Abstract
Choroideremia (CHM) is associated with progressive degeneration of the retinal pigment epithelium (RPE), choriocapillaris (CC), and photoreceptors. As animal models of CHM are lacking, most information about cell survival has come from imaging affected patients. This chapter discusses a combination of imaging techniques, including fundus-guided microperimetry, confocal and non-confocal adaptive optics scanning laser ophthalmoscopy (AOSLO), fundus autofluorescence (FAF), and swept-source optical coherence tomography angiography (SS-OCTA) to analyze macular sensitivity, cone photoreceptor outer and inner segment structure, RPE structure, and CC perfusion, respectively. Combined imaging modalities such as those described here can provide sensitive measures of monitoring retinal structure and function in patients with CHM.
Keywords: Choroideremia, Choriocapillaris, Retinal pigment epithelium, Photoreceptors, Degeneration, Fundus autofluorescence, Adaptive optics scanning laser ophthalmoscopy, Optical coherence tomography, Microperimetry
23.1. Introduction
Choroideremia (CHM), which is estimated to affect 1:50,000, is an X-linked recessive disease caused by a mutation in the CHM (REP1) gene on chromosome Xq21 (Aleman et al. 2017). CHM leads to degeneration of the choriocapillaris (CC), retinal pigment epithelium (RPE), and the photoreceptors. Patients develop progressive loss of night vision, subsequent peripheral visual field loss, and eventual central vision loss. The pathogenetic mechanism underlying the degeneration is not clearly understood but may be due to a deficiency in the function of proteins which have a role in organelle formation and trafficking of vesicles (Coussa and Traboulsi 2012).
The order that the retinal layers are affected by degeneration in patients with CHM is not clear. A study using fundus autofluorescence (FAF) images, adaptive optics scanning laser ophthalmoscopy (AOSLO), and spectral-domain optical coherence tomography (SD-OCT) found that early degeneration of RPE cells likely occurs simultaneously with degeneration of photoreceptors (Syed et al. 2013). Studies using a combination of SD-OCT and confocal and non-confocal split-detector AOSLO techniques (Sun et al. 2016) and a study using OCT angiography (OCTA) imaging (Jain et al. 2016) concluded that RPE degenerates before photoreceptors. Another study performed AOSLO, OCT, and FAF imaging and found that RPE is the primary site of degeneration and also that photoreceptors may degenerate independently (Morgan et al. 2014). Other studies (Jacobson et al. 2006; Aleman et al. 2017) used OCT and psychophysical tests to demonstrate loss of photoreceptors first, perhaps independently or in conjunction with RPE depigmentation. A group using OCTA and FAF (Parodi et al. 2018) found that the CC maintained normal structure until RPE loss occurred.
23.2. Fundus-Guided Microperimetry
Fundus-guided microperimetry using Macular Integrity Assessment (MAIA, Centervue Inc., Fremont, CA) can be used to analyze macular sensitivity of patient eyes with CHM (Jolly et al. 2017). This instrument uses scanning laser ophthalmoscopy (SLO) with real-time fundus tracking at a rate of 25 frames/second using fundus landmarks as a reference for perimetry. It uses a superluminescent diode of 850 nm with 1024 × 1024 pixel resolution and a 36 × 36 degree field of view. Goldmann III (26 arcmin) stimuli are presented for 200 ms on a 1.27 cd/m2 background with a dynamic range of 36 dB (Crossland et al. 2012; Dimopoulos et al. 2016).
23.3. Confocal and Split-Detector Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO)
AOSLO confocal imaging is an in vivo, noninvasive technique that records light emerging from the cone waveguide, which comprises scattered light from the IS/OS junction and the posterior tip of the outer segment. If both reflections are missing or are very weak, then the cone does not appear. AOSLO works by measuring higher order ocular aberrations via wavefront sensing and then compensates for these with a deformable mirror (Roorda et al. 2002). AOSLO has been used to visualize photoreceptors in eyes with retinal degeneration (Duncan et al. 2007; Roorda et al. 2007), including choroideremia (Syed et al. 2013; Sun et al. 2016; Morgan et al. 2018).
