Verification and refinement of cellular glycosphingolipid profiles using HPLC
Bibliographic record
Abstract
Glycosphingolipids (GSLs) are hybrid molecules consisting of the sphingolipid ceramide linked to a mono- or oligo-saccharide. In comparison to other membrane lipids, the family of GSLs stands out because of the extensive variation in the carbohydrate headgroup. GSLs are cell surface binding partners, in cis with growth factor receptors, and in trans with bacterial toxins and viruses, and are among the host-derived membrane components of viral particles, including those of HIV. In spite of their biological relevance, GSL profiles of commonly used cell lines have been analyzed to different degrees. Here, we directly compare the GSL complements from CHO-K1, COS-7, HeLa, HEK-293, HEPG2, Jurkat, and SH-SY5Y cells using an HPLC-based method requiring modest amounts of material. Compared to previous studies, the HPLC-based analyses provided more detailed information on the complexity of the cellular GSL complement, qualitatively as well as quantitatively. In particular for cells expressing multiple GSLs, we found higher numbers of GSL species, and different levels of abundance. Our study thus extends our knowledge of biologically relevant lipids in widely used cell lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".