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American Journal of Respiratory and Critical Care Medicine Vol 166. pp. 1257-1262, (2002)
© 2002 American Thoracic Society

Original Article

Computer-assisted Analysis Helps Detect Inner Dynein Arm Abnormalities

Estelle Escudier, Michel Couprie, Bénédicte Duriez, Françoise Roudot-Thoraval, Marie-Claude Millepied, Virginie Prulière-Escabasse, Laurent Labatte and André Coste

Unité Fonctionnelle de Biologie de la Reproduction, Département de Génétique, Cytogénétique et Embryologie, Groupe hospitalier Pitié-Salpêtrière (AP-HP), Paris; Unité INSERM 492, Créteil; Groupe Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique (ESIEE), Noisy le Grand; Unité INSERM 468; Unité Evaluation Etudes, Hôpital H. Mondor (AP-HP); Service d'Anatomie pathologique (Microscopie Électronique), Hôpital Intercommunal; and Service d'Otorhinolaryngologie et de Chirurgie cervico-faciale, Hôpitaux H. Mondor (AP-HP) et Intercommunal, Créteil, France

Correspondence and requests for reprints should be addressed to Dr. Estelle Escudier, Laboratoire de Biologie de la Reproduction, Bâtiment Benjamin Delessert, Groupe hospitalier Pitié-Salpêtrière, 47-83 boulevard de l'Hôpital, 75651 Paris Cedex 13, France. E-mail: escudier{at}im3.inserm.fr


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The diagnosis of primary ciliary dyskinesia is based on demonstration of ciliary defects, mainly concerning dynein arms. Whereas the absence of outer dynein arms can be easily distinguished, the absence of inner dynein arms is difficult to confirm because of their low contrast on electron microscopy. Ciliary ultrastructure was studied in 40 patients suffering from respiratory tract infections. Conventional transmission electron microscopy showed normal cilia in 6 patients, confirmed a diagnosis of primary ciliary dyskinesia in 26 patients, and was inconclusive in 8 patients. All doubtful cases were related to inner dynein arm determination. Conventional electron microscopic analysis was able to define the ultrastructural phenotype of inner dynein arms in 40.5% of cases (6 presence of inner dynein arms, 13 absence of inner dynein arms). We developed computer-assisted analysis of electron microscopic micrographs to improve inner dynein arm visualization. Computer-assisted analysis consisted of image transformations designed to enhance the signal/noise ratio, based on the symmetry of ciliary axonemes. The sensitivity and specificity of computer-assisted analysis were 100 and 98%, respectively. The efficiency of computer-assisted analysis to visualize inner dynein arms, evaluated in the patients with undetermined phenotype after electron microscopy, was 86% (three normal cilia, seven primary ciliary dyskinesia with absence of outer dynein arms, three primary ciliary dyskinesia with absence of inner dynein arms, five partial absence of inner dynein arms). Computer-assisted analysis of ciliary micrographs improves the characterization of inherited axonemal defects.

Key Words: primary ciliary dyskinesia • ciliary ultrastructure • dynein arms • computer-assisted analysis

Primary ciliary dyskinesia is a congenital disorder of respiratory cilia characterized by abnormal motility generally related to ultrastructural ciliary defects, responsible for impaired mucociliary transport (1). Mucociliary transport is an important defense mechanism dependent on ciliary motion and the rheologic properties of mucus. The axoneme, the core of the cilium, is highly conserved and includes nine peripheral doublet microtubules with attached dynein arms and radial spokes, surrounding two central single microtubules. Inner and outer dynein arms are the transducers of mechanical forces necessary for ciliary motion. Ciliary dysfunction leads to chronic respiratory tract infections beginning in early childhood and characterized by bronchiectasis and chronic sinusitis, sometimes associated with situs inversus and male sterility (1). In the presence of a suggestive clinical presentation, the diagnosis of primary ciliary dyskinesia is mostly based on demonstration of specific defects of respiratory cilia detected by electron microscopy (2). Primary ciliary dyskinesia appears to be a heterogeneous group of genetic disorders with various ciliary ultrastructural defects, including absence of dynein arms, abnormal microtubular arrangements and numbers, or lack of radial spokes (1). The earliest and commonest axonemal defect detected in patients with primary ciliary dyskinesia concerns dynein arms, which contain ATPase activity essential for ciliary motion (3). Inner, outer, or both dynein arms may be defective, and the absence of dynein arms may be either partial or complete (4).

