This work was funded by the National Council for Scientific and Technological Development (CNPq, Brazil) [grant numbers 428048/2018-8 and 402556/2022-4] and the University of Brasilia (Brazil).&#160;The author thanks Dr. Duarte Nuno Carvalho and Dr. Evelyn Santos (i3s, Porto, Portugal) for providing access to their smartphones used in this research.

1. Preparation of the Bradford protein assay reagent
<ol>
	<li>Dissolve 100 mg of Coomassie Brilliant Blue G in 50 mL 95% (w/v) ethanol. Mix until Coomassie Brilliant Blue G is completely dissolved. 
	CAUTION: Ethanol is flammable and causes eye irritation. Avoid flames and use goggles.</li>
	<li>To the previous solution, add 100 mL of 85% (w/v) phosphoric acid carefully. 
	CAUTION: Phosphoric acid is corrosive to metals and causes skin corrosion, serious eye damage, and acute oral toxicity. Wear gloves and goggles.</li>
	<li>Add the solution containing Coomassie Brilliant Blue G, ethanol, and phosphoric acid slowly to 600 mL of deionized water.</li>
	<li>Dilute the solution to a final volume of 1,000 mL. Scale up or down according to the number of samples to be analyzed. As described in the original method7, final concentrations in the working Bradford reagent should be 0.01% (w/v) Coomassie Brilliant Blue G, 4.7% (w/v) ethanol, and 8.5% (w/v) phosphoric acid.</li>
	<li>Remove any insoluble material filtering through filter paper (Whatman #1 paper or equivalent).</li>
	<li>The reagent is stable for several weeks when stored at room temperature (RT) and protected from light. Filter as necessary since precipitates may form over time. 
	​NOTE: Alternatively, ready to dilute and use Bradford reagents are commercially available. Follow the manufacturer's instructions for preparing the reagent and proceed to the next step.</li>
</ol>
2. Preparation of protein standard solutions
<ol>
	<li>Prepare a stock solution (refer to step 2.4) of an isolated protein to be used as standard. An affordable and commonly used protein is bovine serum albumin (BSA). Other options are ovalbumin and bovine gamma globulin.</li>
	<li>If the molar absorptivity of the protein used as standard is known, check the concentration of the stock solution in a spectrophotometer.</li>
	<li>For BSA, a commonly used formula is BSA (mg/mL) = (A280/6.6) × 10, where A280 is the absorbance at 280 nm with a path length of 1 cm read against an appropriate blank (i.e., ε2801% = 6.6)7. For example, 0.8 mg/mL BSA has an absorbance of 0.528 at 280 nm.</li>
	<li>To generate the standard curve, prepare several dilutions of BSA within 0.025 mg/mL and 1.0 mg/mL. These will result in 0.25-10 µg of BSA per well after adding a sample volume of 10 µL per well. 
	NOTE: Protein standard solutions should be prepared in a medium with the same composition (final concentration) of the medium used to prepare samples.</li>
</ol>
3. Assay
<ol>
	<li>Dilute the samples to achieve a protein concentration within the standard curve range of 0.025-1.0 mg/mL. Have several (at least 3) sample dilutions within the range. 
	NOTE: Samples can be diluted with phosphate-buffered saline (PBS) or any other medium/buffer composition compatible with the Bradford reagent. The final concentration of medium components should not differ between standard and samples.</li>
	<li>Add 10 µL of each protein standard solution to three wells (i.e., in triplicate) of a 96-well microplate. For the 0 (zero) protein point, add 10 µL of the buffer/medium used to prepare the standard solutions and sample dilutions.</li>
