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Evolution of fibroblasts in the lung metastatic microenvironment is driven by stage-specific transcriptional plasticity

  1. Ophir Shani
  2. Yael Raz
  3. Lea Monteran
  4. Ye'ela Scharff
  5. Oshrat Levi-Galibov
  6. Or Megides
  7. Hila Shacham
  8. Noam Cohen
  9. Dana Silverbush
  10. Camilla Avivi
  11. Roded Sharan
  12. Asaf Madi
  13. Ruth Scherz-Shouval
  14. Iris Barshack
  15. Ilan Tsarfaty
  16. Neta Erez  Is a corresponding author
  1. Department of Pathology, Sackler Faculty of Medicine, Tel Aviv University, Israel
  2. Department of Obstetrics and Gynecology, Tel Aviv Sourasky Medical Center, Israel
  3. Department of Biomolecular Sciences, The Weizmann Institute of Science, Israel
  4. Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Israel
  5. Blavatnik School of Computer Sciences, Faculty of Exact Sciences, Tel Aviv University, Israel
  6. Department of Pathology, Sheba Medical Center, Tel Hashomer, affiliated with Sackler Faculty of Medicine, Tel Aviv University, Israel
Research Article
Cite this article as: eLife 2021;10:e60745 doi: 10.7554/eLife.60745
7 figures, 1 table and 5 additional files

Figures

Figure 1 with 1 supplement
Fibroblasts are activated and transcriptionally reprogrammed in the lung metastatic niche.

(A) Representative immunofluorescent staining of αSMA (red), FSP-1 (green), and Podoplanin (PDPN) (purple) in normal lungs from FVB/n mice (n = 3), and metastases-bearing lungs from MMTV-PyMT mice (n = 4). Scale bar: 200 µM. (B–D) Quantification of mean fluorescent intensity (MFI) in 5 fields of view (FOV) per mouse of staining shown in (A). (E) Workflow illustration of fibroblast isolation (CD45-EpCAM-YFP+) from normal FVB/n;col1a1-YFP mice (NLF) and of micro- or macrometastasis-associated fibroblasts from MMTV-PyMT;Col1a1-YFP mice (MIF and MAF). (F) Quantification of number of fibroblasts per lung, based on flow cytometry analysis. *p<0.05, **p<0.01. Data are represented as mean ± SD, n = 5. (G) Flow cytometry gating strategy for isolation of fibroblasts prior to RNA-sequencing. (H, I) Principal component analysis (PCA) (H) and hierarchical clustering (I) of 11,115 protein coding genes identified in RNA-seq.

Figure 1—figure supplement 1
Volcano plots of differential expression analysis vs. mean expression of micrometastasis-associated fibroblast (MIF) vs. normal lung fibroblast (NLF), macrometastasis-associated fibroblast (MAF) vs. NLF and MAF vs. MIF using DESeq2.
Figure 2 with 1 supplement
Transcriptome profiling of metastasis-associated fibroblasts reveals dynamic stage-specific changes in gene expression.

(A) Hierarchical clustering of genes upregulated or downregulated in macrometastasis-associated fibroblast (MAF) vs. normal lung fibroblast (NLF) based on fold change (FC) > |2|. (B) Presentation of the average Z-scored gene expression of genes differentially expression in MAF vs. NLF in all three groups: NLF, micrometastasis-associated fibroblast (MIF) and MAF. Dashed lines demarcate genes upregulated in MIF vs. NLF. Dotted lines demarcate genes downregulated in MIF vs. NLF. (C) Hierarchical clustering of genes upregulated or downregulated in MIF vs. NLF based on FC > |1.5|. (D) Venn diagram of upregulated or downregulated genes in the different comparisons. (E, F) Hierarchical clustering (E) and principal component analysis (PCA) (F) of genes upregulated or downregulated in the different comparisons (MIF vs. NLF, MAF vs, NLF, MAF vs. MIF). (G) Protein-protein interaction analysis of the differentially expressed genes per comparison performed in STRING platform. Interconnected genes were selected for subsequent analysis.

Figure 2—figure supplement 1
Protein-protein interactions of differentially expressed genes in each comparison (micrometastasis-associated fibroblast [MIF] vs. normal lung fibroblast [NLF] [1], macrometastasis-associated fibroblast [MAF] vs. NLF [2], MAF vs. MIF [3]), derived from the STRING platform.

Confidence 0.3, text mining connections were excluded.

Figure 3 with 1 supplement
Fibroblast metastases-promoting tasks are driven by functional gene signatures related to stress response, inflammation, and extracellular matrix (ECM) remodeling.

