Integrating multiple references for single-cell assignment.

Efficient single-cell assignment is essential for single-cell sequencing data analysis. With the explosive growth of single-cell sequencing data, multiple single-cell sequencing data sources are available for the same kind of tissue, which can be integrated to further improve single-cell assignment; however, an efficient integration strategy is still lacking due to the great challenges of data heterogeneity existing in multiple references. To this end, we present mtSC, a flexible single-cell assignment framework that integrates multiple references based on multitask deep metric learning designed specifically for cell type identification within tissues with multiple single-cell sequencing data as references. We evaluated mtSC on a comprehensive set of publicly available benchmark datasets and demonstrated its state-of-the-art effectiveness for integrative single-cell assignment with multiple references.

Elucidation of the dimeric interplay of dual MRAP2 proteins in the zebrafish.

The melanocortin receptor accessory protein 2 (MRAP2) plays an essential role in the regulation of metabolic homeostasis and deletion of which results in severe obesity syndrome in mice and human. Mammalian MRAP2 is recognized as an endogenous physiological mediator through the potentiation of the MC4R signaling in vivo. Two isoforms of MRAP2 are identified in zebrafish genome, zMRAP2a and zMRAP2b. However, the mechanism of assembling dual topology and the regulatory roles of each complex on the melanocortin cascades remains unclear. In this study, we showed the bidirectional homo- and hetero-dimeric topologies of two zebrafish MRAP2 isoforms on the plasma membrane. Orientation fixed chimeric proteins could affect the trafficking and pharmacological properties of zMC4R signaling. Reciprocal replacement of zMRAP2a and zMRAP2b proteins elucidated the major participation of the carboxyl terminal as the functional domain for modulating zMC4R signaling. Our findings revealed the complex and dynamic conformational regulation of dual zebrafish MRAP2 proteins in vitro.

Differential analysis of chromatin accessibility and histone modifications for predicting mouse developmental enhancers.

Enhancers are distal cis-regulatory elements that modulate gene expression. They are depleted of nucleosomes and enriched in specific histone modifications; thus, calling DNase-seq and histone mark ChIP-seq peaks can predict enhancers. We evaluated nine peak-calling algorithms for predicting enhancers validated by transgenic mouse assays. DNase and H3K27ac peaks were consistently more predictive than H3K4me1/2/3 and H3K9ac peaks. DFilter and Hotspot2 were the best DNase peak callers, while HOMER, MUSIC, MACS2, DFilter and F-seq were the best H3K27ac peak callers. We observed that the differential DNase or H3K27ac signals between two distant tissues increased the area under the precision-recall curve (PR-AUC) of DNase peaks by 17.5-166.7% and that of H3K27ac peaks by 7.1-22.2%. We further improved this differential signal method using multiple contrast tissues. Evaluated using a blind test, the differential H3K27ac signal method substantially improved PR-AUC from 0.48 to 0.75 for predicting heart enhancers. We further validated our approach using postnatal retina and cerebral cortex enhancers identified by massively parallel reporter assays, and observed improvements for both tissues. In summary, we compared nine peak callers and devised a superior method for predicting tissue-specific mouse developmental enhancers by reranking the called peaks.

Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy.

The rapid accumulation of large-scale single-cell RNA-seq datasets from multiple institutions presents remarkable opportunities for automatically cell annotations through integrative analyses. However, the privacy issue has existed but being ignored, since we are limited to access and utilize all the reference datasets distributed in different institutions globally due to the prohibited data transmission across institutions by data regulation laws. To this end, we present scPrivacy, which is the first and generalized automatically single-cell type identification prototype to facilitate single cell annotations in a data privacy-preserving collaboration manner. We evaluated scPrivacy on a comprehensive set of publicly available benchmark datasets for single-cell type identification to stimulate the scenario that the reference datasets are rapidly generated and distributed in multiple institutions, while they are prohibited to be integrated directly or exposed to each other due to the data privacy regulations, demonstrating its effectiveness, time efficiency and robustness for privacy-preserving integration of multiple institutional datasets in single cell annotations.

Personalized tumor combination therapy optimization using the single-cell transcriptome.

BACKGROUND: The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. METHODS: Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. RESULTS: We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. CONCLUSIONS: Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .

A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data.

Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

Detection of ovarian cancer using plasma cell-free DNA methylomes.

