A rapid reproducible method for determination of basement membrane thickness in biological structures

Compur. Biol. Med. Vol. 17, No. 3, pp. 193-197, Printed in Great Britain.

1987. 0

OOlO-48X/87 $3.00+ .oO 1987 Pergamon Jourmls Ltd.

A RAPID REPRODUCIBLE METHOD FOR DETERMINATION OF BASEMENT MEMBRANE THICKNESS IN BIOLOGICAL STRUCTURES TOM

A. J. MCE‘WEN, SUBRATA CHAKRABARTI and ANDERS A. F. SIMA*

Neuropathology Research Laboratory, University of Manitoba, Winnipeg, Manitoba, Canada (Received 21 July 1986; in revisedform 10 December 1986) Abstract-A computerized method for the determination of basement membrane thickness is described. The program is written in HPL. The accuracy and reproducibility of the method and the program were determined using constructed and biological models. Basement membrane thickness

Morphometry

Hewlett Packard digitizer

INTRODUCTION The common methods for the measurement of basement membrane thickness are the minimum thickness method of Williamson et al. [l], and the multiple point method of Siperstein et al. [2]. In Williamson’s method basement membrane thickness is measured at two points where the basement membrane is thinnest. The points selected must be at least 1 cm apart as measured on an electronmicrograph and must not overlie pericytes. In Siperstein’s multiple point basement membrane thickness technique, a transparent plastic sheet, on which is drawn twenty equidistant radial lines, is placed over the vessel. Measurements are made by calipers at each point where a radial line intersects the basement membrane, except in the places where pericytes are present. Shannon et al. [3], described a method for measuring muscle capillary basement membrane thickness. This method was later used for measuring the basement membrane thickness of retinal capillaries by Robison et al. [4]. In the present communication we present a modification of Shannon’s procedure which was developed for measuring retinal capillary basement membrane thickness in rats. This method takes into account all the variations in basement membrane thickness and produces a mean basement membrane thickness. By using this method different cellular and lumenal areas of the vessel can be measured simultaneously. Although the program was written for the measurement of retinal capillary basement membrane thickness, it can be used successfully for measurements of capillary basement membrane thickness in any tissue in which the membrane boundaries are discernable.

HARDWARE

AND

DATA

DESCRIPTION

The system consists of a Hewlett Packard 9825A desktop computer interfaced with a Hewlett Packard 9874A digitizer and a Hewlett Packard 7225A plotter (Hewlett Packard, Fort Collins, Colorado). The HP 9825A is connected by means of a serial interface, (Hewlett Packard 98036A) to an Epson QX-16 microprocessor (Epson Canada Ltd., Willowdale, Ontario) with 512 kilobytes random access memory and two 5.25 inch floppy disk drives (Bi-Tech Enterprises Inc., Bohemia, NY). The printer is an Epson LX-80. The data consists of continuously digitized X, Y, co-ordinates, which are generated by the 9874A digitizer on a 315 x 435 mm tablet. The resolution of the digitizing tablet is 25 pm, *To whom all correspondence should be addressed at: Neuropathology Bannatyne Ave., Winnipeg, Manitoba, R3E OW3, Canada. 193

Research Laboratory,

223-770,

TOM A. J. MCEWEN. SUBRATA CHAKRABARTI and ANDERS A. F. SIMA

194

cursor accuracy is + 125 pm and repeatability is f 25 pm at zero magnification. New X, Y positions are generated each time the cursor moves over the digitizing tablet by a distance of 25 pm. The program compares new X, Y co-ordinates with previous values and discards them until different co-ordinates are received. The digitized points are read into the 9825A computer where the initial processing is done. The program is written in HPL. Areas and line lengths are computed using standard analytic geometry formulae, where Area = C [(X, - X,)( Y, + Y,)/2 and line length (LL) are calculated using the formula; LL = C [(X2 - X1)’ + ( Y2 - Y1)*]. Special function keys on the 9874A digitizer are programmed to indicate to the program whether area or length measurements are being made and into which category these measurements should be placed. Typical features of a retinal capillary are shown in Fig. 1. Each capillary lumen is lined by endothelial cells (E), which are covered by a basement membrane (BM). The basement membrane is split at various places to accomodate pericytes and their processes (P). Total basement membrane area (BMA) is calculated by subtracting the sum of pericyte profile (P), endothelial cell profile (E) and lumenal areas (L) from the total capillary area (T), BMA (pm*) = T - (L + P + E). Basement membrane length (BML) is derived from the total length of the lines delimiting the basement membrane divided by two, BML (pm) = BML total/Z Mean basement membrane thickness is calculated from the digitized length of the basement membrane and the total basement membrane area, BMT (nm) = [BMA (pm*)/BML (pm)] x 1000.

