Abstract

In this work, we report on a series of transitions in morphology and texture as 5–1000 parts per million of nitrogen were added to 2% and 1% methane–hydrogen depositions of polycrystalline diamond films. Five results are reported. (1) The threshold for transition into the {100}-faceted morphology occurred at lower parts per million nitrogen for the 1% versus the 2% methane–hydrogen series, opposite from the transition thresholds previously reported. (2) At 1000 parts per million nitrogen the film quality of both series had not yet seriously degraded. (3) A well defined sequence of intermediate texture transitions as a function of increasing parts per million nitrogen was observed for both series. (4) A pretransition morphology of large crystallites interspersed among microcrystalline material directly preceding the transitions to the {100}-faceted morphology was observed for both series. (5) A layered growth and/or etched morphology at high nitrogen concentrations was observed for both series. We discuss …

I. Introduction

Controlled, textured growth of polycrystalline diamond films would be desirable for many applications. Very small concentrations of nitrogen have been shown to dramatically change the morphology and crystallographic texture of polycrystalline diamond films grown using microwave plasma chemical vapor deposition ͑MPCVD͒ methods. [1] [2] [3] [4] [5] Previous studies have identified a sharp transition from various initial morphologies into ͕100͖-faceted morphologies as a function of low concentrations of nitrogen. For diamond films grown in a microwave plasma tubular reactor ͑4 cm diam cylindrical quartz tube͒, an upper end concentration of nitrogen, about 400 parts per million ͑ppm͒, is reached beyond which the film crystallinity and quality are reported to degrade. [1] [2] [3] 6 In this work, a detailed study of the evolution of the morphologies and corresponding textures of methanehydrogen grown CVD diamond films, as a function of 5-1000 ppm of nitrogen (N 2 ) added to the depositions, is reported. The film morphologies are investigated using Tap-pingMode™ atomic force microscopy, and the complementary texture studies are performed using x-ray diffraction with a four-circle goniometer. Investigations are presented for two series of polycrystalline diamond films: 2% CH 4 /H 2 and 1% CH 4 /H 2 . Five results are reported and discussed.

Ii. Experimental Approach

A 2.45 GHz microwave plasma cavity reactor was used to deposit polycrystalline diamond films on 7.62 cm diamond scratch-seeded ͑100͒ p-type silicon wafers. The Michigan State University reactor geometry is specially designed for wide-area depositions 7 up to 12.5 cm in diameter. In the present experiments, the diameter of the plasma ball was approximately 8 cm. Substrate heating is only through the formation and breaking of bonds with no external heat source. The substrate temperature depends mainly on the operating pressure, with minor dependence on the microwave input power, as discussed in Ref. 7 . Therefore, the plasma volume, temperature gradients, and other parameters in our reactor system differed significantly from the tubular reactors of Refs. 1-3 as noted in Table I . The Michigan State University microwave plasma reactor system combined with a computer controlled ultrahigh vacuum and gas handling system allows careful control of the gas input variables. The nitrogen was introduced via a premixed gas of 2% N 2 in H 2 . Input gases of research and ultrahigh purity, hydrogen, 99.9995%, methane, 99.99%, and hydrogen/nitrogen mix, 99.9995%, were used. Gas phase nitrogen concentrations were monitored by optical emission spectroscopy ͑OES͒ using the CN emission band. In calibration tests, an increase of 10 ppm nitrogen in the gas phase was detectable in the OES emission spectrum. The flow rates of CH 4 and H 2 were held constant at ratios of CH 4 /H 2 equal to 1% and 2% while the mix of N 2 /H 2 was varied. For each of these series, nitrogen gas was added in amounts which varied between 0 and 1000 ppm. For the zero nitrogen flow condition, we estimate an actual value of 5 ppm N 2 based on the known leak rate of laboratory air into our growth chamber of 0.25 mTorr/h. For the 1% methane-hydrogen experiments, the minimum total flow rate was 202 standard cubic centimeters per minute ͑sccm͒ for the deposition with no nitrogen introduced and the maximum total flow rate was 212 sccm for the 1000 ppm nitrogen deposition. For the 2% methane-hydrogen experiments, the minimum total flow rate was 204 sccm for the deposition with no nitrogen introduced and the maximum total flow rate was 214 sccm for the 1000 ppm nitrogen deposition.

Each sample in this study was grown for 8 h at a reactor pressure of 38.4 Torr. Our calibration experiments showed that, at our operating pressure of 38.4 Torr, the substrate temperature was approximately 825°C. The actual temperatures were also measured by a single color optical pyrometer. The samples used in this study were approximately 1 cm 2 squares cut from the center of each deposition.

A. Atomic Force Microscopy Surface Morphology Measurements

TappingMode atomic force microscope ͑AFM͒ imaging and analyses were performed using a Digital Instruments™ Nanoscope IIIa operated in ambient air. Silicon tapping mode tips with nominal tip radii of 5-10 nm were used for all images. Scans 5ϫ5 m 2 are compared in this article; however, large-area scans, corresponding scanning electron microscopy ͑SEM͒ measurements, and optical microscopy were used to ensure that the morphologies shown in these scans are representative. The 5ϫ5 m 2 images were acquired using an E-scanner with a 13ϫ13 m 2 maximum scan range and the high resolution image shown in Fig. 4͑b͒ was acquired using an A scanner with a 1ϫ1 m 2 maximum scan range.

B. Texture Analysis By X-Ray Diffraction

The x-ray texture measurements were performed on a four-circle goniometer ͑Scintag XDS 2000 system͒ using Cu K␣ radiation at 1.54 Å with a 1 mm collimator at the x-ray source and a 2 mm slit near the detector to minimize intensity from regions away from the sample. For each sample, pole figures were measured by keeping the detector position fixed at 2 hkl and rotating the sample about the axis normal to the substrate through 0°рр360°in 5°increments ͑azimuth angle ͒ and tilting the sample about an axis parallel to the substrate through 0°рр80°in 5.0°increments ͑polar angle ͒. Long count times of 12-25 s/datum were used to minimize the noise due to background from the substrate. The four-circle goniometer was used to measure ͑111͒, ͑220͒, and ͑400͒ pole figures. The pole figures were corrected using an analytical defocusing curve which approximates the more complex actual defocusing optics. Therefore the measured textures are suitable for comparison, but they also contain small systematic errors particularly at high tilt angles. Data reduction to obtain the orientation distribution functions was done assuming only mirror symmetry using standard procedures with popLA software. 8 Inverse pole figures were extracted from the projection of the sample orientation distribution. 8

A. 2% Methane-Hydrogen Series

The major transitions in morphology for the 2% methane-hydrogen series as a function of increasing ppm N 2 are shown in Fig. 1 . The 2% methane-hydrogen depositions showed a uniform coverage of microcrystalline material between about 5 and 150 ppm nitrogen. Large crystallites interspersed among the microcrystalline material appeared with about 175-225 ppm nitrogen. The surface morphology changed abruptly into a ͕100͖-faceted morphology between 225 and 250 ppm nitrogen. The 2% series remained in ͕100͖faceted morphologies after the initial transition. With 900-1000 ppm N 2 , rounded and scalloped edges of the ͕100͖faceted crystallites and a layered morphology were observed. Raman spectroscopy of the 2% series, reported elsewhere, 9 showed unambiguous diamond peaks with full widths half maxima ͑FWHMs͒ of 6-10 cm Ϫ1 for all films after the initial transition into well-faceted material, including those with 900-1000 ppm N 2 in the depositions. Broad peaks were observed for the pretransition microcrystalline material. A minimum value of 6.3 cm Ϫ1 was observed at 400 ppm nitrogen. All other posttransition FWHM values were about 10 cm Ϫ1 . No progressive deterioration of the film quality was observed as a function of increasing ppm nitrogen.

