A periodic table of cell types

Allometry of cell types in planarians by single-cell transcriptomics

Allometry explores the relationship between an organism's body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet the study of cell-type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell-type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for these studies. We used scRNA-seq to examine cell-type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the regulated gene modules of distinct cell types in response to body size. This research sheds light on the molecular and cellular aspects of cell-type allometry in planarians and underscores the utility of scRNA-seq in these investigations.

Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis

Cell type repertoires have expanded extensively in metazoan animals, with some clade-specific cells being crucial to evolutionary success. A prime example are the skeletogenic cells of vertebrates. Depending on anatomical location, these cells originate from three different precursor lineages, yet they converge developmentally towards similar cellular phenotypes. Furthermore, their 'skeletogenic competency' arose at distinct evolutionary timepoints, thus questioning to what extent different skeletal body parts rely on truly homologous cell types. Here, we investigate how lineage-specific molecular properties are integrated at the gene regulatory level, to allow for skeletogenic cell fate convergence. Using single-cell functional genomics, we find that distinct transcription factor profiles are inherited from the three precursor states and incorporated at lineage-specific enhancer elements. This lineage-specific regulatory logic suggests that these regionalized skeletogenic cells are distinct cell types, rendering them amenable to individualized selection, to define adaptive morphologies and biomaterial properties in different parts of the vertebrate skeleton.

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles that specialize to accomplish complimentary functions 1-3. All mitochondria share general features, but not all mitochondria are created equal 4.Here we develop a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes - or mitotypes. By distilling hundreds of validated mitochondrial genes/proteins into 149 biologically interpretable MitoPathway scores (MitoCarta 3.0 5) the simple mitotyping pipeline allows investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomics or proteomics data across a variety of natural and experimental contexts. We show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, contrasting anabolic (liver) and catabolic (brain) tissues. In cultured primary human fibroblasts exhibiting robust time-dependent and treatment-induced metabolic plasticity 6-8, we demonstrate how the mitotype of a given cell type recalibrates i) over time in parallel with hallmarks of aging, and ii) in response to genetic, pharmacological, and metabolic perturbations. Investigators can now use MitotypeExplorer.org and the associated code to visualize, quantify and interpret the multivariate space of mitochondrial biology.

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles that specialize to accomplish complimentary functions1-3. All mitochondria share general features, but not all mitochondria are created equal4. Here we develop a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes - or mitotypes. By distilling hundreds of validated mitochondrial genes/proteins into 149 biologically interpretable MitoPathway scores (MitoCarta 3.05) the simple mitotyping pipeline allows investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomics or proteomics data across a variety of natural and experimental contexts. We show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, contrasting anabolic (liver) and catabolic (brain) tissues. In cultured primary human fibroblasts exhibiting robust time-dependent and treatment-induced metabolic plasticity6-8, we demonstrate how the mitotype of a given cell type recalibrates i) over time in parallel with hallmarks of aging, and ii) in response to genetic, pharmacological, and metabolic perturbations. Investigators can now use MitotypeExplorer.org and the associated code to visualize, quantify and interpret the multivariate space of mitochondrial biology.

High order expression dependencies finely resolve cryptic states and subtypes in single cell data

Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells' local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator's finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator's fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.

Dissecting the cellular architecture and genetic circuitry of the soybean seed

Seeds are complex structures composed of three regions, embryo, endosperm, and seed coat, with each further divided into subregions that consist of tissues, cell layers, and cell types. Although the seed is well characterized anatomically, much less is known about the genetic circuitry that dictates its spatial complexity. To address this issue, we profiled mRNAs from anatomically distinct seed subregions at several developmental stages. Analyses of these profiles showed that all subregions express similar diverse gene numbers and that the small gene numbers expressed subregion specifically provide information about the biological processes that occur in these seed compartments. In parallel, we profiled RNAs in individual nuclei and identified nuclei clusters representing distinct cell identities. Integrating single-nucleus RNA and subregion mRNA transcriptomes allowed most cell identities to be assigned to specific subregions and cell types and/or cell states. The number of cell identities exceeds the number of anatomically distinguishable cell types, emphasizing the spatial complexity of seeds. We defined gene coexpression networks that underlie distinct biological processes during seed development. We showed that network distribution among subregions and cell identities is highly variable. Some networks operate in single subregions and/or cell identities, and many coexpression networks operate in multiple subregions and/or cell identities. We also showed that single subregions and cell identities possess several networks. Together, our studies provide unique insights into the biological processes and genetic circuitry that underlie the spatial landscape of the seed.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

