Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

Swamy, Vinay; Fufa, Temesgen; Hufnagel, Robert B.; McGaughey, David

doi:10.1101/2021.03.26.437190

Cited by 10 publications

(9 citation statements)

References 63 publications

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“…The power of large-scale single-cell cross-dataset analysis has been demonstrated in several recent studies [9][10][11][12][13][14][15] . We previously performed the first single-cell meta-analysis of the respiratory system, aggregating expression data of three genes important for SARS-CoV-2 cell entry from 31 datasets, spanning over 200 individuals 11 .…”

Section: Introductionmentioning

confidence: 99%

“…Especially when large numbers of individuals, rather than cells, are aggregated, it becomes possible to answer population-level questions using single-cell data. However, currently available integrated atlases are limited in the number of genes 11 , human samples 12,14 , datasets 14 , or cell types 9,12 , and include only non-harmonized or limited cell type annotations [9][10][11][12][13] and subject metadata [9][10][11][12][13] (e.g. age, BMI, smoking status).…”

Section: Introductionmentioning

confidence: 99%

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An integrated cell atlas of the human lung in health and disease

Sikkema

Strobl

Zappia

et al. 2022

Preprint

126

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Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated cell atlas of the human lung in health and disease

Sikkema

Strobl

Zappia

et al. 2022

Preprint

126

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“…Human single cell transcriptome data from the scEiaD resource at plae.nei.nih.gov was searched for machine predicted RPE cells from fovea or peripheral punches. We found 217 cells, 159 from the fovea and 58 from the periphery, across four studies [26][27][28][29][30]. The Seurat object was downloaded on 2022-01-14 from plae.nei.nih.gov and subsetted to these 217 cells.…”

Section: Rna Extractionmentioning

confidence: 99%

Transcriptional mapping of the macaque retina and RPE-choroid reveals conserved inter-tissue transcription drivers and signaling pathways

Mungales

McGaughey

Zhang

et al. 2022

Preprint

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Purpose: The macula and fovea comprise a highly sensitive visual detection tissue that is susceptible to common disease processes like age-related macular degeneration (AMD). Our understanding of the molecular determinants of high acuity vision remains unclear, as few model organisms possess a human-like fovea. We explore transcription factor networks and receptor-ligand interactions to elucidate tissue interactions in the macula and peripheral retina and concomitant changes in the underlying retinal pigment epithelium (RPE)/choroid. Methods: Poly-A selected, 100 bp paired-end RNA-sequencing (RNA-seq) was performed across the macular/foveal, perimacular, and temporal peripheral regions of the neural retina and RPE/choroid tissues of four adult Rhesus macaque eyes to characterize region- and tissue-specific gene expression. RNA-seq reads were mapped to both the macaque and human genomes for maximum alignment and analyzed for differential expression and Gene Ontology (GO) enrichment. Results: Comparison of the neural retina and RPE/choroid tissues indicated distinct, contiguously changing gene expression profiles from fovea through perimacula to periphery. Top GO enrichment of differentially expressed genes in the RPE/choroid included cell junction organization and epithelial cell development. Expression of transcriptional regulators and various disease-associated genes show distinct location-specific preference and retina-RPE/choroid tissue-tissue interactions. Conclusions: Regional gene expression changes in the macaque retina and RPE/choroid is greater than that found in previously published transcriptome analysis of the human retina and RPE/choroid. Further, conservation of human macula-specific transcription factor profiles and gene expression in macaque tissues suggest a conservation of programs required for retina and RPE/choroid function and disease susceptibility.

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“…Merging and integrating the count matrices derived from multiple independent public single-cell RNA sequencing (scRNA-seq) experiments allows for better evaluation of the biological patterns in cell composition of tissues, as well as the identification of patterns of gene expression and gene regulation that are consistent across cells of the same cell type obtained from independent samples ( Swamy et al , 2021 ). A good quality integration of multiple datasets allowing comparison and contrasting of the data across different projects begins with a consistent preprocessing of the samples, using the same reference genome and the same quantification method to generate the count matrices ( Lachmann et al , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

2021

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Motivation Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes’ unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

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Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

Cited by 10 publications

References 63 publications

An integrated cell atlas of the human lung in health and disease

An integrated cell atlas of the human lung in health and disease

Transcriptional mapping of the macaque retina and RPE-choroid reveals conserved inter-tissue transcription drivers and signaling pathways

rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

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