The influence of the increasing ppm N 2 on texture development after the transition into well-faceted morphologies is shown in Fig. 2 for the 2% methane-hydrogen series. Texture is defined here as the distribution of crystallographic vectors aligned with the normal direction. The samples grown with 5 ppm nitrogen had a strong ͗101͘ texture component and a weaker ͗203͘ texture component. With increasing nitrogen up to 225 ppm, the ͗203͘ component strengthened and the ͗101͘ component weakened. With 250 ppm nitrogen, a broad ͗102͘ peak developed and the ͗203͘ and ͗101͘ components weakened ͑we note that the abrupt morphology transition to square facets occurred with 250 ppm nitrogen͒. With increasing nitrogen up to 400 ppm, a band of orientations between ͗102͘ and ͗114͘ developed. With 500 ppm nitrogen, strong ͗001͘ and ͗104͘ components developed, with retention of the strong ͗114͘ component and loss of the ͗102͘ component. With increasing nitrogen from 600 through 1000, a band of orientations between ͗104͘ and ͗114͘ was consistently observed, but the additional strong ͗001͘ component was only observed for the samples grown with 500 and 700 ppm nitrogen. We note that the minimum FWHM of the Ref. 9 Raman measurements corresponded more closely with the first transition to ͗001͘ texture between 400 and 500 ppm N 2 than with the observed transition in morphology at 250 ppm N 2 .

B. 1% Methane-Hydrogen Series

The major transitions in morphology for the 1% methane-hydrogen series as a function of increasing ppm N 2 are shown in Fig. 3 . The 1% methane-hydrogen depositions showed a uniform coverage of well-faceted, small ͑less than 0.5 m 2 ͒ crystallites with 5 ppm nitrogen. The small crystallites appeared to be mainly of the roof-shaped morphology that has a ͗101͘ direction close to the sample normal, an observation which was supported by the x-ray texture analysis. ͑For a ͓101͔ direction, there are two ͗111͘ directions with 35.3°inclination: ͓111͔ and ͓111͔. The corresponding faces form roof-shaped structures that can be observed in AFM or SEM images of the surfaces of these diamond films.͒ With 25 ppm, the morphology changed into one of larger crystallites interspersed among a smaller crystallite field. With 50 ppm nitrogen, a transition into a uniform coverage of larger micron-sized crystallites was observed. These appeared to be mainly roof-shaped morphologies. With 75 ppm nitrogen, micron-sized crystallites with roof-shaped morphologies were still observed but a few micron-sized ͕100͖-faceted crystallites were interspersed among them. A uniform coverage of micron-sized ͕100͖-faceted crystallites occurred with 100 ppm nitrogen.

The crystallite morphologies of the depositions with 100-1000 ppm nitrogen all showed ͕100͖-faceted surface morphologies. However, for the depositions with 300, 400, and 500 ppm nitrogen, the surface morphology varied from place to place over a millimeter-sized area similar to the x-ray measurement area. For example, in the lower part of Fig. 3͑e͒ , multiple facets are observed with the same orientation for both roof-top and square faceted morphologies. From 600 to 1000 ppm nitrogen, the morphologies again had the appearance of a uniform coverage of well-faceted crystallites with ͕100͖ facets on the surface. For the 700-1000 ppm nitrogen depositions, rounded and scalloped edges were observed on the ͕100͖ facets, with a layered appearance on the ͑100͒ surfaces. The rounded and scalloped edges and the layering were observed at lower ppm N 2 and were more pronounced in the 1% series than in the 2% series. This was also observed in a SEM analysis of the same samples previously reported in Ref. ration of the film quality was observed as a function of increasing ppm nitrogen.

The influence of the increasing N 2 on texture development is shown in Fig. 5 for the 1% methane-hydrogen series. The observed changes in texture mirrored the changes observed for the 2% methane-hydrogen series but the transitions occurred at systematically lower levels of nitrogen. Once again, the minimum FWHM of the Ref. 9 Raman measurements corresponded more closely with the first transition to ͗001͘ texture with 200 ppm N 2 than with the observed transition in morphology at 100 ppm N 2 .

One difference for the 1% methane-hydrogen series was that a multicomponent texture was observed for the samples grown with 300-500 ppm nitrogen which correlated with the multiple types of facet morphologies observed on these samples. Another difference was that the high nitrogen ͗114͘-͗104͘ band was stronger for the 1% methanehydrogen series and that an additional strong ͗001͘ texture component was observed three times, at 200, 800, and 1000 ppm nitrogen.

Iv. Discussion Of Results

We report the following five results. ͑1͒ The threshold for transition into the ͕100͖-faceted morphology occurred at lower ppm N 2 for the 1% than for the 2% methane-hydrogen series, opposite from that which has been reported in a tubular reactor. [1] [2] [3] Table I shows the differences in reactor conditions between our reactor and those of Refs. 1-3. Important variables affecting the formation, concentrations, and resonance times of the various chemical species present may have been very different, despite similarities in the methane-hydrogen ratios and substrate temperatures. Therefore, we cannot interpret the effect of nitrogen in our reactor in terms of its influence on the previously reported ͑100͒/͑111͒ competitive growth parameter curves. [10] [11] [12] [13] In this previously reported work, a transition into crystallites with small ͑100͒-faceted surfaces and Ͻ5°tilt was observed, from which a corresponding transition in growth rate parameter ␣ ϭͱ3 v 100 /v 111 from about 1.5 to near 3.0 was postulated. In our work, we observe crystallites with large ͑100͒-faceted surfaces and often a band of orientations between ͗104͘ and ͗114͘. This band structure corresponds to ͑100͒ faces with a tilt of 14.04°towards ͗010͘ and a tilt of 19.47°towards ͗111͘. We are exploring the possibility that the corresponding transition in growth parameter ␣ may be in the opposite sense, from about 1.7 to 1.5.

͑2͒ At 1000 parts per million nitrogen the film quality of both series had not seriously degraded, which is the highest value reported to date. The difference in reactor geometry from narrow to wide area may be responsible for the high gas phase nitrogen tolerance without loss of diamond film quality by decreasing the concentration of nitrogen over a given surface area. This has allowed us to see the sequence of transitions and texture in more detail than has been previously reported.

͑3͒ A well defined sequence of intermediate texture transitions was observed for the evolution from an initial ͗101͘ texture to a final range of orientations between ͗104͘-͗001͘-͗114͘ for both the 2% and 1% methane-hydrogen series as a function of increasing parts per million nitrogen.

The sequence of crystal orientation transitions indicates a shift from ͗101͘ to near-͗001͘ texture, as has been reported in prior work. [1] [2] [3] [4] [5] [6] 9 We have observed and reported that specific crystal orientations between ͗101͘ and ͗001͘ are sequentially stabilized with increasing nitrogen. This implies that either ͑111͒ or ͑001͒ crystal planes have preferred growth advantages that may depend on different reactions that are favored with a particular concentration of nitrogen. With sufficient nitrogen, orientations near ͗001͘ but commonly closer to ͗104͘ and ͗114͘ have the highest growth advantage, such that these crystal orientations dominate. However, under some conditions many orientations may be stabilized, such as in Figs. 2͑e͒ and 5͑f͒, suggesting that different reactions occurred preferentially on different orientations. Similar phenomena have been observed in related studies with different growth times. 14 Finally, similar transitions for the 1% methane series occur at about half of the nitrogen concentration for the 2% methane conditions. These observations all suggest that particular concentrations of nitrogen are needed to promote growth of a favored crystal orientation by means of a distinct stoichiometric reaction path. We are currently investigating molecular models of how nitrogen could change the specific reaction paths for ͗110͘ nucleation and growth which are described in Ref. 15 .

͑4͒ A pretransition morphology of large crystallites interspersed among microcrystalline material which seemed to directly precede the transitions to the ͕100͖-faceted morphology was observed for both the 2% and 1% methanehydrogen depositions.

͑5͒ A layered growth and/or etching at high nitrogen concentrations was observed for both the 2% and the 1% methane-hydrogen depositions.

