Single-cell Sequencing Methods Research Articles

The next generation of single-cell sequencing methods can be microfluidics-free.

The next generation of single-cell sequencing methods can be microfluidics-free.

Multi-omics analyses of CD276 in pan-cancer reveals its clinical prognostic value in glioblastoma and other major cancer types

BackgroundCD276 (also known as B7-H3) is one of the most important immune checkpoints of the CD28 and B7 superfamily, and its abnormal expression is closely associated with various types of cancer. It has been shown that CD276 is able to inhibit the function of T cells, and that this gene may potentially be a promising immunotherapy target for different types of cancer.MethodsSince few systematic studies have been published on the role of CD276 in cancer to date, the present study has employed single-cell sequencing and bioinformatics methods to analyze the expression patterns, clinical significance, prognostic value, epigenetic alterations, DNA methylation level, tumor immune cell infiltration and immune functions of CD276 in different types of cancer. In order to analyze the potential underlying mechanism of CD276 in glioblastoma (GBM) to assess its prognostic value, the LinkedOmics database was used to explore the biological function and co-expression pattern of CD276 in GBM, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In addition, a simple validation of the above analyses was performed using reverse transcription-quantitative (RT-q)PCR assay.ResultsThe results revealed that CD276 was highly expressed, and was often associated with poorer survival and prognosis, in the majority of different types of cancer. In addition, CD276 expression was found to be closely associated with T cell infiltration, immune checkpoint genes and immunoregulatory interactions between lymphoid and a non-lymphoid cell. It was also shown that the CD276 expression network exerts a wide influence on the immune activation of GBM. The expression of CD276 was found to be positively correlated with neutrophil-mediated immunity, although it was negatively correlated with the level of neurotransmitters, neurotransmitter transport and the regulation of neuropeptide signaling pathways in GBM. It is noteworthy that CD276 expression was found to be significantly higher in GBM compared with normal controls according to the RT-qPCR analysis, and the co-expression network, biological function and chemotherapeutic drug sensitivity of CD276 in GBM were further explored. In conclusion, the findings of the present study have revealed that CD276 is strongly expressed and associated with poor prognosis in most types of cancer, including GBM, and its expression is strongly associated with T-cell infiltration, immune checkpoint genes, and immunomodulatory interactions between lymphocytes and non-lymphoid cells.ConclusionsTaken together, based on our systematic analysis, our findings have revealed important roles for CD276 in different types of cancers, especially GBM, and CD276 may potentially serve as a biomarker for cancer.

Scm6A-seq reveals single-cell landscapes of the dynamic m6A during oocyte maturation and early embryonic development

N6-methyladenosine (m6A) has been demonstrated to regulate RNA metabolism and various biological processes, including gametogenesis and embryogenesis. However, the landscape and function of m6A at single cell resolution have not been extensively studied in mammalian oocytes or during pre-implantation. In this study, we developed a single-cell m6A sequencing (scm6A-seq) method to simultaneously profile the m6A methylome and transcriptome in single oocytes/blastomeres of cleavage-stage embryos. We found that m6A deficiency leads to aberrant RNA clearance and consequent low quality of Mettl3Gdf9 conditional knockout (cKO) oocytes. We further revealed that m6A regulates the translation and stability of modified RNAs in metaphase II (MII) oocytes and during oocyte-to-embryo transition, respectively. Moreover, we observed m6A-dependent asymmetries in the epi-transcriptome between the blastomeres of two-cell embryo. scm6A-seq thus allows in-depth investigation into m6A characteristics and functions, and the findings provide invaluable single-cell resolution resources for delineating the underlying mechanism for gametogenesis and early embryonic development.

Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing

Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

Systematic single-cell pathway analysis to characterize early Tcell activation.

Pathway analysis is a key analytical stage in the interpretation of omics data, providing a powerful method for detecting alterations in cellular processes. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package single-cell pathway analysis (SCPA). We demonstrate the effectiveness of SCPA over commonly used methods, generate a scRNA-seq Tcell dataset, and characterize pathway activity over early cellular activation. This reveals regulatory pathways in Tcells, including an intrinsic type I interferon system regulating Tcell survival and a reliance on arachidonic acid metabolism throughout Tcell activation. A systems-level characterization of pathway activity in Tcells across multiple tissues also identifies alpha-defensin expression as a hallmark of bone-marrow-derived Tcells. Overall, this work provides a widely applicable tool for single-cell pathway analysis and highlights regulatory mechanisms of Tcells.

Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy.

Recently, immunotherapy has gained increasing popularity in oncology. Several immunotherapies obtained remarkable clinical effects, but the efficacy varied, and only subsets of cancer patients benefited. Breaking the constraints and improving immunotherapy efficacy is extremely important in precision medicine. Whereas traditional sequencing approaches mask the characteristics of individual cells, single-cell sequencing provides multiple dimensions of cellular characterization at the single-cell level, including genomic, transcriptomic, epigenomic, proteomic, and multi-omics. Hence, the complexity of the tumor microenvironment, the universality of tumor heterogeneity, cell composition and cell-cell interactions, cell lineage tracking, and tumor drug resistance mechanisms are revealed in-depth. However, the clinical transformation of single-cell technology is not to the point of in-depth study, especially in the application of immunotherapy. The newly discovered vital cells and tremendous biomarkers facilitate the development of more efficient individualized therapeutic regimens to guide clinical treatment and predict prognosis. This review provided an overview of the progress in distinct single-cell sequencing methods and emerging strategies. For perspective, the expanding utility of combining single-cell sequencing and other technologies was discussed.

Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution

The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis.

Epigenetic Mechanisms of Brain Sexual Differentiation.

Across vertebrate species, gonadal hormones coordinate physiology with behavior to facilitate social interactions essential for reproduction and survival. In adulthood, these hormones activate neural circuits that regulate behaviors presenting differently in females and males, such as parenting and territorial aggression. Yet long before sex-typical behaviors emerge at puberty, transient hormone production during sensitive periods of neurodevelopment establish the circuits upon which adult hormones act. How transitory waves of early-life hormone signaling exert lasting effects on the brain remains a central question. Here we discuss how perinatal estradiol signaling organizes cellular and molecular sex differences in the rodent brain. We review classic anatomic studies revealing sex differences in cell number, volume, and neuronal projections, and consider how single-cell sequencing methods enable distinction between sex-biased cell-type abundance and gene expression. Finally, we highlight the recent discovery of a gene regulatory program activated by estrogen receptor α (ERα) following the perinatal hormone surge. A subset of this program displays sustained sex-biased gene expression and chromatin accessibility throughout the postnatal sensitive period, demonstrating a bona fide epigenetic mechanism. We propose that ERα-expressing neurons throughout the social behavior network use similar gene regulatory programs to coordinate brain sexual differentiation.

High-throughput total RNA sequencing in single cells using VASA-seq

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.

Abstract 59: Optimization of high-throughput, high-depth, targeted single-cell DNA sequencing to pancreatic ductal adenocarcinoma

Abstract Purpose of study: Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve “mixed signals”, i.e. the admixture of normal and tumor cells, and/or of distinct tumor subclones. Bulk sequencing of pancreatic ductal adenocarcinoma (PDAC) has been particularly problematic due to the high stromal content and resulting low tumor cellularity. Strategies to account for this have included laser capture microdissection yet this is a laborious process not amenable to high-throughput pipelines. We sought to develop and apply a high-throughput, high-depth, targeted single-cell DNA sequencing (scDNA-seq) method to account for these issues, including the ability to extract high quality genomic information from low purity and archival samples. Experimental procedures: Bulk whole exome sequencing (WES) was performed on 29 biologically distinct samples. For single cell sequencing, we developed a custom panel containing 186 amplicons covering 93 genes that represent the most common germline and somatic targets reported in PDAC. Based on a commercially available system that enables automatic enzymatic and mechanical tissue disruption with integrated fluidic processes, we optimized a nuclei extraction workflow from frozen tissues that is highly compatible with downstream microdroplet-based single-cell encapsulation and library preparation. With it we generated scDNA-seq data from archival tissues of 15 PDAC patients at varying stages of the disease. Results were compared to that found in matched bulk sequencing data. Summary of new, unpublished data and conclusions: 42 samples were analyzed by single cell sequencing. Our nuclei extraction workflow generated on average 2867 single cell libraries per sample at >80X read depth. The single-cell results aligned well with matched bulk data in terms of the detection of key genetic variants and their variant allele frequency (VAF). We also identified additional driver variants not seen by WES, some with direct clinical evidence. Benefits of this workflow over preexisting methods are its speed of sample preparation, efficiency of sample use (particularly for small samples), flexibility by allowing for storage of excess extracted nuclei, and economy of scale. Together, these features support preparation of large numbers of cancer samples in a relatively short period of time. Citation Format: Haochen Zhang, Elias-Ramzey Karnoub, Ronan Chaligné, Ignas Masilionis, Alvin Makohon-Moore, Jungeui Hong, Christine Iacobuzio-Donahue. Optimization of high-throughput, high-depth, targeted single-cell DNA sequencing to pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 59.

