Literature DB >> 28702480

Dataset on transcriptional profiles and the developmental characteristics of PDGFRα expressing lung fibroblasts.

Mehari Endale1, Shawn Ahlfeld1, Erik Bao1, Xiaoting Chen2, Jenna Green1, Zach Bess1, Matthew Weirauch2, Yan Xu1, Anne Karina Perl1.   

Abstract

The following data are derived from key stages of acinar lung development and define the developmental role of lung interstitial fibroblasts expressing platelet-derived growth factor alpha (PDGFRα). This dataset is related to the research article entitled "Temporal, spatial, and phenotypical changes of PDGFRα expressing fibroblasts during late lung development" (Endale et al., 2017) [1]. At E16.5 (canalicular), E18.5 (saccular), P7 (early alveolar) and P28 (late alveolar), PDGFRαGFP mice, in conjunction with immunohistochemical markers, were utilized to define the spatiotemporal relationship of PDGFRα+ fibroblasts to endothelial, stromal and epithelial cells in both the proximal and distal acinar lung. Complimentary analysis with flow cytometry was employed to determine changes in cellular proliferation, define lipofibroblast and myofibroblast populations via the presence of intracellular lipid or alpha smooth muscle actin (αSMA), and evaluate the expression of CD34, CD29, and Sca-1. Finally, PDGFRα+ cells isolated at each stage of acinar lung development were subjected to RNA-Seq analysis, data was subjected to Bayesian timeline analysis and transcriptional factor promoter enrichment analysis.

Entities:  

Keywords:  Development; Lung; PDGFRα-fibroblast; Transcription

Year:  2017        PMID: 28702480      PMCID: PMC5484972          DOI: 10.1016/j.dib.2017.06.001

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications Table

Data

The data presented herein are representative of the key stages of acinar lung development and define the developmental role of lung interstitial fibroblasts expressing platelet-derived growth factor alpha (PDGFRα). Cells expressing PDGFRα were analyzed at E16.5, E18.5, P7 and P28. The spatiotemporal localization of PDGFRαGFP E18.5 at (Fig. 1) demonstrates the relationship of PDGFRα+ fibroblasts to proximal and distal saccular lung structures. Flow cytometry using direct flow cytometry of whole-lung single cell suspension preparation and selection by differential adherence in tissue culture to enrich and analyze PDGFRα+ fibroblast populations is presented in Fig. 2. PDGFRαGFP expression was assessed at E16.5, P7, and P28 for GFPdim and GFPbright sub-populations. For the two distinct sub-populations present at P7, the relative abundance of myofibroblasts (αSMA+) and lipofibroblasts (LipidTOX+) within each population is presented (Fig. 3). Fig. 4 shows data on temporal changes in neutral lipid, αSMA, proliferation, and cell surface expression of CD34, CD29, and Sca-1 in CD326+, CD31+, CD140+ and CD140aneg stromal cells. The gene expression profile from RNA-Seq data provides information in cell-cycle gene changes of isolated PDGFRα+ fibroblasts throughout acinar lung development (Fig. 5 and Table 1), individual genes upregulated at E18.5 in PDGFRα+ fibroblasts (Table 2), and changes in contractile gene expression in PDGFRα+ fibroblasts (Table 3). Additionally, data from computational transcription factor binding site analyses (Table 4), ChIP-Seq enrichment profiles (Table 5), and promoter sequences of individual genes dynamically expressed by PDGFRα+ fibroblasts during acinar lung development. The three transcription factors identified by ChIP-Seq analysis are presented in Table 6.
Fig. 1

Spatial distribution of PDGFRαGFP cells during the saccular stage of development. Confocal microscopy of lung sections from E18.5 PDGFRαGFP mouse lungs co-stained with αSMA and pro-SPC to demonstrate the relationship of PDGFRαGFP cells to saccular epithelial cells and contribution to αSMA-containing developing conducting airways (A) and blood vessels (B). Images obtained with 40X objective.

Fig. 2

Proportions and characteristics of PDGFRαGFP cells obtained by direct FACS compared to isolation by differential adherence. Flow cytometry profile of lung cell lineage proportions and PDGFRαGFP proportions of fresh whole cell suspension or after selection by differential adherence. Single cell suspensions were stained and subjected to FACS directly after isolation (whole prep) or following incubation for 2 h to obtain non-adherent cells (supernatant) and adherent stromal cells (adherent). (A) Relative proportion of hematopoietic (CD45+), epithelial (CD326+), endothelial (CD31+), CD140a+ (PDGFRα+) stromal, and CD140aneg stromal cell populations analyzed by FACS in samples obtained from whole-lung suspension vs. non-adherent and adherent cells following differential adherence. (B) Distribution of PDGFRα-GFPbright and PDGFRα-GFPdim stromal cells contained in non-adherent and adherent cell fractions following differential adherence. (C) Proportion of PDGFRα-GFPbright (dark green) and PDGFRα-GFPdim (light green) fibroblasts contained in single cell suspensions by direct FACS or FACS following differential adherence. (D) The relative proportion CD29+, CD34+CD29+, CD34+ or CD29-CD34- subpopulations in CD140a+ cells analyzed by direct FACS or FACS following differential adherence.

Fig. 3

Assessment of PDGFRα-GFP interstitial fibroblasts for PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations. Flow cytometry profiles of PDGFRαGFP dim and bright sub-populations throughout acinar lung development. (A) Relative proportions at E16.5, P7, and P28 of PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations. (B-C) Contribution of αSMA+ myofibroblasts (B) and LipidTOX+ lipofibroblasts (C) to the discernible PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations that comprise the pool of P7 PDGFRα-GFP interstitial fibroblasts. (E) Quantification of GFPdim and GFPbright in CD140 positive cells. (F) Quantification of GFPdim and GFPbright in all GFP positive cells. (F) Histogram overlay of GFP intensity in E16.5, PN7 and PN28 GFP cells.

