Neurulation of the cynomolgus monkey embryo achieved from 3D blastocyst culture.

Neural tube (NT) defects arise from abnormal neurulation and result in the most common birth defects worldwide. Yet, mechanisms of primate neurulation remain largely unknown due to prohibitions on human embryo research and limitations of available model systems. Here, we establish a three-dimensional (3D) prolonged in vitro culture (pIVC) system supporting cynomolgus monkey embryo development from 7 to 25 days post-fertilization. Through single-cell multi-omics analyses, we demonstrate that pIVC embryos form three germ layers, including primordial germ cells, and establish proper DNA methylation and chromatin accessibility through advanced gastrulation stages. In addition, pIVC embryo immunofluorescence confirms neural crest formation, NT closure, and neural progenitor regionalization. Finally, we demonstrate that the transcriptional profiles and morphogenetics of pIVC embryos resemble key features of similarly staged in vivo cynomolgus and human embryos. This work therefore describes a system to study non-human primate embryogenesis through advanced gastrulation and early neurulation.

Engineered cell entry links receptor biology with single-cell genomics.

Cells communicate with each other via receptor-ligand interactions. Here, we describe lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to display ligand proteins, deliver payloads, and record receptor specificity. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. A viral presentation strategy allows ENTER to capture interactions between B cell receptor and any antigen. We engineer ENTER to deliver genetic payloads to antigen-specific T or B cells to selectively modulate cellular behavior in mixed populations. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of antigen specificities, TCR clonality, cell type, and states of individual T cells. ENTER-seq of CMV-seropositive patient blood samples reveals the viral epitopes that drive effector memory T cell differentiation and inter-clonal vs. intra-clonal phenotypic diversity targeting the same epitope. ENTER technology enables systematic discovery of receptor specificity, linkage to cell fates, and antigen-specific cargo delivery.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility.

Microdroplet single-cell ATAC-seq is widely used to measure chromatin accessibility, however, highly scalable and simple sample multiplexing procedures are not available. Here, we present a transposome-assisted single nucleus barcoding approach for ATAC-seq (SNuBar-ATAC) that utilizes a single oligonucleotide adaptor for multiplexing samples during the existing tagmentation step and does not require a pre-labeling procedure. The accuracy and scalability of SNuBar-ATAC was evaluated using cell line mixture experiments. We applied SNuBar-ATAC to investigate treatment-induced chromatin accessibility dynamics by multiplexing 28 mice with lung tumors that received different combinations of chemo, radiation, and targeted immunotherapy. We also applied SNuBar-ATAC to study spatial epigenetic heterogeneity by multiplexing 32 regions from a human breast tissue. Additionally, we show that SNuBar can multiplex single cell ATAC and RNA multiomic assays in cell lines and human breast tissue samples. Our data show that SNuBar is a highly accurate, easy-to-use, and scalable system for multiplexing scATAC-seq and scATAC and RNA co-assay experiments.

Single-Cell Analyses Reveal Megakaryocyte-Biased Hematopoiesis in Myelofibrosis and Identify Mutant Clone-Specific Targets.

Myelofibrosis is a severe myeloproliferative neoplasm characterized by increased numbers of abnormal bone marrow megakaryocytes that induce fibrosis, destroying the hematopoietic microenvironment. To determine the cellular and molecular basis for aberrant megakaryopoiesis in myelofibrosis, we performed single-cell transcriptome profiling of 135,929 CD34+ lineage- hematopoietic stem and progenitor cells (HSPCs), single-cell proteomics, genomics, and functional assays. We identified a bias toward megakaryocyte differentiation apparent from early multipotent stem cells in myelofibrosis and associated aberrant molecular signatures. A sub-fraction of myelofibrosis megakaryocyte progenitors (MkPs) are transcriptionally similar to healthy-donor MkPs, but the majority are disease specific, with distinct populations expressing fibrosis- and proliferation-associated genes. Mutant-clone HSPCs have increased expression of megakaryocyte-associated genes compared to wild-type HSPCs, and we provide early validation of G6B as a potential immunotherapy target. Our study paves the way for selective targeting of the myelofibrosis clone and illustrates the power of single-cell multi-omics to discover tumor-specific therapeutic targets and mediators of tissue fibrosis.

