Supplementary Materials for...2020/07/06 · Supplementary Materials Fig. S1. Controlling for Batch Effects Does Not Alter Overall Data Architecture. (A-B) UMAPs of lung epithelial

advances.sciencemag.org/cgi/content/full/6/28/eaba1983/DC1

Supplementary Materials for

Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell

populations in idiopathic pulmonary fibrosis

Taylor S. Adams, Jonas C. Schupp, Sergio Poli, Ehab A. Ayaub, Nir Neumark, Farida Ahangari, Sarah G. Chu, Benjamin A. Raby, Giuseppe DeIuliis, Michael Januszyk, Qiaonan Duan, Heather A. Arnett, Asim Siddiqui,

George R. Washko, Robert Homer, Xiting Yan, Ivan O. Rosas*, Naftali Kaminski*

*Corresponding author. Email: [email protected] (I.O.R.); [email protected] (N.K.)

Published 8 July 2020, Sci. Adv. 6, eaba1983 (2020)

DOI: 10.1126/sciadv.aba1983

This PDF file includes:

Figs. S1 to S9 Table S1 Legends for data S1 to S13 References

Other Supplementary Material for this manuscript includes the following: (available at advances.sciencemag.org/cgi/content/full/6/28/eaba1983/DC1)

Data S1 to S13

Supplementary Materials

Fig. S1. Controlling for Batch Effects Does Not Alter Overall Data Architecture. (A-B)UMAPs of lung epithelial cells (A) without and (B) with correction for batch effects using singlecell variational inference (scVI) (40), colored by subject identity. Uncorrected clustering leads toa single focus of subject-driven cell focus, which is partially mitigated following batchcorrection. (C-D) UMAPs recolored by cell type (C) without and (D) with batch correction. TheIPF-specific epithelial subpopulation of interest is similarly identified in both configurations(92.3% overlap). (E-F) UMAPs recolored by disease state (E) without and (F) with batchcorrection. Three clear foci of non-diseased cells are similarly present in both representations.(G) Overlap between cell type identifications derived with and without scVI batch

normalization, showing high correlation between both analyses. (H) Scatter plot of characteristicdirection (CHDIR) scores for top genes in IPF-specific epithelial subpopulation from bothanalyses. (I-J) Top 10 Reactome (44) pathways overrepresented among the 200 mostdifferentially expressed genes in the IPF-specific epithelial subpopulations derived without (I)and with (J) batch normalization, with overlapping pathways highlighted in parallel colors.Taken together, these findings demonstrate that batch correction does not significantly alter theidentification of this rare epithelial subpopulation, does not significantly change its underlyinggene expression profile, and does not fundamentally alter the inferred functionality of these cells.

Fig. S2. Controlling for Cell Cycle State Does Not Alter Overall Data Architecture. (A-B)UMAPs of lung epithelial cells (A) without and (B) with correction for cell cycle effects usingthe regression implementation in Scanpy per the approach described by Scialdone et al. (41),colored by cell cycle phase. (C-D) UMAPs recolored by cell type (C) without and (D) with cyclecorrection. The IPF-specific epithelial subpopulation of interest is similarly identified in bothconfigurations (89.4% overlap). (E-F) UMAPs recolored by disease state (E) without and (F)

with cycle correction. Three clear foci of non-diseased cells are similarly present in bothrepresentations. (G) Overlap between cell type identifications derived with and without cellcycle normalization, showing high correlation between both analyses. (H) Scatter plot ofcharacteristic direction (CHDIR) scores for top genes in IPF-specific epithelial subpopulationfrom both analyses. (I-J) Top 10 Reactome (44) pathways overrepresented among the 200 mostdifferentially expressed genes in the IPF-specific epithelial subpopulations derived without (I)and with (J) batch normalization, with overlapping pathways highlighted in parallel colors.Taken together, these findings demonstrate that normalizing for cell cycle state does notsignificantly alter the identification of this rare epithelial subpopulation, does not significantlychange its underlying gene expression profile, and does not fundamentally alter the inferredfunctionality of these cells.