Fig. 4

Temporal profiles of proliferation, neutral lipid, αSMA and surface marker expressions of CD45+, CD326+, CD31+, CD140a+ stromal and CD140aneg stromal cell lineages. Proliferation, αSMA, lipid, CD29, CD34 and Sca-1 expressions of CD45+ hematopoietic (A), CD31+ endothelial (B), CD326+ epithelial (C), CD140a+ stromal (D) or CD140a- stromal (E) cells at E16.5 (canalicular), E18.5 (saccular), P7 (early alveolar), P21 (mid alveolar), and P28 (late alveolar) stages of acinar lung development. Data is presented as the relative percentage of cells within each individual cell lineage.

Fig. 5

Transcriptional profile of cell cycle genes expressed in PDGFRα+ fibroblasts throughout critical stages of acinar lung development. Expression profile obtained by Bayesian and STEM analysis of RNA-Seq data to identify cell cycle genes that are differentially expressed in PDGFRα+ fibroblasts between E16.5, E18.5, P7, and P28 during acinar lung development.

Table 1

Cell cycle genes that are differentially expressed in PDGFRα+ fibroblasts during distinct stages of acinar lung development.

GeneE16E18PN7PN28
Calm2−0.00571762−0.8708331.40198−0.525428
Dusp1−0.621371−0.627324−0.22441.47309
Pmp22−0.742166−0.9794940.7986820.922978
Sptbn1−0.789379−0.9360420.9407080.784713
Srsf50.236167−1.084521.26328−0.414932
Hsp90aa11.41313−0.874767−0.484744−0.0536168
Rhoa−0.135108−1.067331.34942−0.146979
Tubb50.832279−0.9580580.894011−0.768232
Thbs10.8724030.828917−0.622006−1.07931
Gnai2−0.147148−1.032871.36953−0.18952
Jun−0.851524−0.510853−0.0556641.41804
Anapc131.1925−0.897243−0.7514970.456244
Gnb2l11.40582−0.040552−0.47046−0.89481
Stmn10.934956−1.092280.757047−0.599728
Smc1a0.883569−1.007340.835485−0.711719
Tuba1a−0.354266−0.8397461.45038−0.256367
Pcna1.26778−0.6424120.313621−0.938985
Rhob−0.535957−1.110430.5737821.0726
Ywhah0.729312−1.249870.880943−0.360389
Calm10.056566−1.429030.7664160.606044
Mapre2−0.362037−0.8617111.44181−0.218067
Sept2−0.191729−0.8693141.43734−0.376292
Plk2−0.294531−0.4635031.47699−0.718961
Rad210.830549−1.057830.876974−0.649691
Txnip−1.04578−0.3218950.02524661.34242
Cdk41.21118−0.8895790.426562−0.74816
Trp530.304485−1.029121.25272−0.528087
Ube2c1.31111−0.7502890.249112−0.809935
Sept4−0.884936−0.734290.3813851.23784
Mapk3−0.467964−1.059531.259620.267868
Chd40.965382−1.066580.73079−0.629589
Csnk1a10.145345−0.4585971.32271−1.00946
Sept110.38547−0.8347811.23797−0.788659
Cetn30.920627−1.398940.4404210.0378935
Nsmce20.927752−1.126490.751502−0.552767
Ppp1cc0.757517−0.9619190.962431−0.758029
Ppp1ca0.769955−1.199870.876423−0.446511
Smc41.16898−0.7984640.49566−0.866176
Ppp1cb−0.197787−1.113431.31193−0.0007138
Smc31.1891−1.039880.422153−0.571375
Cks1b1.34665−0.6296370.15774−0.874757
Sept7−0.183509−0.8786181.43392−0.371793
Mki671.18879−0.7287540.459143−0.919178
Spin10.10549−0.9433861.35379−0.515895
Top2a1.20696−0.8060170.43718−0.838125
Tubb4b0.286298−1.448050.3103880.851369
Kmt2e0.0343548−1.372760.9998740.33853
Rgs2−0.02960691.39706−0.446672−0.920779
Son0.19415−1.316861.108140.0145653
Gnai30.490256−1.249351.05433−0.295233
Cdkn1c1.41418−0.105178−0.397307−0.911693
Usp9x0.0760878−1.43670.727690.632917
Zak−0.700217−1.015320.9181070.797432
Ier30.9804320.73242−0.723419−0.989433
Wapal0.632021−1.233070.971144−0.370096
Anapc50.682133−1.373080.798597−0.10765
Ppm1g0.965661−1.349650.485437−0.101447
Specc1l−0.56199−0.715791−0.1843491.46213
Nedd9−0.579639−1.042441.172820.449265
Ube2i0.696443−1.441550.6547930.0903107
Sirt2−0.128286−1.198341.241050.0855696
Usp470.972221−1.255560.60976−0.326424
Nasp1.35164−0.6506950.151524−0.85247
Ccar11.19936−0.858780.448599−0.789176
Lats2−0.50131−1.121811.105640.517486
Ccny0.0769666−1.097661.30669−0.285998
Calm30.207701−1.121161.25756−0.344101
Eid1−0.207967−0.9324921.41784−0.277379
Foxn3−0.353993−1.185681.146370.393299
Mtus1−0.231669−1.049711.35605−0.0746687
Usp80.0653615−1.321360.1479071.10809
Ran1.27067−0.9822250.291102−0.579552
Cdc1230.684283−1.307010.872431−0.249708
Ccnt10.350793−1.486930.6695010.466636
Ppp6c−0.0845592−1.251981.184140.152397
Dynlt3−0.662532−1.025281.050670.637141
Smarca40.897716−0.9661860.827408−0.758938
Pafah1b1−0.0028538−1.058041.34217−0.281278
Trp53bp20.0115787−1.341681.062030.268075
Marveld1−0.809342−0.8486671.182720.475286
Ccndbp1−0.23958−1.271971.05770.453849
Ccpg1−0.647646−0.9297980.3029941.27445
Cdk60.385309−0.9581411.22701−0.654178
Ctgf−0.476914−1.056910.2774271.2564
Khdrbs10.834267−0.8191320.89601−0.911146
Arl8b0.352736−1.470680.349490.768457
Nudc1.41244−0.834935−0.023113−0.554394
Anxa1−0.049034−0.778171−0.5987991.426
Nipbl0.583539−1.212211.01876−0.390092
Stag10.412112−1.084821.18106−0.508359
Junb0.4109531.17702−1.09807−0.489902
Cdk11b0.0832837−1.205261.2359−0.113924
Cast−0.2003−1.330320.6685220.862097
Klhl90.406837−1.354940.998055−0.0499484
Ctcf0.823152−1.163130.845177−0.505199
Usp160.280098−1.434470.8914830.262886
Pcnp0.473111−1.325070.999318−0.147358
Brd70.444032−1.497620.477130.576461
Cdkn1a0.09162680.462374−1.42250.868503
Cdc5l1.11105−1.15790.483718−0.436864
Psme31.21813−1.164590.254701−0.30825
Ckap50.976807−0.9641730.740723−0.753357
Setd80.947553−1.263960.634908−0.318502
Erh1.03215−1.00910.665275−0.688322
Sept90.555875−0.6844081.11649−0.987952
Gas6−0.610436−0.642252−0.2193121.472
Yeats41.06306−1.193860.532339−0.401537
Ralb0.445575−0.9096591.19804−0.733953
Hcfc11.0087−1.156470.636994−0.489225
H2afx1.36257−0.7603650.137039−0.73924
Ccnd2−0.26808−0.7274081.47332−0.477836
Mapre10.403968−0.8455791.22686−0.78525
Ep3000.310167−1.460510.3416530.80869
Tacc1−0.582128−0.8593330.05598691.38547
Table 2