We are investigating the possibility that nitrogen, acting as a mineralizer or chemical vapor transport agent, may account for these observations. A mineralizing agent ”X,” added to the main deposition, promotes both the dissolution of a solid and its redeposition via a reversible reaction,

A solid ϩX gas k 2 ,T 2 k 1 ,T 1 ͑ AX͒ gas , ͑1͒

where T i is the temperature and k i is the rate constant. Such processes are known to occur in gas phase systems and to promote the growth of well formed large crystals in a large variety of materials. [16] [17] [18] [19] [20] [21] In the gas phase case, a chemical vapor transport reaction is enabled by the added mineralizer. Being reversible, depending on the temperature, these reactions can quickly convert large quantities of microcrystalline material into large crystallites. Previous studies of a similar 2.45 GHz diamond deposition reactor 22 have shown a radial temperature gradient from center to edge of about 100°C/ cm, and a vertical temperature gradient across the plasma sheath of about 600°C/cm. While the nitrogen plasma chemistry might have altered these numbers, we would still expect similar gradients to have been present during growth of the samples in the present work. Additional temperature fluctuations, associated with the cyclic nature of diamond film growth, 23 could also be present. The possibility of using chemical vapor transport for diamond growth has been investigated theoretically 24 and experimentally 25 using a two zone furnace ͑for T 1 and T 2 ͒ and a graphite carbon source. The dissolution of graphite in molecular hydrogen, nitrogen, oxygen, or chlorine gases was calculated theoretically from thermodynamic principles. Hydrogen showed the greatest promise of acting as a chemical vapor transport agent, and was experimentally investigated. These experiments demonstrated the conversion of the graphite into diamond by the mechanism shown in Eq. ͑1͒. Because the calculations indicated that the graphite would not dissolve readily in N 2 , this case was not experimentally investigated. Neither calculations nor experiments were performed for the case where microcrystalline ͗101͘ textured diamond is the carbon source. Therefore, these studies, which are the closest we can find to our own, are not directly comparable but they do indicate that chemical vapor transport reactions can play a role in diamond deposition when temperature gradients are present.

The hypothesis that N or a N-H x species acts as a mineralizer in a gas phase chemical vapor transport reaction would account for several of our observations. A mineralizer would react preferentially with highly energetic sites such as points and edges, and defective material. This effect is consistent with the observed pretransition morphology, large crystallites interspersed in a field of finer-grained material, which seemed to precede the transitions to the ͕100͖-faceted morphology for both the 2% and 1% methane-hydrogen grown films. A similar pretransition morphology has recently been reported by another group. 6 There would be a threshold concentration of mineralizer associated with the onset of the growth of larger well-formed crystals. Above the threshold, an optimum concentration, or range of concentrations, would be present. Beyond this range, growth could become unstable if dissolution were comparable. This could account for both the ledges and the unusually large crystallite sizes observed at high ppm nitrogen for both the 2% and the 1% methane-hydrogen depositions. The reversibility of a mineralizer reaction would also account for the observation that only parts per million of nitrogen are needed to induce the large changes in morphology reported by ourselves and by others, since the mineralizing agent would become free to participate again ͑acting catalytically͒ in the dissolution of defective material and small crystallites at temperature T 1 .

V. Summary

Detailed observations of a series of transitions in morphology and crystallographic texture as ppm N 2 were added to 2% and 1% methane-hydrogen depositions of diamond films have been reported in this article. These results suggest that the concentration of nitrogen promotes growth of particular crystal orientations over others, leading to a growth advantage for well defined low-index crystal orientations. We are investigating the hypothesis that plasma-generated N-H x species act as mineralizing/chemical vapor transport agents that deposit, remove, and redeposit carbon on the film surface.

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J. Appl. Phys., Vol. 89, No. 11, 1 June 2001 Ayres et al. Downloaded 07 Nov 2005 to 131.112.45.39. Redistribution subject to AIP license or copyright, see http://jap.aip.org/jap/copyright.jsp

Downloaded 07 Nov 2005 to 131.112.45.39. Redistribution subject to AIP license or copyright, see http://jap.aip.org/jap/copyright.jsp

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Abstract

A new cell shape index is defined for use with atomic force microscopy height images of cell cultures. The new cell shape index reveals quantitative cell spreading information not included in a conventional cell shape index. A supervised learning‐based cell segmentation algorithm was implemented by texture feature extraction and a multi‐layer neural network classifier. The texture feature sets for four different culture surfaces were determined from the gray level co‐occurrence matrix and local statistics texture models using two feature selection algorithms and by considering computational cost. The quantitative morphometry of quiescent‐like and reactive‐like cerebral cortical astrocytes cultured on four different culture environments was investigated using the new and conventional cell shape index. Inclusion of cell spreading with stellation information through use of the new cell shape index was shown to change biomedical conclusions derived from conventional cell shape analysis based on stellation alone. The new CSI results showed that the quantitative astrocyte spreading and stellation behavior was induced by both the underlying substrate and the immunoreactivity of the astrocytes. © 2015 International Society for Advancement of Cytometry

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A cell shape index (CSI) is a dimensionless quantitative measure of cell morphology acquired from images. Cells have different morphologies depending on their type in vivo, e.g., astrocytes have a stellate morphology in the central nervous system (CNS) for interactions with neurons and capillaries (1) , while endothelial cells in heart arteries have an elongated morphology with actin and microtubule fibers aligned parallel to the direction of blood flow (2) . In vitro, cells also adopt distinctive morphologies that approximately recapitulate their in vivo counterparts and that can be influenced by a controlled environment. Quantitative cell morphology investigations have recently been used to explore the potentially significant directive impact of environments for healthy or pathological cellular outcomes (3) (4) (5) (6) (7) .

A conventional CSI in widespread use is the ratio of perimeter squared to the cell projection area (1):

EQUATION (1): Not extracted; please refer to original document.

where P is cell perimeter and A is cell projection area. This equation describes stellation as a cell’s departure from a circular projection since:

2pr ð Þ 2 4p pr 2 ð Þ 51 (2)

The conventional CSI has been previously used to evaluate morphology of astrocytes (1) and vascular endothelial cells (2) . However, this definition for CSI, which was created for use with two-dimensional (2D) optical microscopy images, does not include three-dimensional (3D) effects, e.g., cellspreading or hypotonic swelling.

New research directions are actively being pursued to enhance the usefulness of cell shape analysis in biomedical research. Two important directions are research to incorporate and analyze 3D information and research to segment cells for CSI analysis. Addressing the former, Canham and Burton have proposed a “sphericity index” for the study of human red blood cells (8) . Their volumetric CSI is highly appropriate for optical microscopy images of blood cells in a 3D environment. The 2D and 3D discrete compactness measures that are invariant under translation, rotation, and scaling were developed by Bribiesca (9) . Chv atal et al. have recently developed a 3D cell morphometry definition for use with z-series confocal microscopy images (10) . A 3D model retrieval that uses both semantic concepts and 2D and 3D classical compactness measures shape indexes was proposed by Kassimi et al. (11) . The 2D shape indexes and texture features measured from cell nuclei were recently used to classify healthy and pathological skin fibroblast by Thibault et al. (12) . Farooque et al. (13) have recently proposed a dimensionality matrix to assess gyration tensor ellipsoids that are fit to each cell, and then classified the ellipsoids as 1D, 2D, or 3D. The L 0: 5 1 =L 0:5 3 measure in Ref. 13 can be used to quantitatively estimate the cell spreading behavior for cell types and/or biological events that are dominated by the cell soma response, e.g., hypotonic swelling. For other cell types such as neurons and astrocytes, the cell process extension response is equally important. This adds an additional level of complexity as the process volumes must be included with the cell soma result.

