scholarly journals Cell-type, single-cell, and spatial signatures of brain-region specific splicing in postnatal development

2020 ◽  
Author(s):  
Anoushka Joglekar ◽  
Andrey Prjibelski ◽  
Ahmed Mahfouz ◽  
Paul Collier ◽  
Susan Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Region ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Cell ◽  
Cell Type ◽  
Anatomic Structure ◽  
Link Type ◽  
Long Read ◽  
Isoform Expression

AbstractAlternative RNA splicing varies across brain regions, but the single-cell resolution of such regional variation is unknown. Here we present the first single-cell investigation of differential isoform expression (DIE) between brain regions, by performing single cell long-read transcriptome sequencing in the mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Using isoform tests for brain-region specific DIE, which outperform exon-based tests, we detect hundreds of brain-region specific DIE events traceable to specific cell-types. Many DIE events correspond to functionally distinct protein isoforms, some with just a 6-nucleotide exon variant. In most instances, one cell type is responsible for brain-region specific DIE. Cell types indigenous to only one anatomic structure display distinctive DIE, where for example, the choroid plexus epithelium manifest unique transcription start sites. However, for some genes, multiple cell-types are responsible for DIE in bulk data, indicating that regional identity can, although less frequently, override cell-type specificity. We validated our findings with spatial transcriptomics and long-read sequencing, yielding the first spatially resolved splicing map in the postnatal mouse brain (www.isoformAtlas.com). Our methods are highly generalizable. They provide a robust means of quantifying isoform expression with cell-type and spatial resolution, and reveal how the brain integrates molecular and cellular complexity to serve function.

scholarly journals A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anoushka Joglekar ◽  
Andrey Prjibelski ◽  
Ahmed Mahfouz ◽  
Paul Collier ◽  
Susan Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Region ◽  
Cell Types ◽  
Brain Regions ◽  
Protein Isoforms ◽  
Cell Type ◽  
Anatomic Structure ◽  
Spatially Resolved ◽  
Long Read ◽  
Isoform Expression

AbstractSplicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.

scholarly journals Genome annotation with long RNA reads reveals new patterns of gene expression and improves single-cell analyses in an ant brain

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Emily J. Shields ◽  
Masato Sorida ◽  
Lihong Sheng ◽  
Bogdan Sieriebriennikov ◽  
Long Ding ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Untranslated Regions ◽  
Specific Cell ◽  
Rna Seq ◽  
Splice Isoforms ◽  
Long Read ◽  
Genome Annotations ◽  
Genome Assemblies ◽  
Traditional Approaches

Abstract Background Functional genomic analyses rely on high-quality genome assemblies and annotations. Highly contiguous genome assemblies have become available for a variety of species, but accurate and complete annotation of gene models, inclusive of alternative splice isoforms and transcription start and termination sites, remains difficult with traditional approaches. Results Here, we utilized full-length isoform sequencing (Iso-Seq), a long-read RNA sequencing technology, to obtain a comprehensive annotation of the transcriptome of the ant Harpegnathos saltator. The improved genome annotations include additional splice isoforms and extended 3′ untranslated regions for more than 4000 genes. Reanalysis of RNA-seq experiments using these annotations revealed several genes with caste-specific differential expression and tissue- or caste-specific splicing patterns that were missed in previous analyses. The extended 3′ untranslated regions afforded great improvements in the analysis of existing single-cell RNA-seq data, resulting in the recovery of the transcriptomes of 18% more cells. The deeper single-cell transcriptomes obtained with these new annotations allowed us to identify additional markers for several cell types in the ant brain, as well as genes differentially expressed across castes in specific cell types. Conclusions Our results demonstrate that Iso-Seq is an efficient and effective approach to improve genome annotations and maximize the amount of information that can be obtained from existing and future genomic datasets in Harpegnathos and other organisms.

scholarly journals FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Genome annotation with long RNA reads reveals new patterns of gene expression in an ant brain

2021 ◽  
Author(s):  
Emily Shields ◽  
Masato Sorida ◽  
Lihong Sheng ◽  
Bogdan Sieriebriennikov ◽  
Long Ding ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Untranslated Regions ◽  
Specific Cell ◽  
Rna Seq ◽  
Splice Isoforms ◽  
Long Read ◽  
Genome Annotations ◽  
Genome Assemblies ◽  
Traditional Approaches