	<li>To another set of wells, add 10 µL of each sample dilution to three wells (i.e., in triplicate) of the same 96-well microplate. One approach is to add different volumes of a sample and complete with a medium of up to 10 µL per well (e.g., 0.5 µL, 1 µL, 2 µL, 4 µL, and 8 µL of the sample plus 9.5 µL, 9 µL, 8 µL, 6 µL, and 2 µL of medium).</li>
	<li>Add 250 µL of Bradford protein assay reagent to all wells. A typical microplate setup is shown in Figure 2. In this example, a set of blank wells containing 260 µL (the final volume per well) water was included to serve as blank in a microplate reader (step 4.5). This is required only if absorbance data will also be collected. 
	NOTE: Prepare a standard curve in the same microplate of samples each time the assay is performed. In other words, if the number of samples requires another plate, prepare another standard curve in the second plate, and so on.</li>
	<li>Record results (section 4) within 5-15 min.</li>
</ol>
<img alt="96-well plate diagram for sample analysis and standard calibration in scientific experiments." class="xfigimg" src="/files/ftp_upload/65547/65547fig02v2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 2"> 
Figure 2: A typical plate layout for the Bradford protein assay. Blank refers to three wells containing 260 µL of water to be used as blank in a microplate reader. STD refers to protein standards. S1-S6 are six different samples. SX_1-SX_4 are four different sample dilutions for each sample. <a href="https://www.jove.com/files/ftp_upload/65547/65547fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. Recording results
<ol>
	<li>In a well-illuminated room, hold the microplate parallel to the bench against a uniform white background (e.g., a paper sheet) with one hand. Ensure accurate alignment by placing a bubble level on the plate.</li>
	<li>With the other hand, hold the smartphone parallelly (some camera applications usefully indicate the inclination of the device) to the bench and the microplate and take one or several pictures of the whole microplate (Figure 3). In iOS devices, turn on the camera level indicator in the camera settings by enabling the Grid option. In Android devices, turn on the camera level indicator in the camera settings by enabling Framing Hints.</li>
	<li>No special lighting apparatus is required, but take care to avoid shadows and reflections. For example, avoid shading the plate or the background with the smartphone and avoid shading the background with the microplate. Small reflections in the edges of the well area are not a problem; color data can be extracted from a very small area in the center of each well.</li>
	<li>Briefly check the picture for background uniformity, shadows, and reflections. Also, take a look at the angle of the wells; the center of each well should be directly visible (i.e., not behind well walls).</li>
	<li>If the comparison between microplate absorbance readings and picture color data is desired, read the microplate at 590 nm and 450 nm in a microplate reader21.</li>
</ol>
<img alt="Lab experiment setup with microplate; data recording using smartphone; biochemical analysis." class="xfigimg" src="/files/ftp_upload/65547/65547fig03v2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 3"> 
Figure 3: Capturing the results of the Bradford protein assay. In a well-illuminated room, the microplate is positioned parallelly to the bench against a uniform background with one hand. With the other hand, the smartphone is hold parallelly to the bench and the microplate. <a href="https://www.jove.com/files/ftp_upload/65547/65547fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Extracting color data-Automatically
