumor responses and improves survival. CR = complete responseAbstract 248 Figure 4Single-cell RNA-sequencing (scRNA-seq) of a pre-treatment microsatellite-instability (MSI-H) colorectal cancer (CRC) patient tumor, refractory to anti-PD-1 (αPD-1) therapy, reveals presence of SNAI1-high immunotherapy persister cellsConclusionsHigh-resolution functional ex vivo profiling identified Sca-1+/Snai1high stem-like ‘immunotherapy persister cells‘ and uncovered their anti-apoptotic dependencies targetable with Birc2/3 antagonism to augment αPD-1 efficacy.Ethics ApprovalThis study was approved by the Dana-Farber Animal Care and Use Committee and Novartis Institutional Animal Care and Use Committee. Informed written consent to participate in Dana-Farber/Harvard Cancer Center institutional review board (IRB)-approved research protocols was obtained from the human subject. A copy of the written consent is available for review by the Editor of this journal. The study was conducted per the WMA Declaration of Helsinki and IRB-approved protocols.ReferencesJenkins RW, Aref AR, Lizotte PH, Ivanova E, Stinson S, Zhou CW, et al. Ex Vivo Profiling of PD-1 Blockade using organotypic tumor spheroids. Cancer Discov. 2018;8(2):196–668 215.Gurjao C, Liu D, Hofree M, AlDubayan SH, Wakiro I, Su MJ, et al. intrinsic resistance to immune checkpoint blockade in a mismatch repair-deficient colorectal cancer. Cancer Immunol Res 2019;7(8):1230–6.
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Feng, Jiaxin, Tianyang Zhou, Yibiao Gu, Chenchen Shu, Kuanyu Zhu, Weiyang Zhang, Hao Zhang, et al. "γ-Aminobutyric Acid Alleviates Salinity-Induced Impairments in Rice Plants by Improving Photosynthesis and Upregulating Osmoprotectants and Antioxidants." Agronomy 14, no. 11 (October 27, 2024): 2524. http://dx.doi.org/10.3390/agronomy14112524.
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Salt stress is a significant abiotic stress that hinders the growth of rice (Oryza sativa L.) and reduces their yield. Previous research has examined the synthesis of γ-aminobutyric acid (GABA) and its role in plant resistance under various abiotic stresses. However, the synthesis of GABA and its ability to mitigate damage caused by salt stress—particularly its effects on osmotic adjustment, antioxidant defense, photosynthesis, and overall plant growth throughout the entire rice lifecycle—remains unclear. Therefore, we conducted two experiments using salt-tolerant rice cultivar Lianjian 5 (J-5) and salt-susceptible cultivar Lianjing 7 (L-7). In Experiment I, RNA-seq (RNA sequencing) was used to analyze the differential expression of the transcriptome between CK and salinity treatments, revealing the key roles of GABA in salt tolerance. In Experiment II, different levels of exogenous GABA were applied to salt-stressed plants to investigate its physiological role in enhancing salt tolerance. Therefore, RNA-seq (RNA sequencing) was used to analyze the differential expression of the transcriptome between CK and salinity treatments, revealing the key roles of GABA in salt tolerance. Subsequently, different levels of exogenous GABA were applied to salt-stressed plants to investigate its physiological role in enhancing salt tolerance. We measured the activities of superoxide dismutase, peroxidase, and catalase, as well as photosynthetic characteristics such as photosynthesis, transpiration, chlorophyll content, stomatal density and size, and leaf anatomical features. The RNA-seq analysis revealed that GABA production is enhanced via the glutamate decarboxylase (GAD) gene (LOC4333932) in the salt-resistant rice cultivar. Exogenous GABA application improves salt-stress tolerance by increasing endogenous ABA and GABA contents, which enhance osmotic adjustment, boost antioxidant defenses, and regulate ion balance. These combined effects help maintain photosynthetic efficiency and support overall plant growth under salt-stressed environments. Additionally, the increased proportion of mesophyll cell periphery covered by chloroplasts (Sc/Sm) indicated enhanced mesophyll conductance. These helped maintain photosynthesis under saline conditions while reducing water consumption.
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Tao, Ping, Zhenyu Wang, Jiongyuan Wang, Jun Chen, Liang Hong, Lijie Ma, Yong Zhang, and Hanxing Tong. "Integrated multi-omics analysis reveals immune landscape of tertiary lymphoid structure in retroperitoneal liposarcoma." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 11563. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.11563.
