Automate tissue processing with STEMprep™ for versatile sample preparation 3334
Bibliographic record
Abstract
Abstract Description Tissue environments are specialized to support distinct functions. Analyzing cell interactions and gene expression within tissues is essential for advancing tissue research and understanding disease mechanisms. Tissue samples must first be processed with protocols that preserve cell viability, yield and functionality. However, achieving an optimal balance between enzymatic and mechanical dissociation for tissues can be time-consuming and technically demanding. To address this, we developed the STEMprep™ Tissue Dissociation system to efficiently generate single cells from tissue samples. This system features an instrument with integrated temperature control and tissue-specific programs, specialized sample tubes, and kits for enzymatic digestion. Our results demonstrate high cell viability and yield for mouse spleen (95.3 ± 2.7%, 1.2E8 ± 3.2E7 cells/tissue), brain (89.2 ± 4.7%, 2.6E6 ± 5.6E5 cells/tissue), lung (90.6 ± 2.9%, 1.5E7 ± 4.0E6 cells/tissue), liver (90.0 ± 5.1%, 6.1E7 ± 6.7E7 cells/tissue), and CT26 tumors (82.9 ± 6.1%, 2.6E4 ± 1.3E4 cells/mg tissue). STEMprep™-processed samples are compatible with EasySep™ cell isolation, and functional in cell-specific downstream assays. High-quality RNA can also be extracted directly using a homogenization protocol, enabling gene analysis workflows. The STEMprep™ Tissue Dissociation system streamlines tissue processing compared to manual procedures, enhancing consistency, throughput, and efficiency to accelerate tissue research. Topic Categories Technological Innovations in Immunology (TECH)
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".