Sample Pooling for Blood Donor Attribute Sieving
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Solution Overview
Problem
Current methods for identifying suitable blood donors are inefficient and costly, particularly for patients requiring regular transfusions, as they rely on serological methods that are time-consuming and expensive, and genotyping is complex and not scalable for routine procurement of platelets and red blood cells with specific antigen and genotype profiles.
Innovation Solution
A process involving pool analysis and sample selection (sieving) to determine attribute patterns in large sample sets, followed by profiling to confirm and resolve ambiguities, allowing for rapid identification of suitable donors by forming pools based on expected abundances of desired attributes, such as alleles or antigens, using DNA analysis and immunophenotyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If serological methods are used to identify suitable blood donors, then the process remains accessible and automated, but it is time-consuming and expensive
Solution Approach 1:
The donor identification process is segmented into two distinct stages: a sieving stage that processes large numbers of donors using high-throughput genotyping to identify candidates with desired attribute patterns, and a profiling stage that performs detailed serological testing on a reduced subset of promising candidates. This segmentation allows the system to leverage the speed of genotyping for initial filtering while maintaining the accuracy of serology for final confirmation, thereby reducing overall time without sacrificing identification quality.
2Productivity
If genotyping is performed on all candidate donors individually, then accurate attribute determination is achieved, but the process becomes complex and not scalable
Solution Approach 1:
Multiple individual genotyping assays are merged into a single pooled genotyping reaction, where DNA from multiple donors is combined and analyzed simultaneously for specific attribute patterns. This pooling approach maintains the ability to accurately determine genetic attributes while dramatically increasing throughput and scalability, as one reaction can evaluate multiple candidates for multiple attributes in parallel rather than requiring separate individual tests for each donor.
3Measurement precision
If large registries of genotyped donors are maintained, then suitable donors can be identified accurately, but the cost increases substantially
Solution Approach 1:
Instead of maintaining comprehensive registries of all potential donors, the system performs partial genotyping only on donors who are actively sought or whose attributes are currently needed, rather than pre-genotyping all possible donors. This approach achieves sufficient measurement precision for accurate matching by testing only the subset of donors relevant to current clinical needs, thereby reducing the total quantity of donors that must be registered and the associated costs.
4Adaptability or versatility
If random donor searches are performed, then the process is simple, but it excludes only cognate epitopes and not allo-epitopes
Solution Approach 1:
The system performs preliminary genotyping to determine the genetic attribute patterns of donors before final selection, allowing proactive identification of individuals with desired characteristics such as absence of specific alleles or presence of protective variants. This preliminary genetic assessment enables the system to adapt its search strategy and prioritize donors with relevant attribute patterns, improving both the adaptability to specific clinical needs and the precision in detecting allo-epitopes that would trigger antibody formation.
Data Source
AI summary
A process of identifying a plurality of biological samples having particular desired attributes by testing pooled samples and selecting, for intended uses such as transfusion, or for subsequent analysis that is thereby enriched for such samples, pooled samples which have, or may have, said desired attributes. The preferred number of samples per pool “d” is determined by selecting an integer value as d which produces the maximum or a value near the maximum of the product of: d times the expected number of unambiguous sample pools, where a sample pool is unambiguous if all of the samples have the desired attributes, and is otherwise ambiguous if at least one sample has the desired attributes. The value selected as d can be greater than the maximum product above, so as to enlarge the total number of samples assayed in determining the desired attributes.