Functional genomic analyses rely on high-quality genome assemblies and annotations. Highly contiguous genome assemblies have become available for a variety of species, but accurate and complete annotation of gene models, inclusive of alternative splice isoforms and transcription start and termination sites remains difficult with traditional approaches. Here, we utilized full-length isoform sequencing (Iso-Seq), a long-read RNA squencing technology, to obtain a comprehensive annotation of the transcriptome of the ant Harpegnathos saltator. The improved genome annotations include additional splice isoforms and extended 3' untranslated regions for more than 4,000 genes. Reanalysis of RNA-seq experiments using these annotations revealed several genes with caste-specific differential expression and tissue- or caste-specific splicing patterns that were missed in previous analyses. The extended 3' untranslated regions afforded great improvements in the analysis of existing single-cell RNA-seq data, resulting in the recovery of the transcriptomes of 18% more cells. The deeper single-cell transcriptomes obtained with these new annotations allowed us to identify additional markers for several cell types in the ant brain, as well as genes differentially expressed across castes in specific cell types. Our results demonstrate that Iso-Seq is an efficient and effective approach to improve genome annotations and maximize the amount of information that can be obtained from existing and future genomic datasets in Harpegnathos and other organisms.

scholarly journals Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

2020 ◽  
Author(s):  
Jiebiao Wang ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Cell ◽  
Expression Data ◽  
Cell Type ◽  
Disease Etiology ◽  
Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

scholarly journals Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Elisabeth Rebboah ◽  
Fairlie Reese ◽  
Katherine Williams ◽  
Gabriela Balderrama-Gutierrez ◽  
Cassandra McGill ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Transcription Start Sites ◽  
Design Flexibility ◽  
Long Reads ◽  
Internal Exon ◽  
Long Read ◽  
Isoform Expression ◽  
Full Length Transcript

AbstractThe rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.

scholarly journals Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT

2017 ◽  
Author(s):  
Bushra Raj ◽  
Daniel E. Wagner ◽  
Aaron McKenna ◽  
Shristi Pandey ◽  
Allon M. Klein ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Marker Genes ◽  
Cell Type ◽  
Zebrafish Brain ◽  
Vertebrate Brain ◽  
Single Cell Rna Sequencing ◽  
Single Cell Profiling ◽  
Lineage Trees

ABSTRACTHundreds of cell types are generated during development, but their lineage relationships are largely elusive. Here we report a technology, scGESTALT, which combines cell type identification by single-cell RNA sequencing with lineage recording by cumulative barcode editing. We sequenced ~60,000 transcriptomes from the juvenile zebrafish brain and identified more than 100 cell types and marker genes. We engineered an inducible system that combines early and late barcode editing and isolated thousands of single-cell transcriptomes and their associated barcodes. The large diversity of edited barcodes and cell types enabled the generation of lineage trees with hundreds of branches. Inspection of lineage trajectories identified restrictions at the level of cell types and brain regions and helped uncover gene expression cascades during differentiation. These results establish scGESTALT as a new and widely applicable tool to simultaneously characterize the molecular identities and lineage histories of thousands of cells during development and disease.

scholarly journals Enhancer viruses and a transgenic platform for combinatorial cell subclass-specific labeling

2019 ◽  
Author(s):  
Lucas T. Graybuck ◽  
Tanya L. Daigle ◽  
Adriana E. Sedeño-Cortés ◽  
Miranda Walker ◽  
Brian Kalmbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Projection Neuron ◽  
Chromatin Accessibility ◽  
Specific Cell ◽  
Cell Type ◽  
Mouse Cortex ◽  
Efficient Access ◽  
Rapid Pace

SummaryThe rapid pace of cell type identification by new single-cell analysis methods has not been met with efficient experimental access to the newly discovered types. To enable flexible and efficient access to specific neural populations in the mouse cortex, we collected chromatin accessibility data from individual cells and clustered the single-cell data to identify enhancers specific for cell classes and subclasses. When cloned into adeno-associated viruses (AAVs) and delivered to the brain by retro-orbital injections, these enhancers drive transgene expression in specific cell subclasses in the cortex. We characterize several enhancer viruses in detail to show that they result in labeling of different projection neuron subclasses in mouse cortex, and that one of them can be used to label the homologous projection neuron subclass in human cortical slices. To enable the combinatorial labeling of more than one cell type by enhancer viruses, we developed a three-color Cre-, Flp- and Nigri-recombinase dependent reporter mouse line, Ai213. The delivery of three enhancer viruses driving these recombinases via a single retroorbital injection into a single Ai213 transgenic mouse results in labeling of three different neuronal classes/subclasses in the same brain tissue. This approach combines unprecedented flexibility with specificity for investigation of cell types in the mouse brain and beyond.

scholarly journals Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

2021 ◽  
Author(s):  
Yongjin Park ◽  
Liang He ◽  
Jose Davila-Velderrain ◽  
Lei Hou ◽  
Shahin Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genetic Variants ◽  
Cell Types ◽  
Brain Regions ◽  
Post Mortem ◽  
Rna Seq ◽  
Cell Type ◽  
Post Mortem Brain ◽  
Cell Type Specific

AbstractThousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

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