Biology subjects

Salehin, N.

Publications and source records attributed to Salehin, N..

5 recordsLinked to original sources

bioRxiv2024

Systematic evaluation of blastoid models of early human development

Recent generation of stem cell-derived blastoids that recapitulate the architecture and cellular constitution of the human blastocyst offers an experimental model for the elucidation of the biology of early human development. To evaluate the fidelity of the blastoids for modelling human blastocysts, we first established a reference map of cell identity and lineage differentiation of the human blastocysts through the integration and curation of single-cell transcriptome data of ex vivo blastocysts profiled at a range of developmental timepoints in culture. We next use this reference map to assess the coverage and the authenticity of cell lineages and the progression of lineage differentiation of various blastoid models generated with different cellular sources and protocols. This reference map generated from this study enables the benchmarking of blastoids that may guide the optimization of the protocol for the generation of high-fidelity blastoid models that faithfully recapitulate the natural human blastocyst.

systems biology↗

bioRxiv2024

Characterization of the mesendoderm progenitors in the gastrulating mouse embryo

A population of putative mesendoderm progenitor cells that can contribute cellular descendants to both mesoderm and endoderm lineages is identified. These progenitor cells are localized to the anterior primitive streak and the adjacent epiblast of E7.0-E7.5 mid-to late-gastrula stage embryos. Lineage tracing in vivo revealed that putative mesendoderm progenitors that are marked by Mixl1 and Mesp1 activity contribute descendants to the endoderm layer. Analysis of the role of Mixl1 transcription factor in endoderm differentiation of the mouse epiblast stem cells revealed the choice for endoderm or mesoderm cell fate depends on the timing of activation of Mixl1 upon exit from pluripotency, suggesting Mixl1 function may underpin the divergence of the mesendoderm progenitor to mesoderm and endoderm lineages. The knowledge gained on the spatial, temporal, and lineage attribute of mesendoderm progenitors enriches our mechanistic understanding of germ layer allocation and endoderm differentiation of mesendoderm progenitor in embryonic development and lineage allocation of primed state pluripotent stem cells in vitro.

developmental biology↗

bioRxiv2024

Unraveling the Role of MIXL1 Activation in Endoderm Differentiation of Isogenic Human Induced Pluripotent Stem Cells

Human induced pluripotent stem cells (hiPSC) possess the ability to differentiate into a multitude of cell and tissue types but display heterogeneous propensity of differentiation into specific lineage. Characterization of the transcriptome of eleven hiPSC lines showed that activation of MIXL1 at the early stage of stem cell differentiation correlated with higher efficacy in generating definitive endoderm and advancing differentiation and maturation of endoderm derivatives. Enforced expression of MIXL1 in the endoderm-inefficient hiPSCs enhanced the propensity of endoderm differentiation, suggesting that modulation of key drivers of lineage differentiation can re-wire hiPSC to the desired lineage propensity to generate the requisite stem cell products.

developmental biology↗

bioRxiv2024

Deep Learning Based Models for Preimplantation Mouse and Human Development

The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell derived models. This avalanche of data about pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states and compare these to in vitro differentiation. Here we utilize a set of deep learning (DL) tools to integrate and classify multiple datasets. This allows for the definition of both mouse and human embryo cell types, lineages and states, thereby maximising the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large scale human organ atlases, but here we focus on the difficult to obtain and process material that spans early mouse, and in particular, human development. Using publicly available data for these stages, we test different deep learning approaches and develop both a model to classify cell types in an unbiased fashion and define the set of genes required to identify lineages, cell types and states. We have used our predictions to probe pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.

developmental biology↗

bioRxiv2021

FEATURE DETECTION TO SEGMENT CARDIOMYOCYTE NUCLEI FOR INVESTIGATING CARDIAC CONTRACTILITY

In vivo quantitative assessment of structural and functional biomarkers is essential for understanding pathophysiology and identifying novel therapies for congenital heart disorders. Cardiac defect analysis through fixed tissue and histology has offered revolutionary insights into the tissue architecture, but section thickness limits the tissue penetration. This study demonstrated the potential of Light Sheet Fluorescence Microscopy (LSFM) for analyzing in vivo 4D (3d + time) cardiac contractility. Furthermore, we have described the utility of an improved feature detection framework for localizing cardiomyocyte nuclei in the zebrafish atrium and ventricle. Using the Hessian Difference of Gaussian (HDoG) scale space in conjunction with the watershed algorithm, we were able to quantify a statistically significant increase in cardiomyocyte nuclei count across different developmental stages. Furthermore, we assessed individual volumes and surface areas for the cardiomyocyte nuclei in the ventricles innermost and outermost curvature during cardiac systole and diastole. Using the segmented nuclei volumes from the feature detection, we successfully performed local area ratio analysis to quantify the degree of deformation suffered by the outermost ventricular region compared to the innermost ventricular region. This paper focuses on the merits of our segmentation and demonstrates its efficacy for cell counting and morphology analysis in the presence of anisotropic illumination across the field-of-view (FOV).

bioengineering↗