Accepted Manuscript Title: Potential and Limitations of Microscopy and Raman Spectroscopy for Live-Cell Analysis of 3D Cell Cultures Author: Verena Charwat Karin Sch¨utze Wolfgang Holnthoner Antonina Lavrentieva Rainer Gangnus Pablo Hofbauer Claudia Hoffmann Brigitte Angres Cornelia Kasper PII: DOI: Reference:

S0168-1656(15)00052-8 http://dx.doi.org/doi:10.1016/j.jbiotec.2015.02.007 BIOTEC 7012

To appear in:

Journal of Biotechnology

Received date: Revised date: Accepted date:

28-8-2014 26-1-2015 2-2-2015

Please cite this article as: Charwat, V., Sch¨utze, K., Holnthoner, W., Lavrentieva, A., Gangnus, R., Hofbauer, P., Hoffmann, C., Angres, B., Kasper, C.,Potential and Limitations of Microscopy and Raman Spectroscopy for Live-Cell Analysis of 3D Cell Cultures, Journal of Biotechnology (2015), http://dx.doi.org/10.1016/j.jbiotec.2015.02.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Journal: Journal of Biotechnology (Elsevier), Special Issue “S.I. 3D Cell Culture 2014”

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Title:

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Potential and Limitations of Microscopy and Raman Spectroscopy for LiveCell Analysis of 3D Cell Cultures

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Authors

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Verena Charwat (1), Karin Schütze (2), Wolfgang Holnthoner(3), Antonina Lavrentieva (4), Rainer Gangnus (2), Pablo Hofbauer (3), Claudia Hoffmann (5), Brigitte Angres (5), Cornelia Kasper (1)

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Affiliations

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(1)

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(2)

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(3) Ludwig-Boltzmann-Institute for Experimental and Clinical Traumatology, Austrian

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niversity of Natural Resources and Life Sciences, Department of Biotechnology, Vienna, Austria

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Cluster for Tissue Regeneration, Vienna, Austria

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eibniz University of Hannover, Institute for Technical Chemistry, Hannover, Germany C

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ellendes GmbH, Reutlingen, Germany

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 We address the timely and highly relevant topic of applying 2D detection methods to 3D samples.

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ellTool GmbH, Bernried, Germany

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Highlights

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Potential and Limitations of Microscopy and Raman Spectroscopy for Live-Cell Analysis of 3D Cell Cultures

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 We critically examine the analytical approaches currently found in research publications on 3D cell cultures and identify possible limitations as well as the need for future developments.

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 We focus on live-cell analytical methods, which enable monitoring of dynamic changes within 3D cell cultures as opposed to endpoint detection methods.

3  Exemplarily we address two cell analytical methods and examine their applicability to 3D samples: microscopy, which is the most widely used cell analytical methods and Raman spectroscopy, a more recently introduced method for cell analysis with great potential to perform 3D cell analysis with high sensitivity.

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Abstract

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Today highly complex 3D cell culture formats that closely mimic the in vivo situation are

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increasingly available. Despite their wide use, the development of analytical methods and

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tools that can work within the depth of 3D-tissue constructs lags behind. In order to get the

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most information from a 3D cell sample, adequate and reliable assays are required. However,

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the majority of tools and methods used today have been originally designed for 2D cell

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cultures and translation to a 3D environment is in general not trivial. Ideally, an analytical

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method should be non-invasive and allow for repeated observation of living cells in order to

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detect dynamic changes in individual cells within the 3D cell culture. Although well-

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established laser confocal microscopy can be used for these purposes, this technique has

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serious limitations including penetration depth and availability. Focusing on two relevant

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analytical methods for live-cell monitoring, we discuss the current challenges of analyzing

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living 3D samples: Microscopy, which is the most widely used technology to observe and

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examine cell cultures, has been successfully adapted for 3D samples by recording of so-called

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“z-stacks”. However the required equipment is generally very expensive and therefore access

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is often limited. Consequently alternative and less advanced approaches are often applied that

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cannot capture the full structural complexity of a 3D sample. Similarly, image analysis tools

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for quantification of microscopic images range from highly specialized and costly to

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simplified and inexpensive. Depending on the actual sample composition and scientific

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question the best approach needs to be assessed individually. Another more recently

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introduced technology for non-invasive cell analysis is Raman micro-spectroscopy. It enables

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label-free identification of cellular metabolic changes with high sensitivity and has already

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been successful applied to 2D and 3D cell cultures. However, its future significance for cell

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analysis will strongly depend on the availability of application oriented and user-friendly

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systems including specific tools for easy analysis and interpretation of spectral data focusing

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on biological relevant information.

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1. Introduction

3D cell cultivation provides well known benefits of improved physiological relevance

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compared to standard 2D cultivation. Therefore 3D culture formats are becoming more

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frequently used in various fields of bio-medical research including in vitro drug and toxin

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testing and tissue engineering for regenerative medicine. While multiple culture formats have

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been developed for 3D cell cultivation, the available tools for analysis of 3D culture are still

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very limited. Most of the currently used bioassays for monitoring and analysis of 3D cell

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cultures have been developed for 2D culture formats Therefore their utility for 3D samples

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such as 3D cell cultures or 3D tissue analogues is often inferior. For example, most of the

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currently used analytical methods rely on antibody-based markers, which can easily access

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cells of 2D cultures but face difficulties in penetrating 3D-cultures. In addition, cells and

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especially delicate primary cells are sensitive towards any chemical substance and may

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change their features drastically. Furthermore, many bioassays are designed as endpoint tests,

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where in general a quite large amount of cells is required which have to be killed (e.g. fixed or

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lysed) in order to be analyzed. Thereby only one single data point can be obtained from each

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batch, which makes it very difficult and time consuming – or in the worst case even

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impossible, to observe dynamic changes in the culture over time. When using an endpoint

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detection method, separate batches have to be prepared for each time point that shall be

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investigated (e.g. different exposure times to a drug or toxic chemical). For many applications

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it is preferable to observe dynamics of the same cell population over time. In order to achieve

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this goal non-invasive live-cell assays need to be developed, which allow to monitor (or to

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observe) cellular changes without influencing the cells and enable data acquisition with good

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time and spatial resolution.

