Xiaojie Qiu

All Publications

• Deciphering cell states and genealogies of human hematopoiesis. Nature Weng, C., Yu, F., Yang, D., Poeschla, M., Liggett, L. A., Jones, M. G., Qiu, X., Wahlster, L., Caulier, A., Hussmann, J. A., Schnell, A., Yost, K. E., Koblan, L., Martin-Rufino, J. D., Min, J., Hammond, A., Ssozi, D., Bueno, R., Mallidi, H., Kreso, A., Escabi, J., Rideout, W. M., Jacks, T., Hormoz, S., van Galen, P., Weissman, J. S., Sankaran, V. G. 2024

  Abstract

  The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived hematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human hematopoiesis and how this changes with age remain incompletely understood. While recent insights have emerged from barcoding studies in model systems4,5,16,17, simultaneous detection of cell states and phylogenies from natural barcodes in humans has been challenging. Here, we introduce an improved single-cell lineage tracing system based on deep detection of naturally-occurring mitochondrial DNA (mtDNA) mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as differences in total HSC output as well as biases toward the production of different mature cell types. We also find that the diversity of HSC clones decreases dramatically with age leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides the first clonally-resolved and cell-state aware atlas of human hematopoiesis at single-cell resolution revealing an unappreciated functional diversity of human HSC clones and more broadly paves the way for refined studies of clonal dynamics across a range of tissues in human health and disease.

  View details for DOI 10.1038/s41586-024-07066-z

  View details for PubMedID 38253266

• Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq. Nature communications Sunshine, S., Puschnik, A. S., Replogle, J. M., Laurie, M. T., Liu, J., Zha, B. S., Nuñez, J. K., Byrum, J. R., McMorrow, A. H., Frieman, M. B., Winkler, J., Qiu, X., Rosenberg, O. S., Leonetti, M. D., Ye, C. J., Weissman, J. S., DeRisi, J. L., Hein, M. Y. 2023; 14 (1): 6245

  Abstract

  Genomic and proteomic screens have identified numerous host factors of SARS-CoV-2, but efficient delineation of their molecular roles during infection remains a challenge. Here we use Perturb-seq, combining genetic perturbations with a single-cell readout, to investigate how inactivation of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our high-dimensional data resolve complex phenotypes such as shifts in the stages of infection and modulations of the interferon response. However, only a small percentage of host factors showed such phenotypes upon perturbation. We further identified the NF-κB inhibitor IκBα (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides massively parallel functional characterization of host factors of SARS-CoV-2 and quantitatively defines their roles both in virus-infected and bystander cells.

  View details for DOI 10.1038/s41467-023-41788-4

  View details for PubMedID 37803001

  View details for PubMedCentralID PMC10558542

• Massively parallel base editing to map variant effects in human hematopoiesis. Cell Martin-Rufino, J. D., Castano, N., Pang, M., Grody, E. I., Joubran, S., Caulier, A., Wahlster, L., Li, T., Qiu, X., Riera-Escandell, A. M., Newby, G. A., Al'Khafaji, A., Chaudhary, S., Black, S., Weng, C., Munson, G., Liu, D. R., Wlodarski, M. W., Sims, K., Oakley, J. H., Fasano, R. M., Xavier, R. J., Lander, E. S., Klein, D. E., Sankaran, V. G. 2023; 186 (11): 2456-2474.e24

  Abstract

  Systematic evaluation of the impact of genetic variants is critical for the study and treatment of human physiology and disease. While specific mutations can be introduced by genome engineering, we still lack scalable approaches that are applicable to the important setting of primary cells, such as blood and immune cells. Here, we describe the development of massively parallel base-editing screens in human hematopoietic stem and progenitor cells. Such approaches enable functional screens for variant effects across any hematopoietic differentiation state. Moreover, they allow for rich phenotyping through single-cell RNA sequencing readouts and separately for characterization of editing outcomes through pooled single-cell genotyping. We efficiently design improved leukemia immunotherapy approaches, comprehensively identify non-coding variants modulating fetal hemoglobin expression, define mechanisms regulating hematopoietic differentiation, and probe the pathogenicity of uncharacterized disease-associated variants. These strategies will advance effective and high-throughput variant-to-function mapping in human hematopoiesis to identify the causes of diverse diseases.