Whereas the ultrastructural defects of outer dynein arms can be distinguished easily, numerous studies (4–7) have highlighted the difficulty of demonstrating the absence of inner dynein arms because of the low contrast of these structures on electron microscopy.

Because the diagnosis of primary ciliary dyskinesia is based on demonstration of ultrastructural defects of cilia (2), it is important to improve inner dynein arm visualization in images obtained by conventional transmission electron microscopy. We therefore developed computer-assisted analysis of electron microscopic images using photographic enhancement of dynein contrast, as proposed by Markham and coworkers (8). The sensitivity and specificity of this new method were evaluated in comparison with conventional electron microscopic results in patients with a defined ultrastructural phenotype after electron microscopy. The efficiency of computer-assisted analysis for identifying inner dynein arms in axonemal sections was evaluated in patients with a doubtful ultrastructural phenotype after electron microscopy.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Patients
Over a 2-year period, ciliary ultrastructure was studied in 40 patients in the Pathology Department of our hospital. All these patients were investigated because of chronic upper and lower respiratory tract infections, i.e., bronchitis and/or bronchiectasis and sinusitis, to confirm a diagnosis of primary ciliary dyskinesia. Other pathologic conditions such as cystic fibrosis, {alpha}1-antitrypsin deficiency, or immunodeficiency were excluded previously. As usually proposed before ultrastructural investigations (9), ciliary motility was studied in 37 of the 40 patients as described previously (10). Informed consent was obtained from all patients, and this study was approved by the Henri Mondor Hospital Ethics Committee.

Tissue Preparation for Ultrastructural Study
Biopsies of ciliated epithelium were obtained from either the inferior nasal turbinate (18 patients) or the bronchi (22 patients) and processed for electron microscopy as described previously (11). At the final dehydration step, the tissue samples were treated with 1% tannic acid in ethanol for 10 minutes. The inclusion of MgSO4 and tannic acid in the processing schedule has been found to markedly increase the visibility and contrast of the axonemal ultrastructure (12).

Quantitative Analysis of Ciliary Ultrastructure by Conventional Electron Microscopy
Ciliary ultrastructural results were expressed as a percentage of abnormal cilia among the total number of cilia analyzed (13). Dynein arms were considered to be absent from axonemal sections when the structure was missing from at least five of the nine peripheral doublets. For each ciliary study, axonemal abnormalities were quantified, and the ultrastructural phenotype was defined by the main ultrastructural defect (involving the outer, inner, or both dynein arms).