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11563 Background: Retroperitoneal liposarcoma (RPLS) is a rare type of mesenchymal tumor characterized by difficult surgical management, immune desert, poor response to immunotherapy and high local recurrence rate. However, how tertiary lymphoid structures (TLS) dictates complex biological processes such as antitumor immunity remains unknown. Thus, we aimed to investigate the spatio-temporal heterogeneity of TLS formation, maturation, and functional involvement in TIME, and the clinical value of TLS in multiple retrospective RPLS clinical cohorts. Methods: 330 patients were retrospectively enrolled into five independent cohorts from the two largest retroperitoneal tumor research centers in China and the TCGA database. Single-cell RNA sequencing (sc-RNA seq) (n=4) and spatial transcriptome seq (n=2) were performed for the estimation of TIME based on treatment-naive RPLS. Transcriptomic profiles of 309 cases in five cohorts were obtained from the ZSFD, GEO, and TCGA databases. TLS was quantified in three different anatomic subregions (intra-tumor, invasion margin and peri-tumor) and correlated with overall survival (OS) and disease-free survival (DFS) by Cox regression and Kaplan-Meier analysis. Multiplex immunohistochemistry (mIHC) was performed to characterize and validate the spatial composition of TLS in another treatment-naive RPLS cohort (n=16), neoadjuvant chemotherapy (n=12) and neoadjuvant radiotherapy (n=20) RPLS cohorts. Results: The joint scoring system of T and P scores stratified RPLS into four immune classes with different TLS distribution patterns and prognoses (p<0.001). The immune class C-index was significantly higher than the TNM staging system (0.798 vs. 0.62, p=0.005). Importantly, mIHC revealed that regulatory T cells (Tregs) and M2 phenotype tumor-associated macrophages (TAMs) were significantly increased in intra-tumoral TLS in DDLPS compared to WDLPS, showing an immunosuppressive pattern. Strikingly, neoadjuvant chemotherapy and radiotherapy could block this status of immunosuppressive, induced TLS formation and restore the antitumor immune balance with significantly more CD38+IgG+ plasma cells (PCs) in responsive RPLS, whereas non-responsive RPLS deteriorated into a more suppressive one. Sc-RNA Seq and ST analysis further revealed significant intra- and inter-tumoral TIME heterogeneity and identified the underlying transcriptomic programs driving each phenotype. Conclusions: Our study provides a high-resolution map of TIME in treatment-naive and neoadjuvant chemotherapy/radiotherapy RPLS. Effective neoadjuvant chemotherapy and radiotherapy can induce TLS formation and restore the antitumor immune balance in RPLS.
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Mitsialis, V., M. Losa, M. Field, L. Collen, J. Barends, A. Ringel, M. Bresnahan, et al. "OP17 IBD ulcers are characterized by bioactive interleukin-1 and transcriptomic hallmarks of stromal cell state reprogramming." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i32—i33. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0017.
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Abstract Background The programs that perpetuate the inflammation and prevent epithelial repair in Inflammatory Bowel Disease (IBD) remain unclear. Interleukin (IL)-1 plays a role in the maintenance of mucosal homeostasis but also in IBD. Expansion of IL-1 expressing myeloid cells is a hallmark of IBD tissue, including severe and anti-TNF unresponsive disease, and IL-1 expression is highly localized to ulcer beds. However, little is known about the presence of bioactive IL-1 proteins in the cell-free mucosal environment of IBD, nor whether IL-1-driven programs affect epithelial regeneration in IBD ulcers. Methods We established a highly sensitive assay (~500-fold greater than ELISA) to assess bioactive extracellular IL-1 from previously cryopreserved IBD biopsies. We assessed a cohort of Crohn’s Disease (CD, n=23), Ulcerative Colitis (UC, n=18) and non-IBD (n=17) patients, with biopsies from paired inflamed/uninflamed regions (primarily colon but also ileal). We assessed IL-1 bioactivity, including IL-1α vs IL-1β contributions, and performed RNA-seq of samples’ matched cellular compartments. An ulcer-associated gene signature was interrogated in a single-cell (sc)RNA-seq cohort (n=42) of very early onset (VEO)IBD including monogenic disorders, as well as publicly available IBD bulk RNA-seq datasets and scRNA-seq of mouse models of colitis. Results IL-1α and IL-1β bioactivity corresponded with disease and ulcer severity in CD and UC. The most extreme signals were seen in select CD patients with deep ulceration. IL-1α was the predominant contributor to total IL-1 bioactivity in most patients although several with ulcers displayed IL-1β predominance. IL-1 bioactivity correlated with IL-1 transcripts in matched RNA-seq, and weighted gene co-expression network analysis revealed a compelling ulcer-specific module. This module contained transcription factors and genes related to cell state, validated in publicly available datasets (e.g. RISK cohort of ileal CD and scRNA-seq of murine DSS colitis), that when interrogated in a scRNA-seq dataset of VEOIBD (including patients with IL-10RA mutations characterized by deep ulcers), suggests an orchestrated IL-1-driven myeloid/stromal response to epithelial ulceration involving cell state reprogramming and loss of key myofibroblast populations. Conclusion Mucosal ulceration in IBD is associated with bioactive IL-1α and IL-1β proteins, and transcriptomic evaluation of the same correlates with bioactive signal. An ulcer-associated gene module, validated in other datasets, sheds light on IL-1 biology in intestinal epithelial repair. Results strongly suggest the IL-1 signaling pathway being an attractive and precision-based therapeutic target in subsets of IBD patients.
