Intracellular Phosphoflow Cytometry of Acute Myeloid Leukemia Patient-Derived Xenotransplants
Bibliographic record
Abstract
To adapt and resist approved treatments, acute myeloid leukemia (AML) cells activate specific molecular pathways that lead to changes in gene expression, protein levels and activity. In this protocol, an approach is reported to explore targets phosphorylated downstream of oncogenic signaling in AML: p-STAT5 (Tyr694), p-4EBP1 (Thr37/46), p-RPS6 (Ser240/244), and p-ERK1/2 (Thr202/Tyr204). This method enables the assessment of how these pathways-major regulators of stemness maintenance, immune evasion, protein synthesis, and adaptation to oxidative and metabolic stress-are modulated by one or more tested compounds in bone marrow cells harvested from live mice by aspiration before and after the treatment phase. This minimally invasive method preserves cell integrity and reduces stress compared to bone-crushing techniques, which can induce damage and potentially affect experimental outcomes. To optimize intracellular antibody staining for flow cytometric analysis, a protocol was developed using paraformaldehyde fixation and methanol permeabilization. This approach ensures high staining precision and minimizes background noise, enabling reliable detection of intracellular signaling markers. One of the main advantages of this protocol is the development of a multiparametric antibody panel, allowing for simultaneous assessment of the four pathways within the same sample. Using a next-generation spectral flow cytometer with high sensitivity, dynamic shifts in pathway activation were observed depending on treatment conditions compared to pretreatment baseline levels in the same mice. This methodology enables precise in vivo analysis of signaling pathway modulation in patient-derived xenograft bone marrow samples without requiring euthanasia of the animals, providing valuable insight into the adaptive mechanisms of AML cells, and can guide the evaluation of therapeutic strategies aimed at targeting these pathways to overcome resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".