Genes upregulated in PDGFRα+ fibroblasts at E18.5 relative to other stages of acinar lung development.

GeneE16E18PN7PN28
ADAMTS1−1.09961.89932−0.7786150.109199
HBA-A1−0.721841.89803−0.602954−0.736058
HBB-BT−0.6982841.89715−0.648254−0.725893
OLFR62−0.6584341.89326−0.719626−0.719626
HBB-B1−0.7010921.8915−0.598885−0.7459
ENPP2−0.6967221.89078−0.611701−0.668638
GM17644−0.7437821.88918−0.860528−0.196981
4930470H14RIK−1.126711.88433−0.7944990.185705
LARS2−0.7120291.88186−0.874364−0.206367
PENK−0.6977631.8681−0.466888−0.81606
PROS1−1.429721.85969−0.092791−0.456466
MT1−0.5474241.82338−0.993104−0.15854
GM10052−0.1668821.70856−0.893127−0.893127
TGFBR3−1.644781.69903−0.3538730.405727
HBB-Y−0.1271851.68753−0.892553−0.910755
TRIB1−0.1151121.65209−1.06878−0.363624
RGS2−0.0705951.59547−0.557646−1.11131
ALDH2−0.9166571.55663−1.196670.944171
NDRG2−0.780571.55655−1.268410.882676
ODC1−0.0385731.55499−0.483119−1.21095
HHIP0.1886241.48161−0.840506−0.864842
SPRED1−0.1490311.45532−0.070748−1.56369
ZFP360.2266831.45129−1.11433−0.463834
BC1170900.3038441.42934−1.00008−1.00008
HBA-X0.3738351.37937−1.01109−1.01109
MYC0.1497691.35186−0.277893−1.49285
SNORA520.4151081.34884−1.01697−1.01697
RMRP0.3517731.29452−1.3491−0.068137
MAFF0.4378031.28682−0.755838−1.05255
JUNB0.4634681.27891−1.14282−0.495453
IIGP10.2462621.27624−0.268592−1.53017
CCDC3−1.243721.256110.861579−1.34577
1500012F01RIK0.455291.25174−0.659759−1.17327
BHLHE400.5445171.19043−1.25119−0.321188
FAU0.6229481.15584−0.922513−0.866115
BTG20.6329111.11406−1.25966−0.320921
HSPB1−0.8668611.1016−1.242011.53001
RPPH1−0.0460671.05871−1.605451.10958
KLF9−1.240711.04622−0.9062751.56238
ATF30.8110230.98054−1.12267−0.576751
SOCS30.8708320.928448−1.02609−0.729075
ITGAV−0.7126330.9167351.02392−1.81532
SLC2A10.8911610.905855−1.0727−0.655793
THBS10.9006030.854342−0.689139−1.17562
IFRD10.2179540.829998−1.657681.15147
SKIL0.1572350.7771330.584415−2.05964
EIF3E0.938360.777077−0.563455−1.31203
H191.032270.774112−0.959012−1.08616
CCNL11.026740.764284−0.973248−0.792439
IER31.015160.75419−0.777739−1.05766
EEF1A10.8939560.731323−0.340296−1.53243
CDKN1A0.2569190.726925−1.662591.24179
IGF21.054410.725075−0.829588−1.09723
ELN−1.818890.7056910.8912420.0251241
LOX1.108470.663704−1.10496−0.568685
SNORA151.174830.621342−1.02944−1.02944
MMP2−0.115030.562767−1.528051.71138
FMO2−1.017890.561459−0.9006951.89521
PTN1.192770.559803−0.808516−1.06382
HES11.134260.553428−0.544956−1.29785
TREM31.237460.5435−1.02013−1.02013
PLAGL11.115390.540813−0.457605−1.38903
SNORA751.246160.532386−1.01866−1.01866
GLUL−1.679740.528690.07901041.29294
EDNRB1.225490.501732−0.735441−1.08308
Table 3

Transcriptional profile of contractile genes differentially expressed in PDGFRα+ fibroblasts over the course of acinar lung development.

