Lysosomes are the terminal end of catabolic pathways in the cell, as well as signaling centers performing important functions such as the recycling of macromolecules, organelles, and nutrient adaptation. The importance of lysosomes in human health is supported by the fact that the deficiency of most lysosomal genes causes monogenic diseases called as a group Lysosomal Storage Diseases (LSDs). A common phenotypic hallmark of LSDs is the expansion of the lysosomal compartment that can be detected by using conventional imaging methods based on immunofluorescence protocols or overexpression of tagged lysosomal proteins. These methods require the alteration of the cellular architecture (i.e., due to fixation methods), can alter the behavior of cells (i.e., by the overexpression of proteins), and require sample preparation and the accurate selection of compatible fluorescent markers in relation to the type of analysis, therefore limiting the possibility of characterizing cellular status with simplicity. Therefore, a quantitative and label-free methodology, such as Quantitative Phase Imaging through Digital Holographic (QPI-DH), for the microscopic imaging of lysosomes in health and disease conditions may represent an important advance to study and effectively diagnose the presence of lysosomal storage in human disease. Here we proof the effectiveness of the QPI-DH method in accomplishing the detection of the lysosomal compartment using mouse embryonic fibroblasts (MEFs) derived from a Mucopolysaccharidosis type III-A (MSP-IIIA) mouse model, and comparing them with wild-type (WT) MEFs. We found that it is possible to identify label-free biomarkers able to supply a first pre-screening of the two populations, thus showing that QPI-DH can be a suitable candidate to surpass fluorescent drawbacks in the detection of lysosomes dysfunction. An appropriate numerical procedure was developed for detecting and evaluate such cellular substructures from in vitro cells cultures. Results reported in this study are encouraging about the further development of the proposed QPI-DH approach for such type of investigations about LSDs.

CNR ISASI Institute of Applied Sciences and Intelligent Systems E Caianiello Pozzuoli Napoli Italy

Department of Medical and Translational Science Federico 2 University Naples Italy

Department of Optics Palacký University Olomouc Czech Republic

Telethon Institute of Genetics and Medicine Pozzuoli Naples Italy

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Ballabio A, Bonifacino JS. Lysosomes as dynamic regulators of cell and organismal homeostasis. Nat Rev Mol Cell Biol. 2020;21(2):101–118.

Yang C, Wang X. Lysosome biogenesis: regulation and functions. J Cell Biol. 2021;220(6):e202102001.

Platt FM, d'Azzo A, Davidson BL, Neufeld EF, Tifft CJ. Lysosomal storage diseases. Nat Rev Dis Primers. 2018;4:27.

Capuozzo A, Montefusco S, Cacace V, Sofia M, Esposito A, Napolitano G, et al. Fluoxetine ameliorates mucopolysaccharidosis type IIIA. Mol Ther. 2022;30(4):1432–1450.

Settembre C, Fraldi A, Jahreiss L, Spampanato C, Venturi C, Medina D, et al. A block of autophagy in lysosomal storage disorders. Hum Mol Genet. 2008;17:119–129.

Valstar MJ, Ruijter GJ, van Diggelen OP, Poorthuis BJ, Wijburg FA. Sanfilippo syndrome: a mini‐review. J Inherit Metab Dis. 2008;31:240–252.

Meyer A, Kossow K, Gal A, Mühlhausen C, Ullrich K, Braulke T, et al. Scoring evaluation of the natural course of mucopolysaccharidosis type IIIA (Sanfilippo syndrome type A). Pediatrics. 2007;120:e1255–e1261.

Sanderson MJ, Smith I, Parker I, Bootman MD. Fluorescence microscopy. Cold Spring Harb Protoc. 2014;2014(10):pdb.top071795.

Ojha A, Ojha NK. Excitation light‐induced phototoxicity during fluorescence imaging. J Biosci. 2021;46:78.

Lee K, Kim K, Jung J, Heo J, Cho S, Lee S, et al. Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications. Sensors. 2013;13:4170–4191.

Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine. Nat Photon. 2018;12(10):578–589.

Memmolo P, Aprea G, Bianco V, Russo R, Andolfo I, Mugnano M, et al. Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning. Biosens Bioelectron. 2022;201:113945.

Memmolo P, Miccio L, Merola F, Gennari O, Netti P, Ferraro P. 3D morphometry of red blood cells by digital holography. Cytometry A. 2014;85:1030–1036. https://doi.org/10.1002/cyto.a.22570

Liu R, Dey DK, Boss D, Marquet P, Javidi B. Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis. J Opt Soc Am A. 2011;28:1204–1210.

Běhal J, Pirone D, Sirico D, Bianco V, Mugnano M, Del Giudice D, et al. On monocytes and lymphocytes biolens clustering by in flow holographic microscopy. Cytometry A. 2023;103(3):251–259.

Zhang JK, He YR, Sobh N, Popescu G. Label‐free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM). APL Photon. 2020;5:040805.

El‐Schich Z, Mölder AL, Wingren AG. Quantitative phase imaging for label‐free analysis of cancer cells—focus on digital holographic microscopy. Appl Sci. 2018;8(7):1027.

Anand A, Chhaniwal VK, Patel NR, Javidi B. Automatic identification of malaria‐infected RBC with digital holographic microscopy using correlation algorithms. IEEE Photon J. 2012;4(5):1456–1464.

Doblas A, Roche E, Ampudia‐Blasco F, Martinez‐Corral M, Saavedra G, Garcia‐Sucerquia J. Diabetes screening by telecentric digital holographic microscopy. J Microsc. 2016;261:285–290.

O'Connor T, Javidi B. COVID‐19 screening with digital holographic microscopy using intra‐patient probability functions of spatio‐temporal bio‐optical attributes. Biomed Opt Express. 2022;13(10):5377–5389.

Javidi B, Markman A, Rawat S, O'Connor T, Anand A, Andemariam A. Sickle cell disease diagnosis based on spatio‐temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy opt. Exp Dermatol. 2018;26:13614.

D'Brant LY, Desta H, Khoo TC, Sharikova AV, Mahajan SD, Khmaladze A. Methamphetamine‐induced apoptosis in glial cells examined under marker‐free imaging modalities. J Biomed Opt. 2019;24(4):1–10.

Zhikhoreva AA, Belashov AV, Belyaeva TN, Salova AV, Litvinov IK, Kornilova ES, et al. Comparative analysis of Radachlorin accumulation, localization, and photobleaching in three cell lines by means of holographic and fluorescence microscopy. Photodiagnosis Photodyn Ther. 2022;39:102973.

Nissim N, Dudaie M, Barnea I, Shaked NT. Real‐time stain‐free classification of cancer cells and blood cells using interferometric phase microscopy and machine learning. Cytometry. 2021;99:511–523.

Campos V, Rappaz B, Kuttler F, Turcatti G, Naveiras O. High‐throughput, nonperturbing quantification of lipid droplets with digital holographic microscopy. J Lipid Res. 2018;59(7):1301–1310.

Kemper B, Bauwens A, Bettenworth D, Götte M, Greve B, Kastl L, et al. Label‐free quantitative in vitro live cell imaging with digital holographic microscopy. In: Wegener J, editor. Label‐free monitoring of cells in vitro. Bioanalytical reviews. Volume 2. Springer, Cham, Switzerland; 2019.

Bieżuńska‐Kusiak K, Kulbacka J, Choromańska A, Rembiałkowska N, Michel O, Saczko J. Evaluation of the anticancer activity of calcium ions introduced into human breast adenocarcinoma cells MCF‐7/WT and MCF‐7/DOX by electroporation. Pharmaceuticals (Basel). 2023;16(6):809.

Fang J, Zhang H, Pan Y, Smith ZJ, Chu K. Label‐free analysis of organelle interactions using organelle‐specific phase contrast microscopy (OS‐PCM). ACS Photon. 2023;10(4):1093–1103.