A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells.

To address how genetic variation alters gene expression in complex cell mixtures, we developed direct nuclear tagmentation and RNA sequencing (DNTR-seq), which enables whole-genome and mRNA sequencing jointly in single cells. DNTR-seq readily identified minor subclones within leukemia patients. In a large-scale DNA damage screen, DNTR-seq was used to detect regions under purifying selection and identified genes where mRNA abundance was resistant to copy-number alteration, suggesting strong genetic compensation. mRNA sequencing (mRNA-seq) quality equals RNA-only methods, and the low positional bias of genomic libraries allowed detection of sub-megabase aberrations at ultra-low coverage. Each cell library is individually addressable and can be re-sequenced at increased depth, allowing multi-tiered study designs. Additionally, the direct tagmentation protocol enables coverage-independent estimation of ploidy, which can be used to identify cell singlets. Thus, DNTR-seq directly links each cell's state to its corresponding genome at scale, enabling routine analysis of heterogeneous tumors and other complex tissues.

Systems analysis of human innate immunity in COVID-19.

Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.

scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.

MOTIVATION: The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements. AVAILABILITY AND IMPLEMENTATION: scMNMF code can be found at https://github.com/yushanqiu/scMNMF.

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis.

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.

A universal framework for single-cell multi-omics data integration with graph convolutional networks.

Single-cell omics data are growing at an unprecedented rate, whereas effective integration of them remains challenging due to different sequencing methods, quality, and expression pattern of each omics data. In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop.

Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis. RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity. CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Spatially Resolved Multi-Omics Single-Cell Analyses Inform Mechanisms of Immune Dysfunction in Pancreatic Cancer.

BACKGROUND & AIMS: As pancreatic ductal adenocarcinoma (PDAC) continues to be recalcitrant to therapeutic interventions, including poor response to immunotherapy, albeit effective in other solid malignancies, a more nuanced understanding of the immune microenvironment in PDAC is urgently needed. We aimed to unveil a detailed view of the immune micromilieu in PDAC using a spatially resolved multimodal single-cell approach. METHODS: We applied single-cell RNA sequencing, spatial transcriptomics, multiplex immunohistochemistry, and mass cytometry to profile the immune compartment in treatment-naïve PDAC tumors and matched adjacent normal pancreatic tissue, as well as in the systemic circulation. We determined prognostic associations of immune signatures and performed a meta-analysis of the immune microenvironment in PDAC and lung adenocarcinoma on single-cell level. RESULTS: We provided a spatially resolved fine map of the immune landscape in PDAC. We substantiated the exhausted phenotype of CD8 T cells and immunosuppressive features of myeloid cells, and highlighted immune subsets with potentially underappreciated roles in PDAC that diverged from immune populations within adjacent normal areas, particularly CD4 T cell subsets and natural killer T cells that are terminally exhausted and acquire a regulatory phenotype. Differential analysis of immune phenotypes in PDAC and lung adenocarcinoma revealed the presence of extraordinarily immunosuppressive subtypes in PDAC, along with a distinctive immune checkpoint composition. CONCLUSIONS: Our study sheds light on the multilayered immune dysfunction in PDAC and presents a holistic view of the immune landscape in PDAC and lung adenocarcinoma, providing a comprehensive resource for functional studies and the exploration of therapeutically actionable targets in PDAC.

Decoding molecular features of bovine oocyte fate during antral follicle growth via single-cell multi-omics analysis.

