Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types

Stereotyped Subclones Revealed by High-Density Single-Cell Lineage Tracing Support Robust Development

Robust development is essential for multicellular organisms. While various mechanisms contributing to developmental robustness are identified at the subcellular level, those at the intercellular and tissue level remain underexplored. This question is approached using a well-established in vitro directed differentiation model recapitulating the in vivo development of lung progenitor cells from human embryonic stem cells. An integrated analysis of high-density cell lineage trees (CLTs) and single-cell transcriptomes of differentiating colonies enabled the resolution of known cell types and developmental hierarchies. This dataset showed little support for the contribution of transcriptional memory to developmental robustness. Nevertheless, stable terminal cell type compositions are observed among many subclones, which enhances developmental robustness because the colony can retain a relatively stable composition even if some subclones are abolished by cell death. Furthermore, it is found that many subclones are formed by sub-CLTs resembling each other in terms of both terminal cell type compositions and topological structures. The presence of stereotyped sub-CLTs constitutes a novel basis for developmental robustness. Moreover, these results suggest a unique perspective on individual cells' function in the context of stereotyped sub-CLTs, which can bridge the knowledge of the atlas of cell types and how they are organized into functional tissues.

A statistical method for quantifying progenitor cells reveals incipient cell fate commitments

Quantifying the number of progenitor cells that found an organ, tissue or cell population is of fundamental importance for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. This strategy is, however, often hindered by the lack of ideal markers. Here we propose a general statistical method to quantify the progenitors of any tissues or cell populations in an organism, even in the absence of progenitor-specific markers, by exploring the cell phylogenetic tree that records the cell division history during development. The method, termed targeting coalescent analysis (TarCA), computes the probability that two randomly sampled cells of a tissue coalesce within the tissue-specific monophyletic clades. The inverse of this probability then serves as a measure of the progenitor number of the tissue. Both mathematic modeling and computer simulations demonstrated the high accuracy of TarCA, which was then validated using real data from nematode, fruit fly and mouse, all with related cell phylogenetic trees. We further showed that TarCA can be used to identify lineage-specific upregulated genes during embryogenesis, revealing incipient cell fate commitments in mouse embryos.

Reconstructing cell lineage trees with genomic barcoding: approaches and applications

In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.

A reference cell tree will serve science better than a reference cell atlas

Single-cell biology is facing a crisis of sorts. Vast numbers of single-cell molecular profiles are being generated, clustered and annotated. However, this is overwhelmingly ad hoc, and we continue to lack a principled, unified, and well-moored system for defining, naming, and organizing cell types. In this perspective, we argue against an atlas or periodic table-like discretization as the right metaphor for a reference taxonomy of cell types. In its place, we advocate for a data-driven, tree-based nomenclature that is rooted in a "consensus ontogeny" spanning the life cycle of a given species. We explore how such a reference cell tree, inclusive of both lineage histories and molecular states, could be constructed, represented, and segmented in practice. Analogous to the taxonomic classification of species, a consensus ontogeny would provide a universal, stable, and extendable framework for precise scientific communication, both contemporaneously and across the ages.

A topological look into the evolution of developmental programs

Rapid advance of experimental techniques provides an unprecedented in-depth view into complex developmental processes. Still, little is known on how the complexity of multicellular organisms evolved by elaborating developmental programs and inventing new cell types. A hurdle to understanding developmental evolution is the difficulty of even describing the intertwined network of spatiotemporal processes underlying the development of complex multicellular organisms. Nonetheless, an overview of developmental trajectories can be obtained from cell type lineage maps. Here, we propose that these lineage maps can also reveal how developmental programs evolve: the modes of evolving new cell types in an organism should be visible in its developmental trajectories and therefore in the geometry of its cell type lineage map. This idea is demonstrated using a parsimonious generative model of developmental programs, which allows us to reliably survey the universe of all possible programs and examine their topological features. We find that, contrary to belief, tree-like lineage maps are rare, and lineage maps of complex multicellular organisms are likely to be directed acyclic graphs in which multiple developmental routes can converge on the same cell type. Although cell type evolution prescribes what developmental programs come into existence, natural selection prunes those programs that produce low-functioning organisms. Our model indicates that additionally, lineage map topologies are correlated with such a functional property: the ability of organisms to regenerate.

Plant Cell Identity in the Era of Single-Cell Transcriptomics

High-throughput single-cell transcriptomic approaches have revolutionized our view of gene expression at the level of individual cells, providing new insights into their heterogeneity, identities, and functions. Recently, technical challenges to the application of single-cell transcriptomics to plants have been overcome, and many plant organs and tissues have now been subjected to analyses at single-cell resolution. In this review, we describe these studies and their impact on our understanding of the diversity, differentiation, and activities of plant cells. We particularly highlight their impact on plant cell identity, including unprecedented views of cell transitions and definitions of rare and novel cell types. We also point out current challenges and future opportunities for the application and analyses of single-cell transcriptomics in plants. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Cell Tracking for Organoids: Lessons From Developmental Biology

Organoids have emerged as powerful model systems to study organ development and regeneration at the cellular level. Recently developed microscopy techniques that track individual cells through space and time hold great promise to elucidate the organizational principles of organs and organoids. Applied extensively in the past decade to embryo development and 2D cell cultures, cell tracking can reveal the cellular lineage trees, proliferation rates, and their spatial distributions, while fluorescent markers indicate differentiation events and other cellular processes. Here, we review a number of recent studies that exemplify the power of this approach, and illustrate its potential to organoid research. We will discuss promising future routes, and the key technical challenges that need to be overcome to apply cell tracking techniques to organoid biology.

CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.

Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.