Anal. Chem. 1995,67, 2883-2891
Investigating the Relationship between Surface Chemistry and Endothelial Cell Growth: Partial Least-Squares Regression of the Static Secondary Ion Mass Spectra of OxygenGontaining Plasma-DepositedFilms Ashutosh Chilkoti,t Ann E. Schmierer,t Victor H. M ~ z = L u M , *and Buddy D. Ratner**t** Center for Bioengineering and Department of Chemical Engineering, BF- 10, University of Washington, Seattle, Washington 98 195
'he relationship between endothelial cell growth and surface properties of plasma-depositedfilms (PDFs) was investigated using partial least-squares regression (PLS). PDFs of oxygen-containing precursors were prepared under various conditions, and bovine arterial endothelial cells (BAECs) were grown on these substrates. Secondary ion mass spectrometry (SIMS)in the static mode was used to characterize the surface chemistry of these substrates. The growth of BAECs on the PDFs was correlated to the positive and negative static SIMS spectra of the PDFs by PIS. A good correlation between the SIMS spectra of PDFs and endothelial cell growth was obtained. Qualitative information was also extracted from the multivariate model, giving some information as to the most important variables influencing BAEC growth. The interactions of anchorage-dependentcell lines with synthetic polymeric substrates are of great interest for the design of biomaterials, in biotechnology, and for understanding biofouling. This study is part of an ongoing research program in our laboratories to identify the surface chemical/structural determinants of cell growth on synthetic polymeric substrates. We show here how a complex biological event can be correlated to aspects of a complex surface chemistry using multivariate statistical methods. We are specifically interested in the interactionsof endothelial cells with organic plasma-deposited thin films. Our interest in endothelial cells stems from the poor adherence to and growth of endothelial cells on currently available materials used in synthetic smalldiametervascular Endothelial cells, like other anchorage-dependent cells, need to spread on a substrate in order to pr~liferate.~.~ Inadequate spreading,possibly related to poor cell adhesion to the substrate, will inhibit +
Center for Bioengineering.
* Department of Chemical Engineering. (1)Van Wachem, P. B.; Beugeling, T.; Feijen, J.; Bantjes. A; Detmers, J. P.; van Aken, G. Biomaterials 1985,6,403-408. (2)Van Wachem, P. B.; Vreriks, C. M.; Beugeling, T.; Feijen, J.; Bantjes, A; Detmers, J. P.; van Aken, W. G. J. Biomed. Mater. Res. 1987,21,701-718. (3) Williams, S. IC;Jarrell, B. E.; Friend, L.; Radomski, J. S.; Carabasi, R A; Koolpe, E.; Mueller, S. N.; Thomton, S. C.; Marinucci. T.; Levine E.J. Surg. Res. 1985,38,618-629. (4)Folkman, J.; Moscona. A Nature 1978,273,345-349. (5)Ireland, G. W.; Dopping-Hepenstal, P. J.; Jordan, P. W.; O'Neill, C. H. Cell. Biol. Int. Rep. 1989, 13,781-90. 0003-2700/95/0367-2883$9.00/0 0 1995 American Chemical Society
proliferation. Since the endothelial cells that line the blood vessels offer an almost perfect non-thrombogenic surface, lining of the inside of vascular grafts with endothelial cells is thought to be a viable strategy to provide a non-thrombogenic surface. However, the materials currently used in vascular grafts (Dacron, Teflon) do not support endothelial cell attachment and growth. Efforts to enhance endothelial cell growth on substrates by altering surface properties using ~ h e m i c a l ,biochemical,8-12 ~,~ or plasma treatment m e t h o d ~ l ~are - ~ ' in progress. We have focused on the plasma deposition of ultrathin organic films to promote the endothelialization of surfaces. The term plasma in this context refers to a complex, partially ionized gas mixture comprising electrons, ions, radicals, and gas atoms and molecules in ground and excited states. The energy required to generate a plasma from a gas at room temperature is provided by an electromagnetic field?2323 All species (ions, radicals, atoms) are reactive with surfaces exposed to the plasma. The deposition of thii films from the plasma environment involves inter- and intramolecular reactions between plasma species and (6)McAuslan, B. R.; Johnson, G. J Biomed. Muter. Res. 1987,21,921-935. (7)McAuslan, B. R;Johnson, G.; Hannan, G. N.; Noms, W. D.; Exner T. J. Biomed. Mater. Res. 1988,22,963-976. (8) Noms, W. D.; Donald, G. S.; Johnson, G.; Ho, T.; Mc Auslan, B. R Clin. Mater. 1989,4, 13-22. (9)Massia, S. P.; Hubbell, J. A J. Cell Biol. 1991, 114,1089-1100. (10)Massia, S. P.; Hubbell, J. A Anal. Biochem. 1990,187,292-301. (11)Massia, S. P.; Hubbell, J. A Ann. N. E.: Acad. Sci. 1990,589,261-270. (12)Massia, S.P.; Hubbell, J. A J Biomed. Mater. Res. 1991,25,223-242. (13) Chinn, J. A;Horbett, T. A: Ratner, B. D.; Schway, M. B.; Haque, Y.; Hauschka, S. D. J. Colloid Intetface Sci. 1989,127,67-87. (14)Dekker, A.; Reitsma, IC;Beugeling, T.; Bantjes, A; Feijen, J.; van Aken, W. G. Biomaterials 1991,12,130-138. (15)Ertel, S. I.; Ratner, B. D.; Horbett, T. A J Biomed. Mater. Res. 1990,24, 1637-1659. (16)Ertel, S. I.; Chilkoti, A; Horbett, T. A; Ratner, B. D.J. Biomater. Sci.: Polym. Ed. 1991,3,163-183. (17)Greisler, H. P.; Dennis, J. W.; Schwarcz, T. H.; Klosak, J. J.; Ellinger, J.; Kim, D. U.Arch. S u q . 1989, 124,967-972. (18)Griesser, H. J.; Johnson, G.: Steele, J. G. Polym. Mater. Sci. Eng. 1990,62, 828-832. (19)Klee. D.; Breuers, W.; Hocker, H.; Mittermayer, C. Makromol. Chem., Macromol. Symp. 1988,19, 179-187. (20)Pratt, K. J.; Williams, S. IC;Jarrell, B. E. J Biomed. Mater. Res. 1989,23, 1131-1147. (21)Sipehia, R Biomater., Art. Cells, Art. 0%.1990, 18,437-446. (22)Hollahan, J. R,Bell, A T., Eds. Techniques and Applications of Plasma Chemistry; John Wiley & Sons: New York, 1974. (23)d'Agostino, R., Ed. Plasma deposition, treatment, and etching of polymen; Academic Press: Boston, 1990.
