Myelofibrosis is a progressive bone marrow malignancy associated with monocytosis, and is believed to promote the pathological remodelling of the extracellular matrix. Here we show that the mechanical properties of myelofibrosis, namely the liquid-to-solid properties (viscoelasticity) of the bone marrow, contribute to aberrant differentiation of monocytes. Human monocytes cultured in stiff, elastic hydrogels show proinflammatory polarization and differentiation towards dendritic cells, as opposed to those cultured in a viscoelastic matrix. This mechanically induced cell differentiation is blocked by inhibiting a myeloid-specific isoform of phosphoinositide 3-kinase, PI3K-γ. We further show that murine bone marrow with myelofibrosis has a significantly increased stiffness and unveil a positive correlation between myelofibrosis grading and viscoelasticity. Treatment with a PI3K-γ inhibitor in vivo reduced frequencies of monocyte and dendritic cell populations in murine bone marrow with myelofibrosis. Moreover, transcriptional changes driven by viscoelasticity are consistent with transcriptional profiles of myeloid cells in other human fibrotic diseases. These results demonstrate that a fibrotic bone marrow niche can physically promote a proinflammatory microenvironment.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Phase-separation facilitated one-step fabrication of multiscale heterogeneous two-aqueous-phase gel
Nature Communications Open Access 16 May 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Digital data supporting the findings of this article are available at https://dataverse.harvard.edu/dataverse.xhtml?alias=Vining_et_al_2022. In vitro mouse bulk RNA-seq and human in vitro and patient-derived nanoString datasets are available at the NCBI GEO database, GEO accession code GSE206773. The following scRNA-seq datasets were utilized for analysis in this study: liver cirrhosis, GEO accession GSE136103; IPF, GEO accession GSE122960.
Distler, J. H. W. et al. Shared and distinct mechanisms of fibrosis. Nat. Rev. Rheumatol. 15, 705–730 (2019).
Leiva, O. et al. The role of the extracellular matrix in primary myelofibrosis. Blood Cancer J. 7, e525 (2017).
Lampi, M. C. & Reinhart-King, C. A. Targeting extracellular matrix stiffness to attenuate disease: from molecular mechanisms to clinical trials. Sci. Transl. Med. 10, eaao0475 (2018).
Laklai, H. et al. Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat. Med. https://doi.org/10.1038/nm.4082 (2016).
Shin, J.-W. & Mooney, D. J. Extracellular matrix stiffness causes systematic variations in proliferation and chemosensitivity in myeloid leukemias. Proc. Natl Acad. Sci. USA 113, 12126–12131 (2016).
Vining, K. H. & Mooney, D. J. Mechanical forces direct stem cell behaviour in development and regeneration. Nat. Rev. Mol. Cell Biol. 18, 728–742 (2017).
Chaudhuri, O. et al. Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat. Mater. 15, 326–334 (2016).
Chaudhuri, O. et al. Substrate stress relaxation regulates cell spreading. Nat. Commun. 6, 6364 (2015).
Gong, Z. et al. Matching material and cellular timescales maximizes cell spreading on viscoelastic substrates. Proc. Natl Acad. Sci. USA 115, E2686–E2695 (2018).
Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix elasticity directs stem cell lineage specification. Cell 126, 677–689 (2006).
Wong, W. J. et al. Gene expression profiling distinguishes prefibrotic from overtly fibrotic myeloproliferative neoplasms and identifies disease subsets with distinct inflammatory signatures. PLoS ONE 14, e0216810 (2019).
Fisher, D. A. C. et al. Cytokine production in myelofibrosis exhibits differential responsiveness to JAK-STAT, MAP kinase, and NFκB signaling. Leukemia 33, 1978–1995 (2019).
Tefferi, A. et al. Monocytosis is a powerful and independent predictor of inferior survival in primary myelofibrosis. Br. J. Haematol. 183, 835–838 (2018).