A new dawn for the study of cell type evolution

Animal evolution is influenced by the emergence of new cell types, yet our understanding of this process remains elusive. This prompts the need for a broader exploration across diverse research organisms, facilitated by recent breakthroughs, such as gene editing tools and single-cell genomics. Essential to our understanding of cell type evolution is the accurate identification of homologous cells. We delve into the significance of considering developmental ontogeny and potential pitfalls when drawing conclusions about cell type homology. Additionally, we highlight recent discoveries in the study of cell type evolution through the application of single-cell transcriptomics and pinpoint areas ripe for further exploration.

Decoding the nature and complexity of extracellular mtDNA: Types and implications for health and disease

The mitochondrial DNA (mtDNA) is replicated and canonically functions within intracellular mitochondria, but recent discoveries reveal that the mtDNA has another exciting extracellular life. mtDNA fragments and mitochondria-containing vesicular structures are detected at high concentrations in cell-free forms, in different biofluids. Commonly referred to as cell-free mtDNA (cf-mtDNA), the field is currently without a comprehensive classification system that acknowledges the various biological forms of mtDNA and whole mitochondria existing outside the cell. This absence of classification hampers the creation of precise and consistent quantification methods across different laboratories, which is crucial for unraveling the molecular and biological characteristics of mtDNA. In this article, we integrate recent findings to propose a classification for different types of Extracellular mtDNA [ex-mtDNA]. The major biologically distinct types include: Naked mtDNA [N-mtDNA], mtDNA within non-mitochondrial Membranes [M-mtDNA], Extracellular mitochondria [exM-mtDNA], and mtDNA within Mitochondria enclosed in a Membrane [MM-mtDNA]. We outline the challenges associated with accurately quantifying these ex-mtDNA types, suggest potential physiological roles for each ex-mtDNA type, and explore how this classification could establish a foundation for future research endeavors and further analysis and definitions for ex-mtDNA. By proposing this classification of circulating mtDNA forms, we draw a parallel with the clinically recognized forms of cholesterol, such as HDL and LDL, to illustrate potential future significance in a similar manner. While not directly analogous, these mtDNA forms may one day be as biologically relevant in clinical interpretation as cholesterol fractions are currently. We also discuss how advancing methodologies to reliably quantify distinct ex-mtDNA forms could significantly enhance their utility as health or disease biomarkers, and how their application may offer innovative therapeutic approaches.

Preparation of polypeptide-metal complexes-coated Hosenkoside A and its inhibitory effect in cervical cancer

We reported the anti-cervical cancer effect of proprietary saponin content from seeds of Impatiens balsamina L., Hosenkoside A. Our study found that Hosenkoside A significantly promotes cell apoptosis and cell cycle arrest after administration, exhibiting anti-tumor effects. Then the transcriptome sequencing results after administration showed that Hosenkoside A had a significant inhibitory effect on Histone deacetylase 3 (HDAC3). After sufficient administration time, the inhibition of HDAC3 expression level leads to a significant decrease in lysine acetylation at histone 3 sites 4 and 9, blocking the activation of Signal transducer and activator of transcription 3 (STAT3) and achieving anti-tumor effects. In addition, we encapsulated Hosenkoside A into polypeptide metal complexes (PMC) to form slow-release spheres. This material breaks down in the tumor environment, not only does it solve the problem of low drug solubility, but it also achieves targeted sustained-release drug delivery. Under the same concentration of stimulation, the PMC complex group showed better anti-tumor effects in both in vitro and in vivo experiments.

A full-body transcription factor expression atlas with completely resolved cell identities in C. elegans

Invariant cell lineage in C. elegans enables spatiotemporal resolution of transcriptional regulatory mechanisms controlling the fate of each cell. Here, we develop RAPCAT (Robust-point-matching- And Piecewise-affine-based Cell Annotation Tool) to automate cell identity assignment in three-dimensional image stacks of L1 larvae and profile reporter expression of 620 transcription factors in every cell. Transcription factor profile-based clustering analysis defines 80 cell types distinct from conventional phenotypic cell types and identifies three general phenotypic modalities related to these classifications. First, transcription factors are broadly downregulated in quiescent stage Hermaphrodite Specific Neurons, suggesting stage- and cell type-specific variation in transcriptome size. Second, transcription factor expression is more closely associated with morphology than other phenotypic modalities in different pre- and post-differentiation developmental stages. Finally, embryonic cell lineages can be associated with specific transcription factor expression patterns and functions that persist throughout postembryonic life. This study presents a comprehensive transcription factor atlas for investigation of intra-cell type heterogeneity.