Analytical Chemistry, Vol. 67, No. 17, September 1, 1995 2883
surface species. Plasma-deposited films show an excellent adhesion to different substrates, thus providing a stable modfication of surface properties while maintaining desirable bulk properties of the materials, such as porosity and c o m p l i a n ~ e . ~ ~ * ~ ~ Plasma-depositedfilms (F'DFs) offer several advantages for the surface modification of bi0materials.2~These include deposition as ultrathin films in a continuous process, conformal adherence to substrates largely independent of the geometry or chemistry of the substrate, and the sterile nature of the plasma environment. Furthermore, the surface chemistry of PDFs engenders unique interactionswith serum proteins and anchoragedependent~ells.'~2~ However, the chemistry of PDFs that is responsible for their unusual biological interactions also poses a considerable analytical challenge. The fact that these materials are multifunctional,26 c r o ~ s - l i n k e d ,and ~ ~ ~deposited ~* as ultrathin films (thicknesses on the order of a few hundred angstroms)29precludes their characterization by bulk polymer analysis techniques. In a previous study, PDFs were created from oxygencontaining precursors such as acetone, methanol, formic acid, and mixtures of these organic vapors with oxygen gas.16 The surface chemistry of these oxygen-containiigPDFs was studied by X-ray photoelectron spectroscopy (XPS) and chemical derivatization assays. The biological interactions of these films were investigated by a clonal growth assay using bovine aortic endothelial cells (BAECs) at low seeding densities16and the adsorption and retention of serum proteins of interest (e.g., cell antiadhesive proteins such as albumin and IgG and cell adhesive proteins such as fibronectin).25 No correlation was observed between the water wettability of the PDFs (contact angle) and cell growth.16 A qualitative relationship between the surface oxygen concentration (as determined by XPS) and BAEC growth was observed. We use the term qualitative because many exceptions to this trend were observed. Since PDFs are usually multifunctional, the surface oxygen concentration poorly describes their surface chemistry. Hence, the surface concentrations of hydroxyl, carbonyl, and carboxyl groups were ascertained by derivatization reactions in conjunction with XPS.16~26 The concentration of hydroxyl and carboxyl groups did not correlate with cell growth, which contradicted previous studies by other i n v e ~ t i g a t o r s . ~The ~ - ~concentration ~ of surface carbonyl groups showed good correlation with BAEC growth.16 However, these results were inconsistent with experiments on ketone-rich polymers, (e.g., poly(viny1methyl ketone)), which did not support cell growth. The inability of a single chemical factor to adequately account for BAEC growth on a variety of oxygencontaining polymeric surfaces has led us to postulate that multiple surface chemical factors are responsible for mediating BAEC growth for this class of materials. PDFs are usually multifunctional and cross-linked, (24) Ratner, B. D.; Chilkoti, A.; Lopez, G. P. In Plasma Deposition, Treatment and Etching ofpolymers;d'Agostino, R, Ed.; Plasma-Materials Interactions 3; Auciello, O., Flamm, D. L.,Series Eds.; Academic Press: San Diego, 1990; Chapter 7. (25) Ertel, S. I.; Ratner, B. D.; Horbett T. A. J. Colloid Interface Sci. 1991,147, 433-442. (26) Chilkoti, A; Ratner, B. D.; Briggs D. Chem. Mater. 1991,3, 51-61. (27) Morosoff, N.; Patel D. L. Polym. Prepr., Am. Chem. Sot. Diu. Polym. Chem. 1986,27,82-84. (28) Ohno, M.; Ohno, K; Sohma, J. J. Polym. Sci. 1987,25,1273-1284. (29) Unpublished observations. (30) Curtis, A. S. G.; Forrester, J. V.; Clark, P. J Cell Sci. 1986,86, 9-24. 1197-1211. (31) Vogler, E. A; Bussian, R. W.J.Biomed. Mater. Res. 1987,21, (32) Ramsey, W. S.; Hertl, W.; Nowlan, E. D.; Binkowski N. J. In Vitro 1984, 20, 802-808.
