Name: analysis and specification of starch granule size distributions
Uploaded: 2021-03-04T12:30:00.000+00:00
Duration: 8 min 46 s
Description: Watch this Scientific Journal Video about Analysis and Specification of Starch Granule Size Distributions at JoVE.com

This work is partly supported by the Cooperative Agriculture Research Center, and Integrated Food Security Research Center of the College of Agriculture and Human Sciences, Prairie View A&#38;M University. We thank Hua Tian for his technical support.

1. Preparation of starch samples
<ol>
	<li>Prepare two (or three) gram-scale replicate starch samples from starch-accumulating tissues of various plant species following the established procedures (e.g., potatoes15, sweetpotatoes28, wheat grains13,29, and maize kernels30, etc.).</li>
	<li>Thoroughly wash starch samples with acetone or toluene 3-4x to minimize granule aggregates and dry them completely. 
	NOTE: Use extraction procedures that yield more than 1 g of starch per preparation. One or two 0.5-g aliquots from each of the three or two replicate extracts, respectively, are sampled for granule sizing analysis of one starch extract.</li>
</ol>
2. Electrolyte preparation
<ol>
	<li>Prepare 500 mL of 50 g/L lithium chloride in methanol for four sizing runs for replication starch samples (100 mL per run plus an extra 100 mL). Preferably, make the electrolyte in large-volume batches, e.g., 4 to 8 L at a time, to minimize the concentration variation.</li>
	<li>Cool the container on ice or in a 4 &#176;C cabinet to speed up dissolution of the lithium chloride.</li>
</ol>
3. Setting up the analyzer
<ol>
	<li>Choose an aperture tube with a particle diameter range covering the known (in the literature or through trial runs) granule size range of starch samples to be analyzed, e.g., a 100 &#956;m aperture for sweetpotato starches. For starch samples of unknown granule size range, select an appropriate aperture through trial runs using several aperture tubes having overlapping particle diameter ranges. 
	NOTE: The particle diameter range of an aperture tube is its accurate sizing range between 2 to 60% of, and with an extended sizing range to 80% of its orifice diameter. Table 1 lists properties of three most useful aperture tubes for sizing granules of major crop starches. If the granule size range of a starch sample is wider than the sizing range of a single aperture tube, perform a multi-tube overlap analysis combining up to five particle size distributions measured with apertures of different sizes. Each aperture is identifiable by its diameter and part number labeled on the tube. Its diameter and serial number contained in a barcode on the tube can be scanned into the analyzer software using the Bar Code Reader on the Control Panel of the analyzer.</li>
	<li>Chose a 100 or 200 mL analytical beaker (over cuvettes) for the determination of starch granule sizes, and set up automatic stirring (below) to maintain a good granule suspension during measurement.</li>
	<li>Create a Standard Operating Method (SOM) to specify run settings, and a Preferences file for analyzing, viewing, and printing the results. Combine SOM and Preferences file into a Standard Operating Procedure (SOP) as needed. 
	NOTE: For non-standardizable analyses, use SOM to run the analyses, and adjust the SOM settings between runs through the Edit the SOM window (see below) as needed. After run completion, analyze, view, and print the run results by changing the Preferences as desired. For standardizable granule sizing analyses, use an SOP to run the analyses.
	<ol>
		<li>Start the analyzer software. On the Main Manu, click SOP | Create SOM Wizard or Edit the SOM, or on the Status Panel, click Edit SOM. Use the wizard or the Edit the SOM window to select settings for an SOM. Settings typically used for sizing granules of sweetpotato starch samples are summarized in Table 2.</li>
		<li>Save the created SOM to a File in the SOM Wizard-Summary of Settings window, or in the Edit the SOM window.</li>
		<li>On the Main Manu, Click SOP | Create Preferences Wizard or Edit Preferences. Use the wizard or tabs in the Preferences edit window to select preference settings as those in Table 3 or others as desired.</li>
		<li>Save the selected Preferences to a file in the Create Preferences Wizard-Summary of Settings window or in the Edit Preferences window.</li>
