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The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

ABSTRACT: Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the "recognition fitness deviation (RFD)," as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.

SUBMITTER: Shirai K

PROVIDER: S-EPMC7229423 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

Shirai Kazuhide K Kato Hirohito H Imai Yuta Y Shibuta Mayu M Kanie Kei K Kato Ryuji R

Regenerative therapy 20200514

Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent ...[more]

PMID: 32435672

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Project description:Cells of the vascular system release spherical vesicles, called microparticles, in the size range of 0.1-1 ?m induced by a variety of stress factors resulting in variable concentrations between health and disease. Furthermore, microparticles have intercellular communication/signaling properties and interfere with inflammation and coagulation pathways. Today's most used analytical technology for microparticle characterization, flow cytometry, is lacking sensitivity and specificity, which might have led to the publication of contradicting results in the past.We propose the use of nano-liquid chromatography two-stage mass spectrometry as a nonbiased tool for quantitative MP proteome analysis.For this, we developed an improved microparticle isolation protocol and quantified the microparticle protein composition of twelve healthy volunteers with a label-free, data-dependent and independent proteomics approach on a quadrupole orbitrap instrument.Using aliquots of 250 ?l platelet-free plasma from one individual donor, we achieved excellent reproducibility with an interassay coefficient of variation of 2.7 ± 1.7% (mean ± 1 standard deviation) on individual peptide intensities across 27 acquisitions performed over a period of 3.5 months. We show that the microparticle proteome between twelve healthy volunteers were remarkably similar, and that it is clearly distinguishable from whole cell and platelet lysates. We propose the use of the proteome profile shown in this work as a quality criterion for microparticle purity in proteomics studies. Furthermore, one freeze thaw cycle damaged the microparticle integrity, articulated by a loss of cytoplasm proteins, encompassing a specific set of proteins involved in regulating dynamic structures of the cytoskeleton, and thrombin activation leading to MP clotting. On the other hand, plasma membrane protein composition was unaffected. Finally, we show that multiplexed data-independent acquisition can be used for relative quantification of target proteins using Skyline software. Mass spectrometry data are available via ProteomeXchange (identifier PXD003935) and panoramaweb.org (https://panoramaweb.org/labkey/N1OHMk.url).

Dataset Information

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

Publications

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

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