An update on human pre- and peri-implantation development: a blueprint for blastoids

David, Laurent; Bruneau, Alexandre; Fréour, Thomas; Rivron, Nicolas; Van de Velde, Hilde

doi:10.1016/j.gde.2023.102125

Cited by 4 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Together, FFAVES and ESFW seek to identify genes that are highly structured within the data and are therefore more likely to be predictive of cell identity. Gene selection using these tools exposed the hitherto ambiguous inner cell mass (ICM) population in human pre-implantation embryo data ( Radley et al, 2023 ; David et al, 2023 ). However, FFAVES and ESFW operate on binarised data, such that genes are considered active or inactive in individual cells.…”

Section: Introductionmentioning

confidence: 99%

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

Radley,

Boeing,

Smith

2024

Development

View full text Add to dashboard Cite

Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after sub-setting to highly variable genes (HVGs). Here we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous entropy sort feature weighting (cESFW) outperforms HVG selection in distinguishing cell state specific genes. We apply cESFW to six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, cESFW generates a high-resolution embedding displaying coherent developmental progression from 8-cell to post-implantation stages and delineating 15 distinct cell states. The embedding highlights sequential lineage decisions during blastocyst development while unsupervised clustering identifies branch point populations obscured in previous analyses. The first branching region, where morula cells become specified for inner cell mass or trophectoderm, includes cells previously asserted to lack a developmental trajectory. We quantify the relatedness of different pluripotent stem cell cultures to distinct embryo cell types and identify marker genes of naïve and primed pluripotency. Finally, by revealing genes with dynamic lineage-specific expression we provide markers for staging progression from morula to blastocyst.

show abstract

Section: Introductionmentioning

confidence: 99%

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

Radley,

Boeing,

Smith

2024

Development

View full text Add to dashboard Cite

show abstract

Embryoids, models, embryos? We need to take a new look at legal norms concerning the beginning of organismic development

Denker

2023

Molecular Human Reproduction

View full text Add to dashboard Cite

deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Fan,

Li,

Jin

et al. 2024

Preprint

View full text Add to dashboard Cite

Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them suitable for high-throughput screening, which often involves analyzing vast numbers of images. However, automated methods for evaluating and characterizing blastoid morphology are still underdeveloped. We developed a deep-learning model capable of recognizing and classifying blastoid brightfield images into five distinct quality categories. The model processes 53.2 images per second with an average accuracy of 87%, without signs of overfitting or batch effects. By integrating a Confidence Rate (CR) metric, the accuracy was further improved to 97%, with low-CR images flagged for human review. In a comparison with human experts, the model matched their accuracy while significantly outperforming them in throughput. We demonstrate the value of the model in two real-world applications: (1) systematic assessment of the effect of lysophosphatidic acid (LPA) concentration on blastoid formation, and (2) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoids for drug screening. In the applications involving over 15,000 images, the model identified significant effects of LPA and DMSO, which may have been overlooked in manual assessments. The deepBlastoid model is publicly available and researchers can train their own model according to their imaging conditions and blastoid culture protocol. deepBlastoid thus offers a precise, automated approach for blastoid classification, with significant potential for advancing mechanism research, drug screening, and clinical in vitro fertilization (IVF) applications.

show abstract

An update on human pre- and peri-implantation development: a blueprint for blastoids

Cited by 4 publications

References 44 publications

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

Embryoids, models, embryos? We need to take a new look at legal norms concerning the beginning of organismic development

deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Contact Info

Product

Resources

About