<ol>
	<li>Download ImageJ and ReadPlate22, an ImageJ plugin is available at https://imagej.nih.gov/ij/plugins/readplate/index.html (this is a .txt file).</li>
	<li>Open ImageJ, click on Plugins &gt; Install and select the file downloaded in step 6.1.</li>
	<li>Set the measurement parameters by clicking on Analyze &gt; Set Measurements and check the following options: Area; Standard deviation; Min &amp; max gray value; Mean gray value; Modal gray value. On the bottom of the window, set Redirect to: None and Decimal places (0-9): 3.</li>
	<li>Go to File &gt; Open and select the picture of the microplate taken in step 4.</li>
	<li>Go to Plugins &gt; ReadPlate. Read the instructions and then click OK.</li>
	<li>Select the number of wells: 96.</li>
	<li>Using the Rectangular selection tool automatically loaded by the plugin, make a rectangle beginning in the center of the A1 well and ending in the center of the H12 well. Then, click OK.</li>
	<li>Select the Blue channel and click OK.</li>
	<li>Confirm the default parameters by clicking on OK.</li>
	<li>Check if the software delineated an area inside each well and if the selected areas are not covering areas with abnormal shadows or reflection. Click OK.</li>
	<li>Save the results and repeat steps 5.8-5.10 for the green channel.</li>
	<li>Calculate the blue-to-green ratio using the mode for each color. 
	​NOTE: The calculation can be done manually or using the software of the reader preferences, such as R, Microsoft Excel, or GraphPad Prism.</li>
</ol>
6. Extracting color data-Manually
<ol>
	<li>Download Inkscape, a free and open-source graphics editor. 
	NOTE: Any software with the color picker tool (usually depicted as an eye dropper) can be used to identify the color and extract RGB data.</li>
	<li>Open Inkscape, go to File &gt; Open. Select the picture of the microplate taken in Step 4.</li>
	<li>Pick the Select and Transform Objects (S) tool, which is depicted as an arrow on the top-left side, and click on the picture. A dashed line border will indicate the selection.</li>
	<li>Select the Pick Colors from Image (D) tool, which is depicted as an eye dropper on the left side.</li>
	<li>Click in the center of a well. The color on the bottom panel ("Fill:") will change accordingly. Click on the color and a Fill and Stroke tab will pop up on the right side.</li>
	<li>Change the Flat Color dropdown menu to RGB. Record the values shown for the blue and green channels for each well.</li>
	<li>Calculate the blue-to-green ratio using the recorded values. 
	​NOTE: The calculation can be done manually or using the software of the reader preferences, such as R, Microsoft Excel, or GraphPad Prism.</li>
</ol>
7. Building standard curves and extrapolating unknowns
<ol>
	<li>Plot the average green-to-blue intensity ratio as a function of protein standard concentration. 
	NOTE: Plot the data manually or using the software of the reader preferences, such as R, Microsoft Excel, or GraphPad Prism.</li>
	<li>Calculate the average green-to-blue intensity ratio for each sample and dilution.</li>
	<li>Check if the signal obtained for the sample lies within the linear range of the protein standard.</li>
	<li>Ignore any value below or above the minimum or maximum values of the standard curve.</li>
	<li>Use the linear equation describing the standard curve to extrapolate the amount of protein in the sample. Multiply the calculated values by the dilution factor accordingly.</li>
</ol>

