Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry
Bibliographic record
Abstract
Deleterious changes, collectively termed as storage lesions, alter the characteristics of red blood cell (RBC) morphology during in vitro storage. Due to gradual loss of cellular membrane, RBCs lose their original biconcave disk shape and begin a process of spherical deformation that is characterized by well-defined morphological criteria. At the spheroechinocyte stage, the structure of RBC is irreversibly damaged and lacks the elasticity necessary to efficiently deliver oxygen. Quantifying the prevalence of spheroechinocytes could provide an important morphological measure of the quality of stored blood products. Unlike the conventional RBC morphology characterization assay involving light microscopy, we introduce a label-free assay using imaging flow cytometry (IFC). The technique captures 100,000 images of a sample and calculates a relative measure of spheroechinocyte population in a fraction of the time required by the conventional method. A comparative method study, measuring a morphological index for 11 RCC units through storage, found that the two techniques measured similar trends with IFC reporting the metric at an average of 3.9% higher. We monitored 18 RCC units between Weeks 1 and 6 of storage and found that the spheroechinocyte population increased by an average of 26.2%. The large (3.5-64.1%) variation between the units' spheroechinocyte population percentage at Week 1 suggests a possible dependence of blood product quality on donor characteristics. Given our method's ability to rapidly monitor large samples and refine morphological characterization beyond conventional methods, we believe our technique offers good potential for studying the underlying relationships between RBC morphology and blood storage lesions. © 2019 International Society for Advancement of Cytometry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".