Ciliary Ultrastructure Analysis after Computer-assisted Analysis
The principle of computer-assisted analysis was based on an idea originally proposed by Markham and coworkers in 1963 (8). The Markham rotation method takes advantage of axoneme symmetry and uses optical and mechanical means to superimpose the nine pairs of peripheral microtubules to obtain a single composite image. Based on this approach, computer-assisted analysis of ciliary micrographs was developed and consisted of image rotations, associated with contrast enhancement. The software was run on a standard personal computer platform under the Linux operating system. Dark regions corresponding to peripheral microtubules and dynein arms were merged together in a composite image. Dark regions corresponding to noise were attenuated by the merging process because they are randomly placed, whereas dark regions corresponding to dynein arms get reinforced because their position with regard to the microtubule pair is always the same. Roughly speaking, in a "normal image of cilia," the nine pairs of peripheral microtubules form a circle surrounding the single central pair. The first step of analysis consisted of identifying the center of this circle as well as the center of each peripheral microtubule. The location of the main center was obtained by a procedure based on the Hough transform (14). In the case of an off-centered ciliary section, in which the shape of the cilia is elongated, a global geometric correction was first performed. The centers of all microtubules were detected by a procedure based on mathematic morphology (14). The inner part of each tubule forms a light spot surrounded by a dark circle: a simple dilatation by a circular structuring element is sufficient to extract such patterns in the whole image. In case of failure of the automatic detection, for example in case of broken or filled-up tubules, a manual procedure using mouse clicking was used. These extracted points were grouped to form nine triangles corresponding to the nine peripheral pairs of microtubules (Figure 1) . The affined transformations allowing these nine triangles to be superimposed were then easily identified by solving a set of linear equations. Classical image warping methods (15) were used to interpolate the pixel values within the transformed sectors, and a mean image of these nine resulting sectors was calculated. Due to the marked variations of contrast between different axoneme images, grayscale normalization and contrast enhancement were performed to provide a well-contrasted and uniform presentation of the results, by identifying the mean gray level in the microtubules and in the background and by applying histogram transformations of the axoneme image (14). The mean overall time to process one image was approximately 1 minute, including the validation done by the operator (the positions of the triangles were carefully checked) and the manual correction when necessary.



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  		Figure 1. Normal axonemal ciliary section with the position of the different markers used to perform the nine 40° rotations to form a single composite image after computer-assisted analysis: center of the circle defined by the nine doublets, center of each peripheral microtubule of the nine doublets, nine triangles corresponding to each doublet (bar = 0.05 µm).

 
For each patient, the 10 best-defined axonemal sections were selected, and the corresponding micrographs were submitted to computer-assisted analysis. The results were expressed as the number of inner dynein arms visualized in the 10 composite images.

Statistical Analysis
The sensitivity and specificity of computer-assisted analysis were evaluated in comparison with the results obtained by conventional electron microscopy when conclusive. The positive and negative predictive values for detecting the absence or presence of inner dynein arms after computer-assisted analysis were then calculated. The efficiency of computer-assisted analysis was evaluated in the patients in whom conventional electron microscopic analysis was unable to determine whether inner dynein arms were present.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Quantitative Analysis of Ciliary Ultrastructure by Conventional Electron Microscopy
The results of ciliary studies are given in Table 1 . Ciliary ultrastructure was strictly normal in six patients (15%). A diagnosis of primary ciliary dyskinesia was confirmed in 26 patients (65%) in the presence of typical clinical features associated with the absence of dynein arms concerning all cilia (Figure 2) . Among these 26 patients with primary ciliary dyskinesia, the ultrastructural phenotype could be precisely identified in 13 cases (absence of inner dynein arms with or without absence of outer dynein arms). In the other 13 patients with primary ciliary dyskinesia, outer dynein arms were absent, but it was impossible to conclude whether or not this absence was associated with absence of inner dynein arms. In the eight remaining patients (20%), outer dynein arms were clearly detected and it was impossible to confirm or exclude the diagnosis of primary ciliary dyskinesia, as the presence of inner dynein arms could not be determined.


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  		TABLE 1. Ciliary ultrastructure and motility, according to clinical features of the study population

 




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  		Figure 2. Ciliary ultrastructure after conventional electron microscopy. (A) Normal ciliary ultrastructure, (B) isolated absence of inner dynein arms (arrowhead), and (C) absence of both dynein arms (bar = 0.1 µm).

 
The patients could be divided into two groups on the basis of inner dynein arm visualization. In 19 patients, conventional electron microscopic analysis demonstrated the presence (6 cases) or absence (13 cases) of inner dynein arms and was inconclusive in 21 patients. Conventional electron microscopic analysis was therefore unable to define the ultrastructural phenotype in 52.5% of cases (Figure 3) .



