Spatiotemporal Impact of Sars-Cov-2 Infection on the Transcriptome of Bone Marrow Megakaryocytes
Bibliographic record
Abstract
Megakaryocytes, integral to platelet production, predominantly reside in the bone marrow, undergoing regulated fragmentation within sinusoid vessels to release platelets into the bloodstream. Inflammatory states and infections have been shown to influence megakaryocyte transcription, potentially affecting platelet functionality. Notably, COVID-19 has been associated with altered platelet transcriptome. In this study, we hypothesized that SARS-CoV-2 infection could impact the transcriptome of bone marrow megakaryocytes, particularly those in direct proximity to the blood. Utilizing spatial transcriptomic analysis and machine learning techniques, we observed that the transcriptome of healthy mouse bone marrow megakaryocytes exhibited minimal alteration based on proximity to sinusoid vessels. Similar findings were observed during peak SARS-CoV-2 viremia, when the disease primarily affected the lungs. Conversely, a significant divergence in the transcriptome of megakaryocytes was observed during systemic inflammation, even when SARS-CoV-2 was no longer detectable in the lungs or bone marrow. Under these conditions, the transcriptional landscape was enriched in genes associated with structural and translational activities, platelet degranulation, netosis, and auto-immunity. Of the approximately ~19,000 genes identified in megakaryocytes during systemic inflammation, machine learning pinpointed 30 genes significantly altered in cells closely adjacent to sinusoid vessels, with gene ontology attributing these changes primarily to protein deSUMOylation. Intriguingly, the type-I interferon signature and calprotectin (S100A8/A9) were not induced under any condition. Inflammatory cytokines were elevated in the blood of COVID-19 mice, but not in bone marrow plasma, suggesting a preferential impact of inflammation on this specific subset of cells. Collectively, our data indicate that distinct subpopulations of bone marrow megakaryocytes may emerge based on spatial localization and the stage of COVID-19 pathogenesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".