GeneE16E18PN7PN28
Sparc−0.782064−0.8840340.5022011.1639
Npm11.3844−0.154564−0.225484−1.00435
Sod1−0.106237−1.298610.3052181.09963
Vim−0.32131−0.8673331.44228−0.253635
Eln−1.430450.6101920.7601750.0600839
Tpm1−0.297888−0.09210751.38385−0.993852
Pfn11.07808−1.017290.602467−0.663255
Tmsb4x0.783135−1.23122−0.3966060.844691
Tgfbi−0.2013840.3218781.13319−1.25368
Fhl1−0.834609−0.8258040.4816461.17877
Myadm−0.950365−0.747321.069360.628329
Gng50.799333−1.396430.63971−0.0426118
Tmsb100.134493−0.8687771.35647−0.622189
Fn1−0.568048−0.828443−0.0144631.41095
Rac1−0.170999−0.9672671.402−0.263733
Cfl10.952984−0.7980670.771927−0.926844
Msn−0.353353−1.042341.336080.0596072
Arpc20.673331−1.256460.923198−0.340072
Fbn1−0.745338−0.9717350.744450.972623
Itgb1−0.573494−0.6061451.48646−0.306817
Arf10.0803654−1.297981.141490.0761276
Cdh110.801392−1.3670.68846−0.122848
Rhoa−0.135108−1.067331.34942−0.146979
Cald1−0.306922−0.7237121.47733−0.446696
Mmp2−0.201580.299274−1.245721.14803
Myl60.415826−1.297621.0551−0.173303
App−0.830414−0.8776261.055870.652173
Ctnnb1−0.136582−1.314221.043030.40777
Gnai2−0.147148−1.032871.36953−0.18952
Cdc420.368935−1.111461.19372−0.451196
Igf11.346530.102391−0.472771−0.976149
Mylk0.0820497−0.8924431.37175−0.561356
Cav1−0.71922−0.7445831.386480.0773188
Flna0.713322−1.179970.936876−0.470232
Gnb2l11.40582−0.0405521−0.47046−0.89481
Tnc−0.424942−0.2950521.47202−0.752022
Tpm30.592418−0.6965411.09092−0.986797
Capzb0.642076−1.232330.964042−0.373784
Ctnnd1−0.483005−0.8860221.4072−0.0381741
Akap2−0.574534−0.490271.49745−0.432647
Myh100.222398−1.366761.034630.109729
Gnb10.0972075−1.157171.26814−0.208171
Hspb1−0.7186610.699476−0.9889261.00811
F2r−0.749158−0.9121920.4940551.1673
Ednra1.13125−1.15915−0.4197920.447694
Rhob−0.535957−1.110430.5737821.0726
Sptan1−0.603347−0.9034811.338410.168423
Cul30.560345−1.286610.982377−0.256117
Myh110.459331−0.1023650.986597−1.34356
Pdlim3−0.5710510.02860091.39505−0.852598
Rdx−0.197531−1.103171.32045−0.0197473
Myh9−0.079762−0.6357711.43791−0.722382
Zyx−0.379613−1.088240.1887981.27906
Dstn−0.768233−0.7330850.1401561.36116
Actr31.3969−0.260784−0.977504−0.158609
Bmp4−0.288633−1.283640.9144520.657823
Cyb5r3−0.745596−0.9072061.18360.4692
Cdk41.21118−0.8895790.426562−0.74816
Aldoa0.826149−1.14381−0.5335230.851188
Cdh5−0.650899−0.6447391.46062−0.164986
Ghr0.275738−0.762861.29793−0.810804
Atp2a2−0.51177−1.147860.9770060.682622
Ltbp2−0.596826−0.08239891.4365−0.757278
Pls30.23697−1.203581.20027−0.233664
Cap10.554204−1.360450.911563−0.105319
Lima1−0.323465−1.154671.223660.254468
Dpysl2−0.696809−1.018720.8071330.908401
Ppp1ca0.769955−1.199870.876423−0.446511
S100a100.0334959−1.01683−0.3689881.35232
Slc9a3r2−0.786913−0.8332941.242450.377755
Marcks−0.095569−0.2866771.38184−0.999596
Sorbs3−0.673773−0.9149121.26710.32158
Fus0.644405−0.7555231.0575−0.946377
Gsn−0.512083−0.503138−0.4846821.4999
Nckap1−0.301663−1.201171.161870.340968
Add1−0.311211−1.2181.118430.410786
Tgfbr2−0.717995−0.9127990.4144361.21636
Cd440.0409719−0.8053951.39648−0.632058
Tns3−0.464562−1.035930.2092861.29121
Actn10.458479−1.07621.15762−0.539902
Wasf2−0.352504−1.079251.301540.130213
Dlc1−0.741294−0.8792961.237010.383584
Rap1gap−0.481962−0.533931−0.4836721.49956
Dnajb61.06517−1.349270.1895530.0945508
Lcp11.41617−0.90207−0.101197−0.412904
Cdkn1c1.41418−0.105178−0.397307−0.911693
Sdc4−0.63846−0.381346−0.4716631.49147
Tmod3−0.388687−1.175161.132010.431829
Net1−0.145783−0.078991.32632−1.10154
Iqgap1−0.257395−1.235110.3625581.12995
Kank2−0.552174−1.127440.9217460.757869
Pecam1−0.675754−0.6208071.45989−0.163329
Slk−0.082074−0.9477221.39944−0.369648
Specc1l−0.56199−0.715791−0.1843491.46213
Rock2−0.752439−0.8939990.4495751.19686
Atf30.7617610.925637−1.10758−0.579823
Actn4−0.441928−1.030761.309850.162832
Myo1b−0.595032−1.098860.8906570.803232
Cxcl12−0.377019−0.8397481.44875−0.23198
Vcam1−0.118534−0.76449−0.5613941.44442
Chchd21.12552−1.041780.525748−0.609493
Arhgef12−0.728362−0.8014871.338030.191823
Bcl20.699560.366960.417325−1.48384
Cryab−0.831001−0.8255240.4705551.18597
Tpm20.358849−0.06458771.0411−1.33536
Stat3−0.951892−0.420543−0.0120861.38452
Capza10.945866−0.945050.778122−0.778938
Il1b1.47332−0.225586−0.622641−0.625097
Rnd31.34749−0.5764980.141145−0.912138
Slit2−0.72262−0.638973−0.0750361.43663
Dnm2−0.296382−1.160681.230260.226806
Emp2−0.964067−0.6786160.4601951.18249
Marcksl11.2413−0.2746690.193641−1.16027
Myc0.1848161.20839−0.179336−1.21387
Mif1.49932−0.52861−0.458383−0.512332
Fnbp10.540304−1.460990.7339850.186696
Clec2d−0.773785−0.8630320.4203831.21643
Crh1.2280.402804−0.815403−0.815403
Smarca40.897716−0.9661860.827408−0.758938
Rhoj−0.69625−0.6523731.44251−0.0938856
Palld−0.0577830.008008091.24868−1.19891
Pafah1b1−0.002853−1.058041.34217−0.281278
Pik3r1−0.845334−0.292798−0.3118451.44998
Fblim1−0.364685−1.247920.7376080.875
Cdk60.385309−0.9581411.22701−0.654178
Ctgf−0.476914−1.056910.2774271.2564
S1pr1−0.673846−0.7006031.43239−0.0579363
Shc11.07814−0.512540.558318−1.12392
Coro1b0.085574−1.322881.107120.130185
Mapk14−0.2419581.28682−1.130580.0857137
Junb0.4109531.17702−1.09807−0.489902
Mprip−0.666687−1.025811.034630.657863
Rock10.158822−1.392640.9871590.246661
Hax10.684126−1.477160.2632230.529807
Akap130.0260559−1.18589−0.0984621.25829
Gng12−0.35838−1.005891.363730.000544535
Cdkn1a0.09162680.462374−1.42250.868503
Gna13−0.44997−0.9361241.386072.05e−05
Gnaq−0.091048−1.031931.36656−0.243584
Sept90.555875−0.6844081.11649−0.987952
Gna12−0.300922−0.8717651.44129−0.268605
Sh3pxd2a−0.403176−0.8083651.45726−0.245722
Prnp−1.327940.522858−0.1627630.967841
Dab21.12043−1.10753−0.5153320.502433
Mapre10.403968−0.8455791.22686−0.78525
Fat10.7273810.5074630.234335−1.46918
Clec7a1.49561−0.390523−0.549357−0.555734
Table 4