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Analytical Chemistry, Vol. 67, No. 17, September 1, 1995
which leads us to hypothesize that PDFs stimulate the growth of endothelial cells because their surfaces present an optimal distribution of certain functional moieties that are embedded in a rigid macromolecularmatrix (due to the presence of surface crosslinking). The presence of an optimal distribution of surface functional moieties embedded in a dense, cross-linked, macromolecular matrix, we hypothesize, promotes the selective adsorp tion of cell adhesive proteins in conformations favorable to subsequent cellular interaction. Conventional polymers, on the other hand, rarely display the range of functional moieties exhibited by PDFs; their surfaces are also more mobile at a segmental level. These differences in surface chemistry/structure may be responsible for the ultimate differences in the cell growth properties of conventional polymers and PDFs. These factors are diflicult to investigate with XPS and provide the motivation for the investigation of organic PDFs by static secondary ion mass spectrometry (SIMS). Static SIMS is a powerful technique for the characterization of polymeric surfaces. This is due to its surface sensitivity33 and the direct relationship between the SIMS fragmentation pattern and polymer ~ t r u c t u r e . ~The . ~ ~examination of the SIMS fragmentation patterns of these PDFs for correlations with cell growth, and the translation of this information into relevant surface structural moieties, however, are not trivial. S i c e static SIMS, like all mass spectrometries, is inherently multivariate (in that relevant chemical information is distributed within many mass peaks), the most efficient method to relate the SIMSfragmentation patterns with endothelial cell growth is to use multivariate regression methods. Various multivariate regression methods exist: inverse least squares (ILS) ,36 which is usually undertaken with best subset selection, classical least squares (CLS),36J7 principal components regression (PCR)?* and partial least-squares regression (PLS) ?9-43 Of the various multivariate regression methods available, PLS regression was selected in view of its being a full spectrum method and its bias toward optimal prediction rather than best fit of the independent variables, and also because it is a soft modeling technique which does not require an a priori knowledge of the structure of the model. The aims of this study are (1) to further clarify the role of surface functional groups in mediating BAEC growth on oxygencontainiig PDFs and (2) to determine the relevance of other surface chemical factors in mediating BAEC attachment and growth on oxygencontaining PDFs. PDFs incorporating a wide range of carbonyl concentrations were created by plasma deposition of a variety of ketonefunctionalized precursors, e.g., vinyl ketones, diones, and mixtures of aliphatic ketones and oxygen. The surface chemistry of these PDFs was characterized by XPS and static SIMS, and their interactions with endothelial cells were probed by a BAEX clonal (33) Hearn, M. J.; Briggs, D.; Yoon, S. C.; Ratner, B. D. S u 6 Interface Anal. 1987,10,384-391. (34) Briggs, D., Brown, A, Vickerman, J. C., Eds. Handbook ofStatic Seconday Ion Mass Spectrometry (SIMS); John Wiley & Sons: Chichester, 1989. (35) Briggs, D. Su?f Intelface Anal. 1986,9,391-404. (36) Haaland, D. M.; Easterling, R G.; Vopicka, D. A Appl. Spectrosc. 1985, 39,73-84. (37) Beebe, K. R; Kowalski B. R Anal. Chem. 1987,59,1007A-1017A (38) Wold, S.; Esbensen, K; Geladi, P. Chemom. Int. Lab. Sys. 1987,2, 37-52. (39) Haaland, D. M.; Thomas, E. V. Anal. Chem. 1988,60,119331202, (40) Haaland, D. M.; Thomas, E. V. AlaaI. Chem. 1988,60,1202-1208. (41) Geladi, P.; Kowalski, B. R. Anal. Chzm. Acta 1986,185, 1-17. (42) Geladi, P. J. Chemom. 1988,2,231-246. (43) Lorber, A.; Wangen, L. E.; Kowalski. B. R. J. Chemom. 1987,I , 19-31.