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In this review we present two examples of approaches for live-cell analysis that originate in

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2D analysis and are currently also employed for monitoring of 3D cell cultures: Microscopy is

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the most widely used technology to obtain information from in vitro cell cultures. Currently, a

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wide range of set ups for continuous cell imaging is available. Importantly, the spectrum of

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imaging technologies does not only include optical approaches based on light scattering or

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fluorescence detection (brightfield and phase contrast microscopy, standard fluorescence

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microscopy, confocal microscopy, multi-photon microscopy, optical coherence tomography).

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Other relevant imaging approaches are based on ionizing radiation (x-ray, computed

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tomography, positron emission tomography), magnetic fields (magnetic resonance imaging)

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or ultrasound. The different imaging platforms vary greatly in price, complexity, penetration

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depth, resolution, image quality and range of application. Furthermore, the complex structure

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of live 3D samples poses additional challenge not only for image acquisition but also for

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image analysis and quantification. The most suitable technological approach needs to be

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selected based on the properties (e.g. size, optical characteristics, thickness) of the whole

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sample and the features of interest(Lodish et al., 2000). Several recent review articles provide

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a detailed evaluation of different imaging approaches for 3D cell samples (Baker, 2010; Graf

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and Boppart, 2010; Nam et al., 2014). A common drawback of most imaging platforms is

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their high cost for installation as well as operation. Especially when living cultures should be

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monitored over extended time periods, access to equipment is often a limiting factor.

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Although in most institutions one or more imaging technologies suitable for analysis of 3D

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samples are available, the equipment can usually not be used exclusively for a single long-

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term experiment. Therefore in this review article we point-out incubator microscopes as a low

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cost alternative for long-term live cell imaging that can easily be established in any cell

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culture laboratory. We describe different available platforms and imaging set ups and

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highlight their potential and limitations for analysis of 3D cell samples.

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In addition to the widely used method of microscopic cell imaging, we present and discuss

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Raman spectroscopy as a recently developed and more advanced technology for non-invasive

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cell analysis. Raman spectra contain a complex “fingerprint” of the analyzed cells, which is

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composed of information about cellular components such as proteins, nucleic acids and lipids.

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Therefore Raman spectra can be used to identify different cell types and altered phenotypes

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with high sensitivity. In order to evaluate the applicability of Raman spectroscopy for analysis

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of live 3D samples, first the penetration depth as well as sensitivity and specificity within a

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complex 3D environment need to be explored. We identify which limitations occur in a 3D

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setting and highlight current efforts to adapt and optimize the technology.

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2. Live-cell imaging and image analysis of 3D cell cultures 2.1. Requirements for microscopy of live 3D samples

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Recent and ongoing research efforts result in fast technological advances, which promote the

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creation of cell assays of increased complexity and improved biological relevance. Most

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prominently, 3D cell culture has been introduced as a highly promising approach for

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improved basic biological science, drug testing as well as tissue engineering. Despite the

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increasingly widespread use of 3D culture formats, the available analysis methods are still

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very limited. In current publications microscopy is still the prevailing – and often only –

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methods of analyzing 3D cell cultures. Therefore finding adequate imaging solutions is an

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important precondition for successful use of 3D cultures, especially in academic settings

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where financial means for sophisticated but expensive systems are usually limited. Compared page 5 Page 5 of 35

to 2D cell culture, 3D samples pose a much higher challenge for microscopy, since the

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information is not contained within a single plane but is distributed over a range of equally

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important planes in z-direction. For 2D samples information of geometric relation between

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the different structural components can be easily captured, visualized and analyzed by taking

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a single image from the plane of interest. In contrast, this is not possible for 3D samples. Here

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the morphological details can be adequately represented not on a single image but a sequence

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of images at different focus depth (z-stack) is required to reveal the full complexity of the

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specimen. However, depending on available instrumentation, acquiring z-stacks of 3D cell

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cultures is not always feasible. Therefore often 2D imaging techniques are applied to 3D

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structures. This approach is reasonable if a) the sample thickness is relatively thin b) only

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representative cross-section of the sample is required or c) if the focus plane is carefully

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chosen to highlight and analyze one feature of the sample (e.g. diameter of spheroid cultures).

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Therefore, depending on the sample properties and question to be answered 2D imaging of

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3D samples may or may not be appropriate. For example, one key question in most cell

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culture experiments is cell number or cell proliferation.

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proliferation can be monitored without problems and is usually estimated as the mean of cell

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confluence (referring to the percentage of covered culture flask bottom area). Cell growth in

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three-dimensional (3D) cell cultures (e.g. cells growing in spheroids) generally cannot be that

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easily determined. For spheroid cultures, often the spheroid diameter (or volume calculated

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from the diameter) is used as a measure to compare proliferation between different samples.

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(Tung et al., 2011) In order to obtain a better understanding of spheroid growth dynamics it

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can be helpful to follow diameter changes over the cultivation time (live-cell time-lapse

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microscopy) instead of taking only a single end-point image.

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2.2. Platforms for live-cell imaging of 3D samples

In monolayer cultures, cell

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In the following an overview of different equipment for time-lapse imaging and their potential

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for application to 3D cell cultures is provided. Although laser confocal microscopy seems to

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be the first choice for 3D imaging, several limitations like penetration depth of only 100 µM

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(Smithpeter et al., 1998), expensiveness and availability makes it difficult to use this

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technique routinely. Recent developments of equipment and software provide now the

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opportunity to perform time-lapse microscopy following computer analysis. Currently, there

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are several commercially available solutions for time-lapse microscopy: (I) heating/gas

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incubation (environmental) chamber systems which can be installed on standard microscope

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tables, (II) miniaturized incubators with integrated camera and (III) humidity-resistant

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incubator microscopes. Although being reasonable in price, heating chambers need to be

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installed on the lab microscope, which makes it difficult to perform long-term time-lapse

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imaging, moreover, humidifiers, gas mixers and pH controllers, as well as suitable software

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must be additionally purchased. Other stand-alone systems provide costumer tailored

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equipment that allows the combination of high end 3D imaging (z-stacks from (spinning disk)

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confocal microscopy, widefield and high content screening systems) including image analysis

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software, live-cell imaging in cell cultivation chambers and automated sample handling steps