Computational transcriptional factor binding site motif enrichment analyzed in the differential gene expression pattern of six profiles.

ProfileGene/TF−log Pval
Profile_1KLF329.92
Profile_1ELK329.31
Profile_1YBX119.67
Profile_1SP119.52
Profile_1HBP112.97
Profile_1FOXF26.998
Profile_1ID36.854
Profile_1CUX13.526
Profile_1CTCF2.103
Profile_13KLF628.86
Profile_13ELK413.64
Profile_13SMARCC26.029
Profile_13NFE2L14.113
Profile_13NFIA3.98
Profile_13NFIX3.98
Profile_13MEF2A3.491
Profile_13MAX2.221
Profile_13FOXN33.203
Profile_10RUNX34.56
Profile_10JUNB4.264
Profile_18KLF75.862
Profile_23FOSB4.769
Profile_39KLF436.43
Table 5

Previously published ChIP-Seq data with significant overlap of genes differentially expressed in CD140+ fibroblasts throughout lung development.

ProfileTrackCellTFOverlapTotalRatioEnrichmentp-Val
Profile_1Caltech_TfbsC2C12NRSF2074650.453.415.80E−66
Profile_1Licr_ChipMELCTCF2864650.622.224.26E−46
Profile_13Sydh_TfbsCH12Max1932570.752.171.62E−37
Table 6

Genes identified in the ChIP-Seq enrichment analysis and differentially expressed in CD140+ cells.