SOX2 and SOX9 Expression in Developing Postnatal Opossum (Monodelphis domestica) Cortex

(1) Background: Central nervous system (CNS) development is characterized by dynamic changes in cell proliferation and differentiation. Key regulators of these transitions are the transcription factors such as SOX2 and SOX9. SOX2 is involved in the maintenance of progenitor cell state and neural stem cell multipotency, while SOX9, expressed in neurogenic niches, plays an important role in neuron/glia switch with predominant expression in astrocytes in the adult brain. (2) Methods: To validate SOX2 and SOX9 expression patterns in developing opossum (Monodelphis domestica) cortex, we used immunohistochemistry (IHC) and the isotropic fractionator method on fixed cortical tissue from comparable postnatal ages, as well as dissociated primary neuronal cultures. (3) Results: Neurons positive for both neuronal (TUJ1 or NeuN) and stem cell (SOX2) markers were identified, and their presence was confirmed with all methods and postnatal age groups (P4-6, P6-18, and P30) analyzed. SOX9 showed exclusive staining in non-neuronal cells, and it was coexpressed with SOX2. (4) Conclusions: The persistence of SOX2 expression in developing cortical neurons of M. domestica during the first postnatal month implies the functional role of SOX2 during neuronal differentiation and maturation, which was not previously reported in opossums.

Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.

Craniofacial developmental biology in the single-cell era

The evolution of a unique craniofacial complex in vertebrates made possible new ways of breathing, eating, communicating and sensing the environment. The head and face develop through interactions of all three germ layers, the endoderm, ectoderm and mesoderm, as well as the so-called fourth germ layer, the cranial neural crest. Over a century of experimental embryology and genetics have revealed an incredible diversity of cell types derived from each germ layer, signaling pathways and genes that coordinate craniofacial development, and how changes to these underlie human disease and vertebrate evolution. Yet for many diseases and congenital anomalies, we have an incomplete picture of the causative genomic changes, in particular how alterations to the non-coding genome might affect craniofacial gene expression. Emerging genomics and single-cell technologies provide an opportunity to obtain a more holistic view of the genes and gene regulatory elements orchestrating craniofacial development across vertebrates. These single-cell studies generate novel hypotheses that can be experimentally validated in vivo. In this Review, we highlight recent advances in single-cell studies of diverse craniofacial structures, as well as potential pitfalls and the need for extensive in vivo validation. We discuss how these studies inform the developmental sources and regulation of head structures, bringing new insights into the etiology of structural birth anomalies that affect the vertebrate head.

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.

Single-cell gene expression analysis of cryopreserved equine bronchoalveolar cells

The transcriptomic profile of a cell population can now be studied at the cellular level using single-cell mRNA sequencing (scRNA-seq). This novel technique provides the unprecedented opportunity to explore the cellular composition of the bronchoalveolar lavage fluid (BALF) of the horse, a species for which cell type markers are poorly described. Here, scRNA-seq technology was applied to cryopreserved equine BALF cells. Analysis of 4,631 cells isolated from three asthmatic horses in remission identified 16 cell clusters belonging to six major cell types: monocytes/macrophages, T cells, B/plasma cells, dendritic cells, neutrophils and mast cells. Higher resolution analysis of the constituents of the major immune cell populations allowed deep annotation of monocytes/macrophages, T cells and B/plasma cells. A significantly higher lymphocyte/macrophage ratio was detected with scRNA-seq compared to conventional cytological differential cell count. For the first time in horses, we detected a transcriptomic signature consistent with monocyte-lymphocyte complexes. Our findings indicate that scRNA-seq technology is applicable to cryopreserved equine BALF cells, allowing the identification of its major (cytologically differentiated) populations as well as previously unexplored T cell and macrophage subpopulations. Single-cell gene expression analysis has the potential to facilitate understanding of the immunological mechanisms at play in respiratory disorders of the horse, such as equine asthma.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

What is a cell type and how to define it?

Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.

