- Li, D., Velazquez, J. J., Ding, J., Hislop, J., Ebrahimkhani, M. R., & Bar-Joseph, Z. (2022). TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data. Genome biology, 23(1), 1-19.
- Hasanaj, E., Wang, J., Sarathi, A.,Ding, J., * & Bar-Joseph, Z. * (2022). Interactive single-cell data analysis using Cellar. Nature Communications, 13(1), 1-6.
- Bhattarai, S., Li, Q., Ding, J., Liang, F., Gusev, E., Lapohos, O., ... & Petrof, B. J. (2022). TLR4 is a regulator of trained immunity in a murine model of Duchenne muscular dystrophy. Nature communications, 13(1), 1-15.
- Ding, J., Sharon, N., & Bar-Joseph, Z. (2022). Temporal modelling using single-cell transcriptomics. Nature Reviews Genetics, 1-14.
- Aloufi, N., Haidar, Z., Ding, J., Nair, P., Benedetti, A., Eidelman, D. H., ... & Baglole, C. J. (2021). Role of human antigen R (HuR) in the regulation of pulmonary ACE2 expression. Cells, 11(1), 22.
- Jun Ding, Amir Alavi, Mo R Ebrahimkhani, Ziv Bar-Joseph. Computational tools for analyzing single-cell data in pluripotent cell differentiation studies .Cell Reports Methods. 2021
- Ding J. A versatile model for single-cell data analysis. Nature Computational Science. 2021 July 22;1:460-461
- Ding J, Hostallero DE, El Khili MR, Fonseca GJ, Milette S, Noorah N, Guay-Belzile M, Spicer J, Daneshtalab N, Sirois M, Tremblay K. A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes’ interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.. PLoS Computational Biology. 2021 Mar 8;17(3):e1008810.
- Guerrina N, Traboulsi H, Souza A, Bossé Y, Thatcher TH, Robichaud A, Ding J, Li P, Simon L, Pareek S, Bourbeau J, Tan WC, Benedetti A, Obeidat M, Sin DD, Brandsma C, Nickle DC, Sime PJ, Phipps RP, Nair P, Zago M, Hamid Q, Smith BM, Eidelman DH, Baglole CJ. Aryl hydrocarbon receptor deficiency causes the development of chronic obstructive pulmonary disease through the integration of multiple pathogenic mechanisms.. FASEB journal: official publication of the Federation of American Societies for Experimental Biology, 2021, 35.3: e21376.
- Aloufi N, Traboulsi H, Ding J, Fonseca GJ, Nair P, Huang SK, Hussain SN, Eidelman DH, Baglole CJ. Angiotensin-converting enzyme 2 expression in COPD and IPF fibroblasts: the forgotten cell in COVID-19.. American Journal of Physiology-Lung Cellular and Molecular Physiology. 2021 Jan 1;320(1):L152-7.
- Li D, Ding J,Bar-Joseph Z. Identifying signaling genes in spatial single cell expression data. Bioinformatics, 2021; 37(7), 968-975.
- Ding J, Bar-Joseph Z. Analysis of time series regulatory networks. Current Opinion in Systems Biology. 2020 June; 21 Pages 16-24
- Lin C, Ding J, Bar-Joseph Z. Inferring TF activation order in time series scRNA-Seq studies. PLoS computational biology. 2020 Feb 18;16(2):e1007644.
- Hurley K, Ding J (co-first), Villacorta-Martin C, Herriges MJ, Jacob A, Vedaie M, Alysandratos KD, Sun YL, Lin C, Werder RB, Huang J. , ..., Bar-Joseph Z, Kotton DN. Reconstructed single-cell fate trajectories define lineage plasticity windows during differentiation of human PSC-derived distal lung progenitors. Cell Stem Cell. 2020 Jan 30.