Jutzi, J. S. & Mullally, A. Remodeling the bone marrow microenvironment—a proposal for targeting pro-inflammatory contributors in MPN. Front. Immunol. 11, 2093–2093 (2020).
Campanelli, R. et al. CD14brightCD16low intermediate monocytes expressing Tie2 are increased in the peripheral blood of patients with primary myelofibrosis. Exp. Hematol. 42, 244–246 (2014).
de la Guardia, R. D. et al. Detection of inflammatory monocytes but not mesenchymal stem/stromal cells in peripheral blood of patients with myelofibrosis. Br. J. Haematol. 181, 133–137 (2018).
Brauer, E. et al. Collagen fibrils mechanically contribute to tissue contraction in an in vitro wound healing scenario. Adv. Sci. 6, 1801780 (2019).
Vining, K. H., Stafford, A. & Mooney, D. J. Sequential modes of crosslinking tune viscoelasticity of cell-instructive hydrogels. Biomaterials 188, 187–197 (2019).
Lee, K. Y. & Mooney, D. J. Alginate: properties and biomedical applications. Prog. Polym. Sci. 37, 106–126 (2012).
Rowley, J. A., Madlambayan, G. & Mooney, D. J. Alginate hydrogels as synthetic extracellular matrix materials. Biomaterials 20, 45–53 (1999).
Iurlo, A. et al. Spleen stiffness measurement by transient elastography as a predictor of bone marrow fibrosis in primary myelofibrosis patients. Blood 124, 1825–1825 (2014).
Sundström, G., Hultdin, M., Engström-Laurent, A. & Dahl, I. M. S. Bone marrow hyaluronan and reticulin in patients with malignant disorders. Med. Oncol. 27, 618–623 (2010).
Jansen, L. E., Birch, N. P., Schiffman, J. D., Crosby, A. J. & Peyton, S. R. Mechanics of intact bone marrow. J. Mech. Behav. Biomed. Mater. 50, 299–307 (2015).
Pardanani, A., Begna, K., Finke, C., Lasho, T. & Tefferi, A. Circulating levels of MCP-1, sIL-2R, IL-15, and IL-8 predict anemia response to pomalidomide therapy in myelofibrosis. Am. J. Hematol. 86, 343–345 (2011).
Maekawa, T. et al. Increased SLAMF7high monocytes in myelofibrosis patients harboring JAK2V617F provide a therapeutic target of elotuzumab. Blood 134, 814–825 (2019).
Farren, M. R. et al. Tumor-induced STAT3 signaling in myeloid cells impairs dendritic cell generation by decreasing PKCβII abundance. Sci. Signal. 7, ra16–ra16 (2014).
Garris, C. S. et al. Successful anti-PD-1 cancer immunotherapy requires T cell–dendritic cell crosstalk involving the cytokines IFN-γ and IL-12. Immunity 49, 1148–1161.e7 (2018).
Vasquez-Dunddel, D. et al. STAT3 regulates arginase-I in myeloid-derived suppressor cells from cancer patients. J. Clin. Investig. 123, 1580–1589 (2013).
Poschke, I., Mougiakakos, D., Hansson, J., Masucci, G. V. & Kiessling, R. Immature immunosuppressive CD14+HLA-DR−/low cells in melanoma patients are Stat3hi and overexpress CD80, CD83, and DC-sign. Cancer Res. 70, 4335–4345 (2010).
Chan, L. L. Y., Cheung, B. K. W., Li, J. C. B. & Lau, A. S. Y. A role for STAT3 and cathepsin S in IL-10 down-regulation of IFN-γ-induced MHC class II molecule on primary human blood macrophages. J. Leukoc. Biol. 88, 303–311 (2010).
Humphrey, J. D., Dufresne, E. R. & Schwartz, M. A. Mechanotransduction and extracellular matrix homeostasis. Nat. Rev. Mol. Cell Biol. 15, 802–812 (2014).