		<li>On the Main Menu, click SOP | Create SOP Wizard. Following the step-by-step guide of the wizard, enter a description, select the SOM and Preferences file to create and save an SOP.</li>
	</ol>
	</li>
</ol>
4. Granule sizing analyses of the starch samples
<ol>
	<li>Prepare the Analyzer
	<ol>
		<li>Turn on the analyzer, open the software in the computer and verify the Ready status at the top of the Status Panel after its automatic connection to the analyzer.</li>
		<li>Fill the electrolyte jar with electrolyte, empty the waste jar if necessary.</li>
		<li>Properly install and secure the chosen aperture tube following the guide in the user&#8217;s manual. For an uncalibrated new aperture tube, calibrate it following the step-by-step guide under Calibration | Calibrate Aperture on the Main Menu. For a calibrated aperture tube, verify the calibration following the step-by-step guide of the Change Aperture Tube Wizard under the Run or Calibration | Verify Aperture Calibration on the Main Menu.</li>
		<li>Unlock the assay platform by pushing the lock-release clip (on the middle front of the left sample compartment wall) and manually lower the platform to the bottom. Place an analytical beaker containing 100 mL of electrolyte on the platform, move the stirrer to the stirring position, and manually raise the platform to the self-locking upper position to immerse the aperture tube and stirrer in the electrolyte.</li>
		<li>Click Fill on the bottom instrument Toolbar to have the analyzer automatically fill the system with the electrolyte and click Flush to have the analyzer automatically flush the system.</li>
		<li>Load the SOM by clicking SOP | Load an SOM on the Main Menu, and use the SOM to run an analysis without a Preferences file. Alternatively, load an SOP by clicking SOP | Load an SOP on the Main Menu or Load SOP on the Status Panel, and use the SOP to run an analysis.</li>
		<li>If using an SOP, click SOP | SOM Info or Preference Info on the Main Menu to verify the SOM and Preference settings. Click Sample | Enter Sample Info on the Main Menu or Edit Info on the Status Panel to enter the sample information for the run.</li>
	</ol>
	</li>
	<li>Prepare starch-methanol sample and sizing suspensions
	<ol>
		<li>Weigh two or one 0.5 g sample from each of the two or three replicate starch extracts, respectively.</li>
		<li>Add each of the 0.5 g starch aliquots to 5 mL methanol in a 50 mL conical centrifuge tube, and fully disperse starch granules using several pulses of low intensity ultrasound (12&#8211;24 W/cm2) from an ultrasonic processor.</li>
		<li>Using a disposable transfer pipette, apply one small drop of the starch-methanol suspension (~0.2 mL) to the 100 mL of 50 g/L LiCl methanol electrolyte under constant stirring in the beaker. Close the sample compartment door.</li>
	</ol>
	</li>
	<li>Perform a sizing run
	<ol>
		<li>Click Preview in the bottom Instrument Toolbar to start a preview run. On the Status Panel, verify that the dynamically displayed concentration bar is in green, and shows a 5 to 8% nominal concentration range for the suspension.</li>
		<li>Click Stop on the bottom Toolbar to stop the Preview run. If necessary, dilute the starch-electrolyte suspension by replacing an aliquot of the suspension with the electrolyte, and then repeat a Preview run. 
		NOTE: The 5% to 8% nominal concentration range of the suspension is critical for completion of a run without stoppage due to aperture blockage by aggregated granules. If needed, adjust the drop-sample size, and/or the concentration of the starch-methanol suspension to make a new starch-electrolyte suspension having the nominal concentration in the optimal range.</li>
		<li>After the verification, click Start on the bottom Toolbar to start the run. The analyzer automatically completes the run once the total count of sized granules, which is displayed along with the run time on the Status Panel in a run, reaches the set Total Count (125,000 or 250,000) by the Control Mode of the SOM. Depending on the suspension concentration (within 5-8% range or lower), a single run takes 2 to 5 min or more. 