Fig. S3. Manual Annotations of Single Cells are Consistent with Automated AnnotationsDrawn from Multiple Cell Type Definition Databases. Using the SingleR software package,annotations were assigned to each cell in an automated fashion based on established definitionsderived from either the (A) Human Primary Cell Atlas (HPCA) or (B) Blue ENCODE databases.

Correlation scores were then assigned to pairwise comparisons of each definition based on thefraction of overlap between the two categories. (C) For reference, a comparison between the twoautomated databases is included, demonstrating comparable overlap. (D-F) Aggregation tablesdetailing the mappings of (D) HPCA, (E) Blue ENCODE, and (F) this manuscript’s cell typedefinitions to the larger group categories used to derive scores in panels (A-C).

Fig. S4. Deconvolution of Bulk RNAseq Dataset with scRNAseq Data as Reference. (A) Signature matrix of markers used for deconvolution, with average expression per subject, unity normalized, grouped by IPF and control to demonstrate within cell-type consistency and across cell-type specificity. (B) Predicted proportions of cell fractions across bulk RNAseq libraries from 26 control and 46 transplant stage IPF lungs, performed with the R package MuSiC (42).

Fig. S5. Aberrant Basaloid Cell Signature Unobserved in Control Immunohistochemistry. Immunohistochemistry of control lung serial sections, bronchus cross sections (top), bronchus longitudinal sections (middle) and alveoli (bottom). Positive stains are brown.

Fig. S6. Aberrant Basaloid Cells Identified in Reyfman et al.'s Data Demonstrate IdenticalFeatures. (A) UMAPs of all epithelial cells identified in Reyfman et al.'s (3) dataset labeled bycell type. (B) Heatmap of average gene expression per subject for each of the identified epithelialcell types in Reyfman et al.' s dataset. Column are hierarchically ordered by disease status andcell type. Right: Zoom annotation of distinguishing markers for aberrant basaloid cells identifiedin Reyfman et al.' s dataset. The portrayed genes and the order of the genes are the same as infigure 2C of the main manuscript. (C) Enlarged heatmap of the correlation matrix of figure 2E inthe main manuscript, with the Spearman correlation coefficients ρ added as numbers. The

heatmap portrays the correlation matrix of ATI, ATII, goblet, club, ciliated and aberrant basaloidcell average gene expression with analogous cell populations we identified in Reyfman et al.'s(3) dataset. Matrix cells are colored by Spearman's rho, cell populations are ordered withhierarchical clustering. The origin dataset for each cell population is denoted by differentgreyscale tones in the annotation bars.

Fig. S7. Validation of COL15A1 Protein Expression in Peribronchial Endothelial Cells inthe Human Protein Atlas. COL15A1 immunostainings - in brown - of bronchi (A, B) and lungparenchyma (C, D) specimen from the Human Protein Atlas (19). Pronounced COL15A1positivity of peribronchial vessels (A, B), while neither pulmonary capillaries nor largerpulmonary vessels stained positive for COL15A1 (C, D).

Fig. S8. Thresholding Cell Barcodes to Define Cells. (A) The relationship between the numberintron-spanning unique molecular identifiers (UMIs) and exon-spanning UMIs, for the top10,000 cell-barcodes per library ranked by number of UMIs, for all 107 libraries used in the data.The same cell-barcodes are plotted in all subsequent figures of the panel. (B) Relationshipbetween the fraction of UMIs that are intron spanning and the total number of UMIs, the dotted

line represents the cutoff of having at least 0.12 proportion intron-spanning to qualify as a validcell. (C) The relationship between the fraction UMIs of mitochondrial origin and total number ofUMIs. The dotted line represents the threshold of having no more than 0.2 proportionmitochondrial reads to qualify as a valid cell. cell-barcodes are colored based on whether theypassed the previous test. (D) Relationship between the number of unique genes and the totalnumber of UMIs for cell barcodes, blue contour lines represent population density estimation,the dotted line represents the cutoff of having at least 1000 unique genes to qualify as a validcell. Cell barcodes are colored by whether they passed or failed previous thresholding tests.