Smartphone as an Analytical Device to Quantify Protein Using Bradford Assay

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	<li>Zaguri, M., Kandel, S., Rinehart, S. A., Torsekar, V. R., Hawlena, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+quantification+in+ecological+studies:+A+literature+review+and+empirical+comparisons+of+standard+methodologies.">Protein quantification in ecological studies: A literature review and empirical comparisons of standard methodologies.</a> Methods in Ecology and Evolution. 12 (7), 1240-1251 (2021).</li><li>Koga, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mild+electrical+stimulation+and+heat+shock+ameliorates+progressive+proteinuria+and+renal+inflammation+in+mouse+model+of+Alport+syndrome.">Mild electrical stimulation and heat shock ameliorates progressive proteinuria and renal inflammation in mouse model of Alport syndrome.</a> PLoS One. 7 (8), e43852 (2012).</li><li>Peterson, G. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+total+protein.">Determination of total protein.</a> Methods in Enzymology. 91, 95-119 (1983).</li><li>Goldfarb, A. R., Saidel, L. J., Mosovich, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ultraviolet+absorption+spectra+of+proteins.">The ultraviolet absorption spectra of proteins.</a> The Journal of Biological Chemistry. 193 (1), 397-404 (1951).</li><li>Lowry, O. H., Rosebrough, N. J., Farr, A. L., Randall, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+measurement+with+the+Folin+phenol+reagent.">Protein measurement with the Folin phenol reagent.</a> The Journal of Biological Chemistry. 193 (1), 265-275 (1951).</li><li>Smith, P. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+protein+using+bicinchoninic+acid.">Measurement of protein using bicinchoninic acid.</a> Analytical Biochemistry. 150 (1), 76-85 (1985).</li><li>Bradford, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+and+sensitive+method+for+the+quantitation+of+microgram+quantities+of+protein+utilizing+the+principle+of+protein-dye+binding.">A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.</a> Analytical Biochemistry. 72 (1-2), 248-254 (1976).</li><li>Datki, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+BisANS+fluorescent+dye+for+developing+a+novel+protein+assay.">Application of BisANS fluorescent dye for developing a novel protein assay.</a> PLoS One. 14 (4), e0215863 (2019).</li><li>Van Noorden, R., Maher, B., Nuzzo, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+top+100+papers.">The top 100 papers.</a> Nature. 514 (7524), 550-553 (2014).</li><li>. 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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensitivity+and+variability+of+the+Bradford+protein+assay+in+the+presence+of+detergents.">Sensitivity and variability of the Bradford protein assay in the presence of detergents.</a> Analytical Biochemistry. 178 (2), 263-268 (1989).</li><li>Stoscheck, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+uniformity+in+the+response+of+the+Coomassie+blue+G+protein+assay+to+different+proteins.">Increased uniformity in the response of the Coomassie blue G protein assay to different proteins.</a> Analytical Biochemistry. 184 (1), 111-116 (1990).</li><li>Zor, T., Selinger, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linearization+of+the+Bradford+protein+assay+increases+its+sensitivity:+Theoretical+and+experimental+studies.">Linearization of the Bradford protein assay increases its sensitivity: Theoretical and experimental studies.</a> Analytical Biochemistry. 236 (2), 302-308 (1996).</li><li>Gee, C. T., Kehoe, E., Pomerantz, W. C. K., Penn, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+protein+concentrations+using+smartphone+colorimetry:+A+new+method+for+an+established+test.">Quantifying protein concentrations using smartphone colorimetry: A new method for an established test.</a> Journal of Chemical Education. 94 (7), 941-945 (2017).</li><li>de Camargo, C., Vicentini, M., Gobbi, A., Martinez, D., Lima, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smartphone+for+point-of-care+quantification+of+protein+by+Bradford+assay.">Smartphone for point-of-care quantification of protein by Bradford assay.</a> Journal of the Brazilian Chemical Society. 28 (4), 689-693 (2016).</li><li>Moreira, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RGBradford:+Accurate+measurement+of+protein+concentration+using+a+smartphone+camera+and+the+blue+to+green+intensity+ratio.">RGBradford: Accurate measurement of protein concentration using a smartphone camera and the blue to green intensity ratio.</a> Analytical Biochemistry. 655, 114839 (2022).</li><li>Ernst, O., Zor, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linearization+of+the+Bradford+Protein+Assay.">Linearization of the Bradford Protein Assay.</a> Journal of Visualized Experiments. (38), 1918 (2010).</li><li>Angelani, C. 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The author has no conflicts of interest to declare.

This paper describes RGBradford, a method that uses a smartphone camera to record data from a Bradford protein assay, extract color data, and accurately quantify protein levels in biological samples as originally described recently20. One difference from the original RGBradford method is that here a procedure for obtaining color data automatically with an ImageJ plugin22 was used. The main novelty of the RGBradford method is the use of RGB data as analytical signals; thus, ...

