Therapeutic Potential of Human Nasal Inferior Turbinate-Derived Stem Cells: Microarray Analysis of Multilineage Differentiation

Park, Sun Hwa; Kim, Do Hyun; Lim, Mi Hyun; Back, Sang A; Yun, Byeong Gon; Jeun, Jung Ho; Lim, Jung Yeon; Kim, Su Young; Hwang, Se Hwan

doi:10.1159/000516016

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Derived from the human nasal inferior turbinate, human nasal inferior turbinatederived stem cells (hNTSCs) offer a conveniently accessible resource obtainable during the treatment of chronic rhinitis, and have emerged as a valuable repository of MSCs, as highlighted across various studies [11][12][13][14][15][16][17][18]. These hNTSCs exhibit a remarkable resilience to their microenvironment, showcasing an elevated potential for proliferation and consistently retaining their distinct characteristics throughout successive cell passages [19,20]. Despite their recognition for their MSC potential, the specific impact of hypoxic conditions on these cells has remained an enigmatic realm yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Human Nasal Turbinate Stem Cells under Hypoxic Conditions

Kim,

Park

et al. 2023

Cells

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This study investigated the influence of hypoxic culture conditions on human nasal inferior turbinate-derived stem cells (hNTSCs), a subtype of mesenchymal stem cells (MSCs). It aimed to discern how hypoxia affected hNTSC characteristics, proliferation, and differentiation potential compared to hNTSCs cultured under normal oxygen levels. After obtaining hNTSCs from five patients, the samples were divided into hypoxic and normoxic groups. The investigation utilized fluorescence-activated cell sorting (FACS) for surface marker analysis, cell counting kit-8 assays for proliferation assessment, and multiplex immunoassays for cytokine secretion study. Differentiation potential—osteogenic, chondrogenic, and adipogenic—was evaluated via histological examination and gene expression analysis. Results indicated that hNTSCs under hypoxic conditions preserved their characteristic MSC phenotype, as confirmed by FACS analysis demonstrating the absence of hematopoietic markers and presence of MSC markers. Proliferation of hNTSCs remained unaffected by hypoxia. Cytokine expression showed similarity between hypoxic and normoxic groups throughout cultivation. Nevertheless, hypoxic conditions reduced the osteogenic and promoted adipogenic differentiation potential, while chondrogenic differentiation was relatively unchanged. These insights contribute to understanding hNTSC behavior in hypoxic environments, advancing the development of protocols for stem cell therapies and tissue engineering.

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Section: Introductionmentioning

confidence: 99%

Characteristics of Human Nasal Turbinate Stem Cells under Hypoxic Conditions

Kim,

Park

et al. 2023

Cells

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High throughput screening of mesenchymal stem cell lines using deep learning

Kim

Jeon

Park

et al. 2022

Sci Rep

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Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of appropriate quality control (QC) methods for functional screening of MSC lines; thus, clinical outcomes are inconsistent. Recently, machine learning (ML)-based methods, in conjunction with single-cell morphological profiling, have been proposed as alternatives to conventional in vitro/vivo assays that evaluate MSC functions. Such methods perform in silico analyses of MSC functions by training ML algorithms to find highly nonlinear connections between MSC functions and morphology. Although such approaches are promising, they are limited in that extensive, high-content single-cell imaging is required; moreover, manually identified morphological features cannot be generalized to other experimental settings. To address these limitations, we propose an end-to-end deep learning (DL) framework for functional screening of MSC lines using live-cell microscopic images of MSC populations. We quantitatively evaluate various convolutional neural network (CNN) models and demonstrate that our method accurately classifies in vitro MSC lines to high/low multilineage differentiating stress-enduring (MUSE) cells markers from multiple donors. A total of 6,120 cell images were obtained from 8 MSC lines, and they were classified into two groups according to MUSE cell markers analyzed by immunofluorescence staining and FACS. The optimized DenseNet121 model showed area under the curve (AUC) 0.975, accuracy 0.922, F1 0.922, sensitivity 0.905, specificity 0.942, positive predictive value 0.940, and negative predictive value 0.908. Therefore, our DL-based framework is a convenient high-throughput method that could serve as an effective QC strategy in future clinical biomanufacturing processes.

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Therapeutic Potential of Human Nasal Inferior Turbinate-Derived Stem Cells: Microarray Analysis of Multilineage Differentiation

Cited by 2 publications

References 21 publications

Characteristics of Human Nasal Turbinate Stem Cells under Hypoxic Conditions

Characteristics of Human Nasal Turbinate Stem Cells under Hypoxic Conditions

High throughput screening of mesenchymal stem cell lines using deep learning

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