- McDonough JE, Ahangari F, ..., Ding J.,Maes K, Sadeleer LD, Vos R, Neyrinck A, Benos PV, Bar-Joseph Z, Tantin D, Hogg JC, Vanaudenaerde BM, Wuyts WA, Kaminski N. Transcriptional regulatory model of fibrosis progression in the human lung. JCI insight. 2019 Nov 14;4(22).
- Ding J, Ahangari F, Espinoza CR, Chhabra D, Nicola T, Yan X, Lal CV, Hagood JS, Kaminski N, Bar-Joseph Z, Ambalavanan N. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. American Journal of Physiology-Lung Cellular and Molecular Physiology. 2019 Nov 1;317(5):L556-68.
- Liu H, Zhang CH, Ammanamanchi N, Suresh S, ..., Ding J, Bar-Joseph Z, Wu Y, Yechoor V, Moulik M, Johnson J, Weinberg J, Reyes-Mugica M, Steinhauser ML, Kuhn B. Control of cytokinesis by beta-adrenergic receptors indicates an approach for regulating cardiomyocyte endowment. Sci Transl Med., 2019; 11(513)
- Ding J, Lin C, Bar-Joseph Z. Cell lineage inference from SNP and scRNA-Seq data. Nucleic Acids Research, 2019 47(10), pp.e56-e56.
- Friedman C, Nguyen Q, Lukowski S, ..., Ding J, Wang Y, Hudson J, Ruohola-Baker H, Bar-Joseph Z, Tam P, Powell J, Palpant N. Single-Cell Transcriptomic Analysis of Cardiac Differentiation from Human PSCs Reveals HOPX-Dependent Cardiomyocyte Maturation. Cell Stem Cell. 2018; 23(4):586-598
- Nguyen QH, Lukowski SW, Chiu HS, Friedman CE, Senabouth A, Crowhurst L, Bruxner TJ, Christ AN, Hudson J, Ding J, Bar-Joseph Z, Tam PP, Palpant NJ, Powell JE. Genetic networks modulating cell fate specification and contributing to cardiac disease risk in hiPSC-derived cardiomyocytes at single cell resolution. Human Genomics. 2018 Mar 9;12.
- Ding J, Aronow B, Kaminski N, Kitzmiller J, Whitsett J, Bar-Joseph Z. Reconstructing differentiation networks and their regulation from time series single cell expression data. Genome research. 2018 Jan 9:gr-225979.
- Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLoS computational biology. 2018 Mar 14;14(3):e1006019.
- Ding J, Bar-Joseph Z. MethRaFo: MeDIP-seq methylation estimate using a Random Forest Regressor. Bioinformatics. 2017 Jul 13;33(21):3477-9.
- Ding J, Li X, Hu H. CCmiR: a computational approach for competitive and cooperative microRNA binding prediction. Bioinformatics. 2017 Sep 25;34(2):198-206.
- Roqueta-Rivera M, Esquejo RM, Phelan PE, Sandor K, Daniel B, Foufelle F, Ding J, Li X, Khorasanizadeh S, Osborne TF. SETDB2 links glucocorticoid to lipid metabolism through Insig2a regulation . Cell metabolism. 2016 Sep 13;24(3):474-84.
- Ding J, Li X, Hu H. TarPmiR: a new approach for microRNA target site prediction. Bioinformatics. 2016 May 20;32(18):2768-75.
- Ding J, Li X, Hu H. MicroRNA modules prefer to bind weak and unconventional target sites. Bioinformatics .2014; doi: 10.1093/bioinformatics/btu833.
- Ding J, Dhillon V, Li X, Hu H. Systematic discovery of cofactor motifs from ChIP-seq data by SIOMICS. Methods . 2014; doi:10.1016/j.ymeth.2014.08.006
- Ding J, Hu H, Li X. SIOMICS: a Novel Approach for Systematic Identification of Motifs in ChIP-seq Data. Nucleic Acids Research . 2014; 42(5): e35.
