Single-cell transcriptomics: background, technologies, applications, and challenges.

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Transposable Element Insertions Are Associated with Batesian Mimicry in the Pantropical Butterfly Hypolimnas misippus.

Hypolimnas misippus is a Batesian mimic of the toxic African Queen butterfly (Danaus chrysippus). Female H. misippus butterflies use two major wing patterning loci (M and A) to imitate three color morphs of D. chrysippus found in different regions of Africa. In this study, we examine the evolution of the M locus and identify it as an example of adaptive atavism. This phenomenon involves a morphological reversion to an ancestral character that results in an adaptive phenotype. We show that H. misippus has re-evolved an ancestral wing pattern present in other Hypolimnas species, repurposing it for Batesian mimicry of a D. chrysippus morph. Using haplotagging, a linked-read sequencing technology, and our new analytical tool, Wrath, we discover two large transposable element insertions located at the M locus and establish that these insertions are present in the dominant allele responsible for producing mimetic phenotype. By conducting a comparative analysis involving additional Hypolimnas species, we demonstrate that the dominant allele is derived. This suggests that, in the derived allele, the transposable elements disrupt a cis-regulatory element, leading to the reversion to an ancestral phenotype that is then utilized for Batesian mimicry of a distinct model, a different morph of D. chrysippus. Our findings present a compelling instance of convergent evolution and adaptive atavism, in which the same pattern element has independently evolved multiple times in Hypolimnas butterflies, repeatedly playing a role in Batesian mimicry of diverse model species.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Bioinformatic analysis of differentially expressed genes as prognostic markers in pheochromocytoma and paraganglioma tumors.

The pathogenesis of pheochromocytoma and paraganglioma (PCPG) catecholamine-producing tumors is exceedingly complicated. Here, we sought to identify important genes affecting the prognosis and survival rate of patients suffering from PCPG. We analyzed 95 samples obtained from two microarray data series, GSE19422 and GSE60459, from the Gene Expression Omnibus (GEO) repository. First, differentially expressed genes (DEGs) were identified by comparing 87 PCPG tumor samples and eight normal adrenal tissue samples using R language. The GEO2R tool and Venn diagram software were applied to the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO). We further employed Cytoscape with the Molecular Complex Detection (MCODE) tool to make protein-protein interactions visible for the Search Tool for Retrieval of Interacting Genes (STRING). These procedures resulted in 30 candidate DEGs, which were subjected to Kaplan-Meier analysis and validated by Gene Expression Profiling Interactive Analysis (GEPIA) to determine their influence on overall survival rate. Finally, we identified ALDH3A2 and AKR1B1, two genes in the glycerolipid metabolism pathway, as being particularly enriched in PCPG tumors and correlated with T and B tumor-infiltrating immune cells. Our results suggest that these two DEGs are closely associated with the prognosis of malignant PCPG tumors.

Identification of vital prognostic genes related to tumor microenvironment in pheochromocytoma and paraganglioma based on weighted gene co-expression network analysis.

Pheochromocytoma and paraganglioma (PCPG) is a rare neuroendocrine tumor. This study aims to identify vital prognostic genes which were associated with PCPG tumor microenvironment (TME). We downloaded transcriptome data of PCPG from TCGA database and calculated the immune scores and stromal scores by using the ESTIMATE algorithm. DEGs related to TMB were then identified. We conducted WGCNA to further extract the TME-related modules. GO, KEGG pathway analysis, and PPI network were performed. Survival analysis was conducted to identify the hub genes associated with the prognosis of PCPG. A total of 150 PCPG samples were included in this study. We obtained 1507 and 2067 DEGs based on immune scores and stromal scores, respectively. WGCNA analysis identified the red module and brown module were correlated with immune sores while the turquoise module and red module were significantly associated with stromal scores. Functional enrichments analysis revealed that 307 TME-related genes were correlated with the inflammation or immune response. Survival analysis showed that three TME-relate genes (ADGRE1, CCL18, and LILRA6) were associated with PCPG prognosis. These three hub genes including ADGRE1, CCL18, and LILRA6 might be involved in the progression of PCPG and could serve as potential biomarkers and novel therapeutic targets.