Addressing the latter, CSI analysis is a revealing but currently under-utilized approach because CSI calculation requires a clearly defined cell perimeter, which is a segmentation issue. Automatic extraction of cell boundary information using, e.g., NIH Image J 1.46r is limited to isolated cells with perimeters that display sharp contrast. In recent work by Pincus and Theriot (14) , a mask and template matching approach was innovatively applied to confluent cells in culture to create an accurate numerical 2D representation for individual cells with extractable boundaries. Tiryaki et al. developed a new CSI that incorporates volumetric information acquired from high-resolution AFM height images of cell-scaffold/substrates interactions (15) . This utilized a Gaussian high-pass filter (GHPF) design followed by histogram equalization that enabled the edges and processes to be clearly distinguished; however, the final cell boundaries were determined by manual segmentation. In the present investigation, a new cell segmentation approach that is based on supervised learning and texture analysis is developed to semiautomatically extract cell-substrate boundaries with minimum user bias, which greatly facilitates use of the new AFM-based CSI.

AFM is becoming increasingly important for biomedical research as it provides direct high-resolution information with minimal sample preparation. Recent AFM-based biomedical investigations include studies of multi-scale nano-mechanical hierarchies present within hydrogel intermediate filament phantoms (16) , multi-scale nano-mechanical assembly present during tendon embryogenesis (17) , cytoskeletal rounding during mitosis (18) , and myosin walking with head torsion (19) . In the present work, we present investigations using the new volumetric CSI definition based on analysis of highresolution AFM images (15) that incorporates the new semiautomatic texture-based cell segmentation analysis. AFM height images retain volumetric information for both cell spreading and cell processes, making the new CSI appropriate for investigations of all substrate-adherent cell cultures and surface seeding of 3D matrix cultures.

In the present investigation, the new AFM-based CSI is used to quantitatively analyze the responses of quiescent-like and reactive-like (dibutyryl cyclic adenosine monophosphate (dBcAMP)-treated) astrocytes to the nanophysical cues provided by four culture environments including a biomimetic polyamide nanofibrillar scaffold environment. Astrocyte reactivity induced by dBcAMP-treatment recapitulates elements of CNS traumatic injury. We are especially interested in astrocyte responses to polyamide nanofibrillar scaffolds, as these appear to favorably modulate the glial scar response that blocks axon regeneration in CNS traumatic injury (20, 21) . Our previous AFM studies determined that significant responses include cell spreading as well as process formation, and that the cell spreading can vary, depending on the surface polarity of the cell environment (3) . In the present work, we use the new CSI to perform a quantitative 3D cell spreading and stellation response investigation of astrocytes in response to (1) changes in nanophysical environment cues and (2) dBcAMPtreatment.

Nanofibrillar Scaffolds And Comparative Culture Surfaces

Four cell culture surfaces were investigated: poly-L-lysinefunctionalized planar glass (PLL glass), unfunctionalized planar Aclar (Aclar), PLL-functionalized planar Aclar (PLL Aclar), and polyamide nanofibrillar scaffolds. Glass coverslips (12 mm, No. 1 coverglass, Fisher Scientific, Pittsburgh, PA) and Aclar coverslips (12 mm, Ted Pella, Redding, CA) were used as underlying surfaces for the PLL functionalization. Glass or Aclar coverslips were placed in a 24-well tissue culture plate (one coverslip/well) and covered with 1 mL of poly-Llysine (PLL) solution (50 lg PLL mL 21 in dH 2 O) overnight. The coverslips used for the cultures were then rinsed with dH 2 O and sterilized with 254 nm UV light using a Spectronics Spectrolinker XL-1500 (Spectroline Corporation, Westbury, NY). The polyamide nanofibrillar scaffolds electrospun on Aclar substrates were obtained from Donaldson (Minneapolis, MN) and Corning Life Sciences (Lowell, MA). The fiber diameter for the nanofibrillar scaffolds has a range from $100 to $300 nm.

Promising in vivo and in vitro results have been obtained for astrocytes in contact with these nanofibrillar scaffolds, as implants or as culture surfaces. In our recent work (3, 4) , astrocyte responses to the nanofibrillar scaffolds were studied in comparison with their responses to three additional culture surfaces: PLL glass, Aclar, and PLL Aclar. PLL glass is a standard astrocyte culture surface, and astrocyte responses to it are well characterized, making it useful for identifying differences in astrocyte responses to other surfaces. The polyamide nanofibrillar scaffolds were electrospun on Aclar substrates. Astrocyte responses to PLL Aclar surfaces were studied to clarify the role of the underlying substrate versus surface functionalization.

Primary Quiescent-Like And Reactive-Like Astrocyte Cultures

Primary quiescent-like astrocyte cultures were prepared from new born Sprague Dawley (postnatal Day 1 or 2) rats (3). All procedures were approved by the Rutgers Animal Care and Facilities Committee (IACUC Protocol #02-004). The rat pups were sacrificed by decapitation and the cerebral hemispheres were isolated aseptically. The cerebral cortices were dissected out, freed of meninges, and collected in Hank’s buffered saline solution (HBSS; Mediatech, Herndon, VA). The cerebral cortices were then minced with sterile scissors and digested in 0.1% trypsin and 0.02% DNase for 20 min at 378C. The softened tissue clumps were then triturated by passing several times through a fine bore glass pipette to obtain a cell suspension. The cell suspension was washed twice with culture medium [Dulbecco’s Modified Eagle’s Medium (DMEM; Life Technologies, Carlsbad, CA) 110% fetal bovine serum (FBS, Life Technologies)] and filtered through a 40-lm nylon mesh. For culturing, the cell suspension was placed in 75-cm 2 flasks (one brain/flask in 10 mL growth medium) and incubated at 378C in a humidified CO 2 incubator. After 3 days of incubation, the growth media was removed, cell debris was washed off, and fresh medium was added. The medium was changed every 3-4 days. After reaching confluency ($7 days), the cultures were shaken to remove macrophages and other loosely adherent cells.

To obtain reactive-like astrocytes, 0.25 mM dibutyryl cyclic adenosine monophosphate (dBcAMP) was added to the culture medium of 7-day-old semi-confluent quiescent astrocyte cultures and the serum concentration was reduced to 1%. The cultures in dBcAMP containing medium were incubated for additional 7-8 days with a media change every 3-4 days. The morphology of the cells was observed on alternate days under a phase contrast microscope. In the control cultures, the cells were fed with DMEM 1 1% FBS (without dBcAMP).

Quiescent-like and reactive-like astrocytes were harvested at the same time point using 0.25% Trypsin/ethylene-diaminetetraacetic acid (EDTA, Sigma-Aldrich, St. Louis, MO) and re-seeded at a density of 30,000 cells per well directly on 12mm Aclar or PLL Aclar coverslips, PLL glass coverslips, or on Aclar coverslips coated with nanofibers in 24-well plates in astrocyte medium containing dBcAMP (0.5 mL). After culturing the astrocytes on the aforementioned substrates for 24 h, they were fixed with 4% paraformaldehyde for 10 min. Parallel cultures were immunostained with GFAP, an identification marker for astrocytes, and >95% were found to be GFAPpositive.

For atomic force microscopy investigation, the astrocytes cultured on coverslips were fixed in 4% paraformaldehyde for 10 min, rinsed with distilled water, and air-dried, but were not immunostained.

Afm Imaging

AFM investigations were performed using a Nanoscope IIIa (Bruker AXS, Madison WI, formerly Veeco Metrology) operated in contact mode and in ambient air. A J scanner with 125 lm 3 125 lm 3 5.548 lm x-y-z scan range, and Bruker DNP silicon nitride probes with a 358 6 28 cone angle, and a nominal 20-nm tip radius of curvature were used for AFM investigations. Cell segmentation for both the conventional and new CSI calculations was implemented with MAT-LAB version 7.7.0 (R2012b) (The MathWorks, Natick, MA) using the neural network and image processing toolboxes. For each culture surface and immunoreactivity group at least 50 astrocyte images were captured from different regions of at least three different cell substrates. AFM height and deflection images were 512 3 512 or 256 3 256 pixels with 8 bit gray level depth. The field of view of the images was 100 lm 3 100 lm. AFM height images were stitched manually when a single astrocyte was in multiple AFM images.