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  		Figure 3. Results of the ultrastructural phenotype analysis of the 40 patients after conventional electron microscopy, expressed as the number of patients with each type of axonemal defect. The ultrastructural phenotype was well characterized after conventional electron microscopy in 19 patients (right part of the pie), whereas the presence of inner dynein arms remained questionable for 21 patients (left part of the pie). IDA = inner dynein arm; ODA = outer dynein arm; + = presence; - = absence.

 
Ciliary Ultrastructure Analysis after Computer-assisted Analysis
The results of inner dynein arm visualization after computer-assisted analysis are given in Figures 4 and 5 .



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  		Figure 4. Original computerized image (left) obtained after conventional electron microscopy and the corresponding digitally processed composite image (right) obtained after computer-assisted analysis. (A) Normal cilia, (B) cilia with absence of inner dynein arms, and (C) cilia with absence of both dynein arms. Note that inner dynein arm visualization was doubtful on the original images (left), whereas dynein arm defects (arrowhead) are clearly confirmed after computer-assisted analysis (bar = 0.05 µm).

 


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  		Figure 5. Results of inner dynein arm visualization after computer-assisted analysis of ciliary micrographs in the study population. The results obtained by conventional electron microscopy (x-axis), i.e., inner dynein arm presence (IDA+), inner dynein arm absence (IDA-), doubtful inner dynein arms (IDA?), are compared with the number of inner dynein arms detected in the 10 composite images for each patient (y-axis). Note that only 37 patients are represented in this graph, as computer-assisted analysis was inconclusive for 3 patients. The various ultrastructural phenotypes are illustrated: normal ciliary ultrastructure (open circle), absence of both dynein arms (dark-gray square), isolated absence of inner dynein arms (dark-gray circle), isolated absence of outer dynein arms (light-gray square).

 
For the six patients with normal ciliary ultrastructure, inner dynein arms were present on at least 8 of the 10 computerized images. For the 13 patients with absence of inner dynein arms on conventional electron microscopy, no inner dynein arm images were generated by computer-assisted analysis. For 3 of the 130 cilia analyzed in these patients, the loss of inner dynein arms concerned some but not all microtubules. We verified in the three corresponding computerized images, coming from three different patients, that the loss of inner structures was not masked by the superimposition process. Thus, in these 19 patients, computer-assisted analysis always confirmed the diagnosis obtained by conventional electron microscopy. The ciliary sections (n = 190) of these 19 patients were used to test the sensitivity and specificity of computer-assisted analysis, giving values of 100 and 98%, respectively. In the population of patients in which conventional electron microscopy gave a precise ultrastructural phenotype, the positive and negative predictive values for detecting the absence or presence of inner dynein arms by computer-assisted analysis were 99 and 100%, respectively.

In 3 of the 21 patients with undetermined ultrastructural phenotype after conventional electron microscopy, no conclusions could be drawn, even after computer-assisted analysis, as inner dynein arm visualization on composite images remained unclear. In 10 patients with undetermined ultrastructural phenotype after conventional electron microscopy, an inner dynein arm image was present on at least 8 of the 10 computerized images. Because the results were in the same range as in patients with normal ultrastructure, we concluded on the presence of inner dynein arms in these 10 patients, finally inferring a normal ultrastructure for three patients and isolated absence of outer dynein arms for seven patients with primary ciliary dyskinesia. In three other patients, the inner dynein arms were absent from all composite images, allowing to precisely determine the ultrastructural phenotype of these patients with primary ciliary dyskinesia (isolated absence of inner dynein arms for one patient and absence of both dynein arms for two patients). In the last five patients, inner dynein arms were visualized in only some of the 10 composite images, suggesting partial absence of inner dynein arms (isolated for one patient or associated with total absence of outer dynein arms for the other four patients). In summary, in the 21 patients with inconclusive conventional electron microscopic findings, the efficiency of computer-assisted analysis to visualize inner dynein arms was therefore 86%.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In the presence of chronic respiratory tract infections, the diagnosis of primary ciliary dyskinesia is based on demonstration of specific ultrastructural defects of cilia responsible for abnormal function. However, even with an extensive experience of ultrastructural analysis, ciliary evaluation may be problematic in some patients because of the difficulty of visualizing inner dynein arms.

