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Related Experiment Video

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Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

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Published on: January 2, 2020

Seeing More: A Future of Augmented Microscopy.

Devin P Sullivan¹, Emma Lundberg¹

¹Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 171 21, Sweden.

|April 21, 2018

Summary

Label-free microscopy images can predict cell characteristics like type and state. Deep learning enables computational multiplexing from inexpensive label-free imaging, advancing cell analysis.

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Area of Science:

Cell biology
Computational imaging
Machine learning

Background:

Microscopy is crucial for cell biology research.
Traditional methods often require fluorescent labels, which can be expensive and perturb cellular function.
There is a need for cost-effective and less invasive methods for detailed cell analysis.

Purpose of the Study:

To investigate the potential of label-free microscopy images for predicting cellular information.
To develop a deep-learning framework capable of extracting rich biological data from label-free images.
To demonstrate the feasibility of computationally multiplexed assays using label-free microscopy.

Main Methods:

Utilized a deep-learning framework applied to label-free microscopy images of cells.
Trained the model to predict fluorescent labels associated with cell type, cell state, and organelle distribution.
Validated the predictive accuracy of the model.

Main Results:

Label-free cell images were successfully used to predict fluorescent labels.
The deep-learning model accurately inferred cell type, state, and organelle distribution.
Demonstrated the capability of predicting multiple biological features from a single label-free image.

Conclusions:

Label-free microscopy, combined with deep learning, offers a powerful alternative to traditional fluorescent labeling.
This approach enables computationally multiplexed assays, significantly reducing costs and complexity.
The findings open new avenues for high-content, inexpensive cell analysis in biological research.