		NOTE: When the analyzer automatically detects an aperture blockage per blockage detection settings of the SOM, it will abort the run, flush to unblock the aperture and start a new run. This blockage action is set to maximally repeat for four times before the analyzer cancels the run operation. This run-aborting blockage problem may be overcome by using two technical methods as noted in Table 2 and detailed in the discussion.</li>
		<li>If needed, perform a technical repeat run (see Table 2 and detailed in Discussion) using the same starch-electrolyte suspension by simply clicking Start or Repeat on the bottom Toolbar.</li>
		<li>After completion of a run or repeat runs, empty the beaker, rinse it with methanol, and refill it with 100 mL fresh electrolyte solution for the next run.</li>
		<li>During a run, if an Extended Size Range notification dialog appears when the count of granules larger than 60 &#181;m exceeds 0.1% of the total count (per the SOM setting), click Run 60% to 80% for running an extended dynamic sizing range to 80% of the aperture diameter. 
		NOTE: The Extended Size Range setting controls actions for granules larger than 60% of the aperture diameter (100 &#181;m, in this case). The setting in the SOM specifies inclusion of starch granules larger than 60 &#181;m when their counts reach over 0.1% of the total count. The completion of the run is still controlled by the total count, and may take slightly less time than otherwise without inclusion of the larger granules totaling less than 0.1% (presumed statically insignificant amount) of the total count.</li>
	</ol>
	</li>
	<li>Analyze the run results
	<ol>
		<li>If an SOM was used to control the runs, select Preferences settings as desired for viewing, printing, and statistical analyses of the results using the Create Preferences Wizard or the Edit Preferences under the SOP on the Main Menu.</li>
		<li>Overlay results from multiple runs on a single graph for comparison.
		<ol>
			<li>Click Overlay on the Main Toolbar or File | Overlay on the Main Menu to access the Overlay window. Navigate to and select multiple desired result files in the Files box, click Add to move them to the Selected Files box, and click OK to overlay the selected results on a single graph.</li>
			<li>To add a file to an open overlay, click RunFile | Open for Overlay on the Run Menu to access the Overlay window, navigate to the desired file, and click to add.</li>
		</ol>
		</li>
		<li>Average results from replicate analyses (2 extracts x 2 starch-sampling or 3 extracts x 1 starch-sampling), and view or print the average granule size distribution and statistics in a list or graph.
		<ol>
			<li>On the Main Menu, click File | FileTool | Average to open the Average window. Navigate to and select multiple desired result files in the Files box, click Add to move them to the Selected Files box, and click OK to average the selected results and display the average on a single graph.</li>
			<li>To include an additional result file in an average distribution, on the Run Menu, click RunFile | Open and Add to Average to open the Add to Average window, navigate to and add the file. The new average appears on the graph in the Run (result) window or listing.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
5. Specifying the average distribution
<ol>
	<li>In the Run-Menu window displaying the average distribution, click Calculate | Averaged Statistics on the Run Menu to open the statistics summary window, which displays the average statistics in rows, and the graph statistics for the average distribution in the columns.</li>
	<li>Use the graphic geometric mean (<img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" />) and S.D. (s*) in the graph statistics column to specify the average distribution in the <img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" /> x/ s* form. Calculate the CV measuring variations among the averaged replicate distributions by dividing the mean (&#181;, the same as the <img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" /> of the average distribution) of the geometric means of the averaged distributions with the average S.D. (&#963;) listed in the average statistics row. 
	NOTE: The average S.D. (for &#181;) assessing variations among the means of the replicate distributions is different from the graphic geometric S.D. (for <img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" />) measuring the spread of the average distribution.</li>
</ol>

