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

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.

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 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 Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E. Wiarda ◽  
Sathesh K. Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

scholarly journals Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain

2022 ◽  
Author(s):  
Matthew T Buckley ◽  
Eric Sun ◽  
Benson M. George ◽  
Ling Liu ◽  
Nicholas Schaum ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Level ◽  
Transcriptomic Data ◽  
Precise Quantification ◽  
Cell Type Specific ◽  
Tissue Aging ◽  
The Brain

Aging manifests as progressive dysfunction culminating in death. The diversity of cell types is a challenge to the precise quantification of aging and its reversal. Here we develop a suite of 'aging clocks' based on single cell transcriptomic data to characterize cell type-specific aging and rejuvenation strategies. The subventricular zone (SVZ) neurogenic region contains many cell types and provides an excellent system to study cell-level tissue aging and regeneration. We generated 21,458 single-cell transcriptomes from the neurogenic regions of 28 mice, tiling ages from young to old. With these data, we trained a suite of single cell-based regression models (aging clocks) to predict both chronological age (passage of time) and biological age (fitness, in this case the proliferative capacity of the neurogenic region). Both types of clocks perform well on independent cohorts of mice. Genes underlying the single cell-based aging clocks are mostly cell-type specific, but also include a few shared genes in the interferon and lipid metabolism pathways. We used these single cell-based aging clocks to measure transcriptomic rejuvenation, by generating single cell RNA-seq datasets of SVZ neurogenic regions for two interventions - heterochronic parabiosis (young blood) and exercise. Interestingly, the use of aging clocks reveals that both heterochronic parabiosis and exercise reverse transcriptomic aging in the niche, but in different ways across cell types and genes. This study represents the first development of high-resolution aging clocks from single cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

scholarly journals Whole Animal Multiplexed Single-Cell RNA-Seq Reveals Plasticity of Clytia Medusa Cell Types

2021 ◽  
Author(s):  
Tara Chari ◽  
Brandon Weissbourd ◽  
Jase Gehring ◽  
Anna Ferraioli ◽  
Lucas Leclère ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Cell Types ◽  
Model Organisms ◽  
Traditional Model ◽  
Rna Seq ◽  
Batch Effects ◽  
Future Studies ◽  
Component Cell ◽  
Jellyfish Species

AbstractWe present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica, and determine how its component cell types respond to starvation. Utilizing multiplexed scRNA-seq, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and plasticity in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole animal, multiplexed single-cell genomics (WHAM-seq) that is readily adaptable to other traditional or non-traditional model organisms.

scholarly journals A probabilistic model-based bi-clustering method for single-cell transcriptomic data analysis

2017 ◽  
Author(s):  
Sha Cao ◽  
Tao Sheng ◽  
Xin Chen ◽  
Qin Ma ◽  
Chi Zhang
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Computational Techniques ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
Large Numbers ◽  
A Cell ◽  
Multiple Cell ◽  
Cell Data

AbstractWe present here novel computational techniques for tackling four problems related to analyses of single-cell RNA-Seq data: (1) a mixture model for coping with multiple cell types in a cell population; (2) a truncated model for handling the unquantifiable errors caused by large numbers of zeros or low-expression values; (3) a bi-clustering technique for detection of sub-populations of cells sharing common expression patterns among subsets of genes; and (4) detection of small cell sub-populations with distinct expression patterns. Through case studies, we demonstrated that these techniques can derive high-resolution information from single-cell data that are not feasible using existing techniques.

scholarly journals Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing

2021 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E Wiarda ◽  
Sathesh K Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B cell, conventional CD4 and CD8 αβ T cells, NK cells, and γδ T cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

scholarly journals Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael F. Z. Wang ◽  
Madhav Mantri ◽  
Shao-Pei Chou ◽  
Gaetano J. Scuderi ◽  
David W. McKellar ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Annotation ◽  
Gene Annotation ◽  
Active Regions ◽  
Sequencing Analysis ◽  
Biologically Relevant ◽  
Mole Rat ◽  
Genome Annotations ◽  
Cell Expression ◽  
High Quality Genome

AbstractConventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

scholarly journals treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Thomas S.B. Schmidt ◽  
Christian Von Mering ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Driven ◽  
Rna Seq ◽  
Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

scholarly journals Annotating cell types in human single-cell RNA-seq data with CellO

2021 ◽  
Vol 2 (3) ◽  
pp. 100705
Author(s):  
Matthew N. Bernstein ◽  
Colin N. Dewey
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq
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