Chrysanthemum (Chrysanthemum moriforlium Ramat.) is one of the most popular flowers worldwide, with very high ornamental and economic values. However, the limitations of available DNA molecular markers and the lack of full genomic sequences hinder the study of genetic diversity and the molecular breeding of chrysanthemum. Here, we developed simple sequence repeat (SSR) from the full-length transcriptome sequences of chrysanthemum cultivar 'Hechengxinghuo'. A total of 11,699 SSRs with mono-, di-, tri-, tetra-, penta- and hexanucleotide repeats were identified, of which eight out of eighteen SSR loci identified based on sixteen transcripts participated in carotenoid metabolism or anthocyanin synthesis were validated as polymorphic SSR markers. These SSRs were used to classify 117 chrysanthemum accessions with different flower colors at the DNA and cDNA levels. The results showed that four SSR markers of carotenoid metabolic pathway divided 117 chrysanthemum accessions into five groups at cDNA level and all purple chrysanthemum accessions were in the group III. Furthermore, the SSR marker CHS-3, LCYE-1 and 3MaT may be related to green color and the PSY-1b marker may be related to yellow color. Overall, our work may be provide a novel method for mining SSR markers associated with specific traits.

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank.

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD.

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

The characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers, thus shedding light on the de novo detection of nucleotide modifications.

Altered islet architecture is associated with β cell dysfunction and type 2 diabetes (T2D) progression, but molecular effectors of islet spatial organization remain mostly unknown. Although Notch signaling is known to regulate pancreatic development, we observed "reactivated" β cell Notch activity in obese mouse models. To test the repercussions and reversibility of Notch effects, we generated doxycycline-dependent, β cell-specific Notch gain-of-function mice. As predicted, we found that Notch activation in postnatal β cells impaired glucose-stimulated insulin secretion and glucose intolerance, but we observed a surprising remnant glucose intolerance after doxycycline withdrawal and cessation of Notch activity, associated with a marked disruption of normal islet architecture. Transcriptomic screening of Notch-active islets revealed increased Ephrin signaling. Commensurately, exposure to Ephrin ligands increased β cell repulsion and impaired murine and human pseudoislet formation. Consistent with our mouse data, Notch and Ephrin signaling were increased in metabolically inflexible β cells in patients with T2D. These studies suggest that β cell Notch/Ephrin signaling can permanently alter islet architecture during a morphogenetic window in early life.

Type 2 diabetes (T2D) is associated with defective insulin secretion and reduced β cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of β cell failure is a key translational question. Here, we reverse engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic inflexibility and endocrine progenitor/stem cell features. Single-cell gain- and loss-of-function and glucose-induced Ca2+ flux analyses of top candidate master regulatory (MR) proteins in islet cells validated transcription factor BACH2 and associated epigenetic effectors as key drivers of T2D cell states. BACH2 knockout in T2D islets reversed cellular features of the disease, restoring a nondiabetic phenotype. BACH2-immunoreactive islet cells increased approximately 4-fold in diabetic patients, confirming the algorithmic prediction of clinically relevant subpopulations. Treatment with a BACH inhibitor lowered glycemia and increased plasma insulin levels in diabetic mice, and restored insulin secretion in diabetic mice and human islets. The findings suggest that T2D-specific populations of failing β cells can be reversed and indicate pathways for pharmacological intervention, including via BACH2 inhibition.

The maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.

Type 2 diabetes (T2D) is associated with β-cell dedifferentiation. Aldehyde dehydrogenase 1 isoform A3 (ALHD1A3) is a marker of β-cell dedifferentiation and correlates with T2D progression. However, it is unknown whether ALDH1A3 activity contributes to β-cell failure, and whether the decrease of ALDH1A3-positive β-cells (A+) following pair-feeding of diabetic animals is due to β-cell restoration. To tackle these questions, we (i) investigated the fate of A+ cells during pair-feeding by lineage-tracing, (ii) somatically ablated ALDH1A3 in diabetic β-cells, and (iii) used a novel selective ALDH1A3 inhibitor to treat diabetes. Lineage tracing and functional characterization show that A+ cells can be reconverted to functional, mature β-cells. Genetic or pharmacological inhibition of ALDH1A3 in diabetic mice lowers glycemia and increases insulin secretion. Characterization of β-cells following ALDH1A3 inhibition shows reactivation of differentiation as well as regeneration pathways. We conclude that ALDH1A3 inhibition offers a therapeutic strategy against β-cell dysfunction in diabetes.

