Single-Cell Multiomics Techniques: From Conception to Applications

OneSC: A computational platform for recapitulating cell state transitions

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Origins of tissue and cell-type specificity in mitochondrial DNA (mtDNA) disease

Mutations of mitochondrial (mt)DNA are a major cause of morbidity and mortality in humans, accounting for approximately two thirds of diagnosed mitochondrial disease. However, despite significant advances in technology since the discovery of the first disease-causing mtDNA mutations in 1988, the comprehensive diagnosis and treatment of mtDNA disease remains challenging. This is partly due to the highly variable clinical presentation linked to tissue-specific vulnerability that determines which organs are affected. Organ involvement can vary between different mtDNA mutations, and also between patients carrying the same disease-causing variant. The clinical features frequently overlap with other non-mitochondrial diseases, both rare and common, adding to the diagnostic challenge. Building on previous findings, recent technological advances have cast further light on the mechanisms which underpin the organ vulnerability in mtDNA diseases, but our understanding is far from complete. In this review we explore the origins, current knowledge, and future directions of research in this area.

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BACKGROUND

Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.

RESULTS

We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.

CONCLUSIONS

Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research

The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

Multi-omics analysis in developmental bone biology

Single-cell omics and multi-omics have revolutionized our understanding of molecular and cellular biological processes at a single-cell level. In bone biology, the combination of single-cell RNA-sequencing analyses and in vivo lineage-tracing approaches has successfully identified multi-cellular diversity and dynamics of skeletal cells. This established a new concept that bone growth and regeneration are regulated by concerted actions of multiple types of skeletal stem cells, which reside in spatiotemporally distinct niches. One important subtype is endosteal stem cells that are particularly abundant in young bone marrow. The discovery of this new skeletal stem cell type has been facilitated by single-cell multi-omics, which simultaneously measures gene expression and chromatin accessibility. Using single-cell omics, it is now possible to computationally predict the immediate future state of individual cells and their differentiation potential. In vivo validation using histological approaches is the key to interpret the computational prediction. The emerging spatial omics, such as spatial transcriptomics and epigenomics, have major advantage in retaining the location of individual cells within highly complex tissue architecture. Spatial omics can be integrated with other omics to further obtain in-depth insights. Single-cell multi-omics are now becoming an essential tool to unravel intricate multicellular dynamics and intercellular interactions of skeletal cells.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Single-Nucleus RNA-Sequencing in Brain Tissue

Neuroscience research greatly benefits from single-cell sequencing technologies, which can reveal transcriptional alterations on a cellular level. However, preparing single-cell suspensions is technically challenging, requires experience, and has several limitations that can influence the transcriptional readout. Performing sequencing of single nuclei instead of single cells alleviates several of the challenges of sample preparation and highlights acute nuclear transcription. Here, we provide a protocol to prepare a nuclei suspension for single-nucleus RNA-sequencing for cell type-specific transcriptional profiling of brain tissue using the 10x Genomics single-cell gene expression assay. Furthermore, we highlight important aspects to consider during experimental design and data analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation of single-nucleus suspension Basic Protocol 2: Preparation and sequencing of single-nucleus libraries for RNA-seq.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataract. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq, and CUT&RUN-seq to discover novel mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Further, we divulge a conserved epigenetic paradigm of cellular differentiation, defined by progressive loss of H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

CMOT: Cross-Modality Optimal Transport for multimodal inference

Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.

Epigenetic Control of Cell Potency and Fate Determination during Mammalian Gastrulation

Pluripotent embryonic stem cells have a unique and characteristic epigenetic profile, which is critical for differentiation to all embryonic germ lineages. When stem cells exit the pluripotent state and commit to lineage-specific identities during the process of gastrulation in early embryogenesis, extensive epigenetic remodelling mediates both the switch in cellular programme and the loss of potential to adopt alternative lineage programmes. However, it remains to be understood how the stem cell epigenetic profile encodes pluripotency, or how dynamic epigenetic regulation helps to direct cell fate specification. Recent advances in stem cell culture techniques, cellular reprogramming, and single-cell technologies that can quantitatively profile epigenetic marks have led to significant insights into these questions, which are important for understanding both embryonic development and cell fate engineering. This review provides an overview of key concepts and highlights exciting new advances in the field.

Enhlink infers distal and context-specific enhancer-promoter linkages

Enhancers play a crucial role in regulating gene expression and their functional status can be queried with cell type precision using using single-cell (sc)ATAC-seq. To facilitate analysis of such data, we developed Enhlink, a novel computational approach that leverages single-cell signals to infer linkages between regulatory DNA sequences, such as enhancers and promoters. Enhlink uses an ensemble strategy that integrates cell-level technical covariates to control for batch effects and biological covariates to infer robust condition-specific links and their associated p-values. It can integrate simultaneous gene expression and chromatin accessibility measurements of individual cells profiled by multi-omic experiments for increased specificity. We evaluated Enhlink using simulated and real scATAC-seq data, including those paired with physical enhancer-promoter links enumerated by promoter capture Hi-C and with multi-omic scATAC-/RNA-seq data we generated from the mouse striatum. These examples demonstrated that our method outperforms popular alternative strategies. In conjunction with eQTL analysis, Enhlink revealed a putative super-enhancer regulating key cell type-specific markers of striatal neurons. Taken together, our analyses demonstrate that Enhlink is accurate, powerful, and provides features that can lead to novel biological insights.

