The emergence and promise of single-cell temporal-omics approaches

https://doi.org/10.1016/j.copbio.2019.12.005Get rights and content

Highlights

  • Pseudotime trajectory inference algorithms harness the variance of asynchronously changing cells.

  • RNA velocity uses expression dynamics to extrapolate future cellular state.

  • New approaches record information on clonal relatedness and other past cellular events.

  • Temporal-omics methods offer insights on development and pathology by integrating information from past and present.

Single-cell transcriptomics enables the measurement of gene expression in complex biological systems at the resolution of individual cells. Multivariate analysis of single-cell data helps describe the variation in expression accompanying cellular processes during embryonic development, disease progression and in response to stimuli. Likewise, new methods have extended the possibilities of single-cell analysis by measuring the transcriptome while simultaneously capturing information on lineage or past molecular events. These emerging approaches have the common strategy of querying a static snapshot for information related to different temporal stages. Single-cell temporal-omics methods open new possibilities such as extrapolating the future or correlating past events to present gene expression. We highlight advancements in the single-cell field, describe novel toolkits for investigation, and consider the potential impact of temporal-omics approaches for the study of disease progression.

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.

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