Creating a semi-opened micro-cavity ovary through sacrificial microspheres as an in vitro model for discovering the potential effect of ovarian toxic agents

The bio-engineered ovary is an essential technology for treating female infertility.Especially the development of relevant in vitro models could be a critical step in a drug study.Herein,we develop a semi-opened culturing system(SOCS)strategy that maintains a 3D structure of follicles during the culture.Based on the SOCS,we further developed micro-cavity ovary(MCO)with mouse follicles by the microsphere-templated technique,where sacrificial gelatin microspheres were mixed with photo-crosslinkable gelatin methacryloyl(GelMA)to engineer a micro-cavity niche for follicle growth.The semi-opened MCO could support the follicle growing to the antral stage,secreting hormones,and ovulating cumulus-oocyte complex out of the MCO without extra manipulation.The MCO-ovulated oocyte exhibits a highly similar transcriptome to the in vivo counterpart(correlation of 0.97)and can be fertilized.Moreover,we found that a high ROS level could affect the cumulus expansion,which may result in anovulation disorder.The damage could be rescued by melatonin,but the end of cumulus expansion was 3h earlier than anticipation,validating that MCO has the potential for investigating ovarian toxic agents in vitro.We provide a novel approach for building an in vitro ovarian model to recapitulate ovarian functions and test chemical toxicity,suggesting it has the potential for clinical research in the future.

Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)data.Compared with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity network.We mathematically find the optimal expression patterns that can maximize the proposed scoring function.In comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation analysis.Also,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.