  View details for DOI 10.1016/j.cell.2023.03.035

  View details for PubMedID 37137305

  View details for PubMedCentralID PMC10225359

• Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape. Nature methods Ren, J., Zhou, H., Zeng, H., Wang, C. K., Huang, J., Qiu, X., Sui, X., Li, Q., Wu, X., Lin, Z., Lo, J. A., Maher, K., He, Y., Tang, X., Lam, J., Chen, H., Li, B., Fisher, D. E., Liu, J., Wang, X. 2023; 20 (5): 695-705

  Abstract

  Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.

  View details for DOI 10.1038/s41592-023-01829-8

  View details for PubMedID 37038000

  View details for PubMedCentralID PMC10172111

• Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science (New York, N.Y.) Wei, X., Fu, S., Li, H., Liu, Y., Wang, S., Feng, W., Yang, Y., Liu, X., Zeng, Y. Y., Cheng, M., Lai, Y., Qiu, X., Wu, L., Zhang, N., Jiang, Y., Xu, J., Su, X., Peng, C., Han, L., Lou, W. P., Liu, C., Yuan, Y., Ma, K., Yang, T., Pan, X., Gao, S., Chen, A., Esteban, M. A., Yang, H., Wang, J., Fan, G., Liu, L., Chen, L., Xu, X., Fei, J. F., Gu, Y. 2022; 377 (6610): eabp9444

  Abstract

  The molecular mechanism underlying brain regeneration in vertebrates remains elusive. We performed spatial enhanced resolution omics sequencing (Stereo-seq) to capture spatially resolved single-cell transcriptomes of axolotl telencephalon sections during development and regeneration. Annotated cell types exhibited distinct spatial distribution, molecular features, and functions. We identified an injury-induced ependymoglial cell cluster at the wound site as a progenitor cell population for the potential replenishment of lost neurons, through a cell state transition process resembling neurogenesis during development. Transcriptome comparisons indicated that these induced cells may originate from local resident ependymoglial cells. We further uncovered spatially defined neurons at the lesion site that may regress to an immature neuron-like state. Our work establishes spatial transcriptome profiles of an anamniote tetrapod brain and decodes potential neurogenesis from ependymoglial cells for development and regeneration, thus providing mechanistic insights into vertebrate brain regeneration.

  View details for DOI 10.1126/science.abp9444

  View details for PubMedID 36048929

• Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proceedings of the National Academy of Sciences of the United States of America Gupta, A., Martin-Rufino, J. D., Jones, T. R., Subramanian, V., Qiu, X., Grody, E. I., Bloemendal, A., Weng, C., Niu, S. Y., Min, K. H., Mehta, A., Zhang, K., Siraj, L., Al' Khafaji, A., Sankaran, V. G., Raychaudhuri, S., Cleary, B., Grossman, S., Lander, E. S. 2022; 119 (34): e2207392119

  Abstract

  Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

  View details for DOI 10.1073/pnas.2207392119

  View details for PubMedID 35969771

  View details for PubMedCentralID PMC9407670

• Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., Horns, F., Qiu, X., Chen, M. Z., Freed-Pastor, W. A., McGinnis, C. S., Patterson, D. M., Gartner, Z. J., Chow, E. D., Bivona, T. G., Chan, M. M., Yosef, N., Jacks, T., Weissman, J. S. 2022; 185 (11): 1905-1923.e25

  Abstract

  Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

  View details for DOI 10.1016/j.cell.2022.04.015

  View details for PubMedID 35523183

• Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., Wang, X., Lu, H., Chen, X., Liu, X., Huang, X., Li, Z., Hong, Y., Jiang, Y., Peng, J., Liu, S., Shen, M., Liu, C., Li, Q., Yuan, Y., Wei, X., Zheng, H., Feng, W., Wang, Z., Liu, Y., Wang, Z., Yang, Y., Xiang, H., Han, L., Qin, B., Guo, P., Lai, G., Muñoz-Cánoves, P., Maxwell, P. H., Thiery, J. P., Wu, Q. F., Zhao, F., Chen, B., Li, M., Dai, X., Wang, S., Kuang, H., Hui, J., Wang, L., Fei, J. F., Wang, O., Wei, X., Lu, H., Wang, B., Liu, S., Gu, Y., Ni, M., Zhang, W., Mu, F., Yin, Y., Yang, H., Lisby, M., Cornall, R. J., Mulder, J., Uhlén, M., Esteban, M. A., Li, Y., Liu, L., Xu, X., Wang, J. 2022; 185 (10): 1777-1792.e21

