Improved quantification of immune-gold labeling and its use to compare the distribution of cellular factors among sub-chloroplast compartments
Copyright © 2021 Elsevier Ltd. All rights reserved..
Quantification of immuno-gold labeling can provide valuable information on the quantity and localization of a target within a region of interest (ROI). Background subtraction usually requires preparation of material with a deliberately reduced amount of target component often by gene knockout/knockdown. This paper reports a modified method without the need for gene knockout/knockdown, by using a region outside the ROI as a background and non-immune serum to verify the reliability of the data. An optimized parameter for use in image processing was also developed to improve semi-automatic segmentation of gold particles, by using the standard deviation of pixel intensity together with default parameters (size and intensity) to improve specificity. The modified methods were used to quantify the gold labeling of various components within chloroplasts and their 3 sub-organelle compartments (thylakoid, stroma and starch). Rubisco, actin, myosin, β-tubulin, Endoplasmic reticulum-retention signal HDEL, Sterol methyltransferase 1, and double stranded RNA were all effectively and consistently quantified at the level of the different sub-chloroplast compartments. The approach should be applicable more widely for high resolution labelling of samples in which a background requiring gene knockout/knockdown is not a realistic option.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2021 |
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Erschienen: |
2021 |
Enthalten in: |
Zur Gesamtaufnahme - volume:145 |
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Enthalten in: |
Micron (Oxford, England : 1993) - 145(2021) vom: 01. Juni, Seite 103060 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Lv, Ming-Fang [VerfasserIn] |
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Links: |
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Themen: |
7440-57-5 |
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Anmerkungen: |
Date Completed 28.10.2021 Date Revised 28.10.2021 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1016/j.micron.2021.103060 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM323545742 |
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520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
520 | |a Quantification of immuno-gold labeling can provide valuable information on the quantity and localization of a target within a region of interest (ROI). Background subtraction usually requires preparation of material with a deliberately reduced amount of target component often by gene knockout/knockdown. This paper reports a modified method without the need for gene knockout/knockdown, by using a region outside the ROI as a background and non-immune serum to verify the reliability of the data. An optimized parameter for use in image processing was also developed to improve semi-automatic segmentation of gold particles, by using the standard deviation of pixel intensity together with default parameters (size and intensity) to improve specificity. The modified methods were used to quantify the gold labeling of various components within chloroplasts and their 3 sub-organelle compartments (thylakoid, stroma and starch). Rubisco, actin, myosin, β-tubulin, Endoplasmic reticulum-retention signal HDEL, Sterol methyltransferase 1, and double stranded RNA were all effectively and consistently quantified at the level of the different sub-chloroplast compartments. The approach should be applicable more widely for high resolution labelling of samples in which a background requiring gene knockout/knockdown is not a realistic option | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, Non-U.S. Gov't | |
650 | 4 | |a Background subtraction | |
650 | 4 | |a Distribution frequency | |
650 | 4 | |a Image processing | |
650 | 4 | |a Immuno-gold labeling | |
650 | 4 | |a Quantification | |
650 | 4 | |a Sub-chloroplast compartment | |
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700 | 1 | |a Li, Yun-Qin |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Xiao-Min |e verfasserin |4 aut | |
700 | 1 | |a Li, Mei |e verfasserin |4 aut | |
700 | 1 | |a Liao, Zhen-Feng |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Zhong-Kai |e verfasserin |4 aut | |
700 | 1 | |a Hong, Jian |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Heng-Mu |e verfasserin |4 aut | |
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