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(e.g. Operetta, PerkinElmer, USA; UltraVIEW VoX, PerkinElmer, USA). The Japanese

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company Yokogawa developed a confocal quantitative image cytometer CQ1, which can

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quantify the morphological features of 3D cell spheroids and tissue sections. However, while

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extremely useful for industry, these systems are usually difficult to afford in an academic

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setting. Even if available, operating costs are often high and systems are not intended for

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long-term use by a single operator. Similarly, incubators with integrated cameras (e.g. Cell-

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IQ® - Imaging and Automated Analysis System, CM Technologies, Finland; Cytell Cell

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Imaging System, GE Healthcare Life Science, UK; BioStation IM-Q, Nikon, Japan) provide

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fully automated cell imaging with various fluorescence filters and possibility of Z-stack image

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acquisition. However, also for these systems a major limitation is their high price. Widely

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distributed and reasonably priced (typically 10.000 to 20.000 USD) are incubator microscopes

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– compact, humidity and high temperature resistant microscopes with bright field, phase

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contrast or fluorescence microscopy features. In the following we focus on the potential and

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limitations of incubator microscopes for 3D live cell samples.

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There are several suppliers of incubator microscopes. Table 1 provides an overview of three

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commercially available incubator microscopes and their technical specifications. JuLITM

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(Just Look At It) Live Fluorescence Cell Imaging system (NanoEnTek, Seoul, Republic of

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Korea) was the first commercially available incubator microscope (Choi et al., 2014) (Figure

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1A). This microscope can be placed into an incubator and can be provided with fixed 10x

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objective or fixed 4x objective each one in 2 different combinations: bright field/ blue LED

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(GFP, Emission 520 nm) or bright field/ green LED (RFP, Emission 590 ± 40 nm). Another

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small incubator microscope is LumaScope manufactured by Etalumna (Carlsbad, CA, USA)

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(Gouveia et al., 2013; Kahle et al., 2011; Zhang et al., 2014) (Figure 1B). LumaScope offers

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bright field, phase contrast and 3-color fluorescence, with objectives that can be changed

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manually (4x, 10x, 20x, 40x, 100x). Time lapse images can be acquired from three filters

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simultaneously and combined with bright field/phase contrast image. A USB interface enables

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remote cell monitoring. A third incubator microscope recently (fall 2013) introduced to the

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market is CytoMate provided by CytoMate Technologies BV, The Netherlands (Figure 1C).

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The device can be remotely operated and collected data are stored on the CytoMate fee-based

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cloud server (https://www.cytomate.com/). The CytoMate system has no fluorescence

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detection, but includes a temperature sensor and provides automated image analysis to

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identify cell confluence. Even cheaper options (down to 10 USD) of in situ cell culture

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monitoring involve different kinds of self-made microscopes or adapters for cell-phone

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cameras. These systems however, require technical skills for fabrication and generally have

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fewer features and inferior image quality compared to off-the-shelf solutions (Kim et al.,

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2012; Smith et al., 2011).

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Clearly, incubator microscopes have not been specifically designed for monitoring of 3D

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structures and provide only a very limited possibility to obtain z-stack images. All the more, it

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is interesting, that many of the articles using incubator microscopes for in situ cell imaging

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present examples of 3D applications. These include imaging of plant sections, rainwater

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microorganisms and fluorescently labeled C. elegans worms (Kahle et al., 2011) as well as

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observation and counting of metastases in mouse lungs (Zhang et al., 2014). Importantly, the

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full complexity of a 3D specimen can only be grasped with more costly imaging equipment

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that enables reconstruction of 3D images from z-stacks. However, existing literature examples

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suggest that 2D time-lapse imaging of 3D samples provides useful information for certain

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applications.

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Apart from difficulties of image acquisition, another issue arises with microscopy of 3D

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samples: While microscopic images hold valuable qualitative information such as

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morphology, geometric relation of the imaged objects to each other, presence of certain

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biomarkers in immunofluorescence images, they do not provide quantitative data. Therefore,

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images are usually difficult to analyze and compare. In order to overcome this limitation,

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image analysis software has been developed concurrently with image acquisition hardware.

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Different free and commercial products for image analysis are available today that enable

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quantification of certain parameters from cell culture images (Baker, 2010). For example

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particle counting, area measurements and fluorescence signal quantification are among the

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most widely use approaches to obtain quantitative information from images. Here again, 3D

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samples pose an even greater challenge than traditional 2D samples. For example out-of-focus

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structures can produce high background signals and even if equipment for obtaining z-stacks

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is available, usually resolution in z-direction is a lot lower than x-y resolution. In order to get

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most information from 3D samples, in the past few years, considerable effort has been put

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into developing tools for the accurate and efficient quantification of cellular structures in a 3D

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setting with varying degrees of success. Several sophisticated and less sophisticated methods

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have already been developed by different companies and institutes(Baker, 2010). Popular

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examples include MetaMorph® (Molecular Devices LLC, Sunnyvale, CA, USA), Volocity®

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(PerkinElmer, USA), Photoshop (Adobe Systems Inc., San Jose, CA, USA), MatLab with

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add-on image analysis tools (MathWorks, Natick, MA, USA) as well as freely available

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software such as ImageJ (National Institutes of Health (NIH), Bethesda, MD, USA) and

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CellProfiler™ (Broad Institute, MA, USA). Many other software tools have been developed

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for special applications. For example, Wimasis (Wimasis GmbH, Munich, Germany) and

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Angiosys (TCS Cellworks, Buckingham, UK) provide tools for quantification of tube

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formation assays based on image analysis. Depending on a variety of factors, including the

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experimental setup based on the final research question, the costs as well as the usability and

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the accuracy researchers have employed or preferred different solutions. Generally, all these

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methods involve the analyses of as many 2D images as possible such as the analysis of z-

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stacks. Evidently, these different methods and solutions have advantages and disadvantages

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that have to be taken into account. Herein, exemplified by an established in vitro endothelial

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cell (EC)/adipose-derived stem cell (ASC) co-culture model in a 3D fibrin scaffold

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(Holnthoner et al., 2012) we discuss the potential benefits and drawbacks of several available

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methods in a 3D setting (Holnthoner et al., 2012; Jones et al., 2008; Khoo et al., 2011;

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Lamprecht et al., 2007). We hereby focus on approaches that can be useful in settings without

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previous access to proprietary image analysis solutions. High-budget solutions as well as

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approaches that require extensive bioinformatics knowledge of the operator were omitted.

