SEARCH

SEARCH BY CITATION

Keywords:

  • human adipose tissue;
  • rat adipose tissue;
  • image analysis

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Objective: Fat cell size is a fundamental parameter in the study of adipose tissue metabolism, because it markedly influences the cellular rates of metabolism. Previous techniques for the sizing of adipocytes are often complicated or time-consuming. The aim of this study was to develop a new, computerized method for rapid and accurate determination of adipocyte size in a cell suspension obtained by incubating human or rat adipose tissue biopsies with collagenase.

Research Methods and Procedures: The cell suspension was placed between a siliconized glass slide and a cover slip. Using the reference method [designated as (R)], the cell diameters were determined manually using a microscope with a calibrated ocular. The new method presented here [designated as (C)] was based on computerized image analysis.

Results: After two well-defined corrective adjustments, measurements with (R) and (C) agreed very well. The small remaining differences seemed, in fact, to depend on inconsistencies in (R).

Discussion: We propose that (C) constitutes a valuable tool to study fat cell size, because this method is fast and allows the assessment of a sufficient number of cells to get reliable data on size distribution. Furthermore, images of cell preparations may be stored for future reference.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

The major function of the fat cell is to store and release energy. Depending on the amount of lipid stored, the diameter and the volume of the adipocyte can change ∼20-fold and several thousand fold, respectively. In clinical and metabolic studies of adipose tissue, an accurate method for the determination of fat cell size is of importance, because adipocyte size markedly influences the cellular rates of metabolism. Both glucose metabolism and lipid mobilization are increased in enlarged fat cells (1, 2, 3, 4). In contrast, the stimulating effects of insulin on the rates of glucose oxidation and incorporation into the triglycerides are inversely related to the size of the fat cells (5, 6). Factors regulating cytokine release within adipose tissue also seem to include the size of the fat cells (7, 8), and differences in adipose tissue cellularity have been implicated as a possible link between obesity and diabetes (9). Moreover, not only adipocyte size but also adipocyte number may be affected by metabolic, endocrine, and pharmacological influences through, for example, differentiation of pre-adipocytes (i.e., recruitment of small fat cells) (10).

There are several different techniques for counting and sizing adipose cells in small samples of human or animal adipose tissue. Microscopic measurements on conventional histological preparations or frozen-cut adipose tissue (11, 12), determination of DNA content (13), and automatic counting or sizing of osmium tetroxide-fixed fat cells or unfixed adipocytes in a suspension using a Coulter electronic counter (12, 14, 15) are some of the methods that have been described. In our laboratory, fat cell isolation with collagenase, followed by microscopic determination of adipocyte diameter (Ref. 16, slightly modified), has been used for several years, and a similar technique has been used by others (17). However, after the collagenase procedure, cell diameter must be determined immediately, because the suspension cannot be stored without affecting cell morphology. This is a considerable drawback when several samples must be analyzed. In a study by Lavau et al. (18), the time spent handling the isolated fat cells was minimized by measuring adipocyte diameter, not under the microscope, but on printed and enlarged photomicrographs. Still, this method and the others that have been described are often both tedious and time-consuming. Recently, a new method measuring the cross-sectional area of adipocytes in histology sections with computer image analysis has been published (19). This method allows a large number of adipocytes to be measured rapidly. However, when isolated adipocytes are studied, as in many ex vivo and in vitro experimental set-ups, fast and accurate determination of the size of adipocytes in a suspension is important.

In this paper, we describe a new computerized technique [designated as (C)1] for determination of the size of adipocytes isolated by collagenase digestion. The main advantage with this technique is that a large amount of visual data may be stored and processed in a short period of time.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Source of Tissue

Biopsies of subcutaneous adipose tissue were obtained from 26 subjects (6 men and 20 women; age, 21 to 53 years). The subjects were healthy volunteers or patients undergoing surgery for nonmalignant conditions. Biopsies from healthy volunteers were obtained by needle aspiration. Adipose tissue from the parametrial or epididymal depots was also obtained from 16 lean or obese rats that were being used in research projects not involving adipose tissue. However, with few exceptions, data from these rats will not be presented in this study; the general conclusions were the same as those obtained from human data. Immediately after excision, the tissue was placed in prewarmed Parker medium 199 (SBL, Stockholm, Sweden) supplemented with 4% albumin (bovine albumin, fraction V; Sigma Chemical Co., St. Louis, MO) that had a pH of 7.4. This medium was used throughout the study.