Verdijk, P. et al. Morphological changes during dendritic cell maturation correlate with cofilin activation and translocation to the cell membrane. Eur. J. Immunol. 34, 156–164 (2004).
Kustermans, G. et al. Actin cytoskeleton differentially modulates NF-κB-mediated IL-8 expression in myelomonocytic cells. Biochem. Pharmacol. 76, 1214–1228 (2008).
Shutt, D. C., Daniels, K. J., Carolan, E. J., Hill, A. C. & Soll, D. R. Changes in the motility, morphology, and F-actin architecture of human dendritic cells in an in vitro model of dendritic cell development. Cell Motil. 46, 200–221 (2000).
Rullo, J. et al. Actin polymerization stabilizes α4β1 integrin anchors that mediate monocyte adhesion. J. Cell Biol. 197, 115–129 (2012).
Ma, A. D., Metjian, A., Bagrodia, S., Taylor, S. & Abrams, C. S. Cytoskeletal reorganization by G protein-coupled receptors is dependent on phosphoinositide 3-kinase γ, a Rac guanosine exchange factor, and Rac. Mol. Cell. Biol. 18, 4744–4751 (1998).
Go, Y.-M. et al. Phosphatidylinositol 3-kinase γ mediates shear stress-dependent activation of JNK in endothelial cells. Am. J. Physiol. Heart Circulatory Physiol. 275, H1898–H1904 (1998).
Hannigan, M. et al. Neutrophils lacking phosphoinositide 3-kinase γ show loss of directionality during N-formyl-Met-Leu-Phe-induced chemotaxis. Proc. Natl Acad. Sci. USA 99, 3603–3608 (2002).
Kaneda, M. M. et al. PI3Kγ is a molecular switch that controls immune suppression. Nature https://doi.org/10.1038/nature19834 (2016).
Bartalucci, N. et al. Inhibitors of the PI3K/mTOR pathway prevent STAT5 phosphorylation in JAK2V617F mutated cells through PP2A/ CIP2A axis. Oncotarget 8, 96710–96724 (2017).
Bartalucci, N., Guglielmelli, P. & Vannucchi, A. M. Rationale for targeting the PI3K/Akt/mTOR pathway in myeloproliferative neoplasms. Clin. Lymphoma Myeloma Leuk. 13, S307–S309 (2013).
Frausto-Del-Río, D. et al. Interferon gamma induces actin polymerization, Rac1 activation and down regulates phagocytosis in human monocytic cells. Cytokine 57, 158–168 (2012).
Grove, L. M. et al. Translocation of TRPV4-PI3Kγ complexes to the plasma membrane drives myofibroblast transdifferentiation. Sci. Signal. 12, eaau1533 (2019).
Nam, S. et al. Cell cycle progression in confining microenvironments is regulated by a growth-responsive TRPV4-PI3K/Akt-p27Kip1 signaling axis. Sci. Adv. 5, eaaw6171 (2019).
Rahaman, S. O. et al. TRPV4 mediates myofibroblast differentiation and pulmonary fibrosis in mice. J. Clin. Investig. 124, 5225–5238 (2014).
Scheraga, R. G., Southern, B. D., Grove, L. M. & Olman, M. A. The role of TRPV4 in regulating innate immune cell function in lung inflammation. Front. Immunol. 11, 1211–1211 (2020).
Tefferi, A. et al. Circulating interleukin (IL)-8, IL-2R, IL-12, and IL-15 levels are independently prognostic in primary myelofibrosis: a comprehensive cytokine profiling study. J. Clin. Oncol. 29, 1356–1363 (2011).
Mullally, A. et al. Distinct roles for long-term hematopoietic stem cells and erythroid precursor cells in a murine model of Jak2V617F-mediated polycythemia vera. Blood 120, 166–172 (2012).
Reyfman, P. A. et al. Single-cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am. J. Respiratory Crit. Care Med. 199, 1517–1536 (2019).
Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).