Photoreceptor inner segments have been visualized in vivo using non-confocal split-detector AOSLO (Scoles et al. 2014). This technique uses a reflective mask with an annulus in the image plane in place of a regular pinhole typically used for confocal detection. This method allows the confocal signal to be reflected into one detector and then directs the multiply scattered, non-confocal light from opposing sides of the annular aperture into two separate detectors. The split-detector signal is calculated as the difference between the two non-confocal detectors divided by their sum (Scoles et al. 2014). Non-confocal split-detector AOSLO can be especially useful in distinguishing areas where cone inner segments remain but outer segments are not waveguiding (Scoles et al. 2017).
23.4. Fundus Autofluorescence (FAF)
Photoreceptor outer segments and RPE cells naturally exhibit autofluorescence from bisretinoid constituents such as A2E which can be excited from an external light source and then imaged (Sparrow et al. 2012). Many FAF images are acquired using in vivo confocal scanning laser ophthalmoscopy (SLO) with short-wavelength (SW-AF) (488 nm) excitation and a 500 nm barrier filter to block reflected light and permit autofluorescent light from the fundus to pass through (Schmitz-Valckenberg 2008). SW-AF SLO imaging uses a confocal pinhole, which selectively allows imaging of a single plane to reduce noise from structures other than the retina (crystalline lens) that may contain fluorophores (Sparrow 2018). FAF can reveal lipofuscin fluorophores that amass in healthy normal and diseased RPE cells from pigment granules composed of lipid residues (Sparrow 2018). When excess lipofuscin accumulates in RPE cells due to incomplete photoreceptor outer segment degradation due to disease, it builds up and appears hyperfluorescent on FAF images (Schmitz-Valckenberg 2008).
Near-infrared autofluorescence (NIR-AF) excitation with a laser diode at 787 nm excitation and a barrier filter allowing light to pass at >810 nm can also be used to image fundus fluorophores, most likely derived from melanin pigment (Weinberger 2006). NIR-AF is more comfortable for patients and may pose less risk of RPE damage than SW-AF (Cideciyan et al. 2007; Cideciyan et al. 2015). NIR-reflectance (NIR-REF) imaging has been shown to be strongly correlated with NIR-AF imaging (Weinberger 2006) and uses similar wavelengths as are used to acquire infrared fundus images during OCT scans, which suggests that signals from OCT imaging might be comparable to NIR-REF and NIR-AF. Studies of patients with CHM quantified areas of preserved RPE and inner segment/outer segment (IS/OS) junction or inner segment ellipsoid zone (EZ) on FAF and OCT images and found that RPE degenerated prior to photoreceptors (Hariri et al. 2017) and found SW-AF to be repeatable over time in CHM patients (Jolly et al. 2016). A more recent study found differences in areas of preservation when comparing NIR-AF to short-wavelength autofluorescence (SW-AF) (Paavo et al. 2018).
23.5. Swept-Source Optical Coherence Tomography (SS-OCT)
OCT is a noninvasive, in vivo imaging technique used to image the fundus layers in cross section (Podoleanu and Rosen 2008; Huang et al. 2014). Swept-source OCT (PLEX Elite 9000, Carl Zeiss Meditec Inc., Dublin, CA) uses a 1060 nm tunable laser which can scan up to 100,000 A-scans/second with an axial resolution of 6.3 μm (Akman 2018). En face SS-OCT slabs have been used to assess geographic atrophy associated with age-related macular degeneration using large scans, up to 12 × 12 mm, comparable to 40 degree field of view (Thulliez et al. 2019). Unlike FAF which represents autofluorescent lipofuscin, when SS-OCT en face slabs are used to observe the RPE layer, the signal comes from RPE melanin (Greenstein et al. 2017). SS-OCT can be used to visualize RPE and semiautomatically identify borders of preserved RPE from en face slabs extending from the outer boundary of the outer plexiform layer (outer retina) to 8 μm beneath Bruch’s membrane (CC) (Zhang et al. 2017).