Informed consent was obtained from each subject, and the study was approved by the Medical Ethics Committee and the Animal Ethics Committee of Göteborg University.

Cell Isolation with Collagenase

Approximately 250 to 500 mg of tissue was cut into smaller pieces weighing 5 to 20 mg each and transferred to a plastic vial containing 10 mL prewarmed medium supplemented with 8 mg collagenase (Type A; Boehringer, Mannheim, Germany). The vial was incubated for 1 hour at 37 °C in a shaking water bath. To liberate the fat cells, the vial was shaken more vigorously twice during the end of the incubation. After filtration through a nylon mesh (250 μm), the cells were washed and allowed to rise to the surface for 5 to 10 minutes. This was repeated twice, and the cells were resuspended in fresh medium (20% cells and 80% medium) for the final cell suspension.

Fat Cell Preparation

All glassware was siliconized to avoid rupture of cells. To obtain a small chamber where the fat cell preparation could be studied, two layers of adhesive tape (Leukoplast Hospital, Beiersdorf, Hamburg, Germany) were stuck on each end of a glass slide (76 × 26 mm). The cell suspension was cautiously suctioned in and out four to five times with a plastic pipette to obtain a homogenous preparation. Three to four droplets of the cell suspension were placed on the slide, and a cover slip (24 × 60 mm) was immediately put on top. Ideally, the cell preparation should be dense enough to give a sufficient number of cells to be counted but should not contain too many cells that overlap. This meant that it was sometimes necessary to add or remove medium from the cell suspension.

Measurement Techniques

A schematic overview of the entire analytical procedure is depicted in Figure 1.

image

Figure 1. Flow chart of adipocyte size measurement, according to the computerized method (top part) and the reference method (bottom part). (I) Cell preparations are made from fat cell suspensions. (II) A selection of the desired number of cells is obtained, and (III) diameters are measured. (IV) Measurement data are processed and presented.

Download figure to PowerPoint

Reference Method [Designated as (R)]

In accordance with a previously published procedure (12, 16), 100 cell diameters were measured with a standard laboratory photomicroscope (Carl Zeiss, Oberkochen, Germany) using an eyepiece micrometer at 256× magnification (objective magnification, 16×; ocular magnification, 16×). The eyepiece was supplied with a scale graded in 100 parts, each of which corresponded to five units (5 × 0.616 μm). An area of the preparation was selected with sufficient cell density but without too much overlapping. All cells adjacent to an imagined vertical line in the visual field were taken into focus one after the other, and the diameter was determined. The visual field was scrolled, and new cells were taken into focus. Cells along a second parallel line were also included, if needed, to reach the desired number of 100 cells.

Computer-Assisted Image Analysis [(C)]

For this method, the cell preparation was made in the same way as for (R). The preparation was placed on the microscopic stage (Axiovert 135 M; Carl Zeiss), and the magnification was adjusted to 8×. Preliminary experiments had indicated that, with this magnification, a satisfactory number of cells was obtained per visual field without losing too much detail. The opening of the condenser diaphragm was narrowed to increase the focal depth, so that a maximum number of cells came into focus.

A visual field containing ∼30 to 80 cells, depending on cell size and density, was registered by means of a video camera (3 CCD Color Video Camera; Sony, Japan) mounted on the microscope. The image was fed into a computerized image analysis system (KS 400; Carl Zeiss) and digitized. The program for the image analysis process is accessible at http:www.wlab.gu.sepublicationsmacro. After automatic contrast enhancement and delineating of contours, the image could be transformed into a bitmap consisting of a number of discrete areas, the majority of which represented the fat cells. By introducing conditions on the roundness of the areas as well as the smoothness of the contours, the computer program could discriminate between healthy fat cell contours and contours arising from deformed cells, cells cut along the borders of the image, and areas between cells. The surface of the relevant areas was measured automatically, and the diameter of the corresponding circle was calculated. In this way, a distribution of fat cell diameters in the visual field was obtained. Another visual field was then registered, and the analysis was repeated. Alternatively, a desired number of visual fields could be registered, and the analysis could be performed on all images at a later stage. The images of several hundred to more than 1000 cells could be stored and evaluated in a couple of minutes.