Table I.Calibration Set Samples, the Static SlYS Fragmentation Patterns of Which Were Correlated with Their BAEC Growth Results
Table 2. Description of Conventional Ketone-Functionallzed Polymers and Argon-Etched Polymers
sample"
precursor
deposition conditions
sample code
1 2 3 4 5 6 7
Falcon TCPS butanedione butanedione pentanedione vinyl methyl ketone vinyl methyl ketone vinyl ethyl ketone vinyl ethyl ketone acetoneb
control (+) P = 200, W = 20, t = 5 P = 500, W = 50, t = 5 P = 300, W = 50, t = 5 P = 250, W = 20, t = 5 P = 200, W = 50, t = 5 P = 200, W = 20, t = 5 P = 180, W = 50, t = 5 P = 44, w =5, F = 1, t = 10 P = 52, W = 5, F = 1, t = 10 P = 59, W = 5, F = 1, t = 10
PVMK PVMK-Ar PVEK PVEK-Ar PAAEMA PAAEMA-Ar PET PS
8
9 10 11
12 13 14 15
acetone- 10%02
acetone-20%02 acetone-30%02 acetone-40%02 methanol*
methanol- 10%02
casting solution 1%(w/v) in THF
2%(w/v) in toluene 2% (w/v) in THF
film film
descriptiona PVMK
argon-etched PVMK PVEK argon-etched PVEK PAAEMA argon-etched PAAEMA negative control control
Refer to Experimental Section for details on preparation of polymer films and argon etching. (I
P=60,W=5,F=l,t=lO P=65,W=5,F=l,t=lO P = 25, W = 5, F = 1, t = 10
P = 31, W = 5,F= 1 , t = 10
100%ethanol for 12 min, followed by repeated ultrasonic rinses in deionizedheverse osmosis purified water. a The sample numbers listed may be used to identify the samples in the concentration vector plot. See refs 26 and 47 for a description Poly(viny1 methyl ketone) (F'VMK) was acquired from Scienof the surface chemistry of the acetone-02 (and methanol-02) PDFs. tific Polymer Products (Ontario, NY). It was purified by dissoluFlow rates (fl for samples 10-13 and 15 are combined flow rates for both precursors in cm3(STP)/min-'; pressure (P) in mTorr, power (w) tion in chloroform and precipitation in methanol. The polymer in W, and reaction time (t) in min. was dried under vacuum and stored in a clean glass bottle for further use. Poly(viny1 ethyl ketone) 0 and poly(acetoacetoxyethyl methacrylate) (PAAEMA) were prepared by free radical initiated polymerization of vinyl ethyl ketone (Aldrich growth assay. The static SIMS results for these PDFs were then Chemical Co., Milwaukee, wr> and acetoacetoxyethyl methacrycorrelated with BAEC growth by PLS regression. The PLS late (Eastman Kodak Research Chemicals, Rochester, NY), calibration model was examined to determine the spectral features respectively. Details of the polymerization and purification of that strongly correlate with BAEC growth, and the relevant these polymers can be found el~ewhere.~? The polymers were spectral information was then qualitatively translated into surface centrifugally cast onto 12 mm diameter glass disks for surface structural information based on the large body of literature on analysis and 30 mm diameter poly(ethy1ene terephthalate) (Pm? the relationship of static SIMS of hydrocarbon and oxygencover slips for BAEC growth experiments. The glass disks were containing polymers to their surface s t r u ~ t u r e . ~ ~ ~ ~ ~ ~ ~ ~ - ~ * acquired from VWR Scientific (San Francisco, CA), while the PET A related set of experiments which attempt to directly examine disks were ordered from Nunc Inc. Prior to the polymer films the role of surface cross-linking on BAEC growth are also being centrifugally cast onto these substrates, the substrate were described in this study. Previous studies have indicated that cleaned as follows: the glass disks were cleaned by ultrasonication exposure of a polymer surface to a low-temperature plasma of an in a 1.5% (v/v) solution of Isopanasol (C.R. Callen Inc., Seattle, inert gas results in surface c r o ~ s - l i n k i n g .Hence, ~ ~ ~ ~ ~ketone WA) in deionized/reverse osmosis purified water, followed by functionalized polymers were etched with an argon plasma to ultrasonic rinses in deionizedheverse osmosis purified water, induce surface cross-linking. The surfaces were characterized by while the PET disks were cleaned by ultrasonication in reagent XPS and static SIMS, and their BAEC growth was assayed. These grade toluene, acetone, and methanol, consecutively. results are also reported in this manuscript. The polymer films were centrifugally cast onto the glass disks and PET cover slips from solutions in appropriate solvents (refer EXPERIMENTALSECTION Materials. Bacteriological grade polystyrene (F'S) (35 mm to Table 2 for details). Typically, 30 p L (12 mm diameter glass diameter, Falcon, No. 1008) and Falcon tissue culture polystyrene disk) or 80 p L (30 mm diameter PET cover slip) of the polymer (TCPS) dishes (35 mm diameter, No. 3001) were purchased from solution was pipetted onto the substrate, which was then spun Becton-Dickinson Corp. (Lincoln Park, NJ) . The bacteriological for 30-60 s at 4000 rpm on an EC-101 spin coater (Headway grade PS disheswere subsequently treated with plasmas of various Research Inc., Garland, ?x). The