Molecular spikes: a gold standard for single-cell RNA counting

Single-cell sequencing methods rely on molecule-counting strategies to account for amplification biases, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes—RNA spike-ins containing built-in unique molecular identifiers (UMIs) that we use to identify critical experimental and computational conditions for accurate RNA counting in single-cell RNA-sequencing (scRNA-seq). Using molecular spikes, we uncovered impaired RNA counting in methods that were not informative for cellular RNA abundances due to inflated UMI counts. We further leverage molecular spikes to improve estimates of total endogenous RNA amounts in cells, and introduce a strategy to correct experiments with impaired RNA counting. The molecular spikes and the accompanying R package UMIcountR (https://github.com/cziegenhain/UMIcountR) will improve the validation of new methods, better estimate and adjust for cellular mRNA amounts and enable more indepth characterization of RNA counting in scRNA-seq.

BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data.

Single-cell sequencing methods provide previously impossible resolution into the transcriptome of individual cells. Cell hashing reduces single-cell sequencing costs by increasing capacity on droplet-based platforms. Cell hashing methods rely on demultiplexing algorithms to accurately classify droplets; however, assumptions underlying these algorithms limit accuracy of demultiplexing, ultimately impacting the quality of single-cell sequencing analyses. We present Bimodal Flexible Fitting (BFF) demultiplexing algorithms BFFcluster and BFFraw, a novel class of algorithms that rely on the single inviolable assumption that barcode count distributions are bimodal. We integrated these and other algorithms into cellhashR, a new R package that provides integrated QC and a single command to execute and compare multiple demultiplexing algorithms. We demonstrate that BFFcluster demultiplexing is both tunable and insensitive to issues with poorly behaved data that can confound other algorithms. Using two well-characterized reference datasets, we demonstrate that demultiplexing with BFF algorithms is accurate and consistent for both well-behaved and poorly behaved input data. cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477. Supplementary data are available at Bioinformatics online.

Low-frequency somatic copy number alterations in normal human lymphocytes revealed by large-scale single-cell whole-genome profiling

Genomic-scale somatic copy number alterations in healthy humans are difficult to investigate because of low occurrence rates and the structural variations’ stochastic natures. Using a Tn5-transposase-assisted single-cell whole-genome sequencing method, we sequenced over 20,000 single lymphocytes from 16 individuals. Then, with the scale increased to a few thousand single cells per individual, we found that about 7.5% of the cells had large-size copy number alterations. Trisomy 21 was the most prevalent aneuploid event among all autosomal copy number alterations, whereas monosomy X occurred most frequently in over-30-yr-old females. In the monosomy X single cells from individuals with phased genomes and identified X-inactivation ratios in bulk, the inactive X Chromosomes were lost more often than the active ones.

Intra-Tumoral Expression of SLC7A11 Is Associated with Immune Microenvironment, Drug Resistance, and Prognosis in Cancers: A Pan-Cancer Analysis.

