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Precise Quantitative Analysis of Cell Targeting by Particle‐Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network

Mochalova, Elizaveta N. ; Kotov, Ivan A. ; Rozenberg, Julian M. ; Nikitin, Maxim P.

Cytometry. Part A, 2020-03, Vol.97 (3), p.279-287 [Periódico revisado por pares]

Hoboken, USA: John Wiley & Sons, Inc

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Título:
Precise Quantitative Analysis of Cell Targeting by Particle‐Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network
Autor: Mochalova, Elizaveta N. ; Kotov, Ivan A. ; Rozenberg, Julian M. ; Nikitin, Maxim P.
Assuntos: Artificial neural networks ; cancer cell targeting ; Cell interactions ; convolutional neural network ; Cytology ; Data analysis ; Data interpretation ; Diagnostic systems ; Drug delivery systems ; faster R‐CNN ; Field of view ; Flow cytometry ; Fluorescence ; Gene transfer ; HER2/neu ; Image detection ; Image processing ; imaging flow cytometry ; Imaging techniques ; Localization ; Morphology ; Neural networks ; object detection ; Object recognition ; particles ; Pattern recognition ; Phototherapy ; Process parameters ; Quantitative analysis ; targeted drug delivery
É parte de: Cytometry. Part A, 2020-03, Vol.97 (3), p.279-287
Descrição: Understanding the intricacies of particle–cell interactions is essential for many applications such as imaging, phototherapy, and drug/gene delivery, because it is the key to accurate control of the particle properties for the improvement of their therapeutic and diagnostic efficiency. Recently, high‐throughput methods have emerged for the detailed investigation of these interactions. For example, imaging flow cytometry (IFC) collects up to 60,000 images of cells per second (in 12 optical channels) and provides information about morphology and organelle localization in combination with fluorescence and side scatter intensity data. However, analysis of IFC data is extremely difficult to perform using conventional methods that calculate integral parameters or use mask‐based object recognition. Here, we show application of a convolutional neural network (CNN) for precise quantitative analysis of particle targeting of cells using IFC data. CNN provides high‐throughput object detection with almost human precision but avoids the subjective choice of image processing parameters that often leads to incorrect data interpretation. The method allows accurate counting of cell‐bound particles with reliable discrimination from the nonbound particles in the field of view. The proposed method expands capabilities of spot counting applications (such as organelle counting, quantification of cell–cell and cell–bacteria interactions) and is going to be useful not only for high‐throughput analysis of IFC data but also for other imaging techniques. © 2019 International Society for Advancement of Cytometry
Editor: Hoboken, USA: John Wiley & Sons, Inc
Idioma: Inglês

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Precise Quantitative Analysis of Cell Targeting by Particle‐Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network

Mochalova, Elizaveta N. ; Kotov, Ivan A. ; Rozenberg, Julian M. ; Nikitin, Maxim P.

Hoboken, USA: John Wiley & Sons, Inc

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