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Force Spectrum Microscopy (FSM) is an application of active microrheology developed to measure aggregate random forces in the cytoplasm.[1] Large, inert flow tracers are injected into live cells and become lodged inside the cytoskeletal mesh, wherein it is oscillated by repercussions from active motor proteins. The magnitude of these random forces can be inferred from the frequency of oscillation of tracer particles. Tracking the fluctuations of tracer particles using optical microscopy can isolate the contribution of active random forces to intracellular molecular transport from that of Brownian motion.

Basic principlesEdit

FSM was developed by Ming Guo and David A. Weitz to probe stochastic intracellular forces generated by motor proteins.[1] Far from a liquid void, the cytoplasm contains a complex meshwork of actin and myosin conferring structural support to the cell, as well as harbouring vesicles and mitochondria among other organelles.[2] Recent research on the macromolecular crowding inside the cytoplasm raises concerns whether diffusive-like motion of large molecules have been mistakenly attributed to Brownian forces.[3] Instead, there are suspicions that myosin motor proteins, which tug randomly on the actin filaments embedded with large molecules, give rise to diffusive-like motion of molecules inside cells.[3][4] Guo et al. developed an assay to distinguish whether particle motion inside cells are driven by thermal diffusion or by repercussions from active motor proteins like non-muscle myosin II shaking the cellular cytoskeleton.

FSM relies on injecting tracer particles coated with polyethylene glycol (PEG) larger than the cytoskeletal mesh size (>50 nm),[5] settling in between an internetwork of actin filaments and myosin motor proteins. As myosin motor proteins tug on actin filaments to perform cellular work, these actin fluctuations invariably oscillate neighboring PEGylated particles. The magnitude of tracer fluctuation is proportional to the magnitude of aggregate active motor forces. Thus, by recording the displacement of tracer oscillations, FSM can gauge and derive the magnitude of forces exerted by active motor proteins.[1]

Force measurementEdit

The fluctuations of PEGylated tracers coupled to aggregate myosin motor forces can be likened to a Hookean spring,

 

where the force   applied to generate the oscillation displacement   is proportional to the effective spring constant   of the intracellular environment. The displacement during oscillation is a spatial function of time, which can be directly measured using optical microscopy.[1] A Fourier transform then maps information in the temporal domain to the frequency domain to derive a useful dimension as a function of frequency,

 

where  ,   and   are quadratic forms of averaged force, elasticity and displacement used to account for stochastic forces.[1] Time-averaged Mean squared displacement,   can be retrieved by a Fourier Transform from the frequency domain back to the temporal domain. In the context of oscillation frequency, the higher the force frequency spectrum, the greater the metabolic activity of the cell.[6] Independent micromechanical measurements can calculate the elasticity of the cytoplasm. By using an optical tweezer to apply a prescribed force to a tracer particle, FSM can measure the resulting displacement in order to estimate the elastic spring constant.[7][8]

ApplicationsEdit

Cytoplasmic fluidityEdit

Directed oscillation of tracer particles using optical tweezers resulted in displacement that was nearly synchronized with applied force, suggesting that the cytoplasm is materially closer to an elastic solid.[1] This is in stark contrast to previous hypothesis that the cytoplasm is a viscoelastic fluid in which large molecules can freely diffuse.[9] In ATP-depleted cells, in which non-muscle myosin II are inactivated, FSM experiments reveal that tracer particles cease to oscillate as if the cytoplasm had solidified.[1] Myosin IIs are motor proteins that bind and tugs on actin filaments through ATP hydrolysis.[10] This further corroborates the finding that in nutrient-starved bacteria, the cytoplasm transitions into a glasslike substance.[11] Thus, ATP-hydrolysis by motor proteins appear to be critical to sustain cytoplasmic fluidity, which is crucial to vesicle transport and diffusive motion in the cytoskeleton.[1]

Differential diagnosis of malignant cancerEdit

By measuring the general state of activity inside a cell, FSM can be applied to identify malignant cancerous cells, which are characteristically more elastic[12] and more motile. FSM measurements on malignant MCF-7 breast cancer cells and benign MCF-10A breast cancer cells revealed a statistically significant separation in force spectrum that allows FSM to assay for metastatic cancer.[1] Dimensionality of extracellular environment greatly influences FSM measurements of cancerous cells. In a 3D matrix, MDA-MB-231 metastatic breast cancer cells had comparatively more solid cytoplasm than counterparts cultured on 2D plates.[13]