While many anti-cancer modalities have shown potent efficacy in clinical practices, cancer prevention, timely detection, and effective treatment are still challenging. As a newly recognized iron-dependent cell death mechanism characterized by excessive generation of lipid peroxidation, ferroptosis is regarded as a potent weapon in clearing cancer cells. The cystine/glutamate antiporter solute carrier family 7 member 11 (SLC7A11) is the core target for ferroptosis regulation, the overexpression of which dictates downregulated sensitivity to ferroptosis in cancer cells. Hence, we elaborated the pan-cancer level bioinformatic study and systematically elucidated the role of intra-tumoral expression of SLC7A11 in the survival of cancer patients and potential immunotherapeutic response. Specifically, 25/27 (92.6%) cancers were featured with upregulated SLC7A11 expression, where SLC7A11 overexpression is a risk factor for worse overall survival in 8 cancers. We also validated SLC7A11 expression in multiple pancreatic cancer cell lines in vitro and found that it was upregulated in most pancreatic cancer cell lines (p < 0.05). Single-cell sequencing method revealed the SLC7A11 was majorly expressed in cancer cells and mononuclear cells. To further explore the function of SLC7A11 in cancer progression, we analyzed the influence on cell proliferation after the knockdown or knockout of SLC7A11 by either CRISPR or RNAi methods. Besides, the association between SLC7A11 and drug resistance was characterized using bioinformatic approaches as well. We also analyzed the association between the expression of SLC7A11 in multi-omics level and the intra-tumoral infiltration of immune cells based on cell annotation algorithms. Moreover, the relationship between SLC7A11 and the expression of MHC, immune stimulators, immune inhibitors as well as the response to immunotherapy was investigated. In addition, the SLC7A11 expression in colon adenocarcinoma, uterine corpus endometrial carcinoma, and stomach adenocarcinoma (STAD) is also positively associated with microsatellite instability and that in head and neck squamous cell carcinoma, STAD, and prostate adenocarcinoma is positively associated with neoantigen level, which further revealed the potential relationship between SLC7A11 and immunotherapeutic response.

Abstract LBA006: Identification of novel, tumor specific pHLA targets recognized by TILs from CPI responders in a high-throughput, high-diversity pHLA library screening platform (3T-TRACE)

Abstract Background The ideal cancer target antigen is selectively expressed on neoplastic cells, absent on normal tissues, and contributes to the survival of cancer cells. Recent clinical trial reports identified T cell receptors (TCRs) recognizing intracellular targets presented as peptides in the context of HLA (pHLA) molecules as the critical molecular mediators of deep and long-lasting anti-tumor responses in a few solid tumor patients with complete responses to checkpoint inhibitor (CPI) treatment. The pHLA targets recognized by these TCRs may thus represent the long sought-after, ideal targets for solid tumor targeting with high-potency therapeutic modalities. Here we describe an immune-response guided target identification platform (3T-TRACE) and its utility to identify novel, intracellular targets, and their corresponding T cell receptors (TCRs) for the treatment of solid tumors. We provide an overview of the frequency and expression levels of the most promising, novel cancer testis antigens (CTAs) and tumor associated antigens (TAAs) identified. Methods By employing single cell sequencing methods, we identified TCRs that are uniquely associated with CPI responses and generated recombinant TCRs for screening against yeast display libraries expressing synthetic peptides in the context of the matching HLA complex, reviewed in (Figure 1). We analyzed over 140,000 CD8/CD4+ TILs from over 90 patient tumor biopsies and/or peripheral blood samples, including colorectal, lung, breast, ovarian, melanoma, and renal cancer (Figure 2). High value TCRs were selected based on the following criteria: high TCR clonality, immunophenotype indicative of T cell activation, shared alpha and/or beta TCR chains across patient samples (Public TCRs) and TCRs with anti-tumor activities. Statistical- and machine learning algorithms were developed to predict human peptides from the library selection data. Predicted target peptides were tested in in vitro T cell co-cultures for TCR activation. Antigen processing assays or mass spec analysis was employed to validate immunological processing of the epitope to identify bona fide targets. Results Among the first 100 targets identified by 3T-TRACE, we found 15 novel cancer testis antigens (CTAs) and 42 tumor associated antigens (TAAs) that were not previously described as targets in CPI treated solid tumor patients (Figure 3). Conclusions By combining TCR repertoire profiling with a library-based, antigen identification approach we found a strong enrichment of CTA- and TAA targets recognized by TCRs on TILs from CPI responders (Figure 4). Our findings are consistent with previous reports, demonstrating long-lasting responses induced by epitope spreading and CD8 T cell responses towards self-antigens, including CTAs and TAAs. Citation Format: Hans-Peter Gerber, Marvin Gee, Leah Sibener. Identification of novel, tumor specific pHLA targets recognized by TILs from CPI responders in a high-throughput, high-diversity pHLA library screening platform (3T-TRACE) [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA006.

