Retinal aging transcriptome and cellular landscape in association with the progression of age-related macular degeneration

Wang, Jiang-Hui; Wong, Raymond C.B.; Liu, Guei‐Sheung

doi:10.1101/2022.04.03.22273375

Cited by 2 publications

(4 citation statements)

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“…To identify genes related to the progressive severity of DR, the selected RNA-Seq raw count was analyzed by DESeq2 (version 1.32.0), 7 using the likelihood-ratio test (LRT), as we previously described. 8 Briefly, gene expression was determined by three variables due to limited access to clinical labeling, consisting of gender, age, and disease status (severity). To simplify the design in the LRT, the progressive disease status was indexed using integer scaling: control-1, diabetic-2, NPDR-3, and NPDR/PDR+DME-4.…”

Section: Methodsmentioning

confidence: 99%

“…To infer the retinal cellular profile, we used a previously derived retinal signature matrix using the HCA scRNA-Seq dataset of the human retina. 8 In brief, the retinal signature matrix contains rod, Müller glia, bipolar, cone, amacrine, microglia, ganglion, astrocytes, and horizontal cells. The retinal signature matrix was used to impute retinal cellular fraction from the bulk transcriptome at 100 permutations with S-mode batch correction.…”

Section: Methodsmentioning

confidence: 99%

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Retinal Transcriptome and Cellular Landscape in Relation to the Progression of Diabetic Retinopathy

Wang

Wong

Liu

2022

Invest. Ophthalmol. Vis. Sci.

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Purpose Previous studies that identify putative genes associated with diabetic retinopathy are only focusing on specific clinical stages, thus resulting genes are not necessarily reflective of disease progression. This study identified genes associated with the severity level of diabetic retinopathy using the likelihood-ratio test (LRT) and ordinal logistic regression (OLR) model, as well as to profile immune and retinal cell landscape in progressive diabetic retinopathy using a machine learning deconvolution approach. Methods This study used a published transcriptomic dataset (GSE160306) from macular regions of donors with different degrees of diabetic retinopathy (10 healthy controls, 10 cases of diabetes, 9 cases of nonproliferative diabetic retinopathy, and 10 cases of proliferative diabetic retinopathy or combined with diabetic macular edema). LRT and OLR models were applied to identify severity-associated genes. In addition, CIBERSORTx was used to estimate proportional changes of immune and retinal cells in progressive diabetic retinopathy. Results By controlling for gender and age using LRT and OLR, 50 genes were identified to be significantly increased in expression with the severity of diabetic retinopathy. Functional enrichment analyses suggested these severity-associated genes are related to inflammation and immune responses. CCND1 and FCGR2B are further identified as key regulators to interact with many other severity-associated genes and are crucial to inflammation. Deconvolution analyses demonstrated that the proportions of memory B cells, M2 macrophages, and Müller glia were significantly increased with the progression of diabetic retinopathy. Conclusions These findings demonstrate that deep analyses of transcriptomic data can advance our understanding of progressive ocular diseases, such as diabetic retinopathy, by applying LRT and OLR models as well as bulk gene expression deconvolution.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Retinal Transcriptome and Cellular Landscape in Relation to the Progression of Diabetic Retinopathy

Wang

Wong

Liu

2022

Invest. Ophthalmol. Vis. Sci.

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show abstract

“…To identify genes related to the progressive severity of diabetic retinopathy, the selected RNA-Seq raw count was analysed by DESeq2 (v1.32.0), 7 using the likelihood-ration test (LRT) as we previously described. 8 Briefly, gene expression was determined by three variables, consisting of gender, age and disease status (severity). Significant severity-associated genes were determined by adjusted p < 0.05, controlled for gender and age.…”

Section: Identification Of Severity-associated Genes In Macular Regio...mentioning

confidence: 99%

“…To infer retinal cellular profile, we used a previously derived retinal signature matrix using the HCA scRNA-Seq dataset of the human retina. 8 In brief, the retinal signature matrix contains rod, Müller glia, bipolar, cone, amacrine, microglia, ganglion, astrocytes, and horizontal cells. The retinal signature matrix was used to impute retinal cellular fraction from the bulk transcriptome at 100 permutations with S-mode batch correction.…”

Section: Deconvolution Analyses Of the Immune Cells And Retinal Cells...mentioning

confidence: 99%

Retinal transcriptome and cellular landscape in relation to the progression of diabetic retinopathy

Wang

Wong

Liu

2022

Preprint

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ImportancePrevious studies identify putative genes associated with diabetic retinopathy only focusing on specific clinical stage, thus resulting genes are not necessarily reflective of disease progression.ObjectiveTo identify genes associated with the severity level of diabetic retinopathy using likelihood-ration test (LRT) and ordinal logistic regression (OLR) model, as well as to profile immune and retinal cell landscape in progressive diabetic retinopathy using a machine learning deconvolution approach.Design, Setting, and ParticipantsThis study used published transcriptomic dataset (GSE160306) from macular regions of donors with different degrees of diabetic retinopathy (10 healthy controls, 10 cases of diabetes, 9 cases of nonproliferative diabetic retinopathy and 10 cases of proliferative diabetic retinopathy or combined with diabetic macular edema). Transcriptomic dataset was released on April 19, 2021; data were analyzed on 28th March 2022.Main Outcomes and MeasuresIdentification of severity-associated genes with LRT and OLR model and proportional changes of immune and retinal cells in progressive diabetic retinopathy.ResultsBy controlling for gender and age using LRT and OLR, 50 genes were identified to be significantly increased in expression with the severity of diabetic retinopathy. Functional enrichment analyses suggested these severity-associated genes are related to inflammation and immune responses. CCND1 and FCGR2B are further identified as key regulators to interact with many other severity-associated genes and are crucial to inflammation. Deconvolution analyses demonstrated that the proportions of memory B cells, M2 macrophages and Müller glia were significantly increased with the progression of diabetic retinopathy.Conclusions and RelevanceThese findings demonstrate the deep analyses of transcriptomic data can advance our understanding of progressive ocular diseases, such as DR, by applying LRT and OLR model as well as bulk gene expression deconvolution.Key PointsQuestionDo genes change in expression or immune cells and retinal cells change in proportions in the human retina with the severity of diabetic retinopathy?FindingsIn this in-depth retinal transcriptomic analysis of 39 participants, consisting of 10 heathy controls, 29 cases of diabetic retinopathy at different clinical stages, severity-associated genes were identified, and the estimated proportions of immune cell and retinal cells were profiled, respectively.MeaningThese findings demonstrate that a group of genes are significantly increased in expression, and the proportions of memory B cells, M2 macrophages and Müller glia were significantly increased with the severity of diabetic retinopathy.

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Retinal aging transcriptome and cellular landscape in association with the progression of age-related macular degeneration

Cited by 2 publications

References 76 publications

Retinal Transcriptome and Cellular Landscape in Relation to the Progression of Diabetic Retinopathy

Retinal Transcriptome and Cellular Landscape in Relation to the Progression of Diabetic Retinopathy

Retinal transcriptome and cellular landscape in relation to the progression of diabetic retinopathy

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