Cross platform validation of a unique method for deep genomic and proteomic analysis of rare immune cell populations
Bibliographic record
Abstract
Abstract Advances in flow and mass cytometry expand the number of cell parameters that can be interrogated improving our understanding of the myriad immune cell types. These technologies, however, are limited in the types of analytes profiled in a single sample. In addition to efforts to understand the immune system for the treatment of autoimmune diseases, the immune system is increasingly a target for cancer therapeutics. These efforts require deep characterization to understand rare cell populations, requiring next-generation methods that expand on flow cytometry. 3D Flow™ Analysis integrates flow cytometry cell sorting and the nCounter® Vantage 3D™ RNA:Protein Immune Cell Profiling Assay* to characterize sorted immune cell populations with direct digital counting of 770 RNA and 30 proteins. To determine if the method is agnostic to the cell sorting technology used, 3D Flow analysis was run with the Becton Dickenson FACSARIA IIu, Sony SH800z, and Beckman Coulter MoFloXDP. Stimulated and unstimulated PBMC were co-stained with flow antibodies for CD3, CD8, and CD4 plus 30 DNA-labeled antibodies for NanoString readout. 5,000 CD3+/CD4+ and CD3+/CD8+ cells were sorted on each platform and immediately lysed to release cellular RNA and DNA tags from NanoString antibodies. RNA and DNA tags were directly hybridized to NanoString barcodes and run on the nCounter system. Expression of 30 proteins and 770 RNA across cell populations correlated between the three sorting platforms, showing 3D Flow is agnostic to the sorting technology used. Importantly, high-plex RNA data was obtained without additional molecular biology methods, such as RNA purification or library construction, simplifying incorporation into a flow cytometry laboratory.
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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 | 0.001 | 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".