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The first method for the analysis of microvascular structures described here is the use of

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Photoshop and Angiosys software on a co-culture model in fibrin (Holnthoner et al., 2012).

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This model, described briefly, consists of ECs and ASCs embedded in a fibrin matrix

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eventually leading to a capillary network which displays varying degrees of tube lengths,

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amount of junctions and other vascular parameters. Unfortunately, most software including

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Angiosys and Wimasis will have troubles gathering all structures accurately from the

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microscopic image. This is mostly due to the inconsistently stained regions that inevitably

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arise from most immunohistochemical stainings and non-specific binding of antibodies

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producing background. As a consequence, the resulting accuracy of the analyses is directly

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dependent on the quality of the raw image. However, even with a high-quality image false

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positive and false negative results will likely arise. 3D vascular tubes run through the entire

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scaffold and some out-of-focus-tubes still lead to false values in terms of tube length, number

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of tubules, number of junctions, mean tube length and other parameters. Thus, correct image

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processing is a prerequisite for further analysis. Based on the protocol of Khoo et al. (Khoo et

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al., 2011) we developed a pipeline by which an image can be enhanced in a way that the

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resulting analysis by Angiosys software leads to the most accurate result possible. This

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Photoshop pipeline comprises the adjustment of brightness and contrast, if needed, and

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duplicating the image into a second layer where all further steps will be performed (Image A,

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Figure 2). Subsequently, the image is desaturated (ImageAdjustmentsDesaturate; Image

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B),

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(ImageAdjustments Levels; Image D). Moreover, Photoshop provides the possibility of

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saving the levels in a preset that can then be easily applied to all following images to be

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analyzed in order to maintain reproducibility. After adjusting the levels, a threshold is applied

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(ImageAdjustmentsThreshold; Image E), Gaussian blur with a radius of 3 pixel is used

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(ImageFilterBlurGaussian Blur; Image F), and finally another threshold is employed.

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This creates a black and white image that still has to be manually adjusted (Image G) to match

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the accurate representation of the tubular network using the pencil tool while having the

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original image shine through from the bottom layer. By painting either black or white on the

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modified layer while having the bottom layer with the original image shine through with the

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“Darken” and “Lighten” option of Photoshop, it is possible to manually correct the image and

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maintain the scientific integrity. This final image is seen in Image H. All in all, this system of

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manually enhancing the images in Photoshop and analyzing them in Angiosys allows for the

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most accurate acquisition of structures by introducing corrections manually, regardless of the

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image background and staining quality. Generally, the better the staining and image quality,

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the shorter it takes to process and analyze the image. Using Photoshop and Angiosys has great

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benefits but its main drawback is that it is very time consuming. However, it involves human

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interaction and therefore introduces possible bias that has to be averted by randomizing the

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pictures.

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Using Wimasis to analyze structures in 3D on the other hand brings other advantages and

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disadvantages. Wimasis functions by uploading images to a server that does the identification

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of objects and their analysis. These analysis algorithms are based on images obtained from

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over 100 institutes in order to make their results reproducible. Its algorithms include filtering,

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segmenting, object recognition and their processing (Khoo et al., 2011). The main benefits of

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Wimasis encompass the possibility of uploading as many images as you want anytime and

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anywhere and the relatively fast analysis time. However, the lack of transparency leads to a

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company dependency and could make reproducibility difficult. Most importantly however,

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the costs are calculated per picture and thus a great number of analyses can get very

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expensive. Additionally, the software, which has been designed for quantification of images

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from 2D tube-formation assays, does not recognize all the structures correctly either.

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Therefore, Photoshop enhancement is needed as well and thus leads to a more time-

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consuming procedure.

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Other methods include the employment of CellProfiler or ImageJ. CellProfiler allows the user

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to set a pipeline of different actions (e.g. skeletonizing the tubular structures and obtaining a

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black and white image of the network) that can be applied to an indefinite amount of images,

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thereby ensuring a high degree of reproducibility (Jones et al., 2008; Lamprecht et al., 2007).

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This can also be achieved in a similar way with ImageJ macros. By programming a series of

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commands one is able to always apply the same actions on a stack of pictures. Nevertheless,

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these two programs also introduce false results partly due to inconsistent staining and

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backgrounds.

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In summary, available methods are based on acquiring and analyzing one or several 2D

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images to be able to get an idea of the 3D results. In order to choose the right methods, it is

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necessary to consider a variety of parameters such as experimental setup, costs, time, usability

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and accuracy. Simpler and less expensive approaches are often limited by the fact that they

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apply 2D analysis to a 3D problem. Future research and development activities should

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therefore be targeted at the establishment of novel low-cost and flexible tools that take into

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account the specialties of 3D cell cultures. Accessibility of affordable, reliable and accurate

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image analysis for 3D cell assays is essential exploit the full benefit of 3D cell cultivation.

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Future improvements should also include faster analysis of images to allow the incorporation

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of more images in a shorter period of time into the analysis. This would make image analysis

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better compatible with time-lapse microscopy and help to better analyze and understand

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dynamic processes within a 3D environment.

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3. Raman spectroscopy – a novel tool for live-cell analysis and its applicability to 3D cell cultures 3.1. The potential of Raman spectroscopy for analysis of biological samples page 13 Page 13 of 35

Despite the limitations associated with imaging and quantification of 3D samples, microscopy

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is still the most widely used technology for analysis of 3D cell cultures. In order to obtain a

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deeper insight into the characteristics of 3D cell cultures, additional analytical methods are

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required. So far many different assays that have been originally designed for 2D cell cultures

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have been applied to 3D cultures with varying success. Here we present Raman spectroscopy

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as a recently developed label-free approach for cellular analysis of living or fixed cells with

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great potential for 3D cell culture analysis.

