Reducing Assay Reaction Volumes of Plasma and Serum for Hemoglobin Testing

Biomarkers and metabolites are compounds that can be measured in clinical patients that help inform physicians about the progression of various illnesses. Thus, detection of those compounds in plasma and serum is an area of current focus in biomedical research. Hemoglobin in a researcher’s samples can interfere with biomarker measurement, so it is important to determine the quantity of hemoglobin in those samples to accurately account for its effect on their results. The amount of hemoglobin contamination is measured in advance by reacting the plasma or serum samples with reagent and generating a color in the mixtures’ solutions. The intensity of those reactions are measured and reported as a unitless value and their averages, standard deviations, percent differences, and percent recoveries can be calculated. This reaction uses some of the sample, which is a limited resource. This project looks to confirm if the calculated statistics of samples are the same, even when a lower amount of samples are used. The sample and reagent volumes are tested at both full and reduced volumes, and then their results compared.

Biomarkers & Metabolites: Biomarkers are used to determine the presence and progression of a disease in a patient. Metabolites are items the body uses to sustain itself. Metabolites can be used as biomarkers. An example of a metabolite would be oxygen, and the levels of oxygen present would determine someone's state of health.

Hemoglobin: The protein responsible for transporting blood from the lungs to red blood cells in the bloodstream throughout the body.

Plasma: Produced when an anticoagulant is added to a sample of blood. Plasma contains white and red blood cells which are suspended in the plasma because anticoagulants are present.

Serum: Produced when a sample of blood is allowed to clot. Serum does not contain white and red blood cells, as they have clotted and settled at the bottom due to no anticoagulants being present.

Sub-aliquoting: The process of distributing a singular sample into several others. The use of reagents may be used in this process to allow the sample to be less viscous.

The sample wells receive the corresponding volume of reagent. The negative control well is the blank, which is filled with only water. This is used as the minimum, which is subtracted from each of the well's data points to account for noise in the assay. The positive control is the maximum, which none of the samples should exceed. The color of the samples can range from clear to yellow to red, as well as appearing either cloudy or clear. Sample appearance is dependent upon the presence of metabolites or hemoglobin in the sample. The sample is then added to the sample wells containing reagent. The plate is then covered with a lid to prevent possible contamination or splashing and mixing of the samples, and is placed in a shaker for 5 minutes to fully incorporate the sample into the reagent. The plate can then be ran through a machine utilizing a program to generate the data points in the Excel sheet for data analysis.

The exported data is copied over and reformatted into another Excel file, referenced here, where conditional formatting and formulas are utilized to assess the quality of the results and to determine trends. The difference and percent difference calculated is between the control and variable wells of the same sample. Any differences exceeding 15 were to be marked red to be looked into more to determine if that data point needed to be excluded. This decision was reinforced by the percent difference where anything over 50% would be marked red and would be excluded. 50% being the cutoff was chosen because the assays being greatly out of each other's ranges would render the overall standard deviations higher. Also, the sample wells exhibiting over 50% tended to be the wells which did not have full volumes of sample, and therefore should be excluded anyways. The samples that ended up getting excluded were as a result of an incomplete sample volume pipetted, which can lead to bubbles formed which leads to inaccurate readings, as well as dilution of the sample due to a different reagent-sample volume ratio. A possible cause of the incomplete sample volumes is the control plate needing to dilute the sample, as the readings for the well exceeded the positive control. Further diluting the sample in order to get a good read will lead to more of the sample being used, which could be less than the 30 uL required for the variable plate to be ran correctly. All of the plates consistently read at lower averages individually. However, in the total statistics the plates resulted in relatively equal averages, which was only a difference of 0.248. Amongst the individual plates, all but one plate exhibited equal or lower standard deviations, while the total statistics show that the variable plates were on average about 2.114 standard deviations lower than the control plates.