Inner dynein arms are difficult to analyze because they have a low contrast on electron microscopy due to the complexity of their structure. The description of dynein arms in Chlamydomonas helps understand the difference in contrast between inner and outer dynein arms (16–18). Whereas all outer arms appear to be identical, uniformly distributed at 24-nm intervals over the length of the peripheral microtubules, the inner arms present a diverse composition and distribution. When the inner arms are viewed longitudinally by electron microscopy, different regions of density, repeated every 96 nm, are distinguished and represent different isoforms of inner arms (19–21). Thus, in each transverse ultrathin section of cilia, at least three identical outer dynein arms are strictly superimposed, whereas different isoforms of inner dynein arms are represented, therefore explaining the discrepancy in outer and inner arm contrast.

Various methods, such as tannic acid staining, have been proposed for improving inner dynein arm visualization (4, 22, 23). In our ciliary processing schedule, we introduced tannic acid processing at the final dehydration step (12), but this staining modification did not resolve the problems of inner dynein arm visualization.

In 1963, Markham described a technique that photographically reinforced the ultrastructural contrast of virus particles exhibiting radial symmetry (8). Markham's rotation method has been applied to dynein arms by using a rotation technique taking advantage of the symmetry of ciliary axonemes (24, 25). After nine successive 40° rotations around the central axis of the axoneme, the dynein arms on a single ciliary cross section were photographically superimposed to form an image corresponding to the summation of all arms present in a given ciliary cross section. The dynein arms, especially the inner ones, then became easier to identify. However, the use of a photographic darkroom and the intervention of an operator for each image rotation limited the use of this original method for routine primary ciliary dyskinesia diagnosis. Some recent studies have adapted Markham rotation, using a personal computer and commercially available software (e.g., Adobe Photoshop) to rotate the axoneme image (26). However, only few cilia obtained from five cases including three primary ciliary dyskinesia were studied, and the efficiency of the method was not evaluated in doubtful cases of primary ciliairy dyskinesia. The computer-assisted analysis developed in our study has several advantages over all previous rotation methods. First, superimposition of the nine peripheral doublets is performed automatically and is not obtained by successive manual rotations of 40°. In fact, 40° rotations do not always correspond to the exact angle between doublets, especially when ciliary ultrastructure is abnormal. In addition, our software automatically corrects the orientation of each doublet. In fact, each peripheral doublet is not perpendicular to the radius of the axoneme circle, but its orientation is variable as illustrated by the different shapes of the nine triangles identified in Figure 1. This doublet orientation should be normalized to obtain strict superimposition of the nine peripheral doublets. Second, unlike previous studies (24, 25), contrast enhancement was not subjective but was automatically normalized among all cilia images before processing, using two internal standards, corresponding to the lightest and darkest areas of each computerized axoneme. Lastly, it should be stressed that 80% of the composite images obtained by computer-assisted analysis did not require any manual intervention. For the remaining 20%, operator intervention was limited to identification of one or two peripheral doublets, which were not automatically located in some axonemes, usually because of the darkness of their center. The ease of computer-assisted analysis allowed us to perform this study, requiring the processing of 400 ciliary micrographs to evaluate the efficiency of computer-assisted analysis of ciliary micrographs, which was never performed before. However, further improvements of our software are necessary before marketing, especially to simplify the user interface and to adapt it to the Windows environment.