Fat Cell Size Measurements

Establishment of Measurement Conditions

In a series of experiments on cell preparations from rat and human adipose tissue, the optimal conditions for (C) were established. In this process, adjustment of the microscope settings and settings of the video camera and the computer software were tested systematically, but only selected aspects need to be accounted for here.

Influence of Magnification

Cells in one cell preparation (1451 cells) were measured at 5×, 8×, 12.5×, and 25× magnification and the distribution of cell diameters was assessed.

Influence of Focal Adjustment

The influence of the position of the focal plane on the calculated cell diameters was tested in nine visual fields (255 cells) at three different focal heights—30, 50, and 100 μm—corresponding to maximum focus on small, intermediate, and big cells, respectively. The measurements were carried out to allow the comparison of cell diameters cell by cell. Furthermore, cell diameters were also measured interactively on the obtained images.

Influence of Segmentation Parameter

After the initial experiments in which different computer settings were tested, only one parameter remained that needed to be set at each measurement occasion. This parameter, the segmentation parameter, determined the sensitivity at which cell borders were discriminated from other structures in the image. A too-high sensitivity would lead to an increased number of irrelevant contours, whereas a low sensitivity might cause the loss of detectable cells. In a separate experiment, this parameter was allowed to vary between extremes for each of five visual fields (400 cells), and the influence on the calculated cell diameters was assessed.

Validation of the Computerized Technique

To validate the results obtained with (C), measurements were performed with this method as well as with (R) on 23 fat cell preparations. Between 560 and 746 cells (nine visual fields) were registered from each preparation.

Control Experiments

Certain differences in cell size, as estimated by (C) and (R), were observed. One factor contributing to these differences could have been the computerized processing of the obtained images (step III in Figure 1). For this reason, two control experiments were performed where cell diameters were measured on the obtained images, both interactively and with the automatic procedure.

In one experiment (21 visual fields, 1551 cells), the whole spectrum of cell sizes was covered, and the visual fields were deliberately selected to include an exaggerated proportion of large cells. In this way, any systematic difference in size estimation could be revealed, and the precision of the method could be evaluated. Data from this experiment also included rat cells, because this increased the proportion of smaller cells, resulting in a more complete distribution of cell sizes.

In the other experiment (97 visual fields, 300 cells), all cells in the lowest size range (<44 μm) were counted. The purpose was to find out whether all small cells were identified with (C).

In addition, the overall measurement procedure (steps II to III in Figure 1) was studied in an experiment in which single cells, identified on the slide, were measured cell by cell, first with (R) and then with (C).

Sampling Error

This type of error would affect the distribution of cell size in the cell population but not measurements of individual fat cells.

Variation between Cell Preparations from the Same Biopsy

To determine the variation between preparations obtained from the same fat biopsy (step I in Figure 1), two experiments were carried out where 9 and 10 consecutive determinations of ∼250 and 150 cells, respectively, were made. In this case, the video registrations were made as fast as possible to avoid changes in fat cell morphology with time.

Error Caused by Visual Field Sampling

When cells are counted on a visual field, bigger cells are more likely to fall on the border and hence become excluded during the automatic measuring as in (C) (step III in Figure 1). This will lead to an under-representation of bigger cells and a skewness toward the left of the distribution curve. The error may be corrected mathematically (see Appendix 1), and this has been done in all the data presented in this study. This corrective adjustment constitutes the first of two that were defined for the new computerized method.

Error Caused by the Sampling Procedure of the Reference Method

In the original method (12, 16), cells were collected along a line scanning across the visual field, resulting in a minimal systematic sampling error. As applied in this study, cells adjacent to a vertical line were sampled (step II in Figure 1), which leads to an over-representation of bigger cells. The influence of the resulting skewness toward the right has not been compensated for here but may be approximated mathematically (see Appendix 2), as mentioned in the discussion.

