Ocular macrophage origin and heterogeneity during steady state and experimental choroidal neovascularization

Abstract Background Neovascular age-related macular degeneration (nAMD) commonly causes vision loss from aberrant angiogenesis, termed choroidal neovascularization (CNV). Macrophages are heterogeneous cells that are necessary for experimental CNV, present in human CNV samples, and can display diverse functions, which are dependent upon both their origin and tissue microenvironment. Despite these associations, choroidal macrophage heterogeneity remains unexplored. Methods We performed multi-parameter flow cytometry on wildtype (WT) and Ccr2−/− mice after laser injury to identify macrophage subtypes, and determine which subsets originate from classical monocytes. To fate map tissue resident macrophages at steady state and after laser injury, we used the Cx3cr1CreER/+ ; Rosa26zsGFP/+ mouse model. We reanalyzed previously published single-cell RNA-seq of human choroid samples from healthy and nAMD patients to investigate human macrophage heterogeneity, disease association, and function. Results We identified 4 macrophage subsets in mice: microglia, MHCII+CD11c−, MHCII+CD11c+, and MHCII−. Microglia are tissue resident macrophages at steady state and unaffected by laser injury. At steady state, MHCII− macrophages are long lived, tissue resident macrophages, while MHCII+CD11c− and MHCII+CD11c+ macrophages are partially replenished from blood monocytes. After laser injury, MHCII+CD11c− macrophages are entirely derived from classical monocytes, MHCII− macrophages originate from classical monocytes (90%) and an expansion of tissue resident macrophages (10%), and MHCII+CD11c+ macrophages are derived from classical monocytes (70%), non-classical monocytes (10%), and an expansion of tissue resident macrophages (20%). Single-cell RNA-seq analysis of human choroid found 5 macrophage subsets: two MHCII+CD11C− and three MHCII+CD11C+ populations. One MHCII+CD11C+ subset was 78% derived from a patient with nAMD. Differential expression analysis identified up-regulation of pro-angiogenic gene expression in one MHCII+CD11C− and two MHCII+CD11C+ subsets, including the disease-associated cluster. The upregulated MHCII+CD11C− pro-angiogenic genes were unique compared to the increased MHCII+CD11C+ angiogenesis genes. Conclusions Macrophage origin impacts heterogeneity at steady state and after laser injury in mice. Both mice and human patients demonstrate similar macrophage subtypes. Two discrete pro-angiogenic macrophage populations exist in the human choroid. Targeting specific, pro-angiogenic macrophage subsets is a potential novel therapeutic for nAMD.

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Dendritic cells play no significant role in the laser-induced choroidal neovascularization model

Scientific Reports ◽

10.1038/s41598-021-96704-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Steven Droho ◽

Harris Perlman ◽

Jeremy A. Lavine

Keyword(s):

Dendritic Cells ◽

Steady State ◽

Choroidal Neovascularization ◽

Significant Role ◽

Age Related Macular Degeneration ◽

Complement Components ◽

Innate And Adaptive Immunity ◽

Laser Injury ◽

Age Related

AbstractAge-related macular degeneration (AMD) is genetically associated with complement. Dendritic cells (DCs) play key roles during innate and adaptive immunity, and express complement components and their receptors. We investigated ocular DC heterogeneity and the role of DCs in the laser-induced choroidal neovascularization (CNV) model. In order to determine the function of DCs, we used two models of DC deficiency: the Flt3−/− and Flt3l−/− mouse. We identified three types of ocular DCs: plasmacytoid DC, classical DC-1, and classical DC-2. At steady-state, classical DCs were found in the iris and choroid but were not detectable in the retina. Plasmacytoid DCs existed at very low levels in iris, choroid, and retina. After laser injury, the number of each DC subset was up-regulated in the choroid and retina. In Flt3−/− mice, we found reduced numbers of classical DCs at steady-state, but each DC subset equally increased after laser injury between wildtype and Flt3−/− mice. In Flt3l−/− mice, each DC subsets was severely reduced after laser injury. Neither Flt3−/− or Flt3l−/− mice demonstrated reduced CNV area compared to wildtype mice. DCs do not play any significant role during the laser-induced CNV model of neovascular AMD.

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Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Andrew E. Teschendorff

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Large Scale ◽

Single Cells ◽

Differential Expression Analysis ◽

Dropout Rate ◽

Rna Seq ◽

Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

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Two-phase differential expression analysis for single cell RNA-seq

Bioinformatics ◽

10.1093/bioinformatics/bty329 ◽

2018 ◽

Vol 34 (19) ◽

pp. 3340-3348 ◽

Cited By ~ 11

Author(s):

Zhijin Wu ◽

Yi Zhang ◽

Michael L Stitzel ◽

Hao Wu

Keyword(s):

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Rna Seq ◽

Two Phase

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A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽

10.3390/genes12121947 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1947

Author(s):

Samarendra Das ◽

Anil Rai ◽

Michael L. Merchant ◽

Matthew C. Cave ◽

Shesh N. Rai

Keyword(s):

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Differential Expression Analysis ◽

Individual Performance ◽

Rna Seq ◽

Gene Expressions ◽

Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

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Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data

10.1101/143289 ◽

2017 ◽

Cited By ~ 16

Author(s):

Charlotte Soneson ◽

Mark D. Robinson

Keyword(s):

