image processing of rep deposits, segmentation, and quantification

Modelando e analisando a degeneração da retina

The retinal pigment epithelium (RPE) is a monolayer of cells located in the back of the eye adjacent to retinal photoreceptors. RPE plays a vital role in maintaining proper vision by providing metabolic and structural support to the photoreceptors. Healthy RPE cells are characterized by a distinct hexagonal morphology. They are connected by tight junctions, which allow the RPE to act as a barrier between the choriocapillaris located on its basal side and photoreceptors located apically. To maintain the retinal ecosystem, RPE shuttles key metabolites, e.g., glucose, to photoreceptors in a way that minimizes glucose consumption in the RPE1. Due to this limitation, RPE depends on other metabolites to maintain their metabolic needs, including fatty acids, which RPE converts to ketones through &#946;-oxidation2. Given the propensity of RPE to utilize fatty acids, which are likely recycled from photoreceptor outer segment (POS) digestion, as an energy source, detrimental changes to the lipid processing pathways in RPE often lead to, or are implicated in, both monogenic and polygenic degenerative retinal diseases3.
Age-related macular degeneration (AMD), a polygenic degenerative eye disease that causes RPE degeneration, has also been linked to aberrant autophagy and lipid metabolism in the RPE monolayer. The failure of a dysfunctional RPE monolayer to process POS and perform other critical functions leads to extracellular (sub-RPE) deposits called basal linear deposits (BLinD) located between the RPE and Bruch&#39;s membrane - a hallmark of AMD pathologies. Major components of BLinD include lipoproteins, the most abundant of which is apolipoprotein E (APOE)4. Accumulation of thin layers of BLinD can lead to soft drusen, which is recognized as a clinical symptom of AMD5,6.
Several groups have shown that stem cell-derived in vitro disease models that cause RPE dysfunction feature sub-RPE lipid accumulation7,8,9. Hallam et al. (2017) generated induced pluripotent stem cell-derived RPE (iRPE) from patients with a high risk for AMD due to a polymorphism of the CFH gene. The iRPE showed drusen accumulation, as marked by APOE, and the high-risk RPE accumulated larger deposits than iRPE generated from low-risk patients10.
To create an in vitro model that recapitulates cellular hallmarks of AMD, such as lipid droplets and drusen deposition, iRPE lines generated from patient blood samples were established using a previously published developmentally guided protocol11. The iRPE were subjected to complement-competent human serum (CC-HS), a solution containing anaphylatoxins that mimic one possible cause of AMD: increased alternate complement signaling8. The resulting cellular and sub-cellular deposition of lipid deposits was measured using commonly used lipid and lipoprotein markers, APOE, Nile Red, and&#160;BODIPY. Through these markers, it was shown that activated complement signaling via CC-HS exacerbated lipid accumulation in iRPE cells8.
To develop a disease model for a monogenic retinal degenerative disease, iRPE lines were developed from patients with Stargardt disease, a disease caused by mutations to the ABCA4 gene in RPE. It has previously been shown that when ABCA4 is knocked out, A2E lipofuscin, an intracellular deposit known to contain high levels of phospholipids and light-dependent lipid peroxidation products, accumulates inside the RPE12. ABCA4 knockout lines were developed alongside the patient lines, and both were subjected to POS feeding. The Stargardt iRPE demonstrated POS phagocytosis-induced pathology, exhibiting increased lipid accumulation quantified by BODIPY staining. RPE derived from ABCA4 KO iPSCs were subjected to CC-HS treatment; quantification of the BODIPY signal showed a defect in lipid handling in the Stargardt disease model as well9.
Given the prevalence of these diseases and the need for effective therapeutics, along with the relevant disease models described above, there is a need to establish robust methods for quantifying the efficacy of potential treatments. To quantify lipid deposits in a way that is objective, automated, and standardized, a machine-learning-based software, LipidUNet, was created so that, when paired with mask analysis tools, lipid deposition can quickly and effectively be identified using the common markers Nile Red, BODIPY, and APOE. The summary statistics obtained using this analysis pipeline can then be analyzed and displayed graphically, allowing for easy comparison of treatment conditions. The schematic of the protocol is shown in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65503/65503fig01.jpg" /> 
Figure 1: Schematic of the protocol: RPE cells are grown on a 96-well plate and challenged with active human serum or purified bovine outer segments to model different types of retinal degenerations in vitro. RPE cells are fixed and stained for lipoprotein deposits with Nile Red, BODIPY, and APOE. A confocal microscope is used to acquire Z-stacks of fluorescently-labeled lipid particles, which are subsequently processed into 2D maximum intensity projections. A machine-learning algorithm was trained to recognize and correctly segment lipoprotein particles. Summary tables containing particle count and various shape metrics are generated and can be used for subsequent plotting and statistical analysis. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Autor em destaque: Desvendando a patogênese da degeneração macular relacionada à idade e descobrindo possíveis terapias 