Figure S9. Quality Control of Library Sequencing Depth. (A) Distribution of uniquelymapped reads per number of cells captured across libraries of samples from each disease states(38 control libraries, 24 COPD libraries and 45 IPF libraries). Biases across the threedistributions were assessed via Kruskal-Wallis one-way analysis of variance (p-value:0.29). (B,C) Distribution of the mean UMIs and genes per library per cell type respectively, representedper library across disease states and cell variety.

SubjectID

Library IDs Sex Age Race EverSmoker

DiseaseGroup

133C 133C-a Female 32 white No Control

1372C 137C-a, 137C-b Female 21 white No Control

034C 034C Male 49 asian Yes Control

218C 218C-a, 218C-b, 219C-a, 219C-b Male 29 white No Control

226C 226C-a, 226C-b Male 32 white Yes Control

244C 244C Male 50 white Yes Control

098C 098C-a, 098C-b Female 41 white No Control

465C 465C Male 56 white No Control

396C 396C Female 37 white No Control



222C 222C, 022C-a, 022C-b Male 65 white Yes Control


092C 092C Male 29 latino No Control

439C 439C, 439C-b Female 66 white No Control



192C 92C, 192C-a Female 62 white No Control






253C 253C Female 66 white Yes Control

484C 484C Male 31 white Yes Control



084C 084C Male 46 black No Control

152CO 152CO, 152CO-a Male 57 white Yes COPD

153CO 153CO-a, 153CO-b Male 62 white Yes COPD

178CO 178CO Female 58 white Yes COPD

184CO 184CO-a, 184CO-b Female 55 white No COPD

186CO 186CO-b Male 66 white Yes COPD

192CO 192CO, 192CO-a, 192CO-b Male 66 white Yes COPD

193CO 193CO-a, 193CO-b Male 63 white Yes COPD

194CO 194CO-a Male 59 white Yes COPD


217CO 217CO-a Female 70 white Yes COPD

23CO 23CO Male 66 white Yes COPD





052CO 052CO-a Female 62 white Yes COPD



210I 210CO Male 68 white Yes IPF

135I 135I-a, 135I-b Male 59 white Yes IPF

138I 138I, 138I-a Male 56 white Yes IPF

145I 145I-a Male 67 white Yes IPF

157I 157I, 157I-a, 157I-b Female 66 white Yes IPF


174I 174I-a Female 67 white Yes IPF

179I 179I Male 70 white Yes IPF

209I 209I-a Female 65 asian No IPF

212I 212I-a Male 71 white No IPF


221I 221I-a Female 67 white No IPF

222I 222I-a, 222I-b Male 59 white No IPF

225I 225I-a, 225I-b Female 70 white Yes IPF


29I 29I Male 61 white No IPF

051I 051I, 051I-a Male 62 other Yes IPF



021I 021I Male 69 white No IPF


041I 041I-b Male 59 white No IPF

040I 040I, 040I-b Male 70 white Yes IPF


49I 49I-a, 49I-b Male 66 white Yes IPF

053I 53I, 053I-d, 053I-n Male 60 white Yes IPF

59I 59I Female 66 white No IPF

063I 063I-b Male 68 white No IPF



158I 158I-b Male 68 white Yes IPF


Table S1. Sample Demographics. Basic characteristics of all included patients and controls. Age is given in years.

Data S1. Library Read Processing SummariesSummary of read processing pipeline outcome for each library.

Data S2. Epithelial Subset Marker Table

Results of Wilcoxon rank-sum test of each epithelial cell-type against the other epithelial varieties, using the average gene expression per subject, per cell type.