Hayman, A. R., Macary, P., Lehner, P. J. & Cox, T. M. Tartrate-resistant acid phosphatase (Acp 5): identification in diverse human tissues and dendritic cells. J. Histochem. Cytochem. 49, 675–683 (2001).
Desai, R. M., Koshy, S. T., Hilderbrand, S. A., Mooney, D. J. & Joshi, N. S. Versatile click alginate hydrogels crosslinked via tetrazine-norbornene chemistry. Biomaterials 50, 30–37 (2015).
pheatmap: Pretty Heatmaps. R package version 1.0.12 https://github.com/raivokolde/pheatmap (2019).
Mullally, A. et al. Physiological Jak2V617F expression causes a lethal myeloproliferative neoplasm with differential effects on hematopoietic stem and progenitor cells. Cancer Cell 17, 584–596 (2010).
Akhtar, R. et al. Nanoindentation of histological specimens: mapping the elastic properties of soft tissues. J. Mater. Res. 24, 638–646 (2009).
Akhtar, R., Draper, E. R., Adams, D. J. & Pfaff, H. in Mechanics of Biological Systems and Materials Vol. 6 (eds Zavattieri, P., Tekalur, S. & Korach, C.) 141–145 (Springer, 2016).
Cohen, S. R. & Kalfon-Cohen, E. Dynamic nanoindentation by instrumented nanoindentation and force microscopy: a comparative review. Beilstein J. Nanotechnol. 4, 815–833 (2013).
Choi, A. P. & Zheng, Y. P. Estimation of Young’s modulus and Poisson’s ratio of soft tissue from indentation using two different-sized indentors: finite element analysis of the finite deformation effect. Med. Biol. Eng. Comput. 43, 258–264 (2005).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
AnnotationDbi: manipulation of SQLite-based annotations in Bioconductor. R package version 1.46.1 https://bioconductor.riken.jp/packages/3.10/bioc/html/AnnotationDbi.html (2019).
RMariaDB: database interface and ‘MariaDB’ driver. R package version 1.0.6 https://github.com/r-dbi/RMariaDB (2018).
Soneson, C., Love, M. & Robinson, M. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; peer review: 2 approved]. F1000Research https://doi.org/10.12688/f1000research.7563.2 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D. & Woolf, P. J. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinf. 10, 161 (2009).
Swerdlow, S. H., International Agency for Research on Cancer & World Health Organization. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 4th edn (International Agency for Research on Cancer, 2008).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Stuart, T. et al. Comprehensive Integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
At the Wyss Institute, we thank T. Ferrante for assistance with imaging, and group members D. Zhang and A. Gonzalez-Pujana for their assistance and feedback on this work. At Massachusetts General Hospital, we thank D. Lagares for his feedback and editing. At the Massachusetts Institute of Technology, the authors thank T. Gierahn and the BioMicroCenter for assistance with bulk RNA-sequencing. At Dana-Farber Cancer Research Institute, we thank C. Laurore for technical assistance. Part of this work was performed at the Bauer Core Facility at Harvard University. We thank S. Reinke and his Cell Harvest Unit of the Berlin Institute of Health Center for Regenerative Therapies and the Charité for supply of the human fracture haematoma samples. We acknowledge Sysmex America, M. Sola-Visner and her laboratory members E. Nolton and P. Davenport (Boston Children’s Hospital) for kindly sharing Sysmex equipment for complete blood counts. Research reported in this manuscript was supported by the National Cancer Institute of the National Institutes of Health under award number U01CA214369 (D.J.M.), the National Institute of Dental & Craniofacial Research of the National Institutes of Health under award numbers K08DE025292 and K99DE030084 (K.H.V.), the Federal Drug Administration R01FD006589 (D.J.M.), and the Harvard University Materials Research Science and Engineering Center (grant DMR 1420570). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Parts of the work were supported by the Einstein foundation and the German Research Foundation in the context of the DFG FOR 2165 grant and CRC 1444 grants (G.N.D.). A.E.M. was supported by a long-term fellowship from the European Molecular Biology Organization (EMBO, ALTF 268-2017) and Horizon award from the US Department of Defense (W81XWH-20-1-0904). A.M. is a Scholar of the Leukaemia and Lymphoma Society (LLS). O.P. was supported by a Brigham and Women’s Hospital Faculty Career Development Award and by the Brigham Research Institute and the Center for Faculty Development and Diversity’s Office for Research Careers Microgrant Program.