Método LipidUNet-Machine Learning de Caracterização e Quantificação de Depósitos Lipídicos Usando Epitélio Pigmentado Retiniano Derivado de iPSC

In this study, we aim to understand the age-related macro degeneration pathogenesis and find the potential treatment for it. This semi-automated pipeline for lipid droplet analysis provides a robust system to study metabolism and aging diseases. Our lab focuses on finding drug targets for the treatment of AMD either using genetic or small molecule screens that aimed at reducing the accumulation of lipid deposits.

Watch this Scientific Journal Video about Image Processing of REP Deposits, Segmentation, and Quantification at JoVE.com

Image Processing of REP Deposits, Segmentation, and Quantification  

Processamento de Imagens de Depósitos de REP, Segmentação e Quantificação

Usando um microscópio confocal e uma objetiva de 40X, crie um perfil de varredura com os canais fluorescentes apropriados para o marcador lipídico utilizado. Depois de configurar o modo de aquisição e os canais, otimize a estratégia de foco clicando em Foco automático de software. Em seguida, defina o intervalo da Pilha Z para 20 mícrons.Depois disso, otimize os blocos e abra o visualizador para selecionar as posições dos blocos. Em seguida, usando um método de processamento de imagem em lote, crie projeções máximas de cada Pilha Z com o método de profundidade de foco estendida. Antes de exportar as imagens, defina a compactação como nenhuma e verifique se os dados originais estão marcados.A imagem resultante é uma escala de cinza de projeção máxima TIFF apenas do canal de fluorescência, expressando o marcador lipídico. Identifique as imagens TIFF que representam Nile Red, BODIPY ou APOE e mova-as para a pasta Images dentro de um diretório chamado Nile Red, BODIPY ou APOE, dependendo do método que está sendo usado. Abra o software da unidade lipídica.Na guia Prever, selecione o diretório relevante clicando nas reticências e navegando até o diretório nomeado. Confirme se a unidade lipídica identificou as imagens corretamente verificando a entrada da classe. Clique em Prever para observar o progresso da tarefa e, em seguida, usando a ferramenta Análise de máscara, itere pelas máscaras geradas e forneça uma contagem quantitativa dos depósitos lipídicos de limite das imagens da máscara.O sucesso na diferenciação e maturação do EPR mostrou uma monocamada confluente com pigmentação e morfologia poligonal. Em contraste, a diferenciação malsucedida mostrou aglomerados de células moribundas. Nas imagens fluorescentes, os depósitos de Nile Red e BODIPY apareceram como pequenos pontos circulares brilhantes.Um resultado negativo mostra segmentação incorreta da imagem ao confundir fluorescência de fundo com um depósito, seja devido à coloração fraca ou alta intensidade de fundo. Os depósitos de APOE variam em tamanho, forma e intensidade de sinal e requerem otimização de métodos de coloração e imagem para minimizar a variação são mostrados.

image-processing-of-rep-deposits-segmentation-and-quantification

Research

JoVE Journal

Biology

Image Processing of REP Deposits, Segmentation, and Quantification

355 visualizações.  National Institutes of Health (NIH). Usando um microscópio confocal e uma objetiva de 40X, crie um perfil de varredura com os canais fluorescentes apropriados para o marcador lipídico utilizado. Depois de configurar o modo de aquisição e os canais, otimize a estratégia de foco clicando em Foco automático de software. Em seguida, defina o intervalo da Pilha Z para 20 mícrons.Depois disso, otimize os blocos e abra o visualizador para selecionar as posições dos blocos. Em seguida, usando um método de processamento ...

