scholarly journals Hypoxic Conditions Promote the Angiogenic Potential of Human Induced Pluripotent Stem Cell-Derived Extracellular Vesicles

2021 ◽  
Vol 22 (8) ◽  
pp. 3890
Author(s):  
André Cronemberger Andrade ◽  
Martin Wolf ◽  
Heide-Marie Binder ◽  
Fausto Gueths Gomes ◽  
Felix Manstein ◽  
...  
Keyword(s):  
Stem Cell ◽  
Extracellular Vesicles ◽  
Pluripotent Stem Cell ◽  
Large Scale ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Communication ◽  
Paracrine Factors ◽  
Angiogenic Potential ◽  
Oxygen Conditions ◽  
Induced Pluripotent

Stem cells secrete paracrine factors including extracellular vesicles (EVs) which can mediate cellular communication and support the regeneration of injured tissues. Reduced oxygen (hypoxia) as a key regulator in development and regeneration may influence cellular communication via EVs. We asked whether hypoxic conditioning during human induced pluripotent stem cell (iPSC) culture effects their EV quantity, quality or EV-based angiogenic potential. We produced iPSC-EVs from large-scale culture-conditioned media at 1%, 5% and 18% air oxygen using tangential flow filtration (TFF), with or without subsequent concentration by ultracentrifugation (TUCF). EVs were quantified by tunable resistive pulse sensing (TRPS), characterized according to MISEV2018 guidelines, and analyzed for angiogenic potential. We observed superior EV recovery by TFF compared to TUCF. We confirmed hypoxia efficacy by HIF-1α stabilization and pimonidazole hypoxyprobe. EV quantity did not differ significantly at different oxygen conditions. Significantly elevated angiogenic potential was observed for iPSC-EVs derived from 1% oxygen culture by TFF or TUCF as compared to EVs obtained at higher oxygen or the corresponding EV-depleted soluble factor fractions. Data thus demonstrate that cell-culture oxygen conditions and mode of EV preparation affect iPSC-EV function. We conclude that selecting appropriate protocols will further improve production of particularly potent iPSC-EV-based therapeutics.

scholarly journals Induced Pluripotent Stem Cell (iPSC)–Derived Extracellular Vesicles Are Safer and More Effective for Cardiac Repair Than iPSCs

2018 ◽  
Vol 122 (2) ◽  
pp. 296-309 ◽  
Author(s):  
Marta Adamiak ◽  
Guangming Cheng ◽  
Sylwia Bobis-Wozowicz ◽  
Lin Zhao ◽  
Sylwia Kedracka-Krok ◽  
...  
Keyword(s):  
Stem Cell ◽  
Extracellular Vesicles ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cardiac Repair ◽  
Induced Pluripotent

scholarly journals Extracellular Vesicles Isolated from Human Induced Pluripotent Stem Cell-Derived Neurons Contain a Transcriptional Network

2020 ◽  
Vol 45 (7) ◽  
pp. 1711-1728 ◽  
Author(s):  
David A. Hicks ◽  
Alys C. Jones ◽  
Nicola J. Corbett ◽  
Kate Fisher ◽  
Stuart M. Pickering-Brown ◽  
...  
Keyword(s):  
Stem Cell ◽  
Extracellular Vesicles ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Transcriptional Network ◽  
Induced Pluripotent

Induced pluripotent stem cell‐derived extracellular vesicles: A novel approach for cell‐free regenerative medicine

2018 ◽  
Vol 234 (6) ◽  
pp. 8455-8464 ◽  
Author(s):  
Behnaz Taheri ◽  
Masoud Soleimani ◽  
Sedigheh Fekri Aval ◽  
Elaheh Esmaeili ◽  
Zahra Bazi ◽  
...  
Keyword(s):  
Stem Cell ◽  
Regenerative Medicine ◽  
Extracellular Vesicles ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Novel Approach ◽  
Induced Pluripotent

scholarly journals Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Henry Joutsijoki ◽  
Markus Haponen ◽  
Jyrki Rasku ◽  
Katriina Aalto-Setälä ◽  
Martti Juhola
Keyword(s):  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Distance Measure ◽  
Classification Tree ◽  
Induced Pluripotent Stem Cell ◽  
Classification Problems ◽  
Induced Pluripotent ◽  
Error Correcting Output Codes

The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of themk-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design andk-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

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