scholarly journals Single cell RNA-seq reveals cellular diversity and developmental characteristics of human infant retina

2019 ◽  
Author(s):  
Jixing Zhong ◽  
Dongsheng Chen ◽  
Fangyuan Hu ◽  
Fang Chen ◽  
Zaoxu Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Developmental Process ◽  
Retinal Disease ◽  
Amacrine Cells ◽  
Bipolar Cells ◽  
Human Infant ◽  
Retinal Cell ◽  
Rna Seq ◽  
Human Infants ◽  
Retina Development

AbstractRetina, located in the innermost layer of the eye of human, holds the decisive role in visual perception. Dissecting the heterogeneity of retina is essential for understanding the mechanism of vision formation and even the development of central nervous system (CNS). Here, we performed single cell RNA-seq, analyzed 57,832 cells from human infant donors, resulting in 20 distinct clusters representing major cell types in retina: rod photoreceptors, cone photoreceptors, bipolar cells, horizontal cells, amacrine cells, Muller glia cells and microglia. We next constructed extensive networks of intercellular communication and identified ligand-receptor interactions playing crucial roles in regulating neural cell development and immune homeostasis in retina. Though re-clustering, we identified known subtypes in cone PRs and additional unreported subpopulations and corresponding markers in rod PRs as well as bipolar cells. Additionally, we linked inherited retinal disease to certain cell subtypes or subpopulations through enrichment analysis. Intriguingly, we found that status and functions of photoreceptors changed drastically between early and late retina. Overall, our study offers the first retinal cell atlas in human infants, dissecting the heterogeneity of retina and identifying the key molecules in the developmental process, which provides an important resource that will pave the way for retina development mechanism research and regenerative medicine concerning retinal biology.

scholarly journals Combined analysis of single cell RNA-Seq and ATAC-Seq data reveals putative regulatory toggles operating in native and iPS-derived retina

2020 ◽  
Author(s):  
Anouk Georges ◽  
Haruko Takeda ◽  
Arnaud Lavergne ◽  
Michiko Mandai ◽  
Fanny Lepiemme ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Retinal Development ◽  
Amacrine Cells ◽  
Regulatory Elements ◽  
Precursor Cells ◽  
Retinal Cell ◽  
Rna Seq ◽  
Combined Analysis ◽  
Temporal Sampling

AbstractBackgroundIt has recently become possible to recapitulate retinal development from induced pluripotent stem cells, opening new investigative and therapeutic opportunities. Single cell RNA sequencing allows comparison of transcriptome unfolding during in vivo and in vitro development at single cell resolution, which can be integrated with information about accessible regulatory elements identified by ATAC-Seq.ResultsWe report the generation and analysis of single-cell RNA-Seq data (> 38,000 cells) from native and iPSC-derived murine retina at four matched developmental stages spanning the emergence of the major retinal cell types. We combine information from temporal sampling, visualization of 3D UMAP manifolds, and RNA velocity to show that iPSC-derived 3D retinal aggregates broadly recapitulate the native developmental trajectories with evidence supporting re-specification from amacrine cells to horizontal and photoreceptor precursor cells, as well as a direct differentiation of Tbr1+ retinal ganglion cells from neuro-epithelium cells. We show relaxation of spatial and temporal transcriptome control, premature emergence and dominance of photoreceptor precursor cells, and susceptibility of dynamically regulated pathways and transcription factors to culture conditions in iPSC-derived retina. We generate bulk ATAC-Seq data for native and iPSC-derived murine retina identifying ∼125,000 peaks. We combine single-cell RNA-Seq with ATAC-Seq information and obtain evidence that approximately halve the transcription factors that are dynamically regulated during retinal development may act as repressors rather than activators. We propose that sets of activators and repressors with cell-type specific expression control “regulatory toggles” that lock cells in distinct transcriptome states underlying differentiation, with subtle but noteworthy differences between native and iPSC-derived retina.ConclusionsCombined analysis of single-cell RNA-Seq and ATAC-Seq information has refined the comparison of native and iPS-derived retinal development.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Molecular identification of sixty-three amacrine cell types completes a mouse retinal cell atlas

