1490 High-plex co-detection of RNA and protein to explore tumor-immune interactions utilizing RNAscope with imaging mass cytometry
Bibliographic record
Abstract
<h3>Background</h3> Future advancements in immuno-oncology will be propelled by the tools capable of deciphering the spatial organization of distinct cell types within the tumor microenvironment (TME). Imaging Mass Cytometry™ (IMC™) has proven its effectiveness in studying complex cellular interactions within the TME. By utilizing CyTOF® technology, IMC allows for the simultaneous assessment of over 40 protein markers with subcellular resolution, eliminating spectral overlap and background autofluorescence. However, the inclusion of certain targets in IMC is impossible if there are no commercially available antibodies that successfully detect these protein targets or if the targets are soluble factors such as cytokines and chemokines. Here we present a new workflow that synergizes the highly sensitive and specific RNAscope™ technology for RNA detection with IMC multiplexing capability to visualize crucial RNA and protein markers simultaneously. <h3>Methods</h3> To evaluate the expression of both RNA and protein targets in human FFPE tumor tissue microarrays (TMAs), we combined the RNAscope HiPlex v2 assay with protein detection on the Hyperion XTi™ Imaging System. The RNAscope assay employed 12 target RNA marker probes and their associated metal-labeled detection probes, specifically designed for compatibility with IMC. The recommended workflow for the RNAscope HiPlex v2 assay was followed, with the exception that for RNA detection, metal-conjugated probes were used instead of fluorophores. Metal-conjugated antibodies were used to detect proteins within the same tissue, resulting in a combined 31-marker co-detection panel. <h3>Results</h3> The identified target protein markers encompassed a diverse range of extracellular matrix, immune, tumor, stromal, and endothelial cells. Detection of RNA enabled the visualization of various cytokines and chemokines, including <i>CXCL13</i>, <i>CXCL9</i>, <i>CXCL10</i>, <i>IFNγ</i>, <i>IL10</i>, and <i>IL8</i>, thereby facilitating the identification of the cellular sources for these secreted factors. Additionally, the use of marker-specific antibodies allowed for the visualization of immune cell subpopulations and their activation states. Immune cell hubs associated with anti-tumor immune responses were detected in tumor niches throughout the TMA. <h3>Conclusions</h3> By integrating RNAscope with the IMC platform, we achieved simultaneous visualization of RNA and protein targets on the same sample to investigate the TME. The superior sensitivity for RNA detection offered by the RNAscope assay unlocks targets previously inaccessible through antibody detection. Thus, this new workflow complements existing multiplexing capabilities of IMC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".