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Singh, Harshabad, Kevin S. Kapner, Joanne Xiu, Matthew James Oberley, Alex Patrick Farrell, Jimmy Guo, Rishi Surana, et al. "Clinical genomic implications of transcriptional subtypes in pancreatic cancer." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 4145. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.4145.
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4145 Background: Transcriptional profiling of pancreatic cancers (PC) has defined classical and basal subtypes; basal tumors have worse outcomes. Mesenchymal (MES) and neural-like progenitor (NRP) subtypes are increasingly recognized and enriched post-therapy. Initial data suggests worse outcomes to FOLFIRINOX (FFX) compared with gemcitabine nab-Paclitaxel (GnP) in basal tumors. Several clinical trials are ongoing to investigate this. Here, we examined the clinical implications of transcriptional subtypes in a large, real-world dataset. Methods: Retrospective IRB exempt, deidentified data was examined from NextGen DNA and RNA sequencing performed on PCs at Caris Life Sciences (Phoenix, AZ). Classical and basal cell states were identified using RNA-seq and the PurIST algorithm in a genomic cohort or GATA6 and KRT5 expression levels in a clinical cohort. Tumor microenvironment immune cell composition on RNA seq was performed using QuantiSeq. Survival was obtained from insurance claims data and calculated from first treatment date to last known contact. Kaplan-Meier estimates were calculated for patient cohorts. P values were adjusted using Benjamini-Hochberg correction. Results: A total of 7,250 PCs were profiled in the genomic cohort. 3,063 tumors (42.2%) were strongly classical (SC), 2,015 tumors (27.8%) were strongly basal (SB) and the remaining had mixed phenotypes. MES and NRP marker genes were significantly co-expressed with each other, with basal genes, and anti-correlated with classical genes. When compared to SC, SB had significantly higher mutation rates in KRAS (93% vs. 88%), TP53 (83% vs. 72%) and ARID1A (12% vs. 8%), whereas SMAD4 (23% vs. 17%) mutations were more common in SC (all q < 0.05). There were no differences in mutation rates in homologous recombination or mismatch repair genes. SB had a significantly higher fraction of M1 macrophages (fold change [FC]: 1.14) and neutrophils (FC 1.16), whereas SC tumors had higher M2 macrophages (FC 1.18), NK (FC 1.2), and dendritic cells. Overall proportions of CD4/8 T cells were low and not different. Interestingly, SB had higher levels of PD-L1 by IHC (4.8% vs. 35%) and higher expression of immune exhaustion genes including CTLA4 (FC 1.19), TIM3 (FC 1.22) and PD-1 (FC 1.43) (all q < 0.05). The clinical cohort had 1,623 patients. Basal tumors had an inferior survival (median survival: 8.2 months (mo) vs 13.3 mo (Hazard Ratio (HR) 0.67, p < 0.00001)) and showed a significant improvement in outcomes when treated with upfront FFX vs GnP (n = 80 vs 90, Median: 15.8 vs 7.4 mos., HR 0.68, p = 0.021). This difference between FFX vs GnP was less pronounced in classical tumors (n = 70 vs 89, Median: 17.3 vs 15.4 mos, HR 0.70, p = 0.049). Conclusions: Our work represents the largest known real world molecular comparison of transcriptional subtypes of PC. Differential outcomes for patients with basal tumors treated with FFX versus GnP warrants further investigation in prospective studies.