Vídeo: Image Processing of REP Deposits, Segmentation, and Quantification  

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to photoreceptors and choroidal capillaries, performs phagocytosis of photoreceptor outer segments (POS), and secretes cytokines in a polarized manner for maintaining the homeostasis of the outer retina. Dysfunctional RPE, caused by mutations, aging, and environmental factors, results in the degeneration of other retinal layers and causes vision loss. A hallmark phenotypic feature of degenerating RPE is intra and sub-cellular lipid-rich deposits. These deposits are a common phenotype across different retinal degenerative diseases. To reproduce the lipid deposit phenotype of monogenic retinal degenerations in vitro, induced pluripotent stem cell-derived RPE (iRPE) was generated from patients&#39; fibroblasts. Cell lines generated from patients with Stargardt and Late-onset retinal degeneration (L-ORD) disease were fed with POS for 7 days to replicate RPE physiological function, which caused POS phagocytosis-induced pathology in these diseases. To generate a model for age-related macular degeneration (AMD), a polygenic disease associated with alternate complement activation, iRPE was challenged with alternate complement anaphylatoxins. The intra and sub-cellular lipid deposits were characterized using Nile Red, boron-dipyrromethene (BODIPY), and apolipoprotein E (APOE). To quantify the density of lipid deposits, a machine learning-based software, LipidUNet, was developed. The software was trained on maximum-intensity projection images of iRPE on culture surfaces. In the future, it will be trained to analyze three-dimensional (3D) images and quantify the volume of lipid droplets. The LipidUNet software will be a valuable resource for discovering drugs that decrease lipid accumulation in disease models.

Degenerative eye diseases that affect the retinal pigment epithelium layer of the eye have monogenic and polygenic origins. Several disease models and a software application, LipidUNet, have been developed to study mechanisms of disease, as well as potential therapeutic interventions.

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to ...

Biologia

Staining of Sub-Retinal Pigment Epithelium (RPE) Deposits

To begin, dissolve Nile Red powder in acetone at three milligrams per milliliter concentration. Incubate the solution for 15 minutes at room temperature with periodic mixing. Filter the solution using a 0.22-micrometer filter once or twice depending on the amount of precipitate remaining in the solution.Then prepare the working solution by diluting the stock solution at a one-to-500 ratio in DPBS. Add 200 microliters of the working solution to the paraformaldehyde-fixed RPE cells and incubate for 30 minutes at room temperature on a shaker protecting from light. After incubation, wash the samples twice with DPBS, then add 200 microliters of DPBS, and store them at four degrees Celsius.For BODIPY staining, prepare a 3.8-millimolar stock concentration by dissolving BODIPY in anhydrous DMSO. Then prepare the working solution by diluting the stock solution at a one-to-300 ratio in DPBS. Add 200 microliters of the working solution of BODIPY to paraformaldehyde-fixed RPE cells and incubate overnight on a rocker at room temperature.The next day, wash the cells twice with DPBS and add 200 microliters of DPBS before storing them at four degrees Celsius. For APOE immunostaining, prepare a buffer solution by combining DPBS with 1%BSA, 0.5%Tween 20, and 0.5%Triton X-100. Block and permeabilize the paraformaldehyde-fixed RPE cells in 200 microliters of buffer solution for one hour.After incubation, add APPLE primary antibody diluted at one-to-100 in the buffer solution and incubate overnight at room temperature. The following day, wash the samples twice with DPBS. Then add 200 microliters of secondary antibody solution diluted at one-to-1000 for one hour at room temperature.After incubation, wash the cells twice with DPBS and add 200 microliters of DPBS before storing them at four degrees Celsius.

Coloração de depósitos de epitélio pigmentado sub-retiniano (EPR)

Using a confocal microscope and a 40X objective, create a scan profile with the appropriate fluorescent channels for the lipid marker used. After setting up the acquisition mode and channels, optimize the focus strategy by clicking on Software Autofocus. Then set the range of the Z Stack to 20 microns.After this, optimize tiles and open the viewer to select the tile positions. Next, using a batch image processing method, create maximum projections of each Z Stack with the extended depth of focus method. Before exporting the images, set the compression to none and ensure original data is checked.The resulting image is a maximum projection gray scale TIFF of only the fluorescence channel, expressing the lipid marker. Identify the TIFF images representing either Nile Red, BODIPY, or APOE and move them into the Images folder within a directory named either Nile Red, BODIPY, or APOE, depending on the method being used. Open the lipid unit software.In the Predict tab, select the relevant directory by clicking on the ellipsis and navigating to the named directory. Confirm that lipid unit has identified the images correctly by checking the class entry. Click Predict to observe the task progress, then using the Mask Analysis tool, iterate through the generated masks and provide a quantitative count of the threshold lipid deposits from the mask images.Successful differentiation and maturation of RPE showed a confluent monolayer with pigmentation and polygonal morphology. In contrast, unsuccessful differentiation showed clusters of dying cells. In fluorescent images, Nile Red and BODIPY deposits appeared as small, bright circular points.A negative result shows incorrect image segmentation by mistaking background fluorescence as a deposit, either due to weak staining or high background intensity. APOE deposits varying in size, shape, and signal intensity and requiring optimization of staining and imaging methods to minimize variation are shown.