Data S3. Mesenchymal Subset Marker Table

Results of Wilcoxon rank-sum test of each mesenchymal cell-type against the other mesenchymal varieties, using the average gene expression per subject, per cell type.

Data S4. Myeloid Subset Marker Table

Results of Wilcoxon rank-sum test of each myeloid cell-type against the other myeloid varieties, using the average gene expression per subject, per cell type.

Data S5. Lymphoid Subset Marker Table

Results of Wilcoxon rank-sum test of each lymphoid cell-type against the other lymphoid varieties, using the average gene expression per subject, per cell type.

Data S6. Marker Specificity Table

Log transformed diagnostics odds ratio of genes for cell types across cells, using genes with log transformed fold change > 0.3 for each cell type population compared against the other cell type populations.

Data S7. Subject Cell Count Table

Number of cells profiled from each described cell type, for each subject.

Data S8. IPF Versus Control Differential Expression Table

Results of Wilcoxon rank-sum test of average per subject gene expression, within each cell type comparing IPF to control. Cell types represented by less than 5 subjects in either disease category are not tested.

Data S9. COPD Versus Control Differential Expression Table

Results of Wilcoxon rank-sum test of average per subject gene expression, within each cell type comparing COPD to control. Cell types represented by less than 5 subjects in either disease category are not tested.

Data S10. IPF Versus COPD Differential Expression Table

Results of Wilcoxon rank-sum test of average per subject gene expression, within each cell type comparing IPF to COPD. Cell types represented by less than 5 subjects in either disease categoryare not tested.

Data S11. UMAP Plot Parameters Values for all non-default parameters used during the generation of each UMAP.

Data S12. Cell Population Composition Statistics Test statistics for Wilcoxon rank-sum test of IPF versus control cell-type non-zero proportion makeup, as a fraction of their respective cell group.

Data S13. Antibodies Utilized Table of primary antibodies used in the immunohistochemical stains.

REFERENCES AND NOTES

1. D. J. Lederer, F. J. Martinez, Idiopathic pulmonary fibrosis. N. Engl. J. Med. 378, 1811–1823 (2018).

2. G. Y. Yu, G. H. Ibarra, N. Kaminski, Fibrosis: Lessons from OMICS analyses of the human lung.

Matrix Biol. 68-69, 422–434 (2018).

3. P. A. Reyfman, J. M. Walter, N. Joshi, K. R. Anekalla, A. C. McQuattie-Pimentel, S. Chiu, R.

Fernandez, M. Akbarpour, C.-I. Chen, Z. Ren, R. Verma, H. Abdala-Valencia, K. Nam, M. Chi, S.

H. Han, F. J. Gonzalez-Gonzalez, S. Soberanes, S. Watanabe, K. J. N. Williams, A. S. Flozak, T. T.

Nicholson, V. K. Morgan, D. R. Winter, M. Hinchcliff, C. L. Hrusch, R. D. Guzy, C. A. Bonham, A.

I. Sperling, R. Bag, R. B. Hamanaka, G. M. Mutlu, A. V. Yeldandi, S. A. Marshall, A. Shilatifard, L.

A. N. Amaral, H. Perlman, J. I. Sznajder, A. C. Argento, C. T. Gillespie, J. Dematte, M. Jain, B. D.

Singer, K. M. Ridge, A. P. Lam, A. Bharat, S. M. Bhorade, C. J. Gottardi, G. R. S. Budinger, A. V.

Misharin, Single-cell transcriptomic analysis of human lung provides insights into the pathobiology

of pulmonary fibrosis. Am. J. Respir. Crit. Care Med. 199, 1517–1536 (2019).

4. M. S. B. Raredon, T. S. Adams, Y. Suhail, J. C. Schupp, S. Poli, N. Neumark, K. L. Leiby, A. M.

Greaney, Y. Yuan, C. Horien, G. Linderman, A. J. Engler, D. J. Boffa, Y. Kluger, I. O. Rosas, A.