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In short, the principle of Raman spectroscopy is the recording of scattered photons caused by

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collision of incident photons with target molecules, which ultimately results in an energy

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transfer. This so-called “inelastic” scattering is a very rare event, and only one of ten million

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impinging photons will result in Raman scattering (Movasaghi et al., 2007). For the present

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work, we used Stokes-Raman, where the scattered photons have a lower frequency, i.e. less

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energy, compared to the incident photons. Thus, only photons with a wavelength above the

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incident 785 nm laser wavelength are collected and form the Raman spectrum. All

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biomolecules within the focus of the laser beam contribute to a sum spectrum (Ferraro, 2003),

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which is a unique fingerprint and may serve as “photonic” marker – a process also known as

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“photonic fingerprinting®”. When applied to cells, Raman spectra contain information of

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different cellular components such as proteins, nucleic acids and lipids, which makes the

19

method highly sensitive towards the metabolic status of a cell. Importantly, Raman

20

spectroscopy can be performed within liquids, which enables analysis of fixed as well as

21

living cells. This provides a wide range of possibilities in biological research and medical

22

applications. In particular, Raman spectroscopy enables simultaneous investigation of cellular

23

components and gives insight into the cells metabolism in a completely non-destructive way.

24

Thus, changes in molecular composition and activation can be measured coming along with

25

cell cycle, cell differentiation or are induced by drug exposure or environmental impact

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(Brauchle et al., 2014; Ichimura et al., 2014; Pudlas et al., 2011; Schütze et al., 2013). As

2

Raman spectroscopy measures completely non-invasively, cells are unaffected and the same

3

sample can be re-measured several times or used for further investigations.

4

In recent years numerous publications appeared that clearly demonstrated the feasibility of

5

Raman spectroscopy to identify and examine cells. For example, Raman spectroscopy

6

discriminates fibroblasts from mesenchymal stem cells (MSC), which cannot be distinguished

7

by standard techniques such as antibody-based fluorescence labeling because common

8

markers are not specific. Classical differentiation assays usually require 2–3 weeks. In

9

comparison, Raman spectra are taken in minutes. Osteogenic differentiation in MSC was

10

detected at day 7 whilst common methods require long-term cultivation periods of 21–28 days

11

(Pudlas et al., 2011; Schütze et al., 2013). In the study of Brauchle et al (Brauchle et al.,

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2014), room temperature (RT)-induced apoptosis and heat-triggered necrosis were analyzed in

13

individual Saos-2 and SW-1353 cells using Raman micro-spectroscopy. A targeted analysis

14

on early and late apoptosis as well as necrosis was facilitated based on the combination of

15

Raman spectroscopy with fluorescence microscopy. Spectral shifts were identified in the two

16

cell lines that reflect biochemical changes specific for either RT-induced apoptosis or heat-

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mediated necrosis. A supervised classification model specified apoptotic and necrotic cell

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death based on single cell Raman spectra.

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3.2. Technological approaches to make Raman spectroscopy amenable to the biomedical field

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Given the outlined advantages, sensitivity and potential of Raman spectroscopy, the method

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can be envisioned to become another of those emerging highly physical technologies like

23

MTR or PET that most probably once will be used in the hospital to support the pathologist at

24

appraisal of tissue sections (Diem et al., 2013), to find patient-specific most effective

25

anticancer drugs or to assist the surgeon during operation for minimal tumor tissue extraction page 15 Page 15 of 35

(Popp et al., 2014). However, despite its great potential and promising results Raman

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spectroscopy did not yet make its way into routine cell analysis and clinical diagnostics.

3

Herein a major factor is the highly interdisciplinary approach of applying a physical method

4

to a biological or medical problem. Biomedical scientists are often reluctant to embrace

5

highly physical methods, because they often lack the training for understanding and

6

interpretation of complex data such as Raman spectra. In order to read and interpret the

7

spectral peaks, usually collaboration with a physicist is required. Therefore in most cases

8

where Raman spectroscopy was applied to investigate biological questions, a physicist having

9

a Raman system available collaborated with a biologist or physician who provided the

10

samples. In order to make the technology more accessible for biomedical scientists, several

11

companies are working on the marketing of Raman systems tailored for application in the

12

biological and medical field. Scientists need a tool that can be integrated in routine lab work

13

handling cells cultured in common culture ware or on routine glass slides and ideally includes

14

software for simple analysis of the spectral information. Currently, available Raman

15

spectroscopy set ups for cell culture, bioprocessing and biomedical analysis include systems

16

from Kaiser Optical Systems, Inc. (Kaiser Raman probe; Michigan, U.S.A.) (Abu‐Absi et al.,

17

2011; Short et al., 2005), Thermo Fischer Scientific (DXR Raman microscope; Milan, Italy)

18

(Avti et al., 2013) Renishaw plc (Gloucestershire, U.K.) (Liu et al., 2008; McAughtrie et al.,

19

2013), Horiba (HORIBA Jobin Yvon S.A.S. Lille, France) (Sánchez et al., 2012) as well as

20

from Witec (WITec GmbH, Ulm, Germany) (Kann et al., 2014). Those systems were mainly

21

established to analyze metals, alloys, pure substances or compounds for example for quality

22

control in material science, chemistry or pharmacology but are now more and more are used

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for analyses of biological samples. In the following example the BioRam® system (CellTool

2

GmbH, Bernried, Germany) and its potential and limitations are described in greater detail.

3

BioRam® is a confocal Raman trapping microscope system that was especially developed to

4

fulfil the needs of biomedical research and applications. The applied near infrared 785 nm

5

laser wavelength is well tolerated by living cells and tissue. Various inserts have been

6

designed to hold commonly used consumables and lab ware. The inverted microscope

7

platform allows safe and comfortable working with cells in culture without danger of

8

contamination. All the complex Raman related issues are running in the background.

9

Specimen are simply placed on the microscope table and observed using bright field

10

illumination. Cells adherently growing can be pin-pointed prior to automated spectra retrieval.

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The microscope table moves with high precision from cell to cell. Furthermore, the user can

12

define areas of interest to automatically let the system run to retrieve spectra of an entire field

13

in a preselected pattern. Focusing of the Raman laser beam through the objective generates a

14

focal spot of about 1 µm in diameter, depending on the objective magnification. This enables

15

for example to distinguish between cytoplasm or nucleus – depending on the experiment and

16

questions to be answered (Figure 3). On the other hand, focusing of the laser beam generates

17

an electromagnetic gradient which can be used to align microorganisms or cells in suspension.

