What is optical density in Lowry's protein estimation method?

I have a few questions:

  1. What is an OD value?

  2. Why do we use blank solution in Lowry's protein estimation method?

  3. If The OD of a protein is 0.01, what does it mean?

Thanks in advance.

  1. The OD measurement is the output of what the photometer measures. It is actually the amount of light which is scattered or absorbed by your sample - scientifically called extinction.
  2. A blank is used to be able to substract the influence of reagents, light that is scattered on the surfaces of the cuvette (which is probably also not completely clean) and so on. Every of this influences reduces the amount of light which should reach the detector on the other side of the probe and give you false extinction readings. If you are using a colored detection sample (which colors deeper or looses color during the reaction) this is more obvious. The solution already has an extinction on its own which is not of interest. So you make a blank (which basically substracts all these influences from you sample reading) to correct for this. How this is done depends on the photometer. Some make a correction reading at the beginning, while others use a second cuvette (a blank cuvette) which is permanently measured.
  3. It means that the protein gives you this extinction. Without a calibration curve (made from serial dilutions with known protein concentrations) it is impossible to make any further comment on this number.

  • Path length l is usually in units of cm. (note: most spectrophotometers are designed to accept 1cm wide cuvettes)
  • Molar extinction coefficient&epsilon has units of M -1 cm -1 and is a constant of proportionality that relates the absorption of molar solutions
  • Mass extinction coefficient&epsilon 1% refers to the absorbance of a 1% by mass solution. Typically this refers to an aqueous solution that we can take to have a density of 1000g/L. A 1% by mass aqueous solution would therefore refer to the dissolution of 10g/L, or a 10mg/ml solution of the molecule of interest.
  • Since the absorbance of a molecule is a function of the wavelength (i.e. the absorption is not equal for every wavelength) the extinction coefficient must also reference a wavelength. This is typically done using a subscript:

&epsilon 1% 280nm = 14.5 g -1 L cm -1

· In this case a 10mg/ml solution of the molecule will have an absorbance reading of 14.5 (dimensionless units) at l = 280nm (the absorption at other wavelengths may not be known). The units of concentration are g/L, thus e will have dimensions of g -1 L cm -1 .

Why is it important to be able to quantitate protein concentration in a sample?

An important application of "Biotechnology" is the production of proteins as commercial products. Such products might have pharmaceutical applications (e.g. insulin, human growth hormone, tissue plasminogen activator, erythropoietin, blood clotting factor VIII.), industrial applications (e.g. subtilisin (an enzyme in detergents), 2,5-diketo-D-gluconate reductase (an enzyme in vitamin C production), as materials (e.g. silk protein in textiles, barnacle adhesion protein as a glue). In these cases, there are various aspects of successful production that require quantitation:

  • How much of the protein can be produced (i.e. what is the efficiency of production)?
  • How pure is the protein that is produced (industrial applications may require 90% pure, pharmaceutical applications may require 99.999% pure)

Such proteins may be isolated from natural sources (e.g. blood clotting factor VIII may be extracted from human blood), or they may be produced recombinantly (e.g. E. coli bacterial cells can be genetically engineered to produce human growth hormone). In both cases, it may be necessary to purify the protein using a series of fractionation steps. We will go into more detail about such fractionation steps in a later lecture, but the general idea is that a heterogeneous mixture of molecules can be fractionated based upon some physical property of the molecules. The following are properties that can be used to fractionate a heterogeneous mixture of biomolecules:

  • Molecular mass (i.e. "big" molecules can be separated from "small" molecules)
  • pKa (i.e. "acidic" molecules can be separated from "basic" molecules)
  • Hydrophobicity (i.e. non-polar molecules can be separated from polar molecules)

For such fractionation steps involving proteins, we need to keep track of how much of the contaminating proteins went into one fraction and how much of our desired protein went into the other fraction. Although the details are somewhat more complicated than this simple description, it is important to be able to quantitate protein concentration to be able to effectively purify a protein of interest.

Once a protein is pure, it may be of considerable economic interest to be able to quantify the yield (and, therefore, be able to determine how much it cost to produce a given mass of protein). For example, the only source for human growth hormone (to treat small stature) used to be to extract it from human pituitary glands harvested from the brains of cadavers. Suffice it to say, this made the protein extremely expensive. Furthermore, the isolation from human tissues meant that the sample could also be potentially contaminated with human pathogens (hepatitis, CJD, AIDS, etc.). With the advent of genetic engineering, the production of human growth hormone by bacterial cells (i.e. E. coli) meant that relative large quantities could be produced far cheaper (and with no threat of human pathogens).

Why not just weigh the protein?

  • Most samples are typically quantities of milligrams or even micrograms, not grams, and thus, it is difficult to transfer and measure such small amounts
  • Water is present in proteins, and it is extremely difficult to remove all the water (some water molecules hydrogen bond extremely tightly to proteins). Thus, the mass measurement would include some waters, and would increase the apparent mass of the protein

Cell Viability and Cytotoxicity: 5 Assays

As the cells are removed from the living (in vivo) environment and subjected to experimental manipulations in the culture systems (in vitro), their viability assumes significance. Viability of the cells represents the capability of their existence, survival and development.

Many experiments are carried out with cells in the culture rather than using the animal models. This is particularly so with regard to the determination of safety and cytotoxicity of several compounds (pharmaceuticals, cosmetics, anticancer drugs, food additives). In vitro testing for cytotoxicity and safety evaluation is in fact cost-effective, besides reducing the use of animals.

Studies on cytotoxicity broadly involve the metabolic alterations of the cells, including the death of cells as a result of toxic effects of the compounds. For instance, in case of anti­cancer drugs, one may look for death of cells, while for cosmetics the metabolic alterations and allergic responses may be more important.

There are several assays developed in the laboratory for measuring the cell viability and cytotoxicity.

They are broadly categorized into the following types:

i. Cytotoxicity and viability assays.

I. Cytotoxicity and Viability Assays:

A majority of the cytotoxicity and viability assays are based on the measurement of membrane integrity, cellular respiration, radioisotope incorporation, colorimetric assays and luminescence- based tests.

Based on membrane integrity:

The most common measurements of cell viability are based on membrane integrity. The damage to membrane may occur due to cell disaggregation, cell separation or freezing and thawing. Membrane integrity can be determined by uptake of dyes to which viable cells are impermeable (e.g. naphthalene black, trypan blue, erythrosin) or release of dyes normally taken up and retained by viable cells (e.g. neutral red, diacetyl fluorescein).

The other assays for membrane integrity are release of labeled chromium ( 51 Cr), enzymes and use of fluorescent probes. Cell viability measurements, based on membrane integrity are immediate that can be detected within a few hours. However, these measurements cannot predict the ultimate survival of cells.

The principle of this assay is based on the fact that viable cells are impermeable to several dyes such as naphthalene black, trypan blue, eosin Y, nigrosin green and erythrocin B. The technique basically consists of mixing the cells in suspension with the dye and examining them under the microscopy. The stained cells and the total number of cells are counted. The percentage of unstained cells represents the viable cells.

Dye exclusion assay is convenient and suitable to suspension cultures than to monolayers. This is due to the fact that as the dead cells detatch from the monolayers they are lost from the assay. The major limitation of this assay is that reproductively dead cells do not take up the dye, and will be counted as though they are viable.

The viable cells can take up the dye diacetyl fluorescein and hydrolyse it to fluorescein. The latter is held up by the viable cells, as it is impermeable to membrane. The viable cells therefore emit fluorescein green while the dead cells do not. Thus, the viable cells can be identified.

Labeled chromium uptake assay:

Labeled chromium ( 51 Cr) binds to the intracellular proteins through basic amino acids. When the cell membrane is damaged, the labeled proteins leak out of the cell and the degree of leakage is proportional to the amount of damage. Labeled 51 Cr uptake method is used in the immunological studies to determine the cytotoxic activity of T-lymphocytes against target cells.