- Ding J, Hu H, Li X. NIM, A novel computational method for predicting nuclear-encoded chloroplast proteins. Journal of Medical and Bioengineering. 2013; 2(2): 115-119.
- Ding J, Cai X, Wang Y, Hu H, Li X. ChIPModule: Systematic discovery of transcription factors and their cofactors from ChIP-seq data. Pac Symp Biocomput. 2013.
- Ding J, Li X, Hu H. Systematic discovery of cis-regulatory elements in Chlamydomonas reinhardtii genome using comparative genomics. Plant Physiology. 2012;160(2):613-23.
- Ying Wang, Ding J, Daniell H, Hu H, Li X. Motif analysis unveils the possible co-regulation of chloroplast genes and nuclear genes encoding chloroplast proteins. Plant Molecular Biology . 2012;80(2):177-87.
- Ding J, Hu H, Li X. Thousands of cis-regulatory sequences are shared by Arabidopsis and populus. Plant Physiology. 2012;158(1):145-55. Epub 2011 Nov 4.
- Ding J, Liu Falin. Novel Tag Anti-Collision Algorithm with Adaptive Grouping. Wireless Sensor Network, 2009 1, 475-481
Abstract: A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids. link
Abstract: Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and modalities, we developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. We discuss the different methods implemented by Cellar, how these can be used with different data types, how to combine complementary data types and how to analyze and visualize spatial data. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spa link
Abstract: Dysregulation of the balance between pro-inflammatory and anti-inflammatory macrophages has a key function in the pathogenesis of Duchenne muscular dystrophy (DMD), a fatal genetic disease. We postulate that an evolutionarily ancient protective mechanism against infection, known as trained immunity, drives pathological inflammation in DMD. Here we show that bone marrow-derived macrophages from a murine model of DMD (mdx) exhibit cardinal features of trained immunity, consisting of transcriptional hyperresponsiveness associated with metabolic and epigenetic remodeling. The hyperresponsive phenotype is transmissible by bone marrow transplantation to previously healthy mice and persists for up to 11 weeks post-transplant. Mechanistically, training is induced by muscle extract in vitro. The functional and epigenetic changes in bone marrow-derived macrophages from dystrophic mice are TLR4-dependent. Adoptive transfer experiments further support the TLR4-dependence of trained macrophages homing to damaged muscles from the bone marrow. Collectively, this suggests that a TLR4-regulated, memory-like capacity of innate immunity induced at the level of the bone marrow promotes dysregulated inflammation in DMD. link
Abstract: Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for. link
Abstract: Patients with COPD may be at an increased risk for severe illness from COVID-19 because of ACE2 upregulation, the entry receptor for SARS-CoV-2. Chronic exposure to cigarette smoke, the main risk factor for COPD, increases pulmonary ACE2. How ACE2 expression is controlled is not known but may involve HuR, an RNA binding protein that increases protein expression by stabilizing mRNA. We hypothesized that HuR would increase ACE2 protein expression. We analyzed scRNA-seq data to profile ELAVL1 expression in distinct respiratory cell populations in COVID-19 and COPD patients. HuR expression and cellular localization was evaluated in COPD lung tissue by multiplex immunohistochemistry and in human lung cells by imaging flow cytometry. The regulation of ACE2 expression was evaluated using siRNA-mediated knockdown of HuR. There is a significant positive correlation between ELAVL1 and ACE2 in COPD cells. HuR cytoplasmic localization is higher in smoker and COPD lung tissue; there were also higher levels of cleaved HuR (CP-1). HuR binds to ACE2 mRNA but knockdown of HuR does not change ACE2 protein levels in primary human lung fibroblasts (HLFs). Our work is the first to investigate the association between CE2 and HuR. Further investigation is needed to understand the mechanistic underpinning behind the regulation of ACE2 expression. link
Abstract: Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation. link
Abstract: Making sense of single-cell data requires various computational efforts such as clustering, visualization and gene regulatory network inference, often addressed by different methods. DeepSEM provides an all-in-one solution. link
Abstract: Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders. link