polymer films were then heatprecursors (Table l), while the Falcon TCPS dishes were used sealed in sterile bags and stored until further use. with no further modification. PS coupons for surface analysis were Plasma Deposition and Treatment. The PDFs were created die-cut from custom-ordered PS paddles (Corning Inc., Horsein a 13.56 MHz, capacitively coupled, radio frequency plasma heads, NY). The PS coupons were cleaned by ultrasonication in reactor, which has been previously described.13 Bacteriological grade PS dishes and PS coupons were supported in the interelec(44) Chilkoti, A.: Ratner, B. D.; Briggs. D. S U Interface ~ Anal. 1992,18,604trode region (-8 in. electrode spacing) of the plasma reactor on 618. a glass plate. The precursors used for creating the PDFs, and (45) Chilkoti, A; Castner, D. G.: Ratner, B. D.Appl. Spectrosc. 1991,45,209217. the reactor conditions, are listed in Table 1. The precursors were (46) Chilkoti, A.; Ratner, B. D.; Briggs, D. Anal. Chem. 1991,63, 1612-1620. repeatedly freeze-thawed under vacuum to remove dissolved air (47) Chilkoti, A; Ratner, B. D.; Briggs. D.; Reich, F.]. Polym. Sci., Polym. Chem. Ed. 1992,30, 1261-1278. and prevent nitrogen incorporation into the PDFs. Prior to the (48) Heam, M. J.; Briggs D. S U Intelface ~ Anal. 1988,11, 198-213. plasma being generated from the precursor of interest, the (49) Clark, D. T.; Dilks, A. ]. Polym. Sci., Polym. Chem. Ed. 1977,15, 2321samples were etched with an argon plasma at the following 2345. (50)Clark, D. T.; Dilks, A]. Polym. Sci., Polym. Chem. Ed. 1978,16, 911-936. conditions: pressure ( p ) , 175 mTorr; power (W), 30 W, flow rate Analytical Chemistry, Vol. 67, No. 17, September 1, 1995
2885
(F),4 cm3(STP) min-I; time (t),5 min. The base pressure in the reactor was -10 mTorr. To ensure that PDFs from a particular precursor were not contaminated by previous depositions from other precursors, the following precautions were observed after the preparation of PDFs from any one precursor: the reactor was disassembled, and the glass reaction vessel was baked out overnight at 365 "C. The Ultratorr fittings and bellows used to connect the reactor parts were soaked overnight in an acetone/ methanol bath and dried the next day in an oven at 80 "C. The reactor was then reassembled and etched with an argon plasma (the same conditions as above, except that a higher power of 80 W was used) to further clean the inside of the glass reaction vessel. PVMK, PVEK and PAAEMA were etched with an argon plasma in the reactor described above. Centrifugally cast films on 12 mm diameter glass disks (for surface analysis) and 30 mm diameter PET disks (for BAEC growth) were introduced horizontally into the interelectrode region of the reactor such that the polymer-coated side was exposed to the plasma. The reaction conditions used for the argon etch were as follows: P = 225 mTorr, W = 30 W, F = 4 cm3(STP) min-I, and t = 30 s. Upon removal from the reactor, the PDFs and the argon-etched polymers were immediately stored in heat-sealed bags to reduce adventitious contamination. Chemical Derivatization. The PDFs were derivatized with hydrazine vapor (Aldrich Chemical Co., Milwaukee, Wl) for 1 h using the following reaction:
The experimental protocol has been published elsewhere.26 PVMK was also derivatized with the samples of interest as a positive control; this allowed the extent of reaction to be ascertained, so that the results for samples derivatized at different times could be compared. X-ray Photoelectron Spectroscopy. The PDFs were analyzed by XPS in a SSX-100 spectrometer (Surface Science Instruments Inc., Mountain View, CA), which includes a monochromatized Al Ka X-ray source, a hemispherical analyzer, and a positionsensitive detector. All polymers were analyzed at a 35" takeoff angle. The take-off angle is defined as the angle between the sample plane and the axis of the analyzer. Survey scans (0-1000 BE) were run at 150 eV analyzer pass energy and 1000 pm X-ray spot size to determine the elemental composition of each polymer. The experimental peak areas were numerically integrated and normalized to account for the number of scans, the number of channels per eV, the Scofield photoionization cross section,51and the sampling depth. The SSX-100transmission function for a pass energy of 150 eV was assumed to be constant over the appropriate range of photoelectron kinetic energies.Sz The sampling depth was assumed to vary as where KE is the kinetic energy of the photoelectron^.^^ High-resolution C1, spectra were obtained at a pass energy of 25 eV. The CI, spectra were resolved into individual Gaussian peaks using a least-squares fitting program. A combination of a (51) Scofield, J. H. J, Elect. Spectrosc. Relut. Phenom. 1976,8,129-137. (52) Transmission function characterization. Application note from Surface Science Instruments, Mountainview, CA, 1987.
