One-Cell Analysis as a Technique for True Single-Cell Analysis of Organelles in Breast Tumor and Adjacent Normal Tissue to Profile Fatty Acid Composition of Triglyceride Species
Bibliographic record
Abstract
Breast cancer develops in an adipose rich environment of normal adipocytes that are known to aid in tumor progression through an unknown method of lipid transfer from normal cells to tumor cells. Much research is built around lipid analysis of breast tumor and adjacent normal tissues to identify variations in the lipidome to gain an understanding of the role lipids play in progressing cancer. Ideally, single-cell analysis methods coupled to mass spectrometry that retain spatial information are best suited for this endeavor. However, many single-cell analysis methods are not capable of subcellular analysis of intact lipids while maintaining spatial information. One-Cell analysis is a true single-cell technique with the precision to extract single organelles from intact tissues while not interfering or disrupting adjacent cells. This method is used to extract and analyze single organelles from individual cells using nanomanipulation coupled to nanoelectrospray ionization mass spectrometry. Presented here is a demonstration of the analysis of single lipid bodies from two different sets of breast tumor and normal adjacent tissues to elucidate the fatty acid composition of triglycerides using One-Cell analysis coupled to tandem mass spectrometry. As a result, thirteen fatty acid species unique to the tumor tissues were identified, five in one set of tissues and eight in the other set.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".