Abstract: Emphysema, a component of chronic obstructive pulmonary disease (COPD), is characterized by irreversible alveolar destruction that results in a progressive decline in lung function. This alveolar destruction is caused by cigarette smoke, the most important risk factor for COPD. Only 15%-20% of smokers develop COPD, suggesting that unknown factors contribute to disease pathogenesis. We postulate that the aryl hydrocarbon receptor (AHR), a receptor/transcription factor highly expressed in the lungs, may be a new susceptibility factor whose expression protects against COPD. Here, we report that Ahr-deficient mice chronically exposed to cigarette smoke develop airspace enlargement concomitant with a decline in lung function. Chronic cigarette smoke exposure also increased cleaved caspase-3, lowered SOD2 expression, and altered MMP9 and TIMP-1 levels in Ahr-deficient mice. We also show that people with COPD have reduced expression of pulmonary and systemic AHR, with systemic AHR mRNA levels positively correlating with lung function. Systemic AHR was also lower in never-smokers with COPD. Thus, AHR expression protects against the development of COPD by controlling interrelated mechanisms involved in the pathogenesis of this disease. This study identifies the AHR as a new, central player in the homeostatic maintenance of lung health, providing a foundation for the AHR as a novel therapeutic target and/or predictive biomarker in chronic lung disease. link
Abstract: The COVID-19 pandemic is associated with severe pneumonia and acute respiratory distress syndrome leading to death in susceptible individuals. For those who recover, post-COVID-19 complications may include development of pulmonary fibrosis. Factors contributing to disease severity or development of complications are not known. Using computational analysis with experimental data, we report that idiopathic pulmonary fibrosis (IPF)- and chronic obstructive pulmonary disease (COPD)-derived lung fibroblasts express higher levels of angiotensin-converting enzyme 2 (ACE2), the receptor for SARS-CoV-2 entry and part of the renin-angiotensin system that is antifibrotic and anti-inflammatory. In preclinical models, we found that chronic exposure to cigarette smoke, a risk factor for both COPD and IPF and potentially for SARS-CoV-2 infection, significantly increased pulmonary ACE2 protein expression. Further studies are needed to understand the functional implications of ACE2 on lung fibroblasts, a cell type that thus far has received relatively little attention in the context of COVID-19. link
Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell–cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. Results
We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. link
Abstract: The vast majority of biological processes are dynamic, changing over time. Several studies profile high-throughput time-series data and use it for analyzing and modeling various biological processes. In this review, we focus on data, methods, and analysis for reconstructing dynamic regulatory network models from high-throughput time-series data sets. We discuss methods focused on a single data type, methods that integrate several omics data types, methods that integrate static and time-series data, and methods that focus on single-cell data. For each of these categories, we present some of the top methods and discuss their underlying assumptions, advantages, and potential shortcomings. As the quantity and types of time-series omics data continue to increase, we expect that these methods, and additional methods extending and improving them, would play an increasingly important role in our ability to accurately model biological processes. link
Abstract: Methods for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize information about transcription factors (TFs) and their targets or only study these as a post-processing step. Using such information can both, improve the accuracy of the reconstructed model and cell assignments, while at the same time provide information on how and when the process is regulated. We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TFs to specific activation points in the model. TFs are assumed to influence the emission probabilities for cells assigned to later time points allowing us to identify not just the TFs controlling each path but also their order of activation. We tested CSHMM-TF on several mouse and human datasets. As we show, the method was able to identify known and novel TFs for all processes, assigned time of activation agrees with both expression information and prior knowledge and combinatorial predictions are supported by known interactions. We also show that CSHMM-TF improves upon prior methods that do not utilize TF-gene interaction. link
