Systems Immunology: Learning the Rules of the Immune System.

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Vision-dependent specification of cell types and function in the developing cortex.

The role of postnatal experience in sculpting cortical circuitry, while long appreciated, is poorly understood at the level of cell types. We explore this in the mouse primary visual cortex (V1) using single-nucleus RNA sequencing, visual deprivation, genetics, and functional imaging. We find that vision selectively drives the specification of glutamatergic cell types in upper layers (L) (L2/3/4), while deeper-layer glutamatergic, GABAergic, and non-neuronal cell types are established prior to eye opening. L2/3 cell types form an experience-dependent spatial continuum defined by the graded expression of ∼200 genes, including regulators of cell adhesion and synapse formation. One of these genes, Igsf9b, a vision-dependent gene encoding an inhibitory synaptic cell adhesion molecule, is required for the normal development of binocular responses in L2/3. In summary, vision preferentially regulates the development of upper-layer glutamatergic cell types through the regulation of cell-type-specific gene expression programs.

A functional cellular framework for sex and estrous cycle-dependent gene expression and behavior.

Sex hormones exert a profound influence on gendered behaviors. How individual sex hormone-responsive neuronal populations regulate diverse sex-typical behaviors is unclear. We performed orthogonal, genetically targeted sequencing of four estrogen receptor 1-expressing (Esr1+) populations and identified 1,415 genes expressed differentially between sexes or estrous states. Unique subsets of these genes were distributed across all 137 transcriptomically defined Esr1+ cell types, including estrous stage-specific ones, that comprise the four populations. We used differentially expressed genes labeling single Esr1+ cell types as entry points to functionally characterize two such cell types, BNSTprTac1/Esr1 and VMHvlCckar/Esr1. We observed that these two cell types, but not the other Esr1+ cell types in these populations, are essential for sex recognition in males and mating in females, respectively. Furthermore, VMHvlCckar/Esr1 cell type projections are distinct from those of other VMHvlEsr1 cell types. Together, projection and functional specialization of dimorphic cell types enables sex hormone-responsive populations to regulate diverse social behaviors.

Innovation, conservation, and repurposing of gene function in root cell type development.

Plant species have evolved myriads of solutions, including complex cell type development and regulation, to adapt to dynamic environments. To understand this cellular diversity, we profiled tomato root cell type translatomes. Using xylem differentiation in tomato, examples of functional innovation, repurposing, and conservation of transcription factors are described, relative to the model plant Arabidopsis. Repurposing and innovation of genes are further observed within an exodermis regulatory network and illustrate its function. Comparative translatome analyses of rice, tomato, and Arabidopsis cell populations suggest increased expression conservation of root meristems compared with other homologous populations. In addition, the functions of constitutively expressed genes are more conserved than those of cell type/tissue-enriched genes. These observations suggest that higher order properties of cell type and pan-cell type regulation are evolutionarily conserved between plants and animals.

Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

From Cell Types to an Integrated Understanding of Brain Evolution: The Case of the Cerebral Cortex.

With the discovery of the incredible diversity of neurons, Cajal and coworkers laid the foundation of modern neuroscience. Neuron types are not only structural units of nervous systems but also evolutionary units, because their identities are encoded in the genome. With the advent of high-throughput cellular transcriptomics, neuronal identities can be characterized and compared systematically across species. The comparison of neurons in mammals, reptiles, and birds indicates that the mammalian cerebral cortex is a mosaic of deeply conserved and recently evolved neuron types. Using the cerebral cortex as a case study, this review illustrates how comparing neuron types across species is key to reconciling observations on neural development, neuroanatomy, circuit wiring, and physiology for an integrated understanding of brain evolution.

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior.

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain.

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

CRISPR Tools for Physiology and Cell State Changes: Potential of Transcriptional Engineering and Epigenome Editing.

Given the large amount of genome-wide data that have been collected during the last decades, a good understanding of how and why cells change during development, homeostasis, and disease might be expected. Unfortunately, the opposite is true; triggers that cause cellular state changes remain elusive, and the underlying molecular mechanisms are poorly understood. Although genes with the potential to influence cell states are known, the historic dependency on methods that manipulate gene expression outside the endogenous chromatin context has prevented us from understanding how cells organize, interpret, and protect cellular programs. Fortunately, recent methodological innovations are now providing options to answer these outstanding questions, by allowing to target and manipulate individual genomic and epigenomic loci. In particular, three experimental approaches are now feasible due to DNA targeting tools, namely, activation and/or repression of master transcription factors in their endogenous chromatin context; targeting transcription factors to endogenous, alternative, or inaccessible sites; and finally, functional manipulation of the chromatin context. In this article, we discuss the molecular basis of DNA targeting tools and review the potential of these new technologies before we summarize how these have already been used for the manipulation of cellular states and hypothesize about future applications.

Mime-seq 2.0: a method to sequence microRNAs from specific mouse cell types.