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The authors have nothing to disclose

The outlined procedure has resolved some critical issues in several existing methods for starch granule size analyses, including inappropriate 1D or 2D sizing of 3D granules, distortion of sizing measurements due to none-uniform granule shapes, poor reproducibility and dubious statistical validity due to limited granule-sample sizes, inaccurate or improper specification (especially the use of the average size) of granule sizes in the presence of both granule shape and none-normal size distributions. It uses the ESZ techn...

Starch granules are the physical structure in which two main reserve homoglucan polymers in plant photosynthesis and storage tissues, the linear or sparsely branched amylose and the highly branched amylopectin, are orderly packed along with some minor components, including lipids and proteins. Starch granules from various plant species exhibit many three-dimensional (3D) shapes (reviewed in ref.1,2), including spheres, ellipsoids, polyhedrons, platelets, cubes, cuboids, and irregular tubules. Even those from the same tissue or different tissues of the same plant species could have a set of shapes with varying occurrence frequencies. In other words, starch granules from a plant species may have a characteristic statistical shape distribution, rather than a specific shape. The non-uniform and non-spherical granule shapes make it difficult to properly measure and define starch granule sizes. Additionally, starch granules from the same tissues of a plant species are of a range of sizes with different proportions, i.e., exhibiting a characteristic size distribution. This size distribution further complicates the analysis and description of starch granule sizes.
Starch granule sizes have been analyzed using several categories of particle sizing techniques (reviewed in ref.3), including microscopy, sedimentation/steric field-flow fractionation (Sd/StFFF), laser diffraction and electrical sensing zone (ESZ). However, these techniques are not equally suited for the determination of starch granule sizes in the presence of a granule shape and a size distribution. Microscopy, including light, confocal and scanning electron microscopy, is excellent for the studies of morphology4,5,6,7, structure8,9 and development10,11 of starch granules, but hardly suited for defining their size distributions due to some inherent shortcomings. Direct measurements of microscopic granule images or software-assisted image analysis of optical microscopy data (IAOM), which have been used for the determination of granule sizes of starches from several species, including maize12, wheat13,14, potato15 and barley16, can measure only 1D (usually maximal length) or 2D (surface area) sizes of very limited numbers (tens to a few thousands) of starch granule images. The small granule sampling sizes that are inherently constrained by the techniques could rarely be statistically representative, considering the enormous granule numbers per unit weight of starch (~120 x 106 per gram, assuming all 10 &#181;m spheres at 1.5 g/cm&#179; density), and, therefore, could lead to the poor reproducibility of the results. The Sd/StFFF technique may have high speed and resolution, and narrow size fractions of starch granules17, but has been rarely used probably because its accuracy could be severely affected by damages, different shapes, and density of starch granules. The laser diffraction technique is the most widely used, and has been applied for starch granule size analyses for all major crop species3,14,16. Although the technique has many advantages, it is actually not suited for determinations of starch granule sizes in the presence of a granule shape distribution. Most of the concurrent laser diffraction instruments rely on the Mie light-scattering theory18 for uniform spherical particles and the modified Mie theory18 for some other shapes of uniformity. The technique is, therefore, inherently very sensitive to particle shapes, and not entirely suited even for certain shapes of uniformity19, let alone for starch granules having a set of shapes of varying proportions. The ESZ technique measures the electric field disturbance proportional to the volume of the particle passing through an aperture. It provides granule volume sizes, as well as the number and volume distribution information, etc., at high resolutions. Since the ESZ technique is independent of any optical properties of particles including color, shape, composition or refractive index, and results are very reproducible, it is particularly suited for determining size distributions of starch granules having a set of shapes.
Starch granule sizes have also been defined by using many parameters. They were often simplistically described by average diameters, which in some cases were the arithmetic means of the microscopically measured maximal lengths of 2D images12,20, or averages of equivalent sphere diameters3. In other cases, the granule size distributions were specified by using size ranges21,22, the distribution mean volume or mean diameter (sphere equivalent, weighted by number, volume, or surface area) assuming a normal distribution14,23,24,25,26. These descriptors of starch granule sizes from various analyses are of a vastly different nature, and not strictly comparable. It could be very misleading if these &#8220;sizes&#8221; of starch granules from different species or even the same tissues of the same species were directly compared. Furthermore, the spread (or shape) parameter of the assumed normal distributions, i.e., the standard deviation &#963; (or graphic standard deviation &#963;g) measuring the width of the distribution (i.e., the spread of the sizes), has been ignored in most studies.
To resolve the aforementioned critical issues facing starch granule sizing analyses, we outlined a procedure for reproducible and statistically valid determinations of granule size distributions of starch samples using the ESZ technique, and for properly specifying the determined granule lognormal size distributions using an adopted two-parameter multiplicative form27 with improved accuracy and comparability. For validation and demonstration, we performed replicate granule sizing analyses of sweetpotato starch samples using the procedure, and specified the lognormal differential volume-percentage volume-equivalent-sphere diameter distributions using their graphic geometric means <img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" /> and multiplicative standard deviations s* in a <img alt="Equation 1" src="/files/ftp_upload/61586/61586eq1.jpg" /> x/ (multiply and divide) s* form.

