Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published: January 18, 2022

doi:

Giuseppe Ciccone, Mariana Azevedo Gonzalez Oliva, Nelda Antonovaite, Ines Lüchtefeld, Manuel Salmeron-Sanchez, Massimo Vassalli

¹Centre for the Cellular Microenvironment, James Watt School of Engineering,University of Glasgow, ²Optics 11 life, ³Laboratory of Biosensors and Bioelectronics,ETH Zürich

Summary

The protocol presents a complete workflow for soft material nanoindentation experiments, including hydrogels and cells. First, the experimental steps to acquire force spectroscopy data are detailed; then, the analysis of such data is detailed through a newly developed open-source Python software, which is free to download from GitHub.

Abstract

Nanoindentation refers to a class of experimental techniques where a micrometric force probe is used to quantify the local mechanical properties of soft biomaterials and cells. This approach has gained a central role in the fields of mechanobiology, biomaterials design and tissue engineering, to obtain a proper mechanical characterization of soft materials with a resolution comparable to the size of single cells (μm). The most popular strategy to acquire such experimental data is to employ an atomic force microscope (AFM); while this instrument offers an unprecedented resolution in force (down to pN) and space (sub-nm), its usability is often limited by its complexity that prevents routine measurements of integral indicators of mechanical properties, such as Young's Modulus (E). A new generation of nanoindenters, such as those based on optical fiber sensing technology, has recently gained popularity for its ease of integration while allowing to apply sub-nN forces with µm spatial resolution, therefore being suitable to probe local mechanical properties of hydrogels and cells.

In this protocol, a step-by-step guide detailing the experimental procedure to acquire nanoindentation data on hydrogels and cells using a commercially available ferrule-top optical fiber sensing nanoindenter is presented. Whereas some steps are specific to the instrument used herein, the proposed protocol can be taken as a guide for other nanoindentation devices, granted some steps are adapted according to the manufacturer's guidelines. Further, a new open-source Python software equipped with a user-friendly graphical user interface for the analysis of nanoindentation data is presented, which allows for screening of incorrectly acquired curves, data filtering, computation of the contact point through different numerical procedures, the conventional computation of E, as well as a more advanced analysis particularly suited for single-cell nanoindentation data.

Introduction

The fundamental role of mechanics in biology is nowadays established¹^,². From whole tissues to single cells, mechanical properties can inform about the pathophysiological state of the biomaterial under investigation³^,⁴. For example, breast tissue affected by cancer is stiffer than healthy tissue, a concept that is the basis of the popular palpation test⁵. Notably, it has been recently shown that the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is underlined by changes in the mechanical properties of blood cells, including decreased erythrocyte deformability and decreased lymphocyte and neutrophil stiffness as compared to blood cells from SARS-CoV-2-naïve individuals⁶.

In general, the mechanics of cells and tissues are inherently intertwined: each tissue has specific mechanical properties that simultaneously influence and depend on those of the constituent cells and extracellular matrix (ECM)⁵. Because of this, strategies to study mechanics in biology often involve engineering substrates with physiologically relevant mechanical stimuli to elucidate cell behavior in response to those stimuli. For example, the seminal work by Engler and colleagues demonstrated that mesenchymal stem cell linage commitment is controlled by matrix elasticity, as studied on soft and stiff two-dimensional polyacrylamide (PAAm) hydrogels⁷.

Many strategies to mechanically characterize the biomaterial under investigation exist, varying in spatial scale (i.e., local to bulk) and in the mode of deformation (e.g., axial vs shear), consequently yielding different information, which needs careful interpretation³^,⁸^,⁹^,¹⁰. The mechanics of soft biomaterials is commonly expressed in terms of stiffness. However, stiffness depends on both material properties and geometry, whereas elastic moduli are fundamental properties of a material and are independent of the material's geometry¹¹. As such, different elastic moduli are related to the stiffness of a given sample, and each elastic modulus encompasses the material's resistance to a specific mode of deformation (e.g., axial vs shear) under different boundary conditions (e.g., free expansion vs confinement)¹¹^,¹². Nanoindentation experiments allow the quantification of mechanical properties through the E which is associated with uniaxial deformation (indentation) when the biomaterial is not laterally confined¹⁰^,¹¹^,¹².

The most popular method to quantify E of biological systems at the microscale is AFM¹³^,¹⁴^,¹⁵^,¹⁶. AFM is an extremely powerful tool with force resolution down to the pN level and spatial resolution down to the sub-nm scale. Further, AFM offers extreme flexibility in terms of coupling with complementary optical and mechanical tools, extending its capabilities to extract a wealth of information from the biomaterial under investigation¹³. Those attractive features, however, come with a barrier-to-entry represented by the complexity of the experimental set-up. AFM requires extensive training before users can acquire robust data, and its use for everyday mechanical characterization of biological materials is often unjustified, especially when its unique force and spatial resolutions are not required.

Because of this, a new class of nanoindenters has recently gained popularity due to their ease of use, while still offering AFM-comparable data with sub-nN force resolution and µm spatial resolution, reflecting forces exerted and perceived by cells over relevant length scales². Particularly, ferrule-top nanoindentation devices based on optical fiber sensing technology¹⁷^,¹⁸ have gained popularity among researchers active in the field of mechanobiology and beyond; and a wealth of works reporting the mechanical properties of biomaterials using these devices, including cells¹⁹^,²⁰, hydrogels⁸^,²¹, and tissues²²^,²³ have been published. Despite the capabilities of these systems to probe local dynamic mechanical properties (i.e., storage and loss modulus), quasi-static experiments yielding E remain the most popular choice⁸^,¹⁹^,²⁰^,²¹. In brief, quasi-static nanoindentation experiments consists of indenting the sample with a constant speed up to a set-point defined either by a maximum displacement, force, or indentation depth, and recording both the force and the vertical position of the cantilever in so-called force-distance (F-z) curves. F-z curves are then converted into force-indentation (F-δ) curves through the identification of the contact point (CP), and fitted with an appropriate contact mechanics model (usually the Hertz model¹³) to compute E.

