Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Wang, Michael F. Z.; Mantri, Madhav; Chou, Shao‐Pei; Scuderi, Gaetano J.; McKellar, David W.; Butcher, Jonathan T.; Danko, Charles G.; Vlaminck, Iwijn De

doi:10.1101/2020.07.31.229575

Cited by 4 publications

(8 citation statements)

References 39 publications

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“…To evaluate the impact of our scRNA-seq datasets on gene detection, we used the hidden Markov model approach described in an associated paper 24 to identify Transcriptionally Active Regions (TARs) of the genome, genomic locations with significant read coverage, using the 10x generated sequencing reads from our scRNA-seq datasets (Fig. 1a).…”

Section: Resultsmentioning

confidence: 99%

“…To uncover unannotated transcriptionally active regions (uTARs), we employed the workflow developed by Wang et al 24 for scRNA-seq data that identifies transcriptionally active regions (TARs), genome regions with abundant transcript alignments, using a previously published tool groHMM 151 . Briefly, all mouse lemur 10x datasets were aligned to genome assembly Mmur 3.0 using STAR with default parameters, without gene annotation indexing, and transcribed regions were predicted using the groHMM tool.…”

Section: Methodsmentioning

confidence: 99%

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Mouse lemur transcriptomic atlas informs primate genes, mutations, physiology, and disease

Ezran¹,

Liu²,

Ming³

et al. 2022

Preprint

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Mouse lemurs (Microcebus spp.) are an emerging model organism for primate biology, behavior, health, and conservation. Although little has been known about their cellular and molecular biology, in the accompanying paper we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types and their full transcriptomic profiles. Here we use this extensive transcriptomic dataset to uncover thousands of previously unidentified genes and hundreds of thousands of new splice junctions in the reference genome that globally define lemur gene structures and cell-type selective expression and splicing and to investigate gene expression evolution. We use the atlas to explore the biology and function of the lemur immune system, including the expression profiles across the organism of all MHC genes and chemokines in health and disease, and the mapping of neutrophil and macrophage development, trafficking, and activation, their local and global responses to infection, and primate-specific aspects of the program. We characterize other examples of primate-specific physiology and disease such as unique features of lemur adipocytes that may underlie their dramatic seasonal rhythms, and spontaneous metastatic endometrial cancer that models the human gynecological malignancy. We identify and describe the organism-wide expression profiles of over 400 primate genes missing in mice, some implicated in human disease. Thus, an organism-wide molecular cell atlas and molecular cell autopsies can enhance gene discovery, structure definition, and annotation in a new model organism, and can identify and elucidate primate-specific genes, physiology, diseases, and evolution.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mouse lemur transcriptomic atlas informs primate genes, mutations, physiology, and disease

Ezran¹,

Liu²,

Ming³

et al. 2022

Preprint

Self Cite

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“…The mouse lemur atlas includes a small number of gonadotrophs that might be expected to express the LHB transcripts. Therefore, we searched for transcripts expressed in the gonadotrophs and identified a candidate gene region (Wang et al, 2021), currently unannotated in NCBI and Ensembl, with high sequence similarity to human and macaque LHB. The putative LHB region is located next to the RUVBL2 gene, as in humans and mice (Parfait et al, 2000).…”

Section: Resultsmentioning

confidence: 99%

“…For MC1R and SCT, we used their annotation in the Ensembl Microcebus murinus genome. For LHB, we used the uTAR-scRNA-seq pipeline described by (Wang et al, 2021) to first predict its chromosome location in the mouse lemur genome. In brief, the pipeline employs the approach of (Chae et al, 2015) to detect transcriptionally active regions (TARs) from aligned sequencing data and annotates these TARs as unannotated (uTARs) or annotated (aTARs) based on their overlap with the existing annotation.…”

Section: Mapping and Counting Transcript Readsmentioning

confidence: 99%

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Liu

Ezran

Wang

et al. 2021

Preprint

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Hormones coordinate long-range cell-cell communication in multicellular organisms and play vital roles in normal physiology, metabolism, and health. Using the newly-completed organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), we have systematically identified hormone-producing and -target cells for 87 classes of hormones, and have created a browsable atlas for hormone signaling that reveals previously unreported sites of hormone regulation and species-specific rewiring. Hormone ligands and receptors exhibited cell-type-dependent, stereotypical expression patterns, and their transcriptional profiles faithfully classified the discrete cell types defined by the full transcriptome, despite their comprising less than 1% of the transcriptome. Although individual cell types generally exhibited the same characteristic patterns of hormonal gene expression, a number of examples of similar or seemingly-identical cell types (e.g., endothelial cells of the lung versus of other organs) displayed different hormonal gene expression patterns. By linking ligand-expressing cells to the cells expressing the corresponding receptor, we constructed an organism-wide map of the hormonal cell-cell communication network. The hormonal cell-cell network was remarkably densely and robustly connected, and included classical hierarchical circuits (e.g. pituitary → peripheral endocrine gland → diverse cell types) as well as examples of highly distributed control. The network also included both well-known examples of feedback loops and a long list of potential novel feedback circuits. This primate hormone atlas provides a powerful resource to facilitate discovery of regulation on an organism-wide scale and at single-cell resolution, complementing the single-site-focused strategy of classical endocrine studies. The network nature of hormone regulation and the principles discovered here further emphasize the importance of a systems approach to understanding hormone regulation.

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“…The reads are aligned to the probe set reference and assigned to the genes they target and the aligned bam file is tagged with the probe and gene ID. 10X Visium FFPE spatial transcriptomics data of five public datasets and two in-house were used as a control in a study to identify potential novel non-coding transcripts in FF tissue samples using a pipeline that identifies transcriptionally active regions (TARs) and classifies them as annotated (aTARs) and unannotated (uTARs) based on their overlap with existing gene annotations [2]. The 10X Visium FFPE protocol sequences a probe that is complementary to the mRNA it is hybridized with.…”

Section: Visium Probesmentioning

confidence: 99%

Spurious off-target signals from potential lncRNAs by 10X Visium probes

Prakrithi

Sami

Jain

et al. 2022

Preprint

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Spatial transcriptomics has revolutionized molecular profiling of tissues in a spatial context, especially in the study of cancer heterogeneity. 10X Genomics facilitates spatial gene expression profiling platforms to help work with fresh-frozen (FF) and formalin fixed paraffin embedded (FFPE) tissues. FF analysis is based on polyA capture of RNAs while FFPE analysis uses a pre-designed set of probes to capture transcripts of coding genes. Previously, we used FFPE spatial data as a 'negative control' in a study to identify novel non-coding RNAs in FF data. Interestingly, we find and report that certain target probes used in FFPE show off-target signals from lncRNAs. The Space Ranger pipeline of 10X Visium counts the expression of these potential off-targets to be that of the corresponding target gene, some of which have known implications in cancer and its diagnosis. Therefore, relying on this technology is not ideal to investigate expression of the genes reported in this study. We hereby recommend excluding those genes in any downstream analysis of FFPE datasets and to design probes with better specificity, considering the sequence similarity between genes and non-coding RNAs.

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Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Cited by 4 publications

References 39 publications

Mouse lemur transcriptomic atlas informs primate genes, mutations, physiology, and disease

Mouse lemur transcriptomic atlas informs primate genes, mutations, physiology, and disease

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Spurious off-target signals from potential lncRNAs by 10X Visium probes

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