3D RNA and Functional Interactions from Evolutionary Couplings.

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces the research on the structure and functional interactions of these RNA gene sequences. We mine the evolutionary sequence record to derive precise information about the function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions-e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by increasing sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA.

Spontaneous behaviour is structured by reinforcement without explicit reward.

Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences1,2. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.

Lifelong multilineage contribution by embryonic-born blood progenitors.

Haematopoietic stem cells (HSCs) arise in the embryo from the arterial endothelium through a process known as the endothelial-to-haematopoietic transition (EHT)1-4. This process generates hundreds of blood progenitors, of which a fraction go on to become definitive HSCs. It is generally thought that most adult blood is derived from those HSCs, but to what extent other progenitors contribute to adult haematopoiesis is not known. Here we use in situ barcoding and classical fate mapping to assess the developmental and clonal origins of adult blood in mice. Our analysis uncovers an early wave of progenitor specification-independent of traditional HSCs-that begins soon after EHT. These embryonic multipotent progenitors (eMPPs) predominantly drive haematopoiesis in the young adult, have a decreasing yet lifelong contribution over time and are the predominant source of lymphoid output. Putative eMPPs are specified within intra-arterial haematopoietic clusters and represent one fate of the earliest haematopoietic progenitors. Altogether, our results reveal functional heterogeneity during the definitive wave that leads to distinct sources of adult blood.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ('syllables') from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis.

Bone marrow transplantation therapy relies on the life-long regenerative capacity of haematopoietic stem cells (HSCs)1,2. HSCs present a complex variety of regenerative behaviours at the clonal level, but the mechanisms underlying this diversity are still undetermined3-11. Recent advances in single-cell RNA sequencing have revealed transcriptional differences among HSCs, providing a possible explanation for their functional heterogeneity12-17. However, the destructive nature of sequencing assays prevents simultaneous observation of stem cell state and function. To solve this challenge, we implemented expressible lentiviral barcoding, which enabled simultaneous analysis of lineages and transcriptomes from single adult HSCs and their clonal trajectories during long-term bone marrow reconstitution. Analysis of differential gene expression between clones with distinct behaviour revealed an intrinsic molecular signature that characterizes functional long-term repopulating HSCs. Probing this signature through in vivo CRISPR screening, we found the transcription factor TCF15 to be required and sufficient to drive HSC quiescence and long-term self-renewal. In situ, Tcf15 expression labels the most primitive subset of true multipotent HSCs. In conclusion, our work elucidates clone-intrinsic molecular programmes associated with functional stem cell heterogeneity and identifies a mechanism for the maintenance of the self-renewing HSC state.

Population snapshots predict early haematopoietic and erythroid hierarchies.

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

Clonal analysis of lineage fate in native haematopoiesis.

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate. Here we use transposon tagging to clonally trace the fates of progenitors and stem cells in unperturbed haematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte lineage arises largely independently of other haematopoietic fates. Our data, combined with single-cell RNA sequencing, identify a functional hierarchy of unilineage- and oligolineage-producing clones within the multipotent progenitor population. Finally, our results demonstrate that traditionally defined long-term haematopoietic stem cells are a significant source of megakaryocyte-restricted progenitors, suggesting that the megakaryocyte lineage is the predominant native fate of long-term haematopoietic stem cells. Our study provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

Lineage reconstruction from clonal correlations.

A central task in developmental biology is to learn the sequence of fate decisions that leads to each mature cell type in a tissue or organism. Recently, clonal labeling of cells using DNA barcodes has emerged as a powerful approach for identifying cells that share a common ancestry of fate decisions. Here we explore the idea that stochasticity of cell fate choice during tissue development could be harnessed to read out lineage relationships after a single step of clonal barcoding. By considering a generalized multitype branching process, we determine the conditions under which the final distribution of barcodes over observed cell types encodes their bona fide lineage relationships. We then propose a method for inferring the order of fate decisions. Our theory predicts a set of symmetries of barcode covariance that serves as a consistency check for the validity of the method. We show that broken symmetries may be used to detect multiple paths of differentiation to the same cell types. We provide computational tools for general use. When applied to barcoding data in hematopoiesis, these tools reconstruct the classical hematopoietic hierarchy and detect couplings between monocytes and dendritic cells and between erythrocytes and basophils that suggest multiple pathways of differentiation for these lineages.

Fundamental limits on dynamic inference from single-cell snapshots.

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.

A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice.

Hematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation causes a multitude of diseases. However, the differentiation journeys toward specific hematopoietic lineages remain ill defined, and system-wide disease interpretation remains challenging. Here, we have profiled 44 802 mouse bone marrow HSPCs using single-cell RNA sequencing to provide a comprehensive transcriptional landscape with entry points to 8 different blood lineages (lymphoid, megakaryocyte, erythroid, neutrophil, monocyte, eosinophil, mast cell, and basophil progenitors). We identified a common basophil/mast cell bone marrow progenitor and characterized its molecular profile at the single-cell level. Transcriptional profiling of 13 815 HSPCs from the c-Kit mutant (W41/W41) mouse model revealed the absence of a distinct mast cell lineage entry point, together with global shifts in cell type abundance. Proliferative defects were accompanied by reduced Myc expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

The Molecular Dialog between Flowering Plant Reproductive Partners Defined by SNP-Informed RNA-Sequencing.

