In recent decades, microfluidic technology has witnessed remarkable advancements, offering novel ways to study cellular behavior and interactions under dynamic flow conditions. However, a significant gap has persisted in the field – the lack of automated and adaptable imaging and analytical tools to fully exploit the potential of these experimental methods. Enter iCLOTS, a revolutionary software that addresses this critical need. In this blog post, we delve into the groundbreaking capabilities of iCLOTS, exploring its applications, features, and the potential it holds for biomedical research.
“We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs.”, the authors explained.
Microfluidics has enabled researchers to mimic complex physiological conditions, such as blood flow through vessels, offering unprecedented insights into cellular responses and interactions. However, manually analyzing the vast amount of data generated by these experiments is time-consuming, error-prone, and often limits the scope of research. iCLOTS bridges this gap by providing automated, user-friendly solutions for image segmentation, feature quantification, and machine learning analysis, specifically tailored for time-dependent, fluid flow-based microfluidic experiments.
iCLOTS, short for “Interactive Cell Lineage and Omics Tracking System,” is a versatile software designed to adapt to any microfluidic device or static system. It guides researchers through data analysis and interpretation in one simple, standalone package. Unlike other bioimage analysis tools, iCLOTS is not limited to specific experimental setups or cellular types. Its modular design allows for easy integration with other advanced AI-based tools, enhancing its analytical capabilities further.
iCLOTS transforms microscopy data into high-dimensional quantitative datasets, which are then subjected to machine learning algorithms for further analysis. The software offers a wide range of applications, from tracking cellular deformability and suspension velocity to assessing cell adhesion and accumulation within microfluidic devices. Notably, iCLOTS has been validated against manual analyses and has demonstrated robustness across various experimental conditions.
One of the most promising aspects of iCLOTS is its potential to advance both basic research and clinical diagnostics. By streamlining data analysis, iCLOTS eliminates the biases and errors associated with manual analysis, contributing to more robust and reproducible results. Its user-friendly interface makes it accessible to researchers with varying levels of computational expertise, while its adaptability ensures its utility in clinical laboratories for making critical medical treatment decisions.
“In summary, this work identifies a highly efficient mechanism for platelet generation outside of the body, by repeated passage of MKs through lung vasculature under air ventilation, involving enucleation and final TPM4-dependent steps to generate platelets. The findings will inform new approaches, such as the microfluidic system reported here, to large scale generation of human platelets.“, the authors explained.
iCLOTS marks a significant leap forward in microfluidic analysis, addressing a long-standing gap in the field and opening up new avenues for research and diagnostics. Its ability to process vast amounts of data, automate analysis, and provide robust results holds immense potential for advancing our understanding of cellular behavior under dynamic flow conditions. With iCLOTS, the promise of microfluidic technology is more attainable than ever before, empowering researchers to unlock groundbreaking discoveries in biomedical research.
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Fay, M.E., Oshinowo, O., Iffrig, E. et al. iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays. Nat Commun 14, 5022 (2023). https://doi.org/10.1038/s41467-023-40522-4 under a Creative Commons Attribution 4.0 International License)
Read the original article: iCLOTS software enables quantification of microscopy data from a wide range of established hematology assays
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