BACKGROUND: Ovarian cancer (OC) is a highly lethal gynecologic cancer, and it is hard to diagnose at an early stage. Clinically, there are no ovarian cancer-specific markers for early detection. Here, we demonstrate the use of cell-free DNA (cfDNA) methylomes to detect ovarian cancer, especially the early-stage OC. EXPERIMENTAL DESIGN: Plasma from 74 epithelial ovarian cancer patients, 86 healthy volunteers, and 20 patients with benign pelvic masses was collected. The cfDNA methylomes of these samples were generated by cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). The differentially methylated regions (DMRs) were identified by the contrasts between tumor and non-tumor groups, and the discrimination performance was evaluated with the iterative training and testing method. RESULTS: The DMRs identified for cfDNA methylomes can well discriminate tumor groups and non-tumor groups (ROC values from 0.86 to 0.98). The late-stage top 300 DMRs are more late-stage-specific and failed to detect early-stage OC. However, the early-stage markers have the potential to discriminate all-stage OCs from non-tumor samples. CONCLUSIONS: This study demonstrates that cfDNA methylomes generated with cfMeDIP-seq could be used to identify OC-specific biomarkers for OC, especially early OC detection. To detect early-stage OC, the biomarkers should be directly identified from early OC plasma samples rather than mix-stage ones. Further exploration of DMRs from a k larger early-stage OC cohort is warranted.

Identification of MRAP protein family as broad-spectrum GPCR modulators.

BACKGROUND: The melanocortin receptor accessory proteins (MRAP1 and MRAP2) are well-known endocrine regulators for the trafficking and signalling of all five melanocortin receptors (MC1R-MC5R). The observation of MRAP2 on regulating several non-melanocortin G protein-coupled receptors (GPCRs) has been sporadically reported, whereas other endogenous GPCR partners of the MRAP protein family are largely unknown. METHODS: Here, we performed single-cell transcriptome analysis and drew a fine GPCR blueprint and MRAPs-associated network of two major endocrine organs, the hypothalamus and adrenal gland at single-cell resolution. We also integrated multiple bulk RNA-seq profiles and single-cell datasets of human and mouse tissues, and narrowed down a list of 48 GPCRs with strong endogenous co-expression correlation with MRAPs. RESULTS: 36 and 46 metabolic-related GPCRs were consequently identified as novel interacting partners of MRAP1 or MRAP2, respectively. MRAPs exhibited protein-protein interactions and varying pharmacological properties on the surface translocation, constitutive activities and ligand-stimulated downstream signalling of these GPCRs. Knockdown of MRAP2 expression by hypothalamic administration of adeno-associated virus (AAV) packed shRNA stimulated body weight gain in mouse model. Co-injection of corticotropinreleasing factor (CRF), the agonist of corticotropin releasing hormone receptor 1 (CRHR1), suppressed feeding behaviour in a MRAP2-dependent manner. CONCLUSIONS: Collectively, our study has comprehensively elucidated the complex GPCR networks in two major endocrine organs and redefined the MRAP protein family as broad-spectrum GPCR modulators. MRAP proteins not only serve as a vital endocrine pivot on the regulation of global GPCR activities in vivo that could explain the composite physiological phenotypes of the MRAP2 null murine model but also provide us with new insights of the phenotyping investigation of GPCR-MRAP functional complexes.

Learning for single-cell assignment.

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.

MetaMed: Linking Microbiota Functions with Medicine Therapeutics.

Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research. Here, we present MetaMed (http://metamed.rwebox.com/index), a novel and integrative system-wide correlation mapping system to link bacterial functions and medicine therapeutics, providing novel hypotheses for deep investigation of microbe therapeutic effects on human health. Furthermore, comprehensive relationships between microbes living in the environment and drugs were discovered, providing a rich source for discovering microbiota metabolites with great potential for pharmaceutical applications.

Deep Surveying of the Transcriptional and Alternative Splicing Signatures for Decidual CD8+ T Cells at the First Trimester of Human Healthy Pregnancy.

Decidual CD8+ (dCD8) T cells have been proposed to play important roles in immune protection against the invading pathogens and in tolerance toward the growing semi-allogeneic fetus during early pregnancy. However, their phenotypic and functional characteristics remain poorly defined. Here, we performed the first analysis of the transcriptional and alternative splicing (AS) signatures for human first-trimester dCD8 T cells using high-throughput mRNA sequencing. Our data revealed that dCD8 T cells have distinct transcriptional and AS landscapes when compared with their autologous peripheral blood CD8+ (pCD8) T counterparts. Furthermore, human dCD8 T cells were observed to contain CD8-Treg and effector-memory T-cell subsets, and display enhanced functionality in terms of degranulation and cytokine production on a per-cell basis. Additionally, we have identified the novel splice junctions that use a high ratio of the non-canonical splicing motif GC-AG and found that AS is not a major contributor to the gene expression-level changes between paired pCD8 and dCD8 T cells. Together, our findings not only provide a comprehensive framework of the transcriptional and AS landscapes but also reveal the functional feature of human dCD8 T cells, which are of great importance in understanding the biology of these cells and the physiology of human healthy pregnancy.