Levchenko, N. Kaminski, L. E. Niklason, Single-cell connectomic analysis of adult mammalian

lungs. Sci. Adv. 5, eaaw3851 (2019).

5. N. Neumark et al., in IPF Cell Atlas (2019); www.ipfcellatlas.com.

6. Y. Xu, T. Mizuno, A. Sridharan, Y. du, M. Guo, J. Tang, K. A. Wikenheiser-Brokamp, A. K. T. Perl,

V. A. Funari, J. J. Gokey, B. R. Stripp, J. A. Whitsett, Single-cell RNA sequencing identifies diverse

roles of epithelial cells in idiopathic pulmonary fibrosis. Jci Insight 1, (2016), e90558.

7. C. Morse, T. Tabib, J. Sembrat, K. L. Buschur, H. T. Bittar, E. Valenzi, Y. Jiang, D. J. Kass, K.

Gibson, W. Chen, A. Mora, P. V. Benos, M. Rojas, R. Lafyatis, Proliferating SPP1/MERTK-

expressing macrophages in idiopathic pulmonary fibrosis. Eur. Respir. J. 54, 1802441 (2019).

8. P. Sivakumar, J. R. Thompson, R. Ammar, M. Porteous, C. McCoubrey, E. Cantu III, K. Ravi, Y.

Zhang, Y. Luo, D. Streltsov, M. F. Beers, G. Jarai, J. D. Christie, RNA sequencing of transplant-

stage idiopathic pulmonary fibrosis lung reveals unique pathway regulation. ERJ Open Res. 5,

00117–02019 (2019).

9. J. Ordovas-Montanes, D. F. Dwyer, S. K. Nyquist, K. M. Buchheit, M. Vukovic, C. Deb, M. H.

Wadsworth II, T. K. Hughes, S. W. Kazer, E. Yoshimoto, K. N. Cahill, N. Bhattacharyya, H. R.

Katz, B. Berger, T. M. Laidlaw, J. A. Boyce, N. A. Barrett, A. K. Shalek, Allergic inflammatory

memory in human respiratory epithelial progenitor cells. Nature 560, 649–654 (2018).

10. M. Zeisberg, E. G. Neilson, Biomarkers for epithelial-mesenchymal transitions. J. Clin. Invest. 119,

1429–1437 (2009).

http://www.ipfcellatlas.com/

11. P. J. Barnes, J. Baker, L. E. Donnelly, Cellular senescence as a mechanism and target in chronic lung

diseases. Am. J. Respir. Crit. Care Med. 200, 556–564 (2019).

12. Y. Z. Zhang, M. Jiang, M. Nouraie, M. G. Roth, T. Tabib, S. Winters, X. Chen, J. Sembrat, Y. Chu,

N. Cardenes, R. M. Tuder, E. L. Herzog, C. Ryu, M. Rojas, R. Lafyatis, K. F. Gibson, J. F. Mc Dyer,

D. J. Kass, J. K. Alder, GDF15 is an epithelial-derived biomarker of idiopathic pulmonary fibrosis.

Am. J. Physiol. Lung Cell. Mol. Physiol. 317, L510–L521 (2019).

13. F. R. Zuo, N. Kaminski, E. Eugui, J. Allard, Z. Yakhini, A. Ben-Dor, L. Lollini, D. Morris, Y. Kim,

B. De Lustro, D. Sheppard, A. Pardo, M. Selman, R. A. Heller, Gene expression analysis reveals

matrilysin as a key regulator of pulmonary fibrosis in mice and humans. Proc. Natl. Acad. Sci.

U.S.A. 99, 6292–6297 (2002).

14. J. S. Munger, X. Huang, H. Kawakatsu, M. J. D. Griffiths, S. L. Dalton, J. Wu, J. F. Pittet, N.

Kaminski, C. Garat, M. A. Matthay, D. B. Rifkin, D. Sheppard, The integrin αvβ 6 binds and

activates latent TGF β1: A mechanism for regulating pulmonary inflammation and fibrosis. Cell 96,

319–328 (1999).