  Abstract

  Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

  View details for DOI 10.1016/j.cell.2022.04.003

  View details for PubMedID 35512705

• Mapping transcriptomic vector fields of single cells. Cell Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Hoi Joseph Min, K., Wang, L., Grody, E. I., Shurtleff, M. J., Yuan, R., Xu, S., Ma, Y., Replogle, J. M., Lander, E. S., Darmanis, S., Bahar, I., Sankaran, V. G., Xing, J., Weissman, J. S. 2022; 185 (4): 690-711.e45

  Abstract

  Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

  View details for DOI 10.1016/j.cell.2021.12.045

  View details for PubMedID 35108499

  View details for PubMedCentralID PMC9332140

• Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nature methods Qiu, Q., Hu, P., Qiu, X., Govek, K. W., Cámara, P. G., Wu, H. 2020; 17 (10): 991-1001

  Abstract

  Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

  View details for DOI 10.1038/s41592-020-0935-4

  View details for PubMedID 32868927

  View details for PubMedCentralID PMC8103797

• Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe. Cell systems Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., Kannan, S. 2020; 10 (3): 265-274.e11

  Abstract

  Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

  View details for DOI 10.1016/j.cels.2020.02.003

  View details for PubMedID 32135093

  View details for PubMedCentralID PMC7223477

• The single-cell transcriptional landscape of mammalianorganogenesis Spielmann, M., Cao, J., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., Trapnell, C., Shendure, J. NATURE PUBLISHING GROUP. 2019: 1043-1044

  View details for Web of Science ID 000489313900003

• A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. Nature genetics McFaline-Figueroa, J. L., Hill, A. J., Qiu, X., Jackson, D., Shendure, J., Trapnell, C. 2019; 51 (9): 1389-1398

  Abstract

  Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-β. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete 'partial' stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory 'checkpoints' in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.

  View details for DOI 10.1038/s41588-019-0489-5

  View details for PubMedID 31477929

  View details for PubMedCentralID PMC6756480

• Thyroid hormone regulates distinct paths to maturation in pigment cell lineages. eLife Saunders, L. M., Mishra, A. K., Aman, A. J., Lewis, V. M., Toomey, M. B., Packer, J. S., Qiu, X., McFaline-Figueroa, J. L., Corbo, J. C., Trapnell, C., Parichy, D. M. 2019; 8

  Abstract

  Thyroid hormone (TH) regulates diverse developmental events and can drive disparate cellular outcomes. In zebrafish, TH has opposite effects on neural crest derived pigment cells of the adult stripe pattern, limiting melanophore population expansion, yet increasing yellow/orange xanthophore numbers. To learn how TH elicits seemingly opposite responses in cells having a common embryological origin, we analyzed individual transcriptomes from thousands of neural crest-derived cells, reconstructed developmental trajectories, identified pigment cell-lineage specific responses to TH, and assessed roles for TH receptors. We show that TH promotes maturation of both cell types but in distinct ways. In melanophores, TH drives terminal differentiation, limiting final cell numbers. In xanthophores, TH promotes accumulation of orange carotenoids, making the cells visible. TH receptors act primarily to repress these programs when TH is limiting. Our findings show how a single endocrine factor integrates very different cellular activities during the generation of adult form.

  View details for DOI 10.7554/eLife.45181

  View details for PubMedID 31140974

  View details for PubMedCentralID PMC6588384

• The single-cell transcriptional landscape of mammalian organogenesis. Nature Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., Trapnell, C., Shendure, J. 2019; 566 (7745): 496-502

  Abstract

  Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting 'mouse organogenesis cell atlas' (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.

