An improved method for isolation and flow cytometric characterization of intrahepatic leukocytes from fatty and fibrotic liver tissues
Bibliographic record
Abstract
Flow cytometry is an imperative tool to characterize alterations in a wide range of immune cell populations during inflammatory conditions and disease states that affect the liver such as the obesity-induced non-alcoholic fatty liver disease and liver fibrosis. Identification and quantification of immune cell subsets from the liver is critically dependent on efficient isolation of intrahepatic leukocytes. The isolation of leukocytes from fatty and fibrotic livers and processing the cells for flow cytometry can be challenging with respect to cell yields, purity and most importantly, the level of autofluorescence resulting from fat deposition. Here, we describe an efficient method for isolating intrahepatic leukocytes from mice fed with high fat diet and propose a strategy to alleviate autofluorescence during phenotyping by multicolor flowcytometry. We also describe a gating strategy for robust identification of granulocytes, pro-inflammatory, anti-inflammatory and transitional state monocyte subsets, dendritic cells, B cell, T lymphocyte subpopulations and NK cell subsets. Overall, the procedures described here will allow simultaneous processing of several samples while ensuring reproducible cell isolation and efficient noise reduction required for reliable characterization of intrahepatic leukocytes from the fatty liver tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".