Biology subjects

Hickman, C.

Publications and source records attributed to Hickman, C..

3 recordsLinked to original sources

bioRxiv2026

Holotomography reveals biophysical remodeling of mouse oocytes during post-ovulatory aging

Routine assessment of oocyte state in reproductive biology and clinical embryology still relies largely on two-dimensional transmitted-light morphology, leaving the underlying three-dimensional intracellular organization largely unmeasured. Here, we use holotomography to reconstruct volumetric refractive index (RI) distributions in unlabeled live mouse oocytes and combine this imaging with sparse-annotation, AI-assisted compartment segmentation and multidimensional feature extraction across geometry, RI statistics, dry-mass density, and intracellular texture. Applied to an in vitro model of post-ovulatory aging, holotomography-derived biophysical profiles separated fresh and aged oocytes more strongly than two-dimensional brightfield-derived features, and feature-family ablation indicated that non-morphological descriptors contributed substantially to this separation. At the population level, aged oocytes showed coordinated ooplasmic remodeling, including reduced volume, increased RI heterogeneity, and increased dry-mass density while total dry mass was broadly conserved. These findings demonstrate a label-free quantitative framework for detecting post-ovulatory aging-associated biophysical remodeling in intact mammalian oocytes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/733271v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@c7257aorg.highwire.dtl.DTLVardef@6d92f0org.highwire.dtl.DTLVardef@1c9f7eborg.highwire.dtl.DTLVardef@b36637_HPS_FORMAT_FIGEXP M_FIG C_FIG

Developmental Biology↗

bioRxiv2025

Noncoding and Coding Mechanisms of Aging Heart Failure with Preserved Ejection Fraction with Thyroid Dysfunction

Heart Failure with preserved Ejection Fraction (HFpEF) is a heterogeneous geriatric syndrome with complex pathophysiology and comorbidities. Long noncoding RNAs (lncRNAs) account for a large majority of the functional mammalian transcriptome and act as key regulators in complex physiological and pathological processes. However, the role of lncRNAs and the development of thyroid dysfunction in aging HFpEF is not clear. We investigated the ZSF1 model in both early and more severe stages of HFpEF (5-, 13- and 20-months old [mo]). We assessed molecular, biochemical, and pathophysiological roles involving lncRNAs, mRNAs, and inflammatory markers. Thyroid hormone (TH) immuno-sorbent assays showed significant decreases in serum T3 levels in 5-mo and serum T4 levels in 5-mo and 13-mo obese HFpEF groups compared to lean controls. Morphometric analyses showed significant increases in heart and LV weights in obese HFpEF rats indicating cardiac hypertrophy. LncRNA microarray and RT-qPCR revealed that three key lncRNAs were significantly increased in 13-mo obese HFpEF but not in 5-mo obese HFpEF left ventricles compared to the ZSF1 lean controls. Microarray analyses showed that Sik1 mRNA was significantly upregulated and Anxa13 was downregulated in early obese HFpEF hearts compared to the lean controls. Additionally, we also uncovered previously unreported tissue and serum inflammatory cytokine profiles in early and late HFpEF. This study has identified key novel lncRNA and inflammatory markers in early and late hypothyroid HFpEF. Further studies may help in better understanding and development of diagnostic and therapeutic targets for HFpEF that presents with severe morbidity and mortality.

genomics↗

bioRxiv2018

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.\n\nTo test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images. Using clinical data for 2,182 embryos, we then created a decision tree that integrates clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.\n\nIn conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

bioinformatics↗