The base level for all culture surfaces was determined by taking the average of substrate/scaffold region height values in the AFM height image. The pixel intensities of the astrocytes on nanofibrillar scaffolds were not always higher than the base level. Therefore, for the nanofibrillar scaffold cultures the cell region was divided into two regions: cell surface that is higher and lower than the base level. For the pixels that are higher than the base level, the cell volume was calculated according to the base level. When the astrocyte surface pixel intensities were lower than the base level, the thickness of the cell region was set to average astrocyte process thickness. Fifty AFM cross-section measurements, typically midway between the cell soma and the process end, were performed to determine the astrocyte process thickness, with results 193.2 6 20.9 nm (mean 6 SE) for quiescent-like astrocytes and 154.6 6 20.1 nm (mean 6 SE) for reactive-like astrocytes.

Cell Segmentation Of Afm Cell Culture Images

A supervised texture-based cell segmentation method was developed by extracting texture features from both AFM deflection and height images. We briefly explain the nature of these imaging modalities. In contact mode AFM (22, 23) , a probe attached to the end of a cantilever is scanned over the sample surface while the magnitude of cantilever deflection is measured from the reflected laser beam by a photodiode detector. A feedback loop maintains constant deflection via the input from the photodiode detector by applying a voltage to a piezoelectric actuator, which is capable of moving the sample stage in the x-y-z direction at high resolution. The constant deflection means a constant force is applied to the sample by the cantilever. AFM height data is constructed by recording the voltage applied to the z piezo at the same time the probe’s (x, y) position. The deflection data is obtained by recording the cantilever deflection that occurs prior to reestablishing the constant force. Therefore, an AFM deflection image is the height intensity differences between consecutive pixels, and hence mathematically the first-order derivative of the height image along the fast scan axis. This was tested by taking the first derivative of AFM height images using [1, 21] and “conv2” command in MATLAB, and confirming the correspondence of the deflection image captured by the instrument and the derivative of height image.

For cell segmentation, AFM raw data were exported as an ASCII file and AFM height and deflection images were loaded. To eliminate the nonlinearity from piezo scanners in the AFM height images, the images were flattened by subtracting a correction plane from the height image. The nonlinearity in the height images was in the vertical direction (slow scan axis). The correction plane was constructed by searching the column that has the minimum standard deviation, using a leastsquares fit of that column to a second degree polynomial, and assuming each column has the same nonlinearity effect. The flattening step is different from the “flatten” command in the Nanoscope IIIa software and the step was implemented using “lsqcurvefit” command in MATLAB.

Sequential forward selection (SFS) and sequential forward floating selection (SFFS) (24) feature selection algorithms were used to identify the most discriminative features, avoid the effects of curse of dimensionality (25) , and reduce the computational cost for cell segmentation. SFS and SFFS were implemented to determine the sub-optimal feature set from a total of 27 types of textural features including local statistics and GLCM texture feature models. The texture features were extracted from each pixel of AFM height and deflection images by setting the moving window size to 3 3 3, 5 3 5, and 7 3 7 determined by the minimal time required to classify culture surface and cell membrane texture patterns. The total time required for SFS calculation of PLL glass, Aclar, PLL Aclar, and nanofibrillar scaffolds using 5 3 5 mask size were 9 h 25 min, 5 h 48 min, 13 h 52 min, and 18 h 56 min, respectively.

Local statistics were used to estimate textures related to first, second, and higher order statistics. Standard deviation and mean by standard deviation features were defined as:

EQUATION (4): Not extracted; please refer to original document.

where

l5 1 N p 1 1 . . . 1p N ð Þ , p i

is the pixel intensity, and N is the number of pixels in the moving window.

GHPF textural feature was calculated as described before (26) . Texture measure A was defined as:

f 3 5 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi C h x; y ð Þ 2 1C v x; y ð Þ 2 q (5)

where C h x; y ð Þ5IÃB, * represents 2D convolution, C h and C v are the horizontal and vertical convolution, I is input AFM image, and B is a bar mask of the form [21 2 21]

or [21 21 2 2-1 21] (27).

Entropy is a measure of the uncertainty of a random variable (28) , and was calculated by:

f 4 52 X K k51 P X5k ½ log P X5k ½ ð Þ (6)

where X is the discrete random variable with S x 5 1; 2; . . . ; K f gand pmf p k 5P X5k ½ . Skewness is a measure of symmetry and kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. These features were calculated as follows:

EQUATION (8): Not extracted; please refer to original document.

where l and r are the means and standard deviations of the pixel intensities in the moving window. Power spectrum of GHPF was more discriminative than the power spectrum of height images, and it was calculated from GHPF images by:

EQUATION (10): Not extracted; please refer to original document.

where G(x, y) is the GHPF image, and F(u, v) is the discrete Fourier transform, which was calculated by using “fft2” command in MATLAB (29) . Local binary pattern (LBP), proposed in 1994 (30) , is a type of feature which was shown to be useful for texture analysis and face recognition, and was defined as:

LBP5 X N 21 i50 u t i 2t c ð Þ2 i (11)

where u(x) 5 1 if x ! 0 and u(x) 5 0 otherwise. N is the number of pixels in the moving window, t i is the intensity of neighboring pixel i, and t c is the intensity of the center pixel in the moving window. Moving window size was constant and 3.

The rotational invariant version of LBP was calculated as (31) :

EQUATION (12): Not extracted; please refer to original document.

where ROR(x, i) performs a circular bit-wise shift on the 8 bit number x i times until a maximal number of the most significant bits is 0. Gray level co-occurrence matrix (GLCM) is a statistical method of extracting texture features from images (32) .

GLCM texture features including contrast, correlation, energy, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, and information measures of correlation 1 and 2 were described and formulated by Haralick et al. (32) . GLCM texture feature extraction was performed using fast calculation of Haralick’s texture features (33) . A total of 18 different types of GLCM features were calculated using equations given in the Supporting Information section.

The texture feature selection for cell segmentation is critical since it affects cell perimeter and cell area calculations, and hence the conventional and new CSI results. In SFS and SFFS algorithms, the newly found best texture feature was added to the current feature set when inclusion of the new feature resulted in at least 0.5% improvement in the cell segmentation recognition rate. Cell segmentation performance evaluation was based on the correctly classified pixels that belong to cell and substrate surface, and recognition rate was defined as: (34, 35) . In the present work, an artificial neural network classifier was used to form the decision boundary for pixel-by-pixel classification of AFM cell culture images. The neural network has an input layer, two hidden layers, and an output layer. The activation functions of the first and second hidden layer were hyperbolic tangent and logarithmic sigmoid transfer function, respectively. The training of the neural network is performed by determining weights by resilient backpropagation algorithm (36) . The average number of cell and scaffold/substrate surface training pixels was $42,000 and 60,000, respectively. The number of weights was $10% of the number of training patterns (37) . The number of training iteration was determined empirically and set to 30 times the number of input features to avoid the overtraining of the classifier. The output layer of the neural network has two nodes that are trained to take [1 21 ] when the output is cell pixel, and [21 1] when the output is scaffold/substrate pixel. This is done to increase the performance of the neural network (37) . Both nodes of the output layer are fed by the nodes in the second hidden layer. The number of nodes in the second hidden layer is 60.

where n i 3 is the value of ith node of the second hidden layer, w i;j 34 denote the weights between n i 3 and n j 4 . The neural net-work is used to classify each AFM image pixel either as a “cell pixel” or a “substrate pixel,” and the final classification method becomes the whole artificial neural network. The implementation of the neural network was performed using “newff” command in MATLAB. The cell versus substrate surface ground truth images were determined by observation for the Aclar, PLL Aclar, and PLL glass cell culture images. As reported in (26) , cell boundaries on nanofibrillar scaffolds, are not easily distinguishable by human observation of either AFM height or deflection images. The cell and nanofibrillar scaffolds ground truth data were therefore determined using AFM GHPF cell culture images (26) . After the texture feature set was determined for each culture surface by SFS, SFSS algorithm, and human observation, texture features in the set were extracted, binarized, concatenated, and applied to the input of the classifier for training. The trained neural networks were saved, and the binary cell masks were obtained using the AFM images and trained networks. The morphological close operation and image filling were used to reduce the segmentation error. Small regions in the segmented images that were <1% of the whole image were eliminated, and then cell masks were ready for CSI and new CSI calculations.

