103 A deep-learning approach to guide acquisition region selection for Imaging Mass Cytometry
Notice bibliographique
Résumé
<h3>Background</h3> Imaging Mass Cytometry™ (IMC™) is the method of choice for single-step staining and high-plex imaging of tissues while avoiding the complications of autofluorescence and cyclic imaging. IMC has three new imaging modes: Preview Mode (PM), Cell Mode (CM) and Tissue Mode (TM). PM rapidly scans a stained tissue to provide a comprehensive overview, mapping out the distribution of over 40 markers and revealing tissue heterogeneity. This enables researchers to make informed decisions about which areas warrant closer examination. Following PM, regions of interest (ROIs) are selected for high-resolution imaging. This is a critical step that is informed by biomarker expression using automated AI algorithms. CM offers high-resolution imaging for detailed analysis of the ROIs identified during PM, all using the same slide. TM provides fast acquisition of the entire tissue at 5-micron resolution, optimal for quantitative pixel-based analysis. These modes support automated, continuous imaging of more than 40 large tissue samples (400 mm<sup>2</sup>) weekly. <h3>Methods</h3> Tissue sections of colon adenocarcinoma were stained with a 30-marker IMC panel of structural, tumor, stromal, immune cell and immune activation markers. Images were acquired on the Hyperion XTi™ Imaging System (Standard BioTools™), first in PM and then in CM with automatic selection of ROIs using Phenoplex™ software (Visiopharm®). ROIs were automatically selected based on two criteria: 1) actively proliferating and non-proliferating tumor regions; 2) cold and hot tumor regions as identified by immune hotspots within stromal or epithelial tumor regions. An adjacent serial section was acquired in TM. Single-cell analysis of the images obtained in CM was performed using Phenoplex. Tissue segmentation divided the tissue into tumor epithelial and stromal regions; cell segmentation was based on Iridium DNA channels; and phenotyping was performed using the Guided Workflow. This data was used to compare the immune contexture and spatial distributions via interactive t-SNE plots. <h3>Results</h3> A high degree of immune infiltration was observed in the tumor, with significant levels of infiltrating myeloid cells. Hotspots of tumor-associated neutrophils expressing granzyme B were found, implicating their role in the recruitment and activation of intratumor CD4+ and cytotoxic CD8+ T cells. <h3>Conclusions</h3> This work demonstrates that Hyperion™ XTi can greatly accelerate the ability of IMC users to gain useful insights from complex biological samples. Phenoplex enables a comprehensive workflow for the analysis of this data, providing automated ROI selection, phenotyping, and spatial analyses of high-resolution IMC images for biological assessment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».