Accelerating the understanding of cancer biology through the lens of genomics

A core mission of cancer genomics is to comprehensively chart molecular underpinnings of cancer-driving events and to provide personalized therapeutic strategies. Primarily focused on cancer cells, cancer genomics studies have successfully uncovered many drivers for major cancer types. Since the emergence of cancer immune evasion as a critical cancer hallmark, the paradigm has been elevated to the holistic tumor ecosystem, with distinct cellular components and their functional states elucidated. We highlight the milestones of cancer genomics, depict the evolving path of the field, and discuss future directions in completing the understanding of the tumor ecosystem and in advancing therapeutic strategies.

Omics-based approaches for the systematic profiling of mitochondrial biology

Mitochondria are essential for cellular functions such as metabolism and apoptosis. They dynamically adapt to the changing environmental demands by adjusting their protein, nucleic acid, metabolite, and lipid contents. In addition, the mitochondrial components are modulated on different levels in response to changes, including abundance, activity, and interaction. A wide range of omics-based approaches has been developed to be able to explore mitochondrial adaptation and how mitochondrial function is compromised in disease contexts. Here, we provide an overview of the omics methods that allow us to systematically investigate the different aspects of mitochondrial biology. In addition, we show examples of how these methods have provided new biological insights. The emerging use of these toolboxes provides a more comprehensive understanding of the processes underlying mitochondrial function.

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

Impact of the Human Cell Atlas on medicine

Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand the mechanisms of disease at the cellular and tissue levels and can use this understanding to develop powerful disease diagnostics; identify promising new drug targets; predict their efficacy, toxicity and resistance mechanisms; and empower new kinds of therapies, from cancer therapies to regenerative medicine. Here, we lay out a vision for the potential of cell atlases to impact the future of medicine, and describe how advances over the past decade have begun to realize this potential in common complex diseases, infectious diseases (including COVID-19), rare diseases and cancer.

A guide to systems-level immunomics

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.

High-throughput scNMT protocol for multiomics profiling of single cells from mouse brain and pancreatic organoids

Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth.

For complete details on the use and execution of this protocol, please refer to Clark (2019), Clark et al. (2018), and Clark et al., 2018, Hagemann-Jensen et al., 2020a, Hagemann-Jensen et al., 2020b.

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Miniaturization of scNMT protocol to increase cost-effectiveness

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Throughput increase from 96-well plate to 384-well plate format

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Incorporation of Smart-seq3 pipeline, obtaining higher number of genes per cell

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Normalization steps to optimally distribute per-cell sequencing depth

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Emerging microfluidic technologies for microbiome research

The importance of the microbiome is increasingly prominent. For example, the human microbiome has been proven to be strongly associated with health conditions, while the environmental microbiome is recognized to have a profound influence on agriculture and even the global climate. Furthermore, the microbiome can serve as a fascinating reservoir of genes that encode tremendously valuable compounds for industrial and medical applications. In the past decades, various technologies have been developed to better understand and exploit the microbiome. In particular, microfluidics has demonstrated its strength and prominence in the microbiome research. By taking advantage of microfluidic technologies, inherited shortcomings of traditional methods such as low throughput, labor-consuming, and high-cost are being compensated or bypassed. In this review, we will summarize a broad spectrum of microfluidic technologies that have addressed various needs in the field of microbiome research, as well as the achievements that were enabled by the microfluidics (or technological advances). Finally, how microfluidics overcomes the limitations of conventional methods by technology integration will also be discussed.

Whole-Genome Amplification—Surveying Yield, Reproducibility, and Heterozygous Balance, Reported by STR-Targeting MIPs

Whole-genome amplification is a crucial first step in nearly all single-cell genomic analyses, with the following steps focused on its products. Bias and variance caused by the whole-genome amplification process add numerous challenges to the world of single-cell genomics. Short tandem repeats are sensitive genomic markers used widely in population genetics, forensics, and retrospective lineage tracing. A previous evaluation of common whole-genome amplification targeting ~1000 non-autosomal short tandem repeat loci is extended here to ~12,000 loci across the entire genome via duplex molecular inversion probes. Other than its improved scale and reduced noise, this system detects an abundance of heterogeneous short tandem repeat loci, allowing the allelic balance to be reported. We show here that while the best overall yield is obtained using RepliG-SC, the maximum uniformity between alleles and reproducibility across cells are maximized by Ampli1, rendering it the best candidate for the comparative heterozygous analysis of single-cell genomes.