Comparing the transcriptome of developing native and iPSC-derived mouse retinae by single cell RNA sequencing

Georges, Anouk; Lavergne, Arnaud; Mandai, Michiko; Lepiemme, Fanny; Karim, Latifa; Demeulenaere, Loïc; Aguilar, Diego; Schyns, Michaël; Nguyen, Laurent; Rakic, Jean-Marie; Takahashi, Masayo; Georges, Michel; Takeda, Hitoshi

doi:10.1038/s41598-023-28429-y

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Preliminary studies have shown that embryonic stem cells can be directed to differentiate into lung progenitor cells. This study laid the foundation for the subsequent induction of more mature lung organoids 13 …”

Section: Introductionmentioning

confidence: 94%

“…This study laid the foundation for the subsequent induction of more mature lung organoids. 13 As an animal model, porcine characteristics are closer to human than mice; however, there is no established lung organoid model in pigs. The normal process of lung development in vivo involves the progression from definitive endoderm and abdominal foregut endoderm to thyroid or lung development, which requires specific factors for lung specification.…”

Section: Introductionmentioning

confidence: 99%

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Induction of lung progenitor cell‐like organoids by porcine pluripotent stem cells

Yang,

Wu,

Wang

et al. 2024

The FASEB Journal

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Organoids are in vitro 3D models that are generated using stem cells to study organ development and regeneration. Despite the extensive research on lung organoids, there is limited information on pig lung cell generation or development. Here, we identified five epithelial cell types along with their characteristic markers using scRNA‐seq. Additionally, we found that NKX2.1 and FOXA2 acted as the crucial core transcription factors in porcine lung development. The presence of SOX9/SOX2 double‐positive cells was identified as a key marker for lung progenitor cells. The Monocle algorithm was used to create a pseudo‐temporal differentiation trajectory of epithelial cells, leading to the identification of signaling pathways related to porcine lung development. Moreover, we established the differentiation method from porcine pluripotent stem cells (pPSCs) to SOX17+FOXA2+ definitive endoderm (DE) and NKX2.1+FOXA2+CDX2− anterior foregut endoderm (AFE). The AFE is further differentiated into lung organoids while closely monitoring the differentiation process. We showed that NKX2.1 overexpression facilitated the induction of lung organoids and supported subsequent lung differentiation and maturation. This model offers valuable insights into studying the interaction patterns between cells and the signaling pathways during the development of the porcine lung.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Induction of lung progenitor cell‐like organoids by porcine pluripotent stem cells

Yang,

Wu,

Wang

et al. 2024

The FASEB Journal

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“…For example, pooling of datasets generated in different studies enabled cross-condition comparisons 1,2 , population-level analysis 3,4 , and revealed evolutionary relationships between individual cell types 5 . The selection of pre-clinical models, such as organoids and animals, and the characterization of their limitations likewise rely on comparison with human tissues [6][7][8][9][10][11][12][13][14] . Similarly, the selection of the optimal sequencing protocols requires a comparison of datasets generated with different protocols 15,16 .…”

Section: Introductionmentioning

confidence: 99%

Integrating single-cell RNA-seq datasets with substantial batch effects

Hrovatin,

Moinfar,

Zappia

et al. 2023

Preprint

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Computational methods for integrating scRNA-seq datasets often struggle to harmonize datasets with substantial differences driven by technical or biological variation, such as between different species, organoids and primary tissue, or different scRNA-seq protocols, including single-cell and single-nuclei. Given that many widely adopted and scalable methods are based on conditional variational autoencoders (cVAE), we hypothesize that machine learning interventions to standard cVAEs can help improve batch effect removal while potentially preserving biological variation more effectively. To address this, we assess four strategies applied to commonly used cVAE models: the previously proposed Kullback–Leibler divergence (KL) regularization tuning and adversarial learning, as well as cycle-consistency loss (previously applied to multi-omic integration) and the multimodal variational mixture of posteriors prior (VampPrior) that has not yet been applied to integration. We evaluated performance in three data settings, namely cross-species, organoid-tissue, and cell-nuclei integration. Cycle-consistency and VampPrior improved batch correction while retaining high biological preservation, with their combination further increasing performance. While adversarial learning led to the strongest batch correction, its preservation of within-cell type variation did not match that of VampPrior or cycle-consistency models, and it was also prone to mixing unrelated cell types with different proportions across batches. KL regularization strength tuning had the least favorable performance, as it jointly removed biological and batch variation by reducing the number of effectively used embedding dimensions. Based on our findings, we recommend the adoption of the VampPrior in combination with the cycle-consistency loss for integrating datasets with substantial batch effects.

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