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Yan, Zunqiang, Pengfei Wang, Qiaoli Yang, Xiaoli Gao, Shuangbao Gun, and Xiaoyu Huang. "Change in Long Non-Coding RNA Expression Profile Related to the Antagonistic Effect of Clostridium perfringens Type C on Piglet Spleen." Current Issues in Molecular Biology 45, no. 3 (March 9, 2023): 2309–25. http://dx.doi.org/10.3390/cimb45030149.
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LncRNAs play important roles in resisting bacterial infection via host immune and inflammation responses. Clostridium perfringens (C. perfringens) type C is one of the main bacteria causing piglet diarrhea diseases, leading to major economic losses in the pig industry worldwide. In our previous studies, piglets resistant (SR) and susceptible (SS) to C. perfringens type C were identified based on differences in host immune capacity and total diarrhea scores. In this paper, the RNA-Seq data of the spleen were comprehensively reanalyzed to investigate antagonistic lncRNAs. Thus, 14 lncRNAs and 89 mRNAs were differentially expressed (DE) between the SR and SS groups compared to the control (SC) group. GO term enrichment, KEGG pathway enrichment and lncRNA-mRNA interactions were analyzed to identify four key lncRNA targeted genes via MAPK and NF-κB pathways to regulate cytokine genes (such as TNF-α and IL-6) against C. perfringens type C infection. The RT-qPCR results for six selected DE lncRNAs and mRNAs are consistent with the RNA-Seq data. This study analyzed the expression profiling of lncRNAs in the spleen of antagonistic and sensitive piglets and found four key lncRNAs against C. perfringens type C infection. The identification of antagonistic lncRNAs can facilitate investigations into the molecular mechanisms underlying resistance to diarrhea in piglets.
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Yeo, In-Cheol, Nam Keun Lee, Byung Wook Yang, and Young Tae Hahm. "RNA-seq Analysis of Antibiotic-Producing Bacillus subtilis SC-8 in Response to Signal Peptide PapR of Bacillus cereus." Applied Biochemistry and Biotechnology 172, no. 2 (October 9, 2013): 580–94. http://dx.doi.org/10.1007/s12010-013-0516-4.
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Tu, Shu, and Jian Zuo. "Systematic single cell RNA sequencing analysis reveals unique transcriptional regulatory networks of Atoh1-mediated hair cell conversion in adult mouse cochleae." PLOS ONE 18, no. 12 (December 11, 2023): e0284685. http://dx.doi.org/10.1371/journal.pone.0284685.
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Regeneration of mammalian cochlear hair cells (HCs) by modulating molecular pathways or transcription factors is a promising approach to hearing restoration; however, immaturity of the regenerated HCs in vivo remains a major challenge. Here, we analyzed a single cell RNA sequencing (scRNA-seq) dataset during Atoh1-induced supporting cell (SC) to hair cell (HC) conversion in adult mouse cochleae (Yamashita et al. (2018)) using multiple high-throughput sequencing analytical tools (WGCNA, SCENIC, ARACNE, and VIPER). Instead of focusing on differentially expressed genes, we established independent expression modules and confirmed the existence of multiple conversion stages. Gene regulatory network (GRN) analysis uncovered previously unidentified key regulators, including Nhlh1, Lhx3, Barhl1 and Nfia, that guide converted HC differentiation. Comparison of the late-stage converted HCs with the scRNA-seq data from neonatal mouse cochleae (Kolla et al. (2020)) revealed that they closely resemble postnatal day 1 wild-type OHCs, in contrast to other developmental stages. Using ARACNE and VIPER, we discovered multiple key regulators likely to promote conversion to a more mature OHC-like state, including Zbtb20, Nfia, Zmiz1, Gm14418, Bhlhe40, Six2, Fosb and Klf9. Our findings provide insights into the regulation of HC regeneration in adult mammalian cochleae in vivo and demonstrate an approach for analyzing GRNs in large scRNA-seq datasets.