Statistics

Results of measurements are given as mean ± SD or SE, as indicated. Linear regression was performed using the least-squares method.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Influence of Microscope Magnification

Eight times magnification was used routinely. A change in magnification to 5×, 12.5×, or 25× moved the predefined cut-off size (22 μm at 8×), at which small cells were included, to bigger or smaller sizes, respectively. This affected the calculated mean diameter accordingly: 78.4 ± 32.8, 70.5 ± 23.4, 64.1 ± 31.8, and 57.4 ± 32.8 μm (mean ± SD) at 5×, 8×, 12.5×, and 25×, respectively. Above the predefined cut-off size, however, the degree of magnification did not change the size distribution pattern (Figure 2).

image

Figure 2. Adipocytes were obtained from human adipose tissue, and cell size was measured with the computerized method. This diagram illustrates the influence of different degrees of microscope magnification (5×, 8×, 12.5×, and 25×) on the distribution of adipocyte diameters (n = 1451). The apparent shift in cut-off point in the lower size range is because of recalculation from pixels to micrometers. The cut-off point was, in fact, constant (15 pixels). With this in mind, the distribution patterns are similar for all degrees of magnification.

Download figure to PowerPoint

Influence of Focal Adjustment

Three levels of focusing relevant to the practical procedure were tested. As seen in Figure 3, the differences in mean cell diameter were small (90.7 ± 1.5, 92.6 ± 1.4, and 94.0 ± 1.4 μm, mean ± SE at 30, 50, and 100 μm, respectively), and the cell size distribution patterns were similar. The distribution pattern assessed from interactive measurements was also very similar, and the cell diameter was 91.4 ± 1.4 μm (mean ± SE).

image

Figure 3. Adipocytes were obtained from human adipose tissue, and cell size was measured with the computerized method. The bar diagrams illustrate the influence of different focus heights (30, 50, and 100 μm) on the distribution of adipocyte diameters (n = 765). The same cells were also measured interactively on the obtained computer images, which give a measure free from any aberration that may be caused by the way the computer behaved at varying focus settings (line curve). The mean cell diameters were 90.7 ± 1.5, 92.6 ± 1.4, and 94.0 ± 1.4 μm at a focus height of 30, 50, and 100 μm, respectively. The interactive measurements gave a mean cell diameter of 91.4 ± 1.4 μm. The distribution patterns are similar for all focus settings.

Download figure to PowerPoint

The agreement among estimated cell diameters, measured cell by cell, was extremely good (Figure 4), indicating that the observed differences between average cell size at different focal heights reflect a small number of cells being included or omitted differently by the computer.

image

Figure 4. This figure shows the same data as Figure 3; however, cell diameters were measured with the computerized method plotted cell by cell. Thus, the cell diameters estimated at a focus height of 30 (y axis, top panel) or 100 μm (y axis, bottom panel) are plotted against the cell diameters estimated at a focus height of 50 μm (x axis), which is used routinely. The agreement was excellent.

Download figure to PowerPoint

Influence of Segmentation Parameter

Mean cell diameter determined for five settings of the only parameter that needed observer input agreed extremely well (range, 88.8 to 89.2 μm). Furthermore, the parameter setting had only minor effects on the size distribution pattern (Figure 5).

image

Figure 5. Adipocytes were obtained from human adipose tissue, and cell size was measured with the computerized method. The lines in the diagram illustrate the influence of different settings of the segmentation parameter (n = 400). The distribution patterns agree well for all values of the parameter.

Download figure to PowerPoint

Validation of the Computerized Technique

Size Distribution

In Figure 6 the size distribution of all measured human cells is shown, estimated by (C) and (R). The general pattern is similar, with a main peak around 80 to 100 μm and a small hump for small cells. An obvious shift to the left is seen in (C) compared with (R), reflected by the following mean cell sizes, respectively, 78.2 ± 23.1 and 89.4 ± 31.1 μm (mean ± SD). As may be understood from Figure 7, the computerized image processing, per se, was one factor contributing to this difference. In this figure, cell size calculated by the computerized method is plotted against cell size measured interactively on the digitized image, cell by cell. The linear regression line (y = 0.94x − 0.91) is shown, and the agreement is excellent over the whole size range, which means that the regression coefficient may be introduced as a correction factor in the calculations. This corrective adjustment constitutes the second of two that were defined for the new computerized method. Another discrepancy between the distribution patterns was the observation that small cells seemed to be less numerous as assessed by (C) than by (R).

image

Figure 6. Size distribution of human adipose cells assessed with the computerized method (bottom panel) and the reference method (top panel). The distribution is similar, with a main peak around 80 to 90 μm and a small hump for small cells. However, there is a shift to the left for the computerized method, resulting in a lower mean value (78.2 ± 23.1 μm) compared with the reference (89.4 ± 31.1 μm).