Enzyme release assays:

The membrane integrity of cells can also be assessed by estimating the enzymes released. Lactate dehydrogenase (LDH) has been the most widely used enzyme for this purpose.

Based on cellular respiration:

Respiration of the cells measured by oxygen utilization or carbon dioxide production can be used to assess cell viability. This is usually done by using Warburg manometer.

Based on radioisotope incorporation:

By using radiolabeled substrates or metabolites, the radiolabel in the products formed can be detected. This method is particularly useful for the cytotoxicity assays of drugs. Some of the important radioisotope incorporation methods are briefly given.

Incorporation of ( 3 H) thymidine into DNA and ( 3 H) uridine into RNA are widely used for the measurement of drug toxicity.

The cells are pre-labeled with 32 P. When the damage occurs to cells, they release labeled phosphate which can be measured. The efficacy of drugs can be evaluated by this approach.

Based on colorimetric assays:

The recent developments in the colorimetric assays by using sophisticated micro-plate readers are fruitfully utilized for quantitation of cells. A good correlation between the cell number and colorimetric assay are observed.

Some highlights of this approach are given below:

i. Protein content can be estimated by methylene blue, amido black, sulforhodamine. In the Lowry method for protein estimation, Folin-Ciocalteau reagent is used.

ii. DNA can be quantitated by staining with fluorescence dyes e.g. 2-diaminodino- phenylindone.

iii. Lysosomal and Golgi body activity by using neutral red.

iv. Enzyme activity assays e.g. hexosaminidase, mitochondrial succinate dehydrogenase.

Based on luminescence test:

The viability of cells can be measured with good sensitivity by estimating ATP levels by luminescence based test. The principle is based on the following reaction.

A good sensitivity of this test is reported for the cells in the range of 20 to 2 × 10 7 cells/ml.

Most of the anticancer drugs kill cells by apoptosis which can be measured for the assessment of cytotoxicity. Apoptosis can be detected by the following ways.

i. Changes in the morphology.

ii. Detection of phosphatidyl serine in the membrane by using annexin V conjugated to fluorescein isothiocyanate (FITC) or biotin.

Ii. Survival Assays:

The tests described above for measurement of cell viability and cytotoxicity are short-term, and they identify the dead/live cells at the time of assay. Many times, when the cells are subjected to toxicity (i.e. exposed to drugs, irradiated), the effects are not immediate, but may be observed after several hours or sometimes even days. The assays based on the survival of cells (i.e. retention of regenerative capacity or reproductive integrity) are preferred.

In clonogenic assay, the survival of the cells is measured by plating efficiency (i.e. the percentage of cells seeded at subculture that give rise to colonies). The plating efficiency measures the proliferative capacity for several cell generations.

Clonogenic assay broadly consists of the following stages:

1. Treatment of the cells with varying concentrations of experimental agent for about 24 hours.

2. Trypsinization followed by seeding of cells at low density.

3. Incubation of the cells for 1-3 weeks.

4. Staining and counting of the colonies.

A survival curve (semi-log plot) representing the survival fraction of the cells against drug concentration is depicted in Fig 38.1. The inhibitory concentration (IC) refers to the drug concentration required to inhibit the viability of cells. Thus, IC50 and IC90 represent the concentrations of a compound that respectively inhibit 50% and 90% of colony formation.

As is evident from the graph, the curve has a knee wherein IC50 lies, while IC90 falls in the linear range. Therefore, the differences will be more significant in the linear range.

The clonogenic assay is influenced by several factors, the important ones are listed:

i. Concentration of the toxic agent.

iii. Cell density during exposure.

iv. Cell density during cloning.

MTT-based cytotoxicity assay:

The tetrazolium salt 3, (4.5-dimethyl-thiazol- 2-yl)-2, 5-diphenyl tetrazolium bromide) is commonly known as MTT. It is dye, and is widely used in cytotoxicity assays. The growing cells in the log phase are exposed to cytotoxic drug. The drug is then removed and the cells are allowed to proliferate for 2-3 population doubling times (PDTs). The number of surviving cells can be detected by MTT dye reduction. The concentration of MTT-formazan formed can be determined spectrophotometrically.

MTT-based cytotoxicity assay is carried out in the following stages:

1. Incubation of monolayer cultures with varying drug concentrations in micro-titration plates.

2. Removal of drug and feeding of plates to achieve 2-3 PDTs.

3. Treatment of plates with MTT, and removal of medium and MTT.

4. Measurement of MTT-formazan in an ELISA plate reader.

When the absorbance of test wells/control wells of the micro-plate is plotted against the concentration of the cytotoxic drug, a sigmoid curve is obtained.

Iii. Metabolic Assays:

The metabolic assays are based on the measurements of metabolic responses of the cells. These test are carried out after exposure of the cells to cytotoxic drugs (either immediately or after 2-3 population doublings). The most commonly used metabolic measurements are DNA, RNA or protein synthesis (by estimating their concentration), besides the assay of certain dehydrogenase enzymes.

Limitations of metabolic assays:

The estimation of the total content of DNA protein may or may not be indicative of increase in cell number. This is because these assays cannot discriminate between the proliferative and metabolic activity of cells. Some workers therefore prefer to confirm the metabolic measurements by colonogenic survival assay.

Iv. Transformation Assays:

The following are the commonly used assays for measurement of in vitro transformation:

i. Evidence of mutagenesis.

ii. Anchorage independence.

iii. Reduced density limitation of cell proliferation.

Mutagenesis can be assayed by sister chromatid exchange (SCE). SCE basically involves the reciprocal exchange of DNA segments between sister chromatids at identical loci in the S-phase of cell cycle. Sister chromatid exchanges are more sensitive to mutagenesis than chromosomal breaks. For this reason, SCEs are preferred in mutagenesis research and transformation assay. The SCE technique basically involves the incorporation of radioactive nucleotides into replicating DNA and detection of SCEs by fluorescence plus Giemsa (FPG) technique.

V. Inflammation Assays:

Inflammation assays are required for testing the various forms of allergy induced by cosmetics, pharmaceuticals and other xenobiotic. These assays are at the early stages of development in the culture cells.


To evaluate the three candidate OD calibration protocols, we organized an interlaboratory study as part of the 2018 International Genetically Engineered Machine (iGEM) competition. The precision and robustness of each protocol is assessed based on the variability between replicates, between reference levels, and between laboratories. The overall efficacy of the protocols was then further evaluated based on the reproducibility of cross-laboratory measurements of cellular fluorescence, as normalized by calibrated OD measurements.

Experimental data collection

Each contributing team was provided with a set of calibration materials and a collection of eight engineered genetic constructs for constitutive expression of GFP at a variety of levels. Specifically, the constructs consisted of a negative control, a positive control, and six test constructs that were identical except for promoters from the Anderson library 11 , selected to give a range of GFP expression (illustrated in Fig. 1a, with complete details provided in Supplementary Data 1). In particular, the positive and negative controls and the J23101, J23106, and J23117 promoters were chosen based on their prior successful use in the 2016 iGEM interlaboratory study 9 as controls and “high”, “medium”, and “low” test levels, respectively. Beyond these, J23100 and J23104 were chosen as potential alternatives for J23101 (about which there were previous reports of difficulty in transformation), and J23116 was chosen as an intermediate value in the large gap in expression levels between J23106 and J23117 (expected values were not communicated to teams, however). These materials were then used to follow a calibration and cell measurement protocol (see the “Methods” section Supplementary Note: Plate Reader and CFU Protocol and Supplementary Note: Flow Cytometer Protocol).

a Each team cultured eight strains of engineered E. coli expressing GFP at various levels: positive and negative controls plus a library of six test constructs with promoters selected to give a range of levels of expression. Each team also collected four sets of calibration measurements, b fluorescein titration for calibration of GFP fluorescence, plus three alternative protocols for calibration of absorbance at 600 nm: c dilution and growth for colony-forming units (CFU), d LUDOX and water, and e serial dilution of 0.961 μm-diameter monodisperse silica microspheres.