The computer-assisted analysis of cilia images turned out to be highly sensitive and specific in patients with a precise ultrastructural phenotype on conventional electron microscopy, always confirming the results of conventional electron microscopic analysis. In the cases in which inner dynein arm visualization was doubtful on electron microscopy, it was possible to reach a conclusion in most patients after computer-assisted analysis. First, computer-assisted analysis was able to distinguish between normal ciliary ultrastructure and primary ciliary dyskinesia with absence of inner dynein arms in four patients. This point has an important clinical relevance because the assertion of absent inner dynein arms in isolation is a strong argument for establishing the diagnosis of primary ciliary dyskinesia. Second, in almost all patients with primary ciliary dyskinesia with absent outer dynein arms, computer-assisted analysis indicated whether this defect was isolated or affected both dynein arms. In patients with primary ciliary dyskinesia, the determination of a precise ultrastructural phenotype is an essential step before genetic studies that require precise definition of the ultrastructural phenotype to guide subsequent molecular analysis (17, 21, 27).

Inner dynein arm analysis on composite images raised a number of problems in some patients. In three cases, the presence of inner dynein arms was questionable because of very poor definition of the structure, even after contrast enhancement. This could reflect a technical problem requiring another biopsy. In other patients, computer-assisted analysis detected partial absence of inner dynein arms, i.e., missing in some of the 10 composite images. This finding could be related to the decreased number of inner dynein arms per cilia found with conventional electron microscopy in patients with chronic respiratory tract infections (7, 12) or could correspond to the defects of only some inner structures as already described in Chlamydomonas mutants such as ida4, pf9, or pf2 (17, 21, 25, 28). Further improvements in computer-assisted analysis could allow evaluation of the shape, surface, and intensity of the shadow of inner arms to test this hypothesis.

Lastly, despite clinical and functional features of primary ciliary dyskinesia, the ciliary ultrastructure was considered to be normal after conventional electron microscopy and after computer-assisted analysis for four patients. Several cases of primary ciliary dyskinesia with normal ciliary ultrastructure, corresponding to molecular anomalies undetectable by electron microscopy, have been reported in the literature (29–31).

In this study, we have developed computer-assisted analysis of conventional electron microscopic micrographs to improve inner dynein arm visualization. We demonstrated the high sensitivity and the specificity of the computer-assisted analysis and proved the efficiency of this method in doubtful cases. In addition, computer-assisted analysis identified partial absence of inner dynein arm. Computer-assisted analysis could also be used to screen other axonemal structures, such as radial spokes or central shafts, which are also complex and composed of several proteins (32). Further improvements in our software, allowing computer-assisted analysis to be associated with each ciliary electron microscopic analysis, could greatly improve the identification and characterization of inherited axonemal defects.


   Acknowledgments
 
The authors thank Francine Jezequel and Gérard Ziverec for their help in ciliary studies.


   FOOTNOTES
 
Supported by grants from the network INSERM/AFM 2000 for Orphan diseases ("Réseaux de Recherche sur les Maladies Rares"), the Chancellerie des Universités (Legs Poix), and the Assistance Publique-Hôpitaux de Paris (CRC 96125).

Received in original form December 5, 2001; accepted in final form August 9, 2002


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 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
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P. G. Noone, M. W. Leigh, A. Sannuti, S. L. Minnix, J. L. Carson, M. Hazucha, M. A. Zariwala, and M. R. Knowles
Primary Ciliary Dyskinesia: Diagnostic and Phenotypic Features
Am. J. Respir. Crit. Care Med., February 15, 2004; 169(4): 459 - 467.
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M. J. Tobin
Sleep-Disordered Breathing, Control of Breathing, Respiratory Muscles, and Pulmonary Function Testing in AJRCCM 2002
Am. J. Respir. Crit. Care Med., February 1, 2003; 167(3): 306 - 318.
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Proc. Am. Thorac. Soc. Am. J. Respir. Cell Mol. Biol.
Copyright © 2002 American Thoracic Society