Regardless of the downstream use (e.g., ELISA, enzyme kinetics, western blotting, protein purification, and mass spectrometry), protein quantification is crucial for accurate analysis in life sciences laboratories. In addition to their use as secondary readouts (i.e., to calculate relative levels of analytes per mass of protein), protein levels in a sample can also be the desired output itself. For example, one can be interested in protein levels in food resources1 or in urine2. There are many methods available to measure protein concentration in samples3, including direct UV absorbance readings4, protein-copper chelation5,6, protein-dye binding colorimetric assays7, and protein-dye binding fluorescent assays8. The relevance of protein quantitation is evidenced by the presence of two papers describing protein measurement methods5,7 in the top-3 of the most cited literature9,10. Despite the fact that many authors neglect their actual citation by citing non-primary references or not citing anything at all, the original papers describing the Lowry protein assay and the Bradford protein assay amount &gt;200,000 citations each10.
The popularity of the Bradford assay stems from its affordability, simplicity, speed, and sensitivity. The assay is based on the interaction between proteins and the dye Coomassie Brilliant Blue G under acidic conditions. Under the conditions of the assay (i.e., low pH), the dye exists in three forms: a red cationic form with λmax at 470 nm; a green neutral form with λmax at 650 nm; and a blue anionic form with λmax at 590 nm11,12 (Figure 1). The cationic form predominates in the absence of proteins. As proteins interact with the dye, they stabilize the blue anionic form, causing a noticeable change in the color of the solution, from brownish to blue. Usually, the change in the concentration of the blue form of the dye is quantified spectrophotometrically, whose absorbance at 590-595 nm is proportional to the quantity of protein in the assay.
<img alt="Absorbance vs. wavelength graph; spectral peaks at 470, 590, 650 nm; indicating optical analysis." class="xfigimg" src="/files/ftp_upload/65547/65547fig01v2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1: Coomassie brilliant blue G absorption spectra under the conditions of the Bradford assay. The three main peaks are marked with arrows indicating the λmax of the red (470 nm), green (650 nm), and blue (590 nm) forms of the dye. Spectra were recorded in the absence of protein (yellow line) and in the presence of 3 µg (gray line) and 10 µg (blue line) of bovine serum albumin. <a href="https://www.jove.com/files/ftp_upload/65547/65547fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The widespread use of the Bradford assay has led to the identification of several limitations (e.g., variable responses to different proteins11, and interference by lipids13 and detergents7) and the development of modifications to improve its performance (e.g., the addition of detergents14,15, alkalinization14,16 and use of the ratio of absorbances17). In addition to modifications in the assay itself, the use of alternative devices, such as smartphones or cameras, to capture analytical signals have also been described18,19,20. Indeed, the development of methods that make use of smartphones as portable chemical analyzers has been an active area of research. The motivation for the use of smartphones stems from the affordability, portability, ease of use, and widespread availability of these devices.
This paper provides a protocol for protein quantification using the RGBradford assay20, which uses a smartphone as an analytical device. In contrast to the original RGBradford publication20, here, a procedure that streamlines the color extraction process has been introduced. It involves the utilization of a freely available software application to extract color information from each well of a microplate picture automatically, saving significant time and effort. This is an alternative to the previous method of manually acquiring color data from each well one by one using a graphics editor software application20. Ultimately, protein levels in samples can be quantified using color data extracted from a picture of a microplate taken with a smartphone.

<table><tbody><tr><td>96-well flat-bottom polystyrene microtiter plates&amp;nbsp;</td><td>Jet Biofil, Guangzhou, China</td><td>TCP011096</td><td>Any flat-bottom microplate compativle with optical reading will suffice.&amp;nbsp;</td></tr><tr><td>Bovine serum albumin</td><td>Sigma-Aldrich, St. Louis, MO</td><td>A2153</td><td></td></tr><tr><td>Coomassie Brilliant Blue G</td><td>Sigma-Aldrich, St. Louis, MO</td><td>B0770</td><td></td></tr><tr><td>Ethyl alcohol</td><td></td><td></td><td></td></tr><tr><td>iPhone 11</td><td>Apple</td><td>MWM02BR/A</td><td>Can be substituted with other smartphone equiped with a camera</td></tr><tr><td>iPhone 14 Pro</td><td>Apple</td><td>N/A</td><td></td></tr><tr><td>Phosphoric acid</td><td>Sigma-Aldrich, St. Louis, MO</td><td>695017</td><td></td></tr><tr><td>Redmi Note 9 Pro</td><td>XIAOMI&amp;nbsp;</td><td>N/A</td><td></td></tr><tr><td>S22 Ultra</td><td>Samsung&amp;nbsp;</td><td>N/A</td><td></td></tr><tr><td>SpectraMax 384 Plus. Microplate reader.</td><td>Molecular Devices, San Jose, CA</td><td>PLUS 384</td><td>Any microplate reader capable of reading at 450 nm and 590 nm will work. This is optional. The method was actually created to dismiss the need of a microplate reader.</td></tr></tbody></table>

