The single-cell transcriptome program of nodule development cellular lineages in Medicago truncatula.

Legumes establish a symbiotic relationship with nitrogen-fixing rhizobia by developing nodules. Nodules are modified lateral roots that undergo changes in their cellular development in response to bacteria, but the transcriptional reprogramming that occurs in these root cells remains largely uncharacterized. Here, we describe the cell-type-specific transcriptome response of Medicago truncatula roots to rhizobia during early nodule development in the wild-type genotype Jemalong A17, complemented with a hypernodulating mutant (sunn-4) to expand the cell population responding to infection and subsequent biological inferences. The analysis identifies epidermal root hair and stele sub-cell types associated with a symbiotic response to infection and regulation of nodule proliferation. Trajectory inference shows cortex-derived cell lineages differentiating to form the nodule primordia and, posteriorly, its meristem, while modulating the regulation of phytohormone-related genes. Gene regulatory analysis of the cell transcriptomes identifies new regulators of nodulation, including STYLISH 4, for which the function is validated.

Human iPSC-derived Committed Cardiac Progenitors Generate Cardiac Tissue Grafts in a Swine Ischemic Cardiomyopathy Model without Triggering Ventricular Arrhythmias.

BACKGROUND: The adult human heart following a large myocardial infarction is unable to regenerate heart muscle and instead forms scar with the risk of progressive heart failure. Large animal studies have shown that intramyocardial injection of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) following a myocardial infarction result in cell grafts but also ventricular arrhythmias. We hypothesized that intramyocardial injection of committed cardiac progenitor cells (CCPs) derived from iPSCs, combined with cardiac fibroblast-derived extracellular matrix (cECM) to enhance cell retention will: i) form cardiomyocyte containing functional grafts, ii) be free of ventricular arrhythmias and iii) restore left ventricular contractility in a post-myocardial infarction (MI) cardiomyopathy swine model. METHODS: hiPSCs were differentiated using bioreactors and small molecules to produce a population of committed cardiac progenitor cells (CCPs). MI was created using a coronary artery balloon occlusion and reperfusion model in Yucatan mini pigs. Four weeks later, epicardial needle injections of CCPs+cECM were performed in a small initial feasibility cohort, and then transendocardial injections (TEI) of CCPs+cECM, CCPs alone, cECM alone or vehicle control into the peri-infarct region in a larger randomized cohort. A 4-drug immunosuppression regimen was administered to prevent rejection of human CCPs. Arrhythmias were evaluated using implanted event recorders. Magnetic resonance imaging (MRI) and invasive pressure volume assessment were used to evaluate left ventricular anatomic and functional performance, including viability. Detailed histology was performed on the heart to detect human grafts. RESULTS: A scalable biomanufacturing protocol was developed generating CCPs which can efficiently differentiate to cardiomyocytes or endothelial cells in vitro. Intramyocardial delivery of CCPs to post-MI porcine hearts resulted in engraftment and differentiation of CCPs to form ventricular cardiomyocyte rich grafts. There was no significant difference in cardiac MRI-based measured cardiac volumes or function between control, CCP and CCP+cECM groups; however, dobutamine stimulated functional reserve was improved in CCP and CCP+cECM groups. TEI delivery of CCPs with or without cECM did not result in tumors or trigger ventricular arrhythmias. CONCLUSIONS: CCPs are a promising cell source for post-MI heart repair using clinically relevant TEI with a low risk of engraftment ventricular arrhythmias.

Human left ventricular cardiac fibroblasts undergo a dynamic shift in secretome and gene expression toward a cardiac myofibroblast phenotype during early passage in typical culture expansion conditions.

Cardiac fibroblasts (CFs) are critical components of the cardiac niche and primarily responsible for assembly and maintenance of the cardiac extracellular matrix (ECM). CFs are increasingly of interest for tissue engineering and drug development applications, as they provide synergistic support to cardiomyocytes through direct cell-to-cell signaling and cell-to-ECM interactions via soluble factors, including cytokines, growth factors and extracellular vesicles. CFs can be activated to a cardiac myofibroblast (CMF) phenotype upon injury or stimulation with transforming growth factor beta 1. Once activated, CMFs assemble collagen-rich ECM, which is vitally important to stabilize scar formation following myocardial infarction, for example. Although there is greater experience with culture expansion of CFs among non-human strains, very little is known about human CF-to-CMF transitions and expression patterns during culture expansion. In this study, we evaluated for shifts in inflammatory and angiogenic expression profiles of human CFs in typical culture expansion conditions. Understanding shifts in cellular expression patterns during CF culture expansion is critically important to establish quality benchmarks and optimize large-scale manufacturing for future clinical applications.

Predicting patient-specific enhancer-promoter interactions.

Computational methods that can predict hard-to-measure modalities from those that are easier to measure, in a patient-specific manner, play a critical role in personalized medicine. In this issue of Cell Reports Methods, Khurana et al. present differential gene targets of accessible chromatin (DGTAC), an approach which predicts patient-specific enhancer-promoter interactions.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

Identifying molecular and functional similarities and differences between human primary cardiac valve interstitial cells and ventricular fibroblasts.