Each team transformed E. coli K-12 DH5-alpha with the provided genetic constructs, culturing two biological replicates for each of the eight constructs. Teams measured absorbance at 600 nm (OD600) and GFP in a plate reader from four technical replicates per biological replicate (for a total of eight replicates and fitting on a 96-well plate) at the 0 and 6 h time points, along with media blanks, thus producing a total of 144 OD600 and 144 GFP measurements per team. Six hours was chosen as a period sufficient for exponential growth, and the zero-hour measurement used only for comparison to exclude samples that failed to grow well. Teams with access to a flow cytometer were asked to also collect GFP and scatter measurements for each sample, plus a sample of SpheroTech Rainbow Calibration Beads 12 for fluorescence calibration.

Measurements of GFP fluorescence were calibrated using serial dilution of fluorescein with PBS in quadruplicate, using the protocol from ref. 9 , as illustrated in Fig. 1b. Starting with a known concentration of fluorescein in PBS means that there is a known number of fluorescein molecules per well. The number of molecules per arbitrary fluorescence unit can then be estimated by dividing the expected number of molecules in each well by the measured fluorescence for the well a similar computation can be made for concentration.

Measurements of OD via absorbance at 600 nm (OD600) were calibrated using three protocols and for each of these a model was devised for the purpose of fitting the data obtained in the study (Methods):

Calibration to colony-forming units (CFU), illustrated in Fig. 1c: Four overnight cultures (two each of positive and negative controls), were sampled in triplicate, each sample diluted to 0.1 OD, then serially diluted, and the final three dilutions spread onto bacterial culture plates for incubation and colony counting (a total of 36 plates per team). The number of CFU per OD per mL is estimated by multiplying colony count by dilution multiple. This protocol has the advantage of being well established and insensitive to non-viable cells and debris, but the disadvantages of an unclear number of cells per CFU, potentially high statistical variability when the number of colonies is low, and being labor intensive.

Comparison of colloidal silica (LUDOX CL-X) and water, illustrated in Fig. 1d: This protocol is adapted from ref. 9 by substitution of a colloidal silica formulation that is more dense and freeze-tolerant (for easier shipping). Quadruplicate measurements are made for both LUDOX CL-X and water, with conversion from arbitrary units to OD measurement in a standard spectrophotometer cuvette estimated as the ratio of their difference to the OD measurement for LUDOX CL-X in a reference spectrophotometer. This protocol has the advantage of using extremely cheap and stable materials, but the disadvantage that LUDOX CL-X provides only a single reference value, and that it calibrates for instrument differences in determination of OD but cannot be used to estimate the number of cells, as all grades of LUDOX particles are far smaller than cells (<50 nm).

Comparison with serial dilution of silica microspheres, illustrated in Fig. 1e: This new protocol, inspired by the relationship between particle size, count, and OD 7 , uses quadruplicate serial dilution protocol of 0.961-μm-diameter monodisperse silica microspheres in water, similar to fluorescein dilution, but with different materials. These particles are selected to match the approximate volume and optical properties of E. coli, with the particles having a refractive index of 1.4 (per manufacturer specification) and typical E. coli ranging from 1.33 to 1.41 7 . With a known starting concentration of particles, the number of particles per OD600 unit is estimated by dividing the expected number of particles in each well by the measured OD for the well. This protocol has the advantages of low cost and of directly mapping between particles and OD, but the disadvantage that the microspheres tend to settle and are freeze-sensitive.

Data from each team were accepted only if they met a set of minimal data quality criteria (Supplementary Note: Data Acceptance Criteria), including values being non-negative, the positive control being notably brighter than the negative control, and measured values for calibrants decreasing as dilution increases. In total, 244 teams provided data meeting these minimal criteria, with 17 teams also providing usable flow cytometry data. Complete anonymized data sets and analysis results are available in Supplementary Data 2.

Robustness of calibration protocols

We assessed the robustness of the calibration protocols under test in two ways: replicate precision and residuals. Replicate precision can be evaluated simply in terms of the similarity of values for each technical replicate of a protocol. The smaller the coefficient of variation (i.e., ratio of standard deviation to mean), the more precise the protocol. With regards to residuals, on the other hand, we considered the modeled mechanism that underlies each calibration method and assess how well it fits the data. Here, the residual is the distance between each measured value provided by a team and the predicted value of a model fit using that same set of data (see Methods for details of each mechanism model and residual calculations). The smaller the residual value, the more precise the protocol. Moreover, the more similar the replicate precision and residuals across teams, the more robust the protocol is to variations in execution conditions.

Figure 2 shows the distribution of the coefficients of variation (CVs) for all valid replicates for each of the calibrant materials (see Methods for validity criteria). For CFU, basic sampling theory implies that the dilution with the largest number of countably distinct colonies (lowest dilution) should have the best CV, and indeed this is the case for 81.6% of the samples. This percentage is surprisingly low, however, and indicates a higher degree of variation than can be explained by the inherent stochasticity of the protocol: CFU sampling should follow a binomial distribution and have a little over 3-fold higher CV with each 10-fold dilution, but on average it was much less. This indicates the presence of a large component of variation with an unknown source, which is further confirmed by the fact that even the best CVs are quite high: the best of the three dilutions for each team has CV ≤ 0.1 for only 2.1% of all data sets and CV ≤ 0.2 for only 16.4% of all data sets.

CFU models are generated from only the best CV dilution (blue) other dilutions are shown separately above. Even the best CV CFU dilutions, however, have a distribution far worse than the other four methods, and are surprisingly often not the lowest dilution (red crosses). Of the others, LUDOX (magenta) and water (light blue) have the best and near-identical distributions, while microspheres (black) and fluorescein (green) are only slightly higher.

LUDOX and water have the lowest CV, at CV ≤ 0.1 for 86.9% (LUDOX) and 88.1% (water) of all replicate sets and CV ≤ 0.2 for 97.1% (LUDOX) and 98.0% (water) of all replicate sets. Microspheres and fluorescein have slightly higher CV, at CV ≤ 0.1 for 80.8% (microspheres) and 76.9% (fluorescein) of all replicate sets and CV ≤ 0.2 for 93.9% (microspheres) and 92.4% (fluorescein) of all replicate sets. The difference between these two pairs likely derives from the fact that the LUDOX and water samples are each produced in only a single step, while the serial dilution of microspheres and fluorescein allows inaccuracies to compound in the production of later samples.

The accuracy of a calibration protocol is ultimately determined by how replicate data sets across the study are jointly interpreted to parameterize a model of the calibration protocol, one part of which is the scaling function that maps between arbitrary units and calibrated units. As noted above, this can be assessed by considering the residuals in the fit between observed values and their fit to the protocol model. To do this, we first estimated the calibration parameters from the observed experimental values (see Methods for the unit scaling computation for each calibration method), then used the resulting model to “predict” what those values should have been (e.g., 10-fold less colonies after a 10-fold dilution). The closer the ratio was to one, the more the protocol was operating in conformance with the theory supporting its use for calibration, and thus the more likely that the calibration process produced an accurate value.

Here we see a critical weakness of the LUDOX/water protocol: the LUDOX and water samples provide only two measurements, from which two model parameters are set: the background to subtract (set by water) and the scaling between background-subtracted LUDOX and the reference OD. Thus, the dimensionality of the model precisely matches the dimensionality of the experimental samples, and there are no residuals to assess. As such, the LUDOX/water protocol may indeed be accurate, but its accuracy cannot be empirically assessed from the data it produces. If anything goes wrong in the reagents, protocol execution, or instrument, such problems cannot be detected unless they are so great as to render the data clearly invalid (e.g., the OD of water being less than the OD of LUDOX).