23.6. Swept-Source OCT Angiography (OCTA)
SS-OCTA can be used to visualize the CC in vivo and its associated flow voids (FV) (Zhang et al. 2018). CC perfusion can be measured as FV, defined as a percentage of the imaged region without measurable CC flow, using a threshold of one standard deviation below the mean CC flow from a normative database of 20 normal subjects aged 20–39 years old (Zhang et al. 2018). Prior studies of CHM using OCTA have suggested that the RPE area of loss was more extensive than the CC nonperfusion area, which in turn was larger than the area of retinal vascular nonperfusion (Jia et al. 2015). These results suggest RPE cells to be the primary site of degeneration, followed by loss of the CC and then photoreceptors. However, correlation of all the modalities discussed acquired concurrently should provide additional insight into the relationship between cellular function and structure in patients with CHM.
23.7. Conclusion
The mechanisms of degeneration in CHM remain unclear. While some studies using AOSLO suggest that RPE cells degenerate earliest (Morgan et al. 2014), others suggest RPE cells and photoreceptors degenerate independently (Syed et al. 2013), and structural measures may not demonstrate early changes in photoreceptor function (Jacobson et al. 2006; Aleman et al. 2017; Duncan et al. 2002). Recent advances in imaging technology permit assessment of eyes with CHM using multiple modalities to improve the study of these cells. The use of multimodal, noninvasive imaging may provide better understanding of the sequence of degeneration in eyes with CHM. Future studies are necessary to examine longitudinal data and degeneration using multimodal techniques, including those described here. While microperimetry can provide a measure of macular sensitivity, AOSLO can visualize photoreceptor morphology. FAF as well as SS-OCT can provide images of RPE structure, and SS-OCTA can display CC perfusion. Greater understanding of degeneration and disease progression is crucial to advance the development of novel therapies for this relentless, sight-threatening disease.
Contributor Information
Katharina G. Foote, School of Optometry and Vision Science Graduate Group, University of California, Berkeley, CA, USA; Department of Ophthalmology, University of California, San Francisco, CA, USA
Austin Roorda, School of Optometry and Vision Science Graduate Group, University of California, Berkeley, CA, USA.
Jacque L. Duncan, Department of Ophthalmology, University of California, San Francisco, CA, USA
References
- Akman A (2018) Optical coherence tomography: manufacturers and current systems. In: Optical coherence tomography in glaucoma, pp 27–37 [Google Scholar]
- Aleman TS, Han G, Serrano LW, Fuerst NM, Charlson ES, Pearson DJ, Chung DC, Traband A, Pan W, Ying GS, Bennett J (2017) Natural history of the central structural abnormalities in choroideremia: a prospective cross-sectional study. Ophthalmology 124(3):359–373 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cideciyan AV, Swider M, Aleman TS, Roman MI, Sumaroka A, Schwartz SB, Stone EM, Jacobson SG (2007) Reduced-illuminance autofluorescence imaging in ABCA4-associated retinal degenerations. JOSA A 24(5):1457–1467 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cideciyan AV, Swider M, Jacobson SG (2015) Autofluorescence imaging with near-infrared excitation: normalization by reflectance to reduce signal from choroidal fluorophores. Invest Ophthalmol Vis Sci 56(5):3393–3406 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Coussa RG, Traboulsi EI (2012) Choroideremia: a review of general findings and pathogenesis. Ophthalmic Genet 33(2):57–65 [DOI] [PubMed] [Google Scholar]