Calabuig A, Mugnano M, Miccio L, Grilli S, Ferraro P. Investigating fibroblast cells under “safe” and “injurious” blue‐light exposure by holographic microscopy. J Biophotonics. 2017;10(6–7):919–927. https://doi.org/10.1002/jbio.201500340

Pu Y, Meng H. An advanced off‐axis holographic particle image velocimetry (HPIV) system. Exp Fluids. 2000;29:184–197.

Kim M. Principles and techniques of digital holographic microscopy. SPIE‐Rev. 2010;1. https://doi.org/10.1117/6.0000006

Cotte Y, Toy F, Jourdain P, Pavillon N, Boss D, Magistretti P, et al. Marker‐free phase nanoscopy. Nat Photon. 2013;7:113–117.

Memmolo P, Distante C, Paturzo M, Finizio A, Ferraro P, Javidi B. Automatic focusing in digital holography and its application to stretched holograms. Opt Lett. 2011;36:1945–1947.

Zuo C, Chen Q, Qu W, Asundi A. Phase aberration compensation in digital holographic microscopy based on principal component analysis. Opt Lett. 2013;38(10):1724–1726.

Huang ZZ, Cao LC. Phase aberration separation for holographic microscopy by alternating direction sparse optimization. Opt Express. 2023;31:12520–12533.

Min J, Yao B, Ketelhut S, Engwer C, Greve B, Kemper B. Simple and fast spectral domain algorithm for quantitative phase imaging of living cells with digital holographic microscopy. Opt Lett. 2017;42(2):227–230.

Colomb T, Kühn J, Charrière F, Depeursinge C, Marquet P, Aspert N. Total aberrations compensation in digital holographic microscopy with a reference conjugated hologram. Opt Express. 2006;14:4300–4306.

Bioucas‐Dias JM, Valadao G. Phase unwrapping via graph cuts. IEEE Trans Image Process. 2007;16:698–709.

Kemao Q. Windowed Fourier transform for fringe pattern analysis. Appl Optics. 2004;43:2695–2702.

Sánchez‐Ortiga E, Doblas A, Saavedra G, Martínez‐Corral M, Garcia‐Sucerquia J. Off‐axis digital holographic microscopy: practical design parameters for operating at diffraction limit. Appl Optics. 2014;53:2058–2066.

Daniele P, Daniel S, Lisa M, Vittorio B, Martina M, Pietro F, et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning. Lab Chip. 2022;22:793–804.

Liu PY, Chin LK, Ser W, Chen HF, Hsieh CM, Lee CH, et al. Cell refractive index for cell biology and disease diagnosis: past, present and future. Lab Chip. 2016;16:634–644.

Parkinson‐Lawrence EJ, Shandala T, Prodoehl M, Plew R, Borlace GN, Brooks DA. Lysosomal storage disease: revealing lysosomal function and physiology. Physiology (Bethesda). 2010;25(2):102–115.

Rajan DS, Escolar ML. Evolving therapies in neuronopathic LSDs: opportunities and challenges. Metab Brain Dis. 2022;37(7):2245–2256.

Kandel ME, He YR, Lee YJ, Chen THY, Sullivan KM, Aydin O, et al. Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub‐cellular compartments. Nat Commun. 2020;11:6256.

Zhang Z, Leong KW, Van Vliet K, Barbastathis G, Ravasio A. Deep learning for label‐free nuclei detection from implicit phase information of mesenchymal stem cells. Biomed Opt Express. 2021;12(3):1683–1706.

Pirone D, Sirico D, Miccio L, Bianco V, Mugnano M, Giudice D, et al. 3D imaging lipidometry in single cell by in‐flow holographic tomography. Opto‐Electron Adv. 2023;6:220048.

Pirone D, Lim J, Merola F, Miccio L, Mugnano M, Bianco V, et al. Stain‐free identification of cell nuclei using tomographic phase microscopy in flow cytometry. Nat Photon. 2022;16:851–859.