<table><tbody><tr><td>Analytical beaker</td><td>Beckman Coulter Life Sciences</td><td>A35595</td><td>Smart-Technology (ST) beaker</td></tr><tr><td>Aperture tube, 100 &amp;micro;m</td><td>Beckman Coulter Life Sciences</td><td>A36394</td><td>For the MS4E</td></tr><tr><td>Disposable transfer pipettor,</td><td>Fisher Scientific (Fishersci.com)</td><td>13-711-9AM</td><td>Other disposable transfer pipettors with similar orifice can also be used.</td></tr><tr><td>Fisherbrand Conical Polypropylene Centrifuge Tubes, 50 ml</td><td>Fisher Scientific (Fishersci.com)</td><td>05-539-13</td><td>Any other similar types of tubes can be used.</td></tr><tr><td>Glass beakers, 150 to 250 ml</td><td>Fisher Scientific (Fishersci.com)</td><td>02-540K</td><td>These beakers are used to contain methanol for washing the aperture tube and stirrer between runs.</td></tr><tr><td>LiCl</td><td>Fisher Chemical</td><td>L121-100</td><td></td></tr><tr><td>Methanol</td><td>Fisher Chemical</td><td>A412-500</td><td>Buy in bulk as the analysis uses a large quantity of methanol.</td></tr><tr><td>Mettler Toledo ML-T Precision Balances</td><td>Mettler Toledo</td><td>30243412</td><td>Any other precision balance with a readability 0.01 g to 1 mg will work.</td></tr><tr><td>Multisizer 4e Coulter Counter</td><td>Beckman Coulter Life Sciences</td><td>B23005</td><td>The old model, Multisizer 3 can also be used with slight adjustment of parameters. The 4e model comes with a 100 &amp;mu;m aperture tube. Other aperture tubes of different diameter can be purchased separately from the company.</td></tr><tr><td>Ultrasonic processor UP50H</td><td>Hielscher Ultrasound Technology</td><td>UP50H</td><td>Other laboratory sonicator having a low-power (&amp;lt;50 Watt) output can be also used. Both MS1 and MS2 sonotrodes for the particular sonicator can be used to disperse starch granules in 5 ml methanol. Always use the lowest setting first, 20% amplitude and 0.1 or 0.2 cycle, and raise the setting if aggregates persist in suspension.</td></tr></tbody></table>

analysis and specification of starch granule size distributions

Starch from all plant sources are made up of granules in a range of sizes and shapes having different occurrence frequencies, i.e., exhibiting a size and a shape distribution. Starch granule size data determined using several types of particle sizing techniques are often problematic due to poor reproducibility or lack of statistical significance resulting from some insurmountable systematic errors, including sensitivity to granule shapes and limits of granule-sample sizes. We outlined a procedure for reproducible and statistically valid determinations of starch granule size distributions using the electrical sensing zone technique, and for specifying the determined granule lognormal size distributions using an adopted two-parameter multiplicative form with improved accuracy and comparability. It is applicable to all granule sizing analyses of gram-scale starch samples, and, therefore, could facilitate studies on how starch granule sizes are molded by the starch biosynthesis apparatus and mechanisms; and how they impact properties and functionality of starches for food and industrial uses. Representative results are presented from replicate analyses of granule size distributions of sweetpotato starch samples using the outlined procedure. We further discussed several key technical aspects of the procedure, especially, the multiplicative specification of granule lognormal size distributions and some technical means for overcoming frequent aperture blockage by granule aggregates.

To validate the procedure, and demonstrate reproducibility of the determined granule size distribution, we performed replicate sizing analyses of sweetpotato starch samples. We prepared replicate (S1 and S2) starch samples from field-grown sweetpotatoes of a breeding line SC1149-19 at a similar developmental age using a previously described procedure28. From each starch extract, two 0.5 g aliquots (a and b) were sampled, suspended in 5 mL of methanol and sonicated with several pulses of low-energy...

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Analysis and Specification of Starch Granule Size Distributions

Presented here is a procedure for reproducible and statistically valid determinations of starch granule size distributions, and for specifying the determined granule lognormal size distributions using a two-parameter multiplicative form. It is applicable to all granule sizing analyses of gram-scale starch samples for plant and food science research. &#160;

Starch from all plant sources are made up of granules in a range of sizes and shapes having different occurrence frequencies, i.e., exhibiting a size and ...

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5.0K 조회수.  Prairie View A&M University. Presented here is a procedure for reproducible and statistically valid determinations of starch granule size distributions, and for specifying the determined granule lognormal size distributions using a two-parameter multiplicative form. It is applicable to all granule sizing analyses of gram-scale starch samples for plant and food science research.  

비디오: Analysis and Specification of Starch Granule Size Distributions

Rapid High Throughput Amylose Determination in Freeze Dried Potato Tuber Samples

This protocol describes a high through put colorimetric method that relies on the formation of a complex between iodine and chains of glucose molecules in starch. Iodine forms complexes with both amylose and long chains within amylopectin. After the addition of iodine to a starch sample, the maximum absorption of amylose and amylopectin occurs at 620 and 550 nm, respectively. The amylose/amylopectin ratio can be estimated from the ratio of the 620 and 550 nm absorbance values and comparing them to a standard curve in which specific known concentrations are plotted against absorption values. This high throughput, inexpensive method is reliable and reproducible, allowing the evaluation of large populations of potato clones.&#160;

냉동 건조 된 감자 괴경 샘플의 신속한 높은 처리량 아밀로스 결정

This protocol describes a high through put colorimetric method that relies on the formation of a complex between iodine and chains of glucose molecules in ...