The hidden genomic diversity of ciliated protists revealed by single-cell genome sequencing

BackgroundCiliated protists are a widely distributed, morphologically diverse, and genetically heterogeneous group of unicellular organisms, usually known for containing two types of nuclei: a transcribed polyploid macronucleus involved in gene expression and a silent diploid micronucleus responsible for transmission of genetic material during sexual reproduction and generation of the macronucleus. Although studies in a few species of culturable ciliated protists have revealed the highly dynamic nature of replicative and recombination events relating the micronucleus to the macronucleus, the broader understanding of the genomic diversity of ciliated protists, as well as their phylogenetic relationships and metabolic potential, has been hampered by the inability to culture numerous other species under laboratory conditions, as well as the presence of symbiotic bacteria and microalgae which provide a challenge for current sequencing technologies. Here, we optimized single-cell sequencing methods and associated data analyses, to effectively remove contamination by commensal bacteria, and generated high-quality genomes for a number of Euplotia species.ResultsWe obtained eight high-quality Euplotia genomes by using single-cell genome sequencing techniques. The genomes have high genomic completeness, with sizes between 68 and 125 M and gene numbers between 14K and 25K. Through comparative genomic analysis, we found that there are a large number of gene expansion events in Euplotia genomes, and these expansions are closely related to the phenotypic evolution and specific environmental adaptations of individual species. We further found four distinct subgroups in the genus Euplotes, which exhibited considerable genetic distance and relative lack of conserved genomic syntenies. Comparative genomic analyses of Uronychia and its relatives revealed significant gene expansion associated with the ciliary movement machinery, which may be related to the unique and strong swimming ability.ConclusionsWe employed single-cell genomics to obtain eight ciliate genomes, characterized the underestimated genomic diversity of Euplotia, and determined the divergence time of representative species in this subclass for the first time. We also further investigated the extensive duplication events associated with speciation and environmental adaptation. This study provides a unique and valuable resource for understanding the evolutionary history and genetic diversity of ciliates.

Emerging Single-cell Approaches to Understand HIV in the Central Nervous System.

Purpose of ReviewThis review highlights emerging single-cell sequencing methods relevant to translational studies of HIV in the central nervous system (CNS), summarizes limited single-cell studies of HIV in the CNS, and discusses opportunities for future HIV translational CNS studies.Recent FindingsInnovative methods utilizing single-cell technologies have advanced the study of genomes, proteomes, transcriptomes, and epigenomes at an enhanced resolution and depth. Single-cell analyses of central nervous system tissue, including autopsy brain and CSF cells, may shed light on CNS perturbations in people living with HIV. New strategies can distinguish distinct molecular identifies of rare infected cells at single-cell level, suggesting an opportunity to uncloak the molecular identity of hidden HIV in the CNS reservoir.SummaryAdoption of multimodal “omics” analyses to translational HIV studies and tissue compartments beyond blood will be critical to advancing our understanding of viral establishment, persistence, and eradication.

Complex infections in vivax malaria: the more you look, the more you find

The human malaria parasite Plasmodium vivax commonly causes complex multiclonal infections. Recently, Dia et al. have developed innovative methods for single-cell sequencing (SCS) of P. vivax infections by adapting an approach used previously for Plasmodium falciparum. Their studies provide fascinating new insights into P. vivax intrahost diversity and evolution.

Single-cell Ribo-seq reveals cell cycle-dependent translational pausing.

Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes1, epigenomes2 and transcriptomes3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution4, it remains a major challenge to measure translation in individual cells. Here, building on existing protocols5-7, we have substantially increased the sensitivity of these assaysto enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells.

Single-cell omics: Overview, analysis, and application in biomedical science.

Single-cell sequencing methods provide the highest resolution insight into cellular heterogeneity. Owing to their rapid growth and decreasing cost, they are now widely accessible to scientists worldwide. Single-cell technologies enable analysis of a large number of cells, making them powerful tools to characterise rare cell types and refine our understanding of diverse cell states. Moreover, single-cell application in biomedical sciences helps to unravel mechanisms related to disease pathogenesis and outcome. In this Viewpoint, we briefly describe existing single-cell methods (genomics, transcriptomics, epigenomics, proteomics, and mulitomics), comment on available analysis tools, and give examples of method applications in the biomedical field.