Multi-Molecular Hyperspectral PRM-SRS Imaging
Abstract Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues in situ are critical for understanding aging and diseases. Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes. To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 24. Nov. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Wenxu [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2022.07.25.501472 |
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| PPN (Katalog-ID): |
XBI036628611 |
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| 520 | |a Abstract Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues in situ are critical for understanding aging and diseases. Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes. To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging. | ||
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| 700 | 1 | |a Li, Yajuan |4 aut | |
| 700 | 1 | |a Fung, Anthony A. |0 (orcid)0000-0003-1631-7451 |4 aut | |
| 700 | 1 | |a Li, Zhi |4 aut | |
| 700 | 1 | |a Jang, Hongje |4 aut | |
| 700 | 1 | |a Zha, Honghao |4 aut | |
| 700 | 1 | |a Chen, Xiaoping |4 aut | |
| 700 | 1 | |a Gao, Fangyuan |4 aut | |
| 700 | 1 | |a Wu, Jane Y. |4 aut | |
| 700 | 1 | |a Sheng, Huaxin |0 (orcid)0000-0002-4325-2940 |4 aut | |
| 700 | 1 | |a Yao, Junjie |0 (orcid)0000-0002-2381-706X |4 aut | |
| 700 | 1 | |a Skowronska-Krawczyk, Dorota |0 (orcid)0000-0002-5758-4225 |4 aut | |
| 700 | 1 | |a Jain, Sanjay |0 (orcid)0000-0003-2804-127X |4 aut | |
| 700 | 1 | |a Shi, Lingyan |0 (orcid)0000-0003-1373-3206 |4 aut | |
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