Advances In Single-cell Technologies Research Articles

Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

Understanding glioblastoma at the single-cell level: Recent advances and future challenges.

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

Pi-seq—A customizable multichannel syringe pump for microfluidics

The advent of single cell technologies resulted in growing demand for microfluidics in the biological sciences. Commercial platforms have remained expensive, inflexible, and non-customizable black boxes. We developed an open source, multichannel, zero-backflow microfluidics device based on syringe pumps controlled by a Raspberry Pi computer. It uses both readily available and 3D-printed parts as well as a custom PCB and is easily serviceable. Moreover, it is fully customizable for various applications. Total cost is under €600. We equipped one channel with a custom Peltier-based temperature controller for precise heating or cooling and a mixer mechanism to prevent sedimentation of the cells within the syringe. Depending on the cells in the sample, heating and cooling can be useful to maintain a beneficial environment or to slow down cellular processes and cell death, respectively. Combined with microfluidics consumables and a microscope, the device is capable of integration into a high quality droplet-based single cell RNA sequencing workflow as shown here. Analysis of a mixture of human and insect cells resulted in a dataset of 17,769 single cells and demonstrates reliable operation and separation.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans – a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes’ cellular and molecular biology – which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus – a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Immune mechanisms of granuloma formation in sarcoidosis and tuberculosis.

Sarcoidosis is a complex immune-mediated disease characterized by clusters of immune cells called granulomas. Despite major steps in understanding the cause of this disease, many questions remain. In this Review, we perform a mechanistic interrogation of the immune activities that contribute to granuloma formation in sarcoidosis and compare these processes with its closest mimic, tuberculosis, highlighting shared and divergent immune activities. We examine how Mycobacterium tuberculosis is sensed by the immune system; how the granuloma is initiated, formed, and perpetuated in tuberculosis compared with sarcoidosis; and the role of major innate and adaptive immune cells in shaping these processes. Finally, we draw these findings together around several recent high-resolution studies of the granuloma in situ that utilized the latest advances in single-cell technology combined with spatial methods to analyze plausible disease mechanisms. We conclude with an overall view of granuloma formation in sarcoidosis.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

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.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

Single-cell analysis of chromatin accessibility in the adult mouse brain

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1–4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs—specifically, those identified from a subset of cortical excitatory neurons—are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

Characterization of Niche Cells and Signals Regulating Stepwise Embryonic Hematopoiesis Using Slide-Seq and Merfish Spatial Transcriptomics

Stagewise HSC development initiates in the aorta-gonad-mesonephros (AGM) region during mouse embryonic day (E)9.5-E12.5 and progresses through stages including hemogenic endothelial cell (HEC) specification, the endothelial to hematopoietic transition (EHT), and maturation. The nascent HSCs (pre-HSCs) form in the AGM region and subsequently migrate to the fetal liver where they undergo further maturation and expansion. Recent advances in single cell technologies have significantly enhanced our understanding of intrinsic cellular programs involved in HSC development. However, our knowledge about niche cells and the signals they emanate in the AGM region, as well as their contribution to HSC development, remain limited. Here we've conducted spatial transcriptomics analysis using Slide-seq on mouse trunk tissues, including both the AGM region and the fetal liver, at E10.5, E11.5 and E12.5. In addition, we performed scRNA-seq on the cells collected from the AGM and trunk at E10.5, E11.5 and E12.5, as well as the fetal liver at E12.5. To integrate the Slide-seq and scRNA-seq data, we developed an algorithm that leverages capabilities from both spatial and in-depth transcriptional sequencing. As a result, we were able to successfully reconstitute the structures of the trunk tissues and obtain gene expression profiles of individual cells at 10 µm resolution. From the analysis, we identified 7 cell clusters surrounding the aorta, with one cluster comprised of Nfgr + cells predominantly occupying the dorsal aorta. The ventral part of aorta displayed a complex and multilayered cellular architecture, featuring pre-HSCs, endothelial cells, mesonephric cells, and macrophages. Notably, we identified a unique mesenchymal stromal cell (MSC) population that co-express Cdh2 (encodes N-cadherin or N-cad) and Pdgfra; thus, we call these cells N-cad + MSCs. These N-cad + MSCs were particularly enriched at the ventral part of the aorta, forming a single cell layer positioned between an inner layer of aortic endothelial cells and an outer layer of mesonephric cells. Next, we utilized several bioinformatics tools (CellphoneDB, CellChat, NicheChat, and StringDB) in effort to identify potential signals that could impact embryonic hematopoiesis. The results revealed intricate interactions between N-cad + MSCs and pre-HSCs, for example, involving JAG1-NOTCH1 and SDF-1 (encoded by Cxcl12)-CXCR4 ligand-receptor pairs. To validate the findings obtained by the Slide-seq data, we selected and designed customized RNA probes for 140 genes of interest, including cell markers and potential signals. We then conducted MERFISH (Multiplexed Error-Robust Fluorescence in situ Hybridization) on 18 E11.5 trunk sections. The MERFISH data enabled us to characterize the spatially resolved niche cells and their derived signals in the AGM region at the single-cell level, which largely corroborated the results obtained from the Slide-seq data. Specifically, the MERFISH data indicates that N-cad + MSCs are in direct contact with aortic endothelial cells and are the primary cellular source of Jag1 and Cxcl12 around the aorta. Given the unique position of N-cad + MSCs and their intricate interactions with pre-HSCs, we formulated the hypothesis that N-cad + MSCs play a crucial role in embryonic hematopoiesis. To functionally validate our hypothesis, we are conducting a series of experiments using Ncad CreER; Cxcl12 flox/floxand Ncad CreER; Jag1 flox/flox mouse models. We anticipate that embryos lacking N-cad + MSC derived Jag1 or Cxcl12 will exhibit a substantial decline in intra-aortic hematopoietic clusters, phenotypic pre-HSCs, and functional HSCs. In summary, our study employed sequencing-based (Slide-seq) and imaging-based (MERFISH) spatial transcriptomics technologies to successfully characterize dynamic, spatially resolved profiles of niche components and associated signaling in the AGM region at the single cell level. This work significantly enhances our understanding of niche signaling and its role in regulating embryonic hematopoiesis and may aid the development of novel methods for inducing the transition from pluripotent stem cells to HSCs.