Fig. 1.

Electronmicrograph basement membrane

of a retinal capillary from (BM), pericyte (P), endothelial

the superficial capillary bed showing cell (E) and capillary lumen (L).

Determination of basement membrane thickness in biological structures

195

START

f

DIGlTlZE SVBRDUTINE

INPUT

SUBROUTINE

( Y

RETURN

)

NEXT PIrnO N

t FINAL

CALCULATION I PRINT RESULTS

Fig. 2. Simplified flowchart of Program. * This is a multiple decision diamond although only two decisions are shown. Special function keys on the 9874A digitizer are programmed to set the flag which indicates whether area or length measurements are being made and to which category they belong.

Pericyte profile, endothelial cell profile and lumenal areas were also expressed as a percentage of total capillary area and were used for morphometric evaluation of these parameters. The accuracy of the area and length portions of the digitizing program were verified by three different examiners using drawings of known circle areas and line lengths; the calculated areas and lengths of the drawings being unknown to the examiner. Measurements are initially stored on data tape cartridges and then downloaded to diskfiles for subsequent mathematical and statistical analysis. A simplified flowchart of the program is shown in Fig. 2. ANIMALS The retinas from 5 inbred male Lewis rats of the AC1 (AgB 4/4) strain were used in this study. The animals were sacrificed at 18 months of age, by total body perfusion with a 0.1 M cacodylate buffered (pH 7.4) 2.5% glutaraldehyde solution, 2.5 ml/g body weight. Both eyes were enucleated and post fixed in the same fixative for 4 h at 4°C. After fixation radially oriented retinal segments (1 mm’) from the superior temporal quadrant of the right eye were cut near the optic nerve head and washed overnight in 0.1 M cacodylate buffer (pH 7.4). The tissues were then postfixed in cacodylate buffered 1% osmium tetroxide (pH 7.4) for 2 h, dehydrated through graded alcohols and embedded in Epon. Ultrathin sections, stained with aqueous uranyl acetate and lead citrate, were examined electronmicroscopically. Randomly selected and transversely sectioned capillaries, with a perpendicular diameter ratio less than 2, and which showed sharp basement membrane borders, were photographed from each animal. Electronmicrographs were enlarged to a final magnification of 16000-24000 and

196

TOMA. J.

MCEWEN, SUBRATACHAKRABARTIand

ANDERSA. F. SIMA

Table 1. Digitized test model

Examiner No. I Examiner No. 2 Examiner No. 3 Calculated values

Area (pm)*

(n)

Length (pm)

(4

3 1.998 f 0.089 31.870* 0.109 31.929 + 0.284 31.900

(20) (20) (20)

6.304 f 0.109 6.295 k 0.02 1 6.282 + 0.034 6.290

(20) (20) (20)

Means &SD of 20 consecutive measurements by each of 3 different examiners. Areas and line lengths of constructed test models of known area and length. One way analysis of variance yielded a non-significant F-value, (0.85 and 2.09, respectively, with 2.58 df).

Table 2.