(A) Flow chart of the pathway enrichment over-representation analyses based on GO, Reactome, and KEGG using the Consensus Path DB (CPDB) platform. (B) Bubble graph heat map based on the number of specific enrichment terms and their average log-transformed q-value per group. Circle sizes denote number of terms included in a group; color indicates the average log-transformed q-value. Enrichments based on downregulated genes are presented as negative values. (C–E) Heat maps of gene expression fold-change presenting genes in selected group annotations. Fold change was log2 transformed for presentation. Only genes found in at least two different terms are presented. (C) ‘Stress response and protein folding’ enriched genes. (D) ‘Extracellular matrix remodeling’ enriched genes. (E) ‘Inflammatory signaling’ and/or ‘Cytokine and chemokine activity’ enriched genes. (F) Gene Set Enrichment Analysis (GSEA) for hallmark datasets upregulated in macrometastasis-associated fibroblast (MAF) vs. normal lung fibroblast (NLF) related to inflammatory signaling, false discovery rate (FDR) < 0.05, normalized enrichment score (NES) > 2. (G) GSEA results for ‘Myc targets’ hallmark dataset that were upregulated in all comparisons. FDR < 0.05; NES > 2.

Figure 3—figure supplement 1
qRT-PCR analysis in sorted normal lung fibroblast (NLF), micrometastasis-associated fibroblast (MIF), and macrometastasis-associated fibroblast (MAF).

(1) Relative expression (normalized to NLF) of key genes found to be differentially expressed in RNA-seq. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. Data are presented as mean ± SD, n 3 per group. (2) Expression of THBS1 and HSP90AA1 in lung fibroblasts: representative images of YFP and THBS1 immunostaining (top) or YFP and HSP90AA1 (bottom) in normal lungs, micro- and macrometastases-bearing lungs from MMTV-PyMT mice (n = 3). Arrows denote co-staining. Scale bar THBS1: 25 µM. Scale bar HSP90AA1: 50 µM.

Figure 4 with 1 supplement
Multiple gene network analyses identify Myc as a central transcription factor (TF) in the rewiring of metastasis-associated fibroblasts.

(A) Heat maps of ranking parameters and analyses performed per each comparison to identify the centrality of five candidate TFs: Hif1a, Hsf1, Myc, Nfkb1, Stat3. Orange: STRING protein-protein interaction (PPI) analysis results. Yellow: Advanced Network Analysis Tool (ANAT) pathway analysis results. Green: RegNetwork analysis of connectivity between target genes and TFs. Purple: VarElect analysis results. (B) Representative ANAT protein-protein network using all TFs as anchors (green) and the stage-specific signature as target genes (red). Only interaction confidence > 0.6 are presented. (C) Box plot of VarElect scores for directly related genes to each TF (presenting top 50 per TF). (D) Z-score graphs of the results described in (A). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, one-way ANOVA with Tukey correction for multiple comparisons. Data are presented as mean ± SD. (E) Expression of TFs in micrometastasis-associated fibroblast (MIF) and macrometastasis-associated fibroblast (MAF): representative multiplex immunofluorescent staining (MxIF) staining of YFP (green), STAT3 (cyan), NF-κB (magenta), MYC (red), and HSF1 (yellow) in tissue sections of micro- and macrometastases bearing lungs from PyMT;Col1a1-YFP mice (n = 3). Regions with co-staining of several TFs are denoted with solid lines, unique MYC staining regions are denoted in dashed lines. Scale bar: 50 µM.

Figure 4—figure supplement 1
(13) Advanced Network Analysis Tool (ANAT) pathway networks for each transcription factor (TF) (Hif1a, Hsf1, Myc, Nfkb1, Stat3) and each comparison (micrometastasis-associated fibroblast [MIF] vs. normal lung fibroblast [NLF] [1], macrometastasis-associated fibroblast [MAF] vs. NLF [2], MAF vs. MIF [3]).
Figure 5 with 1 supplement
Myc is a central regulator in metastasis-associated fibroblasts and contributes to their acquisition of tumor-promoting traits.

