Automated acquisition and analysis of airway surface liquid height by confocal microscopy

Abstract

The airway surface liquid (ASL) is a thin-liquid layer that lines the luminal side of airway epithelia. ASL contains many molecules that are involved in primary innate defense in the lung. Measurement of ASL height on primary airway cultures by confocal microscopy is a powerful tool that has enabled researchers to study ASL physiology and pharmacology. Previously, ASL image acquisition and analysis were performed manually. However, this process is time and labor intensive. To increase the throughput, we have developed an automatic ASL measurement technique that combines a fully automated confocal microscope with novel automatic image analysis software that was written with image processing techniques derived from the computer science field. We were able to acquire XZ ASL images at the rate of ∼ 1 image/s in a reproducible fashion. Our automatic analysis software was able to analyze images at the rate of ∼ 32 ms/image. As proofs of concept, we generated a time course for ASL absorption and a dose response in the presence of SPLUNC1, a known epithelial sodium channel inhibitor, on human bronchial epithelial cultures. Using this approach, we determined the IC50 for SPLUNC1 to be 6.53 μM. Furthermore, our technique successfully detected a difference in ASL height between normal and cystic fibrosis (CF) human bronchial epithelial cultures and detected changes in ATP-stimulated Cl(-)/ASL secretion. We conclude that our automatic ASL measurement technique can be applied for repeated ASL height measurements with high accuracy and consistency and increased throughput.

Keywords: CFTR; COPD; ENaC; airway surface liquid; cystic fibrosis; fluorescent microscopy.

Fig. 1.

Initial processing of confocal micrographs using Otsu's threshold. A: representative fluorescent airway surface liquid (ASL) images. The red-colored band represents the ASL region. ASL height is measured in pixels and converted into a real length according to the ratio of pixel-to-length. In this case, 512 pixels = 145 μm. Images can have an ASL region of varying height from a narrow band (top right) to a wide band (bottom right). B: binarized image taken from A (top left). C: binarized image taken from B after morphological operations with several subsections of the ASL region labeled. D–G: histogram profiles of the subsections of ASL regions shown in C.

Fig. 2.

Analysis of histogram profiles using a 7-step filter. A: histogram profile of subsection 2 (taken from Fig. 1C). B: histogram showing the result of a 5-point running average. C: bipolar profile after thresholding of Fig. 1C. D and E: after application of 7-point step function, values are shown either as 1 or −1. D shows the rising edge and E shows the falling edge. F: graph showing the result of filtering with the 7-point rising edge step function. G: graph showing the result of filtering with the 7-point falling edge step function.

Fig. 3.

ASL height measurements according to different subsection configurations. A–E: examples of the detected subsection boundaries (green), and average boundary positions (white) in a fluorescent image using 8 (A), 16 (B), 32 (C), 64 (D), or 128 (E) subsections. F: example of a 32-subsection configuration. G: average (Avg.) and maximum (Max.) of relative differences in the measured heights of 2 neighboring subsection configurations for all the test images.

Fig. 4.

Distribution of inlier and outlier subsections in a 2-dimensional plane. Difference of height is the absolute value of the difference between the height of subsection and the initial average of heights. Relative difference of height is the absolute value of the difference between the height of a subsection and the initial average of heights divided by the initial average of heights. The ground truth inliers (blue circles) and outliers (red asterisks) are suboptimally separated by union of the 2 thresholded regions bounded by the red lines. Therefore, any subsections located within the dotted square are considered outliers. In the process of defining inliers and outliers, inaccurate selection occurred as shown here at point ∼(0.12, 40; see arrow). However, the accuracy of the mean ASL height was not altered because our algorithm is dependent on the distribution of all collected data points.

Fig. 5.

Examples of the detected boundaries of different ASL regions obtained from the test image set. A: broad ASL region. B: moderate ASL region. C: narrow ASL region from normal human bronchial epithelial cultures (HBECs). D: very narrow ASL region from cystic fibrosis (CF) HBECs. The thickness of the ASL band did not affect the detection of accurate boundaries (green lines), average boundaries (white line), and outlier subsections (blue).

Fig. 6.

A comparison of sample size vs. mean ASL height. A: Mark-and-Find designs for acquiring different amounts of fluorescent images per culture (left). Each dot represents a location where an image is acquired. Histograms of measured ASL heights from all of the acquired images corresponding to Mark-and-Find designs using n = 5–40 images per culture (right). B: graph showing mean ASL heights vs. the number of data points used, based on the different Mark-and-Find designs shown in A. Each data point represents a mean ASL height from each culture. The same cultures were reimaged by using each of the 5 different Mark-and-Find algorithms.

Fig. 7.

Validation of our approach: inhibition of ENaC and stimulation of CFTR/Cacc on ASL height. A: ASL height in the presence of varying concentrations of recombinant SPLUNC1 (all n = 24). Compared with 0.1 μM: ***P < 0.001, difference to 100 μM. †P < 0.05, difference to 10 and 25 μM. B: dose response for ASL height following 4-h recombinant SPLUNC1 exposure on HBECs (all n = 28). C: ASL height in the presence and absence of 300 μM of ATP on normal and CF HBECs (n = 30 for normal and n = 20 for CF). ****P < 0.0001 and *P < 0.05.