D.J.M. has sponsored research from Novartis, and consults and/or has stock options/stock in J&J, Samyang Corp., Lyell, Attivare, IVIVA, and Revela; none of these relate to the topic of this manuscript. Within the past 12 months, A.M. has consulted for Janssen, PharmaEssentia, Constellation and Relay Therapeutics and receives research support from Janssen and Actuate Therapeutics. The authors confirm that there are no known conflicts of interest associated with this publication and there has been no notable financial support for this work that could have influenced its outcome. The remaining authors declare no competing interests.
Peer review information
Nature Materials thanks Song Li, Simon Mendez Ferrer, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Related to Fig. 2. Nested scatterplots of normalized gene expression of markers upregulated in cells in viscous and soft-elastic gels (A) and stiff-elastic gels (B).
Each set of data points represents biological replicates from a particular donor (the two sets of data points at each experimental condition represent cells from the n = 2 donors). Soft ~ 0.75 kPa, Stiff ~ 7.5 kPa elastic moduli. P-values indicate statistical significance (p < 0.05) compared to stiff-elastic gels (*, asterix) of nested one-way ANOVA with Tukey’s multiple comparisons tests. All analyses were performed at 3 days after encapsulation.
Extended Data Fig. 2 Related to Fig. 3. Nested scatterplots of flow cytometry performed on samples from n = 3 donors.
HLA-DR mean fluorescence intensity (MFI), fraction of live cells expressing dendritic cell differentiation markers CD11c + CD1c + , CD80 + , and PDL1 + gated on live-HLA-DR + cells for soft viscous (blue), stiff viscous (red), soft elastic (green), and stiff elastic (purple) gels. P-values indicate statistical significance (p < 0.05) of n = 3 biological replicates of two-way ANOVA with Tukey’s multiple comparisons tests compared to stiff elastic gel within each donor (asterix). All analyses were performed at 3 days after encapsulation.
Extended Data Fig. 3 Related to Fig. 3. STAT3 transcription factor is associated with differentially expressed genes in stiff viscous hydrogels.
A) Unsupervised hierarchical clustering of differentially expressed genes from bulk RNA sequencing. Cells from soft and stiff viscous gels are compared to cells in non-adherent tissue culture with or without calcium control. The calcium control are cells exposed to free calcium released from an empty hydrogel. Markers were separated into 11 clusters associated with patterns of gene expression. B) Clusters 5, 6, 7, 8, and 11 were associated with genes upregulated in stiff viscous conditions. These markers were put into the ENCODE ChIP-Seq database, which identified STAT3 as one of the top ranked transcription factors involved in regulating these genes. C) Flow cytometry of HLA-DR MFI and CD11b + CD11c + cells (gated on live-HLA-DR + ) with blocking anti-IL6R antibody (1μg/ml), n = 3 biological replicates. D) Normalized gene expression of NFKBIZ at Day 0 prior to encapsulation and after 3 days in viscous and elastic gels of soft (0.75 kPa) and stiff (7.5 kPa) elastic modulus. Data points indicate biological replicates from n = 2 donors for viscous and elastic gels, and n = 1 donor for day 0 control. P-values<0.05 indicate statistical significance of one-way ANOVA with Tukey multiple comparisons test. Experiments were repeated with at least two donors. Data are presented as mean values + /- SEM.
Extended Data Fig. 4 Related to Fig. 4. F-actin in elastic gels is independent of collagen adhesive ligands and actomyosin contractility.
