58
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis

      research-article
      1 , 1 , 1 , 1 , 1 , 2 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 3 , 3 , 4 , 4 , 4 , 4 , 4 , 4 , 4 , 5 , 6 , 6 , 7 , 3 , 1 , 1 , 2 , 1 , 2 , 1 , 1 , 1 , 6 , 1 , 1 , 2 , 1 , 1 , 1 , 1 ,
      American Journal of Respiratory and Critical Care Medicine
      American Thoracic Society
      pulmonary fibrosis, RNA sequencing, alveolar macrophages, alveolar type II cells

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Rationale: The contributions of diverse cell populations in the human lung to pulmonary fibrosis pathogenesis are poorly understood. Single-cell RNA sequencing can reveal changes within individual cell populations during pulmonary fibrosis that are important for disease pathogenesis.

          Objectives: To determine whether single-cell RNA sequencing can reveal disease-related heterogeneity within alveolar macrophages, epithelial cells, or other cell types in lung tissue from subjects with pulmonary fibrosis compared with control subjects.

          Methods: We performed single-cell RNA sequencing on lung tissue obtained from eight transplant donors and eight recipients with pulmonary fibrosis and on one bronchoscopic cryobiospy sample from a patient with idiopathic pulmonary fibrosis. We validated these data using in situ RNA hybridization, immunohistochemistry, and bulk RNA-sequencing on flow-sorted cells from 22 additional subjects.

          Measurements and Main Results: We identified a distinct, novel population of profibrotic alveolar macrophages exclusively in patients with fibrosis. Within epithelial cells, the expression of genes involved in Wnt secretion and response was restricted to nonoverlapping cells. We identified rare cell populations including airway stem cells and senescent cells emerging during pulmonary fibrosis. We developed a web-based tool to explore these data.

          Conclusions: We generated a single-cell atlas of pulmonary fibrosis. Using this atlas, we demonstrated heterogeneity within alveolar macrophages and epithelial cells from subjects with pulmonary fibrosis. These results support the feasibility of discovery-based approaches using next-generation sequencing technologies to identify signaling pathways for targeting in the development of personalized therapies for patients with pulmonary fibrosis.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Basal cells as stem cells of the mouse trachea and human airway epithelium.

            The pseudostratified epithelium of the mouse trachea and human airways contains a population of basal cells expressing Trp-63 (p63) and cytokeratins 5 (Krt5) and Krt14. Using a KRT5-CreER(T2) transgenic mouse line for lineage tracing, we show that basal cells generate differentiated cells during postnatal growth and in the adult during both steady state and epithelial repair. We have fractionated mouse basal cells by FACS and identified 627 genes preferentially expressed in a basal subpopulation vs. non-BCs. Analysis reveals potential mechanisms regulating basal cells and allows comparison with other epithelial stem cells. To study basal cell behaviors, we describe a simple in vitro clonal sphere-forming assay in which mouse basal cells self-renew and generate luminal cells, including differentiated ciliated cells, in the absence of stroma. The transcriptional profile identified 2 cell-surface markers, ITGA6 and NGFR, which can be used in combination to purify human lung basal cells by FACS. Like those from the mouse trachea, human airway basal cells both self-renew and generate luminal daughters in the sphere-forming assay.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Discovering Motifs in Ranked Lists of DNA Sequences