While the operation of ferrule-top nanoindenters resembles AFM measurements, there are specificities worth considering. In this work, a step-by-step guide to robustly acquire F-z curves from cells and tissue-mimicking hydrogels using a commercially available ferrule-top nanoindenter is provided, in order to encourage standardization of experimental procedures between research groups using this and other similar devices. In addition, advice on how to best prepare hydrogel samples and cells to perform nanoindentation experiments is given, together with troubleshooting tips along the experimental pathway.

Furthermore, much of the variability in nanoindentation results (i.e., E and its distribution) depends on the specific procedure used to analyze data, which is non-trivial. To address this issue, instructions for the use of a newly developed open-source software programmed in Python and equipped with a user-friendly graphical user interface (GUI) for batch analysis of F-z curves are provided. The software allows for fast data screening, filtering of data, computation of the CP through different numerical procedures, the conventional computation of E, as well a more advanced analysis named the elasticity spectra²⁴, allowing to estimate the cell's bulk Young's modulus, actin cortex's Young's modulus, and actin cortex's thickness. The software can be freely downloaded from GitHub and can be easily adapted to analyze data originating from other systems by adding an appropriate data parser. It is emphasized that this protocol can be used for other ferrule-top nanoindentation devices, and other nanoindentation devices in general, granted some steps are adapted according to the specific instrument's guidelines. The protocol is schematically summarized in Figure 1.

Protocol

1. Preparation of substrates/cells for nanoindentation measurements Follow the steps given in the Supplementary Protocol for preparation of PAAm hydrogels/cells for nanoindentation experiments. The procedure is summarized in Figure 2. NOTE: PAAm hydrogels have been chosen as they are the most common hydrogels used within the field of mechanobiology. However, the protocol is equally applicable to any type of hydrogel25</su…

Representative Results

Following the protocol, a set of F-z curves is obtained. The dataset will most likely contain good curves, and curves to be discarded before continuing with the analysis. In general, curves should be discarded if their shape is different from the one shown in Figure 4A. Figure 5AI shows a dataset of ~100 curves obtained on a soft PAAm hydrogel of expected E 0.8 KPa35 uploaded in the NanoPrepare GUI. Most curves present a…

Discussion

This protocol shows how to robustly acquire force spectroscopy nanoindentation data using a commercially available ferrule-top nanoindenter on both hydrogels and single cells. In addition, instructions for the use of an open-source software programmed in Python comprising a precise workflow for the analysis of nanoindentation data are provided.

Critical steps in the protocol
The following steps have been identified to be of particular importance when following this proto…

Declarações

The authors have nothing to disclose.

Acknowledgements

GC and MAGO acknowledge all members of the CeMi. MSS acknowledges support via an EPSRC Programme Grant (EP/P001114/1).

GC: software (contribution to software development and algorithms), formal analysis (analysis of nanoindentation data), validation, Investigation (nanoindentation experiments on polyacrylamide gels), data curation, writing (original draft, review and editing), visualization (figures and graphs). MAGO: investigation (preparation of gels and cells samples, nanoindentation experiments on cells), writing (original draft, review and editing), visualization (figures and graphs). NA: validation, writing (review and editing). IL: software (contribution to software development and algorithms), validation, writing (review and editing); MV: conceptualization, software (design and development of original software and algorithms), validation, resources, writing (original draft, review and editing), supervision, project administration, funding acquisition MSS: resources, writing (review and editing), supervision, project administration, funding acquisition. All authors read and approved the final manuscript.

Materials

12 mm coverslips	VWR	631-1577P
35 mm cell treated culture dishes	Greiner CELLSTAR	627160
Acrylamide	Sigma-Aldrich	A4058
Acrylsilane	Alfa Aesar	L16400
Ammonium Persulfate	Merk	7727-54-0
Bisacrylamide	Merk	110-26-9
Chiaro nanoindenter	Optics 11 Life		no catologue number
Ethanol			general
Fetal bovine serum	Gibco	16140071
High glucose DMEM	Gibco	11995065
Isopropanol			general
Kimwipe	Kimberly Clark	21905-026
Microscope glass slides	VWR	631-1550P
MilliQ system	Merk Millipore	ZR0Q008WW
OP1550 Interferometer	Optics11 Life		no catalogue number
Optics 11 Life probe (k = 0.02-0.005 N/m, R = 3-3.5 um)	Optics 11 Life		no catologue number
Optics 11 Life probe (k = 0.46-0.5 N/m, R = 50-55 um)	Optics 11 Life		no catologue number
Penicillin/Streptomycin	Gibco	15140122
RainX rain repellent	RainX	26012
Standard petri dishes (90 mm)	Thermo Scientific	101RTIRR
Tetramethylethylenediamine	Sigma-Aldrich	110-18-9
Vaccum dessicator	Thermo Scientific	531-0250
Software
Data acquisition software (v 3.4.1)	Optics 11 Life
GitHub Desktop (Optional)	Microsoft
Python 3	Python Software Foundation
Visual Studio Code (Optional)	Microsoft

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Ciccone, G., Azevedo Gonzalez Oliva, M., Antonovaite, N., Lüchtefeld, I., Salmeron-Sanchez, M., Vassalli, M. Experimental and Data Analysis Workflow for Soft Matter Nanoindentation. J. Vis. Exp. (179), e63401, doi:10.3791/63401 (2022).