- Crossland M, Jackson ML, Seiple WH (2012) Microperimetry: a review of fundus related perimetry. Optometry Rep 2(1):2 [Google Scholar]
- Dimopoulos IS, Tseng C, MacDonald IM (2016) Microperimetry as an outcome measure in choroideremia trials: reproducibility and beyond. Invest Ophthalmol Vis Sci 57(10):4151–4161 [DOI] [PubMed] [Google Scholar]
- Duncan JL, Zhang Y, Gandhi J et al. (2007) High-resolution imaging with adaptive optics in patients with inherited retinal degeneration. Invest Ophthalmol Vis Sci 48:3283–3291 [DOI] [PubMed] [Google Scholar]
- Duncan JL, Aleman TS, Gardner LM, De Castro E, Marks DA, Emmons JM, Bieber ML, Steinberg JD, Bennett J, Stone EM, MacDonald IM (2002) Macular pigment and lutein supplementation in choroideremia. Exp Eye Res 74(3):371–381 [DOI] [PubMed] [Google Scholar]
- Greenstein VC, Nunez J, Lee W, Schuerch K, Fortune B, Tsang SH, Allikmets R, Sparrow JR, Hood DC (2017) A comparison of En face optical coherence tomography and fundus autofluorescence in Stargardt disease. Invest Ophthalmol Vis Sci 58(12):5227–5236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hariri AH, Velaga SB, Girach A, Ip MS, Le PV, Lam BL, Fischer MD, Sankila EM, Pennesi ME, Holz FG, MacLaren RE (2017) Measurement and reproducibility of preserved ellipsoid zone area and preserved retinal pigment epithelium area in eyes with choroideremia. Am J Ophthalmol 179:110–117 [DOI] [PubMed] [Google Scholar]
- Huang Y, Zhang Q, Thorell MR, An L, Durbin MK, Laron M, Sharma U, Gregori G, Rosenfeld PJ, Wang RK (2014) Swept-source OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms. Ophthalmic Surg Lasers Imaging Retina 45(5):382–389 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jacobson SG, Cideciyan AV, Sumaroka A, Aleman TS, Schwartz SB, Windsor EA, Roman AJ, Stone EM, MacDonald IM (2006) Remodeling of the human retina in choroideremia: rab escort protein 1 (REP-1) mutations. Invest Ophthalmol Vis Sci 47(9):4113–4120 [DOI] [PubMed] [Google Scholar]
- Jain N, Jia Y, Gao SS, Zhang X, Weleber RG, Huang D, Pennesi ME (2016) Optical coherence tomography angiography in choroideremia: correlating choriocapillaris loss with overlying degeneration. JAMA Ophthalmol 134(6):697–702 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jia Y, Bailey ST, Hwang TS, McClintic SM, Gao SS, Pennesi ME, Flaxel CJ, Lauer AK, Wilson DJ, Hornegger J, Fujimoto JG (2015) Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye. Proc Natl Acad Sci 112:E2395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jolly JK, Edwards TL, Moules J, Groppe M, Downes SM, MacLaren RE (2016) A qualitative and quantitative assessment of fundus autofluorescence patterns in patients with choroideremia. Invest Ophthalmol Vis Sci 57(10):4498–4503 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jolly JK, Xue K, Edwards TL, Groppe M, MacLaren RE (2017) Characterizing the natural history of visual function in choroideremia using microperimetry and multimodal retinal imaging. Invest Ophthalmol Vis Sci 58(12):5575–5583 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morgan JI, Han G, Klinman E, Maguire WM, Chung DC, Maguire AM, Bennett J (2014) High-resolution adaptive optics retinal imaging of cellular structure in choroideremia. Invest Ophthalmol Vis Sci 55(10):6381–6397 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morgan JI, Tuten WS, Cooper RF, Han GK, Young G, Bennett J, Maguire AM, Aleman TS, Brainard DH (2018) Cellular-scale assessment of visual function in Choroideremia. Invest Ophthalmol Vis Sci 59(9):1151 [Google Scholar]
