Similar Datasets
Project description:The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ?13,000 habenular cells with 4× cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions.
| S-EPMC6042852 | biostudies-literature
Project description:Comprehensive identification and spatial mapping of habenular neuronal types using single cell RNAseq
| PRJNA414719 | ENA
Project description:Mouse brain transcriptomic studies are important in the understanding of the structural heterogeneity in the brain. However, it is not well understood how cell types in the mouse brain relate to human brain cell types on a cellular level. We propose that it is possible with single cell granularity to find concordant genes between mouse and human and that these genes can be used to separate cell types across species. We show that a set of concordant genes can be algorithmically derived from a combination of human and mouse single cell sequencing data. Using this gene set, we show that similar cell types shared between mouse and human cluster together. Furthermore we find that previously unclassified human cells can be mapped to the glial/vascular cell type by integrating mouse cell type expression profiles.
| S-EPMC6956653 | biostudies-literature
Project description:The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function. We combined in situ sequencing gene expression data and single-nucleus RNA-sequencing cell type definitions to spatially map cells in sections of the human cortex via probabilistic cell typing. We mapped and classified a total of 59,816 cells into all 75 previously defined subtypes to create a first spatial atlas of human cortical cells in their native position, their abundances and genetic signatures. We also examined the precise within- and across-layer distributions of all the cell types and provide a resource for the cell atlas community. The abundances and locations presented here could serve as a reference for further studies, that include human brain tissues and disease applications at the cell type level.
| S-EPMC8384853 | biostudies-literature
Project description:Optimal selection of threat-driven defensive behaviors is paramount to an animal's survival. The lateral habenula (LHb) is a key neuronal hub coordinating behavioral responses to aversive stimuli. Yet, how individual LHb neurons represent defensive behaviors in response to threats remains unknown. Here, we show that in mice, a visual threat promotes distinct defensive behaviors, namely runaway (escape) and action-locking (immobile-like). Fiber photometry of bulk LHb neuronal activity in behaving animals reveals an increase and a decrease in calcium signal time-locked with runaway and action-locking, respectively. Imaging single-cell calcium dynamics across distinct threat-driven behaviors identify independently active LHb neuronal clusters. These clusters participate during specific time epochs of defensive behaviors. Decoding analysis of this neuronal activity reveals that some LHb clusters either predict the upcoming selection of the defensive action or represent the selected action. Thus, heterogeneous neuronal clusters in LHb predict or reflect the selection of distinct threat-driven defensive behaviors.
| S-EPMC7296347 | biostudies-literature
Project description:BACKGROUND: The use of RNAseq to resolve the transcriptional organization of an organism was established in recent years and also showed the complexity and dynamics of bacterial transcriptomes. The aim of this study was to comprehensively investigate the transcriptome of the industrially relevant amino acid producer and model organism Corynebacterium glutamicum by RNAseq in order to improve its genome annotation and to describe important features for transcription and translation. RESULTS: RNAseq data sets were obtained by two methods, one that focuses on 5'-ends of primary transcripts and another that provides the overall transcriptome with an improved resolution of 3'-ends of transcripts. Subsequent data analysis led to the identification of more than 2,000 transcription start sites (TSSs), the definition of 5'-UTRs (untranslated regions) for annotated protein-coding genes, operon structures and many novel transcripts located between or in antisense orientation to protein-coding regions. Interestingly, a high number of mRNAs (33%) is transcribed as leaderless transcripts. From the data, consensus promoter and ribosome binding site (RBS) motifs were identified and it was shown that the majority of genes in C. glutamicum are transcribed monocistronically, but operons containing up to 16 genes are also present. CONCLUSIONS: The comprehensive transcriptome map of C. glutamicum established in this study represents a major step forward towards a complete definition of genetic elements (e.g. promoter regions, gene starts and stops, 5'-UTRs, RBSs, transcript starts and ends) and provides the ideal basis for further analyses on transcriptional regulatory networks in this organism. The methods developed are easily applicable for other bacteria and have the potential to be used also for quantification of transcriptomes, replacing microarrays in the near future.
| S-EPMC3890552 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.
| S-EPMC11011776 | biostudies-literature
Project description:Intervertebral disc (IVD) degeneration and its medical consequences is still one of the leading causes of morbidity worldwide. To support potential regenerative treatments for degenerated IVDs, we sought to deconvolute the cell composition of the nucleus pulposus (NP) and the annulus fibrosus (AF) of bovine intervertebral discs. Bovine calf tails have been extensively used in intervertebral disc research as a readily available source of NP and AF material from healthy and young IVDs. We used single-cell RNA sequencing (scRNAseq) coupled to bulk RNA sequencing (RNAseq) to unravel the cell populations in these two structures and analyze developmental changes across the rostrocaudal axis. By integrating the scRNAseq data with the bulk RNAseq data to stabilize the clustering results of our study, we identified 27 NP structure/tissue specific genes and 24 AF structure/tissue specific genes. From our scRNAseq results, we could deconvolute the heterogeneous cell populations in both the NP and the AF. In the NP, we detected a notochordal-like cell cluster and a progenitor stem cell cluster. In the AF, we detected a stem cell-like cluster, a cluster with a predominantly fibroblast-like phenotype and a potential endothelial progenitor cluster. Taken together, our results illustrate the cell phenotypic complexity of the AF and NP in the young bovine IVDs.
| S-EPMC8124861 | biostudies-literature
Project description: Not available
| S-EPMC10817898 | biostudies-literature
Project description:MotivationIdentification of bimodally expressed genes is an important task, as genes with bimodal expression play important roles in cell differentiation, signalling and disease progression. Several useful algorithms have been developed to identify bimodal genes from microarray data. Currently, no method can deal with data from next-generation sequencing, which is emerging as a replacement technology for microarrays.ResultsWe present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data. We evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis. We show that the lognormal mixture model performs best in terms of power and robustness under various scenarios. We also compare our method with alternative approaches, including profile analysis using clustering and kurtosis (PACK) and cancer outlier profile analysis (COPA). Our method is robust, powerful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation.AvailabilityThe R package SIBER is available at the website http://bioinformatics.mdanderson.org/main/OOMPA:Overview.
| S-EPMC3582265 | biostudies-literature