Introduction: Fibroblasts are mesenchymal cells that predominantly produce and maintain the extracellular matrix (ECM) and are critical mediators of injury response. In the heart, valve interstitial cells (VICs) are a population of fibroblasts responsible for maintaining the structure and function of heart valves. These cells are regionally distinct from myocardial fibroblasts, including left ventricular cardiac fibroblasts (LVCFBs), which are located in the myocardium in close vicinity to cardiomyocytes. Here, we hypothesize these subpopulations of fibroblasts are transcriptionally and functionally distinct. Methods: To compare these fibroblast subtypes, we collected patient-matched samples of human primary VICs and LVCFBs and performed bulk RNA sequencing, extracellular matrix profiling, and functional contraction and calcification assays. Results: Here, we identified combined expression of SUSD2 on a protein-level, and MEOX2, EBF2 and RHOU at a transcript-level to be differentially expressed in VICs compared to LVCFBs and demonstrated that expression of these genes can be used to distinguish between the two subpopulations. We found both VICs and LVCFBs expressed similar activation and contraction potential in vitro, but VICs showed an increase in ALP activity when activated and higher expression in matricellular proteins, including cartilage oligomeric protein and alpha 2-Heremans-Schmid glycoprotein, both of which are reported to be linked to calcification, compared to LVCFBs. Conclusion: These comparative transcriptomic, proteomic, and functional studies shed novel insight into the similarities and differences between valve interstitial cells and left ventricular cardiac fibroblasts and will aid in understanding region-specific cardiac pathologies, distinguishing between primary subpopulations of fibroblasts, and generating region-specific stem-cell derived cardiac fibroblasts.

Rewiring of the 3D genome during acquisition of carboplatin resistance in a triple-negative breast cancer patient-derived xenograft.

Changes in the three-dimensional (3D) structure of the genome are an emerging hallmark of cancer. Cancer-associated copy number variants and single nucleotide polymorphisms promote rewiring of chromatin loops, disruption of topologically associating domains (TADs), active/inactive chromatin state switching, leading to oncogene expression and silencing of tumor suppressors. However, little is known about 3D changes during cancer progression to a chemotherapy-resistant state. We integrated chromatin conformation capture (Hi-C), RNA-seq, and whole-genome sequencing obtained from triple-negative breast cancer patient-derived xenograft primary tumors (UCD52) and carboplatin-resistant samples and found increased short-range (< 2 Mb) interactions, chromatin looping, formation of TAD, chromatin state switching into a more active state, and amplification of ATP-binding cassette transporters. Transcriptome changes suggested the role of long-noncoding RNAs in carboplatin resistance. Rewiring of the 3D genome was associated with TP53, TP63, BATF, FOS-JUN family of transcription factors and led to activation of aggressiveness-, metastasis- and other cancer-related pathways. Integrative analysis highlighted increased ribosome biogenesis and oxidative phosphorylation, suggesting the role of mitochondrial energy metabolism. Our results suggest that 3D genome remodeling may be a key mechanism underlying carboplatin resistance.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula.

BACKGROUND: Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules. RESULTS: We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia. CONCLUSIONS: Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.

Intracerebroventricular dosing of N-sulfoglucosamine sulfohydrolase in mucopolysaccharidosis IIIA mice reduces markers of brain lysosomal dysfunction.

Mucopolysaccharidosis type IIIA (MPS IIIA) is a lysosomal storage disorder caused by N-sulfoglucosamine sulfohydrolase (SGSH) deficiency. SGSH removes the sulfate from N-sulfoglucosamine residues on the nonreducing end of heparan sulfate (HS-NRE) within lysosomes. Enzyme deficiency results in accumulation of partially degraded HS within lysosomes throughout the body, leading to a progressive severe neurological disease. Enzyme replacement therapy has been proposed, but further evaluation of the treatment strategy is needed. Here, we used Chinese hamster ovary cells to produce a highly soluble and fully active recombinant human sulfamidase (rhSGSH). We discovered that rhSGSH utilizes both the CI-MPR and LRP1 receptors for uptake into patient fibroblasts. A single intracerebroventricular (ICV) injection of rhSGSH in MPS IIIA mice resulted in a tissue half-life of 9 days and widespread distribution throughout the brain. Following a single ICV dose, both total HS and the MPS IIIA disease-specific HS-NRE were dramatically reduced, reaching a nadir 2 weeks post dose. The durability of effect for reduction of both substrate and protein markers of lysosomal dysfunction and a neuroimmune response lasted through the 56 days tested. Furthermore, seven weekly 148 µg doses ICV reduced those markers to near normal and produced a 99.5% reduction in HS-NRE levels. A pilot study utilizing every other week dosing in two animals supports further evaluation of less frequent dosing. Finally, our dose-response study also suggests lower doses may be efficacious. Our findings show that rhSGSH can normalize lysosomal HS storage and markers of a neuroimmune response when delivered ICV.