Download figure to PowerPoint

image

Figure 7. Scatter plot showing the relationship between individual cell diameters measured directly from the images (x axis) and with the computerized technique (y axis). The linear regression line is shown (y = 0.94x − 0.91, r2 = 0.997). The relationship is strictly linear, with a small negative y intercept.

Download figure to PowerPoint

In Figure 8, the correction factor, 1.06, derived from the regression analysis, is introduced, and it is obvious that the distribution patterns are in better agreement. After correction, the mean cell diameter for (C) was estimated to be 82.9 ± 24.5 μm [89.4 ± 31.1 μm for (R); mean ± SD].

image

Figure 8. Size distribution of human adipose cells assessed with the computerized method (bottom panel) and with the reference method (top panel). This figure shows the same data as Figure 6, but in this case, the correction factor (1.06), which was derived from the regression calculation illustrated in Figure 7, has been included for the computerized measurement. The distribution patterns agree even better, and the mean diameter estimated by the computerized method has increased (82.9 ± 24.5 μm).

Download figure to PowerPoint

We wanted to exclude the possibility that the computerized image processing per se was the reason that a smaller number of small cells seemed to be detected by (C) than by (R) within the “hump” region of the distribution. For this reason, comparisons between automatic and interactive measurements were made especially directed at this size range. The results indicated that the number of cells in the smallest size range detected by (C) (<40 μm) was the same as by (R) (303 vs. 304, respectively). In this control experiment, a small number of cells (27) lay below the preset lower limit in (C) (<22 μm) and would, thus, have been counted only by (R).

To further pinpoint the reason for the discrepancy in mean diameter, individual cells were also measured both with (R) and with (C) (Figure 9). The agreement between the measurements was excellent. The regression coefficient, 0.93, was the same as above, supporting its use as a correction factor in the calculations. Notably, however, a significant y intercept, −4.0 ± 0.9 (SE), was also observed.

image

Figure 9. Cell diameters of individual cells measured cell by cell both by the computerized method (C; y axis) and the reference method (R; x axis). The agreement is very good (y = 0.93x − 4.0, r2 = 0.996).

Download figure to PowerPoint

Mean Cell Diameter

In Figure 10, the mean cell diameter, determined by (C), is plotted against (R) for each human and rat fat biopsy. The data points lie scattered around the line of identity (dashed line), and the slope of the best fit of a line drawn through the origin of coordinates (data not shown) is 0.97 ± 0.01 (mean ± SE), indicating a good agreement between (C) and (R). The slope of the linear regression line is 0.84 ± 0.6 (mean ± SE), which seems to indicate that the mean diameter of a cell population with big cells would be somewhat underestimated by (C) compared with (R) and that the mean diameter of a cell population with small cells would be somewhat overestimated by (C) compared with (R). From these data, the mean error could be calculated to 5.0% [SQR(ΣΔ2/2n)/grand mean].

image

Figure 10. Mean cell diameters determined by the computerized method (y axis) and the reference method (x axis) for each human and rat fat biopsy. The linear regression line together with the 95% confidence interval is indicated (y = 0.84x + 11.1, r2 = 0.85). Data points are scattered around the line of identity (dashed line).

Download figure to PowerPoint

Figure 11 shows a Bland-Altman plot of the same data, illustrating the difference between mean cell size determined by (C) and (R) (mean(C) − mean(R)) vs. their average [(mean(C) + mean(R))/2]. The figure illustrates that the discrepancy between (C) and (R) does not depend on average cell size. The coefficient of variation around the regression line was calculated to be 6.5%.

image

Figure 11. A Bland-Altman plot of the same data as in Figure 10. In this plot, the value of the cell diameter obtained with the computerized method (dC) is compared with the average (AveCR) of this value (dC) and the value obtained with the reference method (dR) for the same sample [AveCR = (dC + dR)/2]. This comparison is achieved by plotting the difference between the computerized value and the average (Δ = dC − AveCR) against the average (AveCR). To give a better intuitive understanding of the relative size of the difference (Δ), the values are given as percent, and the x axis is set to cross the y axis at y = −100%. The mean of Δ (−2.5 ± 6.5%) is depicted in the top right corner. The figure indicates that the coefficient of variation of the discrepancy between the two methods is ±6.5%. This discrepancy does not seem to vary with the mean cell diameter of the sample.