(A) qRT-PCR analysis of Myc expression in sorted normal lung fibroblast (NLF), micrometastasis-associated fibroblast (MIF), and macrometastasis-associated fibroblast (MAF). **p<0.01. Data are represented as mean ± SD, n = 3 per group. (B) qRT-PCR analysis in sorted NLF, MIF, and MAF. Relative expression of Myc target genes found to be differentially expressed in RNA-seq. *p<0.05. Data are presented as mean ± SD, n > 3 per group. (C) Myc targeting by siRNA: Myc expression in NLF transfected with siRNA targeting Myc or with control siRNA (siMyc or siCtrl). Following transfection, cells were incubated with serum-free medium (SFM) or with Met-1 conditioned media (CM) supplemented with the same siRNA for additional 24 hr. Data are presented as mean ± SD, n = 3. (D) qRT-PCR analysis of Myc targets following treatment as in (C). Data are represented as mean ± SD, n = 3. (E, F) Representative images and quantification of collagen contraction assay of fibroblasts transfected with siMyc or siCtrl, incubated with Met-1 CM. *p<0.05. Data are represented as mean ± SD, n = 5. (G, H) Representative images and quantification of scratch closure assay of NLF transfected with siMyc or siCtrl and incubated with Met-1 CM. Scale bar: 400 µm. Two-way ANOVA with multiple comparisons. ***p<0.001. Data are presented as mean ± SD, n = 5. (I) Myc overexpression: qRT-PCR analysis of Myc expression in NLF transfected with Myc or with a control plasmid (Myc OE or Ctrl). Data are presented as mean ± SD, n = 3. (J) Quantification of scratch closure assay of NLF transfected with Myc or a control plasmid. Two-way ANOVA with multiple comparisons. *p<0.5, **p<0.01, ***p<0.001, ****p<0.0001. Data are presented as mean ± SD, n = 3. (K) qRT-PCR analysis of Myc target genes following treatment as in (I). Data are represented as mean ± SD, n = 3.

Figure 5—figure supplement 1
Representative images of scratch closure assay at 0 hr and 24 hr following scratch.

(1) Lung fibroblasts were incubated with serum-free medium (SFM) (normal lung fibroblasts [NLF]) or with tumor cell conditioned media (CM) (activated lung fibroblasts [ALF]), scale bar: 300 µM. (2) Quantification of scratch closure assay performed with FVB/n lung fibroblasts incubated with SFM (NLF, n = 3) or with Met1 CM (ALF, n = 3). ****p<0.0001. Two-way ANOVA with multiple comparisons. Data are represented as mean ± SD. (3) Quantification of scratch closure assay performed with BALB/c NLF incubated with SFM (n = 2) or with 4T1 CM (ALF, n = 2). ****p<0.0001. Two-way ANOVA multiple comparisons. Data are represented as mean ± SD. (4) Scratch closure is not a result of enhanced fibroblast proliferation: quantification of scratch closure of lung fibroblasts incubated with SFM (NLF), or with Met1-CM (ALF), and supplemented with the proliferation inhibitor mitomycin C. ***p<0.001, ****p<0.0001. Two-way ANOVA multiple comparisons. Data are presented as mean ± SEM, n = 3. (5) Representative images of collagen contraction assay at 24 hr. Lung fibroblasts were embedded in collagen gel and incubated with SFM (NLF) or in tumor cell CM (ALF). (6) Quantification of collagen contraction with FVB/n lung fibroblasts incubated with SFM (NLF, n = 8) or with Met1 CM (ALF, n = 8). *p<0.05. Data are represented as mean ± SD. (7) Quantification of collagen contraction with BALB/c NLF incubated with SFM (n = 2) or with 4T1 CM (ALF, n = 2). *p<0.05. Data are represented as mean ± SD. (8) Myc targeting by siRNA: Myc expression in NLF that were transfected with individual siRNA targeting Myc, or with control siRNA (siMyc1, siMyc2 siMyc3 or siCtrl). Data are presented as mean ± SD of technical repeats, n = 4. (9) Quantification of scratch closure assay of NLF transfected with individual siMyc1/2/3 or siCtrl and incubated with Met-1 CM. Two-way ANOVA with multiple comparisons. Data are presented as mean ± SEM, n = 4. (10) Flow cytometry analysis of Ki67+ cells in fibroblasts transfected with siMyc as compared with siCtrl. Data are presented as mean % of Ki67+ cells out of live cells ± SD, n = 3 per group. (11) Proliferation analysis (XTT) of fibroblasts transfected with siMyc as compared with siCtrl. Data are presented as mean fold change from siCtrl ± SD, n = 3 per group. (12) Proliferation analysis (XTT) of fibroblasts transfected with Myc overexpression plasmid or a control plasmid. Data are presented as mean fold change from control ± SD, n = 3 per group.

Figure 6 with 1 supplement
High expression of MYC and its downstream target genes is associated with tumor aggressiveness in human breast cancer.