CTCF & NRSFNRSFCTCFMAX
ACTN1ANAPC51110004F10RIK1700016K19RIK
ACTR2AP2M11700020I14RIKACAA2
ANXA6ATP5B2700081O15RIKACO2
AP3B1ATP6V0C-PS26820431F20RIKACTN4
API5BRD2ACAT1ADD1
ARPC1BCALD1ADNPADIPOR1
ARPC2CALUANKRD11AHCYL1
ARPC5CBX3ANKRD17AKAP12
ATP5C1CDK11BANP32AANO6
B230219D22RIKCFDP1ATF7IPAP2B1
BCLAF1CNN3ATP5JARF4
CALM2CXCL12ATXN2LARHGAP1
CALM3DDX1BAZ1BARHGDIA
CANXDDX3XBC005537ARL8B
CAPRIN1DHX15BPTFASAH1
CAPZA1DLDBZW1ATL3
CAPZA2EIF5CCND2ATP1A1
CAPZBFKBP10CCNIATP1B3
CCNYFOXF2CDC123ATP2A2
CDC42FSTL1CDK6ATP6AP1
CFL1FZD1CDV3ATP6V0D1
CHD4GSK3BCLINT1ATP6V0E
CKAP4GTF2A2CNN2ATP6V1A
COMMD3GTF3C6CNOT1BAG1
COPS5HDAC2COPZ2BAG6
CORO1CHNRNPH1CPDBCAP31
CSNK1A1IDH3BCRTAPBRD7
CUL3IMMTCTCFCALM1
CYC1ITSN2CTDNEP1CAPNS1
DDX39BKTN1CTDSP1CAST
DENND5ALGALS1CUL1CCDC47
DHX9MDH2CUX1CCNDBP1
EID1NARSCXXC1CCNT1
EIF3CNCOR1DDB1CD164
EIF3DNDUFA10DNAJC10CD47
EIF4G2NRD1DNAJC7CHMP2A
EIF5BNSMCE2DNMT3ACHTOP
EPB4.1L2NXF1DNTTIP2CIR1
ERHPCNPEIF2S3XCLIP1
EWSR1POMPEIF3GCLN5
FBXO11PPP4R2EIF4A3COPA
FKBP1APRCPEIF4G3COPE
FLNAPRDX2ELK3COPG1
FUSPSMB5ERBB2IPCR1L
GDI2RCN2ESF1CRIPT
GHRRHOAEXT2CSNK1G2
GLUD1RNF4FAM120ACTDSP2
GNB1RTN4FAM193ACTNNB1
H3F3ASDCBPGALNT1CTSA
HMGB3SETD5GINS4CTSB
HMOX2SLC25A3GNG12CTSD
HNRNPA0SND1GNG5CUTA
HNRNPA2B1SNRNP200GOLGA7DAP
HNRNPDLSNRNP70GPBP1DAZAP2
HPRTSTRAPGSK3ADCTN4
HTATSF1TAF13GTPBP4DCTN6
ID3TRP53H1F0DDOST
IKTUBB6HDGFDEGS1
ILF3VDAC3HIC1DHRS1
IVNS1ABPVIMHIPK2DNAJA1
KANSL1YBX1HMGN1DNAJB11
KHDRBS1YEATS4HNRNPA3DPP8
KLF3ZFP207HNRNPH2EGLN1
KPNB1HNRNPLLEIF4EBP2
LEO1HNRNPUL1EIF4ENIF1
LRRC59ILKELK4
LSM14AKLHDC2ELOVL5
MBD2KLHL9EMC3
MFSD1LARP4BEMC4
MSL1LASP1EMC7
MYH9LIX1LEP300
NCBP2LSM12ERGIC3
NDUFA12MAP4ERP29
NEDD4MAP7D1ESYT1
NUP62MAPK1IP1LETFA
P4HBMAPKAP1FADS1
PABPC1MAPRE1FAM114A1
PAICSMAPRE2FBXO22
PCBP1MIDNGANAB
PCBP2MRFAP1GM13363
PFN1MTDHGM6644
PNRC2MTSS1LGOLGA4
POLR2ANDUFS2GORASP2
PPP1CCNONOGRN
PPP1R12ANRBP1GTF2B
PPP3R1NUCKS1H2-K1
PRELID1PABPN1HADHA
PRKAR1APAPOLAHADHB
PRPF40APCM1HAX1
PRPF4BPDCD5HDLBP
PRRC2APDS5AHECTD1
PSMD11PICALMHIAT1
PSMD12PITPNAHIPK1
PSMD6POLR2MHNRNPUL2
PTBP1PPP1CAHSP90B1
PTP4A2PSMA7IFITM2
PTPN12PSMC2IFT20
QKPSMD1IQGAP1
RAB10PTBP3IRF2BP2
RAB14PTCH1ITFG1
RAB6APTOV1JAGN1
RAC1PUM2KCMF1
RNF187RAD21KDELR2
RNF7RAP1AKIF1B
SCAF11RBBP6KLF6
SENP6REREKMT2E
SH3BGRL3RESTKRCC1
SH3GLB1RRP1LAMP1
SLTMSETD8LAMTOR5
SMC6SHFM1LGALS9
SRP72SHOC2LIMA1
SRRM1SKAP2LIMS1
SRSF2SKIV2L2LMAN1
SRSF3SLKMAPK3
SRSF5SMARCA4MAT2A
SSR3SMARCE1MAX
STAG1SMOMEF2A
STMN1SNAI2MLF2
STX12SNHG5MYL12A
SUPT16SNX4NBR1
TAB2SP1NCOA4
TBL1XSPIN1NCSTN
TCF12SUCLG2NDFIP1
TFGSYNCRIPNFE2L1
THRAP3TCEA1NFIX
TMED9THOC7NISCH
TMEM123TOMM22OCIAD1
TMEM131TPRPAFAH1B2
TMEM234TSPAN3PDHB
TMPOUBE2E3PDIA4
TOP2BUBE2IPDZD11
TRIP12UBE2V1PHLDA1
TTC3UBE4BPLXNB2
TUBA1AUSP47PPP2R1A
TUBB5UTP3PPT1
VAMP3VCPPSMC5
VDAC2WAPALPTPRS
WDR1WDR26RAB1
XRN2YWHABRAB7
YTHDC1ZC3H15RAB9
YWHAEZFP664RANBP9
YWHAHZMYND11RAP2A
YWHAQRCN1
ZCRB1REEP5
RHOB
RNH1
RTN3
S100A11
SAR1A
SAR1B
SEC. 31A
SEC. 61A1
SEC. 63
SERINC1
SIRT2
SMDT1
SNAPIN
SRPR
STAU1
STT3A
STT3B
STX4A
STX5A
SUPT6
SWI5
TAGLN2
TAOK1
TCF25
TECR
TLN1
TM9SF2
TMBIM6
TMED10
TMED2
TMOD3
TOR1AIP2
TRAPPC6B
TRP53BP2
TUBB4B
TXNDC5
UBR5
UBXN4
UGP2
USP16
USP8
VGLL4
VPS25
VPS28
YWHAG
ZFP106
ZMIZ1
ZYX
Spatial distribution of PDGFRαGFP cells during the saccular stage of development. Confocal microscopy of lung sections from E18.5 PDGFRαGFP mouse lungs co-stained with αSMA and pro-SPC to demonstrate the relationship of PDGFRαGFP cells to saccular epithelial cells and contribution to αSMA-containing developing conducting airways (A) and blood vessels (B). Images obtained with 40X objective. Proportions and characteristics of PDGFRαGFP cells obtained by direct FACS compared to isolation by differential adherence. Flow cytometry profile of lung cell lineage proportions and PDGFRαGFP proportions of fresh whole cell suspension or after selection by differential adherence. Single cell suspensions were stained and subjected to FACS directly after isolation (whole prep) or following incubation for 2 h to obtain non-adherent cells (supernatant) and adherent stromal cells (adherent). (A) Relative proportion of hematopoietic (CD45+), epithelial (CD326+), endothelial (CD31+), CD140a+ (PDGFRα+) stromal, and CD140aneg stromal cell populations analyzed by FACS in samples obtained from whole-lung suspension vs. non-adherent and adherent cells following differential adherence. (B) Distribution of PDGFRα-GFPbright and PDGFRα-GFPdim stromal cells contained in non-adherent and adherent cell fractions following differential adherence. (C) Proportion of PDGFRα-GFPbright (dark green) and PDGFRα-GFPdim (light green) fibroblasts contained in single cell suspensions by direct FACS or FACS following differential adherence. (D) The relative proportion CD29+, CD34+CD29+, CD34+ or CD29-CD34- subpopulations in CD140a+ cells analyzed by direct FACS or FACS following differential adherence. Assessment of PDGFRα-GFP interstitial fibroblasts for PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations. Flow cytometry profiles of PDGFRαGFP dim and bright sub-populations throughout acinar lung development. (A) Relative proportions at E16.5, P7, and P28 of PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations. (B-C) Contribution of αSMA+ myofibroblasts (B) and LipidTOX+ lipofibroblasts (C) to the discernible PDGFRα-GFPdim and PDGFRα-GFPbright subpopulations that comprise the pool of P7 PDGFRα-GFP interstitial fibroblasts. (E) Quantification of GFPdim and GFPbright in CD140 positive cells. (F) Quantification of GFPdim and GFPbright in all GFP positive cells. (F) Histogram overlay of GFP intensity in E16.5, PN7 and PN28 GFP cells. Temporal profiles of proliferation, neutral lipid, αSMA and surface marker expressions of CD45+, CD326+, CD31+, CD140a+ stromal and CD140aneg stromal cell lineages. Proliferation, αSMA, lipid, CD29, CD34 and Sca-1 expressions of CD45+ hematopoietic (A), CD31+ endothelial (B), CD326+ epithelial (C), CD140a+ stromal (D) or CD140a- stromal (E) cells at E16.5 (canalicular), E18.5 (saccular), P7 (early alveolar), P21 (mid alveolar), and P28 (late alveolar) stages of acinar lung development. Data is presented as the relative percentage of cells within each individual cell lineage. Transcriptional profile of cell cycle genes expressed in PDGFRα+ fibroblasts throughout critical stages of acinar lung development. Expression profile obtained by Bayesian and STEM analysis of RNA-Seq data to identify cell cycle genes that are differentially expressed in PDGFRα+ fibroblasts between E16.5, E18.5, P7, and P28 during acinar lung development. Cell cycle genes that are differentially expressed in PDGFRα+ fibroblasts during distinct stages of acinar lung development. Genes upregulated in PDGFRα+ fibroblasts at E18.5 relative to other stages of acinar lung development. Transcriptional profile of contractile genes differentially expressed in PDGFRα+ fibroblasts over the course of acinar lung development. Computational transcriptional factor binding site motif enrichment analyzed in the differential gene expression pattern of six profiles. Previously published ChIP-Seq data with significant overlap of genes differentially expressed in CD140+ fibroblasts throughout lung development. Genes identified in the ChIP-Seq enrichment analysis and differentially expressed in CD140+ cells.

