The emergence and promise of single-cell temporal-omics approaches
Graphical abstract
Introduction
Over the past decade, an ambition of the single-cell genomics field has been to use high-throughput techniques to interrogate complex biological systems and understand how cells change and respond to different stimuli. Single-cell RNA sequencing (scRNA-seq) can capture the transcriptomic profiles of individual cells, rather than relying on bulk cell measurements, permitting greater resolution of tissue heterogeneity [1].
Single-cell profiling has been used to construct comprehensive tissue atlases defining diverse cell profiles and marker genes [2,3], including for aging tissues [4, 5, 6] and organoids [7]. Rare cell types have been identified that play a role in genetic diseases such as cystic fibrosis [8,9] or the response to pathogens in the intestine [10]. Similar surveys have uncovered greater nuance in multi-factorial diseases, particularly nervous system conditions such as Alzheimer’s disease [11, 12, 13], schizophrenia [14,15], and multiple sclerosis [16]. Gene expression data has also been cleverly adopted to enhance perspectives offered by genome-wide association studies (GWAS). Using linkage disequilibrium regression, disease-associated variants identified in GWAS can be mapped to specific cell types or tissues. For example, polymorphisms related to schizophrenia were linked to a subset of neurons in the brain using single-cell gene expression information [14,15].
Furthermore, single-cell technologies have further revealed the complexity of developmental processes, gene regulatory networks, and cell fate commitment landscapes [17]. Although these techniques can profile several thousand cells per sample [18,19], converting this wealth of data into biological insight is challenging and requires the application of dedicated statistical methods and modeling approaches [1,20•].
Single-cell sampling of a tissue generates a snapshot of cell states during a specific biological process, revealing regions in gene expression space occupied by mature and transient cell types. Profiling dynamical processes with scRNA-seq specifically enables characterization of intermediate cell states. The first methods to represent cells as entities spanning gene expression space were introduced in 2014 [21,22]. Importantly, unlike live cell approaches, these methods depend on variation among cells types within the measured population and do not infer cellular trajectories by tracking change within a single cell. Approaches have been developed to assess different temporal aspects, including RNA velocity [23••], nascent RNA quantification [24••,25••,26,27], lineage tracing [28, 29, 30, 31, 32, 33, 34, 35, 36, 37], and molecular recording [29,38••] (Figure 1).
In this review, we describe the rise of single-cell methods that enable holistic examination of the dynamics of gene expression. Starting from pseudotime trajectory analysis, we highlight tools that collect information relating to events in the remote or recent past or allow estimation of the future state of a cell (Figure 2). These techniques establish impactful means to investigate biological systems, which we propose as deserving its own denomination: single-cell temporal-omics, the genome-wide study of change in individual cells. While presenting the advances in this field, we provide our view on how these emerging methodologies can potentially influence the study of disease.
Section snippets
Inference of single-cell pseudotime trajectories
In addition to being valuable for molecular histology and the identification of cell types, single-cell methods can measure the variation in expression accompanying physiological and pathological processes [17,20•]. Repeated extraction of cellular contents from living cells is still in its infancy [39]. Thus, single-cell genomics techniques are inherently destructive and provide only a snapshot from the time of sampling [40••]. Nonetheless, information on collective cellular dynamics can be
RNA velocity and estimation of future gene expression states
Using the causal relationship between two cellular entities to model the rate of a biological process was proposed in 1952 by biochemist Jacques Monod in the context of enzymatic reactions [53,54]. Monod describes a first-order differential equation representing the enzyme synthesis rate in Escherichia coli, given a constant ratio of the amount of enzyme synthesized to total amount of biological matter. These concepts were revisited in the microarray era by Zeisel et al. for the purpose of
Recording of clonal information and past transcriptional states
Traditional lineage tracing technologies probe the mitotic relatedness of individual cells by marking the progeny of a given set of cells [81•,82]. In the modern single-cell genomics era, the evolution of lineage tracing is represented by techniques that can probe gene expression while simultaneously providing information on mitotic kinship. This can be achieved using inducible recording systems in which barcodes are incorporated into the genome and read out by sequencing or imaging-based
Temporal-omics in the study of multi-factorial diseases
Several obstacles exist when using current approaches to capture tissue alterations and identify the dysregulation that characterizes disease initiation and progression [88,89]. These challenges include the slow onset of disease, the disjointed nature of samples collected from different patients, the scarcity of samples, and the inter-individual variability of disease processes. Importantly, novel tools open the possibility to mitigate some of these limiting factors.