2886 Analytical Chemistry, Vol. 67, No. 17, September 1, 1995
low-energy flood gun set at 5 eV and a metal screen placed on the sample holder was used to minimize sample charging. All polymer binding energies (BEs) were referenced by setting the CH, peak maximum in the resolved CI, spectra to 285.0 eV. Static Secondary Ion Mass Spectrometry. Static SIMS analyses were performed on a Perkin Elmer Model 3700 system with a 1 nA Xe+ ion beam rastered over a 4 x 4 mm2 area. A total acquisition time of 4 min or less was required for each sample. This ensured static conditions for all spectra.53 Charge neutralization was achieved with a low-energy (0-30 ev> electron gun. Clonal Cell Growth Assay. Bovine aortic endothelial cells, a gift of Dr. Steven Schwartz (University of Washington), had been previously tested for the presence of von Willebrand factor and are free of mycoplasma.16 BAECs were cultured in Iscove's modified Dulbecco's medium containing 10%heat-inactivated calf serum, 50 units of penicillin, and 50 pg/mL streptomycin solution (all from GIBCO, Grand Island, NY). Cells were maintained on TCPS dishes in a h u m i d ~ e dincubator at 37 "C, 7%COz. BAECs were removed from growth surfaces for passage or to be used in an assay by mild trypsin/EDTA (0.05%/0.53 mM, GIBCO) digestion. After the cells had detached, serum-containing medium was added to inactivate the trypsin. Viable cells were counted by trypan blue exclusion. Cells were discarded after 12-14 passages. Conditioned medium was prepared by seeding flasks at -30% confluency in growth medium (above) and harvested after 4-5 days. All culture and conditioned media were 0.22 pm filtered before use. Conditioned medium was aliquoted and frozen at -20 "C until needed. For the clonal growth assay used for these experiments, cells were seeded on surfaces at a low density, 3.1 cells/cm*, or 18 cells/dish. Triplicates were run for each sample, including TCPS as a control surface. Each dish contained 1.5 mL of cell suspension and 1.5 mL of conditioned medium, and the samples were allowed to grow for 3 days. The cells were then fixed using cold (-20 "C) 95% ethanol. The cells were Giemsa stained (Sigma) for -10 min, washed with distilled, deionized water, and allowed to dry before counting. The number of cells on each sample was determined by counting the total surface area using light microscopy. Each set of data was normalized to respective TCPS controls. PLS: Experimental Details. The theory of PLS is well established. A number of tutorial^^^,^^,^^ and articles dealing with the theoretical aspects of PLS4334have appeared in the literature. A comprehensive discussion of PLS is not within the scope of this study, and the interested reader is referred to appropriate references. The PLS calibration was developed by relating the positive and negative static SIMS spectra of the PDFs listed in Table 1to their BAEC growth results. The calibration set contained the m/z = 0-300 region of the positive ion spectrum and the m/z = 0-300 region of the negative ion spectrum for each of the 15 PDFs. Since data were acquired for every 0.15 m/z, this resulted in a 15 x 4000 response matrix. The positive and negative ion components of the combined spectra were then separately normalized with respect to the largest peak intensity observed in the respective part of the spectrum (the largest peak intensities in the positive (53) Briggs, D.; Heam, M. J. Vucuum 1986,36,1005-1010. (54) Manne, R. Chemom. Int. Lab. Sys. 1987,2,187-197.
40000 30000
8 v,
25000 20000
*
(+I (-1
4
iI
10000
5000 0 100
200
l&,
"
.
,
300
300
100
300
wwvw4 100 200 300
200
5
0
10 15 20 25 30 35 At. % 0 (XPS)
1.2
1
0.8
8
0.6 0.4
0.2 0
100
200
mlz Figure 1. Positive and negative ion static SIMS spectra of acetone PDF (a) before normalization and (b) after normalization of each component such that the most intense peak in each component of the spectrum is scaled to unit height.
Sample # Figure 2. Concentration vector showing cell growth on PDFs normalized to 100% for TCPS. Sample numbers correspond to PDFs shown in Table 1.
and negative ion components of the spectra were arbitrarily assigned as 1). This was done to compensate for the different absolute intensities observed for the positive and negative ion spectra. It was observed that normalization (of the positive and negative ion spectra to unit height) improved the predictive response of the PLS model as compared to retaining the original intensities. These results are discussed in the next section. The effect of this normalization step can be seen in Figure 1, parts a and b, which show the positive and negative ion spectra of acetone PDF before and after normalization, respectively. Note that this combined spectrum (positive and negative ion peaks) comprises one row of the response matrix. The concentration vector or dependent variable, which is the BAEC growth (normalized to TCPS at 100%)for the PDFs, is shown in Figure 2, plotted versus the sample number of the PDFs (refer to Table 1). The PLS analysis was done on an IBM compatible 386 PC, with a numeric coprocessor and 8 MB RAM. The software used for these calculations (Pirouette) is a PLSl algorithm (Infometrix
5
0
At.
Oh
10
15
20
25
N (Hydrazine-XPS)
Figure 3. (a) Percent BAEC growth versus the XPS-measured percent oxygen in the PDFs. Note the large scatter in the data. (b) Percent BAEC growth versus the XPS-measured percent nitrogen for the hydrazine-derivatized PDFs. The nitrogen tag is a measure of the surface carbonyl concentration of these PDFs. M, PDFs; 0, conventional polymers; and 0, argon-etched polymers.