  View details for DOI 10.1038/s41586-019-0969-x

  View details for PubMedID 30787437

  View details for PubMedCentralID PMC6434952

• Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome. Cell systems Cacchiarelli, D., Qiu, X., Srivatsan, S., Manfredi, A., Ziller, M., Overbey, E., Grimaldi, A., Grimsby, J., Pokharel, P., Livak, K. J., Li, S., Meissner, A., Mikkelsen, T. S., Rinn, J. L., Trapnell, C. 2018; 7 (3): 258-268.e3

  Abstract

  Cellular reprogramming through manipulation of defined factors holds great promise for large-scale production of cell types needed for use in therapy and for revealing principles of gene regulation. However, most reprogramming systems are inefficient, converting only a fraction of cells to the desired state. Here, we analyze MYOD-mediated reprogramming of human fibroblasts to myotubes, a well-characterized model system for direct conversion by defined factors, at pseudotemporal resolution using single-cell RNA-seq. To expose barriers to efficient conversion, we introduce a novel analytic technique, trajectory alignment, which enables quantitative comparison of gene expression kinetics across two biological processes. Reprogrammed cells navigate a trajectory with branch points that correspond to two alternative decision points, with cells that select incorrect branches terminating at aberrant or incomplete reprogramming outcomes. Analysis of these branch points revealed insulin and BMP signaling as crucial molecular determinants of reprogramming. Single-cell trajectory alignment enables rigorous quantitative comparisons between biological trajectories found in diverse processes in development, reprogramming, and other contexts.

  View details for DOI 10.1016/j.cels.2018.07.006

  View details for PubMedID 30195438

• Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Molecular cell Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., Adey, A. C., Steemers, F. J., Shendure, J., Trapnell, C. 2018; 71 (5): 858-871.e8

  Abstract

  Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here, we introduce Cicero, an algorithm that identifies co-accessible pairs of DNA elements using single-cell chromatin accessibility data and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs"-they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD1 in myoblasts exhibit early opening in a PBX1- and MEIS1-dependent manner. Our strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

  View details for DOI 10.1016/j.molcel.2018.06.044

  View details for PubMedID 30078726

• The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., Trapnell, C., Shendure, J., Furlong, E. E. 2018; 555 (7697): 538-542

  Abstract

  Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq), we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2-4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6-8 h after egg laying (predominantly stage 10-11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10-12 h after egg laying (predominantly stage 13), when each of the embryo's more than 20,000 cells are undergoing terminal differentiation. Our results show that there is spatial heterogeneity in the accessibility of the regulatory genome before gastrulation, a feature that aligns with future cell fate, and that nuclei can be temporally ordered along developmental trajectories. During mid-embryogenesis, tissue granularity emerges such that individual cell types can be inferred by their chromatin accessibility while maintaining a signature of their germ layer of origin. Analysis of the data reveals overlapping usage of regulatory elements between cells of the endoderm and non-myogenic mesoderm, suggesting a common developmental program that is reminiscent of the mesendoderm lineage in other species. We identify 30,075 distal regulatory elements that exhibit tissue-specific accessibility. We validated the germ-layer specificity of a subset of these predicted enhancers in transgenic embryos, achieving an accuracy of 90%. Overall, our results demonstrate the power of shotgun single-cell profiling of embryos to resolve dynamic changes in the chromatin landscape during development, and to uncover the cis-regulatory programs of metazoan germ layers and cell types.

  View details for DOI 10.1038/nature25981

  View details for PubMedID 29539636

  View details for PubMedCentralID PMC5866720

• Reversed graph embedding resolves complex single-cell trajectories. Nature methods Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., Trapnell, C. 2017; 14 (10): 979-982

  Abstract

  Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

  View details for DOI 10.1038/nmeth.4402

  View details for PubMedID 28825705

  View details for PubMedCentralID PMC5764547

• Comprehensive single-cell transcriptional profiling of a multicellular organism. Science (New York, N.Y.) Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., Adey, A., Waterston, R. H., Trapnell, C., Shendure, J. 2017; 357 (6352): 661-667

  Abstract

  To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.

  View details for DOI 10.1126/science.aam8940

  View details for PubMedID 28818938

  View details for PubMedCentralID PMC5894354

• Single-cell mRNA quantification and differential analysis with Census. Nature methods Qiu, X., Hill, A., Packer, J., Lin, D., Ma, Y. A., Trapnell, C. 2017; 14 (3): 309-315

  Abstract

  Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.