rgbradford: protein quantitation with a smartphone camera

Protein quantitation is an essential procedure in life sciences research. Amongst several other methods, the Bradford assay is one of the most used. Because of its widespread, the limitations and advantages of the Bradford assay have been exhaustively reported, including several modifications of the original method to improve its performance. One of the alterations of the original method is the use of a smartphone camera as an analytical instrument. Taking advantage of the three forms of the Coomassie Brilliant Blue dye that exist in the conditions of the Bradford assay, this paper describes how to accurately quantify protein in samples using color data extracted from a single picture of a microplate. After performing the assay in a microplate, a picture is taken using a smartphone camera, and RGB color data is extracted from the picture using a free and open-source image analysis software application. Then, the ratio of blue to green intensity (in the RGB scale) of samples with unknown concentrations of protein is used to calculate the protein content based on a standard curve. No significant difference is observed between values calculated using RGB color data and those calculated using conventional absorbance data.

Figure 4 is a picture of a microplate from which color data was extracted, and absorbance at 450 nm and 590 nm was recorded. The RGB color data reported here as representative were obtained automatically as described in section 5. A typical pattern of color data is an increase in the blue values and a decrease in the red and green values (Figure 5). Note that despite the evident reflection in all wells and a not perfectly aligned microplate (<strong class="xfig...

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Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone 

This paper provides a protocol for protein quantification using the Bradford assay and a smartphone as an analytical device. Protein levels in samples can be quantified using color data extracted from a picture of a microplate taken with a smartphone.

RGBradford: Protein Quantitation with a Smartphone Camera

Protein quantitation is an essential procedure in life sciences research. Amongst several other methods, the Bradford assay is one of the most used. ...

rgbradford-protein-quantitation-with-a-smartphone-camera

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Video: Auteur in de schijnwerpers: een alternatieve benadering van eiwitkwantificering door Bradford-test met behulp van een smartphone 

Quantitative Immunofluorescence to Measure Global Localized Translation

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and organogenesis, as well as in multiple diseases. Numerous publications have convincingly shown that specific mechanisms tightly regulate mRNA translation. Increased interest in the translation-induced regulation of protein expression has led to the development of novel methods to study and follow de novo protein synthesis in cellulo. However, most of these methods are complex, making them costly and often limiting the number of mRNA targets that can be studied. This manuscript proposes a method that requires only basic reagents and a confocal fluorescence imaging system to measure and visualize the changes in mRNA translation that occur in any cell line under various conditions. This method was recently used to show localized translation in the subcellular structures of adherent cells over a short period of time, thus offering the possibility of visualizing de novo translation for a short period during a variety of biological processes or of validating changes in translational activity in response to specific stimuli.

This manuscript describes a method to visualize and quantify localized translation events in subcellular compartments. The approach proposed in this manuscript requires a basic confocal imaging system and reagents and is rapid and cost-effective.

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and ...

Real-time Tracking of DNA Fragment Separation by Smartphone

Slab gel electrophoresis (SGE) is the most common method for the separation of DNA fragments; thus, it is broadly applied to the field of biology and others. However, the traditional SGE protocol is quite tedious, and the experiment takes a long time. Moreover, the chemical consumption in SGE experiments is very high. This work proposes a simple method for the separation of DNA fragments based on an SGE chip. The chip is made by an engraving machine. Two plastic sheets are used for the excitation and emission wavelengths of the optical signal. The fluorescence signal of the DNA bands is collected by smartphone. To validate this method, 50, 100, and 1,000 bp DNA ladders were separated. The results demonstrate that a DNA ladder smaller than 5,000 bp can be resolved within 12 min and with high resolution when using this method, indicating that it is an ideal substitute for the traditional SGE method.

