scholarly journals A translational kidney organoid system bolsters human relevance of clinical development candidate

2019 ◽  
Author(s):  
Amy Westerling-Bui ◽  
Thomas W. Soare ◽  
Srinivasan Venkatachalan ◽  
Michael DeRan ◽  
Eva Maria Fast ◽  
...  
Keyword(s):  
Quality Control ◽  
Drug Discovery ◽  
Animal Studies ◽  
Channel Blocker ◽  
Clinical Development ◽  
Rna Seq ◽  
Human System ◽  
Functional Studies ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractA major challenge in drug discovery is gaining confidence in the human relevance of pre-clinical animal studies. While human iPSC-derived organoids offer exciting opportunities to address this, concerns about applicability and scalability remain. Here, we report a high-throughput organoid platform for assessment of kidney disease targeting compounds in a human system. We confirmed platform reproducibility by single cell RNA-Seq (scRNA-Seq) and derived a NanoString panel for efficient quality control (QC). Organoid transplantation in rats for 2 to 4 weeks promoted organoid maturation and vascularization. In functional studies, cyclosporine A (CsA) and GFB-887, a novel TRPC5 channel blocker, protected kidney organoids from injury. Pharmacodynamic studies with GFB-887 delivered orally to rats were also successfully performed in human transplanted organoids. These data show how human organoids can deliver confidence in taking development candidate compounds to the clinic, fulfilling their promise to revolutionize drug discovery.

scholarly journals Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Katherine Vernon ◽  
Nareh Sahakian ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Human Kidney ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Time Points ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

scholarly journals Kidney organoid reproducibility across multiple human iPSC lines and diminished off target cells after transplantation revealed by single cell transcriptomics

2019 ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Nareh Sahakian ◽  
Katherine Vernon ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we used single cell RNA-Seq (scRNA-Seq) to profile 415,775 cells to show that organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes were largely reproducible across iPSC lines, time points, protocols, and replicates, cell proportions were variable between different iPSC lines. Off-target cell proportions were the most variable. Prolonged in vitro culture did not alter cell types, but organoid transplantation under the mouse kidney capsule diminished off-target cells. Our work shows how scRNA-seq can help score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

scholarly journals High throughput single cell RNA-seq of developing mouse kidney and human kidney organoids reveals a roadmap for recreating the kidney

2017 ◽  
Author(s):  
Alexander N. Combes ◽  
Belinda Phipson ◽  
Luke Zappia ◽  
Kynan T. Lawlor ◽  
Pei Xuan Er ◽  
...  
Keyword(s):  
Single Cell ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Population Based ◽  
Mouse Kidney ◽  
Rna Seq ◽  
Kidney Morphogenesis ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractRecent advances in our capacity to differentiate human pluripotent stem cells to human kidney tissue are moving the field closer to novel approaches for renal replacement. Such protocols have relied upon our current understanding of the molecular basis of mammalian kidney morphogenesis. To date this has depended upon population based-profiling of non-homogenous cellular compartments. In order to improve our resolution of individual cell transcriptional profiles during kidney morphogenesis, we have performed 10x Chromium single cell RNA-seq on over 6000 cells from the E18.5 developing mouse kidney, as well as more than 7000 cells from human iPSC-derived kidney organoids. We identified 16 clusters of cells representing all major cell lineages in the E18.5 mouse kidney. The differentially expressed genes from individual murine clusters were then used to guide the classification of 16 cell clusters within human kidney organoids, revealing the presence of distinguishable stromal, endothelial, nephron, podocyte and nephron progenitor populations. Despite the congruence between developing mouse and human organoid, our analysis suggested limited nephron maturation and the presence of ‘off target’ populations in human kidney organoids, including unidentified stromal populations and evidence of neural clusters. This may reflect unique human kidney populations, mixed cultures or aberrant differentiation in vitro. Analysis of clusters within the mouse data revealed novel insights into progenitor maintenance and cellular maturation in the major renal lineages and will serve as a roadmap to refine directed differentiation approaches in human iPSC-derived kidney organoids.

scholarly journals Revisiting The Role of Notch in Nephron Segmentation: Notch is required for proximal, not distal fate selection during mouse and human nephrogenesis

2021 ◽  
Author(s):  
Kathryn Duvall ◽  
Lauren Bice ◽  
Alison J Perl ◽  
Naomi Pode Shakked ◽  
Praneet Chaturvedi ◽  
...  
Keyword(s):  
Notch Signaling ◽  
Human Kidney ◽  
Distal Tubule ◽  
Rna Seq ◽  
Early Markers ◽  
Nephron Segmentation ◽  
Human Ipsc ◽  
Kidney Organoids ◽  
In Cells

Notch signaling promotes maturation of nephron epithelia, but its proposed contribution to nephron segmentation into proximal and distal domains has been called into doubt. We leveraged single cell and bulk RNA-seq, quantitative immunofluorescent lineage/fate tracing, and genetically modified human iPSC to revisit this question in developing mouse kidneys and human kidney organoids. We confirmed that Notch signaling is needed for maturation of all nephron lineages, and thus mature lineage markers fail to detect a fate bias. By contrast, early markers identified a distal fate bias in cells lacking Notch2, and a concomitant increase in early proximal and podocyte fates in cells expressing hyperactive Notch1 was observed. Orthogonal support for a conserved role for Notch signaling in the distal/proximal axis segmentation is provided by the ability of Nicastrin-deficient hiPSCs-derived organoids to differentiate into TFA2B+ distal tubule and CDH1 connecting segment progenitors, but not into HNF4A+ or LTL+ proximal progenitors.

scholarly journals Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cheng-Cheng Deng ◽  
Yong-Fei Hu ◽  
Ding-Heng Zhu ◽  
Qing Cheng ◽  
Jing-Jing Gu ◽  
...  
Keyword(s):  
Single Cell ◽  
Skin Disease ◽  
Skin Diseases ◽  
Rna Seq ◽  
Fibrotic Disease ◽  
Functional Studies ◽  
Fibrotic Diseases ◽  
Fibroblast Heterogeneity ◽  
Keloid Fibroblasts ◽  
Excessive Accumulation

AbstractFibrotic skin disease represents a major global healthcare burden, characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix. Fibroblasts are found to be heterogeneous in multiple fibrotic diseases, but fibroblast heterogeneity in fibrotic skin diseases is not well characterized. In this study, we explore fibroblast heterogeneity in keloid, a paradigm of fibrotic skin diseases, by using single-cell RNA-seq. Our results indicate that keloid fibroblasts can be divided into 4 subpopulations: secretory-papillary, secretory-reticular, mesenchymal and pro-inflammatory. Interestingly, the percentage of mesenchymal fibroblast subpopulation is significantly increased in keloid compared to normal scar. Functional studies indicate that mesenchymal fibroblasts are crucial for collagen overexpression in keloid. Increased mesenchymal fibroblast subpopulation is also found in another fibrotic skin disease, scleroderma, suggesting this is a broad mechanism for skin fibrosis. These findings will help us better understand skin fibrotic pathogenesis, and provide potential targets for fibrotic disease therapies.

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

Introduction to RNA-Seq and its Applications to Drug Discovery and Development

2014 ◽  
Vol 75 (5) ◽  
pp. 324-330 ◽  
Author(s):  
Zainab Khatoon ◽  
Bryan Figler ◽  
Hui Zhang ◽  
Feng Cheng
Keyword(s):  
Drug Discovery ◽  
Rna Seq ◽  
Drug Discovery And Development

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

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