  View details for DOI 10.1038/nmeth.4150

  View details for PubMedID 28114287

  View details for PubMedCentralID PMC5330805

• Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis. Science (New York, N.Y.) Hanchate, N. K., Kondoh, K., Lu, Z., Kuang, D., Ye, X., Qiu, X., Pachter, L., Trapnell, C., Buck, L. B. 2015; 350 (6265): 1251-5

  Abstract

  The sense of smell allows chemicals to be perceived as diverse scents. We used single-neuron RNA sequencing to explore the developmental mechanisms that shape this ability as nasal olfactory neurons mature in mice. Most mature neurons expressed only one of the ~1000 odorant receptor genes (Olfrs) available, and at a high level. However, many immature neurons expressed low levels of multiple Olfrs. Coexpressed Olfrs localized to overlapping zones of the nasal epithelium, suggesting regional biases, but not to single genomic loci. A single immature neuron could express Olfrs from up to seven different chromosomes. The mature state in which expression of Olfr genes is restricted to one per neuron emerges over a developmental progression that appears to be independent of neuronal activity involving sensory transduction molecules.

  View details for DOI 10.1126/science.aad2456

  View details for PubMedID 26541607

  View details for PubMedCentralID PMC5642900

• From understanding the development landscape of the canonical fate-switch pair to constructing a dynamic landscape for two-step neural differentiation. PloS one Qiu, X., Ding, S., Shi, T. 2012; 7 (12): e49271

  Abstract

  Recent progress in stem cell biology, notably cell fate conversion, calls for novel theoretical understanding for cell differentiation. The existing qualitative concept of Waddington's "epigenetic landscape" has attracted particular attention because it captures subsequent fate decision points, thus manifesting the hierarchical ("tree-like") nature of cell fate diversification. Here, we generalized a recent work and explored such a developmental landscape for a two-gene fate decision circuit by integrating the underlying probability landscapes with different parameters (corresponding to distinct developmental stages). The change of entropy production rate along the parameter changes indicates which parameter changes can represent a normal developmental process while other parameters' change can not. The transdifferentiation paths over the landscape under certain conditions reveal the possibility of a direct and reversible phenotypic conversion. As the intensity of noise increases, we found that the landscape becomes flatter and the dominant paths more straight, implying the importance of biological noise processing mechanism in development and reprogramming. We further extended the landscape of the one-step fate decision to that for two-step decisions in central nervous system (CNS) differentiation. A minimal network and dynamic model for CNS differentiation was firstly constructed where two three-gene motifs are coupled. We then implemented the SDEs (Stochastic Differentiation Equations) simulation for the validity of the network and model. By integrating the two landscapes for the two switch gene pairs, we constructed the two-step development landscape for CNS differentiation. Our work provides new insights into cellular differentiation and important clues for better reprogramming strategies.

  View details for DOI 10.1371/journal.pone.0049271

  View details for PubMedID 23300518

  View details for PubMedCentralID PMC3530918

• A transcriptional dynamic network during Arabidopsis thaliana pollen development. BMC systems biology Wang, J., Qiu, X., Li, Y., Deng, Y., Shi, T. 2011; 5 Suppl 3 (Suppl 3): S8

  Abstract

  To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. During the past decade, significant progresses have been made towards understanding pollen development at each development stage under the molecular level, yet the regulatory mechanisms that control the dynamic pollen development processes remain largely unknown. Here, we adopt Networks Component Analysis (NCA) to identify TF activities over time course, and infer their regulatory relationships based on the coexpression of TFs and their target genes during pollen development.We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube). We constructed a regulatory network, including 19 TFs, 101 target genes and 319 regulatory interactions. The computationally estimated TF activities were well correlated to their coordinated genes' expressions during the development process. We clustered the expression of their target genes in the context of regulatory influences, and inferred new regulatory relationships between those TFs and their target genes, such as transcription factor WRKY34, which was identified that specifically expressed in pollen, and regulated several new target genes. Our finding facilitates the interpretation of the expression patterns with more biological relevancy, since the clusters corresponding to the activity of specific TF or the combination of TFs suggest the coordinated regulation of TFs to their target genes.Through integrating different resources, we constructed a dynamic regulatory network of Arabidopsis thaliana during pollen development with gene coexpression and NCA. The network illustrated the relationships between the TFs' activities and their target genes' expression, as well as the interactions between TFs, which provide new insight into the molecular mechanisms that control the pollen development.

  View details for DOI 10.1186/1752-0509-5-S3-S8

  View details for PubMedID 22784627

  View details for PubMedCentralID PMC3287576

• HCCNet: an integrated network database of hepatocellular carcinoma. Cell research He, B., Qiu, X., Li, P., Wang, L., Lv, Q., Shi, T. 2010; 20 (6): 732-4

  View details for DOI 10.1038/cr.2010.67

  View details for PubMedID 20479783