2020 ◽  
Author(s):  
Wenjun Yan ◽  
Mallory A. Laboulaye ◽  
Nicholas M. Tran ◽  
Irene E. Whitney ◽  
Inbal Benhar ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
High Throughput ◽  
Amacrine Cells ◽  
Cell Types ◽  
Retinal Cell ◽  
Mouse Retina ◽  
Single Cell Rna Sequencing ◽  
Neuronal Types ◽  
The Brain

ABSTRACTAmacrine cells (ACs) are a diverse class of interneurons that modulate input from photoreceptors to retinal ganglion cells (RGCs), rendering each RGC type selectively sensitive to particular visual features, which are then relayed to the brain. While many AC types have been identified morphologically and physiologically, they have not been comprehensively classified or molecularly characterized. We used high-throughput single-cell RNA sequencing (scRNA-seq) to profile >32,000 ACs from mouse retina, and applied computational methods to identify 63 AC types. We identified molecular markers for each type, and used them to characterize the morphology of multiple types. We show that they include nearly all previously known AC types as well as many that had not been described. Consistent with previous studies, most of the AC types express markers for the canonical inhibitory neurotransmitters GABA or glycine, but several express neither or both. In addition, many express one or more neuropeptides, and two express glutamatergic markers. We also explored transcriptomic relationships among AC types and identified transcription factors expressed by individual or multiple closely related types. Noteworthy among these were Meis2 and Tcf4, expressed by most GABAergic and most glycinergic types, respectively. Together, these results provide a foundation for developmental and functional studies of ACs, as well as means for genetically accessing them. Along with previous molecular, physiological and morphological analyses, they establish the existence of at least 130 neuronal types and nearly 140 cell types in mouse retina.SIGNIFICANCE STATEMENTThe mouse retina is a leading model for analyzing the development, structure, function and pathology of neural circuits. A complete molecular atlas of retinal cell types provides an important foundation for these studies. We used high-throughput single-cell RNA sequencing (scRNA-seq) to characterize the most heterogeneous class of retinal interneurons, amacrine cells, identifying 63 distinct types. The atlas includes types identified previously as well as many novel types. We provide evidence for use of multiple neurotransmitters and neuropeptides and identify transcription factors expressed by groups of closely related types. Combining these results with those obtained previously, we proposed that the mouse retina contains 130 neuronal types, and is therefore comparable in complexity to other regions of the brain.

scholarly journals Generation of a Retina Reporter hiPSC Line to Label Progenitor, Ganglion, and Photoreceptor Cell Types

2019 ◽  
Author(s):  
Phuong T. Lam ◽  
Christian Gutierrez ◽  
Katia Del Rio-Tsonis ◽  
Michael L. Robinson
Keyword(s):  
Fluorescent Protein ◽  
Ganglion Cells ◽  
Fluorescent Proteins ◽  
Retinal Development ◽  
Retinal Disease ◽  
Cell Types ◽  
Retinal Cell ◽  
Therapeutic Drug ◽  
Retina Development ◽  
Induced Pluripotent

ABSTRACTEarly in mammalian eye development, VSX2, BRN3b, and RCVRN expression marks neural retina progenitors (NRPs), retinal ganglion cells (RGCs), and photoreceptors (PRs), respectively. The ability to create retinal organoids from human induced pluripotent stem cells (hiPSC) holds great potential for modeling both human retinal development and retinal disease. However, no methods allowing the simultaneous, real-time monitoring of multiple specific retinal cell types during development currently exist. Here, we describe a CRISPR/Cas9 gene editing strategy to generate a triple transgenic reporter hiPSC line (PGP1) that utilizes the endogenous VSX2, BRN3b, and RCVRN promoters to specifically express fluorescent proteins (Cerulean in NRPs, eGFP in RGCs and mCherry in PRs) without disrupting the function of the endogenous alleles. Retinal organoid formation from the PGP1 line demonstrated the ability of the edited cells to undergo normal retina development while exhibiting appropriate fluorescent protein expression consistent with the onset of NRPs, RGCs, and PRs. Organoids produced from the PGP1 line expressed transcripts consistent with the development of all major retinal cell types. The PGP1 line offers a powerful new tool to study retinal development, retinal reprogramming, and therapeutic drug screening.