연구

화학

Transverse Sectioning of Mature Rice (Oryza sativa L.) Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

Starch granules (SGs) exhibit different morphologies depending on the plant species, especially in the endosperm of the Poaceae family. Endosperm phenotyping can be used to classify genotypes based on SG morphotype using scanning electron microscopic (SEM) analysis. SGs can be visualized using SEM by slicing through the kernel (pericarp, aleurone layers, and endosperm) and exposing the organellar contents. Current methods require the rice kernel to be embedded in plastic resin and sectioned using a microtome or embedded in a truncated pipette tip and sectioned by hand using a razor blade. The former method requires specialized equipment and is time-consuming, while the latter introduces a new host of problems depending on rice genotype. Chalky rice varieties, particularly, pose a problem for this type of sectioning due to the friable nature of their endosperm tissue. Presented here is a technique for preparing translucent and chalky rice kernel sections for microscopy, requiring only pipette tips and a scalpel blade. Preparing the sections within the confines of a pipette tip prevents rice kernel endosperm from shattering (for translucent or &#8216;vitreous&#8217; phenotypes) and crumbling (for chalky phenotypes). Using this technique, endosperm cell patterning and the structure of intact SGs can be observed.

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

This protocol allows for the preparation of transverse sections of cereal seeds (e.g., rice) for the analysis of endosperm and starch granule morphology using scanning electron microscopy.

성숙한 쌀의 횡단 단면(오리자 사티 라 L.) 파이펫 팁을 사용하여 전자 현미경 이미징을 스캐닝하기 위한 커널

Starch granules (SGs) exhibit different morphologies depending on the plant species, especially in the endosperm of the Poaceae family. Endosperm ...

생화학

Combining Histochemical Staining and Image Analysis to Quantify Starch in the Ovary Primordia of Sweet Cherry during Winter Dormancy

Changes in starch in small structures are associated with key events during several plant developmental processes, including the reproductive phase from pollination to fertilization and the onset of fruiting. However, variations in starch during flower differentiation are not completely known, mainly due to the difficulty of quantifying the starch content in the particularly small structures of the flower primordia. Here, we describe a method for the quantification of starch in the ovary primordia of sweet cherry (Prunus avium L.) by using an image analysis system attached to the microscope, which allows relating the changes in starch content with the different phases of dormancy from autumn to spring. For this purpose, the dormancy status of flower buds is determined by evaluating the bud growth of shoots transferred to controlled conditions at different moments in winter time. For the quantification of starch in the ovary primordia, flower buds are sequentially collected, fixed, embedded in paraffin wax, sectioned, and stained with I2Kl (potassium iodide-iodine). Preparations are observed under the microscope and analyzed by an image analyzer that clearly distinguishes starch from the background. Starch content values are obtained by measuring the optical density of the image that corresponds to the stained starch, considering the sum of the optical density of each pixel as an estimation of the starch content of the frame studied.

We present a methodology to quantify the starch content in the ovary primordia in sweet cherry (Prunus avium L.) during winter dormancy by using an image analysis system combined with histochemical techniques.

조직화 학적인 얼룩과 겨울 휴면 중 달콤한 체리의 난소 Primordia에서 전 분을 계량 하기 이미지 분석

Changes in starch in small structures are associated with key events during several plant developmental processes, including the reproductive phase from ...

발생학

Spectrophotometric Methods for the Study of Eukaryotic Glycogen Metabolism

Glycogen is synthesized as a storage form of glucose by a wide array of organisms, ranging from bacteria to animals. The molecule comprises linear chains of &#945;1,4-linked glucose residues with branches introduced through the addition of &#945;1,6-linkages. Understanding how the synthesis and degradation of glycogen are regulated and how glycogen attains its characteristic branched structure requires the study of the enzymes of glycogen storage. However, the methods most commonly used to study these enzyme activities typically employ reagents or techniques that are not available to all investigators. Here, we discuss a battery of procedures that are technically simple, cost-effective, and yet still capable of providing valuable insight into the control of glycogen storage. The techniques require access to a spectrophotometer, operating in the range of 330 to 800 nm, and are described assuming that the users will employ disposable, plastic cuvettes. However, the procedures are readily scalable and can be modified for use in a microplate reader, allowing highly parallel analysis.