Experimental design, materials and methods

Animals

B6.129S4-PDGFRα/J mouse-line herein designated PDGFRαGFP [2], with PDGFRα promoter driving the expression of the H2B-eGFP fusion gene were used for immunohistochemical, differential plate-down, and flow cytometry analyses. Mice lacking the PDGFRα GFP tag were used for PDGFRα+ cell RNA-Seq analysis.

Confocal microscopy

Lung tissues were harvested, fixed with 4% PFA in PBS and frozen. Tissue was sectioned into 200 μm slices and stained with anti-αSMA (Sigma-Aldrich, St. Louis, MO), Pro-SPC and chicken polyclonal anti-GFP antibody (Abcam, Cambridge, MA). Data was analyzed by Imaris software, version 7.6.

Characterization of PDGFRαGFP Cells by flow cytometry in plate-adhered or suspension cells

Lung tissue from PDGFRαGFP mice was harvested, processed into single cell suspension as previously described [3]. Cells were incubated in Dulbecco׳s DMEM/F12 (10% FBS, 2% pen/strep) after 2 h of culture, the media containing the non-adherent cell fraction was collected, and the adherent fraction was collected using Accutase (1× ACCUTASE enzymes in Dulbecco׳s PBS (0.2 g/L KCl, 0.2 g/L KH2PO4, 8 g/L NaCl, and 1.15 g/L Na2HPO4) containing 0.5 mM EDTA·4Na and 3 mg/L Phenol Red).

Bioinformatics data analysis

RNA-Seq data was quantitated using TopHat and Cufflinks [4], genes were included with the expression level (FPKM) was more than 1 in all samples. Bayesian Analysis of Time Series (BATS) identified genes as differentially expressed at one or more timepoints, co-regulated genes were identified by using pattern recognition using STEM and grouped into Gene expression profiles. Gene expression profiles were subjected to gene set enrichment analysis with Toppgene and Toppcluster [5], [6], [7].

Transcription factor promoter enrichment analysis

Transcription factor promoter enrichment analysis of PDGFRα+ fibroblast RNA-Seq profiles identified three candidate transcription factors: NRSF/REST, CTCF, and MAX. ChIP-Seq has been performed in the following mouse cell lines, and the data available in the public domain: MAX: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM912908 CTCF: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM918744 NRSF/REST: https://www.encodeproject.org/experiments/ENCSR000AIS/ To identify potential candidate genes in PDGFRα+ fibroblasts regulated by NRSF/REST, CTCF, or MAX during acinar lung development, we cross-referenced the dynamically regulated genes identified by our present RNA-Seq analysis with the above, previously-published gene sets [1].
Subject areaDevelopmental Biology
More specific subject areaLung Development
Type of dataTable, image, text file, graph, figure
How data was acquired3D Confocal Microscope inverted A1Rsi (Nikon Instruments, Melville, NY), fluorescent activated sorting flow cytometry (LSR II, BD Bioscience), MACS microbeads (Miltenyi Biotec technology, Gladbach, Germany), RNA-Seq (Illumina Inc. San Diego, CA, USA).
Data formatFiltered, analyzed
Experimental factorsSamples were not pretreated
Experimental featuresThe transcriptional profile, temporal, spatial and functional roles of PDGFαGFP expressing fibroblasts were examined at different stages of acinar lung development using RNA-Seq, confocal microscopy and flow cytometry, respectively.
Data source locationCincinnati, OH 45229, USA
Data accessibilityData is incorporated with this article
Data is accessible at: https://research.cchmc.org/pbge/lunggens/mainportal.html
  7 in total

1.  Diversity of Interstitial Lung Fibroblasts Is Regulated by Platelet-Derived Growth Factor Receptor α Kinase Activity.