Single Nuclei Multiomics Analysis of Transcriptional and Chromatin Accessibility of Tumor Cells Uncovers Molecular Signatures and Regulatory Elements of Malignant Clonal Evolution in Multiple Myeloma

Category Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational Introduction Multiple myeloma (MM) is a hematological cancer resulting from abnormal proliferation of plasma cells (PC) in bone marrow. The progression of the disease from Monoclonal Gammopathy of Undetermined Significance (MGUS), Smoldering Multiple Myeloma (SMM), to MM involves multiple drivers and dynamic interactions of premalignant/malignant cells. In the advancement of single-cell technologies, the same-cell readouts of nucleus RNA sequencing (snRNA-seq) and Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) provide a less biased measurement of gene expressions and chromatin accessible regions. Profiling and analyzing both omics data at different stages of MM may reveal essential molecular signatures and regulatory elements for microenvironmental modification and evolution. These insights could benefit MM patients as novel targets for early therapeutic intervention at different stages of progression. Methods Bone marrow aspirates were collected from 33 individuals: Healthy donors (n=3), patients with MGUS (n=2), SMM (n=5), MM (n=21), and Plasma Cell Leukemia (PCL; n=2). Viable mononuclear cells underwent CD138+ sorting and nuclei isolation prior to library preparation. Libraries were prepared with the Chromium Multiome kit (10X Genomics) and sequenced on NovaSeq S4 flow cells at UAMS. The clonal V(D)J arrangements of each sample and expression level from HD samples were used to distinguish malignant (MM) from normal plasma cells (PC). The MM data from snATAC-seq and snRNA-seq were integrated using Signac with the weighted nearest neighbor methods from Seurat. All inter- and intra-tumor clusters were evaluated for markers of high-risk disease, differential gene expression, differentially accessible regions, and transcription factor activities. Results We performed snRNA-seq and snATAC-seq sequencing to generate multi-stage, molecular classified-specific chromatin accessibility and transcriptional profiles of plasma cell neoplasms comprising 439,134 cells across 33 samples. The malignant plasma cells for each sample were extracted by the unique clonotypic signature of the immunoglobulin gene rearrangement. Of these, we detected the highest clonality in MGUS samples, suggesting clonal competition at the premalignant stage. Based on our primary microarray data, we detected the overexpression of molecular signatures for most cells in each sample, confirming the intratumor cell heterogeneity. Furthermore, we used the ratio of 70-gene signature of aggressive disease and proliferation signatures to investigate the clonal composition of MM cells corresponding to the risk of relapse. Surprisingly, the proportion of high-risk cells increased respectively from low to high-risk samples. The expansion of high-risk clonality might exhibit competition among malignant cell subpopulations regarding microenvironmental adaptation. The multiomics integration generated less distinct clusters between MGUS and SMM, indicating the gradual complex alterations of subclonal gene expression and chromatin accessibility in malignant transformation. Interestingly, most accessible regions were found in intronic/distal intergenic regions implying the long-range gene regulation from cis-regulatory elements. Based on the positive/negative correlation of binding motifs within differentially accessible regions, we identified unique enriched transcription factors for each disease stage, suggesting the mechanism patterns in the evolutionary process of malignant cells. Conclusion In the largest multiomics study to date of CD138+ cells, we demonstrate the malignant clonal evolution using gene expression and chromatin accessibility profiling from patients with early-stage disease to highly aggressive form. Throughout the time of disease progression, we found an increase in the proportion of malignant cells with high-risk signatures. Also, we uncover unique signatures of chromatin accessibility for each molecular classification that can further refine our understanding of functional heterogeneity in multiple myeloma. Collectively, our joint single nuclei profiling reveals additional insights into the malignant clonal evolution and investigates novel regulatory elements involved in disease progression.

ScapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.

A roadmap for delivering a human musculoskeletal cell atlas.

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims tocreate a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Studying Macrophages in the Murine Steatotic Liver Using Flow Cytometry and Confocal Microscopy.

The study of macrophage functions in the context of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction associated steatohepatitis (MASH) has been hampered by the fact that until recently all macrophages in the liver were thought to be Kupffer cells, the resident macrophages of the liver. With the advent of single-cell technologies, it is now clear that the steatotic liver harbors many distinct populations of macrophages, likely each with their own unique functions as well as subsets of monocytes and dendritic cells which can be difficult to discriminate from one another. Here, we detail the protocols we utilize to (i) induce MASLD/MASH in mice, (ii) isolate cells from the steatotic liver, and (iii) describe reliable gating strategies, which can be used to identify the different subsets of myeloid cells. Finally, we also discuss the issue of increased autofluorescence in the steatotic liver and the techniques we use to minimize this both for flow cytometry and confocal microscopy analyses.

Single-cell resolution analysis reveals the preparation for reprogramming the fate of stem cell niche in cotton lateral meristem

BackgroundSomatic embryogenesis is a major process for plant regeneration. However, cell communication and the gene regulatory network responsible for cell reprogramming during somatic embryogenesis are still largely unclear. Recent advances in single-cell technologies enable us to explore the mechanism of plant regeneration at single-cell resolution.ResultsWe generate a high-resolution single-cell transcriptomic landscape of hypocotyl tissue from the highly regenerable cotton genotype Jin668 and the recalcitrant TM-1. We identify nine putative cell clusters and 23 cluster-specific marker genes for both cultivars. We find that the primary vascular cell is the major cell type that undergoes cell fate transition in response to external stimulation. Further developmental trajectory and gene regulatory network analysis of these cell clusters reveals that a total of 41 hormone response-related genes, including LAX2, LAX1, and LOX3, exhibit different expression patterns in the primary xylem and cambium region of Jin668 and TM-1. We also identify novel genes, including CSEF, PIS1, AFB2, ATHB2, PLC2, and PLT3, that are involved in regeneration. We demonstrate that LAX2, LAX1 and LOX3 play important roles in callus proliferation and plant regeneration by CRISPR/Cas9 editing and overexpression assay.ConclusionsThis study provides novel insights on the role of the regulatory network in cell fate transition and reprogramming during plant regeneration driven by somatic embryogenesis.

Monocytes in Tumorigenesis and Tumor Immunotherapy.

Monocytes are highly plastic innate immune cells that display significant heterogeneity during homeostasis, inflammation, and tumorigenesis. Tumor-induced systemic and local microenvironmental changes influence the phenotype, differentiation, and distribution of monocytes. Meanwhile, monocytes and their related cell subsets perform an important regulatory role in the development of many cancers by affecting tumor growth or metastasis. Thanks to recent advances in single-cell technologies, the nature of monocyte heterogeneity and subset-specific functions have become increasingly clear, making it possible to systematically analyze subset-specific roles of monocytes in tumorigenesis. In this review, we discuss recent discoveries related to monocytes and tumorigenesis, and new strategies for tumor biomarker identification and anti-tumor immunotherapy.

Transitioning single-cell genomics into the clinic.

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci.

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.