Over the last decade, technologies in tissue multiplexed imaging have been processing massively, and a significant number of commercial and laboratorial methods have been developed, including MIBI, IMC, CODEX and CyCIF. These new technologies allow researchers and physicians to interrogate clinical samples in great depth, and multiple biomarkers can be examined in a single specimen. Furthermore, the spatial and single-cell resolution conjoining with high-dimensional features moves the field from “hypothesis-driven” to “hypothesis-generating”.
However, despite the great advances of instruments and reagents, the computational toolkits required for discovery and diagnosis are outdated. In addition, there are unfulfilled needs for the data and metadata standards in multiplex imaging, robust and reusable computational workflows in analysis and data processing, as well as the scalable solutions for data sharing and storage. Furthermore, the cross-platform comparison and integration between different multiplexing methods are still lacking, which is particularly important for reconciling biological findings from different labs, institutes or even countries. Thus, this Research Topic serves as the first initiative for both commercial and academic users and innovators to join forces and present their tools and solutions for these challenges. Although further development will be required, we hope to have a centralized location for people who are interested in this multiplexing field to start with.
As such, we are interested in the submission of Original Research, Review, Mini-review, Perspective, and methodological articles, focusing on, but not limited to, the following sub-topics:
• Algorithms and workflows for image processing and segmentation (from image to data)
• Solutions for data and metadata standards
• ML/DL/AI approaches to extract features from images and data
• Supervised and unsupervised methods for cell-type identification
• Robust methods for normalization and clustering of single-cell data
• 3D registration of images & data
• Cross-platform comparison using standard samples
• Machine learning and other heuristics approaches for integrating multimodal data
• From bench to clinical, the challenges and requirements.
Over the last decade, technologies in tissue multiplexed imaging have been processing massively, and a significant number of commercial and laboratorial methods have been developed, including MIBI, IMC, CODEX and CyCIF. These new technologies allow researchers and physicians to interrogate clinical samples in great depth, and multiple biomarkers can be examined in a single specimen. Furthermore, the spatial and single-cell resolution conjoining with high-dimensional features moves the field from “hypothesis-driven” to “hypothesis-generating”.
However, despite the great advances of instruments and reagents, the computational toolkits required for discovery and diagnosis are outdated. In addition, there are unfulfilled needs for the data and metadata standards in multiplex imaging, robust and reusable computational workflows in analysis and data processing, as well as the scalable solutions for data sharing and storage. Furthermore, the cross-platform comparison and integration between different multiplexing methods are still lacking, which is particularly important for reconciling biological findings from different labs, institutes or even countries. Thus, this Research Topic serves as the first initiative for both commercial and academic users and innovators to join forces and present their tools and solutions for these challenges. Although further development will be required, we hope to have a centralized location for people who are interested in this multiplexing field to start with.
As such, we are interested in the submission of Original Research, Review, Mini-review, Perspective, and methodological articles, focusing on, but not limited to, the following sub-topics:
• Algorithms and workflows for image processing and segmentation (from image to data)
• Solutions for data and metadata standards
• ML/DL/AI approaches to extract features from images and data
• Supervised and unsupervised methods for cell-type identification
• Robust methods for normalization and clustering of single-cell data
• 3D registration of images & data
• Cross-platform comparison using standard samples
• Machine learning and other heuristics approaches for integrating multimodal data
• From bench to clinical, the challenges and requirements.