A Cd9+Cd271+ stem/progenitor population and the SHP2 pathway contribute to neonatal-to-adult switching that regulates tendon maturation

Tendon maturation lays the foundation for postnatal tendon development, its proper mechanical function, and regeneration, but the critical cell populations and the entangled mechanisms remain poorly understood. Here, by integrating the structural, mechanical, and molecular properties, we show that post-natal days 7-14 are the crucial transitional stage for mouse tendon maturation. We decode the cellular and molecular regulatory networks at the single-cell level. We find that a nerve growth factor (NGF)-secreting Cd9⁺Cd271⁺ tendon stem/progenitor cell population mainly prompts conversion from neonate to adult tendon. Through single-cell gene regulatory network analysis, in vitro inhibitor identification, and in vivo tendon-specific Shp2 deletion, we find that SHP2 signaling is a regulator for tendon maturation. Our research comprehensively reveals the dynamic cell population transition during tendon maturation, implementing insights into the critical roles of the maturation-related stem cell population and SHP2 signaling pathway during tendon differentiation and regeneration.

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.

Evolutionary Origins of the Oligodendrocyte Cell Type and Adaptive Myelination

Oligodendrocytes are multifunctional central nervous system (CNS) glia that are essential for neural function in gnathostomes. The evolutionary origins and specializations of the oligodendrocyte cell type are among the many remaining mysteries in glial biology and neuroscience. The role of oligodendrocytes as CNS myelinating glia is well established, but recent studies demonstrate that oligodendrocytes also participate in several myelin-independent aspects of CNS development, function, and maintenance. Furthermore, many recent studies have collectively advanced our understanding of myelin plasticity, and it is now clear that experience-dependent adaptations to myelination are an additional form of neural plasticity. These observations beg the questions of when and for which functions the ancestral oligodendrocyte cell type emerged, when primitive oligodendrocytes evolved new functionalities, and the genetic changes responsible for these evolutionary innovations. Here, I review recent findings and propose working models addressing the origins and evolution of the oligodendrocyte cell type and adaptive myelination. The core gene regulatory network (GRN) specifying the oligodendrocyte cell type is also reviewed as a means to probe the existence of oligodendrocytes in basal vertebrates and chordate invertebrates.

Estimating cellular redundancy in networks of genetic expression

Networks of genetic expression can be modeled by hypergraphs with the additional structure that real coefficients are given to each vertex-edge incidence. The spectra, i.e. the multiset of the eigenvalues, of such hypergraphs, are known to encode structural information of the data. We show how these spectra can be used, in particular, in order to give an estimation of cellular redundancy, a novel measure of gene expression heterogeneity, of the network. We analyze some simulated and real data sets of gene expression for illustrating the new method proposed here.

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.

Vision, challenges and opportunities for a Plant Cell Atlas

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.

Evaluating single-cell cluster stability using the Jaccard similarity index

MOTIVATION

One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor and the resolution parameters, among others.

RESULTS

Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat and estimation of cluster stability using the Jaccard similarity index and providing rich visualizations.

AVAILABILITYAND IMPLEMENTATION

R package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow_seuratv3_parameter Tutorial: https://crazyhottommy.github.io/EvaluateSingleCellClustering/.

Defining the variety of cell types in developing and adult human kidneys by single-cell RNA sequencing

The kidney is among the most complex organs in terms of the variety of cell types. The cellular complexity of human kidneys is not fully unraveled and this challenge is further complicated by the existence of multiple progenitor pools and differentiation pathways. Researchers disagree on the variety of renal cell types due to a lack of research providing a comprehensive picture and the challenge to translate findings between species. To find an answer to the number of human renal cell types, we discuss research that used single-cell RNA sequencing on developing and adult human kidney tissue and compares these findings to the literature of the pre-single-cell RNA sequencing era. We find that these publications show major steps towards the discovery of novel cell types and intermediate cell stages as well as complex molecular signatures and lineage pathways throughout development. The variety of cell types remains variable in the single-cell literature, which is due to the limitations of the technique. Nevertheless, our analysis approaches an accumulated number of 41 identified cell populations of renal lineage and 32 of non-renal lineage in the adult kidney, and there is certainly much more to discover. There is still a need for a consensus on a variety of definitions and standards in single-cell RNA sequencing research, such as the definition of what is a cell type. Nevertheless, this early-stage research already proves to be of significant impact for both clinical and regenerative medicine, and shows potential to enhance the generation of sophisticated in vitro kidney tissue.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Assessing evolutionary and developmental transcriptome dynamics in homologous cell types

Background

During development, complex organ patterns emerge through the precise temporal and spatial specification of different cell types. On an evolutionary timescale, these patterns can change, resulting in morphological diversification. It is generally believed that homologous anatomical structures are built—largely—by homologous cell types. However, whether a common evolutionary origin of such cell types is always reflected in the conservation of their intrinsic transcriptional specification programs is less clear.