Techniques to measure the activity of key enzymes of glycogen metabolism are presented, using a simple spectrophotometer operating in the visible range.

Glycogen is synthesized as a storage form of glucose by a wide array of organisms, ranging from bacteria to animals. The molecule comprises linear chains ...

The Extraction of Liver Glycogen Molecules for Glycogen Structure Determination

Liver glycogen is a hyperbranched glucose polymer that is involved in the maintenance of blood sugar levels in animals. The properties of glycogen are influenced by its structure. Hence, a suitable extraction method that isolates representative samples of glycogen is crucial to the study of this macromolecule. Compared to other extraction methods, a method that employs a sucrose density gradient centrifugation step can minimize molecular damage. Based on this method, a recent publication describes how the density of the sucrose solution used during centrifugation was varied (30%, 50%, 72.5%) to find the most suitable concentration to extract glycogen particles of a wide variety of sizes, limiting the loss of smaller particles. A 10 min boiling step was introduced to test its ability to denature glycogen degrading enzymes, thus preserving glycogen. The lowest sucrose concentration (30%) and the addition of the boiling step were shown to extract the most representative samples of glycogen.

An optimal sucrose concentration was determined for the extraction of liver glycogen using sucrose density gradient centrifugation. The addition of a 10 min boiling step to inhibit glycogen-degrading enzymes proved beneficial.

글리코겐 구조 측정을 위한 간 글리코겐 분자 추출

Liver glycogen is a hyperbranched glucose polymer that is involved in the maintenance of blood sugar levels in animals. The properties of glycogen are ...

Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) Method

Glycogen particles are branched polysaccharides composed of linear chains of glucosyl units linked by &#945;-1,4 glucoside bonds. The latter are attached to each other by &#945;-1,6 glucoside linkages, referred to as branch points. Among the different forms of carbon storage (i.e., starch, &#946;-glucan), glycogen is probably one of the oldest and most successful storage polysaccharides found across the living world. Glucan chains are organized so that a large amount of glucose can quickly be stored or fueled in a cell when needed. Numerous complementary techniques have been developed over the last decades to solve the fine structure of glycogen particles. This article describes Fluorophore-Assisted Carbohydrate Electrophoresis (FACE). This method quantifies the population of glucan chains that compose a glycogen particle. Also known as chain length distribution (CLD), this parameter mirrors the particle size and the percentage of branching. It is also an essential requirement for the mathematical modeling of glycogen biosynthesis.

Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis FACE Method

In the present protocol, the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) technique is used to determine the chain length distribution (CLD) and the average chain length (ACL) of glycogen.

형광단 보조 탄수화물 전기영동(FACE) 방법을 이용한 글리코겐의 글루칸 사슬 길이 분포 결정

Glycogen particles are branched polysaccharides composed of linear chains of glucosyl units linked by &#945;-1,4 glucoside bonds. The latter are attached ...

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these organisms form networks of interconnected hyphae, known as pellets, which are heterogeneous in size. Here we describe a method to analyze and sort mycelial pellets using a Complex Object Parametric Analyzer and Sorter (COPAS). Detailed instructions are given for the use of the instrument and the basic statistical analysis of the data. We furthermore describe how pellets can be sorted according to user-defined settings, which enables downstream processing such as the analysis of the RNA or protein content. Using this methodology the mechanism underlying heterogeneous growth can be tackled. This will be instrumental for improving streptomycetes as a cell factory, considering the fact that productivity correlates with pellet size.

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Liquid-grown Streptomyces cultures are characterized by mycelial pellets that are heterogeneous in size. We here describe a method to analyze and sort such pellets in a high-throughput manner. These pellets can be used for further analyses, which will provide leads to understand and control growth heterogeneity.

의 정렬<em&gt; 스트렙토 마이 세스 (Streptomyces)</em복잡한 개체 파라 메트릭 분석기 및 분류기를 사용하여&gt; 셀 펠렛

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these ...