Download figure to PowerPoint

Within-Biopsy Variation

The coefficient of variation of consecutive determinations of average cell size on the same cell preparation was 3.6% and 1.2% for (R) and (C), respectively (steps II to IV in Figure 1).

In two separate experiments, consecutive measurements were made with (C) on cells from different preparations (150 to 250 cells each), derived from the same fat biopsy (steps I to IV in Figure 1). The coefficient of variation differed between the two occasions (±11.5% and ±3.8%), probably reflecting inhomogeneities in the cell suspensions in the former experiment.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Two of the most evaluated methods for determining fat cell size are 1) automatic determination after osmium fixation (12, 14) and 2) microscopic measurements with an eyepiece micrometer after freeze cutting or collagenase digestion (12, 16, 17). The former method is applicable to cells of all sizes, a large number of cells can be counted, and the reproducibility is good. Still, the technique requires osmium fixation for 24 to 72 hours, depending on the amount of tissue, and the fixation may be accompanied by cell swelling (14). Moreover, it is not possible to store information on what has actually been sized. In this paper, the second method has been used as the reference (R). This method requires the assessment of diameters of single cells, one by one, by direct observation. In practice, this means that the total number of measured cells will be restricted and possible conclusions will be curtailed.

Here, we present a computer-based method for determination of fat cell size after collagenase digestion. With this method, a large number of cells may be measured in a short period of time. It is also possible to save images of the measured cells for future reference. In addition, observer bias can be minimized by such a method. The procedure is carried out in two steps (i.e., registration of the images and subsequent analysis of these images).

Registration of Images

One advantage of direct eyepiece measurement of fat cell size is that it gives a very precise measure, because focus is adjusted for each cell. At the same time, however, the need to assess single cells constitutes a disadvantage because it makes the technique slow and tiresome. To capitalize on the speed of the computer-based method, the focus is set only once for each visual field. However, this inevitably means that a number of cells will be somewhat out of focus. Increasing the depth of focus of the microscope reduces this effect. Furthermore, in a separate control experiment, we showed that this effect did not appreciably influence the reliability of the obtained result (Figures 3 and 4).

Image Analysis

The main advantage of computer-based image analysis is that a large amount of visual data may be processed in a short period of time. To obtain reliable results, however, it is essential that the analyzed images are well standardized. This is achieved through standardization of the microscope set-up and through automatic contrast enhancement at the image processing level. Also, the necessary observer inputs during the calculations should be few; in our method, they were restricted to the setting of a single segmentation parameter. Furthermore, selection of the inclusion criteria that determine what should be included as relevant image structures is critical.

Contrast Enhancement

For all practical purposes, day-to-day differences in light intensity of the microscope and in the amplification of the electronic equipment result in a variation of contrast and brightness in the registered image. To compensate for this variation, all images were normalized through automatic contrast enhancement, which expands the intensity levels to the full 8-bit range of the software.

Segmentation Parameter

In the process of segmentation, the intensity level that differentiates relevant and irrelevant structures must be decided. In our technique, this amounted to the setting of one parameter at each measurement occasion, i.e., once for 20 to 40 visual fields, depending on the requirements of the experiment. Although this choice implies a moment of subjective judgment, control experiments indicated that the final results of the measurements were very insensitive to this setting (Figure 5).

Inclusion Criteria

In the final processing of the images, all areas bordered by fat cell contours would be measured by the software, including deformed cells, cells cut along the image borders, and areas between cells. For this reason, two inclusion criteria were defined, i.e., maximum degree of oblongness (Feret ratio, the ratio between the longest and shortest extension of the area > 0.8) and minimum degree of contour smoothness (ratio between true surface area and perimeter-derived surface area > 0.8). A lower limit of the cell size was also set, corresponding to a cell diameter of 22 μm at 8× magnification, reflecting the useful resolution of the image. DeMartinis and Francendese have described a population of very small fat cells of ∼8 to 35 μm in some mammalian species (20). The smallest of those cells, 8 to 21 μm, were not measured using the present technique. However, as reported by DeMartinis and Francendese, the very small fat cells below ∼20 μm are not spherical, but rather oval, and were thus excluded irrespective of size by the computer program. Thus, if these “pre-adipocytes” of ∼8 to 20 μm are to be studied, other methods must be used.

