Blog

Empowering Multiplex Imaging Analysis: Scientific Review

Home » Empowering Multiplex Imaging Analysis: Scientific Review

Category:

Empowering Multiplex Imaging Analysis: A Review of “Cross-platform dataset of multiplex fluorescent cellular object image annotations”

As multiplex imaging technology continues to evolve at a rapid pace, researchers are now equipped with vast amounts of high-quality datasets. However, the analysis of these datasets, which includes tasks such as cell segmentation, cell typing, and marker expression analysis, still presents significant challenges. Achieving precise quantification of protein expression at a sub-cellular resolution demands a multitude of annotations, which are not only costly but also arduous to obtain. Aleynick et al. addresses this critical need in “Cross-platform Dataset of Multiplex Fluorescent Cellular Object Image Annotations”. In this article, we delve into the significance of this paper and explore its potential implications for further research and development.

A Comprehensive Dataset for Multiplex Imaging Analysis

This annotated dataset, comprising over 100,000 cell annotations across various imaging platforms, aims to drive progress in the field of multiplex imaging analysis and contains more than 40 antibody markers across three distinct imaging platforms, covering a broad range of over 15 tissue indications. 

This dataset was obtained using three different multiplex imaging platforms, each offering unique capabilities and challenges. The following platforms were used:

  • Sequential IF with unmixing (Akoya Vectra 3.0)
  • Sequential IF with narrowband capture (Ultivue InSituPlex with Zeiss Axioscan image capture)
  • Cyclical IF with narrowband capture (Akoya CODEX)

By providing this comprehensive resource, the authors aim to propel the advancement and benchmarking of nuclear segmentation algorithms, ultimately driving progress in the field of multiplex imaging analysis. 

Meticulous Annotation Process

(A) Cellular annotation is performed starting from pathology slides which have been stained for a variety of markers with multiplexed imaging platforms. The images are then annotated and reviewed by 2 pathologists to create consensus annotations. (B) Data summary stratified by multiplexed imaging platform. (full article: https://rdcu.be/dfv8M)

By utilizing data from these three multiplex imaging platforms, Aleynick et al. ensure the incorporation of diverse imaging techniques and methodologies into the annotated dataset. This rich variety of data acquisition approaches enhances the dataset’s utility and applicability across different research contexts and enables the development of robust segmentation algorithms.

To ensure the accuracy and reliability of the annotations, Aleynick et al. employed a meticulous annotation process. The open-source image processing software, ImageJ, was used as the primary tool for annotation tasks. 

Each image in the dataset underwent annotation by two to three annotators. This multiple-annotator approach serves to mitigate individual biases and increase the overall consistency of the annotations. By involving multiple annotators, the dataset benefits from diverse perspectives and reduces the potential for subjective interpretations. Following the initial annotation process, the annotated images were then subjected to rigorous review by a pathologist. 

Implications for Research and Development

This paper is a significant step forward in the field of multiplex imaging analysis and has wide-ranging implications in the following areas:

  • Algorithm Development: The availability of this annotated dataset serves as a valuable resource for the development and refinement of nuclear segmentation algorithms. By offering a comprehensive collection of annotations across multiple antibody markers, tissue indications, and imaging platforms, researchers can leverage this dataset to train, test, and benchmark their algorithms. This significantly accelerates the development of robust and accurate segmentation algorithms, enabling more precise analysis of multiplex imaging data.
  • Benchmarking and Validation: The annotated dataset facilitates benchmarking efforts by providing a standardized reference for evaluating the performance of various nuclear segmentation algorithms. Researchers can compare the outputs of their algorithms against the annotations within the dataset, quantifying accuracy, efficiency, and other performance metrics. This enables the identification of novel algorithms or improvements to existing methods, ultimately leading to more reliable and consistent results in multiplex imaging analysis.
  • Advancing Research in Multiplex Imaging: The comprehensive nature of the dataset, covering a broad range of tissue indications and imaging platforms, promotes advancements in multiplex imaging research. Researchers can explore the dataset to investigate cellular phenotypes, protein expression patterns, and marker co-localization, facilitating deeper insights into complex biological processes. This can uncover new discoveries, aid in understanding disease mechanisms, and contribute to the development of targeted therapies and precision medicine approaches.

In summary, Aleynick et al. plays a key role in advancing the field of quantitative multiplex imaging analysis. Through its provision of a comprehensive annotated dataset, researchers are empowered to develop and benchmark nuclear segmentation algorithms, leading to improved accuracy, efficiency, and reliability in the analysis of multiplex imaging data. This work opens up new avenues for research, enhances clinical applications, and ultimately contributes to our understanding of complex biological processes at single cell spatial resolution.

Want to learn more about spatial biology? Join the scientific conversation on Linkedin

References:
Aleynick, N., Li, Y., Xie, Y. et al. Cross-platform dataset of multiplex fluorescent cellular object image annotations. Sci Data 10, 193 (2023). https://doi.org/10.1038/s41597-023-02108-z

Book A Demo

Add Your Heading Text Here