The CFU protocol and the two serial dilution protocols, however, both have multiple dilution levels, overconstraining the model and allowing likely accuracy to be assessed. Figure 3 shows the distribution of residuals for these three protocols, in the form of a ratio between the observed mean for each replicate set and the value predicted by the model fit across all replicate sets. The CFU protocol again performs extremely poorly, as we might expect based on the poor CV of even the best replicates: only 7.3% of valid replicate sets have a residual within 1.1-fold, only 14.0% within 1.2-fold, and overall the geometric standard deviation of the residuals is 3.06-fold—meaning that values are only reliable to within approximately two orders of magnitude! Furthermore, the distribution is asymmetric, suggesting that the CFU protocol may be systematically underestimating the number of cells in the original sample. The accuracy of the CFU protocol thus appears highly unreliable.

a Model fit residual distribution for each replica set in the CFU (blue), microsphere, and fluorescein calibration protocols (all teams included). b Expanding the Y axis to focus on the microsphere and fluorescein distributions shows that incorporating a model parameter for systematic pipetting error (black, green) produces a notably better fit (and thus likely more accurate unit calibration) than a simple geometric mean over scaling factors (red, magenta).

The microsphere dilution protocol, on the other hand, produced much more accurate results. Even with only a simple model of perfect dilution, the residuals are quite low (red line in Fig. 3b), having 61.0% of valid replicates within 1.1-fold, 83.6% within 1.2-fold, and an overall geometric standard deviation of 1.152-fold. As noted above, however, with serial dilution we may expect error to compound systematically with each dilution, and indeed the value sequences in individual data sets do tend to show curves indicative of systematic pipetting error. When the model is extended to include systematic pipetting error (see Methods subsection on “Systematic pipetting error model”), the results improve markedly (black line in Fig. 3b), to 82.4% of valid replicates within 1.1-fold, 95.5% within 1.2-fold, and an overall geometric standard deviation improved to 1.090-fold. Fluorescein dilution provides nearly identical results: with a perfect dilution model (magenta line in Fig. 3b), having 71.1% of valid replicates within 1.1-fold, 88.2% within 1.2-fold, and an overall geometric standard deviation of 1.148-fold, and systematic pipetting error improving the model (green line in Fig. 3b), to 88.1% of valid replicates within 1.1-fold, 98.0% within 1.2-fold, and an overall geometric standard deviation of 1.085-fold.

Based on an analysis of the statistical properties of calibration data, we may thus conclude that the microsphere and fluorescein dilution protocols are highly robust, producing results that are precise, likely to be accurate, and readily assessed for execution quality on the basis of calibration model residuals. The LUDOX/water protocol is also highly precise and may be accurate, but its execution quality cannot be directly assessed due to its lack of residuals. The CFU protocol, on the other hand, appears likely to be highly problematic, producing unreliable and likely inaccurate calibrations.

Reproducibility and accuracy of cell-count estimates

Reproducibility and accuracy of the calibration protocols can be evaluated through their application to calibration of fluorescence from E. coli, as normalized by calibrated OD measurements. Figure 4 shows the fluorescence values computed for each of the three fluorescence/OD calibration combinations, as well as for calibrated flow cytometry, excluding data with poor calibration or outlier values for colony growth or positive control fluorescence (for details see Methods on determining validity of E. coli data). Overall, the lab-to-lab variation was workably small, with the geometric mean of the geometric standard deviations for each test device being 2.4-fold for CFU calibration, 2.21-fold for LUDOX/water calibration, and 2.21-fold for microsphere dilution calibration. These values are quite similar to those previously reported in ref. 9 , which reported a 2.1-fold geometric standard deviation for LUDOX/water.

Measured fluorescence of test devices after 6 h of growth using a CFU calibration, b LUDOX/water calibration, c microsphere dilution calibration, and d flow cytometry. In each box, red plus indicates geometric mean, red line indicates median, top and bottom edges indicate 25th and 75th percentiles, and whiskers extend from 9 to 91%. Team count per condition provided in Supplementary Data 3.

Note that these standard deviations are also dominated by the high variability observed in the constructs with J23101 and J23104, both of which appear to have suffered notable difficulties in culturing, with many teams’ samples failing to grow for these constructs, while other constructs grew much more reliably (see Supplementary Fig. 1). Omitting the problematic constructs finds variations of 2.02-fold for CFU calibration, 1.84-fold for LUDOX/water calibration, and 1.83-fold for microsphere dilution calibration. Flow cytometry in this case is also similar, though somewhat higher variability in this case, at 2.31-fold (possibly due to the much smaller number of replicates and additional opportunities for variation in protocol execution). All together, these values indicate that, when filtered using quality control based on the replicate precision and residual statistics established above, all three OD calibration methods are capable of producing highly reproducible measurements across laboratories.

To determine the accuracy of cell-count estimates, we compared normalized bulk measurements (total fluorescence divided by estimated cell count) against single-cell measurements of fluorescence from calibrated flow cytometry, which provides direct measurement of per-cell fluorescence without the need to estimate cell count (see Methods on “Flow cytometry data processing” for analytical details). In this comparison, an accurate cell count is expected to allow bulk fluorescence measurement normalized by cell count to closely match the per-cell fluorescence value produced by flow cytometry. In making this comparison, there are some differences that must be considered between the two modalities. Gene expression typically has a log-normal distribution 13 , meaning that bulk measurements will be distorted upward compared to the geometric mean of log-normal distribution observed with the single-cell measurements of a flow cytometer. In this experiment, for typical levels of cell-to-cell variation observed in E. coli, this effect should cause the estimate of per-cell fluorescence to be approximately 1.3-fold higher from a plate reader than a flow cytometer. At the same time, non-cell particles in the culture will tend to distort fluorescence per-cell estimates in the opposite direction for bulk measurement, as these typically contribute to OD but not fluorescence in a plate reader, but the vast majority of debris particles are typically able to be gated out of flow cytometry data. With generally healthy cells in log-phase growth, however, the levels of debris in this experiment are expected to be relatively low. Thus, these two differences are likely to both be small and in opposite directions, such that we should still expect the per-cell fluorescence estimates of plate reader and flow cytometry data to closely match if accurately calibrated.

Of the three OD calibration methods, the LUDOX/water measurement is immediately disqualified as it calibrates only to a relative OD, and thus cannot produce comparable units. Comparison of CFU and microsphere dilution to flow cytometry is shown in Fig. 5. The CFU-calibrated measurements are far higher than the values produced by flow cytometry, a geometric mean of 28.4-fold higher, indicating that this calibration method badly underestimates the number of cells. It is unclear the degree to which this is due to known issues of CFU, such as cells adhering into clumps, as opposed to the problems with imprecision noted above or yet other possible unidentified causes. Whatever the cause, however, CFU calibration is clearly problematic for obtaining anything like an accurate estimate of cell count.

Microsphere dilution produces values extremely close to the ground truth provided by calibrated flow cytometry, whereas the CFU protocol produces values more than an order of magnitude different, suggesting that CFU calibration greatly underestimates the number of cells in the sample. Bars show geometric mean and standard deviation. Team count per condition provided in Supplementary Data 3.

Microsphere dilution, on the other hand, produces values that are remarkably close to those for flow cytometry, a geometric mean of only 1.07-fold higher, indicating that this calibration method is quite accurate in estimating cell count. Moreover, we may note that the only large difference between values comes with the extremely low fluorescence of the J23117 construct, which is unsurprising given that flow cytometers generally have a higher dynamic range than plate readers, allowing better sensitivity to low signals.

(1st step)

  1. Pipette 2.0 mL of olive oil emulsion (A) into a test tube and equilibrate at 37 ℃ for about 5 minutes.
  1. Add 0.2 mL of the enzyme solution* and mix.
  2. After exactly 15 minutes at 37 ℃, add 2.0 mL of TCA solution (E) to stop the reaction and remove the precipitate by filtration through filter paper (Toyo-Roshi No.131 or Whatman No.42).

(2nd step)

  1. Pipette 0.05 mL of the filtrate thus obtained into a test tube.
  2. Add 3.0 mL of color developing reagent (G) and incubate at 37 ℃ for 15 minutes.
  3. Measure the optical density at 545 nm against water (OD test).