(A–C) Box plots of MYC (A), NFKB1 (B), and STAT3 (C) expression in tumor-associated stroma from the GSE14548 dataset by disease grade (grade 1: G1; grade 2: G2; grade 3: G3). Data are presented as median and upper and lower quartiles ± SD. One-way ANOVA with Tukey correction for multiple comparisons, *p<0.05. (D) Correlations between the expression of MYC and selected downstream targets in tumor-associated stroma based on GSE14548. Positive correlations are marked in dotted red square. *p-value<0.05. (E) Representative immunohistochemistry staining of MYC in lung metastases of breast cancer patients (n = 9). Scale bars: 200 μm.

Figure 6—figure supplement 1
Correlation graphs between MYC expression and the expression of specific target genes.

p-values of Pearson correlation and correlation coefficient are presented in the graph.

Summary scheme.

The co-evolution of lung fibroblasts at the metastatic microenvironment is driven by stage-specific transcriptional plasticity that activates growth-promoting tasks including stress response, extracellular matrix (ECM) remodeling, and instigation of inflammatory signaling.

Tables

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Cell line (Mus musculus)Met-1Collaborator’s lab
Cell line (M. musculus)4T1Collaborator’s lab
Transfected construct (M. musculus)siRNA to Myc
(Accell SMARTpool)
Dharmacon/
Thermo Fisher Scientific
E-040813
Transfected construct (M. musculus)siRNA to Myc 1Dharmacon/
Thermo Fisher Scientific
A-040813-17CCUCAAACUUAAAUAGUAU
Transfected construct (M. musculus)siRNA to Myc 2Dharmacon/
Thermo Fisher Scientific
A-040813-20CUCUGGUGCAUAAACUGAC
Transfected construct (M. musculus)siRNA to Myc 3Dharmacon/
Thermo Fisher Scientific
A-040813-18GCUUCAGCCAUAAUUUUAA
Transfected construct (M. musculus)Mouse Myc cDNA pCMV-SPORT6Tamar Laboratories#MMM1013-202763479
AntibodyMonoclonal rat anti-mouse EpCAM-APCeBioscience/ Thermo Fisher Scientific17-57911:100
AntibodyMonoclonal rat anti-mouse CD45-PercpCy5.5eBioscience/ Thermo Fisher Scientific45-04511:200
AntibodyMonoclonal rat anti-mouse CD31 PeCy7eBioscience/ Thermo Fisher Scientific25-03111:50
AntibodyMonoclonal rat anti-mouse Ki67-PEBioLegend6524031:100
AntibodyMonoclonal rabbit anti-mouse Nfkb1Cell SignalingCST-8242S1:200
AntibodyMonoclonal rabbit anti-mouse HSP90aa1Cell SignalingCST-4877S1:200
AntibodyMonoclonal rabbit anti-mouse Stat3Cell SignalingCST 12640S1:200
AntibodyPolyclonal chicken anti-GFP/YFPAbcamAB-ab139701:400
AntibodyPolyclonal rabbit anti-GFP/YFPAbcamAB-ab65561:100
AntibodyMonoclonal rabbit anti-mouse MycAbcamAB-ab320721:200
AntibodyMonoclonal rabbit anti-mouse THBS1AbcamAB-ab2639051:50
AntibodyPolyclonal rabbit anti-mouse Hsf1Cell Signaling4356S1:800
AntibodyMonoclonal mouse anti-mouse aSMASigma-AldrichA25471:1000
AntibodyPolyclonal goat anti-mouse PDPNR&D SystemsAF32441:200
AntibodyPolyclonal rabbit anti-mouse FSP-1 (S100A4)AbcamAb415321:600
AntibodyPolyclonal goat anti-rabbitJackson111-035-1441:400
Commercial assay or kitOpal 520 Reagent PackAkoya BiosciencesFP1487001 KT1:400
Commercial assay or kitOpal 570 Reagent PackAkoya BiosciencesFP1488001 KT1:400
Commercial assay or kitOpal 620 Reagent PackAkoya BiosciencesFP1495001 KT1:400
Commercial assay or kitOpal 650 Reagent PackAkoya BiosciencesFP1496001 KT1:400
Commercial assay or kitOpal 690 Reagent PackAkoya BiosciencesFP1497001 KT1:400
Commercial assay or kitIntracellular staining KitBD Biosciences554714