Many microRNAs (miRNAs) are expressed with high spatiotemporal specificity during organismal development, with some being limited to rare cell types, often embedded in complex tissues. Yet, most miRNA profiling efforts remain at the tissue and organ levels. To overcome challenges in accessing the microRNomes from tissue-embedded cells, we had previously developed mime-seq (miRNome by methylation-dependent sequencing), a technique in which cell-specific miRNA methylation in C. elegans and Drosophila enabled chemo-selective sequencing without the need for cell sorting or biochemical purification. Here, we present mime-seq 2.0 for profiling miRNAs from specific mouse cell types. We engineered a chimeric RNA methyltransferase that is tethered to Argonaute protein and efficiently methylates miRNAs at their 3'-terminal 2'-OH in mouse and human cell lines. We also generated a transgenic mouse for conditional expression of this methyltransferase, which can be used to direct methylation of miRNAs in a cell type of choice. We validated the use of this mouse model by profiling miRNAs from B cells and bone marrow plasma cells.

Cell type-specific molecular mechanisms and implications of necroptosis in inflammatory respiratory diseases.

Necroptosis is generally considered as an inflammatory cell death form. The core regulators of necroptotic signaling are receptor-interacting serine-threonine protein kinases 1 (RIPK1) and RIPK3, and the executioner, mixed lineage kinase domain-like pseudokinase (MLKL). Evidence demonstrates that necroptosis contributes profoundly to inflammatory respiratory diseases that are common public health problem. Necroptosis occurs in nearly all pulmonary cell types in the settings of inflammatory respiratory diseases. The influence of necroptosis on cells varies depending upon the type of cells, tissues, organs, etc., which is an important factor to consider. Thus, in this review, we briefly summarize the current state of knowledge regarding the biology of necroptosis, and focus on the key molecular mechanisms that define the necroptosis status of specific cell types in inflammatory respiratory diseases. We also discuss the clinical potential of small molecular inhibitors of necroptosis in treating inflammatory respiratory diseases, and describe the pathological processes that engage cross talk between necroptosis and other cell death pathways in the context of respiratory inflammation. The rapid advancement of single-cell technologies will help understand the key mechanisms underlying cell type-specific necroptosis that are critical to effectively treat pathogenic lung infections and inflammatory respiratory diseases.

Brainstem Circuits Controlling Action Diversification.

Neuronal circuits that regulate movement are distributed throughout the nervous system. The brainstem is an important interface between upper motor centers involved in action planning and circuits in the spinal cord ultimately leading to execution of body movements. Here we focus on recent work using genetic and viral entry points to reveal the identity of functionally dedicated and frequently spatially intermingled brainstem populations essential for action diversification, a general principle conserved throughout evolution. Brainstem circuits with distinct organization and function control skilled forelimb behavior, orofacial movements, and locomotion. They convey regulatory parameters to motor output structures and collaborate in the construction of complex natural motor behaviors. Functionally tuned brainstem neurons for different actions serve as important integrators of synaptic inputs from upstream centers, including the basal ganglia and cortex, to regulate and modulate behavioral function in different contexts.

CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

scTOP: physics-inspired order parameters for cellular identification and visualization.

Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues.

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

How Many Cell Types Are in the Kidney and What Do They Do?

The kidney maintains electrolyte, water, and acid-base balance, eliminates foreign and waste compounds, regulates blood pressure, and secretes hormones. There are at least 16 different highly specialized epithelial cell types in the mammalian kidney. The number of specialized endothelial cells, immune cells, and interstitial cell types might even be larger. The concerted interplay between different cell types is critical for kidney function. Traditionally, cells were defined by their function or microscopical morphological appearance. With the advent of new single-cell modalities such as transcriptomics, epigenetics, metabolomics, and proteomics we are entering into a new era of cell type definition. This new technological revolution provides new opportunities to classify cells in the kidney and understand their functions.

Deep conservation and co-option of programmed cell death facilitates evolution of alternative phenotypes at multiple biological levels.

Alternative phenotypes, such as polyphenisms and sexual dimorphisms, are widespread in nature and appear at all levels of biological organization, from genes and cells to morphology and behavior. Yet, our understanding of the mechanisms through which alternative phenotypes develop and how they evolve remains understudied. In this review, we explore the association between alternative phenotypes and programmed cell death, a mechanism responsible for the elimination of superfluous cells during development. We discuss the ancient origins and deep conservation of programmed cell death (its function, forms and underlying core regulatory gene networks), and propose that it was co-opted repeatedly to generate alternative phenotypes at the level of cells, tissues, organs, external morphology, and even individuals. We review several examples from across the tree of life to explore the conditions under which programmed cell death is likely to facilitate the evolution of alternative phenotypes.

Review of single-cell RNA-seq data clustering for cell-type identification and characterization.

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Unique transcriptomes of sensory and non-sensory neurons: insights from Splicing Regulatory States.

Alternative Splicing (AS) programs serve as instructive signals of cell type specificity, particularly within the brain, which comprises dozens of molecularly and functionally distinct cell types. Among them, retinal photoreceptors stand out due to their unique transcriptome, making them a particularly well-suited system for studying how AS shapes cell type-specific molecular functions. Here, we use the Splicing Regulatory State (SRS) as a novel framework to discuss the splicing factors governing the unique AS pattern of photoreceptors, and how this pattern may aid in the specification of their highly specialized sensory cilia. In addition, we discuss how other sensory cells with ciliated structures, for which data is much scarcer, also rely on specific SRSs to implement a proteome specialized in the detection of sensory stimuli. By reviewing the general rules of cell type- and tissue-specific AS programs, firstly in the brain and subsequently in specialized sensory neurons, we propose a novel paradigm on how SRSs are established and how they can diversify. Finally, we illustrate how SRSs shape the outcome of mutations in splicing factors to produce cell type-specific phenotypes that can lead to various human diseases.