A simple and integrated workflow for deep proteomic and transcriptomic analysis of sorted cell populations
Bibliographic record
Abstract
Abstract In addition to the long standing efforts to understand and manipulate the immune system in the treatment of autoimmune diseases, the immune system is increasingly becoming a direct target for cancer therapeutics. Recent advances in flow and mass cytometry have greatly expanded the number of immune cell parameters that can be interrogated resulting in an improved understanding of the immune system heterogeneity. These technologies, however, remain limited in the number and types of analytes that can be examined in a single clinical sample. The NanoString nCounter® platform enables the highly multiplexed digital analysis of both RNA and protein from a single biological specimen for multiple research applications. We have recently developed the nCounter® Vantage 3D™ RNA:Protein Immune Cell Profiling Assay for research applications, which interrogates 30 cell surface proteins and 770 immune-related RNAs starting with cells in suspension. Expanding on this, we demonstrate the development of a streamlined workflow that integrates standard immune cell sorting with downstream nCounter® analysis. By co-staining PBMCs with both fluorescently-labeled and DNA barcoded antibodies, CD8+ and CD4+ T cells and CD19+ B cells were isolated followed by analysis of dozens of additional proteins and 770 RNA from each sorted population. Demonstrating the value of this workflow in analyzing potentially rare cell populations, the number of target cells were titrated to determine the sensitivity of the workflow. Without the requirement for additional molecular biology methods, such as RNA purification or sequencing library construction, this method is ideally suited for incorporation into any cell sorting workflow.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".