Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

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The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell–cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand–receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

Background Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. Results We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. Conclusions Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub (https://github.com/wanglabtongji/CCI).

Graphical Abstract Highlights d E2F expression during cell division, differentiation, and quiescence is measured in vivo d E2F3A, E2F8, and E2F4 accumulate sequentially in the nucleus of cycling cells d E2F3A-4 nuclear accumulation controls gene expression during cell-cycle exit d Deep learning tools are applied to nuclear segmentation of complex mammalian tissues In Brief The study of E2Fs in vivo has been challenging. Cuitiñ o et al. reconstruct the spatiotemporal expression of E2F activators (E2F3A) and canonical (E2F4) and atypical (E2F8) repressors during the mammalian cell cycle and propose that orchestrated accumulation of different E2F combinations control gene expression in proliferating (E2F3A-8-4) and differentiating (E2F3A-4) cells. SUMMARYOrchestrating cell-cycle-dependent mRNA oscillations is critical to cell proliferation in multicellular organisms. Even though our understanding of cellcycle-regulated transcription has improved significantly over the last three decades, the mechanisms remain untested in vivo. Unbiased transcriptomic profiling of G 0 , G 1 -S, and S-G 2 -M sorted cells from FUCCI mouse embryos suggested a central role for E2Fs in the control of cell-cycle-dependent gene expression. The analysis of gene expression and E2F-tagged knockin mice with tissue imaging and deep-learning tools suggested that post-transcriptional mechanisms universally coordinate the nuclear accumulation of E2F activators (E2F3A) and canonical (E2F4) and atypical (E2F8) repressors during the cell cycle in vivo. In summary, we mapped the spatiotemporal expression of sentinel E2F activators and canonical and atypical repressors at the singlecell level in vivo and propose that two distinct E2F modules relay the control of gene expression in cells actively cycling (E2F3A-8-4) and exiting the cycle (E2F3A-4) during mammalian development.

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

Water reclamation and ecological reuse is gradually becoming a popular solution to address the high pollutant loads and insufficient ecological flow of many urban rivers. However, emerging contaminants in water reuse system and associated human health and ecological risks need to be assessed. This study determined the occurrence and human health and ecological risk assessments of 35 emerging contaminants during one year, including 5 types of persistent organic pollutants (POPs), 5 pharmaceutical and personal care products (PPCPs), 7 endocrine disrupting chemicals (EDCs) and 18 disinfection by-products (DBPs), in a wastewater treatment plant (WWTP) and receiving rivers, as well as an unimpacted river for comparison. Results showed that most of PPCPs and EDCs, especially antibiotics, triclosan, estrogens and bisphenol A, occurred frequently at relatively high concentrations, and they were removed from 20.5% to 88.7% with a mean of 58.9% via WWTP. The highest potential noncarcinogenic and carcinogenic risks in different reuse scenarios were assessed using maximal detected concentrations, all below the acceptable risk limits, with the highest total combined risk value of 9.21 × 10 −9 and 9.98 × 10 −7 , respectively. Ecological risk assessment was conducted using risk quotient (RQ) method and indicated that several PPCPs, EDCs and haloacetonitriles (HANs) pose high risk (RQ N 1) to aquatic ecology in the rivers, with the highest RQ up to 83.8. The study suggested that ecological risks need to be urgently addressed by updating and optimizing the process in WWTPs to strengthen the removal efficiencies of emerging contaminants.