The molecular interactions between reproductive cells are critical for determining whether sexual reproduction between individuals results in fertilization and can result in barriers to interspecific hybridization. However, it is a challenge to define the complete molecular exchange between reproductive partners because parents contribute to a complex mixture of cells during reproduction. We unambiguously defined male- and female-specific patterns of gene expression during Arabidopsis thaliana reproduction using single nucleotide polymorphism-informed RNA-sequencing analysis. Importantly, we defined the repertoire of pollen tube-secreted proteins controlled by a group of MYB transcription factors that are required for sperm release from the pollen tube to the female gametes, a critical barrier to interspecific hybridization. Our work defines the pollen tube gene products that respond to the pistil and are required for reproductive success; moreover, we find that these genes are highly evolutionarily plastic both at the level of coding sequence and expression across A. thaliana accessions.

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data.

Motivation: Single-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies. Results: Force-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and captures complex population topologies. We describe SPRING, a pipeline for data filtering, normalization and visualization using force-directed layouts and show that it reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells and cells of the upper airway epithelium. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. We provide SPRING as an interactive web-tool with an easy to use GUI. Availability and implementation: https://kleintools.hms.harvard.edu/tools/spring.html, https://github.com/AllonKleinLab/SPRING/. Contact: calebsw@gmail.com or allon_klein@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of hierarchical chromatin domains.

MOTIVATION: The three-dimensional structure of the genome is an important regulator of many cellular processes including differentiation and gene regulation. Recently, technologies such as Hi-C that combine proximity ligation with high-throughput sequencing have revealed domains of self-interacting chromatin, called topologically associating domains (TADs), in many organisms. Current methods for identifying TADs using Hi-C data assume that TADs are non-overlapping, despite evidence for a nested structure in which TADs and sub-TADs form a complex hierarchy. RESULTS: We introduce a model for decomposition of contact frequencies into a hierarchy of nested TADs. This model is based on empirical distributions of contact frequencies within TADs, where positions that are far apart have a greater enrichment of contacts than positions that are close together. We find that the increase in contact enrichment with distance is stronger for the inner TAD than for the outer TAD in a TAD/sub-TAD pair. Using this model, we develop the TADtree algorithm for detecting hierarchies of nested TADs. TADtree compares favorably with previous methods, finding TADs with a greater enrichment of chromatin marks such as CTCF at their boundaries. AVAILABILITY AND IMPLEMENTATION: A python implementation of TADtree is available at http://compbio.cs.brown.edu/software/ CONTACT: braphael@cs.brown.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Open adjacencies and k-breaks: detecting simultaneous rearrangements in cancer genomes.

BACKGROUND: The evolution of a cancer genome has traditionally been described as a sequential accumulation of mutations - including chromosomal rearrangements - over a period of time. Recent research suggests, however, that numerous rearrangements may be acquired simultaneously during a single cataclysmic event, leading to the proposal of new mechanisms of rearrangement such as chromothripsis and chromoplexy. RESULTS: We introduce two measures, open adjacency rate (OAR) and copy-number asymmetry enrichment (CAE), that assess the prevalence of simultaneously formed breakpoints, or k-breaks with k >2, compared to the sequential accumulation of standard rearrangements, or 2-breaks. We apply the OAR and the CAE to genome sequencing data from 121 cancer genomes from two different studies. CONCLUSIONS: We find that the OAR and CAE correlate well with previous analyses of chromothripsis/chromoplexy but make differing predictions on a small subset of genomes. These results lend support to the existence of simultaneous rearrangements, but also demonstrate the difficulty of characterizing such rearrangements using different criterion.

Characterizing the structure of mouse behavior using Motion Sequencing.

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

ArMo: An Articulated Mesh Approach for Mouse 3D Reconstruction.

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animal’s surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

Transcriptional profiling of sequentially generated septal neuron fates.

The septum is a ventral forebrain structure known to regulate innate behaviors. During embryonic development, septal neurons are produced in multiple proliferative areas from neural progenitors following transcriptional programs that are still largely unknown. Here, we use a combination of single-cell RNA sequencing, histology, and genetic models to address how septal neuron diversity is established during neurogenesis. We find that the transcriptional profiles of septal progenitors change along neurogenesis, coinciding with the generation of distinct neuron types. We characterize the septal eminence, an anatomically distinct and transient proliferative zone composed of progenitors with distinctive molecular profiles, proliferative capacity, and fate potential compared to the rostral septal progenitor zone. We show that Nkx2.1-expressing septal eminence progenitors give rise to neurons belonging to at least three morphological classes, born in temporal cohorts that are distributed across different septal nuclei in a sequential fountain-like pattern. Our study provides insight into the molecular programs that control the sequential production of different neuronal types in the septum, a structure with important roles in regulating mood and motivation.

Lineage tracing on transcriptional landscapes links state to fate during differentiation.

A challenge in biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Here, we used expressed DNA barcodes to clonally trace transcriptomes over time and applied this to study fate determination in hematopoiesis. We identified states of primed fate potential and located them on a continuous transcriptional landscape. We identified two routes of monocyte differentiation that leave an imprint on mature cells. Analysis of sister cells also revealed cells to have intrinsic fate biases not detectable by single-cell RNA sequencing. Finally, we benchmarked computational methods of dynamic inference from single-cell snapshots, showing that fate choice occurs earlier than is detected by state-of the-art algorithms and that cells progress steadily through pseudotime with precise and consistent dynamics.

COVID-19 and the Chemical Senses: Supporting Players Take Center Stage.

The main neurological manifestation of COVID-19 is loss of smell or taste. The high incidence of smell loss without significant rhinorrhea or nasal congestion suggests that SARS-CoV-2 targets the chemical senses through mechanisms distinct from those used by endemic coronaviruses or other common cold-causing agents. Here we review recently developed hypotheses about how SARS-CoV-2 might alter the cells and circuits involved in chemosensory processing and thereby change perception. Given our limited understanding of SARS-CoV-2 pathogenesis, we propose future experiments to elucidate disease mechanisms and highlight the relevance of this ongoing work to understanding how the virus might alter brain function more broadly.