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Gupta, Pravesh, Minghao Dang, Dapeng Hao, Krishna Bojja, Tuan M. Tran, Huma Shehwana, Carlos Kamiya-Matsuoka, et al. "OTME-23. Single-cell transcriptomic and epigenomic immune landscape of isocitrate dehydrogenase stratified human gliomas." Neuro-Oncology Advances 3, Supplement_2 (July 1, 2021): ii18. http://dx.doi.org/10.1093/noajnl/vdab070.074.
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Abstract The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression, but a comprehensive characterization of the glioma-centric immune cell repertoire beyond a priori cell states is uncharted. In this study, we performed single-cell RNA-sequencing (scRNA-seq) and single cell- Assay for Transposase-Accessible Chromatin using sequencing (sc-ATAC-seq) on ~100,000 tumor-associated immune cells from seventeen isocitrate dehydrogenase (IDH) mutation classified primary and recurrent human gliomas and non-glioma brains (NGBs). Our analyses revealed sixty-two transcriptionally distinct myeloid and lymphoid cell states within and across glioma subtypes and we noted microglial attrition with increasing disease severity concomitant with invading monocyte-derived cells and lymphocytes. Specifically, certain microglial and monocyte-derived subpopulations were associated with antigen presentation gene modules, akin to cross-presenting dendritic cells (DCs). We identified cytotoxic T cells with poly-functional cytolytic states mostly in recurrent IDH-wt gliomas. Furthermore, ligand-receptor interactome analyses showed a preponderance of antigen presentation and phagocytosis over the checkpoint axis in IDH-wt compared to IDH-mut gliomas. Additionally, our sc-ATAC-seq analyses revealed differences in regulatory networks in NGBs, IDH-mut and IDH-wt glioma associated immune cells. In particular, we noted abundant usage of inflammatory transcription factors (TFs) as exemplified by Nuclear factor kappa B and Activator Protein-1 TF family in IDH-wt microglia when compared with microglia from IDH-mut and NGBs. Unique features such as amplification of 11- Zinc Finger Protein accessibility were restricted to monocyte derived cells and were not observed in microglia. Finally, sc-ATAC-seq profiles of CD8+ exhausted T cells from IDH-wt showed strong enhancer accessibility on Cytotoxic T-lymphocyte-associated protein 4, Layilin and Hepatitis A Virus Cellular Receptor 2 but no enrichment on PDCD1 (gene encoding Programmed cell death protein 1) was seen. In summary, our study provides unprecedented granular detail of transcriptionally defined glioma- specific immune contexture that can be exploited for immunotherapy applications. This study in K.B. laboratory was supported by the generous philanthropic contributions to The University of Texas (UT) MD Anderson Cancer Center (MDACC) Moon Shots Program™, Marnie Rose Foundation, NIH grants: R21 CA222992 and R01CA225963. This study was partly supported by the UT MDACC start-up research fund to L.W. and CPRIT Single Core grant RP180684 to N. E. N.
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Lewis, A., B. Pan-Castillo, G. Berti, C. Felice, H. Gordon, R. Gadhok, A. Minicozzi, et al. "DOP23 Single-cell RNA sequencing identifies an important role for class I histone-deacetylase enzymes in intestinal myofibroblasts from patients with Crohn’s Disease strictures." Journal of Crohn's and Colitis 15, Supplement_1 (May 1, 2021): S062. http://dx.doi.org/10.1093/ecco-jcc/jjab073.062.
