Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE178858 | GEO | 2021/07/02
REPOSITORIES: GEO
Similar Datasets
Project description:Human pluripotent stem cell (hPSC)-derived organoids and cells have similar characteristics to human organs and tissues. Thus, in vitro human organoids and cells serve as a superior alternative to conventional cell lines and animal models in drug development and regenerative medicine. For a simple and reproducible analysis of the quality of organoids and cells to compensate for the shortcomings of existing experimental validation studies, a quantitative evaluation method should be developed. Here, using the GTEx database, we construct a quantitative calculation system to assess similarity to the human organs. To evaluate our system, we generate hPSC-derived organoids and cells, and detected organ similarity. To facilitate the access of our system by researchers, we develop a web-based user interface presenting similarity to the appropriate organs as percentages. Thus, this program could provide valuable information for the generation of high-quality organoids and cells and a strategy to guide proper lineage-oriented differentiation.
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Project description:We constructed aquantitative calculation system (organ-specific panels and calculation algorithm) to assess thesimilarity to the human lung, stomach, and heart and confirmed the algorithm using in-houseRNA-seq data (total RNA from 20 tissues). To evaluate our system, we generated hPSC-derived lung organoids, gastric organoids, and cardiomyocytes and detected 33.4%, 51.7%, and 83.4% similarity, respectively, to the corresponding human target organs.
2021-07-02 | GSE178857 | GEO
Project description:We constructed aquantitative calculation system (organ-specific panels and calculation algorithm) to assess thesimilarity to the human lung, stomach, and heart and confirmed the algorithm using in-houseRNA-seq data (total RNA from 20 tissues). To evaluate our system, we generated hPSC-derived lung organoids, gastric organoids, and cardiomyocytes and detected 33.4%, 51.7%, and 83.4% similarity, respectively, to the corresponding human target organs.
2021-07-02 | GSE178855 | GEO
Project description:Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells
| PRJNA741246 | ENA
Project description:The discoveries of human pluripotent stem cells (PSCs) including embryonic stem cells and induced pluripotent stem cells (iPSCs) has led to dramatic advances in our understanding of basic human developmental and cell biology and has also been applied to research aimed at drug discovery and development of disease treatments. Research using human PSCs has been largely dominated by studies using two-dimensional cultures. In the past decade, however, ex vivo tissue "organoids," which have a complex and functional three-dimensional structure similar to human organs, have been created from PSCs and are now being used in various fields. Organoids created from PSCs are composed of multiple cell types and are valuable models with which it is better to reproduce the complex structures of living organs and study organogenesis through niche reproduction and pathological modeling through cell-cell interactions. Organoids derived from iPSCs, which inherit the genetic background of the donor, are helpful for disease modeling, elucidation of pathophysiology, and drug screening. Moreover, it is anticipated that iPSC-derived organoids will contribute significantly to regenerative medicine by providing treatment alternatives to organ transplantation with which the risk of immune rejection is low. This review summarizes how PSC-derived organoids are used in developmental biology, disease modeling, drug discovery, and regenerative medicine. Highlighted is the liver, an organ that play crucial roles in metabolic regulation and is composed of diverse cell types.
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Project description:The brainstem is a posterior region of the brain, composed of three parts, midbrain, pons, and medulla oblongata. It is critical in controlling heartbeat, blood pressure, and respiration, all of which are life-sustaining functions, and therefore, damages to or disorders of the brainstem can be lethal. Brain organoids derived from human pluripotent stem cells (hPSCs) recapitulate the course of human brain development and are expected to be useful for medical research on central nervous system disorders. However, existing organoid models are limited in the extent hPSCs recapitulate human brain development and hence are not able to fully elucidate the diseases affecting various components of the brain such as brainstem. Here, we developed a method to generate human brainstem organoids (hBSOs), containing midbrain/hindbrain progenitors, noradrenergic and cholinergic neurons, dopaminergic neurons, and neural crest lineage cells. Single-cell RNA sequence (scRNA-seq) analysis, together with evidence from proteomics and electrophysiology, revealed that the cellular population in these organoids was similar to that of the human brainstem, which raises the possibility of making use of hBSOs in investigating central nervous system disorders affecting brainstem and in efficient drug screenings.
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Project description:Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R 0), body-weight (BW) normalized central elimination rate (K el/BW(-0.25)) and central volume (V c/BW(1.0)). AAFE of less than three-fold was estimated for the binding affinity constant (K D). The other four parameters, i.e., complex turnover rate (K int), target turnover rate (K deg), central to peripheral distribution rate constant (K pt) and peripheral to central rate constant (K tp) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.
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Project description:The cornea is the transparent outermost surface of the eye, consisting of a stratified epithelium, a collagenous stroma and an innermost single-cell layered endothelium and providing 2/3 of the refractive power of the eye. Multiple diseases of the cornea arise from genetic defects where the ultimate phenotype can be influenced by cross talk between the cell types and the extracellular matrix. Cell culture modeling of diseases can benefit from cornea organoids that include multiple corneal cell types and extracellular matrices. Here we present human iPS cell-derived organoids through sequential rounds of differentiation programs. These organoids share features of the developing cornea, harboring three distinct cell types with expression of key epithelial, stromal and endothelial cell markers. Cornea organoid cultures provide a powerful 3D model system for investigating corneal developmental processes and their disruptions in diseased conditions.
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Project description:Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans-using computationally scaled data from multiple fluidically linked two-channel organ chips-predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials.
| S-EPMC8011576 | biostudies-literature
Project description:tRNA fragments (tRFs) are a novel class of small RNAs comparable to the size and function of miRNAs. We and others have shown that tRFs are generally Dicer independent, can be found in abundance in the miRNA effector protein Ago, and can repress expression of specific genes that have complementarity to their 5’ seed-sequences. Given that this greatly expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, using Gene Ontology analysis, we provide evidence that tRF-3009a targets may be involved in neural development. These improvements to tRF target prediction further our understanding of tRF function broadly across species and tRF types, and provide avenues for testing novel roles for tRFs in biology.
2022-05-16 | GSE189510 | GEO