End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

Derr, Alan; Yang, Chyun-Yu; Žilionis, Rapolas; Sergushichev, Alexey; Blodgett, David M.; Redick, Sambra D.; Bortell, Rita; Luban, Jeremy; Harlan, David M.; Kadener, Sebastián; Greiner, Dale L.; Klein, Allon M.; Artyomov, Maxim N.; Garber, Manuel

doi:10.1101/gr.207902.116

Cited by 62 publications

(60 citation statements)

References 60 publications

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“…Primers and probes used for IAV-M, IBV-HA were previously reported (World Health Organization, 2017) (Macosko and Goldman). Genes were quantified using End Sequence Analysis Toolkit (ESAT, github/garber-lab/ESAT) with parameters -wlen 100 -wOlap 50 -wExt 0 -scPrep (Derr et al, 2016). Finally, UMIs that likely result from sequencing errors were corrected by merging any UMIs that were observed only once and have 1 hamming distance from a UMI detected by two or more aligned reads.…”

Section: Rt-qpcrmentioning

confidence: 99%

Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adults

Cao

Vangala

et al. 2020

Preprint

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Influenza virus infections are major causes of morbidity and mortality. Research using cultured cells, bulk tissue, and animal models cannot fully capture human disease dynamics. Many aspects of virus-host interactions in a natural setting remain unclear, including the specific cell types that are infected and how they and neighboring bystander cells contribute to the overall antiviral response. To address these questions, we 35 performed single-cell RNA sequencing (scRNA-Seq) on cells from freshly collected nasal washes from healthy human donors and donors diagnosed with acute influenza during the 2017-18 season. We describe a previously uncharacterized goblet cell population, specific to infected individuals, with high expression of MHC class II genes. Furthermore, leveraging scRNA-Seq reads, we obtained deep viral genome coverage and developed a model to rigorously identify infected cells that detected influenza infection in all epithelial cell types 40 and even some immune cells. Our data revealed that each donor was infected by a unique influenza variant and that each variant was separated by at least one unique non-synonymous difference. Our results demonstrate the power of massively-parallel scRNA-Seq to study viral variation, as well as host and viral transcriptional activity during human infection.45 50

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Section: Rt-qpcrmentioning

confidence: 99%

Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adults

Cao

Vangala

et al. 2020

Preprint

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“…Cleanup, alignment and transcript quantification Sequencing reads were processed as previously described 103 and the pipeline is available through GitHub (github.com/garber-lab/inDrop_Processing). Briefly, fastq files were generated with bcl2fastq using parameters --use-bases-mask y58n*,y*,I*,y16n* --mask-short-adapter-reads 0 --minimum-trimmed-read-length 0 --barcode-mismatches 1.…”

Section: Data Processingmentioning

confidence: 99%

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Donnard

Shu

Garber

2020

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Despite advances in understanding the pathophysiology of Fragile X syndrome (FXS), its molecular bases are still poorly understood. Whole brain tissue expression profiles have proved surprisingly uninformative. We applied single cell RNA sequencing to profile a FXS mouse model. We found that FXS results in a highly cell type specific effect and it is strongest among different neuronal types. We detected a downregulation of mRNAs bound by FMRP and this effect is prominent in neurons. Metabolic pathways including translation are significantly upregulated across all cell types with the notable exception of excitatory neurons. These effects point to a potential difference in the activity of mTOR pathways, and together with other dysregulated pathways suggest an excitatory-inhibitory imbalance in the FXS cortex which is exacerbated by astrocytes. Our data demonstrate the cell-type specific complexity of FXS and provide a resource for interrogating the biological basis of this disorder.

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“…RNA-seq reads were aligned to the genome and transcriptome (dm3) using tophat 33. Gene expression levels from 3' DGE experiments were determined using ESAT tool 34 and differential expression analysis was performed with DEseq2. We considered genes with fold change>1.5 and pvalue<0.05 as significantly changing.…”

Section: Gene Expression Analysismentioning

confidence: 99%

Anin vivostrategy for knockdown of circular RNAs

Reddy

Patop

Krishnamoorthy

et al. 2020

Preprint

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Exonic circular RNAs (circRNAs) are highly abundant and evolutionarily conserved RNAs generated mostly from exons of protein-coding genes. Assaying the functions of circRNAs is not straightforward as common approaches for circRNA depletion tend to also alter the levels of mRNAs generated from the hosting gene. Here we describe a methodology for specific knockdown of circRNAs in vivo with tissue and cell resolution. We also describe an experimental and computational platform for determining the potential off-target effects as well as for verifying the obtained phenotypes. Briefly, we utilize miRNA-derived shRNAs targeted to the circRNAspecific back-splice junction to specifically downregulate the circRNA. We utilized this methodology to downregulate five circRNAs that are highly expressed in the fly nervous system. There were no effects on levels of the linear RNA or any RNA with complementarity to the expressed shRNA. Interestingly, downregulation of circCtrip resulted in developmental lethality that was recapitulated with a second shRNA. Moreover, we found that downregulation of individual circRNAs caused specific changes in the fly head transcriptome, suggesting roles for these circRNAs in the fly nervous system. Together, our results provide a methodological approach that enables the comprehensive study of circRNAs at the organismal and cell levels.

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End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

Cited by 62 publications

References 60 publications

Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adults

Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adults

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Anin vivostrategy for knockdown of circular RNAs

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