Traditional slab gel electrophoresis (SGE) experiments require a complicated apparatus and high chemical consumption. This work presents a protocol that describes a low-cost method to separate DNA fragments within a short timeframe.

Slab gel electrophoresis (SGE) is the most common method for the separation of DNA fragments; thus, it is broadly applied to the field of biology and ...

Wide-field Fluorescent Microscopy and Fluorescent Imaging Flow Cytometry on a Cell-phone

Fluorescent microscopy and flow cytometry are widely used tools in biomedical research and clinical diagnosis. However these devices are in general relatively bulky and costly, making them less effective in the resource limited settings. To potentially address these limitations, we have recently demonstrated the integration of wide-field fluorescent microscopy and imaging flow cytometry tools on cell-phones using compact, light-weight, and cost-effective opto-fluidic attachments. In our flow cytometry design, fluorescently labeled cells are flushed through a microfluidic channel that is positioned above the existing cell-phone camera unit. Battery powered light-emitting diodes (LEDs) are butt-coupled to the side of this microfluidic chip, which effectively acts as a multi-mode slab waveguide, where the excitation light is guided to uniformly excite the fluorescent targets. The cell-phone camera records a time lapse movie of the fluorescent cells flowing through the microfluidic channel, where the digital frames of this movie are processed to count the number of the labeled cells within the target solution of interest. Using a similar opto-fluidic design, we can also image these fluorescently labeled cells in static mode by e.g. sandwiching the fluorescent particles between two glass slides and capturing their fluorescent images using the cell-phone camera, which can achieve a spatial resolution of e.g. ~ 10 &mu;m over a very large field-of-view of ~ 81 mm2. This cell-phone based fluorescent imaging flow cytometry and microscopy platform might be useful especially in resource limited settings, for e.g. counting of CD4+ T cells toward monitoring of HIV+ patients or for detection of water-borne parasites in drinking water.

We review our recent results on the integration of fluorescent microscopy and imaging flow cytometry tools on a cell-phone using compact and cost-effective opto-fluidic attachments. These cell-phone based micro-analysis devices might be useful for cytometric analysis, such as performing various cell counting tasks as well as for high-throughput screening of e.g., water samples in resource limited settings.

Fluorescent microscopy and flow cytometry are widely used tools in biomedical research and clinical diagnosis. However these devices are in general ...

Bioengineering

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

Environment

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

Smartphone-Based Imaging for C. elegans Lawn Avoidance Assay

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay  

This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting.

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually ...

Behavior

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of applications. The cell surface proteome ("surfaceome") in human disease is of significant interest, as plasma membrane proteins are the primary target of most clinically approved therapeutics, as well as a key feature by which to diagnostically distinguish diseased cells from healthy tissues. However, focused characterization of membrane and surface proteins of the cell has remained challenging, primarily due to the complexity of cellular lysates, which mask proteins of interest by other high-abundance proteins. To overcome this technical barrier and accurately define the cell surface proteome of various cell types using mass spectrometry proteomics, it is necessary to enrich the cell lysate for cell surface proteins prior to analysis on the mass spectrometer. This paper presents a detailed workflow for labeling cell surface proteins from cancer cells, enriching these proteins out of the cell lysate, and subsequent sample preparation for mass spectrometry analysis.

Here, we describe a proteomics workflow for characterization of the cell surface proteome of various cell types. This workflow includes cell surface protein enrichment, subsequent sample preparation, analysis using an LC-MS/MS platform, and data processing with specialized software.

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of ...

