Streamlined mouse tumor processing with STEMprep™: Automated, Efficient, and Reliable 3260
Bibliographic record
Abstract
Abstract Description The tumor microenvironment is highly heterogeneous, composed of diverse cell types with distinct phenotypes and characteristics. Gaining insights into these differences is essential for advancing cancer immunotherapy and research. Reliable results demand high-quality samples and standardized processing, however, generating single-cell suspensions from solid tumors is challenging due to variations in tumor type, size, texture, and developmental stage. Balancing enzymatic and mechanical dissociation is critical to preserve cell viability and yield. We developed the STEMprep™ Mouse Tumor Dissociation Kit for use with the STEMprep™ Automated Tissue Dissociation System, featuring a user-friendly instrument and specially designed sample tubes. This system includes an optimized enzyme formulation and a single protocol that works across diverse tumor types. Our findings demonstrate consistently high cell viability and yield (cells/mg tissue) across tumors: soft B16 melanoma (91.2 ± 3.3% and 1.5E4 ± 7.4E3 [n = 15]), medium-firm CT26 colon carcinoma (82.9 ± 6.1% and 2.6E4 ± 1.3E4 [n = 9]), and firm 4T1 mammary carcinoma (88.5 ± 4.0% and 2.7E4 ± 1.7E4 [n = 4]). This method maintains cell integrity and epitope availability for downstream analysis. Furthermore, STEMprep™-processed samples are compatible with EasySep™ magnetic cell isolation technology, and are functional in T cell suppression assays, enhancing tumor research workflows with efficiency, consistency, and reliability. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".