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

Notch signaling, re-activated in β cells from obese mice and causal to β cell dysfunction, is determined in part by transmembrane ligand availability in a neighboring cell. We hypothesized that β cell expression of Jagged1 determines the maladaptive Notch response and resultant insulin secretory defects in obese mice.

Basal cells are multipotent stem cells of a variety of organs, including the respiratory tract, where they are major components of the airway epithelium. However, it remains unclear how diverse basal cells are and how distinct subpopulations respond to airway challenges. Using single cell RNA-sequencing and functional approaches, we report a significant and previously underappreciated degree of heterogeneity in the basal cell pool, leading to identification of six subpopulations in the adult murine trachea. Among these, we found two major subpopulations, collectively comprising the most uncommitted of all the pools, but with distinct gene expression signatures. Notably, these occupy distinct ventral and dorsal tracheal niches and differ in their ability to self-renew and initiate a program of differentiation in response to environmental perturbations in primary cultures and in mouse injury models in vivo. We found that such heterogeneity is acquired prenatally, when the basal cell pool and local niches are still being established, and depends on the integrity of these niches, as supported by the altered basal cell phenotype of tracheal cartilage-deficient mouse mutants. Finally, we show that features that distinguish these progenitor subpopulations in murine airways are conserved in humans. Together, the data provide novel insights into the origin and impact of basal cell heterogeneity on the establishment of regionally distinct responses of the airway epithelium during injury-repair and in disease conditions.

Myeloid-biased hematopoietic stem cells (MB-HSCs) play critical roles in recovery from injury, but little is known about how they are regulated within the bone marrow niche. Here we describe an auto-/paracrine physiologic circuit that controls quiescence of MB-HSCs and hematopoietic progenitors marked by histidine decarboxylase (Hdc). Committed Hdc+ myeloid cells lie in close anatomical proximity to MB-HSCs and produce histamine, which activates the H2 receptor on MB-HSCs to promote their quiescence and self-renewal. Depleting histamine-producing cells enforces cell cycle entry, induces loss of serial transplant capacity, and sensitizes animals to chemotherapeutic injury. Increasing demand for myeloid cells via lipopolysaccharide (LPS) treatment specifically recruits MB-HSCs and progenitors into the cell cycle; cycling MB-HSCs fail to revert into quiescence in the absence of histamine feedback, leading to their depletion, while an H2 agonist protects MB-HSCs from depletion after sepsis. Thus, histamine couples lineage-specific physiological demands to intrinsically primed MB-HSCs to enforce homeostasis.

Nucleotides in RNA and DNA are chemically modified by numerous enzymes that alter their function. Eukaryotic ribosomal RNA (rRNA) is modified at more than 100 locations, particularly at highly conserved and functionally important nucleotides. During ribosome biogenesis, modifications are added at various stages of assembly. The existence of differently modified classes of ribosomes in normal cells is unknown because no method exists to simultaneously evaluate the modification status at all sites within a single rRNA molecule. Using a combination of yeast genetics and nanopore direct RNA sequencing, we developed a reliable method to track the modification status of single rRNA molecules at 37 sites in 18 S rRNA and 73 sites in 25 S rRNA. We use our method to characterize patterns of modification heterogeneity and identify concerted modification of nucleotides found near functional centers of the ribosome. Distinct, undermodified subpopulations of rRNAs accumulate upon loss of Dbp3 or Prp43 RNA helicases, suggesting overlapping roles in ribosome biogenesis. Modification profiles are surprisingly resistant to change in response to many genetic and acute environmental conditions that affect translation, ribosome biogenesis, and pre-mRNA splicing. The ability to capture single-molecule RNA modification profiles provides new insights into the roles of nucleotide modifications in RNA function.

Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (VAE Enhanced by Gene Annotations), whose decoder wiring mirrors user-provided gene modules, providing direct interpretability to the latent variables. We demonstrate the performance of VEGA in diverse biological contexts using pathways, gene regulatory networks and cell type identities as the gene modules that define its latent space. VEGA successfully recapitulates the mechanism of cellular-specific response to treatments, the status of master regulators as well as jointly revealing the cell type and cellular state identity in developing cells. We envision the approach could serve as an explanatory biological model for development and drug treatment experiments.