18

Floating particles are driven towards the laser focus, where they are trapped during Raman

19

spectra retrieval. In addition to the possibility to measure Raman spectra of suspended cells

20

the trapping effect in combination with a microfluidic set-up also enables cell sorting.

21

The application-oriented operation and data processing software allows quick access to the

22

required spectral information of cell state and fate. The position coordinates and a

23

microscopic image of analyzed cells as well as all relevant data acquisition information are

24

stored together with the spectral data. This enables to relocate for example adherent growing

25

cells in culture or suspended cells scattered in individual microwells for ongoing observation

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such as monitoring drug or toxin induced changes. As a special requirement in biomedical

2

application we have to deal with routine glass slides and the corresponding fluorescence.

3

Therefore, an algorithm was developed to subtract the glass background from the spectral

4

data. Data analysis is performed with Principal Component Analyses (PCA), which is a

5

multivariate technique and the most commonly used statistical tool for the analysis of spectral

6

data. Briefly, each Raman spectrum consists of many data points; however, only a few of

7

them contain useful information for biologically relevant cell analysis. PC Analysis inspects

8

the data set by reducing a multitude of statistic variables to a small number of significant

9

linear combinations (Principal components). This results in structured, simplified and

10

exemplified data sets without much lost information. At the end the PCA analysis searches for

11

differences in data sets. These differences are explained as principal components (PCs) and

12

are expressed as wavenumbers. For biological interpretation two graphic data representations

13

are of relevance – “Scores” and “Loadings”. The Score Plot is a 2D-diagram showing the

14

major PC in x-direction and the second major PC in y-direction. It enables visualization of

15

differences in the data sets. For example data points in Figure 6C marked as blue and red

16

dots) split into two groups. The “Loading” (e.g. see Figure 6B) shows the wavenumber areas

17

(mostly the prominent peaks) that are relevant for the discrimination of the two data sets.

18

Using this information re-analysis of the data sets are performed based on the identified

19

wavenumber areas. This allows isolating and assessing the discriminating wavenumber areas

20

(see black marked peaks in Figure 6B). An additional information is the orientation of the

21

peaks. The positive/negative orientation of the data points in the Scoreplot is linked to the

22

positive/negative orientation of the peaks in the loadings and therefore to its indicated

23

wavenumber area. The negative data points have higher Raman counts in the wavenumber

24

areas indicated through a negative peak in the loading and lower Raman counts on the

25

wavenumber areas indicated through a positive peak. The positive data points have higher

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Raman counts in the wavenumber areas indicated through a positive peak in the loading and

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lower Raman counts on the wavenumber areas indicated through a negative peak. Thereafter,

2

the identified spectral peaks are compared with literature to extract the biological relevant

3

information and to identify the biomolecules responsible for the discrimination. All data can

4

be stored in a data bank to quickly allocate further measured spectra to the corresponding cell

5

type. 3.3. Raman spectroscopy in a 3D environment

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Since 3D in vitro models are now widely developed to investigate mechanisms and

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interrelationships e.g. between tumor and stroma and to screen for drugs targeting this

9

interaction, novel analytical methods are required. Two examples will answer the question if

10

Raman spectroscopy can be used to analyze 3D cell cultures like spheroids and will show its

11

potential in a 3D environment.

12

The main question when talking about Raman in a 3D-environment is the penetration depth to

13

still get significant Raman spectra. To find out how “deep” we can measure we used

14

spheroids composed of cells of the breast epithelial tumor cell line MCF7 with a diameter of

15

about 500µm and compared Raman spectra from different focal planes. Figure 4 shows mean

16

spectra of cells located in 75µm, 150µm, 225µm and 300µm depth within the MCF-7

17

spheroid. All spectra show the same pattern. Only the peak height is reduced when getting

18

deeper into the spheroid which is a hint of decreasing spectral intensity. However, spectra at

19

300µm depth can still be used for detailed statistical analyses.

20

The second approach was to investigate cell-cell interaction in 3D hydrogel matrices. In this

21

study Raman spectroscopy was used to detect changes in fibroblasts and their

22

microenvironment when co-cultured with tumor cells in chemically defined hydrogels and

23

being compared to mono-cultured fibroblasts. The 3-D Life Hydrogel (Cellendes GmbH,

24

Reutlingen, Germany). based on dextran and polyethylenglycol is free of extracellular matrix

25

proteins and growth factors and thus provides a „clean“ background for the detection of

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newly synthesized extracellular components or changes in the microenvironment. MCF-7

2

breast epithelial tumor cells and primary human dermal fibroblasts were grown alone or in co-

3

culture for 14 days in dextran gels that had been modified by covalently attaching a peptide

4

containing the cell adhesion amino acid motif R-G-D and a matrix metalloprotease cleavable

5

crosslinker (Figure 5). Raman spectra were taken from mono-cultured and from co-cultured

6

fibroblasts and analyzed using PCA (details see chapter 3.2). PCA analysis performed on the

7

entire spectral range gave a strong hint that the metabolome of fibroblasts in mono-culture

8

differs from co-cultured cells (Figure 6A). The Scoreplot shows that mono-cultured cells

9

cluster in the lower-right area (red dots), whereas the co-cultured cells cluster more in the

10

upper-left region (blue squares). A subgroup of co-cultured cells was identified located within

11

the cluster of mono-cultured cells (see green circles). In order to find the discriminating

12

wavenumbers the information from “Loadings” (see black bars in Figure 6B) were used and

13

re-analyzed the data sets focusing on the identified wavenumber areas. Now, separation of the

14

two cell types become much more pronounced showing higher relevance (i.e. higher

15

explained variance as seen in increased percentage of PC1)(Figure 6C). Comparing the

16

identified wavenumber areas with literature differences were found within nucleic acids

17

(867cm-1, 915cm-1 and 1223cm-1), lipids (1110cm-1-1140cm-1 ) and proline content (1059cm-

18

1

19

located within the cluster of mono-cultured fibroblasts (see green circles within Scoreplot

20

Figure 6A) was investigated. The goal was to identify differences between the two

21

subpopulations of co-cultured fibroblasts. A discriminating wavenumber area at 1215-1252

22

cm-1 corresponding to collagen could be identified and the PCA-analysis was repeated based

23

on this information ( Figure 6D). Overall, this 3D cell culture experiment showed that Raman

24

spectroscopy was able to identify a different collection of metabolites in co- and mono-

25

cultured fibroblasts and additionally identified a subgroup of co-cultured fibroblasts having

26

characteristics more like mono-cultured cells with respect to collagen content.