Astrocyte Morphology Investigation By New Afmbased Csi

The new CSI that includes volumetric information extracted from AFM images of cells on surfaces is defined as (15)

EQUATION (16): Not extracted; please refer to original document.

Departure from unity reflects the average departure from a 3D hemispherical volume by both stellation and cell spreading. The surface area of each cell was calculated by splitting the AFM topography faces into triangles. The area of a triangle in 3D space was computed using the cross product given by (38) :

SA triangle 5 0:5 j v 2 2v 1 ð Þ 3 v 3 2v 1 ð Þj (17)

where SA triangle is area of a triangle on the cell surface and coordinates of the vertices are given by vi 5 (xi, yi, zi). The surface area of each triangle was computed over the segmented cell area and then cell surface area was obtained. The volume of each astrocyte was calculated by assuming each pixel and its z coordinate as a square prism (39) . The volume under each pixel was computed by multiplying the unit pixel area by the height of that pixel. This was repeated for all of the cell region pixels. The base level for all culture surfaces was determined by taking the average of substrate/scaffold height values in the AFM height image. Variations in the new CSI data among the culture surfaces were analyzed using ANOVA followed by pairwise post hoc comparisons with Tukey’s test. Significance levels were set at P < 0.05.

Astrocyte Morphology Investigation By Conventional Csi And Comparison

The conventional CSI definition given in Eq. (1) was used for comparison investigation of the quantitative morphometry of the cerebral cortical astrocytes cultured on the four different culture surfaces that presented different nanophysical cues (3). Variations in conventional CSI data among the culture surfaces were analyzed using ANOVA followed by pairwise post hoc comparisons with Tukey’s test. Significance levels were set at P < 0.05. The illustration of conventional and new CSI calculation steps is shown in Figure 1 .

Cell Segmentation

Strategy. The individual recognition rates of each textural feature for the four culture surfaces, defined as the recognition rate [Eq. (13) ] rendered as a percent, are shown in Figure 2 . The highest recognition rates were observed for PLL glass because of its relatively smooth surface compared to cell surface. All GLCM features except dissimilarity, inertia, cluster shade, and cluster prominence were highly discriminative for cell segmentation on PLL glass. For nanofibrillar scaffolds and Aclar surfaces, the power spectrum and standard deviation were the most discriminative features, respectively. The lowest recognition rates were observed for the cell segmentation on PLL Aclar. The most discriminative feature for cell segmentation on PLL Aclar was mean by standard deviation. Power spectrum and standard deviation features were good discriminators on nanofibrillar scaffolds, Aclar, and PLL Aclar, while the recognition rates of LBP ri and GLCM features: dissimilarity, inertia, cluster shade, and cluster prominence were low for all culture surfaces.

The sub-optimal textural feature set for each culture surface found by SFS and SFFS algorithms is given in Table 1 . In general, features extracted from height images were more discriminative than deflection images.

The cell segmentation performance depended on the window size as well as the culture surface and the texture features identified in the feature set found by feature selection algorithms. Three different window sizes were investigated in this work: 3 3 3, 5 3 5, and 7 3 7. The recognition rates increased as the window size was increased from 3 3 3 to 7 3 7 for all culture surfaces. The number of input texture feature (dimension) changed from one to four.

Computational demand. The decision for determining the final window size and feature set for cell segmentation was made by considering both the recognition rate and the computational cost. The maximum acceptable feature extraction computation time for a 256 3 256 image was set to 10 s to enable implementation of the cell segmentation algorithm on an ordinary personal computer. The recognition rates of standard deviation, mean by standard deviation, texture measure A, and kurtosis for cell segmentation on PLL glass using the 3 3 3 window size were close. After doing pairwise comparisons of these features on multiple images, kurtosis feature using 3 3 3 moving window size was found to be the most discriminative. For PLL glass, GLCM features were not required and were not used due to their high computational cost. For nanofibrillar scaffolds, the first two features of SFS result: power spectrum and standard deviation were used. The recognition rate of power spectrum plus standard deviation proved better than power spectrum alone for nanofibrillar scaffolds, and the computational cost of adding the standard deviation was low. For cell segmentation on Aclar and PLL Aclar surfaces, the feature set found by SFS using 5×5 moving window size was used. Window size increases were carefully investigated for cells on PLL Aclar surfaces; however, use of up to the 9 3 9 window size did not increase the cell segmentation recognition rate. Therefore, the feature set found by SFS using the 5 3 5 moving window size was also used for cells on PLL Aclar surfaces. Feature extraction computation times for 256 3 256 images via 3 3 3, 5 3 5, and 7 3 7 moving window sizes are compared in Figure 3 . The final window size and feature set performance results for cell segmentation are summarized in Table 2 . Representative examples of input AFM height and deflection images and cell segmentation results are shown in Figure 4 . The low recognition rate for cell segmentation on PLL Aclar surfaces can be seen in the figure.

Comparison Of Conventional And New Csi Analysis Results

The conventional and the new CSIs were used to perform quantitative investigations of astrocyte responses as a function of 1) changes in nanophysical environment cues, and 2) dBcAMP-treatment. At 24 h, quiescent-like and reactive-like astrocytes on all substrates exhibited significant variation in their morphologies. The conventional CSI results, shown in Figure 5a , indicated that quiescent-like astrocytes cultured on

Original Article

Aclar surfaces were more stellate than the ones on other surfaces. The mean CSI of astrocytes cultured on PLL Aclar and nanofibrillar scaffolds were close. The biomedical interpretation of the conventional CSI results would be that astrocytes cultured on nanofibrillar scaffolds and PLL Aclar were responsive to the dBcAMP-treatment whereas ones on Aclar and PLL Aclar were almost unchanged.

However, the new CSI results (Figure 5b) showed that both quiescent-like and reactive-like astrocytes cultured on the Aclar surfaces had the lowest mean CSI values. This implies that, with the same stellation counted in the new CSI, there is also dominant minimal spreading behavior. The new CSI analysis indicated that astrocytes cultured on Aclar surfaces were less spread than the ones on other substrates, and this is consistent with our previous independent AFM crosssection analysis (3) . For all surfaces, the new CSI value was significantly increased after dBcAMP-treatment except the ones on PLL glass. The highest new CSI values were observed for quiescent-like astrocytes on PLL glass and reactive-like astrocytes on PLL Aclar. The new CSI of both the quiescentlike and reactive-like astrocytes on nanofibrillar scaffolds were in the midway between Aclar and PLL Aclar. The new CSI of quiescent-like and reactive-like astrocytes on PLL Aclar surfaces was significantly higher than the corresponding ones on Aclar surfaces. The biomedical interpretation of the new CSI values for astrocytes on PLL functionalized surfaces would be that PLL functionalization induced an increase in cell spreading for both quiescent-like and reactive-like astrocytes. This is Figure 2 . Recognition rates of individual texture features extracted from AFM height images using 5 3 5 moving window size. (std: standard deviation; GHPF: Gaussian high pass filter; TMA: texture measure A; spec: spectrum; LBPri: rotationally invariant local binary pattern; hom: homogeneity; IDM: inverse difference moment; avg: average; var: variance; diff: difference; IMC: information measure of correlation; cluster prom.: cluster prominence).

consistent both with our previous independent AFM crosssection analysis (3) and with the known interaction of positively charged PLL with negatively charged cell membrane moieties (40, 41) .