ReferencesEdit

  1. ^ a b c d e f g h i Guo, Ming; Ehrlicher, Allen J.; Jensen, Mikkel H.; Renz, Malte; Moore, Jeffrey R.; Goldman, Robert D.; Lippincott-Schwartz, Jennifer; Mackintosh, Frederick C.; Weitz, David A. (August 14, 2014). "Probing the Stochastic, Motor-Drive Properties of the Cytoplasm Using Force Spectrum Microscopy". Cell. 158: 822–832. doi:10.1016/j.cell.2014.06.051. PMC 4183065. PMID 25126787.
  2. ^ Brangwynne, C.P.; Koenderink, G.H.; Mackintosh, F.C.; Weitz, D.A. (2007). "Cytoplasmic diffusion: molecular motors mix it up" (PDF). Journal of Cell Biology. 183: 583–587. doi:10.1083/jcb.200806149.
  3. ^ a b MacKintosh, F.C. (2012). "Active Diffusion: the Erratic Dance of Chromosomal Loci". Proceedings of the National Academy of Sciences, USA. 109: 7138–7139. Bibcode:2012PNAS..109.7138M. doi:10.1073/pnas.1204794109. PMC 3358833.
  4. ^ MacKintosh, F.C.; Levine, A.J. (2010). "Nonequilibrium Mechanics and Dynamics of Motor-activated Gels". Physical Review Letters. 100: 018104. arXiv:0704.3794. Bibcode:2008PhRvL.100a8104M. doi:10.1103/physrevlett.100.018104. PMID 18232824.
  5. ^ Luby-Phelps, K. (2000). "Cytoarchitecture and physical properties of cytoplasm: volume, viscosity, diffusion, intracellular surface area". International Review of Cytology. 192: 189–221.
  6. ^ Mourão, MA; Hakim, JB; Schnell, S (2014). "Connecting the dots: the effects of macromolecular crowding on cell physiology". Biophysical Journal. 107: 2761–2766. Bibcode:2014BpJ...107.2761M. doi:10.1016/j.bpj.2014.10.051. PMC 4269789. PMID 25517143.
  7. ^ Tassieri, M; Evans, RML; Warren, R; Bailey, NJ; Cooper, JM (2012). "Microrheology with optical tweezers: data analysis" (PDF). New Journal of Physics. 14: 115032. Bibcode:2012NJPh...14k5032T. doi:10.1088/1367-2630/14/11/115032.
  8. ^ Bausch, AR; MöllerMöller, W; Sackmann, E (1999). "Measurement of local viscoelasticity and forces in living cells by magnetic tweezers". Biophysical Journal. 76: 573–579. Bibcode:1999BpJ....76..573B. doi:10.1016/s0006-3495(99)77225-5. PMC 1302547. PMID 9876170.
  9. ^ Guigas, G; Kalla, C; Weiss, M (2007). "The degree of macromolecular crowding in the cytoplasm and nucleoplasm of mammalian cells is conserved". FEBS Letters. 581: 5094–5098. doi:10.1016/j.febslet.2007.09.054. PMID 17923125.
  10. ^ Vicente-Manzanares, M; Ma, X; Adelstein, RS; Horwitz, AR (2009). "Non-muscle myosin II takes centre stage in cell adhesion and migration". Nature Reviews Molecular Cell Biology. 10: 778–790. doi:10.1038/nrm2786. PMC 2834236. PMID 19851336.
  11. ^ Parry, BR; Surovtsev, IV; cabeen, MT; Corey, SO; Dufresne, ER; Jacobs-Wagner, C (2013). "The Bacterial Cytoplasm Has Glass-like Properties and Is Fluidized by Metabolic Activity". Cell. 156: 1–12. doi:10.1016/j.cell.2013.11.028. PMC 3956598. PMID 24361104.
  12. ^ Plodinec, M; et al. (2013). "The Nanomechanical Signature of Breast Cancer". Biophysical Journal. 104: 321. Bibcode:2013BpJ...104..321P. doi:10.1016/j.bpj.2012.11.1779.
  13. ^ Mak, M; Kamm, RD; Zaman, MH (2014). "Impact of Dimensionality and Network Disruption on Microrheology of Cancer Cells in 3D Environments". PLOS Computational Biology. 10: e1003959. Bibcode:2014PLSCB..10E3959M. doi:10.1371/journal.pcbi.1003959.