Single-nuclei transcriptome analysis of the shoot apex vascular system differentiation in Populus.

Differentiation of stem cells in the plant apex gives rise to aerial tissues and organs. Presently, we lack a lineage map of the shoot apex cells in woody perennials - a crucial gap considering their role in determining primary and secondary growth. Here, we used single-nuclei RNA-sequencing to determine cell type-specific transcriptomes of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of the epidermis, leaf mesophyll and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we applied a pipeline for interspecific single-cell gene expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the principles underlying cell division and differentiation conserved between herbaceous and perennial species while also allowing us to examine species-specific differences at single-cell resolution.

Modular derivation of diverse, regionally discrete human posterior CNS neurons enables discovery of transcriptomic patterns.

Our inability to derive the neuronal diversity that comprises the posterior central nervous system (pCNS) using human pluripotent stem cells (hPSCs) poses an impediment to understanding human neurodevelopment and disease in the hindbrain and spinal cord. Here, we establish a modular, monolayer differentiation paradigm that recapitulates both rostrocaudal (R/C) and dorsoventral (D/V) patterning, enabling derivation of diverse pCNS neurons with discrete regional specificity. First, neuromesodermal progenitors (NMPs) with discrete HOX profiles are converted to pCNS progenitors (pCNSPs). Then, by tuning D/V signaling, pCNSPs are directed to locomotor or somatosensory neurons. Expansive single-cell RNA-sequencing (scRNA-seq) analysis coupled with a novel computational pipeline allowed us to detect hundreds of transcriptional markers within region-specific phenotypes, enabling discovery of gene expression patterns across R/C and D/V developmental axes. These findings highlight the potential of these resources to advance a mechanistic understanding of pCNS development, enhance in vitro models, and inform therapeutic strategies.

An LCO-responsive homolog of NODULE INCEPTION positively regulates lateral root formation in Populus sp.

The transcription factor NODULE INCEPTION (NIN) has been studied extensively for its multiple roles in root nodule symbiosis within plants of the nitrogen-fixing clade (NFC) that associate with soil bacteria, such as rhizobia and Frankia. However, NIN homologs are present in plants outside the NFC, suggesting a role in other developmental processes. Here, we show that the biofuel crop Populus sp., which is not part of the NFC, contains eight copies of NIN with diversified protein sequence and expression patterns. Lipo-chitooligosaccharides (LCOs) are produced by rhizobia and a wide range of fungi, including mycorrhizal ones, and act as symbiotic signals that promote lateral root formation. RNAseq analysis of Populus sp. treated with purified LCO showed induction of the PtNIN2 subfamily. Moreover, the expression of PtNIN2b correlated with the formation of lateral roots and was suppressed by cytokinin treatment. Constitutive expression of PtNIN2b overcame the inhibition of lateral root development by cytokinin under high nitrate conditions. Lateral root induction in response to LCOs likely represents an ancestral function of NIN retained and repurposed in nodulating plants, as we demonstrate that the role of NIN in LCO-induced root branching is conserved in both Populus sp. and legumes. We further established a visual marker of LCO perception in Populus sp. roots, the putative sulfotransferase PtSS1 that can be used to study symbiotic interactions with the bacterial and fungal symbionts of Populus sp.

Evolution of miRNA-Binding Sites and Regulatory Networks in Cichlids.

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA-binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analyzing the selective constraints driving evolution of miRNA and transcription factor (TF)-binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA- and TF-binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared with subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA- and TF-binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired, for example, clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions, and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

Dynamic regulatory module networks for inference of cell type-specific transcriptional networks.

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE.

Transcriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.

Functional and comparative genomics reveals conserved noncoding sequences in the nitrogen-fixing clade.

Nitrogen is one of the most inaccessible plant nutrients, but certain species have overcome this limitation by establishing symbiotic interactions with nitrogen-fixing bacteria in the root nodule. This root-nodule symbiosis (RNS) is restricted to species within a single clade of angiosperms, suggesting a critical, but undetermined, evolutionary event at the base of this clade. To identify putative regulatory sequences implicated in the evolution of RNS, we evaluated the genomes of 25 species capable of nodulation and identified 3091 conserved noncoding sequences (CNS) in the nitrogen-fixing clade (NFC). We show that the chromatin accessibility of 452 CNS correlates significantly with the regulation of genes responding to lipochitooligosaccharides in Medicago truncatula. These included 38 CNS in proximity to 19 known genes involved in RNS. Five such regions are upstream of MtCRE1, Cytokinin Response Element 1, required to activate a suite of downstream transcription factors necessary for nodulation in M. truncatula. Genetic complementation of an Mtcre1 mutant showed a significant decrease of nodulation in the absence of the five CNS, when they are driving the expression of a functional copy of MtCRE1. CNS identified in the NFC may harbor elements required for the regulation of genes controlling RNS in M. truncatula.