Inc., Seattle, WA), based on a bidiagonalizationprocedure." The predictions from this algorithm are equivalent to algorithms that use the NIPALS a l g ~ r i t h m . ~ The ~ ~ ~ 'software permits facile execution of the leave-one-out cross-validation procedure. For the sake of consistency, all results, namely the predicted values, scores, loadings, and the regression vector, were also calculated using the NIPALS algorithm and a singular value decomposition (SVD) routine using the MATLAB computing environment m e Mathworks, Inc., 158 Woodland St., Sherbom, MA 01770). RESULTS AND DISCUSSION
XPS Results. Since the central premise of this investigation rests on the hypothesis that more than one chemical factor is implicated in the growth of BAECs on polymeric substrates, results supporting this hypothesis are briefly discussed. Figure 3a shows the BAEC growth plotted versus the XPSmeasured %O for the PDFs. The scatter in the data indicates that the cell growth results for the PDFs cannot be explained on the basis of simple measures of their surface composition, a result that is in agreement with those previously published.16 While the correlation between BAEC growth results and the surface carbonyl concentration (as determined by the results of the hydrazine derivatization reaction) is somewhat better (Figure 3b), the presence of a number of outliers indicates that the surface carbonyl concentration is not the sole chemical factor that influences BAEC growth on these PDFs. The inability to identify simple chemical parameters from XPS that are relevant to understanding the interaction of BAECs with these multifunctional surfaces provides the impetus for the multivariate correlation of PDF surface chemistry with their cell growth results. PIS Analysis: Static SIMS Versus BAEC Growth. In this section, we demonstrate how PLS regression can be utilized to Analytical Chemistry, Vol. 67, No. 17, September 1, 7995
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Table 3. Comparison of Different Preprocessing Methods and Their Effects in the Predictive Capabilities of the Obtained Models
no preprocessing
mean centering
autoscaling
Raw SIMS Intensities
no. of latent variables of model
5 4 35.01 29.18 12 (predicted vs actual values) 0.819 0.82 SIMS Intensities Normalized to Unit Height no. of latent variables of model 5 5 SEV 20.03 17.99 Z (predicted vs actual values) 0.955 . 0.97
SEV
3 27.49 0.941 I"
2 24.06 0.917
develop a multivariate statistical model that can account for a complex biological phenomenon (Le.,cell growth) on chemically complex and heterogeneous surfaces (PDFs). Based on a multivariate description of surface chemistry/structure provided by static SIMS, we show how the model can then be dissected using appropriate analysis to yield insights into spectral features (e.g., specific secondary ions) that correlate with cell growth, and how, using the large static SIMS this information can be translated into chemical moieties (e.g., functional groups) and surface structural features (e.g., cross-linking) that are implicated in cell growth. PLS calculations were carried out using the software package Pirouette. In exploratory calculations, the effects of normalization and preprocessing on the prediction of BAEC growth were studied. Specifically, the standard error of validation (SEV) for PLS calibrations with different numbers of latent variables and the correlation between the predicted and measured values for the dependent variable were calculated to determine optimal normalization and preprocessing. The SEV is the cumulative rootmean-square error generated during the cross-validation procedure for the entire calibration set; mathematically it is defined as shown below:
I,
where n is the number of samples in the calibration set, k is the number of latent variables used to build the model, ci is the actual value of the dependent variable for the ith sample in the calibration set, and Ei is the predicted value of the dependent variable for the ith sample in the calibration set. The effects of using raw intensities were compared with normalization to unit height for the positive and negative ion components of the combined SIMS spectra of the PDFs; additionally, the effects of mean centering and autoscaling were also evaluated. Table 3 shows the SEV, correlation coefficients (12) of the predicted versus the actual values of cell growth, and the optimum number of latent variables selected for the best predictive models after cross-validation (see below) for different preprocessing steps and scaling of the SIMS data. From Table 3, it is evident that normalization of SIMS spectra to unit height improved the predictive capabilities of the PLS models. Taking the error generated during the validation step as an indicator of the predictive capabilities of the models, it can be seen that the SEV of the PLS models is always smaller when the normalized 2888 Analytical Chemistry, Vol. 67, No. 17, September 1 , 7995
0
20
40
60
80
% BAEC Growth
100 120
(Expt.)
Figure 4. Percent BAEC growth predicted by five latent variables PLS model versus experimentally determined results for the PDFs in the calibration set.
intensities of the SIMS spectra are used, compared to the raw data. Mean centering of the data gave better models (smaller SEV) than using unprocessed data. Although autoscaling gave a smaller SEV than mean centering when the raw intensities were used, autoscaling did not improve the predictive capabilities of the model with normalized intensities. In fact, for normalized intensities, autoscaling resulted in a deterioration of the predictive capabilities of the model. This is evident in the correlation coefficients,12, of the predicted versus actual values of cell growth and the SEVs for autoscaled and mean centered data. Autoscaling makes the contribution of all variables similar, and thus, it also enhances the contribution of noise to the model. In fact, the regression vectors of the models built with autoscaled data (not shown) exhibit a high incorporation of noise, leading to overfitting of the data. A more complete description of the influence of data preprocessing and scaling in PLS models can be found elsewhere.4I The model obtained using mean centering of the normalized intensities of the SIMS data was selected as the best model, and it is the model that will be discussed from now on. The leave-oneout croswalidation was performed with different numbers of latent variables to determine the optimum number of latent variables to be used in the final PLS model. The SEV minimizes for five latent variables, and the difference between the SEV for PLS models with three, four, and five latent variables was found to be signifcant. Thus, a PLS model with five latent variables provided the best correlation between the static SIMS fragmentation patterns of the PDFs and their BAEC growth results. The predictions for the five latent variables calibration model are shown in Figure 4. The good agreement between the predicted values of BAEC growth and the experimental values (# = 0.97) indicates that the SIMS fragmentation patterns of the PDFs in the calibration set can be correlated with their cell growth results. Furthermore, in comparison with other variables, such as water wettability and surface elemental composition,the static SIMS patterns of these PDFs appear to provide a more relevant description of the surface chemisty as it pertains to BAECgrowth. From the PLS model, it is also possible to extract qualitative information relating static SIMS variables to cell growth. An examination of the regression vector would indicate which variables are more important and which variables are unimportant in describing BAEC growth. Figure 5, parts a and b, show the regression coefficients for the positive and negative SIMS variables, respectively. Variables with high (positive) regression coefficients positively correlate with cell growth, whereas variables
0.4 0.2
0
0
0 Ln
0
0
51
m/z
0
a
m/z
8
c)
15 L
0.8
1
0.6
f
0.4
c
t
80
0.2
-5
0
I
-1 5
25 -20
m/z Figure 6. Average SlMS spectra of PDFs samples in the calibration set. (a) Positive SlMS variables. (b) Negative SlMS variables.