Results

Here, we developed a user‐friendly bioinformatics workflow to detect gene co‐expression modules and test for their conservation across developmental stages and species boundaries. Using a paradigm of morphological diversification, the tetrapod limb, and single‐cell RNA‐sequencing data from two distantly related species, chicken and mouse, we assessed the transcriptional dynamics of homologous cell types during embryonic patterning. With mouse limb data as reference, we identified 19 gene co‐expression modules with varying tissue or cell type‐restricted activities. Testing for co‐expression conservation revealed modules with high evolutionary turnover, while others seemed maintained—to different degrees, in module make‐up, density or connectivity—over developmental and evolutionary timescales.

Conclusions

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

Extracellular matrix gene expression signatures as cell type and cell state identifiers

Transcriptomic signatures based on cellular mRNA expression profiles can be used to categorize cell types and states. Yet whether different functional groups of genes perform better or worse in this process remains largely unexplored. Here we test the core matrisome - that is, all genes coding for structural proteins of the extracellular matrix - for its ability to delineate distinct cell types in embryonic single-cell RNA-sequencing (scRNA-seq) data. We show that even though expressed core matrisome genes correspond to less than 2% of an entire cellular transcriptome, their RNA expression levels suffice to recapitulate essential aspects of cell type-specific clustering. Notably, using scRNA-seq data from the embryonic limb, we demonstrate that core matrisome gene expression outperforms random gene subsets of similar sizes and can match and exceed the predictive power of transcription factors. While transcription factor signatures generally perform better in predicting cell types at early stages of chicken and mouse limb development, i.e., when cells are less differentiated, the information content of the core matrisome signature increases in more differentiated cells. Moreover, using cross-species analyses, we show that these cell type-specific signatures are evolutionarily conserved. Our findings suggest that each cell type produces its own unique extracellular matrix, or matreotype, which becomes progressively more refined and cell type-specific as embryonic tissues mature.

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

A comprehensive survey of developmental programs reveals a dearth of tree-like lineage graphs and ubiquitous regeneration

Background

Multicellular organisms are characterized by a wide diversity of forms and complexity despite a restricted set of key molecules and mechanisms at the base of organismal development. Development combines three basic processes—asymmetric cell division, signaling, and gene regulation—in a multitude of ways to create this overwhelming diversity of multicellular life forms. Here, we use a generative model to test the limits to which such processes can be combined to generate multiple differentiation paths during development, and attempt to chart the diversity of multicellular organisms generated.

Results

We sample millions of biologically feasible developmental schemes, allowing us to comment on the statistical properties of cell differentiation trajectories they produce. We characterize model-generated “organisms” using the graph topology of their cell type lineage maps. Remarkably, tree-type lineage differentiation maps are the rarest in our data. Additionally, a majority of the “organisms” generated by our model appear to be endowed with the ability to regenerate using pluripotent cells.

Conclusions

Our results indicate that, in contrast to common views, cell type lineage graphs are unlikely to be tree-like. Instead, they are more likely to be directed acyclic graphs, with multiple lineages converging on the same terminal cell type. Furthermore, the high incidence of pluripotent cells in model-generated organisms stands in line with the long-standing hypothesis that whole body regeneration is an epiphenomenon of development. We discuss experimentally testable predictions of our model and some ways to adapt the generative framework to test additional hypotheses about general features of development.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12915-021-01013-4).

Evolutionary Cell Type Mapping with Single-Cell Genomics

A fundamental characteristic of animal multicellularity is the spatial coexistence of functionally specialized cell types that are all encoded by a single genome sequence. Cell type transcriptional programs are deployed and maintained by regulatory mechanisms that control the asymmetric, differential access to genomic information in each cell. This genome regulation ultimately results in specific cellular phenotypes. However, the emergence, diversity, and evolutionary dynamics of animal cell types remain almost completely unexplored beyond a few species. Single-cell genomics is emerging as a powerful tool to build comprehensive catalogs of cell types and their associated gene regulatory programs in non-traditional model species. We review the current state of sampling efforts across the animal tree of life and challenges ahead for the comparative study of cell type programs. We also discuss how the phylogenetic integration of cell atlases can lead to the development of models of cell type evolution and a phylogenetic taxonomy of cells.