              Introduction Background This paper examines the problem of discovering “interesting” sequence motifs in biological sequence data. A widely accepted and more formal definition of this task is: given a target set and a background set of sequences (or a background model), identify sequence motifs that are enriched in the target set compared with the background set. The purpose of this paper is to extend this formulation and to make it more flexible so as to enable the determination of the target and background set in a data driven manner. Discovery of sequences or attributes that are enriched in a target set compared with a background set (or model) has become increasingly useful in a wide range of applications in molecular biology research. For example, discovery of DNA sequence motifs that are overabundant in a set of promoter regions of co-expressed genes (determined by clustering of expression data) can suggest an explanation for this co-expression. Another example is the discovery of DNA sequences that are enriched in a set of promoter regions to which a certain transcription factor (TF) binds strongly, inferred from chromatin immuno-precipitation on a microarray (ChIP–chip) [1] measurements. The same principle may be extended to many other applications such as discovery of genomic elements enriched in a set of highly methylated CpG island sequences [2]. Due to its importance, this task of discovering enriched DNA subsequences and capturing their corresponding motif profile has gained much attention in the literature. Any approach to motif discovery must address several fundamental issues. The first issue is the way by which motifs are represented. There are several strategies for motif representation: using a k-mer of IUPAC symbols where each symbol represents a fixed set of possible nucleotides at a single position (examples of methods that use this representation include REDUCE [3], YMF [4,5], ANN-SPEC [6], and a hypergeometric-based method [7]) or using a position weight matrix (PWM), which specifies the probability of observing each nucleotide at each motif position (for example MEME [8], BioProspector [9], MotifBooster [10], DME-X [11], and AlignACE [12]). Both representations assume base position independence. Alternatively, higher order representations that capture positional dependencies have been proposed (e.g., HMM and Bayesian networks motif representations [13]). While these representations circumvent the position independence assumption, they are more vulnerable to overfitting and lack of data for determining model parameters. The method described in this paper uses the k-mer model with symbols above IUPAC. The second issue is devising a motif scoring scheme. Many strategies for scoring motifs have been suggested in the literature. One simple yet powerful approach uses the hypergeometric distribution for identifying enriched motif kernels in a set of sequences and then expanding these motifs using an EM algorithm [7]. The framework described in this paper is a natural extension of the approach of [7]. YMF [4,5] is an exhaustive search algorithm which associates each motif with a z-score. AlignACE [12] uses a Gibbs sampling algorithm for finding global sequence alignments and produces a MAP score. This score is an internal metric used to determine the significance of an alignment. MEME [8] uses an expectation maximization strategy and outputs the log-likelihood and relative entropy associated with each motif. Once a scoring scheme is devised, a defined motif search space is scanned (either heuristically or exhaustively) and motifs with significantly high scores are identified. To determine the statistical significance of the obtained scores, many methods resort to simulations or ad hoc thresholds. Several excellent reviews narrate the different strategies for motif detection and use quantitative benchmarking to compare their performance [14–18]. A related aspect of motif discovery, which is outside the scope of this paper, focuses on properties of clusters and modules of TF binding sites (TFBS). Examples of approaches that search for combinatorial patterns and modules underlying TF binding and gene expression include [19–23]. Open Challenges in Motif Discovery One issue of motif discovery that is often overlooked concerns the partition of the input set of sequences into target and background sets. Many methods rely on the user to provide these two sets and search for motifs that are overabundant in the former set compared with the latter. The question of how to partition the data into target and background sets is left to the user. However, the boundary between the sets is often unclear and the exact choice of sequences in each set arbitrary. For example, suppose that one wishes to identify motifs within promoter sequences that constitute putative TFBS. An obvious strategy would be to partition the set of promoter sequences into target and background sets according to the TF binding signal (as measured by ChIP–chip experiments). The two sets would contain the sequences to which the TF binds “strongly” and “weakly,” respectively. A motif detection algorithm could then be applied to find motifs that are overabundant in the target set compared with the background set. In this scenario, the positioning of the cutoff between the strong and weak binding signal is somewhat arbitrary. Obviously, the final outcome of the motif identification process can be highly dependent on this choice of cutoff. A stringent cutoff will result in the exclusion of informative sequences from the target set while a promiscuous cutoff will cause inclusion of nonrelevant sequences—both extremes hinder the accuracy of motif prediction. This example demonstrates a fundamental difficulty in partitioning most types of data. Several methods attempt to circumvent this hurdle. For example, REDUCE [3] uses a regression model on the entire set of sequences. However, it is difficult to justify this model in the context of multiple motif occurrence (as explained below). In other work, a variant of the Kolmogorov-Smirnov test was used for motif discovery [24]. This approach successfully circumvents arbitrary data partition. However, it has other limitations such as the failure to address multiple motif occurrences in a single promoter, and the lack of an exact characterization of the null distribution. Overall, the following four major challenges in motif discovery still require consideration: (c1) the cutoff used to partition data into a target set and background set of sequences is often chosen arbitrarily; (c2) lack of an exact statistical score and p-value for motif enrichment. Current methods typically use arbitrarily set thresholds or simulations, which are inherently limited in precision and costly in terms of running time; (c3) a need for an appropriate framework that accounts for multiple motif occurrences in a single promoter. For example, how should one quantify the significance of a single motif occurrence in a promoter against two motif occurrences in a promoter? Linear models [3] assume that the weight of the latter is double that of the former. However, it is difficult to justify this approach since biological systems do not necessarily operate in such a linear fashion. Another issue related to motif multiplicity is low complexity or repetitive regions. These regions often contain multiple copies of degenerate motifs (e.g., CA repeats). Since the nucleotide frequency underlying these regions substantially deviates from the standard background frequency, they often cause false-motif discoveries. Consequently, most methods mask these regions in the preprocessing stage and thereby lose vital information that might reside therein; (c4) criticism has been made over the fact that motif discovery methods tend to report presumably significant motifs even when applied on randomly generated data [25]. These motifs are clear cases of false positives and should be avoided. Data Lends Itself to Ranking in a Natural Manner In this paper we describe a novel method that attempts to solve the above-mentioned four challenges in a principled manner. It exploits the following observation: data often lends itself to ranking in a natural manner, e.g., ranking sequences according to TF binding signal: ranking according to CpG methylation signal, ranking according to distance in expression space from a set of co-expressed genes, ranking according to differential expression, etc. We exploit this inherent ranking property of biological data in order to circumvent the need for an arbitrary and difficult-to-justify data partition. Consequently, we propose the following formulation of the motif finding task: given a list of ranked sequences, identify motifs that are overabundant at either end of the list. Our solution employs a statistical score termed mHG (minimal hypergeometric) [26]. It is related to the concept of rank-imbalanced motifs, which are sequence motifs that tend to appear at either end of a ranked sequence list. In previous work [26], the authors used mHG to identify sequence motifs in expression data. We use this simple yet powerful approach as the starting point for our study. Overview The rest of this paper is divided into two main parts, each of which is self-contained: in the Results we briefly outline our method and describe new biological findings that were obtained by applying this method to biological data. We address challenge (c4) by testing the algorithm on randomly ranked real genomic sequences. In the Methods, we describe the mHG probabilistic and algorithmic framework and explain how we deal with challenges (c1)–(c3). Results Statistics and Algorithms in a Nutshell Based on the mHG framework, we developed a software tool termed DRIM (discovery of rank imbalanced motifs) for motif identification in DNA sequences. A flow chart of DRIM is provided in Figure 1. The formal introduction and details of the mHG statistics are given in Methods. However, to facilitate the explanation and interpretation of our biological results, we begin with a brief description of the method. Figure 1 DRIM Flow Chart DRIM receives a list of DNA sequences as input and a criterion by which the sequences should be ranked, for example, TF binding signals as measured by ChIP ChIP–chip: (i) The sequences are ranked according to the criterion. (ii) A “blind search” is performed over all the motifs that reside in the restricted motif space (in this study the restricted motif space contains ∼100,000 motifs, see Methods, The DRIM software). For each motif an occurrence vector is generated. Each position in the vector is the number of motif occurrences in the corresponding sequence, (the figure shows the vector for the motif CACGTGW). (iii) The motif significance is computed using the mHG scheme, and the optimal partition into target and background sets in terms of motif enrichment is identified. The promising motif seeds are passed as input to the heuristic motif search model and the rest are filtered out. (iv,v) The motif seeds are expanded in an iterative manner (the mHG is computed in each lap), until a local optimum motif is found. (vi) The exact mHG p-value of the motif is computed. If it has a p-value p. The (0,0) → (N,B) path representing λ visits N distinct grid points (excluding the point (0,0)), representing the N different HGT scores that are considered when calculating its mHG score: mHG(λ) = min1≤n
                Bookmark