Identification of key genes and molecular mechanisms associated with dedifferentiated liposarcoma based on bioinformatic methods.

BACKGROUND: Dedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown. MATERIALS AND METHODS: Microarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified. RESULTS: All 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes CDK4 and MDM2 were universally found to be up-regulated and amplified in gene copy number. CONCLUSION: This study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.

Multi-Omics of Single Cells: Strategies and Applications.

Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.

Identification of carcinogenic potential-associated molecular mechanisms in CD133(+) A549 cells based on microRNA profiles.

This study aimed to identify carcinogenic potential-related molecular mechanisms in cancer stem cells (CSCs) in lung cancer. CD133(+) and CD133(-) subpopulations were sorted from A549 cells using magnetic-activated cell sorting. The abilities to form sphere and clone, proliferate, migrate, and invade were compared between CD133(+) and CD133(-) cells, as well as drug sensitivity. Thereafter, microRNA (miRNA) profiles were performed to identify differentially expressed miRNAs between CD133(+) and CD133(-) subpopulation. Following, bioinformatic methods were used to predict target genes for differentially expressed miRNAs and perform enrichment analysis. Furthermore, the mammalian target of rapamycin (mTOR) signaling pathways and CSC property-associated signaling pathways were explored and visualized in regulatory network among competitive endogenous RNA (ceRNA), miRNA, and target gene. CD133(+) subpopulation showed greater oncogenic potential than CD133(-) subpopulation. In all, 14 differentially expressed miRNAs were obtained and enriched in 119 pathways, including five upregulated (hsa-miR-23b-3p, -23a-3p, -15b-5p, -24-3p, and -4734) and nine downregulated (hsa-miR-1246, -30b-5p, -5096, -6510-5p, has-miR-7110-5p, -7641, -3197, -7108-5p, and -6791-5p). For mTOR signaling pathway, eight differential miRNAs (hsa-miR-23b-3p, -23a-3p, -15b-5p, -24-3p, -4734, -1246, -7641, and -3197) and 39 target genes (e.g., AKT1, AKT2, PIK3CB, PIK3CG, PIK3R1, PIK3CA, and PIK3CD) were involved, as well as some ceRNAs. Besides, for CSC property-related signaling pathways, six miRNAs (hsa-miR-1246, -15b-5p, -30b-5p, -3197, -4734, and -7110-5p) were dramatically enriched in Hedgehog, Notch, and Wnt signaling pathways via regulating 108 target genes (e.g., DVL1, DVL3, WNT3A, and WNT5A). The mTOR and CSC property-associated signaling pathways may be important oncogenic molecular mechanisms in CD133(+) A549 cells.

A strategy to recover a high-quality, complete plastid sequence from low-coverage whole-genome sequencing.

PREMISE OF THE STUDY: We developed a bioinformatic strategy to recover and assemble a chloroplast genome using data derived from low-coverage 454 GS FLX/Roche whole-genome sequencing. METHODS: A comparative genomics approach was applied to obtain the complete chloroplast genome from a weedy biotype of rice from Uruguay. We also applied appropriate filters to discriminate reads representing novel DNA transfer events between the chloroplast and nuclear genomes. RESULTS: From a set of 295,159 reads (96 Mb data), we assembled the chloroplast genome into two contigs. This weedy rice was classified based on 23 polymorphic regions identified by comparison with reference chloroplast genomes. We detected recent and past events of genetic material transfer between the chloroplast and nuclear genomes and estimated their occurrence frequency. DISCUSSION: We obtained a high-quality complete chloroplast genome sequence from low-coverage sequencing data. Intergenome DNA transfer appears to be more frequent than previously thought.