Authors:  Jenna Green; Mehari Endale; Herbert Auer; Anne-Karina T Perl
Journal:  Am J Respir Cell Mol Biol       Date:  2016-04       Impact factor: 6.914

2.  Temporal, spatial, and phenotypical changes of PDGFRα expressing fibroblasts during late lung development.

Authors:  Mehari Endale; Shawn Ahlfeld; Erik Bao; Xiaoting Chen; Jenna Green; Zach Bess; Matthew T Weirauch; Yan Xu; Anne Karina Perl
Journal:  Dev Biol       Date:  2017-04-11       Impact factor: 3.582

3.  Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

Authors:  Cole Trapnell; Adam Roberts; Loyal Goff; Geo Pertea; Daehwan Kim; David R Kelley; Harold Pimentel; Steven L Salzberg; John L Rinn; Lior Pachter
Journal:  Nat Protoc       Date:  2012-03-01       Impact factor: 13.491

4.  Evolutionary divergence of platelet-derived growth factor alpha receptor signaling mechanisms.

Authors:  T Guy Hamilton; Richard A Klinghoffer; Philip D Corrin; Philippe Soriano
Journal:  Mol Cell Biol       Date:  2003-06       Impact factor: 4.272

5.  STEM: a tool for the analysis of short time series gene expression data.

Authors:  Jason Ernst; Ziv Bar-Joseph
Journal:  BMC Bioinformatics       Date:  2006-04-05       Impact factor: 3.169

6.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization.

Authors:  Jing Chen; Eric E Bardes; Bruce J Aronow; Anil G Jegga
Journal:  Nucleic Acids Res       Date:  2009-05-22       Impact factor: 16.971

7.  BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

Authors:  Claudia Angelini; Luisa Cutillo; Daniela De Canditiis; Margherita Mutarelli; Marianna Pensky
Journal:  BMC Bioinformatics       Date:  2008-10-06       Impact factor: 3.169

  7 in total
  8 in total

1.  Insulin-like Growth Factor 1 Supports a Pulmonary Niche that Promotes Type 3 Innate Lymphoid Cell Development in Newborn Lungs.

Authors:  Katherine Oherle; Elizabeth Acker; Madeline Bonfield; Timothy Wang; Jerilyn Gray; Ian Lang; James Bridges; Ian Lewkowich; Yan Xu; Shawn Ahlfeld; William Zacharias; Theresa Alenghat; Hitesh Deshmukh
Journal:  Immunity       Date:  2020-02-18       Impact factor: 31.745

2.  The Tcf21 lineage constitutes the lung lipofibroblast population.

Authors:  Juwon Park; Malina J Ivey; Yanik Deana; Kara L Riggsbee; Emelie Sörensen; Veronika Schwabl; Caroline Sjöberg; Tilda Hjertberg; Ga Young Park; Jessica M Swonger; Taylor Rosengreen; Rory E Morty; Katrin Ahlbrecht; Michelle D Tallquist
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2019-01-24       Impact factor: 5.464

3.  The elephant in the lung: Integrating lineage-tracing, molecular markers, and single cell sequencing data to identify distinct fibroblast populations during lung development and regeneration.

Authors:  Matthew Riccetti; Jason J Gokey; Bruce Aronow; Anne-Karina T Perl
Journal:  Matrix Biol       Date:  2020-05-19       Impact factor: 11.583

4.  Secondary crest myofibroblast PDGFRα controls the elastogenesis pathway via a secondary tier of signaling networks during alveologenesis.

Authors:  Changgong Li; Matt K Lee; Feng Gao; Sha Webster; Helen Di; Jiang Duan; Chang-Yo Yang; Navin Bhopal; Neil Peinado; Gloria Pryhuber; Susan M Smith; Zea Borok; Saverio Bellusci; Parviz Minoo
Journal:  Development       Date:  2019-08-09       Impact factor: 6.868

5.  Neuropilin-1 and platelet-derived growth factor receptors cooperatively regulate intermediate filaments and mesenchymal cell migration during alveolar septation.

Authors:  Stephen E McGowan; Diann M McCoy
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2018-03-15       Impact factor: 5.464

Review 6.  Understanding alveolarization to induce lung regeneration.

Authors:  José Alberto Rodríguez-Castillo; David Bravo Pérez; Aglaia Ntokou; Werner Seeger; Rory E Morty; Katrin Ahlbrecht
Journal:  Respir Res       Date:  2018-08-06

Review 7.  The History and Mystery of Alveolar Epithelial Type II Cells: Focus on Their Physiologic and Pathologic Role in Lung.

Authors:  Barbara Ruaro; Francesco Salton; Luca Braga; Barbara Wade; Paola Confalonieri; Maria Concetta Volpe; Elisa Baratella; Serena Maiocchi; Marco Confalonieri
Journal:  Int J Mol Sci       Date:  2021-03-04       Impact factor: 5.923

8.  Maladaptive functional changes in alveolar fibroblasts due to perinatal hyperoxia impair epithelial differentiation.

Authors:  Matthew R Riccetti; Mereena George Ushakumary; Marion Waltamath; Jenna Green; John Snowball; Sydney E Dautel; Mehari Endale; Bonny Lami; Jason Woods; Shawn K Ahlfeld; Anne-Karina T Perl
Journal:  JCI Insight       Date:  2022-03-08
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.