Antral follicle size is a useful predictive marker of the competency of enclosed oocytes for yielding an embryo following in vitro maturation and fertilization. However, the molecular mechanisms underpinning oocyte developmental potential during bovine antral follicle growth are still unclear. Here, we used a modified single-cell multi-omics approach to analyze the transcriptome, DNA methylome and chromatin accessibility in parallel for oocytes and cumulus cells collected from bovine antral follicles of different sizes. Transcriptome profiling identified three types of oocytes (Small, Medium and Large) that underwent different developmental trajectories, with Large oocytes exhibiting the largest average follicle size and characteristics resembling metaphase-II oocytes. Differential expression analysis and real-time PCR assay showed that most replication-dependent histone genes were highly expressed in Large oocytes. The joint analysis of multi-omics data revealed that the transcription of 20 differentially expressed genes in Large oocytes was associated with both DNA methylation and chromatin accessibility. In addition, oocyte-cumulus interaction analysis showed that inflammation, DNA damage, and p53 signaling pathways were active in Small oocytes, which had the smallest average follicle sizes. We further confirmed that p53 pathway inhibition in in vitro maturation experiments using oocytes obtained from small antral follicles could improve the quality of oocytes and increased the blastocyte rate after in vitro fertilization and culture. Our work provides new insights into the intricate orchestration of bovine oocyte fate determination during antral folliculogenesis, which is instrumental for optimizing in vitro maturation techniques to optimize oocyte quality.

JSNMF enables effective and accurate integrative analysis of single-cell multiomics data.

The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream analysis, including the characterization of markers and functional pathway enrichment analysis. The core of JSNMF is an unsupervised method based on JSNMF, where it assumes different latent variables for the two molecular modalities, and integrates the information of transcriptomic and epigenomic data with consensus graph fusion, which better tackles the distinct characteristics and levels of noise across different molecular modalities in single-cell multiomics data. We applied JSNMF to single-cell multiomics datasets from different tissues and different technologies. The results demonstrate the superior performance of JSNMF in clustering and data visualization of the cells. JSNMF also allows joint analysis of multiple single-cell multiomics experiments and single-cell multiomics data with more than two modalities profiled on the same cell. JSNMF also provides rich biological insight on the markers, cell-type-specific region-gene associations and the functions of the identified cell subpopulation.

Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.

Advances in single-cell biotechnologies simultaneously generate the transcriptomic and epigenomic profiles at cell levels, providing an opportunity for investigating cell fates. Although great efforts have been devoted to either of them, the integrative analysis of single-cell multi-omics data is really limited because of the heterogeneity, noises and sparsity of single-cell profiles. In this study, a network-based integrative clustering algorithm (aka NIC) is present for the identification of cell types by fusing the parallel single-cell transcriptomic (scRNA-seq) and epigenomic profiles (scATAC-seq or DNA methylation). To avoid heterogeneity of multi-omics data, NIC automatically learns the cell-cell similarity graphs, which transforms the fusion of multi-omics data into the analysis of multiple networks. Then, NIC employs joint non-negative matrix factorization to learn the shared features of cells by exploiting the structure of learned cell-cell similarity networks, providing a better way to characterize the features of cells. The graph learning and integrative analysis procedures are jointly formulated as an optimization problem, and then the update rules are derived. Thirteen single-cell multi-omics datasets from various tissues and organisms are adopted to validate the performance of NIC, and the experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods in terms of various measurements. The proposed algorithm provides an effective strategy for the integrative analysis of single-cell multi-omics data (The software is coded using Matlab, and is freely available for academic https://github.com/xkmaxidian/NIC ).

Taming human brain organoids one cell at a time.

Human brain organoids are self-organizing three-dimensional structures that emerge from human pluripotent stem cells and mimic aspects of the cellular composition and functionality of the developing human brain. Despite their impressive self-organizing capacity, organoids lack the stereotypic structural anatomy of their in vivo counterpart, making conventional analysis techniques underpowered to assess cellular composition and gene network regulation in organoids. Advances in single cell transcriptomics have recently allowed characterization and improvement of organoid protocols, as they continue to evolve, by enabling identification of cell types and states along with their developmental origins. In this review, we summarize recent approaches, progresses and challenges in resolving brain organoid's complexity through single-cell transcriptomics. We then discuss emerging technologies that may complement single-cell RNA sequencing by providing additional readouts of cellular states to generate an organ-level view of developmental processes. Altogether, these integrative technologies will allow monitoring of global gene regulation in thousands of individual cells and will offer an unprecedented opportunity to investigate features of human brain development and disease across multiple cellular modalities and with cell-type resolution.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine.

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.