At the same time, prepare the blank by first mixing 2.0 mL of the olive oil emulsion (A) after 15 min incubation at 37 ℃ with 2.0 mL of TCA solution, followed by the addition of the enzyme solution (first step). By using the filtrate obtained from the mixture, carry out the 2nd step using the same procedure as the test and measure the optical density at 545 nm (OD blank).

* Dissolve the enzyme preparation in ice-cold enzyme diluent (H) and dilute to 0.4-1.2 U/mL with the same buffer, immediately before assay.


Modern DNA sequencing techniques have revolutionized genomics [1], but extending these methods to routine proteome analysis, and specifically to single-cell proteomics, remains a global unmet challenge. This is attributed to the fundamental complexity of the proteome: protein expression level spans several orders of magnitude, from a single copy to tens of thousands of copies per cell and the total number of proteins in each cell is staggering [2]. Given the lack of in-vitro protein amplification assays the ability to accurately quantify both abundant and rare proteins hinges on the development of single-protein identification methods that also feature extraordinary-high sensing throughput. To date, however, protein sequencing techniques, such as mass-spectrometry, have not reached single-molecule resolution, and rely on bulk averaging from hundreds of cells or more [3]. Affinity-based method can reach single protein sensitivity [4], but depend on limited repertoires of antibodies, thus severely hindering their applicability for proteome-wide analyses. Consequently, in the past few years single-molecule approaches for proteome analysis based on Edman degradation [5] or FRET [6] have been proposed. To date, however, profiling of the entire proteome of individual cells remains the ultimate challenge in proteomics [7].

Nanopores are single-molecule biosensors adapted for DNA sequencing, as well as other biosensing applications [8,9]. Recent nanopore studies extended nucleic-acid detection to proteins, demonstrating that ion current traces contain information about protein size, charge and structure [10–17]. However, to date, the challenge of deconvolving the electrical ion-current trace to determine the protein’s amino-acid sequence from the time-dependent electrical signal has remained elusive. In an analogy to the field of transcriptomics, in many practical cases it is sufficient to identify and quantify each protein among the repertoire of known proteins, instead of re-sequencing it. Yao and co-workers showed theoretically that most proteins in the human proteome database can be uniquely identified by the order of appearance of just two amino-acids, lysine and cysteine (K and C, respectively) [18]. But taking into account experimental errors, for example due to false calling of an amino-acid, or an unlabeled amino-acid, sharply reduces the ID accuracy. Motivated by recent experiments suggesting the ability to translocate SDS-denatured proteins through either small nanopores (

0.5 nm) [19], or large nanopores [20] (

10 nm), and the possibility to differentiate among polypeptides based on optical sensing in nanopore [21], we here introduce a protein ID method that according to simulation remains robust against the expected experimental errors. We show that relatively low-resolution, tri-color, optical fingerprints produced during the passage of proteins through a nanopore, preserve sufficient information to allow a deep-learning classification algorithm to accurately identify the entire human proteome with >95% accuracy. Even in cases where the apparent spatial and temporal resolutions of the optical system appear to be prohibitively low, and the amino-acids labelling efficiency is incomplete, whole proteome ID efficiency remains high and robust. Particularly, the expected protein ID efficiency is of an extremely high clinical relevancy. We illustrate the broad applicability of the method by analyzing the human plasma proteome, as well as commercially-available cytokine identification panel based on antibodies, showing that our antibody-free method can readily surpass current techniques in a number of key parameters, while displaying a near perfect accuracy.


Determination of protein concentration TIMING 30 min to 1 hour

Determine the concentration of the protein stock solutions. The following three simple spectroscopic methods give rapid accurate measurements of protein concentration and are independent of protein composition, provided the protein is unfolded in 6M Guanidine-HCl or 3% NaOH. Options A and B require that the protein have tyrosines or tryptophans. Option C does not work with collagen-like proteins with high proline contents.

Option A. Determination of protein concentration from the difference spectrum of the protein dissolved in 6 M guanidine at pH 12.5 versus 7.1. 58 , 59

Pipette exactly the same volume (0.4 to 1 ml depending on the sample volume of the cuvette) of each solution into two cuvettes with 1 cm path lengths and scan the baseline from 320 to 270 nm with the pH 7.1 solution in the reference compartment and the 12.5 solution in the sample compartment.

CRITICAL STEP The dilutions of the samples must be exactly the same in the reference and sample compartments.

Add exactly the same volume (e.g 10 to 100 microliters) of protein solution to each cuvette and obtain the difference spectrum. Correct the spectrum for the baseline.

Calculate the concentration of protein. The molar concentration (in moles /liter) of protein in the cuvette = A/(2357Y +830W) where A is the absorbance at 294 nm, Y is the number of tyrosines and W is the number of tryptophans 59 . Correct the measured concentration for dilution. The mean residue concentration can be calculated by multiplying the molar concentration by the number of amino acids in the protein. The number of milligrams per milliliter of protein is calculated by multiplying the molar concentration by the molecular weight. If the proteins are denatured by the Guanidine solution, the difference method should give one band at 294 nm.


Option B. Determination of protein concentration from the aromatic spectrum determined in 6M guanidine-HCl, pH 6.5 58

Run a baseline spectrum of two cells with an equal volume of Guanidine-HCl in each side.

Add a small aliquot of protein solution to the sample side, and an equal volume of the buffer to the reference side.

CRITICAL STEP: The protein must be free of scattering material and 2-mercaptoethanol or dithiothreitol. The oxidized form of these compounds absorb strongly at 280 nm and the oxidation rates are faster in solutions containing protein than in plain buffers, so it is hard to subtract their contributions.

Collect the spectrum of the protein between 350 and 250 nm and calculate the protein concentration using the formulas:

where W, Y and C are the numbers of tryptophans, tyrosines and cystines (oxidized) per mole of protein and ε288 and ε280 are the molar extinction coefficients of the protein at 288 nm and 280 nm, respectively 58 . The protein concentrations in moles/liter are the absorbance value at 288nm/ε288 and 280nm/ε280. The concentrations determined at 288 and 280 nm should agree.


Option C. Determination of protein concentration using a microbiuret procedure 60

Aliquot protein samples in buffer e.g. 0, 0.025, 0.05, and 0.1 ml and add the buffer to a final volume of 0.1 ml in a small clean test tubes.

Prepare a standard curve containing 0.02, 0.04, 0.06, 0.08 and 0.10 ml of BSA diluted to a final volume of 0.1 ml for concentrations of 0.2, 0.4, 0.6, 0.8 and 1.0 mg/ml, respectively.

Add 0.5 ml of 3% NaOH and 0.02 ml of Benedicts reagent to the standards and samples. Mix with a vortex mixer.

Allow to stand at least 15 minutes for the color to develop.

Read the absorbance at 330 nm

Plot the standard curve of absorbance as a function of protein concentration in mg/ml. Correct the absorbance of each unknown sample for the contribution of the buffer and read the protein concentration from the curve. Correct for dilution.

This assay can be done in microtiter plates using half the volume of each reagent. The plates can be read at 340 nm although the color intensity is lower at this wavelength than at 330 nm. The microbiuret method using freshly prepared Benedict's reagent should be linear for protein concentrations ranging between 0 and 1.5 mg/ml, with the absorbances ranging from approx. 0.2 for the blank (100 µl buffer) + 0.5 ml of 3% NaOH and 0.02 ml of Benedicts reagent to approx 0.5 𠄰.6 for the sample with 100 µl of BSA, 1.5 mg/ml.


Sample preparation TIMING 10 to 30 minutes

Prepare the protein samples. For typical measurements in a 0.1 cm cell, depending on the buffer (See Table 1 ), make solutions of 0.05 to 0.2 mg/ml protein. For measurements in 0.01 to 0.02 cm cells use 0.2 to 1 mg/ml protein and for 1 cm cells use 0.005 to 0.01 mg/ml protein.

Equipment preparation TIMING 30 to 40 minutes

Turn on the nitrogen and flush the optics compartment for 15 � minutes before starting the machine (See manufacturer’s suggested time)

CAUTION. Nitrogen displaces oxygen. Only operate a CD machine in a well ventilated room. Do not close the door. If a tank of liquid nitrogen begins to vent because pressure has built up, leave the room and allow the excess nitrogen to disperse before reentering.