scholarly journals Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data

2018 ◽  
Author(s):  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Developmental Process ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Supplementary Information ◽  
Rna Seq ◽  
Inference Algorithms ◽  
Continuous State ◽  
Efficient Learning

AbstractMotivationMethods for reconstructing developmental trajectories from time series single cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods, are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.ResultsWe developed a new method based on continuous state HMMs (CSHMMs) for representing and modeling time series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single cell datasets we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.AvailabilitySoftware and Supporting website: www.andrew.cmu.edu/user/chiehll/CSHMM/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Developing Stage-dependent Retinal Toxicity Induced by l-glutamate in Neonatal Rats

2016 ◽  
Vol 44 (8) ◽  
pp. 1137-1145 ◽  
Author(s):  
Hikaru Mitori ◽  
Takeshi Izawa ◽  
Mitsuru Kuwamura ◽  
Masahiro Matsumoto ◽  
Jyoji Yamate
Keyword(s):  
Subcutaneous Administration ◽  
Amacrine Cells ◽  
Bipolar Cells ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Target Cells ◽  
Retinal Damage ◽  
Neonatal Rats ◽  
Neurotransmitter Glutamate

The neurotransmitter glutamate causes excitotoxicity in the human retina. In neonatal rats, the degree of glutamate-induced retinal damage depends on age at administration. To elucidate the sensitivity to glutamate on various developing stage of retina, we investigated glutamate-induced retinal damage and glutamate target cells on each postnatal day (PND). Newborn rats received a single subcutaneous administration of l-glutamate on PNDs 1 to 14. Retinal cell apoptosis characterized as pyknotic and terminal deoxynucleotidyl transferase–mediated dUTP digoxigenin nick end labeling–positive nuclei was analyzed at 6 hr after treatment, and sequential morphological features of retina were evaluated on PND 21. The inner retina on PND 21 exhibited thinning in rats treated after PND 2. The thinning was most severe in rats treated on PND 8 and the number of apoptotic cells also peaked. No thinning was observed in rats treated on PND 14. In the inner nuclear layer, glutamate target cells were mainly amacrine cells; additionally, bipolar cells and horizontal cells were damaged on PND 8. These retinal changes were more severe in central retina than those in peripheral retina on PND 8. Our findings indicate the morphological consequences of glutamate-induced retinal excitotoxicity and glutamate target cells on each PND and reveal that glutamate-induced retinal damage depends on developing stage.

scholarly journals Different lineage contexts direct common pro-neural factors to specify distinct retinal cell subtypes

2020 ◽  
Vol 219 (9) ◽  
Author(s):  
Mei Wang ◽  
Lei Du ◽  
Aih Cheun Lee ◽  
Yan Li ◽  
Huiwen Qin ◽  
...  
Keyword(s):  
Amacrine Cells ◽  
Photoreceptor Cells ◽  
Chromatin Accessibility ◽  
Bipolar Cells ◽  
Lineage Tracing ◽  
Retinal Cell ◽  
Dependent Manner ◽  
Retinal Neuron ◽  
Neural Factors

How astounding neuronal diversity arises from variable cell lineages in vertebrates remains mostly elusive. By in vivo lineage tracing of ∼1,000 single zebrafish retinal progenitors, we identified a repertoire of subtype-specific stereotyped neurogenic lineages. Remarkably, within these stereotyped lineages, GABAergic amacrine cells were born with photoreceptor cells, whereas glycinergic amacrine cells were born with OFF bipolar cells. More interestingly, post-mitotic differentiation blockage of GABAergic and glycinergic amacrine cells resulted in their respecification into photoreceptor and bipolar cells, respectively, suggesting lineage constraint in cell subtype specification. Using single-cell RNA-seq and ATAC-seq analyses, we further identified lineage-specific progenitors, each defined by specific transcription factors that exhibited characteristic chromatin accessibility dynamics. Finally, single pro-neural factors could specify different neuron types/subtypes in a lineage-dependent manner. Our findings reveal the importance of lineage context in defining neuronal subtypes and provide a demonstration of in vivo lineage-dependent induction of unique retinal neuron subtypes for treatment purposes.