15. D. Lagares, P. Ghassemi-Kakroodi, C. Tremblay, A. Santos, C. K. Probst, A. Franklin, D. M.

Santos, P. Grasberger, N. Ahluwalia, S. B. Montesi, B. S. Shea, K. E. Black, R. Knipe, M. Blati, M.

Baron, B. Wu, H. Fahmi, R. Gandhi, A. Pardo, M. Selman, J. Wu, J.-P. Pelletier, J. Martel-Pelletier,

A. M. Tager, M. Kapoor, ADAM10-mediated ephrin-B2 shedding promotes myofibroblast

activation and organ fibrosis. Nat. Med. 23, 1405–1415 (2017).

16. S. Danopoulos, I. Alonso, M. E. Thornton, B. H. Grubbs, S. Bellusci, D. Warburton, D. Al Alam,

Human lung branching morphogenesis is orchestrated by the spatiotemporal distribution of ACTA2,

SOX2, and SOX9. Am. J. Physiol. Lung Cell. Mol. Physiol. 314, L144–L149 (2018).

17. T. Volckaert, T. Yuan, J. Yuan, E. Boateng, S. Hopkins, J. S. Zhang, V. J. Thannickal, R. Fässler, S.

P. de Langhe, Hippo signaling promotes lung epithelial lineage commitment by curbing Fgf10 and

β-catenin signaling. Development 146, dev166454 (2019).

18. M. Ebina, Pathognomonic remodeling of blood and lymphatic capillaries in idiopathic pulmonary

fibrosis. Respir. Investig. 55, 2–9 (2017).

19. M. Uhlen, L. Fagerberg, B. M. Hallström, C. Lindskog, P. Oksvold, A. Mardinoglu, Å. Sivertsson,

C. Kampf, E. Sjöstedt, A. Asplund, I. M. Olsson, K. Edlund, E. Lundberg, S. Navani, Cristina Al-

Khalili Szigyarto, J. Odeberg, D. Djureinovic, J. O. Takanen, S. Hober, T. Alm, P.-H. Edqvist, H.

Berling, H. Tegel, J. Mulder, J. Rockberg, P. Nilsson, J. M. Schwenk, M. Hamsten, Kalle von

Feilitzen, M. Forsberg, L. Persson, F. Johansson, M. Zwahlen, Gunnar von Heijne, J. Nielsen, F.

Pontén, Tissue-based map of the human proteome. Science 347, 1260419 (2015).

20. F. A. Vieira Braga, G. Kar, M. Berg, O. A. Carpaij, K. Polanski, L. M. Simon, S. Brouwer, T.

Gomes, L. Hesse, J. Jiang, E. S. Fasouli, M. Efremova, R. Vento-Tormo, C. Talavera-López, M. R.

Jonker, K. Affleck, S. Palit, P. M. Strzelecka, H. V. Firth, K. T. Mahbubani, A. Cvejic, K. B. Meyer,

K. Saeb-Parsy, M. Luinge, C. A. Brandsma, W. Timens, I. Angelidis, M. Strunz, G. H. Koppelman,

A. J. van Oosterhout, H. B. Schiller, F. J. Theis, M. van den Berge, M. C. Nawijn, S. A. Teichmann,

A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25,

1153–1163 (2019).

21. J. Green, M. Endale, H. Auer, A. K. T. Perl, Diversity of interstitial lung fibroblasts is regulated by

platelet-derived growth factor receptor α kinase activity. Am. J. Respir. Cell Mol. Biol. 54, 532–545

(2016).

22. G. Jia, S. Chandriani, A. R. Abbas, D. J. DePianto, E. N. N'Diaye, M. B. Yaylaoglu, H. M. Moore, I.

Peng, J. DeVoss, H. R. Collard, P. J. Wolters, J. G. Egen, J. R. Arron, CXCL14 is a candidate

biomarker for Hedgehog signalling in idiopathic pulmonary fibrosis. Thorax 72, 780–787 (2017).