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-1067cm-1 ). In a further step of analysis the nature of the subgroup of co-cultured fibroblasts

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In another example, monitoring cell differentiation of gingival fibroblasts seeded on a

2

collagen tissue matrix mucoderm® (botiss uk ltd) and treated with differentiation medium

3

(Provitro) illustrates the potential of this promising combination. Mucoderm® is a stable tissue

4

matrix consisting of collagen and elastin. It supports revascularization and fast soft tissue

5

integration and is a valid alternative for patients’ own connective tissue. After initial

6

cultivation of gingival fibroblasts for 7 days in normal medium cells haven been divided into

7

two groups. One group was treated with differentiation medium (Provitro) the other group

8

served as control. Raman measurements were performed after further 6 weeks of cultivation

9

within mucoderm®. Mucoderm® has a thickness of roughly 1-2 mm and the fibroblasts are

10

invading and growing within the matrix. First, the penetration depth of the Raman laser was

11

evaluated. Raman spectra were taken from untreated fibroblasts in different depths of the

12

mucoderm® matrix moving the laser focus in micrometer steps (0µm, 20µm, 50µm, 90µm

13

and 120µm). Figure 7 shows mean spectra analysis of different focal planes and bright field

14

images of fibroblasts taken at the same depth. At a depth of 120µm the penetration limit of the

15

laser was not yet reached as spectra still show relevant peaks. By contrast, microscopic

16

visualization of individual cells got worse and was finally impossible. Finally, the potential of

17

Raman spectroscopy to monitor cell differentiation within mucoderm® matrix was

18

demonstrated comparing Raman spectra of untreated fibroblasts and fibroblasts treated with

19

differentiation medium (see Figure 8A and B). The PCA analysis of these two cell types (see

20

Figure 8C) revealed a clear separation with respect to 6 wavenumber areas (see Figure 8D,

21

black bars in Loading Plot), amongst those were wavenumbers representing collagen type1,

22

proline, lipids and amids.

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3.4. Trends in Raman spectroscopy analysis of live 3D samples

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The outlined investigations show the combination of chemically defined hydrogels and

25

Raman spectroscopy provide a platform for the non-invasive and label-free investigation of page 21 Page 21 of 35

tumor-stroma interactions. The combination of 3D models and Raman spectroscopy might

2

detect early stage, tumor-based changes in cells and even unknown types of tumor cells might

3

be discovered, not possible so far due to missing labels and markers. Raman micro-

4

spectroscopy was also applied to monitor invasion of gliablastoma into engineered neuronal

5

tissue (Koch et al., 2013). The existence of a dedicated “cell and user friendly” Raman system

6

as well as the availability of a service lab for Raman analysis of cells and tissue may greatly

7

facilitate the introduction of this important technology into academic research and even

8

clinical routine. Effort has to be put into further automation of Raman spectra retrieval

9

through 3D-tissue and into developing algorithm and software analyzing tools to enable

10

Raman spectra measurements of membrane based 3D culture systems or liquid/air interphase

11

culture devices for example.

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4. Conclusion

In this review article we addressed current issues of analysis of live 3D samples. Increasingly

14

complex 3D cell culture models that closely mimic the tissue specific physiological in vivo

15

situation are widely used in biomedical research and continuously refined. While

16

sophisticated 3D cell culture formats are available today, the currently employed detection

17

methods often lag behind. Usually, methods and technologies that have been originally

18

developed for 2D formats are applied to 3D samples with varying degree of success. We

19

highlighted the potential and challenges of two analytical methods that are capable of non-

20

invasive monitoring of live cell cultures and therefore allow insights into dynamic cell

21

population behavior. First, the current status of microscopy, which is the most widely used

22

cell analytical method, for 3D samples was discussed. A range of different systems ranging

23

from low budget self-assembled devices to costly high-end automated systems is available.

24

Incubator microscopes are a reasonably prized option for live-cell monitoring especially in the

25

academic setting. They enable continuous monitoring of cell cultures and have already been

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used for 3D samples. However, the main drawback is that image acquisition is usually

2

restricted to a single plane, while more expensive systems allow acquisition of z-stacks that

3

capture the whole complexity of a 3D construct. Therefore, the best imaging device needs to

4

be carefully selected for each application depending on the actual sample structure and

5

scientific question. Furthermore, quantification of microscopic pictures poses an additional

6

challenge, especially for 3D samples. The 3D angiogenesis assay was selected as an example

7

to demonstrate different algorithms and approaches that can be applied for quantitative image

8

analysis. In order to promote meaningfulness and reliability of 3D cell culture imaging on a

9

broad basis, affordable solutions that allow fast, objective, accurate and robust image analysis

10

still need to be developed. The second investigated method was Raman spectroscopy, which

11

allows non-invasive analysis of the metabolic status of living cells. It has been demonstrated

12

that Raman spectra can be used to identify different cellular conditions such as differentiation

13

status of stem cells, viability and metabolic activity. Raman spectroscopy also has a

14

promising potential for use with 3D samples, since it provides label-free, sensitive

15

reproducible signals of cells within the depth of spheroids or matrices for up to at least 300µm

16

and 120µm, respectively. By contrast, penetration depth of confocal microscopy is typically

17

limited to less than 100µm due to due light scattering in the sample, which results in

18

defocusing of the laser beam. The applicability of Raman spectroscopy to investigate 3D cell

19

samples was further demonstrated by successful identification of an altered composition of

20

metabolites of fibroblast in 3D hydrogel co- and mono-culture. Raman spectroscopy for label

21

free cell analysis is on its way to become a modern diagnostic tool. Spectral changes provide

22

the clinician with valuable diagnostic information that cannot be obtained with current

23

methods. , However, there are still some obstacles hindering its application to routine clinical

24

diagnosis, such as usability of the equipment and efficiency of the diagnostic model (Chen et

25

al. 2011). The BioRam® system was especially designed to meet the requirements of

26

biologists and clinicians, which will strongly support the introduction of Raman spectroscopy

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into biomedical investigations and diagnosis of disease. Overall, our understanding of

2

increasingly complex 3D cell cultures is going to be determined by the available analytical

3

methods. Only live-cell analysis with non-invasive methods enables us to observe and

4

understand dynamic cellular changes.

