In-flow holographic tomography boosts lipid droplet quantification

Michael John Fanous; Aydogan Ozcan

doi:10.29026/oea.2023.230083

Article navigation > Opto-Electronic Advances > 2023 Vol. 6 > No. 6 > 230083

Next Article Previous Article

Fanous MJ, Ozcan A. In-flow holographic tomography boosts lipid droplet quantification. Opto-Electron Adv 6, 230083 (2023). doi: 10.29026/oea.2023.230083

Citation:

Fanous MJ, Ozcan A. In-flow holographic tomography boosts lipid droplet quantification. Opto-Electron Adv 6, 230083 (2023). doi: 10.29026/oea.2023.230083

News & Views Open Access

In-flow holographic tomography boosts lipid droplet quantification

Michael John Fanous¹,
Aydogan Ozcan^1,2,3,4, ,

1.
Electrical and Computer Engineering Department, University of California, Los Angeles 90095 CA, USA
2.
Bioengineering Department, University of California, Los Angeles 90095 CA, USA
3.
California NanoSystems Institute (CNSI), University of California, Los Angeles 90095 CA, USA
4.
Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles 90095 CA, USA

More Information

^*Corresponding author: A Ozcan, E-mail: ozcan@ucla.edu

Received Date 15 May 2023

Accepted Date 17 May 2023

Published Date 25 June 2023

Abstract

Abstract

A 3D microscopy instrument using flow tomography significantly advances the screening and quantification of intracellular lipid droplets.

FullText(HTML)

References

[1]	Pirone D, Sirico D, Miccio L, Bianco V, Mugnano M et al. 3D imaging lipidometry in single cell by in-flow holographic tomography. Opto-Electronic Adv 6, 220048 (2023). doi: 10.29026/oea.2023.220048 CrossRef Google Scholar
[2]	Olzmann JA, Carvalho P. Dynamics and functions of lipid droplets. Nat Rev Mol Cell Biol 20, 137–155 (2019). doi: 10.1038/s41580-018-0085-z CrossRef Google Scholar
[3]	Imai Y, Cousins RS, Liu SM, Phelps BM, Promes JA. Connecting pancreatic islet lipid metabolism with insulin secretion and the development of type 2 diabetes. Ann N Y Acad Sci 1461, 53–72 (2020). doi: 10.1111/nyas.14037 CrossRef Google Scholar
[4]	Cruz ALS, Barreto EDA, Fazolini NPB, Viola JPB, Bozza PT. Lipid droplets: platforms with multiple functions in cancer hallmarks. Cell Death Dis 11, 105 (2020). doi: 10.1038/s41419-020-2297-3 CrossRef Google Scholar
[5]	Dias SSG, Soares VC, Ferreira AC, Sacramento CQ, Fintelman-Rodrigues N et al. Lipid droplets fuel SARS-CoV-2 replication and production of inflammatory mediators. PLoS Pathog 16, e1009127 (2020). doi: 10.1371/journal.ppat.1009127 CrossRef Google Scholar
[6]	Martins AS, Martins IC, Santos NC. Methods for lipid droplet biophysical characterization in Flaviviridae infections. Front Microbiol 9, 1951 (2018). doi: 10.3389/fmicb.2018.01951 CrossRef Google Scholar
[7]	Bai BJ, Yang XL, Li YZ, Zhang YJ, Pillar N et al. Deep learning-enabled virtual histological staining of biological samples. Light Sci Appl 12, 57 (2023). doi: 10.1038/s41377-023-01104-7 CrossRef Google Scholar
[8]	Rivenson YJ, Yang XL, Li YZ, Zhang YJ, Pillar N et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng 3, 466–477 (2019). doi: 10.1038/s41551-019-0362-y CrossRef Google Scholar
[9]	Rivenson Y, Liu TR, Wei ZS, Zhang YB, De Haan K et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci Appl 8, 23 (2019). doi: 10.1038/s41377-019-0129-y CrossRef Google Scholar
[10]	Choi W, Fang-Yen C, Badizadegan K, Oh S, Lue N et al. Tomographic phase microscopy. Nat Methods 4, 717–719 (2007). doi: 10.1038/nmeth1078 CrossRef Google Scholar
[11]	Sandoz PA, Tremblay C, van der Goot FG, Frechin M. Image-based analysis of living mammalian cells using label-free 3D refractive index maps reveals new organelle dynamics and dry mass flux. PLoS Biol 17, e3000553 (2019). doi: 10.1371/journal.pbio.3000553 CrossRef Google Scholar
[12]	Fanous MJ, He SH, Sengupta S, Tangella K, Sobh N et al. White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS). Sci Rep 12, 20043 (2022). doi: 10.1038/s41598-022-21250-z CrossRef Google Scholar
[13]	Fanous MJ, Li YF, Kandel ME, Abdeen AA, Kilian KA et al. Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM). J Biophotonics 12, e201900178 (2019). doi: 10.1002/jbio.201900178 CrossRef Google Scholar
[14]	Li YF, Fanous MJ, Kilian KA, Popescu G. Quantitative phase imaging reveals matrix stiffness-dependent growth and migration of cancer cells. Sci Rep 9, 248 (2019). doi: 10.1038/s41598-018-36551-5 CrossRef Google Scholar
[15]	Isikman SO, Bishara W, Mavandadi S, Yu FW, Feng S et al. Lens-free optical tomographic microscope with a large imaging volume on a chip. Proc Natl Acad Sci USA 108, 7296–7301 (2011). doi: 10.1073/pnas.1015638108 CrossRef Google Scholar
[16]	Bishara W, Isikman SO, Ozcan A. Lensfree optofluidic microscopy and tomography. Ann Biomed Eng 40, 251–262 (2012). doi: 10.1007/s10439-011-0385-3 CrossRef Google Scholar
[17]	Fanous MJ, Popescu G. GANscan: continuous scanning microscopy using deep learning deblurring. Light Sci Appl 11, 265 (2022). doi: 10.1038/s41377-022-00952-z CrossRef Google Scholar
[18]	Chen XX, Wu TL, Gong ZY, Guo JH, Liu XS et al. Lipid droplets as endogenous intracellular microlenses. Light Sci Appl 10, 242 (2021). doi: 10.1038/s41377-021-00687-3 CrossRef Google Scholar