Breakage of large fat cells during collagenase treatment, resulting in nonrepresentative samples containing lipid droplets, has been reported in the literature (14). However, with the precautions used, i.e., a gently shaking water bath during collagenase treatment, freshly siliconized glassware, and no centrifugation of the cells, there is no indication of an increased rupture of adipocytes during preparation of the cell suspension (16). Thus, the proportion of lipid droplets in the cell suspensions is usually very low. One considerable advantage with the new computerized method is that the images can be stored and later studied regarding what has actually been measured. If separate spherical formations, identified as lipid droplets, are present on the images, these formations can easily be excluded from the calculations. The adipocytes can be stained with crystal violet to facilitate adipocyte identification. However, it has not been found necessary to stain the samples routinely.

Sampling

In the original description (12, 16) of the sampling technique adopted in (R), the representation of the measured cells was ensured by inclusion of all cells that fell along a line, scanning perpendicularly across the visual field. In this study, cells that were adjacent to a vertical line, positioned arbitrarily across the cell preparation, were measured. Such a procedure will inevitably exaggerate the number of big cells, because they will be included even if their centers lie far away. It is also possible that subjective judgment might influence the selection of measured cells, which means that the sampling error in (R) may not easily be corrected by mathematical means.

In (C), a sufficient number of arbitrarily selected visual fields are registered and analyzed. Also in this case, selection is influenced by the procedure; more large cells than small cells along the borders of the visual field become transected and disregarded. However, this error may be corrected fully by mathematical means.

Dispersion

The coefficient of variation of cell size in the cell samples was between 29% and 38%, indicating that a sample size of 100 cells, as in (R), could reveal differences in mean cell diameter of around 7% at p < 0.05 between cell preparations. Considering that the size distribution seems somewhat skewed (Figure 2), the precision is likely to be less. When cells within a defined size range are of interest or when cell distribution pattern is under study, the sample size must be increased in an inverse proportion to the size of the subsample to achieve the same degree of precision, pointing to the need for the presented methodology.

Comparison between (C) and (R)

Size Distribution

When the experimental conditions had been established, fat cell size was measured on 39 biopsies (23 human and 16 rat) both with (C) (around 11, 300 and 8700 cells, respectively) and with (R) (around 11, 700 and 6900 cells, respectively). In Figure 6, the obtained size distribution is shown for human cells. The general pattern of the distribution is similar for the methods, with a dominating peak and a less prominent hump for smaller cell diameters. However, two discrepancies were observed: 1) (C) gave a smaller mean cell diameter than (R): 78.2 ± 23.1 and 89.4 ± 31.1 μm, respectively; and 2) it seemed that there were fewer small cells measured with (C).

A difference in average size could be caused either by a systematic difference in size estimation by the two methods or by a systematic difference in sampling.

In fact, when cells were measured both interactively on the registered image and with the automatic procedure, it was obvious that (C) underestimated cell size by a factor of 1.06 (Figure 7). This difference depended on the image analysis processing, in which the cell diameter is calculated from the surface area inside the cell contours. Because the degree of underestimation was linear over the whole size range, this error could be corrected by including the factor 1.06 in the computer program. After such a correction (Figure 8), the distributions agreed better, and the difference in average cell size was smaller: 82.9 ± 24.5 and 89.4 ± 31.1 μm for (C) and (R), respectively.

Individual cells were also measured with both (C) and (R) (Figure 9). In these measurements, the same proportionate underestimation of cell diameters by (C) (x1/1.07) was obtained, reflecting the computerized processing of the images. There was, however, also a negative y intercept of the linear regression curve of (C) on (R). Because no significant y intercept was obtained when the very same cells were measured interactively on the registered image, the only possible explanation is that there was, in fact, a systematic bias in (R). Such a bias might reflect the way the observer delimited cell contours. If this bias is taken into account, the discrepancy between mean diameters obtained with (C) and (R) would be further reduced: 82.9 ± 24.5 and 84.7 ± 31.1 μm (mean ± SD), respectively.