The slow onset of genetic
Conclusion
A first exploratory phase of single-cell transcriptomics that focused on identification and classification of complex cell types has provided valuable insights into the heterogeneity of development and disease. The rise of a second phase characterized by experimentally articulate methods that pursue more complex questions about biological mechanisms with respect to time [94•] suggests a shift in the single-cell field from a descriptive to a predictive ambition. Temporal-omics techniques are
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
CRediT authorship contribution statement
Alex R Lederer: Conceptualization, Writing - original draft, Writing - review & editing. Gioele La Manno: Conceptualization, Writing - original draft, Writing - review & editing, Supervision.
Acknowledgements
We would like to sincerely thank those who have provided suggestions and feedback on this article. G.L.M. is an EPFL Life Sciences Early Independence Research (ELISIR) Scholar. G.L.M. is further supported by the Chan Zuckerberg Initiative and Swiss National Science Foundation.
References (96)
- et al.
Beyond bulk: a review of single cell transcriptomics methodologies and applications
Curr Opin Biotechnol
(2019) - et al.
Single-cell multi-omics: an engine for new quantitative models of gene regulation
Trends Genet
(2018) - et al.
RNA velocity of single cells
Nature
(2018) - et al.
Diffusion pseudotime robustly reconstructs lineage branching
Nat Methods
(2016) - et al.
Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli
Mol Syst Biol
(2011) - et al.
Circadian clock-dependent and independent posttranscriptional regulation underlies temporal mRNA accumulation in mouse liver
Proc Natl Acad Sci U S A
(2018) - et al.
Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects impairing adult neurogenesis
Cell Rep
(2018) - et al.
Landscape and dynamics of single immune cells in hepatocellular carcinoma
Cell
(2019) - et al.
Combined mRNA and protein single cell analysis in a dynamic cellular system using SPARC Johan
bioRxiv
(2019) - et al.
Simultaneous multiplexed measurement of RNA and proteins in single cells
Cell Rep
(2016)
Deep generative modeling for single-cell transcriptomics
Nat Methods
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Science
CUT&Tag for efficient epigenomic profiling of small samples and single cells
Nat Commun
Dissecting human disease with single-cell omics: application in model systems and in the clinic
Dis Model Mech
Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response
J Am Soc Nephrol
Aging induces aberrant state transition kinetics in murine muscle stem cells
bioRxiv
A human liver cell atlas reveals heterogeneity and epithelial progenitors
Nature
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Nature
A single cell transcriptomic atlas characterizes aging tissues in the mouse
bioRxiv
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Nat Commun
A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging
bioRxiv
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Nature
A revised airway epithelial hierarchy includes CFTR-expressing ionocytes
Nature
A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte
Nature
A single-cell survey of the small intestinal epithelium
Nature
Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk
Nat Genet
Single-cell transcriptomic analysis of Alzheimer’s disease
Nature
A unique microglia type associated with restricting development of Alzheimer’s disease
Cell
Genetic identification of brain cell types underlying schizophrenia
Nat Genet
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types
Nat Genet
Altered human oligodendrocyte heterogeneity in multiple sclerosis
Nature
Transition states and cell fate decisions in epigenetic landscapes
Nat Rev Genet
Exponential scaling of single-cell RNA-seq in the past decade
Nat Protoc
Scaling single-cell genomics from phenomenology to mechanism
Nature
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Nat Biotechnol
Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development
Cell
NASC-seq monitors RNA synthesis in single cells
Nat Commun
scSLAM-seq reveals core features of transcription dynamics in single cells
Nature
SLAM-seq defines direct gene-regulatory functions of the BRD4-MYC axis
Science
Thiol-linked alkylation of RNA to assess expression dynamics
Nat Methods
Whole-organism lineage tracing by combinatorial and cumulative genome editing
Science
Synthetic recording and in situ readout of lineage information in single cells
Nature
Polylox barcoding reveals haematopoietic stem cell fates realized in vivo
Nature
Whole-organism clone tracing using single-cell sequencing
Nature
Developmental barcoding of whole mouse via homing CRISPR
Science
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Nat Biotechnol
Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars
Nat Biotechnol
Molecular recording of mammalian embryogenesis
Nature
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