Low vulnerability of the human retina to SARS-CoV-2 infection-potential role of the choroidal vasculature
Bibliographic record
Abstract
Purpose : Ocular manifestations have been described in COVID-19 patients, but it is unknown whether SARS-CoV-2 - the causal agent in COVID-19 - can directly infect posterior ocular tissues. Here, we investigate SARS-CoV-2 host factor gene expression levels and their distribution across retinal and choroidal cell types. The overall aim is to identify particularly vulnerable cell-types to elucidate a potential pathomechanism for observed retinal lesions in COVID-19 patients. Methods : Datasets from human retina and choroid were retrieved from published datasets. We reprocessed the data from raw quantification matrix following standard Seurat (v.3.1) clustering procedure. To take advantage of the improved Seurat pre processing and normalization workflow, we used the SCTransform function, thereby we “corrected” log-normalized expression values across datasets. To reannotate the cells, multiple clusterings of different resolutions were generated among which the one best matching published clustering was picked and manual annotation was undertaken using marker genes. Results : Key genes for SARS-CoV-2 host cell entry are significantly lower expressed in human retinal cell-types compared to choroid. In detail, we observe no expression of ACE2 or TMPRSS2 in the retina, but a scarce co-expression in vessel associated cell-types of the choroid. No differences could be detected between fovea and periphery for both tissues. Conclusions : Key conclusion: The human retina is not vulnerable for a direct SARS-CoV-2 infection. Thus, retinal lesions as described in a recent Lancet publication by Marinho et al. are most likely due to a secondary damage (Marinho et al., 2020). However, SARS-CoV-2 entry genes are expressed in vessel-associated cell-types of the choroid. Thus, as described in other organs, the virus might infect the vasculature in terms of an infectious endothelitis, and could subsequently lead to vascular occlusions. Further studies are needed to elucidate whether vascular occlusions of the posterior eye segment correlate with life-threatening cardiovascular complications in COVID-19 patients. Thus, the posterior eye segment might qualify as an in vivo biomarker of systemic vascular disease in COVID-19 patients and imaging techniques of the posterior eye segment could be helpful for manage treatment of COVID-19 patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".