Discussion

The present work is one of the first studies to explore the sub-optimal textural feature set for cell-scaffold/substrate segmentation of AFM cell culture images. As demonstrated by the present work, use of a feature set found by SFS and SFFS, enables cell segmentation in comparative situations with average recognition rate higher than 90%. This is a valuable approach for studies in which the biomedical goal is to investigate changes in cell behavior in response to varying environments, including variations in regenerative, stem cell, and cancer cell environments. In the present work, a feature set selected from 27 textural features including GLCM and local statistical models and a multi-layer neural network classifier were used to develop a semiautomatic cell segmentation algorithm for a comparative study of neural cells in four different regenerative environments. SFS and SFFS feature selection algorithms were implemented and identified an individual texture feature set for cell segmentation in the culture environments. The feature set selections were then further revised to meet a 10 s computational cost requirement for a 256 3 256 AFM image, enabling code access by a majority of users. 1 In this work, a new CSI was defined that revealed quantitative cell spreading information not included in a conventional cell shape index. This required mathematical segmentation of cells from scaffolds/substrates. The difficulty of segmentation varied, depending on the textural similarity between cell membranes and substrate/scaffold surfaces. The surface roughness of a cell membrane has a large variance because of the different types of glycoproteins, cell protrusions, channels, and protein assemblies present. In terms of surface roughness, astrocyte cell membranes and PLL Aclar surfaces were closer than those of PLL glass and Aclar surfaces, resulting in a low recognition rate on PLL Aclar surfaces. The segmentation was also challenging on nanofibrillar scaffolds. Cells on nanofibrillar scaffolds interact with these surfaces via nanoscale edges and processes that are not distinguishable from the nanofibrillar background by AFM height or deflection (or phase, not shown) imaging. This is because the cellular edges and processes are approximately the same order in height as the background nanofibers, $100 to $300 nm. This difficulty was partly overcome by using GHPF as described in detail in Ref. 26 .

The development of the new CSI in the present work was motivated by the need to accommodate obvious differences in cell spreading observed in response to regenerative environment differences, identified by the 3D volumetric capability of AFM imaging.

All cells need to attach to a native or synthetic extracellular matrix or to another cell to survive. The attachments may be relatively permanent, e.g., cardiomyocytes in heart tissue or dynamic, e.g., astrocyte perivascular endfeet in contact with capillary basement membranes at the blood-brain barrier. Cell spreading is a consequence of cell and surface interactions that are initiated with cell attachment (42) . Figure 5b of our present work showed that the reactive-like astrocyte spreading on all culture surfaces, except PLL glass, were significantly higher than quiescent-like astrocyte spreading. This result indicates that cell spreading could be an indication of astrocyte immunoreactivity in culture. Cell spreading is related to how cell volume is distributed on a surface. If cell volume is mainly aggregated in the center and cell shape is like a hemisphere, then the cell is unspread. If cell volume is distributed evenly and cell has a flattened shape, then the cell is spread. A conventional CSI ignores the 3D shape of the cell, and uses only cell perimeter and cell projection area data from 2D images to represent the 2D shape of the cell. The new CSI analysis results in a combined measure of cell spreading and stellation information, thus utilizes the volumetric information provided by AFM. The calculation of the new CSI is practical since only cell surface area and cell volume measurements are required. The new CSI is, therefore, a new promising quantitative cell morphology evaluation method that is expected to be useful for biomedical research in regenerative medicine.

Use of the new CSI in the present work enabled quantitative results capable of biomedical interpretation. Our studies showed that the dBcAMP-treatment induced a statistically significant increase in the new CSI of cerebral cortical astrocytes for all surfaces except the ones on PLL glass. The unusual astrocyte response on PLL glass is possibly due to the high substrate stiffness (4). The new CSI of quiescent-like astrocytes on PLL glass was significantly higher than for other substrates. The high CSI for PLL glass indicated that astrocytes spread more on PLL glass than on other substrates, which is consistent with previously published results (43, 44) . Increased spreading with dBcAMP-treatment on Aclar, PLL Aclar, and nanofibrillar scaffolds, is possibly because the increased GFAP expression in astrocyte cytoskeleton induced spreading of astrocytes. The quiescent and reactive-like astrocytes on Aclar were less spread compared to the corresponding ones on PLL Aclar, which indicates that PLL functionalization induced cells to become more spread. The new CSI of astrocytes on nanofibrillar scaffolds was in the midway between Aclar and PLL functionalized surfaces indicating a moderate cell behavior on these surfaces. The quiescent-like astrocyte process thickness was higher than reactive-like astrocyte process thickness on nanofibrillar scaffolds, which is consistent with the increased spreading behavior with dBcAMP-treatment.

The important information about the cell spreading behavior was missing from the conventional CSI analysis. It is crucial to realize that the biomedical interpretation of the conventional CSI results, that astrocytes cultured on nanofibrillar scaffolds and PLL Aclar were responsive to the dBcAMPtreatment whereas ones on Aclar and PLL Aclar were almost unchanged, was incorrect and misleading.

The new CSI in the present work was developed for use with cells in culture. Recently, cell culture standard practice Figure 3 . Feature extraction computation time for 256 3 256 images via 3 3 3, 5 3 5, and 7 3 7 moving window sizes. TMA, std, mean*std, and power spec. extraction time does not strongly depend on the moving window size whereas entropy, skewness, and kurtosis computation time increase as moving window size increase. GLCM textural feature extraction is computationally more expensive than local statistics except LBPri. GLCM feature extraction times do not depend on the window size and feature type except for cluster shade and cluster prominence. (std: standard deviation; TMA: texture measure A; spec: spectrum; LBPri: rotationally invariant local binary pattern; hom: homogeneity; IDM: inverse difference moment; avg: average; var: variance; diff: difference; IMC: information measure of correlation; cluster prom.: cluster prominence). Average computation time is given for 20 randomly selected 256 3 256 pixel AFM images. The texture features extracted from AFM height and deflection images were indicated by (h) and (d), respectively. has changed from using planar glass or plastic substrates to using fibrous and scaffold environments that produce more biomimetic cell morphology results. These changes in cell culture protocols result in cell segmentation texture changes, and therefore advantage in using our strategy of selection from a texture feature set.

Cells in culture are often investigated using optical techniques including confocal laser scanning microscopy (CLSM) (10, 13) and recently superresolution microscopy (SR) in stochastic (PALM, STORM) and optical (STED) formats (45) . AFM is relatively a novel instrument in the biomedical community (4, 14) . Its key advantage for use in quantitative CSI studies is its extremely high axial resolution, <1 nm in the z direction. Axial resolution for CLSM and also for stochastic SR is $200-500 nm, while axial resolution for optical SR can reach $50 nm through splitting its resolving light into two light paths. Total internal reflection fluorescence microscopy (TIRF) is another optical technique, which is used for specifically for its axial resolution, $100-200 nm (46) . For cell volume analysis by any of these optical microscopy techniques, special stains that delineate the plasma membrane and have minimal intracellular diffusion must be used. Their fluorescence must also be compatible with all excitation and probe laser wavelengths, which require careful planning and execution of sample preparation. Furthermore, immunostaining requires permeabilization of the cell membrane, which can significantly affect the cell spreading. AFM sample preparation is relatively easy, and it requires no staining, fixing, or labeling. Therefore, the cell volume and cell surface area calculations and hence the new CSI calculations from AFM images are the most accurate that are currently available and have the greatest integrity and ease of sample preparation. Because AFM is a surface technique, it cannot be used to characterize the cell spreading of cells cultured in 3D scaffolds or hydrogels. However, new techniques designed to combine the advantages of AFM with those of continuously improving optical microscopies are emerging (47) . These techniques, coupled with powerful image processing analyses and interpretations, will lead to key advances in cell morphometry and to significant nanobiomedical discoveries.

SECTION

Cytometry Part A 87A: 1090À1100, 2015

Codes are publically available at http://www.msu.egr.edu/ebnlOriginal ArticleCytometry Part A 87A: 1090À1100, 2015

1. Introduction

Electrospun carbon nanofibers are hollow core carbon structures produced by self-assembly within an evaporating liquid jet. They are of great interest as biocompatible carbonbased structures for tissue scaffold applications [1] .