scholarly journals Continuous-state HMMs for modeling time-series single-cell RNA-Seq data

2019 ◽  
Vol 35 (22) ◽  
pp. 4707-4715 ◽  
Author(s):  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Developmental Process ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Supplementary Information ◽  
Rna Seq ◽  
Inference Algorithms ◽  
Continuous State ◽  
Efficient Learning

Abstract Motivation Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy. Results We developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single-cell datasets, we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types. Availability and implementation Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenbo Yu ◽  
Ahmed Mahfouz ◽  
Marcel J. T. Reinders
Keyword(s):  
Single Cell ◽  
Developmental Process ◽  
Mouse Cell ◽  
Cell Alignment ◽  
Rna Seq ◽  
Cell Type ◽  
Expression Of Genes ◽  
A Cell ◽  
Differential Expression Of Genes ◽  
Cell Cell

The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between these batches. Typically, these batch effects are resolved by matching similar cells across the different batches. Current approaches, however, do not take into account that we can constrain this matching further as cells can also be matched on their cell type identity. We use an auto-encoder to embed two batches in the same space such that cells are matched. To accomplish this, we use a loss function that preserves: (1) cell-cell distances within each of the two batches, as well as (2) cell-cell distances between two batches when the cells are of the same cell-type. The cell-type guidance is unsupervised, i.e., a cell-type is defined as a cluster in the original batch. We evaluated the performance of our cluster-guided batch alignment (CBA) using pancreas and mouse cell atlas datasets, against six state-of-the-art single cell alignment methods: Seurat v3, BBKNN, Scanorama, Harmony, LIGER, and BERMUDA. Compared to other approaches, CBA preserves the cluster separation in the original datasets while still being able to align the two datasets. We confirm that this separation is biologically meaningful by identifying relevant differential expression of genes for these preserved clusters.

scholarly journals Integrating Spatial Transcriptomics and Single-Cell RNA-seq Reveals the Gene Expression Profling of the Human Embryonic Liver

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianliang Hou ◽  
Yane Yang ◽  
Ping Li ◽  
Zhipeng Zeng ◽  
Wenlong Hu ◽  
...  
Keyword(s):  
Liver Disease ◽  
Liver Regeneration ◽  
Single Cell ◽  
Developmental Process ◽  
Fetal Liver ◽  
Cell Types ◽  
Developmental Time ◽  
Clear Understanding ◽  
Rna Seq ◽  
Liver Replacement

The liver is one of vital organs of the human body, and it plays an important role in the metabolism and detoxification. Moreover, fetal liver is one of the hematopoietic places during ontogeny. Understanding how this complex organ develops during embryogenesis will yield insights into how functional liver replacement tissue can be engineered and how liver regeneration can be promoted. Here, we combine the advantages of single-cell RNA sequencing and Spatial Transcriptomics (ST) technology for unbiased analysis of fetal livers over developmental time from 8 post-conception weeks (PCW) and 17 PCW in humans. We systematically identified nine cell types, and defined the developmental pathways of the major cell types. The results showed that human fetal livers experienced blood rapid growth and immigration during the period studied in our experiments, and identified the differentially expressed genes, and metabolic changes in the developmental process of erythroid cells. In addition, we focus on the expression of liver disease related genes, and found that 17 genes published and linked to liver disease mainly expressed in megakaryocyte and endothelial, hardly expressed in any other cell types. Together, our findings provide a comprehensive and clear understanding of the differentiation processes of all main cell types in the human fetal livers, which may provide reference data and information for liver disease treatment and liver regeneration.

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