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Abstract Background Histone-deacetylase (HDAC) enzymes are a broad class of ubiquitously expressed enzymes that modulate histone acetylation, chromatin accessibility and gene expression. In models of Inflammatory bowel disease (IBD), HDAC inhibitors, such as Valproic acid (VPA) are proven anti-inflammatory agents and evidence suggests that they also inhibit fibrosis in non-intestinal organs. However, the role of HDAC enzymes in stricturing Crohn’s disease (CD) has not been characterised; this is key to understanding the molecular mechanism and developing novel therapies. Methods To evaluate HDAC expression in the intestine of SCD patients, we performed unbiased single-cell RNA sequencing (sc-RNA-seq) of over 10,000 cells isolated from full-thickness surgical resection specimens of non-SCD (NSCD; n=2) and SCD intestine (n=3). Approximately, 1000 fibroblasts were identified for further analysis, including a distinct cluster of myofibroblasts. Changes in gene expression were compared between myofibroblasts and other resident intestinal fibroblasts using the sc-RNA-seq analysis pipeline in Partek. Changes in HDAC expression and markers of HDAC activity (H3K27ac) were confirmed by immunohistochemistry in FFPE tissue from patient matched NSCD and SCD intestine (n=14 pairs). The function of HDACs in intestinal fibroblasts in the CCD-18co cell line and primary CD myofibroblast cultures (n=16 cultures) was assessed using VPA, a class I HDAC inhibitor. Cells were analysed using a variety of molecular techniques including ATAC-seq, gene expression arrays, qPCR, western blot and immunofluorescent protein analysis. Results Class I HDAC (HDAC1, p= 2.11E-11; HDAC2, p= 4.28E-11; HDAC3, p= 1.60E-07; and HDAC8, p= 2.67E-03) expression was increased in myofibroblasts compared to other intestinal fibroblasts subtypes. IHC also showed an increase in the percentage of stromal HDAC2 positive cells, coupled with a decrease in the percentage of H3K27ac positive cells, in the mucosa overlying SCD intestine relative to matched NSCD areas. In the CCD-18co cell line and primary myofibroblast cultures, VPA reduced chromatin accessibility at Collagen-I gene promoters and suppressed their transcription. VPA also inhibited TGFB-induced up-regulation of Collagen-I, in part by inhibiting TGFB1|1/SMAD4 signalling. TGFB1|1 was identified as a mesenchymal specific target of VPA and siRNA knockdown of TGFB1|1 was sufficient suppress TGFB-induced up-regulation of Collagen-I. Conclusion In SCD patients, class I HDAC expression is increased in myofibroblasts. Class I HDACs inhibitors impair TGFB-signalling and inhibit Collagen-I expression. Selective targeting of TGFB1|1 offers the opportunity to increase treatment specificity by selectively targeting meschenymal cells.
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Wang, Wenqing, Xianhong Wang, Chunyan Tu, Mengmeng Yang, Jun Xiang, Liping Wang, Ni Hong, Lifeng Zhai, and Guoping Wang. "Novel Mycoviruses Discovered from a Metatranscriptomics Survey of the Phytopathogenic Alternaria Fungus." Viruses 14, no. 11 (November 18, 2022): 2552. http://dx.doi.org/10.3390/v14112552.
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Alternaria fungus can cause notable diseases in cereals, ornamental plants, vegetables, and fruits around the world. To date, an increasing number of mycoviruses have been accurately and successfully identified in this fungus. In this study, we discovered mycoviruses from 78 strains in 6 species of the genus Alternaria, which were collected from 10 pear production areas using high-throughput sequencing technology. Using the total RNA-seq, we detected the RNA-dependent RNA polymerase of 19 potential viruses and the coat protein of two potential viruses. We successfully confirmed these viruses using reverse transcription polymerase chain reaction with RNA as the template. We identified 12 mycoviruses that were positive-sense single-stranded RNA (+ssRNA) viruses, 5 double-strand RNA (dsRNA) viruses, and 4 negative single-stranded RNA (−ssRNA) viruses. In these viruses, five +ssRNA and four −ssRNA viruses were novel mycoviruses classified into diverse the families Botourmiaviridae, Deltaflexivirus, Mymonaviridea, and Discoviridae. We identified a novel −ssRNA mycovirus isolated from an A. tenuissima strain HB-15 as Alternaria tenuissima negative-stranded RNA virus 2 (AtNSRV2). Additionally, we characterized a novel +ssRNA mycovirus isolated from an A. tenuissima strain SC-8 as Alternaria tenuissima deltaflexivirus 1 (AtDFV1). According to phylogenetic and sequence analyses, we determined that AtNSRV2 was related to the viruses of the genus Sclerotimonavirus in the family Mymonaviridae. We also found that AtDFV1 was related to the virus family Deltaflexivirus. This study is the first to use total RNA sequencing to characterize viruses in Alternaria spp. These results expand the number of Alternaria viruses and demonstrate the diversity of these mycoviruses.
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Rodrigues, Fernanda Martins, Kelsey Gallant, Reyka Jayasinghe, Michael Iglesia, Andrew Houston, Siqi Chen, Preet Lal, et al. "Abstract 1773: Deciphering the roles of germline predisposition variants and somatic mutations on breast cancer cells and the tumor microenvironment." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1773. http://dx.doi.org/10.1158/1538-7445.am2024-1773.