                Author and article information

                Journal
                Am J Respir Crit Care Med
                Am. J. Respir. Crit. Care Med
                ajrccm
                American Journal of Respiratory and Critical Care Medicine
                American Thoracic Society
                1073-449X
                1535-4970
                15 June 2019
                15 June 2019
                15 June 2019
                15 June 2019
                : 199
                : 12
                : 1517-1536
                Affiliations
                [ 1 ]Division of Pulmonary and Critical Care Medicine, Department of Medicine
                [ 2 ]Division of Thoracic Surgery, Department of Surgery
                [ 3 ]Division of Rheumatology, Department of Medicine
                [ 5 ]Department of Pathology, and
                [ 6 ]Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
                [ 4 ]Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois; and
                [ 7 ]Department of Chemical and Biological Engineering, Weinberg College of Arts and Sciences, Northwestern University, Evanston, Illinois
                Author notes
                Correspondence and requests for reprints should be addressed to Alexander V. Misharin, M.D., Ph.D., Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, 240 East Huron Street, Room M343, Chicago, IL 60611. E-mail: a-misharin@ 123456northwestern.edu .
                [*]

                These authors contributed equally to this work.

                [‡]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-6435-6001
                http://orcid.org/0000-0001-7428-3101
                http://orcid.org/0000-0001-8330-0663
                http://orcid.org/0000-0002-8366-5782
                http://orcid.org/0000-0002-8909-356X
                http://orcid.org/0000-0001-5775-8427
                http://orcid.org/0000-0002-3114-5208
                http://orcid.org/0000-0003-2879-3789
                Article
                201712-2410OC
                10.1164/rccm.201712-2410OC
                6580683
                30554520
                f8c7531b-f50f-43dc-903b-ac6f383b963e
                Copyright © 2019 by the American Thoracic Society

                This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 ( http://creativecommons.org/licenses/by-nc-nd/4.0/). For commercial usage and reprints, please contact Diane Gern ( dgern@ 123456thoracic.org ).

                History
                : 08 December 2017
                : 14 December 2019
                Page count
                Figures: 10, Tables: 2, Pages: 20
                Categories
                Original Articles
                Interstitial Lung Disease

                pulmonary fibrosis,rna sequencing,alveolar macrophages,alveolar type ii cells

                Comments

                Comment on this article