- Paavo M, Lee W, Sengillo J, Tsang SH, Sparrow JR (2018) Near-infrared autofluorescence imaging in choroideremia. Invest Ophthalmol Vis Sci 59(9):4664. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Parodi MB, Arrigo A, MacLaren RE, Aragona E, Toto L, Mastropasqua R, Manitto MP, Bandello F (2018) Vascular alterations revealed with optical coherence tomography angiography in patients with choroideremia. Retina 187:61–70 [DOI] [PubMed] [Google Scholar]
- Podoleanu AG, Rosen RB (2008) Combinations of techniques in imaging the retina with high resolution. Prog Retin Eye Res 27(4):464–499 [DOI] [PubMed] [Google Scholar]
- Roorda A, Romero-Borja F, Donnelly WJ III, Queener H, Hebert TJ, Campbell MC (2002) Adaptive optics scanning laser ophthalmoscopy. Opt Express 10(9):405–412 [DOI] [PubMed] [Google Scholar]
- Roorda A, Zhang Y, Duncan JL (2007) High-resolution in vivo imaging of the RPE mosaic in eyes with retinal disease. Invest Ophthalmol Vis Sci 48:2297–2303 [DOI] [PubMed] [Google Scholar]
- Schmitz-Valckenberg S, Holz FG, Bird AC, Spaide RF (2008) Fundus autofluorescence imaging: review and perspectives. Retina 28(3):385–409 [DOI] [PubMed] [Google Scholar]
- Scoles D, Sulai YN, Cooper RF, Higgins BP, Johnson RD, Carroll J, Dubra A, Stepien KE (2017) Photoreceptor inner segment morphology in best vitelliform macular dystrophy. Retina 37(4):741–748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Scoles D, Sulai YN, Langlo CS, Fishman GA, Curcio CA, Carroll J, Dubra A (2014) In vivo imaging of human cone photoreceptor inner segments. Invest Ophthalmol Vis Sci 55(7):4244–4251 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sparrow JR, Gregory-Roberts E, Yamamoto K, Blonska A, Ghosh SK, Ueda K, Zhou J (2012) The bisretinoids of retinal pigment epithelium. Prog Retin Eye Res 31(2):121–135 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sparrow JR (2018) Light come shining: fundus autofluorescence. J Pediatr Ophthalmol Strabismus 55(5):285–286 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sun LW, Johnson RD, Williams V, Summerfelt P, Dubra A, Weinberg DV, Stepien KE, Fishman GA, Carroll J (2016) Multimodal imaging of photoreceptor structure in choroideremia. PLoS One 11(12):e0167526. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Syed R, Sundquist SM, Ratnam K, Zayit-Soudry S, Zhang Y, Crawford JB, MacDonald IM, Godara P, Rha J, Carroll J, Roorda A (2013) High-resolution images of retinal structure in patients with choroideremia. Invest Ophthalmol Vis Sci 54(2):950–961 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thulliez M, Motulsky EH, Feuer W, Gregori G, Rosenfeld PJ (2019) En face imaging of geographic atrophy using different swept-source optical coherence tomography scan patterns. Ophthalmol Retina 3(2):122–132 [DOI] [PubMed] [Google Scholar]
- Weinberger AW, Lappas A, Kirschkamp T, Mazinani BA, Huth JK, Mohammadi B, Walter P (2006) Fundus near infrared fluorescence correlates with fundus near infrared reflectance. Invest Ophthalmol Vis Sci 47(7):3098–3108 [DOI] [PubMed] [Google Scholar]
- Zhang Q, Chen CL, Chu Z, Zheng F, Miller A, Roisman L, de Oliveira Dias JR, Yehoshua Z, Schaal KB, Feuer W, Gregori G (2017) Automated quantitation of choroidal neovascularization: a comparison study between spectral-domain and swept-source OCT angiograms. Invest Ophthalmol Vis Sci 58(3):1506–1513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang Q, Zheng F, Motulsky EH, Gregori G, Chu Z, Chen CL, Li C, De Sisternes L, Durbin M, Rosenfeld PJ, Wang RK (2018) A novel strategy for quantifying choriocapillaris flow voids using swept-source OCT angiography. Invest Ophthalmol Vis Sci 59(1):203–211 [DOI] [PMC free article] [PubMed] [Google Scholar]