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Abstract While great progress has been made in the understanding and treatment of cancer, tumor heterogeneity remains a diagnostic and prognostic challenge. Single-cell sequencing techniques and high-resolution cellular imaging now afford researchers the ability to investigate the effects of germline variants (especially when compared to somatic mutations) on molecular characteristics of cancer cells and non-cancer cells in the tumor microenvironment (TME). Here, we investigated a breast cancer (BRCA) cohort of over 100 individuals, for which we have generated sc/sn RNA-seq, snATAC-seq, and CODEX imaging data as well as bulk whole exome sequencing and RNA-seq data. We identified 21 BRCA cases carrying pathogenic and likely pathogenic rare germline variants in known cancer predisposition genes, such as BRCA2, BRIP1, ATM, and TP53. These predisposition genes showed diverse expression patterns across stromal and immune cell types, indicating roles in cell types beyond breast epithelia. For cases carrying pathogenic germline variants in such genes, we use snRNA-seq/snATAC-seq to assess gene expression changes and pathway alterations (e.g.,HRD and apoptosis) across all cell types and investigate phenotypic changes associated with pathogenic germline variants in cancer cells compared to stroma and immune cells in the TME. By evaluating chromatin accessibility changes and allele-specific expression, we reveal the connection between epigenetic regulators and differential roles of germline variants across cell types. Finally, the integrated analysis of germline variants and somatic mutations from predisposition genes at single cell resolution broadened our understanding of the role of germline variants in TME during tumorigenesis. Citation Format: Fernanda Martins Rodrigues, Kelsey Gallant, Reyka Jayasinghe, Michael Iglesia, Andrew Houston, Siqi Chen, Preet Lal, Ryan Fields, William Gillanders, Feng Chen, Li Ding. Deciphering the roles of germline predisposition variants and somatic mutations on breast cancer cells and the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1773.
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Yang, Byung Wook, In-Cheol Yeo, Jae Hee Choi, Chandra Datta Sumi, and Young Tae Hahm. "RNA-Seq Analysis of Antibiotic-Producing Bacillus subtilis SC-8 Reveals a Role for Small Peptides in Controlling PapR Signaling." Applied Biochemistry and Biotechnology 185, no. 2 (November 20, 2017): 359–69. http://dx.doi.org/10.1007/s12010-017-2653-7.
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Timperi, Eleonora, and Emanuela Romano. "Stromal circuits involving tumor-associated macrophages and cancer-associated fibroblasts." Frontiers in Immunology 14 (June 5, 2023). http://dx.doi.org/10.3389/fimmu.2023.1194642.
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The tumor associated macrophages (TAM) represent one of most abundant subpopulations across several solid cancers and their number/frequency is associated with a poor clinical outcome. It has been clearly demonstrated that stromal cells, such as the cancer associated fibroblasts (CAFs), may orchestrate TAM recruitment, survival and reprogramming. Today, single cell-RNA sequencing (sc-RNA seq) technologies allowed a more granular knowledge about TAMs and CAFs phenotypical and functional programs. In this mini-review we discuss the recent discoveries in the sc-RNA seq field focusing on TAM and CAF identity and their crosstalk in the tumor microenvironment (TME) of solid cancers.
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Berg, Marijn, Ilya Petoukhov, Inge van den Ende, Kerstin B. Meyer, Victor Guryev, Judith M. Vonk, Orestes Carpaij, et al. "FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets." BMC Genomics 24, no. 1 (November 29, 2023). http://dx.doi.org/10.1186/s12864-023-09822-3.
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AbstractCell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.
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Song, Zheng, Lara Henze, Christian Casar, Dorothee Schwinge, Christoph Schramm, Johannes Fuss, Likai Tan, and Immo Prinz. "Human γδ T cell Identification from Single-cell RNA Sequencing Datasets by Modular TCR Expression." Journal of Leukocyte Biology, July 12, 2023. http://dx.doi.org/10.1093/jleuko/qiad069.
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Abstract Accurately identifying γδ T cells in large single-cell RNA sequencing (scRNA-seq) datasets without additional sc-γδTCR-seq or CITE-seq data remains challenging. In this study, we developed a TCR module scoring strategy for human γδ T cell identification, that is based on modular gene expression of constant and variable TRA/TRB and TRD genes. We evaluated our method using 5’ scRNA-seq datasets comprising both sc-αβTCR-seq and sc-γδTCR-seq as references and demonstrated that it can identify γδ T cells in scRNA-seq datasets with high sensitivity and accuracy. We observed a stable performance of this strategy across datasets from different tissues and different subtypes of γδ T cells. Thus, we propose this analysis method, based on TCR gene module scores, as a standardized tool for identifying and reanalyzing γδ T cells from 5'-end scRNA-seq datasets.