Smartphone Fundus Photography

Smartphone fundus photography is a simple technique to obtain ocular fundus pictures using a smartphone camera and a conventional handheld indirect ophthalmoscopy lens. This technique is indispensable when picture documentation of optic nerve, retina, and retinal vessels is necessary but a fundus camera is not available. The main advantage of this technique is the widespread&#160;availability of smartphones that allows documentation of macula and optic nerve changes in many settings that was not previously possible. Following the well-defined steps detailed here, such as proper alignment of the phone camera, handheld lens, and the patient&#39;s pupil, is the key for obtaining a clear retina picture with no interfering light reflections and aberrations. In this paper, the optical principles of indirect ophthalmoscopy and fundus photography will be reviewed first. Then, the step-by-step method to record a good quality retinal image using a smartphone will be explained.

Fundus photography normally requires specialized fundus cameras that are not always available in all clinical settings.&#160;Here, a simple method to record ocular fundus images using a smartphone camera and a conventional high-plus handheld indirect ophthalmoscopy lens is described.

Smartphone fundus photography is a simple technique to obtain ocular fundus pictures using a smartphone camera and a conventional handheld indirect ...

Medicine

Smartphone-Integrated Dengue NS1 Paper-Based Diagnostic Device

Portable Paper-Based Immunoassay Combined with Smartphone Application for Colorimetric and Quantitative Detection of Dengue NS1 Antigen

Dengue virus (DENV) infection, which is transmitted by Aedes mosquitoes, is a major public health concern in tropical and subtropical countries. With an annual incidence of approximately 10 million cases and 20,000-25,000 deaths, particularly among children, there is an urgent need for practical diagnostic tools. The presence of dengue non-structural protein 1 (NS1) during early infection has been linked to cytokine release, vascular leakage, and endothelial dysfunction, making it a potential marker for severe dengue. 
Paper-based immunoassays such as lateral flow assays (LFAs) and microfluidic paper-based analytical devices (PADs) have gained popularity as diagnostic tests due to their simplicity, rapidity, inexpensiveness, specificity, and ease of interpretation. However, conventional paper-based immunoassays for dengue NS1 detection typically rely on visual inspection, yielding only qualitative results. To address this limitation and enhance sensitivity, we proposed a highly portable NS1 dengue detection assay on a Paper-based Analytical Device (PAD), namely, DEN-NS1-PAD, that integrates a smartphone application as a colorimetric and quantitative reader. The development system enables direct quantification of NS1 concentrations in clinical samples. 
Serum and blood samples obtained from patients were utilized to demonstrate the system prototype performance. The results were obtained immediately and can be employed for clinical assessment, both in well-equipped healthcare facilities and resource-limited settings. This innovative combination of a paper-based immunoassay with a smartphone application offers a promising approach for enhanced detection and quantification of dengue NS1 antigen. By augmenting sensitivity beyond the capabilities of the naked eye, this system holds great potential for improving clinical decision-making in dengue management, particularly in remote or underserved areas.

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Addressing urgent dengue diagnostic needs, here we introduce a smartphone app-integrated Dengue NS1 Paper-based Analytical Device (DEN-NS1-PAD) for quantifying Dengue NS1 antigen concentration in clinical serum/blood samples. This innovation enhances dengue management by aiding clinical decision-making in various healthcare settings, even resource-limited ones.

Dengue virus (DENV) infection, which is transmitted by Aedes mosquitoes, is a major public health concern in tropical and subtropical countries. With an ...

Immunology and Infection

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein &#945;-subunit) is described in detail.

This protocol describes how to perform absolute quantification assays of target proteins within complex biological samples using selected reaction monitoring. It was used to accurately quantify proteins of the mouse macrophage chemotaxis signaling pathway. Target peptide selection, assay development, and qualitative and quantitative assays are described in detail.

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This ...

RGBradford: Protein Quantitation with a Smartphone Camera

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