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

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.

Download Full-text

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

10.1101/2021.04.20.440671 ◽

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.

Download Full-text

CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues

10.1101/847657 ◽

2019 ◽

Cited By ~ 1

Author(s):

Pawel F. Przytycki ◽

Katherine S. Pollard

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Cell Labeling ◽

Open Chromatin ◽

Specific Cell ◽

Rna Seq ◽

Data Types ◽

Bulk Data ◽

Cell Type Specific

Single-cell and bulk genomics assays have complementary strengths and weaknesses, and alone neither strategy can fully capture regulatory elements across the diversity of cells in complex tissues. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene expression (RNA-seq) and other data types using a network model that simultaneously improves cell labeling in noisy scATAC-seq and annotates cell-type specific regulatory elements in bulk data. We demonstrate CellWalker’s robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing brain. We identify cells transitioning between transcriptional states, resolve enhancers to specific cell types, and observe that autism and other neurological traits can be mapped to specific cell types through their enhancers.

Download Full-text

miRSCAPE - Inferring miRNA expression in single-cell clusters

10.1101/2021.07.29.454389 ◽

2021 ◽

Author(s):

Gulden Olgun ◽

Vishaka Gopalan ◽

Sridhar Hannenhalli

Keyword(s):

Single Cell ◽

Mirna Expression ◽

Regulatory Networks ◽

Cell Types ◽

Specific Cell ◽

Rna Seq ◽

Lung Cancers ◽

Cellular Resolution ◽

Micro Rnas ◽

Cell Type Specific

Micro-RNAs (miRNA) are critical in development, homeostasis, and diseases, including cancer. However, our understanding of miRNA function at cellular resolution is thwarted by the inability of the standard single cell RNA-seq protocols to capture miRNAs. Here we introduce a machine learning tool -- miRSCAPE -- to infer miRNA expression in a sample from its RNA-seq profile. We establish miRSCAPE's accuracy separately in 10 tissues comprising ~10,000 tumor and normal bulk samples and demonstrate that miRSCAPE accurately infers cell type-specific miRNA activities (predicted vs observed fold-difference correlation ~ 0.81) in two independent datasets where miRNA profiles of specific cell types are available (HEK-GBM, Kidney-Breast-Skin). When trained on human hematopoietic cancers, miRSCAPE can identify active miRNAs in 8 hematopoietic cell lines in mouse with a reasonable accuracy (auROC = 0.67). Finally, we apply miRSCAPE to infer miRNA activities in scRNA clusters in Pancreatic and Lung cancers, as well as in 56 cell types in the Human Cell Landscape (HCL). Across the board, miRSCAPE recapitulates and provides a refined view of known miRNA biology. miRSCAPE is freely available and promises to substantially expand our understanding of gene regulatory networks at cellular resolution.

Download Full-text

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

10.1101/2020.08.27.268730 ◽

2020 ◽

Cited By ~ 1

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.

Download Full-text

Single-cell transcriptomics for the 99.9% of species without reference genomes

10.1101/2021.07.09.450799 ◽

2021 ◽

Author(s):

Olga Borisovna Botvinnik ◽

Pranathi Vemuri ◽

N. Tessa Pierce Ward ◽

Phoenix Aja Logan ◽

Saba Nafees ◽

...

Keyword(s):

Single Cell ◽

Gene Annotation ◽

Cell Types ◽

Model Organisms ◽

Mouse Lung ◽

Rna Seq ◽

Transcriptomic Data ◽

A Genome ◽

Genome Annotations ◽

Reference Genomes

Single-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on earth, >99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of "k-mer homology," combining biochemical synonyms in degenerate protein alphabets with uniform data subsampling via MinHash into a pipeline called Kmermaid, to directly detect similar cell types across species from transcriptomic data without the need for a reference genome. Underpinning kmermaid is the tool Orpheum, a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. After validating kmermaid using datasets from human and mouse lung, we applied Kmermaid to the Chinese horseshoe bat (Rhinolophus sinicus), where we propagated cellular compartment labels at high fidelity. Our pipeline provides a high-throughput tool that enables analyses of transcriptomic data across divergent species' transcriptomes in a genome- and gene annotation-agnostic manner. Thus, the combination of Kmermaid and Orpheum identifies cellular type-specific sequences that may be missing from genome annotations and empowers molecular cellular phenotyping for novel model organisms and species.

Download Full-text

The landscape of mouse epididymal cells defined by the single-cell RNA-Seq

10.1101/2020.01.05.895052 ◽

2020 ◽

Author(s):

Jianwu Shi ◽

Mengmeng Sang ◽

Gangcai Xie ◽

Hao Chen

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Principal Cell ◽

Sperm Maturation ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Gene Expression Signatures ◽

Mouse Epididymis

ABSTRACTSpermatozoa acquire their fertilizing ability and forward motility properties during epididymal transit. Although lots of attempts elucidating the functions of different cell types in epididymis, the composition of epididymal tubal and cell types are still largely unknown. Using single-cell RNA sequence, we analyzed the cell constitutions and their gene expression profiles of adult epididymis derived from caput, corpus and cauda epididymis with a total of 12,597 cells. This allowed us to elucidate the full range of gene expression changes during epididymis and derive region-specific gene expression signatures along the epididymis. A total of 7 cell populations were identified with all known constituent cells of mouse epididymis, as well as two novel cell types. Our analyses revealed a segment to segment variation of the same cell type in the three different part of epididymis and generated a reference dataset of epididymal cell gene expression. Focused analyses uncovered nine subtypes of principal cell. Two subtypes of principal cell, c0.3 and c.6 respectively, in our results supported with previous finding that they mainly located in the caput of mouse epididymis and play important roles during sperm maturation. We also showed unique gene expression signatures of each cell population and key pathways that may concert epididymal epithelial cell-sperm interactions. Overall, our single-cell RNA seq datasets of epididymis provide a comprehensive potential cell types and information-rich resource for the studies of epididymal composition, epididymal microenvironment regulation by the specific cell type, or contraceptive development, as well as a gene expression roadmap to be emulated in efforts to achieve sperm maturation regulation in the epididymis.

