Region-specific cellular and molecular basis of liver regeneration after acute pericentral injury.

Liver injuries often occur in a zonated manner. However, detailed regenerative responses to such zonal injuries at cellular and molecular levels remain largely elusive. By using a fate-mapping strain, Cyp2e1-DreER, to elucidate liver regeneration after acute pericentral injury, we found that pericentral regeneration is primarily compensated by the expansion of remaining pericentral hepatocytes, and secondarily by expansion of periportal hepatocytes. Employing single-cell RNA sequencing, spatial transcriptomics, immunostaining, and in vivo functional assays, we demonstrated that the upregulated expression of the mTOR/4E-BP1 axis and lactate dehydrogenase A in hepatocytes contributes to pericentral regeneration, while activation of transforming growth factor ß (TGF-ß1) signaling in the damaged area mediates fibrotic responses and inhibits hepatocyte proliferation. Inhibiting the pericentral accumulation of monocytes and monocyte-derived macrophages through an Arg-Gly-Asp (RGD) peptide-based strategy attenuates these cell-derived TGF-ß1 signalings, thus improving pericentral regeneration. Our study provides integrated and high-resolution spatiotemporal insights into the cellular and molecular basis of pericentral regeneration.

Single-cell and spatially resolved transcriptomics for liver biology.

Single-cell transcriptomics enables the identification of rare cell types and the inference of state transitions, whereas spatially resolved transcriptomics allows the quantification of cells and genes in the context of tissues. The recent progress in these new technologies is improving our understanding of the cell landscape and its roles in diseases. Here, we review key biological insights into liver homeostasis, development, regeneration, chronic liver disease, and cancer obtained from single-cell and spatially resolved transcriptomics. We highlight recent progress in the liver cell atlas that characterizes the comprehensive cellular composition; diversity and function; the spatial architecture such as liver zonation, cell communication, and proximity; the cell identity conversion and cell-specific alterations that are associated with liver pathology; and new therapeutic targets. We further discuss outstanding challenges, advanced experimental technologies, and computational methods that help to address these challenges.

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.

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.

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.

JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Comprehensive analysis of spatial architecture in primary liver cancer.

Heterogeneity is the major challenge for cancer prevention and therapy. Here, we first constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from seven patients. The progressive comparison of spatial tumor microenvironment (TME) characteristics from nontumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity, and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100-µm-wide cluster-cluster boundary contribute to maintaining intratumor architecture and the PROM1+ and CD47+ cancer stem cell niches are related to TME remodeling and tumor metastasis. Last, we proposed a TLS-50 signature to accurately locate tertiary lymphoid structures (TLSs) spatially and unveiled that the distinct composition of TLSs is shaped by their distance to tumor cells. Our study provides previous unknown insights into the diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

scCancer: a package for automated processing of single-cell RNA-seq data in cancer.

Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic level. Here, we develop an R package named scCancer, which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness), gene signatures and cell-cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.