Prospective, international, multisite comparison of platelet isolation techniques for genome-wide transcriptomics: communication from the SSC of the ISTH
Bibliographic record
Abstract
Genome-wide platelet transcriptomics is increasingly used to uncover new aspects of platelet biology and as a diagnostic and prognostic tool. Nevertheless, platelet isolation methods for transcriptomic studies are not standardized, introducing challenges for cross-study comparisons, data integration, and replication. In this prospective multicenter study, called "Standardizing Platelet Transcriptomics for Discovery, Diagnostics, and Therapeutics in the Thrombosis and Hemostasis Community (STRIDE)" by the International Society on Thrombosis and Haemostasis Scientific and Standardization Committees, we assessed how 3 of the most commonly used platelet isolation protocols influence metrics from next-generation bulk RNA sequencing and functional assays. Compared with washing alone, more stringent removal of leukocytes by anti-CD45 beads or PALL filters resulted in a sufficient quantity of RNA for next-generation sequencing and similar quality of RNA sequencing metrics. Importantly, stringent removal of leukocytes resulted in the lower relative expression of known leukocyte-specific genes and the higher relative expression of known platelet-specific genes. The results were consistent across enrolling sites, suggesting that the techniques are transferrable and reproducible. Moreover, all 3 isolation techniques did not influence basal platelet reactivity, but agonist-induced integrin αIIbβ 3 activation is reduced by anti-CD45 bead isolation compared with washing alone. In conclusion, the isolation technique chosen influences genome-wide transcriptional and functional assays in platelets. These results should help the research community make informed choices about platelet isolation techniques in their own platelet studies.
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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 | 0.086 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".