Turn on the water supply or circulating water bath chiller to the lamp housing, if the lamp is water cooled. If a water supply is used, make sure the filter is clean. If a bath is used, make sure the water is clean.

CRITICAL STEP Avoid using ethylene glycol in the water in the circulating bath since it can damage the pumps.

Turn on the circulating water bath for temperature control. If the temperature is controlled using a temperature-regulated cell compartment that requires a heat sink set the temperature of the bath to 20 or 25 degrees. If the temperature is controlled using water-jacketed cells set the temperature of the bath to the desired temperature (see step 9).

CRITICAL STEP Be sure that the water is circulating before turning on the power to the thermal regulators or you can burn out the heating units.


Turn on the lamp, if it has a separate switch, before you turn on the power to the rest of the CD machine or computers.

CRITICAL STEP Firing the lamp may cause a voltage surge that can destroy electronic boards or computers in some machines if they are turned on before the lamp is lit.


Turn on the CD machine and computer and start the CD collection program.


Set the data path of the operating program to store your data.

Set the desired temperature. For a previously uncharacterized protein, one should collect spectra at multiple temperatures and correlate the spectra with some measurement of the activity or the protein, e.g. enzyme activity, or ability to bind ligands or antibodies. Typically, preliminary spectra of proteins are collected at 4, 25, 37, 50, 60 and 70 ଌ. Once a stability range is established, data may be collected at more closely-spaced intervals to determine whether there are spectral changes indicative of folding intermediates using the CCA algorithm 44 or singular value decomposition 47 , 61 , 62

Collecting CD spectra for the determination of secondary structure - TIMING 2 -4 hours for collection of 5 samples and 5 baselines at a single temperature, depending on wavelength range and interval and number of repeat spectra

Set the desired equilibration time. Usually globular proteins reach equilibrium within 2 minutes, but some proteins, e.g. collagen, can literally take days to fold (see preparation of proteins). If unsure of the folding time, incubate the CD sample on ice for several days before starting the CD measurements for measurements at 4 ଌ or at 25 ଌ for measurements near room temperature.

Set the half-band width between 1 and 1.5 nm. These values give spectra with good signal to noise levels and adequate spectral resolution, since UV bands are broad ( Figures 2a and 2b ).

a, The spectra of (black) air (red) water (green) buffer and (orange, blue and magenta) three replicate spectra of lysozyme, 0.085 mg/ml in a 0.1 cm cell. b, The spectra of (black, red, green) buffer and three replicate spectra each of lysozyme at (cyan, purple, brown) 0.41 mg/ml and (blue, magenta, orange) 0.83 mg/ml concentration in a 0.01 cm cell. d, The change in the photomultiplier tube dynode voltage as a function of wavelength for the conditions illustrated in 1a and 1b and for 0.41 mg/ml lysozyme in a 0.1 cm cell. d, The mean residue ellipticity of lysozyme in a 0.1 cm cell (black) 0.41 mg/ml (raw data not shown) (cyan) 0.085 mg/ml and in a 0.01 cm cell (green) 0.41 mg/ml (red) 0.83 mg/ml. e, The mean residue ellipticity of lysozyme (black circles) fit using the method of least squares: (blue) using a peptide data base 20 (black) four basis spectra extracted from 17 proteins1 26 (orange) five basis spectra extracted from 33 proteins 26 and (magenta ) CONTIN 27 (cyan) K2D 38 and (orange) SELCON2 43 . F, The mean residue ellipticity of lysozyme (black circles) fit using the CDPro Package 33 : (magenta) SELCON3 (cyan) CDSSTR and (black) CONTIN. Note. Lyoszyme was obtained from Sigma (L6876) and dissolved in sodium phosphate, 10 mM. The protein concentration was determined using the published extinction coefficient of ε1% of 26.5 (εM =38.2 × I0 3 ) 65 for comparison with previous data 5 . Data were obtained on an Aviv Model 215 spectrometer (Aviv Biomedical, Lakewood, NJ).

Set the wavelength range: from 260 to 185 nm for 0.1 to 0.2 mg/ml protein samples in transparent buffers (see Table 1 ) in 0.1 cm cells from 260 to 178 nm for 0.2 to 0.8 mg/ml samples in 0.01 cm cells and from 260 to 200 nm for 0.01 to 0.02 mg/ml proteins in 1 cm cells.

Set the wavelength interval, 0.5 nm for samples with signal to noise > 20 to 1 or 0.1, 0.2 or 0.25 nm for samples with low ellipticity. Data collected at 0.10 to 0.5 nm intervals with half-band widths of 1 to 1.5 nm will give well-defined spectra (see Figure 2a and b where data were collected using a half-band width of 1.5 nm and a wavelength spacing of 0.25 nm).

CRITICAL STEP Only collect data at even intervals that are a fraction of 1.0 nm since most data analysis programs have databases with ellipticity values collected at 1 nm intervals.

Set the time for data collection at each point (i.e. signal averaging time). For samples with a concentration of approximately 0.1 mg/ml in a transparent buffer, collecting for 1 sec at each point should be sufficient. If one is collecting replicate spectra, at intervals from 0.1 to 0.5 nm, 0.5 sec/point should be adequate. If the protein concentration is low or the buffer has high absorbance, increase the averaging time. The relative signal to noise will increase as the square root of signal averaging time.

Set the instrument time constant. For routine collection of CD spectra this should be 100 milliseconds. For rapid collection of data, e.g. in measurements of stopped flow CD, the time constant should be decreased to 100 µsec or less (no greater than 1/10 the data averaging time at each point).

Set the instrument to record the ellipticity and the photomultiplier tube (PMT) voltage. When light hits the photomultiplier of the CD machine a current is induced. Most CD machines maintain constant current by raising the voltage as the amount of light decreases. As it scans to lower wavelengths, the absorbance will increase and the PMT voltage will rise. The signal to noise will greatly diminish once the dynode voltage goes above 500 volts and the data are often become very noisy and unreliable. (On an Aviv instrument the PMT voltage is called the dynode voltage, on a Jasco it is called HT voltage, on an Applied Photophysics machine it is called the detector gain and on an Olis machine it is called the PMT HV). For protein concentrations ranging from 0.05 to 0.1 mg/ml in 0.1 cm cells in 10 mM phosphate buffer, the signal to noise ratios are usually better than 10:1 over a wavelength range of 260 to 185 nm ( Figure 2a ) with dynode voltages below 500 V ( Figure 2c ). With 0.01 cm cells and 0.4 to 0.8 mg/ml concentrations, the signal to noise ratio of the data is usually > 30:1 between 260 to 178 nm with dynode voltages below 500 V ( Figure 2b, 2c ). When the dynode voltage (2c) goes above 500 V e.g. below 185 nm for data collected in a 0.1 cm cell

0.1 mg/ml concentration, the signal to noise is usually becomes very poor ( Figure 2a ) although the signal may still be linear as a function of concentration on some instruments up to 700 volts.

Determine the spectrum of the blank. Fill the cell with water and determine its spectrum. Suitable buffers should have no ellipticity, but their increased absorbance compared to water decreases the signal to noise. The spectrum of the cell containing water should be relatively flat, but may be displaced from that of pure air due to the birefringence of the cell ( Figure 2a ).


Collect a spectrum of the buffer to make sure that it does not have any ellipticity due to dichroic components or a high absorbance leading to a very poor signal to noise and possibly false peaks. Note that phospholipids are asymmetric and have CD bands. If samples are suspended in phospholipids it is essential that the spectrum of the blank contains the same concentration of protein-free lipids. The spectrum of the buffer and water should overlay each other, within the experimental error, but the spectrum of the buffer usually has a lower signal to noise than the spectrum of water at low wavelengths (see Figure 2a ).


