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Arcanjo, R. B.

Publications and source records attributed to Arcanjo, R. B..

2 recordsLinked to original sources

Primed bovine embryonic stem cell lines can be derived at diverse stages of blastocyst development with similar efficiency and molecular characteristics

In this study, we established bovine embryonic stem cell (bESC) lines from early (eBL) and full (BL) blastocysts to determine the efficiency of bESC derivation from an earlier embryonic stage and compare the characteristics of the resulting lines. Using established medium and protocols for derivation of primed bESCs from expanded blastocysts, we derived bESC lines from eBLs and BLs with the same efficiency (4/12 each, 33%). Regardless of original blastocyst stage, bESC lines had a similar phenotype, including differentiation capacity, stable karyotype, and pluripotency marker expression over feeder-free transition and long-term culture. Transcriptome and functional analyses indicated that eBL- and BL-derived lines were in primed pluripotency. We additionally compared RNA-sequencing data from our lines to bovine embryos and stem cells from other recent reports, finding that base medium was the predominant source of variation among cell lines. In conclusion, our results show that indistinguishable bESC lines can be readily derived from eBL and BL, widening the pool of embryos available for bESC establishment. Finally, our investigation points to sources of variation in cell phenotype among recently reported bESC conditions, opening the door to future studies investigating the impact of factors aside from signaling molecules on ESC derivation, maintenance, and performance. SUMMARY STATEMENTPrimed bovine embryonic stem cell lines can be readily established from early blastocysts and are indistinguishable from full blastocyst-derived lines, widening the pool of embryos available for stem cell derivation.

cell biology↗

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

bioengineering↗