23. F. A. Wolf, F. K Hamey, M. Plass, J. Solana, J. S. Dahlin, B. Göttgens, N. Rajewsky, L. Simon, F. J.

Theis, PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology

preserving map of single cells. Genome Biol. 20, 59 (2019).

24. G. Iacono, R. Massoni-Badosa, H. Heyn, Single-cell transcriptomics unveils gene regulatory

network plasticity. Genome Biol. 20, 110 (2019).

25. M. Selman, T. E. King, A. Pardo; American Thoracic Society, European Respiratory Society,

American College of Chest Physicians, Idiopathic pulmonary fibrosis: Prevailing and evolving

hypotheses about its pathogenesis and implications for therapy. Ann. Intern. Med. 134, 136–151

(2001).

26. M. Chilosi, V. Poletti, B. Murer, M. Lestani, A. Cancellieri, L. Montagna, P. Piccoli, G. Cangi, G.

Semenzato, C. Doglioni, Abnormal re-epithelialization and lung remodeling in idiopathic pulmonary

fibrosis: The role of ΔN-p63. Lab. Invest. 82, 1335–1345 (2002).

27. A. Prasse, H. Binder, J. C. Schupp, G. Kayser, E. Bargagli, B. Jaeger, M. Hess, S. Rittinghausen, L.

Vuga, H. Lynn, S. Violette, B. Jung, K. Quast, B. Vanaudenaerde, Y. Xu, J. M. Hohlfeld, N. Krug, J.

D. Herazo-Maya, P. Rottoli, W. A. Wuyts, N. Kaminski, BAL cell gene expression is indicative of

outcome and airway basal cell involvement in IPF. Am. J. Respir. Crit. Care Med. 199, 622–630

(2019).

28. Y. Zeng, J. Zhu, D. Shen, H. Qin, Z. Lei, W. Li, J.-A. Huang, Z. Liu, Repression of Smad4 by miR-

205 moderates TGF-β-induced epithelial-mesenchymal transition in A549 cell lines. Int. J. Oncol.

49, 700–708 (2016).

29. B. C. Willis, J. M. Liebler, K. Luby-Phelps, A. G. Nicholson, E. D. Crandall, R. M. du Bois, Z.

Borok, Induction of epithelial-mesenchymal transition in alveolar epithelial cells by transforming

growth factor-β1. Am. J. Pathol. 166, 1321–1332 (2005).

30. J. R. Rock, C. E. Barkauskas, M. J. Cronce, Y. Xue, J. R. Harris, J. Liang, P. W. Noble, B. L. M.

Hogan, Multiple stromal populations contribute to pulmonary fibrosis without evidence for epithelial

to mesenchymal transition. Proc. Natl. Acad. Sci. U.S.A. 108, E1475–E1483 (2011).

31. M. Chilosi, V. Poletti, A. Zamò, M. Lestani, L. Montagna, P. Piccoli, S. Pedron, M. Bertaso, A.

Scarpa, B. Murer, A. Cancellieri, R. Maestro, G. Semenzato, C. Doglioni, Aberrant Wnt/beta-catenin

pathway activation in idiopathic pulmonary fibrosis. Am. J. Pathol. 162, 1495–1502 (2003).

32. C. F. Kim, E. L. Jackson, A. E. Woolfenden, S. Lawrence, I. Babar, S. Vogel, D. Crowley, R. T.

Bronson, T. Jacks, Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell

121, 823–835 (2005).

33. M. Ebina, M. Shimizukawa, N. Shibata, Y. Kimura, T. Suzuki, M. Endo, H. Sasano, T. Kondo, T.

Nukiwa, Heterogeneous increase in CD34-positive alveolar capillaries in idiopathic pulmonary

fibrosis. Am. J. Respir. Crit. Care Med. 169, 1203–1208 (2004).