Another explanation contributing to the observed shift in the size distribution (Figure 8) could be that big cells were more likely to be selected in (R). This effect is illustrated in Figure 12.

image

Figure 12. This composite figure is based on the same data as in Figure 6. The distribution of cell diameters is illustrated, determined by the reference method (line curves), with the distribution according to the computerized method given in the background for comparison (gray area). Only the main peaks are shown (compare Figure 6). On the left, noncorrected reference data are shown, whereas the other two diagrams illustrate the distributions after corrections, considering sampling along a 30-μm-broad band (middle) or along a line (right). It is obvious that compensation for this putative sampling error improves the agreement between the distribution profiles obtained by the reference method and the computerized method.

Download figure to PowerPoint

In the lowest size range, (C) seemed to pick up fewer cells than (R). However, when cells <44 μm were scrutinized in the registered video images, all cells above the defined limit (>22 μm) were picked up. Another 10% were cells smaller than 22 μm. This little difference, however, could not explain the difference in cell number. One possibility is that the discrepancy reflects a subjective bias to include too many cells in the smallest range with (R).

Mean Cell Diameter

The agreement between the two methods in the determination of mean cell diameter was good, with the data points scattered around the line of identity when (C) was plotted against (R) (k = 0.97 ± 0.01). The size of the coefficient of variation around the regression line, 6.5%, reflects the variation in the individual methods (Figure 1), together with the systematic sampling error and the suspected subjective bias in (R) (step II in Figure 1). Thus, as applied here, the coefficient of variation of determinations on single cell samples (steps II to IV in Figure 1) was 3.6% and 1.2% in (R) and (C), respectively. The dispersion between determinations of mean cell diameter of cells from the same fat biopsy but from different cell samples (steps I to IV in Figure 1) could be bigger, again highlighting the need for large samples and the importance of homogeneous preparations.

Summary

When measurements made with (C) and (R) were compared, certain differences were observed regarding cell size distribution and mean cell diameter. The differences depended to a certain extent on the computerized processing of the image data. These differences were defined in detail and could be compensated for. The remaining differences seemed to depend on inconsistencies in (R) as applied. They should not be compensated for but should merely highlight the need for caution when data obtained by different methods are compared.

Conclusion

We have presented a computer-based method to measure cell size on adipocyte suspensions obtained from collagenase-treated adipose tissue biopsies. The technique is fast enough to easily allow the assessment of 10-fold more cells from fresh preparations than conventional methods. We propose that the method constitutes a powerful tool to study not only average cell size, but also more subtle variations in the size distribution of fat cells from tissue biopsies. At the same time, images of the measured cells may be stored for future reference.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

This study was supported by Swedish Research Council Grants 8269, 13507, and 14735, the Åke Wiberg Foundation, the Wilhelm and Martina Lundgren Foundation, and the National Board of Health and Welfare. We thank the College of Health and Caring Sciences in Göteborg for support during the work.

Footnotes
  • 1

    Nonstandard abbreviations: (R), reference method; (C), computerized technique.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References
  • image

[(1.) For cells of point size, the effective area will be equal to the true area of the visual field = width (w) × height (h). For cells with Ø = d, cells with the center closer than d/2 from the periphery will be excluded, which means that the effective area for these cells will be (wd) × (hd). Thus, if the apparent relative frequency of cells with Ø = d is f, then the true relative frequency (F) will be F = f × [w × h]/[(wd) × (hd)]. (2.) Strictly, only those cells that are grazed or intersected by the vertical line are sampled. For cells with Ø = d, only those cells with the center closer to the vertical line than d/2 will be included, which means that the effective area for these cells is d × l (l = the traveling distance along the line). Thus, if the apparent relative frequency of cells with Ø = d is f, then the true relative frequency (F) will be F = f × k/d, where k is an arbitrary constant. In order to simulate the practical procedure, one might assume that all cells with the borders within a certain distance (a/2) from the vertical line are included. Then the true relative frequency will be F = f × k/(d + a).]