1490 High-plex co-detection of RNA and protein to explore tumor-immune interactions utilizing RNAscope with imaging mass cytometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it