Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3.

Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

Single-cell RNA sequencing reveals immune cell dysfunction in the peripheral blood of patients with highly aggressive gastric cancer.

Highly aggressive gastric cancer (HAGC) is a gastric cancer characterized by bone marrow metastasis and disseminated intravascular coagulation (DIC). Information about the disease is limited. Here we employed single-cell RNA sequencing to investigate peripheral blood mononuclear cells (PBMCs), aiming to unravel the immune response of patients toward HAGC. PBMCs from seven HAGC patients, six normal advanced gastric cancer (NAGC) patients, and five healthy individuals were analysed by single-cell RNA sequencing. The expression of genes of interest was validated by bulk RNA-sequencing and ELISA. We found a massive expansion of neutrophils in PBMCs of HAGC. These neutrophils are activated, but immature. Besides, mononuclear phagocytes exhibited an M2-like signature and T cells were suppressed and reduced in number. Analysis of cell-cell crosstalk revealed that several signalling pathways involved in neutrophil to T-cell suppression including APP-CD74, MIF-(CD74+CXCR2), and MIF-(CD74+CD44) pathways were increased in HAGC. NETosis-associated genes S100A8 and S100A9 as well as VEGF, PDGF, FGF, and NOTCH signalling that contribute to DIC development were upregulated in HAGC too. This study reveals significant changes in the distribution and interactions of the PBMC subsets and provides valuable insight into the immune response in patients with HAGC. S100A8 and S100A9 are highly expressed in HAGC neutrophils, suggesting their potential to be used as novel diagnostic and therapeutic targets for HAGC.

Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq.

Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq.

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Dissecting the immune discrepancies in mouse liver allograft tolerance and heart/kidney allograft rejection.

The liver is the most tolerogenic of transplanted organs. However, the mechanisms underlying liver transplant tolerance are not well understood. The comparison between liver transplantation tolerance and heart/kidney transplantation rejection will deepen our understanding of tolerance and rejection in solid organs. Here, we built a mouse model of liver, heart and kidney allograft and performed single-cell RNA sequencing of 66,393 cells to describe the cell composition and immune cell interactions at the early stage of tolerance or rejection. We also performed bulk RNA-seq of mouse liver allografts from Day 7 to Day 60 post-transplantation to map the dynamic transcriptional variation in spontaneous tolerance. The transcriptome of lymphocytes and myeloid cells were characterized and compared in three types of organ allografts. Cell-cell interaction networks reveal the coordinated function of Kupffer cells, macrophages and their associated metabolic processes, including insulin receptor signalling and oxidative phosphorylation in tolerance induction. Cd11b+ dendritic cells (DCs) in liver allografts were found to inhibit cytotoxic T cells by secreting anti-inflammatory cytokines such as Il10. In summary, we profiled single-cell transcriptome analysis of mouse solid organ allografts. We characterized the immune microenvironment of mouse organ allografts in the acute rejection state (heart, kidney) and tolerance state (liver).

Mapping Cell Atlases at the Single-Cell Level.

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses.

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq.

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

The complete and fully-phased diploid genome of a male Han Chinese.

Since the release of the complete human genome, the priority of human genomic study has now been shifting towards closing gaps in ethnic diversity. Here, we present a fully phased and well-annotated diploid human genome from a Han Chinese male individual (CN1), in which the assemblies of both haploids achieve the telomere-to-telomere (T2T) level. Comparison of this diploid genome with the CHM13 haploid T2T genome revealed significant variations in the centromere. Outside the centromere, we discovered 11,413 structural variations, including numerous novel ones. We also detected thousands of CN1 alleles that have accumulated high substitution rates and a few that have been under positive selection in the East Asian population. Further, we found that CN1 outperforms CHM13 as a reference genome in mapping and variant calling for the East Asian population owing to the distinct structural variants of the two references. Comparison of SNP calling for a large cohort of 8869 Chinese genomes using CN1 and CHM13 as reference respectively showed that the reference bias profoundly impacts rare SNP calling, with nearly 2 million rare SNPs miss-called with different reference genomes. Finally, applying the CN1 as a reference, we discovered 5.80 Mb and 4.21 Mb putative introgression sequences from Neanderthal and Denisovan, respectively, including many East Asian specific ones undetected using CHM13 as the reference. Our analyses reveal the advances of using CN1 as a reference for population genomic studies and paleo-genomic studies. This complete genome will serve as an alternative reference for future genomic studies on the East Asian population.

Single-cell landscape analysis reveals systematic senescence in mammalian Down syndrome.