생물학

Dynamic Light Scattering Analysis for the Determination of the Particle Size of Iron-Carbohydrate Complexes

Intravenously administered iron-carbohydrate nanoparticle complexes are widely used to treat iron deficiency. This class includes several structurally heterogeneous nanoparticle complexes, which exhibit varying sensitivity to the conditions required for the methodologies available to physicochemically characterize these agents. Currently, the critical quality attributes of iron-carbohydrate complexes have not been fully established. Dynamic light scattering (DLS) has emerged as a fundamental method to determine intact particle size and distribution. However, challenges still remain regarding the standardization of methodologies across laboratories, specific modifications required for individual iron-carbohydrate products, and how the size distribution can be best described. Importantly, the diluent and serial dilutions used must be standardized. The wide variance in approaches for sample preparation and data reporting limit the use of DLS for the comparison of iron-carbohydrate agents. Herein, we detail a robust and easily reproducible protocol to measure the size and size distribution of the iron-carbohydrate complex, iron sucrose, using the Z-average and polydispersity index.

Dynamic light scattering (DLS) has emerged as a suitable assay for evaluating the particle size and distribution of intravenously administered iron-carbohydrate complexes. However, the protocols lack standardization and need to be modified for each iron-carbohydrate complex analyzed. The present protocol describes the application and special considerations for the analysis of iron sucrose.

철-탄수화물 복합체의 입자 크기 측정을 위한 동적 광산란 분석

Intravenously administered iron-carbohydrate nanoparticle complexes are widely used to treat iron deficiency. This class includes several structurally ...

의학

A Simple Dry Sectioning Method for Obtaining Whole-Seed-Sized Resin Section and Its Applications

The morphology, size and quantity of cells, starch granules&#160;and protein bodies in seed determine the weight and quality of seed. They are significantly different among different regions of seed. In order to view the morphologies of cells, starch granules and protein bodies clearly, and quantitatively analyze their morphology parameters accurately, the whole-seed-sized section is needed. Though the whole-seed-sized paraffin section can investigate the accumulation of storage materials in seeds, it is very difficult to quantitatively analyze the morphology parameters of cells and storage materials due to the low resolution of the thick section. The thin resin section has high resolution, but the routine resin sectioning method is not suitable to prepare the whole-seed-sized section of mature seeds with a large volume and high starch content. In this study, we present a simple dry sectioning method for preparing the whole-seed-sized resin section. The technique can prepare the cross and longitudinal whole-seed-sized sections of developing, mature, germinated, and cooked seeds embedded in LR White resin, even for large seeds with high starch content. The whole-seed-sized section can be stained with fluorescent brightener 28, iodine, and Coomassie brilliant blue R250 to specifically exhibit the morphology of cells, starch granules, and protein bodies clearly, respectively. The image obtained can also be analyzed quantitatively to show the morphology parameters of cells, starch granules, and protein bodies in different regions of seed.

This technique allows for the fast and simple preparation of whole-seed-sized resin section for the observation and analysis of cells, starch granules, and protein bodies in different regions of the seed.

전체 종자 크기의 수지 섹션 및 응용 프로그램을 얻기위한 간단한 건조 단면 방법

The morphology, size and quantity of cells, starch granules&#160;and protein bodies in seed determine the weight and quality of seed. They are ...

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles involved in the endo/exocytotic trafficking of solutes and biomolecules. iMSD relies on standard time-lapse imaging, is compatible with any optical setup, and does not need to dwell on single objects to extract trajectories. From each iMSD trace, a unique triplet of average structural and dynamic parameters (i.e., size, local diffusivity, anomalous coefficient) is calculated and combined to build the "iMSD signature" of the nanostructure under study. 
The potency of this approach is proved here with the exemplary case of macropinosomes. These vesicles evolve in time, changing their average size, number, and dynamic properties passing from early to late stages of intracellular trafficking. As a control, insulin secretory granules (ISGs) are used as a reference for subcellular structures that live in a stationary state in which the average structural and dynamic properties of the whole population of objects are invariant in time. The iMSD analysis highlights these peculiar features quantitatively and paves the way to similar applications at the sub-cellular level, both in the physiological and pathological states.

Imaging-derived mean square displacement (iMSD) analysis is applied to macropinosomes to highlight their intrinsic time-evolving nature in terms of structural and dynamic properties. Macropinosomes are then compared to insulin secretory granules (ISGs) as a reference for subcellular structures with time-invariant average structural/dynamic properties.

시공간 변동 분광법에 의한 밀매된 세포하 나노구조체의 구조적 및 동적 특성 조사

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles ...