Color quantification for evaluation of stained tissues

Wang, Defeng; Shi, Lin; Wáng, Yì Xiáng J.; Man, Gene Chi Wai; Heng, Pheng‐Ann; Griffith, James F.; Ahuja, Anil T.

doi:10.1002/cyto.a.21037

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…2A–2H; n = 10 mice per group). Only the spleen showed iron staining, as assessed by Prussian blue, and this staining was physiological because the endogenous levels of iron are high in this organ [27]. Iron content of liver, spleen, and brain were measured by ICP‐MS at the contract laboratory.…”

Section: Resultsmentioning

confidence: 99%

Magnetic Resonance Imaging Tracking of Ferumoxytol-Labeled Human Neural Stem Cells: Studies Leading to Clinical Use

Gutova

Frank

D’Apuzzo

et al. 2013

Stem Cells Translational Medicine

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Numerous stem cell-based therapies are currently under clinical investigation, including the use of neural stem cells (NSCs) as delivery vehicles to target therapeutic agents to invasive brain tumors. The ability to monitor the time course, migration, and distribution of stem cells following transplantation into patients would provide critical information for optimizing treatment regimens. No effective cell-tracking methodology has yet garnered clinical acceptance. A highly promising noninvasive method for monitoring NSCs and potentially other cell types in vivo involves preloading them with ultrasmall superparamagnetic iron oxide nanoparticles (USPIOs) to enable cell tracking using magnetic resonance imaging (MRI). We report here the preclinical studies that led to U.S. Food and Drug Administration approval for first-in-human investigational use of ferumoxytol to label NSCs prior to transplantation into brain tumor patients, followed by surveillance serial MRI. A combination of heparin, protamine sulfate, and ferumoxytol (HPF) was used to label the NSCs. HPF labeling did not affect cell viability, growth kinetics, or tumor tropism in vitro, and it enabled MRI visualization of NSC distribution within orthotopic glioma xenografts. MRI revealed dynamic in vivo NSC distribution at multiple time points following intracerebral or intravenous injection into glioma-bearing mice that correlated with histological analysis. Preclinical safety/toxicity studies of intracerebrally administered HPF-labeled NSCs in mice were also performed, and they showed no significant clinical or behavioral changes, no neuronal or systemic toxicities, and no abnormal accumulation of iron in the liver or spleen. These studies support the clinical use of ferumoxytol labeling of cells for post-transplant MRI visualization and tracking.

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Section: Resultsmentioning

confidence: 99%

Magnetic Resonance Imaging Tracking of Ferumoxytol-Labeled Human Neural Stem Cells: Studies Leading to Clinical Use

Gutova

Frank

D’Apuzzo

et al. 2013

Stem Cells Translational Medicine

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“…Also, the MSE metric is inconsistent with the human perception ability [ 37 ]. Therefore, two other metrics namely structure similarity index (SSIM) [ 37 , 38 ] and color shading similarity (CSS) [ 39 ], which are well-suited for evaluating GAN performances, were utilized.…”

Section: Methodsmentioning

confidence: 99%

“…Another metric called color shading similarity (CSS) [ 39 ] was used to quantify the similarity in the colors of the pixels in the histopathologically stained H&E image (

) and the computationally stained H&E image (

). The CSS is calculated by converting both images in the CIELAB space and utilizing only the color channels A* and B*.…”

Section: Methodsmentioning

confidence: 99%

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Pradhan

Meyer

Stallmach

et al. 2021

Biomed. Opt. Express

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Hematoxylin and Eosin (H&E) staining is the ’gold-standard’ method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for ’real-time’ disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author’s best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.

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“…For this, cells were fixed with 2.5% glutaraldehyde (Sigma G4004), the cells were incubated with 1% potassium ferrocyanide (Sigma P3289) and 2% HCl for 10–15 min, then 1% neutral red (Sigma N8002) was added to stain nucleus. The degree of blueness was quantified using the following methodology [41]. A computer program was written in Matlab 7.1 (Mathworks Inc., Natick, MA, USA) to quantify the blueness of the stained images.…”

Section: Methodsmentioning

confidence: 99%

Efficacy and Durability in Direct Labeling of Mesenchymal Stem Cells Using Ultrasmall Superparamagnetic Iron Oxide Nanoparticles with Organosilica, Dextran, and PEG Coatings

et al. 2011

Self Cite

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We herein report a comparative study of mesenchymal stem cell (MSC) labeling using spherical superparamagnetic iron oxide (SPIO) nanoparticles containing different coatings, namely, organosilica, dextran, and poly(ethylene glycol) (PEG). These nanomaterials possess a similar SPIO core size of 6–7 nm. Together with their coatings, the overall sizes are 10–15 nm for all SPIO@SiO2, SPIO@dextran, and SPIO@PEG nanoparticles. These nanoparticles were investigated for their efficacies to be uptaken by rabbit bone marrow-derived MSCs without any transfecting agent. Experimentally, both SPIO@SiO2 and SPIO@PEG nanoparticles could be successfully uptaken by MSCs while the SPIO@dextran nanoparticles demonstrated limited labeling efficiency. The labeling durability of SPIO@SiO2 and SPIO@PEG nanoparticles in MSCs after three weeks of culture were compared by Prussian blue staining tests. SPIO@SiO2 nanoparticles demonstrated more blue staining than SPIO@PEG nanoparticles, rendering them better materials for MSCs labeling by direct uptake when durable intracellullar retention of SPIO is desired.

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Color quantification for evaluation of stained tissues

Cited by 8 publications

References 18 publications

Magnetic Resonance Imaging Tracking of Ferumoxytol-Labeled Human Neural Stem Cells: Studies Leading to Clinical Use

Magnetic Resonance Imaging Tracking of Ferumoxytol-Labeled Human Neural Stem Cells: Studies Leading to Clinical Use

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Efficacy and Durability in Direct Labeling of Mesenchymal Stem Cells Using Ultrasmall Superparamagnetic Iron Oxide Nanoparticles with Organosilica, Dextran, and PEG Coatings

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