Clean the cell, fill with protein solution and collect the CD spectrum. It is a good idea to collect data 2 or 3 times for new samples to make sure that the sample is at equilibrium and the signal is not changing as a function of time. Many CD machines can collect multiple spectra automatically. If the protein is at equilibrium, replicate scans of the protein solutions should overlay each other and not drift as a function of time ( Figures 2a and 2b ).


If the data sets overlay each other one, average the data sets. For the most accurate estimates of protein secondary structure, data should be collected to 178 nm or lower wavelengths, in 0.01 to 0.02 cm cells. Since data below 200 nm may have low signal to noise, three to five scans should be collected and averaged.

Save the raw data on the hard drive, floppy disk or other media!

CRITICAL STEP Always immediately save the data to prevent loss if there is a power failure.

Smooth the spectra of the sample and blank. Most CD machines have built-in smoothing algorithms and some will automatically pick the best smoothing parameters - refer to the manual. The smoothing algorithms that are used depend on the manufacturer. If the machine uses Savitsky-Golay smoothing 63 , and data is collected at 0.5 nm intervals, a polynomial order of 3 and a smoothing window of 20 points usually gives a good fit. If data is collected at shorter wavelength intervals increase the number of points. Some smoothing protocols give estimates of the goodness of smoothing by calculating whether the difference between the raw and smoothed data has the statistical characteristics of noise.

Check that the data have not been over-smoothed by subtracting the smoothed curve from the raw data. The points should be evenly distributed around zero. Some CD machines will automatically calculate the residuals from the smoothing and these can be viewed on the spectrometer in its data viewing mode.


Subtract the smoothed baseline from the smoothed spectrum of the sample. The ellipticity for most proteins should be close to zero between 260 and 250 nm.


Convert the data to mean residue ellipticity or Δε using the formulas:

Ellipticity, [θ], in deg. · cm 2 /dmol = (millidegrees times mean residue weight) / (path length in mm times concentration in mg/ml) or

[θ] = millidegrees / (path length in mm times the concentration of protein times the number of residues)

The mean residue weight of a peptide is the molecular weight divided by the number of backbone amides (number of amino acids -1 if the protein is not acetylated). It is

115 for most proteins if the molecular weight of the sample is not known, but should be calculated directly if the molecular weight of the protein is known for accurate results. If the protein or peptide is monomeric and does not aggregate under the experimental conditions used to collect the CD data, the spectra collected at different protein concentrations and path lengths should give the same mean residue ellipticities ( Figure 2d )


Save the corrected data of each sample in separate text files (not binary) as mean residue ellipticity, [θ] (y) as a function of wavelength (x), or in files with the ellipticity values which also have information about the starting wavelength, ending wavelength and data interval so that it can be converted into formats that can be used to estimate protein conformation.

CRITICAL STEP Save the data in text (ASCII format) as binary files are in proprietary formats that can only be accessed by the correct CD machine. Binary data cannot be edited by a text editor or imported into a spread sheet or converted to formats that can be used for analysis programs.

If data has been collected at different wavelength ranges, e.g. 260 to 190 nm in 0.1 cm cells and 200 to 178 nm in 0.01 cm cells, and the data agree between 200 and 190 nm, combine the data containing the mean residue ellipticity into a single file using a text editor. If the data do not agree with each other, make sure the proper baseline has been subtracted for each sample and that the path lengths of the cells are correct. If they still do not agree, redo the measurements!

CRITICAL STEP For data analysis, ensure there is a single entry for each wavelength in the final file or it will confuse the data analysis programs.

Data analysis- TIMING 2 to 16 hours

Analyze the data using appropriate methods (Options A𠄽). It is best to use as many methods as possible for the most accurate results.

Option A Evaluating the secondary structure of globular proteins using data collected between 260 and 178 nm

The CDPro package or the on-line analysis programs at DicroWeb are recommended for the most accurate estimates of secondary structure. The DicroProt suite of programs uses older versions of the programs available at DicroWeb and in CDPro, but the results are similar to those of the more modern versions. It is easy to convert data to both the CDPro and the DicroProt 𠇍ic” format and the user interface is simple for both programs. A simple program to convert CD data in columns of ellipticity as a function of wavelength to the DIC format, CONVERT.EXE, is in SUPPLEMENTAL MATERIALS TIMING Converting data to the formats used by the CDPro package and analyzing it using SELCON, CONTINLL and CDSSTR will take about 10� minutes/ spectrum. Converting data to the DIC format and then analyzing it by all the programs in the DicroProt package including VARSLC, SELCON2 and SELCON3, CONTIN and K2D also takes about 10� minutes /spectrum.

Option B. Evaluating the secondary structure of globular proteins using data truncated below 260 nm or above 178 nm

For data sets collected over truncated wavelength ranges (e.g. 240 to 200 nm) use the programs SELCON, VARSLC, K2D, CONTIN, or LINCOMB (with a data base extracted from proteins as references). These programs are all included in the file CD.Zip in SUPPLEMENTAL MATERIALS. The file also includes conversion programs that will edit the reference sets so that they match the range of data in the sample files. Reference sets for LINCOMB are also included.

TIMING Converting raw CD data and running each program will take about 10 minutes/spectrum per program, since these older versions of the program each have a different format for their input files, and the CD spectrum must be converted separately for each program.

Option C. Analyzing the effect of mutations on the helical or β-sheet content of a protein

Use the constrained method of least squares (e.g. LINCOMB) with a fixed reference set so that the same standards will be used to analyze the wild-type and mutant proteins. This method should also be used for quantifying the conformational changes in response to addition of a ligand.

TIMING Analysis of data using the LINCOMB or MLR programs is slow and takes about 20 minutes/ spectrum because the programs are DOS based and use the old DOS graphic screens which take time to display the results. In addition, one must first convert the data to the correct format, then run the program and finally examine the results with a text editor.

Option D. Analysis of the conformation of polypeptides or short protein fragments

Use LINCOMB (with polypeptide references), CONTIN or K2D. These programs are in the CD.Zip file in SUPPLEMENTAL MATERIALS.


Use ultrapure water (≥18 MΩxcm resistivity at 25 °C) for the preparation of reagents.

Buffer (50 mM Potassium Phosphate Buffer, pH 6.24, at 25 °C) – To prepared 550 mL:

  • Add 20.125 mL of 1.0 M Potassium phosphate monobasic solution (P8709).
  • Add 7.375 mL of 1.0 M Potassium phosphate dibasic solution (P8584).
  • Add ultrapure water to make up the final volume to 550 mL.
  • Adjust the pH to 6.24 at 25 °C using 1 M KOH or 1 M HCl.

Substrate Suspension (0.015% [w/v] Micrococcus lysodeikticus Cell Suspension) – Prepare a 0.15 mg/mL suspension in Buffer using Micrococcus lysodeikticus, ATCC No. 4698, lyophilized cells (M3770).

Substrate Suitability: The A450 of this suspension must be between 0.6–0.7 versus a Buffer blank. If necessary, adjust the absorbance using appropriate amount of Buffer or Micrococcus lysodeikticus cells.

Enzyme Solution (Lysozyme) – Immediately before use, prepare a solution containing 200‑400 units/mL of Lysozyme in cold (2–8 °C) Buffer.

Materials and methods

Microorganisms and growth conditions

Escherichia coli DSMZ 613 (DSMZ, Germany) was grown overnight in Luria-Bertani (LB) medium at 37 °C in an incubator shaker (100 rpm). The cultures were centrifuged at 4000 x g for 10 min and resuspended in 1 mL of 0.1 M phosphate buffer (PB, pH = 7.2). Optical density of the cell suspensions was measured at 600 nm using a Smartspec Plus spectrophotometer (Bio-rad, California, USA) and diluted to the required concentration using 0.1 M PB. Bacterial concentrations were determined by viable plate counts and expressed as colony forming unit per mL (cfu/mL).

