DOI: 10.5061/dryad.9s4mw6ms0
title: Raw fluorescent images of the stem cell based embryoids

description: In this study, post-implantation human epiblast and amnion development are
 modeled using a stem cell-based embryoid system. A dataset of 3,697
 fluorescent images, along with tissue, cavity, and cell masks, is
 generated from experimental data. A computational pipeline analyzes
 morphological and marker expression features, revealing key developmental
 processes such as tissue growth, cavity expansion, and cell
 differentiation. To uncover hidden developmental dynamics, a novel deep
 manifold learning framework is introduced. This framework uses an
 autoencoder to project embryoid images into a 20-dimensional latent space
 and models the dynamics using a mean-reverting stochastic process of mixed
 Gaussians. The approach accurately captures phenotypic changes observed at
 discrete experimental time points. Moreover, it enables the generation of
 artificial yet realistic embryoid images at finer temporal resolutions,
 providing deeper insights into the progression of early human development.
description: <strong>(1) Acquisition of fluorescent
 images </strong> The experimental protocol for
 the generation of embryoids is provided in the work: <em>Kejie Chen,
 Yi Zheng, Xufeng Xue, Yue Liu, Agnes M. Resto Irizarry, Huaijing Tang, and
 Jianping Fu(*). Branching development of early post-implantation human
 embryonic-like tissues in 3D stem cell culture. Biomaterials, 275:120898,
 2021.</em> Specifically, the isolated human
 embryoid stem cells (hESCs) were aggregated in the PDMS pyramidal wells
 and cultured for 6 hours using mTeSR1 medium (STEMCELL Technologies)
 supplemented with Y2763 (10 μM). hESC aggregates were then transferred
 into the 128-well plate for suspension culture in 5% Geltrex. A proportion
 of embryoids were collected from the well plates and stained with
 fluorescent markers including DAPI, NANOG, GATA3 and T. Four-channel
 fluorescent images were acquired using an Olympus IX81 fluorescent
 microscope at ×20 magnifaction.  <strong>(2)
 Image pre-processing</strong> First, a proportion
 of embryoid images was selected. Boundaries of the embryoid tissues,
 cavities, and single cells in these images (DAPI channel) were manually
 annotated using an open-source annotation software. The annotation results
 were automatically read using the custom Python scripts. Next, a
 Transformer-CNN model was developed for accurate segmentation of embryoid
 tissues, cavities, and single cells. Annotated images were used to train
 and validate the Transformer-CNN model. Then, the model was applied to all
 the images. The predicted masks were verified by annotators visualizing
 the raw images and the predicted masks image-by-image. Finally, the
 centers of embryoid tissues were moved to the center of the images for
 both raw images and masks. The longest axes of the embryoids were aligned
 vertically.
description: # Raw images of the stem cell based embryoids
 [https://doi.org/10.5061/dryad.9s4mw6ms0](https://doi.org/10.5061/dryad.9s4mw6ms0) ## Description of the data and file structure **1.Pre-processed images:** zip file (.zip) whose name is "preprocessed_dataset.zip" includes all the preprocess images. * .npz file is a compressed file using python numpy package. It can be opened using python script as ``` data = np.load('XXX.npz', allow_pickle=True) ``` * *data['raw_w1405']*, *data['raw_w2488']*, *data['raw_3561']*, *data['raw_w4640']* are pre-processed raw images for DAPI, GATA3, NANOG and T channels. The pre-processing steps only include cropping, adjusting image size and aligning the center of the embryoid to the image center. - *data['tissue_mask']*, *data['cell_mask']* are mask for tissues and cavities, and mask for single cells predicted by the Transformer-CNN model. * *data['cell_type_mask']* are masks for single cells, where the same type of cells are represented by the same mask value. The criteria used for identification of cell types is described in this Sci. Adv. work. ## Code/software To process the data, we provide open-source pipelines (algorithms) embryoid_developmental_dynamics-code.zip developed in the paper "Deep manifold learning reveals hidden developmental dynamics of a human embryo model". The pipelines (algorithms) can also be accessed in Github: [https://github.com/kejiechen/embryoid_developmental_dynamics](https://github.com/kejiechen/embryoid_developmental_dynamics/tree/master).