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Davies, Philip, Matt Jones, Juntai Liu, and Daniel Hebenstreit. "Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision." Briefings in Bioinformatics, May 6, 2021. http://dx.doi.org/10.1093/bib/bbab148.
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Abstract RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.
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Jiang, Ying, Ziyi Chen, Na Han, Jingzhe Shang, and Aiping Wu. "sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq." Frontiers in Immunology 14 (July 20, 2023). http://dx.doi.org/10.3389/fimmu.2023.1223471.
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Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.
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Suphavilai, Chayaporn, Shumei Chia, Ankur Sharma, Lorna Tu, Rafael Peres Da Silva, Aanchal Mongia, Ramanuj DasGupta, and Niranjan Nagarajan. "Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures." Genome Medicine 13, no. 1 (December 2021). http://dx.doi.org/10.1186/s13073-021-01000-y.
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AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.
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Shi, Fei, Guiyun Zhang, Jinshi Li, Liang Shu, Cong Yu, Dabin Ren, Yisong Zhang, and Ping Zheng. "Integrated analysis of single cell‐RNA sequencing and Mendelian randomization identifies lactate dehydrogenase B as a target of melatonin in ischemic stroke." CNS Neuroscience & Therapeutics 30, no. 5 (May 2024). http://dx.doi.org/10.1111/cns.14741.
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AbstractAimsDespite the success of single‐cell RNA sequencing in identifying cellular heterogeneity in ischemic stroke, clarifying the mechanisms underlying these associations of differently expressed genes remains challenging. Several studies that integrate gene expression and gene expression quantitative trait loci (eQTLs) with genome wide‐association study (GWAS) data to determine their causal role have been proposed.MethodsHere, we combined Mendelian randomization (MR) framework and single cell (sc) RNA sequencing to study how differently expressed genes (DEGs) mediating the effect of gene expression on ischemic stroke. The hub gene was further validated in the in vitro model.ResultsWe identified 2339 DEGs in 10 cell clusters. Among these DEGs, 58 genes were associated with the risk of ischemic stroke. After external validation with eQTL dataset, lactate dehydrogenase B (LDHB) is identified to be positively associated with ischemic stroke. The expression of LDHB has also been validated in sc RNA‐seq with dominant expression in microglia and astrocytes, and melatonin is able to reduce the LDHB expression and activity in vitro ischemic models.ConclusionOur study identifies LDHB as a novel biomarker for ischemic stroke via combining the sc RNA‐seq and MR analysis.
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Tirumalasetty, Munichandra Babu, Indrashis Bhattacharya, Mohammad Sarif Mohiuddin, Vijaya Bhaskar Baki, and Mayank Choubey. "Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility." Frontiers in Endocrinology 15 (July 11, 2024). http://dx.doi.org/10.3389/fendo.2024.1394812.
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Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.
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Lall, Snehalika, Abhik Ghosh, Sumanta Ray, and Sanghamitra Bandyopadhyay. "sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data." Briefings in Bioinformatics 23, no. 2 (January 17, 2022). http://dx.doi.org/10.1093/bib/bbab517.
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Abstract Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single-cell data are susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantages of $R{\prime}{e}nyi$ and $Tsallis$ entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter ($q$), $R{\prime}{e}nyi$ and $Tsallis$ entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to determine the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data. Availability: The sc-REnF is available at https://github.com/Snehalikalall/sc-REnF
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Cuomo, Anna S. E., Giordano Alvari, Christina B. Azodi, Davis J. McCarthy, and Marc Jan Bonder. "Optimizing expression quantitative trait locus mapping workflows for single-cell studies." Genome Biology 22, no. 1 (June 24, 2021). http://dx.doi.org/10.1186/s13059-021-02407-x.
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Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
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Adil, Asif, Vijay Kumar, Arif Tasleem Jan, and Mohammed Asger. "Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis." Frontiers in Neuroscience 15 (April 22, 2021). http://dx.doi.org/10.3389/fnins.2021.591122.
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Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.