Single Cell ◽

Differential Expression ◽

Statistical Methods ◽

Expression Analysis ◽

Method Development ◽

Differential Expression Analysis ◽

Data Sets ◽

Rna Seq ◽

Data Set ◽

Extensive Evaluation

AbstractBackgroundAs single-cell RNA-seq (scRNA-seq) is becoming increasingly common, the amount of publicly available data grows rapidly, generating a useful resource for computational method development and extension of published results. Although processed data matrices are typically made available in public repositories, the procedure to obtain these varies widely between data sets, which may complicate reuse and cross-data set comparison. Moreover, while many statistical methods for performing differential expression analysis of scRNA-seq data are becoming available, their relative merits and the performance compared to methods developed for bulk RNA-seq data are not sufficiently well understood.ResultsWe present conquer, a collection of consistently processed, analysis-ready public single-cell RNA-seq data sets. Each data set has count and transcripts per million (TPM) estimates for genes and transcripts, as well as quality control and exploratory analysis reports. We use a subset of the data sets available in conquer to perform an extensive evaluation of the performance and characteristics of statistical methods for differential gene expression analysis, evaluating a total of 30 statistical approaches on both experimental and simulated scRNA-seq data.ConclusionsConsiderable differences are found between the methods in terms of the number and characteristics of the genes that are called differentially expressed. Pre-filtering of lowly expressed genes can have important effects on the results, particularly for some of the methods originally developed for analysis of bulk RNA-seq data. Generally, however, methods developed for bulk RNA-seq analysis do not perform notably worse than those developed specifically for scRNA-seq.

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Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Genes ◽

10.3390/genes11070792 ◽

2020 ◽

Vol 11 (7) ◽

pp. 792 ◽

Cited By ~ 2

Author(s):

Liang Chen ◽

Yuyao Zhai ◽

Qiuyan He ◽

Weinan Wang ◽

Minghua Deng

Keyword(s):

Unsupervised Learning ◽

Single Cell ◽

Supervised Learning ◽

Reference Data ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Marker Genes ◽

Rna Seq ◽

Supervised Clustering ◽

Cell Clustering

As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Most existing single-cell RNA-seq (scRNA-seq) data clustering algorithms do not take into account the available cell annotation results on the same tissues or organisms from other laboratories. Nonetheless, such data could assist and guide the clustering process on the target dataset. Identifying marker genes through differential expression analysis to manually annotate large amounts of cells also costs labor and resources. Therefore, in this paper, we propose a novel end-to-end cell supervised clustering and annotation framework called scAnCluster, which fully utilizes the cell type labels available from reference data to facilitate the cell clustering and annotation on the unlabeled target data. Our algorithm integrates deep supervised learning, self-supervised learning and unsupervised learning techniques together, and it outperforms other customized scRNA-seq supervised clustering methods in both simulation and real data. It is particularly worth noting that our method performs well on the challenging task of discovering novel cell types that are absent in the reference data.

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scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

Bioinformatics ◽

10.1093/bioinformatics/btaa097 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3115-3123 ◽

Cited By ~ 3

Author(s):

Teng Fei ◽

Tianwei Yu

Keyword(s):

Single Cell ◽

Differential Expression Analysis ◽

Distance Matrix ◽

Real Data ◽

R Package ◽

Batch Effect ◽

Supplementary Information ◽

Rna Seq ◽

Sequencing Data ◽

Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

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SC1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis

10.1101/2021.03.19.435534 ◽

2021 ◽

Author(s):

Marmar Moussa ◽

Ion Mandoiu

Keyword(s):

Data Analysis ◽

Single Cell ◽

Differential Expression Analysis ◽

Analysis Tool ◽

Rna Seq ◽

Web Based ◽

Inverse Document Frequency ◽

Sequencing Technologies ◽

Document Frequency ◽

Novel Method

Single cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. Here, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on Term-Frequency Inverse-Document-Frequency (TF-IDF) scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single cell omics data modalities like TCR-Seq and supports several single cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

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deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

10.1101/387621 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aanchal Mongia ◽

Debarka Sengupta ◽

Angshul Majumdar

Keyword(s):

Single Cell ◽

Missing Values ◽

Matrix Completion ◽

Differential Expression Analysis ◽

Learning Problems ◽

Great Success ◽

Rna Seq ◽

The Past ◽

Deep Architecture ◽

Positive Results

AbstractSingle cell RNA-seq has fueled discovery and innovation in medicine over the past few years and is useful for studying cellular responses at individual cell resolution. But, due to paucity of starting RNA, the data acquired is highly sparse. To address this, We propose a deep matrix factorization based method, deepMc, to impute missing values in gene-expression data. For the deep architecture of our approach, We draw our motivation from great success of deep learning in solving various Machine learning problems. In this work, We support our method with positive results on several evaluation metrics like clustering of cell populations, differential expression analysis and cell type separability.

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Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

10.1101/2020.01.15.907964 ◽

2020 ◽

Author(s):

Matteo Calgaro ◽

Chiara Romualdi ◽

Levi Waldron ◽

Davide Risso ◽

Nicola Vitulo

Keyword(s):

Data Analysis ◽

Single Cell ◽

Differential Expression Analysis ◽

Informed Choice ◽

Correct Identification ◽

Rna Seq ◽

Experimental Conditions ◽

Exploratory Data ◽

Microbiome Data ◽

False Discoveries

AbstractBackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.ResultsHere, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

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