Bacteriophage T4 was kindly provided by Dr. M. Llagostera from the Department of Genetics and Microbiology of the Autonomous University of Barcelona. Phage lysates were prepared following the protocol of Bonilla et al. [36] using E. coli as a host. 100 mL of an E. coli culture growing in LB broth supplemented with CaCl2 (1 mM) and MgCl2 (1 mM) were infected with 100 μL of virus suspension. After achieving lysis, the culture was centrifuged at 4000 x g for 20 min. The supernatant was filtered through a 0.22 μm membrane cellulose acetate filter (Whatman) and further treated with chloroform to remove lipids. The resulting suspension was concentrated by ultrafiltration using Amicon Ultra-15 centrifuge tubes with a cutout size of 100 kDa. Additional endotoxin removal, prior to sample storage, was done using 1-octanol as described by Szermer-Olearnik and Boratyński [37] followed by membrane dialysis in a Spectra/Por Float-A-Lyzer G2 Dialysis Device with a MWCO of 3.5–5 kDa. The purified product was stored in SM buffer [36] at 4 °C. Determination of virus concentration was performed by counting plaque forming unit (pfu) using the double layer agar method described by Adams [2]. Prior to their use, virus suspensions were diluted in LB to achieve the desired final concentration.

Experimental design

Our objective was to characterize the optical density kinetics of different combinations of phage/bacteria concentrations in order to assess to what extent kinetic measurements could be used as a reliable indicator of the abundance of phage in a certain sample. Therefore, an experiment was designed in which bacterial concentrations ranging from 10 5 to 5x10 8 cfu/mL were tested in combination with concentrations of T4 phage ranging from 0 to 5x10 8 pfu/mL.

Overnight cultures of E. coli were centrifuged and the pellets resuspended in 0.1 mM PB to achieve a concentration of 10 10 cfu/mL. The resulting suspensions were subject to serial dilution in such a way that after mixing with the phage in LB medium the desired final concentration was obtained. In a similar way, stock lysates of T4 were serially diluted in LB medium in order to achieve the desired concentrations. For each assay, 160 μL of LB were mixed with 20 μL phage solution, 20 μL of bacteria solution and 20μL of PB in transparent 96-well plates (Thermo Scientific, Massachusetts, USA). The plates were incubated at 37 °C in a Varioskan Flash plate reader (Thermo Scientific, Massachusetts, USA) and OD600 was recorded at regular intervals. Samples, controls and blanks were always assayed as triplicates.

Analysis of the experimental data

The experimental design used provides an extensive set of data that has to be further processed in order to carry out a proper interpretation of the results. For each bacteria concentration used we calculated the Start Point of Detection (SPD) as the time required for the different controls (bacteria without phages) to reach the threshold of detectable growth. We arbitrarily defined this threshold as a growth rate of 0.002 OD units per min. For further calculations we also defined the End Point of Detection (EPD) as a time corresponding to SPD + 120 min, thus allocating a 2-hour window for the assay to develop (Fig 1).

Optical density vs time curves of a control (●) and a phage-inoculated culture (○) were integrated and subtracted. The difference, represented by the shaded area, indicates the extent of the inhibition. This area is expressed as a percentage of the area of the control. In cases with little or no phage effect, the shaded area is very small and the percentage of inhibition approaches 0%. In the most extreme cases the shaded area virtually coincides with the area of the control, and the percentage of inhibition approaches 100%. In order to standardize all calculations, integration is carried out between the Start Point of Detection (SPD) and End Point of Detection (EPD) as defined in the text.

Growth inhibition due to lysis

For each bacteria/phage combination, we integrated the area of the curve between the points SPD and EPD. Numerical integration was carried out using the Euler method with the sampling interval as the integration step. The integrated areas were used to calculate a percentage of inhibition (PI) using the following formula based on the procedure described by Xie et al [11]: (1) in which Acontrol corresponds to the area of the curve of a control culture without phage inoculation, Aphage corresponds to the area of the curve of a culture exposed to a certain phage concentration, and Ablank corresponds to the area of the baseline curve consisting only of culture medium without either bacteria or phages (Fig 1). Simplification of Eq 1 yields: (2)

As a rule, in the absence of phage lysis, PI equals 0% and complete lysis gives a PI of 100%. Intermediate results can be correlated to phage concentration for each bacterial concentration used.

Probability of void samples

The probability of void samples (samples containing no phages) was calculated using the probability mass function of the Poisson distribution [38] expressed as follows: (3) Where N is the number of phages expected (in this case 0), c is the concentration of phages in the medium subject to sampling and V is the volume of the sample. For the specific case of N = 0, Eq 3 can be simplified to: (4)

SpectroPhotometer Applications

How to use the spectrophotometer? There are uses of spectrophotometry in biochemistry which are listed below:

1. Qualitative Analysis

The visible and UV spectrophotometer may be used to identify classes of compounds in both the pure and biological preparations. This is done by plotting absorption spectrum curves. Absorption by a compound in different regions gives some hints to its structure.

2. Quantitative Analysis

Spectrophotometer uses in the Quantitative analysis of Biochemistry practicals. Quantitative analysis method developing for determining an unknown concentration of a species by absorption spectrometry.

Most of the organic compounds of biological interest absorb in the UV-visible range of the spectrum.

Thus, several important classes of biological compounds may be measured semi-quantitatively using the UV-visible spectrophotometer. Nucleic acids at 254nm protein at 280nm provide good examples of such use.

The absorbance at 280nm by proteins depends on their “Tyrosine” and “Tryptophan” content.

3. Enzyme Assay

It is the primary application of spectrophotometry. This assay is carried out most quickly and conveniently when the substrate (or) the product is colour (or) absorbs light in the UV range.

Eg 1: Lactate Dehydrogenase (LDH)

Lactate + NAD + ↔ Pyruvate + NADH + H +

  • The LDH is engaged in the transfer of electrons from lactate to NAD+.
  • The products of the reaction are pyruvate, NAD, and a proton
  • One of the products, NADH, absorbs radiation in the UV range at 340 nm while its oxidized counterpart, NAD+, does not.
  • The forward reaction can be followed by measuring the increment in the light absorption of the system at 540nm in a spectrophotometer.

Eg 2: Pyruvate Kinase

Phosphoenolpyruvate + ADP ↔ Pyruvate + ATP

Pyruvate + NADH + H + ↔ Lactate + NAD +

We have added a significant excess of NADH to the system. The system now absorbs at 340nm. According to the above-given reactions, each molecule of Pyruvate formed in the reaction. A molecule of NADH is oxidized to NAD+ in the second reaction when the system converts Pyruvate to locate.

Since NAD+ does not absorb at 340nm, the absorbance goes on decreasing with increased pyruvate generation. Such measurements are known as “Coupled assays”.

4. Molecular Weight determination

Molecular weights of amine picrates, sugars and much aldehyde and ketone compounds have been determined by this method. Molecular weights of only small molecules may be determined by this method.

  1. Study of Cis-Trans Isomerism: Geometrical isomers differ in the spatial arrangement of groups about a plane. The absorption spectra of the isomers also differ. The trans-isomer is usually more elongated than its cis counterpart. Absorption spectrometry can be utilized to study Cis-Trans isomerism.
  2. Control of Purification: Impurities in a compound can be detected very easily by spectrophotometric studies. “Carbon disulfide” impurity in carbon tetrachloride can be detected easily by measuring absorbance at 318nm, where carbon sulfide absorbs. A lot many commercial solutions are routinely tested for purity spectroscopically.

5. Other Physiochemical Studies

Spectrophotometry (UV-VIS) has been used to study the following physiochemical phenomena:

  • Heats of formation of molecular addition compound and complexes in solution
  • Determination of the empirical formula
  • Formation constants of complexes in solution
  • Hydration equilibrium of carbonyl compounds
  • Association constants of weak acids and bases in organic solvents
  • Protein-dye interactions
  • Chlorophyll-Protein complexes
  • Vitamin-A aldehyde–Protein complex
  • Determination of reaction rates
  • Dissociation constants of acids and bases
  • Association of cyanine dyes

These are the basic spectrophotometer instrumentation and its applications.

Watch the video: Lowry method for protein quantification (November 2021).