Abstract: Alveolar epithelial type 2 cells (AEC2s) are the facultative progenitors responsible for maintaining lung alveoli throughout life but are difficult to isolate from patients. Here, we engineer AEC2s from human pluripotent stem cells (PSCs) in vitro and use time-series single-cell RNA sequencing with lentiviral barcoding to profile the kinetics of their differentiation in comparison to primary fetal and adult AEC2 benchmarks. We observe bifurcating cell-fate trajectories as primordial lung progenitors differentiate in vitro, with some progeny reaching their AEC2 fate target, while others diverge to alternative non-lung endodermal fates. We develop a Continuous State Hidden Markov model to identify the timing and type of signals, such as overexuberant Wnt responses, that induce some early multipotent NKX2-1+ progenitors to lose lung fate. Finally, we find that this initial developmental plasticity is regulatable and subsides over time, ultimately resulting in PSC-derived AEC2s that exhibit a stable phenotype and nearly limitless self-renewal capacity. link
Abstract: To develop a systems biology model of fibrosis progression within the human lung we performed RNA sequencing and microRNA analysis on 95 samples obtained from 10 idiopathic pulmonary fibrosis (IPF) and 6 control lungs. Extent of fibrosis in each sample was assessed by microCT-measured alveolar surface density (ASD) and confirmed by histology. Regulatory gene expression networks were identified using linear mixed-effect models and dynamic regulatory events miner (DREM). Differential gene expression analysis identified a core set of genes increased or decreased before fibrosis was histologically evident that continued to change with advanced fibrosis. DREM generated a systems biology model (www.sb.cs.cmu.edu/IPFReg) that identified progressively divergent gene expression tracks with microRNAs and transcription factors that specifically regulate mild or advanced fibrosis. We confirmed model predictions by demonstrating that expression of POU2AF1, previously unassociated with lung fibrosis but proposed by the model as regulator, is increased in B lymphocytes in IPF lungs and that POU2AF1-knockout mice were protected from bleomycin-induced lung fibrosis. Our results reveal distinct regulation of gene expression changes in IPF tissue that remained structurally normal compared with moderate or advanced fibrosis and suggest distinct regulatory mechanisms for each stage. link
Abstract: A comprehensive understanding of the dynamic regulatory networks that govern postnatal alveolar lung development is still lacking. To construct such a model, we profiled mRNA, microRNA, DNA methylation, and proteomics of developing murine alveoli isolated by laser capture microdissection at 14 predetermined time points. We developed a detailed comprehensive and interactive model that provides information about the major expression trajectories, the regulators of specific key events, and the impact of epigenetic changes. Intersecting the model with single-cell RNA-Seq data led to the identification of active pathways in multiple or individual cell types. We then constructed a similar model for human lung development by profiling time-series human omics data sets. Several key pathways and regulators are shared between the reconstructed models. We experimentally validated the activity of a number of predicted regulators, leading to new insights about the regulation of innate immunity during lung development. link
Abstract: One million patients with congenital heart disease (CHD) live in the United States. They have a lifelong risk of developing heart failure. Current concepts do not sufficiently address mechanisms of heart failure development specifically for these patients. Here, analysis of heart tissue from an infant with tetralogy of Fallot with pulmonary stenosis (ToF/PS) labeled with isotope-tagged thymidine demonstrated that cardiomyocyte cytokinesis failure is increased in this common form of CHD. We used single-cell transcriptional profiling to discover that the underlying mechanism of cytokinesis failure is repression of the cytokinesis gene ECT2, downstream of beta-adrenergic receptors (beta-ARs). Inactivation of the beta-AR genes and administration of the beta-blocker propranolol increased cardiomyocyte division in neonatal mice, which increased the number of cardiomyocytes (endowment) and conferred benefit after myocardial infarction in adults. Propranolol enabled the division of ToF/PS cardiomyocytes in vitro. These results suggest that beta-blockers could be evaluated for increasing cardiomyocyte division in patients with ToF/PS and other types of CHD link
Abstract: Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types. link