106 Spatial multi-omic characterization of tumor microenvironment heterogeneity in hepatocellular carcinoma
Notice bibliographique
Résumé
Background Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy, demanding comprehensive multi-omic understanding of its spatial architecture to improve therapeutic strategies. Spatial transcriptomics with the Xenium™ platform enables mapping of hundreds to thousands of RNA targets in tissue sections. However, identification and validation of drug targets requires proteomic assessment to directly reveal the functional mechanisms underlying disease biology. Imaging Mass Cytometry™ (IMC™) is a spatial proteomic technology that utilizes cytometry by time-of-flight (on which CyTOF™ systems are based) to generate high-dimensional, spatially resolved protein expression data at subcellular resolution in tissue sections. Unlike traditional immunohistochemistry or immunofluorescence, IMC platforms use metal-tagged antibodies and laser ablation to simultaneously detect over 40 protein markers without spectral overlap or autofluorescence interference. Here we demonstrate the performance and insights gained from using IMC technology on tissue samples previously processed with the Xenium platform.Methods We profiled HCC FFPE tissue sections using a custom transcriptomic panel with Xenium v1 assay and then performed IMC using a 43-marker immuno-oncology-focused antibody panel on the same tissue section ( figure 1). We used IMC technology in parallel on non-Xenium processed control serial sections and compared performance. To combine the transcriptomic and proteomic datasets, we utilized Xenium Explorer software to co-register the datasets and observe overlay of transcriptomic and proteomic biomarkers.Results IMC technology alone and post-Xenium IMC generated data of similar quality, demonstrating highly consistent tumor and immune cell phenotyping capabilities in HCC ( figure 2). IMC technology alone and post-Xenium IMC similarly detect localization of macrophages (M1 and M2), neutrophils (CD66b), B cells (CD20), cytotoxic T cells (CD8) and T helper cells (CD4) in specific locations around the tissue. Xenium Explorer software permitted import of IMC data through a user-friendly co-registration algorithm that aligned stained nuclei from both datasets. Combining the datasets revealed the presence of subcategories of immune cells (T cells, B cells, macrophages) and their activation states. Additionally, detection of transcript and protein of multiple markers showcased discrepancies in spatial localization, highlighting the importance of validating transcriptomic data with proteomic assessment.Conclusions Here, we highlight the synergistic use of the Xenium platform for transcript detection and IMC technology for protein profiling on the same tissue section, facilitating an integrated understanding of tissue biology. This study provides a multi-omic view of HCC heterogeneity, offering insights into mechanisms of disease and development of potential therapeutic strategies.For Research Use Only. Not for use in diagnostic procedures.Abstract 106 Figure 1Combined workflow for spatial imaging of both RNA and protein on the same slide. The IMC staining protocol can be added onto the end of Xenium acquisition with an additional wash step prior to IMC staining. Slides can be imaged immediately or stored for later acquisitionAbstract 106 Figure 2Low and high abundance proteins were detected by IMC regardless of processing approach. IMC alone and post-Xenium IMC generate the same quality image, demonstrating highly consistent and reproducible data. For post-Xenium slides, the Xenium assay used was a custom panel using the Xenium v1 workflow
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».