Protocol for measuring cellular energetics through noncanonical amino acid tagging in human peripheral blood and murine tissue immune cells
Bibliographic record
Abstract
Cellular metabolism dictates immune cell function, yet we lack tools to functionally profile immunometabolism in low-yield, complex samples. We present a flow cytometry-based protocol for measuring cellular energetics through noncanonical amino acid tagging (CENCAT) in human peripheral blood and murine tissue immune cells. We describe steps for sample preparation, metabolic inhibition, protein synthesis analysis using click chemistry, immunophenotyping, and calculation of metabolic dependencies. For complete details on the use and execution of this protocol, please refer to Vrieling et al. 1 • Instructions for preparing human PBMCs and murine tissues for metabolic profiling • Protocol for βES incorporation in nascent proteins under metabolic inhibition • Step-by-step guide to fluorescent tagging of nascent proteins via click chemistry • Procedure for calculating relative cellular glucose and mitochondrial dependence Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Cellular metabolism dictates immune cell function, yet we lack tools to functionally profile immunometabolism in low-yield, complex samples. We present a flow cytometry-based protocol for measuring cellular energetics through noncanonical amino acid tagging (CENCAT) in human peripheral blood and murine tissue immune cells. We describe steps for sample preparation, metabolic inhibition, protein synthesis analysis using click chemistry, immunophenotyping, and calculation of metabolic dependencies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".