A) Pan-actin staining of cells in viscous and elastic gels. B) F-actin staining of cells in elastic gels without collagen. C) F-actin staining with or without inhibition of actomyosin contractility by non-muscle myosin-II by blebbistatin (10 μM). D) Flow cytometry for dendritic cell differentiation markers with or without blebbistatin. Images are maximum intensity projections of representative cells. Scale bar 10 um. Data points indicate n = 3 biological replicates from 1 donor. P-values<0.05 indicate statistically significant difference by ordinary one-way ANOVA with Tukey multiple comparisons test. All analyses were performed at 3 days after encapsulation. Data are presented as mean values + /- SEM.
Extended Data Fig. 5 Related to Fig. 5. Viscoelasticity impacts Jak2 signaling in Jak2-V167F monocytes.
A) Volcano plot of global transcriptome of differentially expressed genes comparing mouse Jak2-V617F monocytes in stiff, elastic versus stiff, viscous gels. Bulk RNA-sequencing was performed in duplicate. Genes marked in red are above thresholds of both fold change = 21 and p-value = 10-2 threshold (dotted lines). Green indicates genes above fold change threshold, but below the p-value threshold. Blue indicates genes above the p-values threshold, but below the fold change threshold. Grey indicates genes that are not significantly differentially expressed. Data was obtained from n = 2 biological replicates. B) Normalized expression of JAK-STAT pathway genes in Jak2-V617F monocytes cultured in elastic versus viscous hydrogels, with linear regression fit (black line). Genes enriched in elastic gels are marked in red and appear above the linear regression (viscous in blue). Data points indicate mean normalized expression of n = 2 biological replicates from a single experiment (one of each gender). C) IL6 and LIF levels of conditioned media from WT or Jak2-V617F monocytes in viscous (blue) or elastic (red) hydrogels. Data shown from a single experiment with n samples as individual data points collected from n = 2 biological replicates (one of each gender). P-value<0.05 indicates statistically significant difference by Brown–Forsythe and Welch ANOVA test with Dunnett’s T3 multiple comparisons test.
Extended Data Fig. 6 Related to Fig. 5. Pathologic grading of model of MF and flow cytometry analysis of BM neutrophils.
A) Representative MF grading of histologic sections from femurs of recipient retroviral Jak2-V617F mice, repeated n = 3 independent experiments. 60x objective. B) Flow cytometry to determine the fraction of bone marrow Ly6C + LY6G + neutrophils in mouse models of fibrotic (pMEGIX Jak2-V617F) and non-fibrotic (Mx1-cre Jak2-V617F) MPNs, gated on CD11b + cells. In the left panel, mice were untreated. Values were normalized to empty-vector controls, and data shown is from n = 3 independent cohorts of mice. In the right panel, cells were analysed from fibrotic pMEGIX Jak2-V617F recipient mice and treated with either vehicle control (-) or IPI-549 (+) daily for 2 weeks with oral gavage of 15 mg/kg IPI-549 10% DMSO/5%NMP/85% PEG400. Data points indicate n biological replicates from 1 independent experiment. P-values <0.05 indicate statistically significant differences of one-way ANOVA with Tukey multiple comparisons test or unpaired two-tailed Student’s t test. Data are presented as mean values + /- SEM.
Supplementary methods, Figs. 1–8, Tables 1–9 and references.
Supplementary Data 1
Custom human nanoString elements panel.
Rights and permissions
About this article
Cite this article
Vining, K.H., Marneth, A.E., Adu-Berchie, K. et al. Mechanical checkpoint regulates monocyte differentiation in fibrotic niches. Nat. Mater. 21, 939–950 (2022). https://doi.org/10.1038/s41563-022-01293-3
This article is cited by
Phase-separation facilitated one-step fabrication of multiscale heterogeneous two-aqueous-phase gel
Nature Communications (2023)
Cell–extracellular matrix mechanotransduction in 3D
Nature Reviews Molecular Cell Biology (2023)