BACKGROUND: Down syndrome (DS), which is characterized by various malfunctions, is the most common chromosomal disorder. As the DS population continues to grow and most of those with DS live beyond puberty, early-onset health problems have become apparent. However, the cellular landscape and molecular alterations have not been thoroughly studied. METHODS: This study utilized single-cell resolution techniques to examine DS in humans and mice, spanning seven distinct organs. A total of 71 934 mouse and 98 207 human cells were analyzed to uncover the molecular alterations occurring in different cell types and organs related to DS, specifically starting from the fetal stage. Additionally, SA-ß-Gal staining, western blot, and histological study were employed to verify the alterations. RESULTS: In this study, we firstly established the transcriptomic profile of the mammalian DS, deciphering the cellular map and molecular mechanism. Our analysis indicated that DS cells across various types and organs experienced senescence stresses from as early as the fetal stage. This was marked by elevated SA-ß-Gal activity, overexpression of cell cycle inhibitors, augmented inflammatory responses, and a loss of cellular identity. Furthermore, we found evidence of mitochondrial disturbance, an increase in ribosomal protein transcription, and heightened apoptosis in fetal DS cells. This investigation also unearthed a regulatory network driven by an HSA21 gene, which leads to genome-wide expression changes. CONCLUSION: The findings from this study offer significant insights into the molecular alterations that occur in DS, shedding light on the pathological processes underlying this disorder. These results can potentially guide future research and treatment development for DS.

An analytical framework for decoding cell type-specific genetic variation of gene regulation.

A deeper understanding of genetic regulation and functional mechanisms underlying genetic associations with complex traits and diseases is impeded by cellular heterogeneity and linkage disequilibrium. To address these limits, we introduce Huatuo, a framework to decode genetic variation of gene regulation at cell type and single-nucleotide resolutions by integrating deep-learning-based variant predictions with population-based association analyses. We apply Huatuo to generate a comprehensive cell type-specific genetic variation landscape across human tissues and further evaluate their potential roles in complex diseases and traits. Finally, we show that Huatuo's inferences permit prioritizations of driver cell types associated with complex traits and diseases and allow for systematic insights into the mechanisms of phenotype-causal genetic variation.

High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq.

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.

SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.

It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.

Construction of a cross-species cell landscape at single-cell level.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging.

How many cell types are there in nature? How do they change during the life cycle? These are two fundamental questions that researchers have been trying to understand in the area of biology. In this study, single-cell mRNA sequencing data were used to profile over 2.6 million individual cells from mice, zebrafish and Drosophila at different life stages, 1.3 million of which were newly collected. The comprehensive datasets allow investigators to construct a cross-species cell landscape that helps to reveal the conservation and diversity of cell taxonomies at genetic and regulatory levels. The resources in this study are assembled into a publicly available website at http://bis.zju.edu.cn/cellatlas/.

Gut microbiota promotes stem cell differentiation through macrophage and mesenchymal niches in early postnatal development.

Intestinal stem cell maturation and development coincide with gut microbiota exposure after birth. Here, we investigated how early life microbial exposure, and disruption of this process, impacts the intestinal stem cell niche and development. Single-cell transcriptional analysis revealed impaired stem cell differentiation into Paneth cells and macrophage specification upon antibiotic treatment in early life. Mouse genetic and organoid co-culture experiments demonstrated that a CD206+ subset of intestinal macrophages secreted Wnt ligands, which maintained the mesenchymal niche cells important for Paneth cell differentiation. Antibiotics and reduced numbers of Paneth cells are associated with the deadly infant disease, necrotizing enterocolitis (NEC). We showed that colonization with Lactobacillus or transfer of CD206+ macrophages promoted Paneth cell differentiation and reduced NEC severity. Together, our work defines the gut microbiota-mediated regulation of stem cell niches during early postnatal development.

Unraveling Differential Transcriptomes and Cell Types in Zebrafish Larvae Intestine and Liver.

The zebrafish intestine and liver, as in other vertebrates, are derived from the endoderm. Great effort has been devoted to deciphering the molecular mechanisms controlling the specification and development of the zebrafish intestine and liver; however, genome-wide comparison of the transcriptomes between these two organs at the larval stage remains unexplored. There is a lack of extensive identification of feature genes marking specific cell types in the zebrafish intestine and liver at 5 days post-fertilization, when the larval fish starts food intake. In this report, through RNA sequencing and single-cell RNA sequencing of intestines and livers separately dissected from wild-type zebrafish larvae at 5 days post-fertilization, together with the experimental validation of 47 genes through RNA whole-mount in situ hybridization, we identified not only distinctive transcriptomes for the larval intestine and liver, but also a considerable number of feature genes for marking the intestinal bulb, mid-intestine and hindgut, and for marking hepatocytes and cholangiocytes. Meanwhile, we identified 135 intestine- and 97 liver-enriched transcription factor genes in zebrafish larvae at 5 days post-fertilization. Our findings provide rich molecular and cellular resources for studying cell patterning and specification during the early development of the zebrafish intestine and liver.

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.