Employing core regulatory circuits to define cell identity

The interplay between extrinsic signaling and downstream gene networks controls the establishment of cell identity during development and its maintenance in adult life. Advances in next-generation sequencing and single-cell technologies have revealed additional layers of complexity in cell identity. Here, we review our current understanding of transcription factor (TF) networks as key determinants of cell identity. We discuss the concept of the core regulatory circuit as a set of TFs and interacting factors that together define the gene expression profile of the cell. We propose the core regulatory circuit as a comprehensive conceptual framework for defining cellular identity and discuss its connections to cell function in different contexts.

Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.

Gene expression pattern of trophoblast-specific transcription factors in trophectoderm by analysis of single-cell RNA-seq data of human blastocyst

The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in humans due to the lack of material in vivo. Trophoblast (TB) lineage derived from human embryonic stem cells (hESCs) induced by bone morphogenetic protein 4 (BMP4) has been applied as a model for studying TB lineage specification in vitro. With the development of single-cell sequencing technology, it became possible to detect the transcriptome of the post-implantation embryo at unprecedented precision. In this study, we reanalyzed single-cell RNA-seq of post-implantation embryos derived from two separate groups and identified different subtypes of trophoblast cells and their marker, respectively. At the same time, we focused on the gene expression patterns of trophoblast-specific transcription factors in different models. Our analysis sheds new light on the transcription regulation mechanism of trophoblast differentiation at the early stage of pregnancy establishment in human.

Rethinking organoid technology through bioengineering

In recent years considerable progress has been made in the development of faithful procedures for the differentiation of human pluripotent stem cells (hPSCs). An important step in this direction has also been the derivation of organoids. This technology generally relies on traditional three-dimensional culture techniques that exploit cell-autonomous self-organization responses of hPSCs with minimal control over the external inputs supplied to the system. The convergence of stem cell biology and bioengineering offers the possibility to provide these stimuli in a controlled fashion, resulting in the development of naturally inspired approaches to overcome major limitations of this nascent technology. Based on the current developments, we emphasize the achievements and ongoing challenges of bringing together hPSC organoid differentiation, bioengineering and ethics. This Review underlines the need for providing engineering solutions to gain control of self-organization and functionality of hPSC-derived organoids. We expect that this knowledge will guide the community to generate higher-grade hPSC-derived organoids for further applications in developmental biology, drug screening, disease modelling and personalized medicine.

Elucidating memory in the brain via single-cell transcriptomics

Elucidating the neural mechanisms of memory in the brain is a central goal of neuroscience. Here, we discuss modern-day transcriptomics methodologies, and how they are well-poised to revolutionize our insight into memory mechanisms at unprecedented resolution and throughput. Focusing on the hippocampus and amygdala, two regions extensively examined in memory research, we show how single-cell transcriptomics technologies have been leveraged to understand the naïve state of these brain regions. Building upon this foundation, we show that these technologies can be applied to single-trial learning paradigms to comprehensively identify molecules and cells that participate in the encoding and retrieval of memory. Transcriptomics also provides an opportunity to understand the cell-type organization of the human hippocampus and amygdala, and due to conservation of these brain regions between humans and rodents, to infer behavioral and causal contributions in the human brain by leveraging rodent cell-type homologies and interventions. Ultimately, such transcriptomic technologies are poised to usher in a qualitatively novel understanding of memory in the brain.

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.

Untangling biological factors influencing trajectory inference from single cell data

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajectory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.

The evolving concept of cell identity in the single cell era

Fueled by recent advances in single cell biology, we are moving away from qualitative and undersampled assessments of cell identity, toward building quantitative, high-resolution cell atlases. However, it remains challenging to precisely define cell identity, leading to renewed debate surrounding this concept. Here, I present three pillars that I propose are central to the notion of cell identity: phenotype, lineage and state. I explore emerging technologies that are enabling the systematic and unbiased quantification of these properties, and outline how these efforts will enable the construction of a high-resolution, dynamic landscape of cell identity, potentially revealing its underlying molecular regulation to provide new opportunities for understanding and manipulating cell fate.