0
m/z Figure 5. Rearession vector Dlotted as rearession coefficients versus peak m/z-for the five lateni variables PL: model. (a) Positive SlMS variables. (b) Negative SlMS variables.
with low (negative) regression coefficients negatively correlate with cell growth. Variables with small (near zero) regression coefficients are considered unimportant in describing BAEC growth on PDFs. The highest regression coefficients (23) correspond to m/z = 15, 18, 27, 29, 30, 39, 41, 42, 43, 44, 45, 53, 55, and 56 for the positive SIMS spectra (Figure 6a) and m/z = 1,41,42,43,55,59,73,80,87,and 97 for the negative SIMS spectra (Figure 6b). The structure of the ions corresponding to these m/z values can yield clues as to the types of functionalities that are expected to promote BAEC growth on these PDFs. In the positive SIMS spectra, ions with m/z = 15, 27, 39, 41, 43, 53, and 55 are positively correlated with cell growth. These fragments are a mixture of hydrocarbon and oxygen-containing ions, and only m/z = 15 and 27 can be unequivocally assigned to C,H,+ groups. This overlap of hydrocarbon and oxygen-containing ions is common in PDFs prepared from oxygen-containing precursor^.^^,^^.^^ For example, m/z = 41 can be attributed to C*HO+or C3H5+. The identities of some of these ions have been determined with some certainty by static SIMS of stable isotopelabeled We note that the process of identification is facilitated by the use of high-mass-resolutionspectrometers such as reflectron-based time-of-flight mass spectr0mete1-s.~~ Oxygencontaining peaks that correlate positively with BAEC growth (55) Niehuis, E.; van Velzen, P. N. T.; Lub,J.; Heller, T.; Benninghoven, A Surf: Intelface Anal. 1989,14, 135-142.
include m/z = 29, 41, 43, 45, 53, and 55, which, as mentioned before, can overlap with hydrocarbon ions. An ion with m/z = 43, characteristic of ketone functionalities, correlates positively with BAEC growth. This is in agreement with previous studies indicating a role of surface carbonyl groups in cell growth.16 However, the concentration of carbonyl groups at the surface is not the only factor accounting for the cell growth on these PDFs. This is evident from the large regression coefficients of variables that do not correspond to carbonyl groups, and also because conventional ketone-functionalized polymers do not support cell growth. Impurities and adventitious contamination of PDFs also play a role in cell growth onto these substrates. Nitrogencontaining positive ions are also correlated with BAEC growth (m/z = 18, 30, 42, 44, and 56). The presence of such even m/z fragments is indicative of nitrogencontaining fragments for polymers.% These results are consistent with the presence of low levels of nitrogen in the XPS spectra of some of the PDFs. In the negative SIMS spectra, some oxygen-containing ions can be identified that correlate positively with cell growth, including m/z = 41, 43, 55, 59, 73, and 87. Also positively correlated with cell growth is m/z = 42, which more likely represents an NCO- ion. High regression coefficients corresponding to m/z = 80 and 97 also correlated positively with BAEC growth on PDFs. These correspond to SO3- and HS04- ions. Although sulfur was not detected by XPS, SOa- and HSOI- have a high ion formation probability in SIMS, leading to a much greater analytical sensitivityfor sulfurcontaining moieties in SIMS than in XPS. The intensity of these ions in the averaged spectrum, and the absence of detectable sulfur in the XPS results, suggest Analytical Chemistry, Vol. 67, No. 17,September 1, 1995
2889
Table 4. Tentative Ion Structure Assignments for the SIMS Variables with the Largest Regresslon Coefficients'
m/z 15+ 18+ 27+ 29+ 30f 39+ 41+
(3.3) (2.8) (7.8) (5.6) (4.9) (10.8) (13.4)
42+ 43+ 44+ 45+ 53+ 55+ 56+
(4.5) (8.9) (4.7) (3.0) (3.7) (5.9) (3.1)
23+ (-4.2)
105+ (-4.5)
ion structure
m/z
ion structure
Positively Correlated with Cell Growth CH3+ 1- (8.7) HNH4' 41- (3.4) CHCOCzH3+
C2H5+,CHO+ CHzNHz+,NO' C3H3+
C3Hj+, CHCO+
4243555973-
(7.3) (6.3) (3.7) (4.2) (7.2)
CzHzNHz+,CzHzO+ CH3CO+, C3H7+ 80- (6.3) CzH4NHz+
CzH5O+
87- (7.0) 97- (13.6)
NCOCH3CO-
CH3CHCOCH3COOHOCHzCH=CHO-, CH3CHzCOOSO3-
CzH5COOCHzHSO4-
C4Hj+, C3HO+
CdH7+,C3H30+ CzHdNz+ Negatively Correlated with Cell Growth Na+ 13- (-9.0) CH24- (-5.9) Cz25- (-19.0) CzH57- (-5.8)
C3H50-
71- (-6.6)
C3H302-
Numbers in parentheses indicate the magnitude of the corresponding regression coefficients.
that the sulfur concentration is