Biological models-comparisons (uaired t-test)

vs A A B

B C C

of the measurements

mean diff.

t-value (df)

- 1.231 -1.411 -0.180

-0.716 (24) NS - 0.772 (24) NS -0.091 (24) NS

A = 1st examiner, 1st time; B = 1st examiner, 2nd time; C = 2nd examiner; NS = non-significant.

were used for the measurement of basement membrane thickness (BMT), endothelial cell profile (E), pericyte profile (P), and lumenal areas (L). To verify the accuracy of the program, constructed circles and lines of known dimensions were measured by three different examiners. These results were compared using one way analysis of variance and are summarised in Table 1. To assess reproducibility, twenty-five capillaries showing transverse cuts and unambiguous basement membrane borders were selected for measurement, (Fig. 1). Measurements were made by two different examiners. One examiner repeated these measurements a second time at an interval of four weeks. The three measurements were compared using paired t-test (Table 2). RESULTS

AND DISCUSSION

The data used to verify the accuracy of the program (Table 1) and to assess the reproducibility of the technique (Table 2) showed no significant differences between examiners. Hence, the technique has advantages over previously described methods; it is fast, accurate and reproducible. Twenty-thirty capillaries can be digitized per hour. As basement membrane is not of uniform thickness the method takes into account this variability by expressing average basement membrane thickness per unit length. The program simultaneously measures pericyte profile, endothelial cell profile and lumenal areas and can be advantageously used for morphometric evaluation of these parameters. Although the program was written for the measurement of retinal capillary basement membrane thicknesses, it can be applied to any tissue in which membrane boundaries are discernible, and could be merged with software which employs a more automated approach to boundary finding techniques [S, 63. SUMMARY A computerized method for rapid measurement of basement membrane thickness and simultaneous measurements of capillary constituent areas in biological structures is described. The technique employs a Hewlett Packard 9874A digitizer and a 9825A desktop computer. The method was tested on constructed and biological models and found to be accurate and reproducible. REFERENCES 1. J. R. Williamson, E. Rowold, P. Hoffman and C. Kilo, Influence of fixation and morphometric techniques on capillary basement-membrane thickening prevalence data in diabetes, Diabetes 25, 604-613 (1976).

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2. M. D. Siperstein, R. H. Unger and L. L. Madison, Studies of muscle capillary basement membrane in normal subjects, diabetic and prediabetic patients, J. din. Invest. 47, 1973-1999 (1968). 3. W. A. Shannon, Jr., D. L. Rockholt and S. B. Bates, Computer assisted measurement of the thickness of biological structures, Comput. Bid. Med. 12, 149-155 (1982). 4. W. G. Robison, Jr., P. F. Kador and J. H. Kinoshita, Retinal capillaries: basement membrane thickening by galactosemia prevented with aldose reductase inhibitor, Science 221,1177-l 179 (1983). 5. P. J. Dyck, J. Karnes, A. Lais et al., Pathologic alterations of the peripheral nervous system of humans. In Peripheral Neuropathy, P. J. Dyck, P. K. Thomas, E. H. Lambert and R. Bunge, Ed.%pp. 760-870. Saunders, Philadelphia (1984). 6. J. M. Lester, H. A. Williams, B. A. Weintraub and J. F. Brenner, Two graph searching techniques for boundary finding in white blood cell images, Comput. Bid. Med. 8,293-308 (1978). About the AuthorANDERS A. F. SIMAis Professor of Pathology and Biochemistry and Head of the Division of Neuropathology at the University of Manitoba. He graduated from the University of Goteborg in 1973 and received his Ph.D. in Experimental Neuropathology from the same University in 1974. He is a fellow of the Royal College of Physicians and Surgeons of Canada. He is the author of over 140 publications related to clinical and experimental neuropathology.

About the Author- SUBRATA CHAKRABARTI received his M.D. and diploma in Ophthalmology from the University ofcalcutta, India. He completed his M.Sc. in Pathology at the University of Manitoba and is now engaged in graduate work at the University of Manitoba. Subrata Chakrabarti’s research interest is in diabetic retinopathy. He is a member of the Association for Research in Vision and Ophthalmology.

A. J. MCEWENgraduated A.I.M.L.S. in bacteriology from the Institute of Medical Laboratory Science in London in 1969. He received the dioloma in Medical Technoloav from the Universit; of the West Indies in 1971 and the A.R.T. in microbiology from the Canadian Society of Laboratory Technologists in 1983. He has taught introductory courses in Computer Science at the Community College level and is currently involved in research in diabetic neuropathies and retinopathy at the University of Manitoba. About the Author-TOM