Download Full-text

Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data

10.1101/456129 ◽

2018 ◽

Cited By ~ 24

Author(s):

Florian Wagner ◽

Itai Yanai

Keyword(s):

Single Cell ◽

Cell Types ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Systematic Analysis ◽

Learning Framework ◽

Classification Framework ◽

Heterogeneous Tissues

AbstractSingle-cell RNA-Seq (scRNA-Seq) enables the systematic molecular characterization of heterogeneous tissues at an unprecedented resolution and scale. However, it is currently unclear how to establish formal cell type definitions, which impedes the systematic analysis of scRNA-Seq data across experiments and studies. To address this challenge, we have developed Moana, a hierarchical machine learning framework that enables the construction of robust cell type classifiers from heterogeneous scRNA-Seq datasets. To demonstrate Moana’s capabilities, we construct cell type classifiers for human immune cells that accurately distinguish between closely related cell types in the presence of experimental perturbations and systematic differences between scRNA-Seq protocols. We show that Moana is generally applicable and scales to datasets with more than ten thousand cells, thus enabling the construction of tissue-specific cell type atlases that can be directly applied to analyze new scRNASeq datasets. A Python implementation of Moana can be found at https://github.com/yanailab/moana.

Download Full-text

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

10.1101/2021.01.30.428954 ◽

2021 ◽

Author(s):

Kai Kang ◽

Caizhi David Huang ◽

Yuanyuan Li ◽

David M. Umbach ◽

Leping Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

In Silico ◽

Cell Types ◽

R Package ◽

Biological Tissues ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific

AbstractBackgroundBiological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and with a new function to aid interpretation of deconvolution outcomes. The R package would be of interest for the broader R community.ResultWe developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating CDSeq-estimated cell types using publicly available single-cell RNA sequencing (scRNA-seq) data (single-cell data from 20 major organs are included in the R package). This function allows users to readily interpret and visualize the CDSeq-estimated cell types. We carried out additional validations of the CDSeqR software with in silico and in vitro mixtures and with real experimental data including RNA-seq data from the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) project.ConclusionsThe existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. However, bulk level analyses neglect tissue heterogeneity and hinder investigation in a cell-type-specific fashion. The CDSeqR package can be viewed as providing in silico single-cell dissection of bulk measurements. It enables researchers to gain cell-type-specific information from bulk RNA-seq data.

Download Full-text

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

10.1101/2021.04.26.441522 ◽

2021 ◽

Author(s):

Elisabeth Rebboah ◽

Fairlie Reese ◽

Katherine Williams ◽

Gabriela Balderrama-Gutierrez ◽

Cassandra McGill ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Rna Seq ◽

Short Read ◽

Whole Cells ◽

Long Read ◽

Isoform Expression ◽

Differential Isoform Expression

AbstractAlternative RNA isoforms are defined by promoter choice, alternative splicing, and polyA site selection. Although differential isoform expression is known to play a large regulatory role in eukaryotes, it has proved challenging to study with standard short-read RNA-seq because of the uncertainties it leaves about the full-length structure and precise termini of transcripts. The rise in throughput and quality of long-read sequencing now makes it possible, in principle, to unambiguously identify most transcript isoforms from beginning to end. 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 a combinatorial barcoding-based method for sequencing single cells and nuclei with long reads. We show that LR-Split-seq can associate isoforms with cell types with relative economy and design flexibility. We characterize LR-Split-seq for whole cells and nuclei by using the well-studied mouse C2C12 system in which mononucleated myoblast cells differentiate and fuse into multinucleated myotubes. We show that the overall results are reproducible when comparing long- and short-read data from the same cell or nucleus. We find substantial evidence of differential isoform expression during differentiation including alternative transcription start site (TSS) usage. We integrate the resulting isoform expression dynamics with snATAC-seq chromatin accessibility to validate TSS-driven isoform choices. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells that can be further quantified with companion deep short-read scRNA-seq from the same cell populations.

Download Full-text

CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues

Genome Biology ◽

10.1186/s13059-021-02279-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Pawel F. Przytycki ◽

Katherine S. Pollard

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Cell Labeling ◽

Open Chromatin ◽

Specific Cell ◽

Rna Seq ◽

Data Types ◽

Bulk Data ◽

Cell Type Specific

AbstractSingle-cell and bulk genomics assays have complementary strengths and weaknesses, and alone neither strategy can fully capture regulatory elements across the diversity of cells in complex tissues. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene expression (RNA-seq) and other data types using a network model that simultaneously improves cell labeling in noisy scATAC-seq and annotates cell type-specific regulatory elements in bulk data. We demonstrate CellWalker’s robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing brain. We identify cells transitioning between transcriptional states, resolve regulatory elements to cell types, and observe that autism and other neurological traits can be mapped to specific cell types through their regulatory elements.

Download Full-text