34. M. P. Keane, D. A. Arenberg, J. P. Lynch III, R. I. Whyte, M. D. Iannettoni, M. D. Burdick, C. A.

Wilke, S. B. Morris, M. C. Glass, B. Di Giovine, S. L. Kunkel, R. M. Strieter, The CXC

chemokines, IL-8 and IP-10, regulate angiogenic activity in idiopathic pulmonary fibrosis. J.

Immunol. 159, 1437–1443 (1997).

35. M. Turner-Warwick, Precapillary systemic-pulmonary anastomoses. Thorax 18, 225–237 (1963).

36. D. W. Waters, K. E. C. Blokland, P. S. Pathinayake, J. K. Burgess, S. E. Mutsaers, C. M. Prele, M.

Schuliga, C. L. Grainge, D. A. Knight, Fibroblast senescence in the pathology of idiopathic

pulmonary fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 315, L162–L172 (2018).

37. L. R. Rodriguez, M. Emblom-Callahan, M. Chhina, S. Bui, B. Aljeburry, L. H. Tran, R. Novak, M.

Lemma, S. D. Nathan, G. M. Grant, Global gene expression analysis in an in vitro fibroblast model

of idiopathic pulmonary fibrosis reveals potential role for CXCL14/CXCR4. Sci. Rep. 8, 3983

(2018).

38. T. Xie, Y. Wang, N. Deng, G. Huang, F. Taghavifar, Y. Geng, N. Liu, V. Kulur, C. Yao, P. Chen, Z.

Liu, B. Stripp, J. Tang, J. Liang, P. W. Noble, D. Jiang, Single-cell deconvolution of fibroblast

heterogeneity in mouse pulmonary fibrosis. Cell Rep. 22, 3625–3640 (2018).

39. S. G. Chu, S. Poli De Frias, Y. Sakairi, R. S. Kelly, R. Chase, K. Konishi, A. Blau, E. Tsai, K. Tsoyi,

R. F. Padera, L. M. Sholl, H. J. Goldberg, H. R. Mallidi, P. C. Camp, S. Y. El-Chemaly, M. A.

Perrella, A. M. K. Choi, G. R. Washko, B. A. Raby, I. O. Rosas, Biobanking and cryopreservation of

human lung explants for omic analysis. Eur. Respir. J. 55, 1801635 (2019).

40. R. Lopez, J. Regier, M. B. Cole, M. I. Jordan, N. Yosef, Deep generative modeling for single-cell

transcriptomics. Nat. Methods 15, 1053−1058 (2018).

41. A. Scialdone, A. Scialdone, K. N. Natarajan, L. R. Saraiva, V. Proserpio, S. A. Teichmann, O.

Stegle, J. C. Marioni, F. Buettner, Computational assignment Of cell-cycle stage from single-cell

transcriptome data. Methods 85, 54–61 (2015).

42. X. Wang, J. Park, K. Susztak, N. R. Zhang, M. Li, Bulk tissue cell type deconvolution with multi-

subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

43. S. Aibar, C. Bravo González-Blas, T. Moerman, V. A. Huynh-Thu, H. Imrichova, G. Hulselmans, F.

Rambow, J.-C. Marine, P. Geurts, J. Aerts, J. van den Oord, Z. K. Atak, J. Wouters, S. Aerts,

SCENIC: Single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086

(2017).

44. A. Fabregat, S. Jupe, L. Matthews, K. Sidiropoulos, M. Gillespie, P. Garapati, R. Haw, B. Jassal, F.

Korninger, B. May, M. Milacic, C. D. Roca, K. Rothfels, C. Sevilla, V. Shamovsky, S. Shorser, T.

Varusai, G. Viteri, J. Weiser, G. Wu, L. Stein, H. Hermjakob, P. D’Eustachio, The reactome

pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

Documents

Supplementary Materials for...2020/07/06 · Supplementary Materials Fig. S1. Controlling for Batch Effects Does Not Alter Overall Data Architecture. (A-B) UMAPs of lung epithelial