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5. Potential conflict of interest

Claudia Hoffmann and Brigitte Angres are affiliated with Cellendes GmbH. Karin Schütze

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and Rainer Gangnus are affiliated with CellTool GmbH. Therefore, we want to clarify that the

9

section on Raman spectroscopy does not provide a balanced comparison between different

10

available set ups. Instead, it describes the general potential and limitations of Raman

11

spectroscopy for 3D live-cell analysis with the example of the BioRam® system from

12

CellTool.

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This project has received funding from the European Union’s Seventh Program for research,

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technological development and demonstration under grant agreement No 279288 (IDEA). We

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also like to thank Erika Herzmann for her expert assistance in the lab and Sven Mesecke for

18

his advanced computational support.

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1 2

Table 1. Technical specifications

3 Incubator microscope

Size/Weight/ Objectives

JuLITM

Bright field/ blue LED (GFP, Emission 520 nm) 240 x 350 x 320 mm/5kg/4x, 10x, or bright field/ green LED (RFP, Emission 590 ± digital zoom 40 nm)

CytoMate

130 x 80 x 90 mm/650g/10x

Bright field, no fluorescence, temperature recording, data acquisition over registration on cloud server (online control and automated alerts)

240 x 140 x 165 mm /3.2 kg/4x, 10x, 20x, 40x, 100x

Bright field/ Phase contrast/Chan 1: Excitation: 390/40nm; Emission: 446nm; Chan 2: Excitation: 482/18nm; Emission: 532nm; Chan 3: Excitation: 589/18nm; Emission: 646nm

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LumaScope600

Features

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Figure 1: Three commercially available incubator microscopes: (A) JuLI, (B) LumaScope 600, (C)

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CytoMate. Images reprinted with permission of the respective manufacturer.

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Figure 2: Succession of enhancements done prior to Angiosys analysis to accurately represent the

5

tubular network present in a 3D environment. Image A: Original Image, Image B: Desaturation, Image

6

C: Inversion, Image D: Levels applied, Image E: Threshold applied, Image F: Gaussian blur applied,

7

Image G: Threshold applied, and Image H: Manual corrections applied. (Scale bars represent 200µm.)

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Figure 3: BioRam® - the cell and user friendly Raman microscope system (CellTool,

4

Bernried, Germany) consists of an inverted microscope platform with a motorized sample

5

holder and integrated 785 nm Raman spectroscope. The Raman laser is focused through the

6

objective to a spot size of about 1 µm in diameter. Application specific software facilitates

7

spectra retrieval and supports data processing.

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Figure 4: Mean spectra of a MCF-7 spheroid having a diameter of about 500µm. Spectra were

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recorded from different depth at a z-focus of 75µm (blue), 150µm (orange), 225µm (green)

5

and 300µm (red).

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Figure 5: Phase contrast microscopy of 14 day mono- and co-cultures of MCF-7 cells and

4

fibroblasts. In monoculture MCF-7 cells form spheroids of varying sizes (A). Primary human

5

dermal fibroblasts grown in monoculture grow mostly as single cells or in loose clusters in a

6

well spread phenotype (B). When MCF-7 cells and fibroblasts are grown together, fibroblasts

7

form long shaped aggregates with tightly packed cells (arrows in C). Asterisks indicate MCF-

8

7 spheroids. (Scale bar represents 100 µm.)

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Figure 6: PCA analysis of mono-cultured Fibroblasts (red dots) and co-cultured Fibroblasts

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(blue squares) shown in a 2D Score Plot indicating differences of the two cell types. (A)

5

Score Plot of the PCA analysis with respect to the entire spectral area with a subgroup of co-

6

cultured fibroblasts located within the mono-cultured cells (green circles). (B) Loadings Plot

7

with peaks indicating the wavenumber areas that might be relevant for the discrimination of

8

the spectral data; Black bars: identified wavenumber peaks relevant for the discrimination of

9

the two cell types. Main differences were found at 867cm-1, 915cm-1 and 1223cm-1 describing

10

nucleic acid, 1110cm-1-1140cm-1 describing lipids and 1059cm-1-1067cm-1 describing

11

difference in the proline content. (C) Score Plot of the repeated PCA analysis focusing on the

12

identified wavenumbers yield an even better explained separation of the cell groups. (D)

13

Score Plot of PCA analysis focusing on wavenumber areas corresponding to collagen. The

14

two groups of co-cultured cells (light blue triangles vs blue squares) differ with respect to

15

their collagen content (i.e. their collagen content seems to match those of the mono-cultured

16

cells).

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Figure 7: Mean spectra (x-axis: wavenumbers, y-axis: relative intensity) and corresponding

3

microscope image of untreated gingival fibroblasts in different depths of mucoderm® matrix

4

(0µm in dark blue, 20µm in red, 50µm in green, 90µm in bright blue, 120µm in orange). For

5

better data representation of the spectra a value of 0.05 was added to each relative intensity

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(y-axis). (Scale bars represent 20µm.)

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Figure 8: Gingival fibroblasts were cultured on a 1-2 mm thick mucoderm® matrix. Cells

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were either cultured normally (control - blue squares) or incubated with differentiation

5

medium (differentiated - red dots). (A) Bright field image of control and (B) of the

6

differentiated fibroblasts within mucoderm®. Scale bars represent 20 µm. (C) Score plot

7

clearly depicts difference between differentiated gingival fibroblasts and untreated control

8

cells. (D) Loading shows that treated cells mainly differ from the control cells in

9

wavenumbers corresponding to type1 collagen, lipids and primary amids (wavenumber area

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1239-1273cm-1 and 1435-1447cm-1, respectively). (Scale bars represent 20µm)

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