Sequence-based reagentBcat1_FHyLabsPCR primersCCCATCGTACCTCTTTCACCC
Sequence-based reagentBcat1_RHyLabsPCR primersGGGAGCGTGGGAATACGTG
Sequence-based reagentCcl7_FHyLabsPCR primersCCTGGGAAGCTGTTATCTTCAA
Sequence-based reagentCcl7_RHyLabsPCR primersGGTTTCTGTTCAGGCACATTTC
Sequence-based reagentChi3l1_FHyLabsPCR primersGGCAGAGAGAAACTCCTGCTCA
Sequence-based reagentChi3l1_RHyLabsPCR primersTGAGATTGATAAAATCCAGGTGTTG
Sequence-based reagentMyc_FHyLabsPCR primersCGGACACACAACGTCTTGGAA
Sequence-based reagentMyc_RHyLabsPCR primersAGGATGTAGGCGGTGGCTTTT
Sequence-based reagentCol5a3_FHyLabsPCR primersAGGGACCAACTGGGAAGAGT
Sequence-based reagentCol5a3_RHyLabsPCR primersAAAGTCAGAGGCAGCCACAT
Sequence-based reagentCol8a1_FHyLabsPCR primersGCCAGCCAAGCCTAAATGTG
Sequence-based reagentCol8a1_RHyLabsPCR primersGTAGGCACCGGCCTGAATGA
Sequence-based reagentCxcl10_FHyLabsPCR primersCACCATGAACCCAAGTGCTG
Sequence-based reagentCxcl10_RHyLabsPCR primersTTGCGAGAGGGATCCCTTG
Sequence-based reagentFosl1_FHyLabsPCR primersCCAGGGCATGTACCGAGACTA
Sequence-based reagentFosl1_RHyLabsPCR primersTGGCACAAGGTGGAACTTCTG
Sequence-based reagentGapdh_FHyLabsPCR primersTGTGTCCGTCGTGGATCTGA
Sequence-based reagentGapdh_RHyLabsPCR primersTTGCTGTTGAAGTCGCAGGAG
Sequence-based reagentHsp90aa1_FHyLabsPCR primersGCGTGTTCATTCAGCCACGAT
Sequence-based reagentHsp90aa1_RHyLabsPCR primersACTGGGCAATTTCTGCCTGA
Sequence-based reagentHspd1_FHyLabsPCR primersCACAGTCCTTCGCCAGATGAG
Sequence-based reagentHspd1_RHyLabsPCR primersCTACACCTTGAAGCATTAAGGCT
Sequence-based reagentHspe1_FHyLabsPCR primersAGTTTCTTCCGCTCTTTGACAG
Sequence-based reagentHspe1_RHyLabsPCR primersTGCCACCTTTGGTTACAGTTTC
Sequence-based reagentHsph1_FHyLabsPCR primersCAACAGAAAGCTCGGATGTGGATAA
Sequence-based reagentHsph1_RHyLabsPCR primersCTTCTGAGGTAAGTTCAGGTGAAG
Sequence-based reagentIl6_FHyLabsPCR primersATACCACTCCCAACAGACCTGTCT
Sequence-based reagentIl6_RHyLabsPCR primersCAGAATTGCCATTGCACAACTC
Sequence-based reagentGusb_FHyLabsPCR primersGCAGCCGCTACGGGAGTC
Sequence-based reagentGusb_RHyLabsPCR primersTTCATACCACACCCAGCCAAT
Sequence-based reagentOdc1_FHyLabsPCR primersGACGAGTTTGACTGCCACATC
Sequence-based reagentOdc1_RHyLabsPCR primersCGCAACATAGAACGCATCCTT
Sequence-based reagentTimp1_FHyLabsPCR primersGTGCACAGTGTTTCCCTGTTTA
Sequence-based reagentTimp1_RHyLabsPCR primersGACCTGATCCGTCCACAAAC
OtherDAPI stainMolecular ProbesD35711:1000
OtherDAPI stainBioLegend4228011:1000
Software, algorithmJMP14 and upJMP

Additional files

Supplementary file 1

Related to Figure 3.

Detailed enrichment results for all comparisons based on selection criteria.

https://cdn.elifesciences.org/articles/60745/elife-60745-supp1-v2.xlsx
Supplementary file 2

Related to Figure 3.

Full Gene Set Enrichment Analysis results for all comparisons, false discovery rate < 0.05, normalized enrichment score > |2|.

https://cdn.elifesciences.org/articles/60745/elife-60745-supp2-v2.xlsx
Supplementary file 3

Related to Figure 4.

List of terms containing transcription factors enriched in all comparisons.

https://cdn.elifesciences.org/articles/60745/elife-60745-supp3-v2.xlsx
Supplementary file 4

Related to Figure 4.

Full results of transcription factor ranking of all comparisons.

https://cdn.elifesciences.org/articles/60745/elife-60745-supp4-v2.xlsx
Transparent reporting form
https://cdn.elifesciences.org/articles/60745/elife-60745-transrepform-v2.pdf

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