Electrospinning-based self-assembly is achieved by the application of an electrostatic force between a charged droplet containing polymer monomers in liquid suspension and a collecting metal electrode [2] [3] [4] . A charged droplet of monomer in liquid suspension is formed at a capillary tip, conveniently a metal syringe tip of known bore radius. A 20-30 kV potential difference is applied between the syringe tip and a metal collecting plate held 10-20 cm apart. At a critical field, the force due to the electric field overcomes the surface tension forces holding the droplet, and the solution starts flowing towards the collecting electrode in the form of a charged jet. Initially, the charged droplet deforms into a cone, which then separates into splayed charged jets due to mutual repulsion. As the liquid in each splayed jet evaporates, the jet diameter shrinks rapidly, creating the conditions for self assembly of the polymers into nanofibers. The diameters of the collected electrospun carbon nanofibers may range from a few microns to as low as 10 nm depending on the complicated interplay of Coulomb forces. Electrospun carbon nanofibers used in tissue scaffold applications typically have diameters on the order of ∼100 nm.

In this work, results of an investigation of monomer choice, to control the initial droplet charge state, and syringe bore size, to control the initial droplet radius/surface tension, are presented. In Series 1, 15% weight of poly methyl methacrylate suspended in tetrahydrofuran (THF)/dimethylformamide (DMF) was electrospun using a 200 μm fixed bore radius, without and with the addition of 6% single walled carbon nanotubes produced by NASA-GSFC Cooled Welding Method added to the suspension. In Series 2, 15% weight of poly (εcaprolactone) suspended in methylene chloride (MC)/dimethylformamide (DMF) was electrospun using bore radii of 152.4, 254.0 and 406.4 μm. The tip to collector plate distance was held constant at 10 cm. The voltage was held constant at 25 kV. A key property under investigation in our group is the effect of electrospinning conditions on resulting carbon nanofiber elasticity. Recent research indicates that the elasticity of the tissue scaffold is an important property for successful entrained cell re-growth. The Force Integration to Equal Limits (FIEL) mapping method [5, 6] was used to measure the relative elasticity between the samples.

2.1. Experimental

Poly(methyl methacrylate) (PMMA) with a molecular weight of 120,000 and poly (ε-caprolactone) with a number average molecular weight (M n ) of 80,000 from Aldrich Chemical were used in these experiments ( Fig. 1a-b ). Single walled carbon nanotubes, 6% by weight, produced by NASA-GSFC Cooled Welding Method were added to one batch of PMMA experiments. The electrospinning set-up consisted of a Sorensen 230-3/12P R&D high voltage DC power supply, with a syringe and an 8 × 12″ aluminum collecting foil held 10 cm apart. The experiments were horizontally configured, with the syringe tilted about 30°to facilitate droplet formation (Fig. 1c) . Field emission scanning electron microscopy (FESEM) of gold coated samples was used to assess the initial non-woven mat topography and nanofiber diameter, and to confirm that subsequent atomic force microscopy results were truly representative. Field emission scanning electron microscopy was performed using a Hitachi S-4700II Field Emission SEM operated at 1.0 kV.

Atomic force microscopy (AFM) with force volume imaging [7] was performed using a Veeco Metrology Nanoscope IIIa Scanning Probe Station, in contact mode with a nominal tip radius of 25 nm, operated in ambient air. A silicon nitride tip was used. The silicon nitride tip (hardness ≈ 35 GPa) was assumed to be much harder than any of the tissues scaffold samples.

2.2. Fiel Mapping Method For Relative Elasticity

In force volume imaging, a single force curve records the force felt by the tip as it approaches to and retracts from a point on the sample surface. A force volume image consists of an array of force curves over a user-specified area. Each force curve is measured at a unique X-Y position within the area and force curves from an array of X-Y points are combined into a three dimensional array of force data.

The Force Integration to Equal Limits (FIEL) mapping method can be used to produce a robust measurement of relative elasticity between samples. In FIEL mapping, force curves (FC) taken during force volume imaging are used to measure the cantilever deflection (d) versus the sample position (Z). The FCs are taken in Relative Trigger mode, in which the sample is advanced until a preset cantilever deflection relative to the zero deflection position is reached, as measured by the AFM optical detection system A corresponding Force Distance (FD) curve is defined by using an absolute distance (D = Z − d), which is the separation between the tip and the sample surface, instead of using sample position (Z). If the AFM tip is approximated as a parabola with a spherical tip (Hertz model), then the force on the AFM cantilever (F) can be calculated by

EQUATION ð1Þ: Not extracted; please refer to original document.

where E is the elastic modulus of the sample, R is the radius of the probe sphere, δ is the indentation and ν is the Poisson ratio. The relationship between elasticity and FD curves derived from FIEL mapping method is

EQUATION ð2Þ: Not extracted; please refer to original document.

where

EQUATION ð3Þ: Not extracted; please refer to original document.

is the local elastic constant, and w is the work done by the cantilever, which is equal to the area under the FD curve. The area under the FD curve (w) can then be graphically calculated and used to represent the elasticity feature of the tissue scaffolding. The FIEL mapping method may be used to define the relative elasticity of the different samples as follows. Using the method of Ref. [5] , the FD curves must be acquired using the same tip/ cantilever. The tip should be much harder than the sample. The elastic properties of the sample are assumed to dominate any viscous properties and plasticity is not considered. Under these conditions, the externally applied force of Eq. (1) may be assumed to be the same and what changes is the elastic response of the sample. Using Eqs. (1)-(3) and graphically evaluating the relative work as the areas under two FD curves,

EQUATION ð4Þ: Not extracted; please refer to original document.

which directly relates the areas under two FD curves to the ratio of their elastic constants. The relative sample elasticity is given by the inverse power relationship of Eq. (3).

3. Results

The PMMA monomers with and without single walled carbon nanotubes were electrospun under conditions of 200 μm bore radius, 10 cm between the tip and the collector foil, and 25 kV electrostatic potential difference. The results are shown in Fig. 2 . Electrospinning of both resulted in micron-scale diameter fibers. This is too large for tissue scaffold applications and PMMA was not investigated further. Anomalous beam and tip deflections observed during the electron and atomic force microscopy experiments indicated highly charged surface states.

The poly (ε-caprolactone) monomers were electrospun under conditions of 10 cm between the tip and the collector foil and 25 kV electrostatic potential difference. The bore radius was varied through (a) 152.4, (b) 254.0, and (c) 406.4 μm. The results are shown in Fig. 3 . The nanofiber average diameter was ∼90-110 nm, increasing slightly as the bore size was increased. Nuisance bubbles were observed in addition to the nanofibers. The average diameter of the nuisance bubbles decreased by ∼ 50% as the bore size was increased.

Atomic force microscopy with force volume imaging followed by Force Integration to Equal Limits (FIEL) mapping was used to measure the relative elasticity between the three poly (ε-caprolactone) nanofiber samples and the metal foil. The results of the FIEL elasticity mapping for the poly (εcaprolactone) samples are shown in Figs. 4 and 5 . A 5 μm 2 region of each sample (Fig. 4, left) was selected for analysis to ensure that a statistically meaningful number of force curves were taken on several nanofibers. As the AFM tip is vertically indenting a cylindrical object, only data collected along the top and center of each nanofiber matches the model. An erosion operation [6, 8] was performed along each nanofiber to restrict the data set to these reliable points. The area under each force distance curve was plotted as a map along each nanofiber. A typical result for the topmost nanofibers is shown in Fig. 4 (middle) . A histogram of the values shown in Fig. 4 (right) indicated that there were differences in the value distributions among the samples. The values of the areas under the force distance curves increased with increased bore radius. The result shown in Fig. 5 is the mean value for data similarly analyzed along several nanofibers. The mean value force distance curve areas increased with increased bore radius as shown in the Fig. 5 insets. As the area (w) is in an inverse power relationship with the elasticity E as shown in Eqs. 3and 4, these results indicate a decrease in sample elasticity (E) with increasing bore radius.