Comparison of fixed and living biological cells parameters investigated with digital holographic microscope

Statistical analysis and comparison of biophysical parameters of living and fixed, mouse embryonic fibroblasts cells are presented. Parameters calculations are based on phase measurements performed by means of a digital, holographic microscope. The phases are retrieved from off-axis, image plane holograms, followed by custom image segmentation and statistical analysis of cells’ surface, phase volume and dry mass. The results indicated statistically significant differences between fixed and living cell parameters, which is an important message for setting the methodology for further diagnosis based on quantitative phase (label-free) analysis. Currently, microscopic studies of cell cultures and histopathology samples slowly convert from imaging of chemically stained or labeled microsamples to label-free quantitative phase analysis of transparent microobjects. One of the most popular methods suitable for such analysis is digital holographic microscopy (DHM) [1]. DHM enables measuring many parameters of biological samples such as integrated optical path difference (integrated phase), dry mass, shape and in some cases cell volume. Currently, a lot of studies are carried out on fixed cells, which are easier to handle than living cells. However, many problems require experiments with living cells. The data from both investigations are often mixed and clear interpretation of the results is difficult if not impossible. Most of the previous studies have devoted little attention to the analysis of the impact of fixation process on the values of cell parameters. This work aims to compare the parameters of fixed and living biological cells and answer the question whether the results of their measurements can be treated equally and used for machine learning purposes and further diagnosis [2]. The measurements described in this article were carried out at a multimodal system combining a white light optical microscope and a digital holographic microscope (Fig. 1). DHM works in the Mach-Zehnder interferometer configuration and in-plane, off-axis hologram capture as described in detail in [3]. * E-mail: patryknienaltowski@gmail.com Fig. 1. a) The scheme of the DHM setup [2], b) a white light image and c) a phase map of example NIH3T3 cell culture. WL, white light source; BE1/BE2, beam expanders; BS1/BS2, nonpolarizing beam splitters; CL, condenser lens; MO, microscope objective; M, mirror. Mouse embryonic fibroblasts cells from NIH3T3 line were chosen for the research, as an example of the basic type of cells. The conditions for the preparation of cell cultures tested in the laboratory of the Westphalian Wilhelm University in Münster were compatible with the international standard [4]. Several holograms have been captured from different areas of the cell culture in order to provide a sufficient number of cells for further statistical analysis. In this work, we have focused on investigations of the influence of the most popular fixation process – paraformaldehyde fixation (PFA) – on biophysical parameters of the cells. The phase mod(2) maps are retrieved from holograms using the Fourier Transform Method. After phase unwrapping, the phase maps have to be postprocessed in order to enable correct phase images segmentation and determination of biophysical parameters of individual cells. Phase postprocessing includes two steps: removal of a residual linear term from the phase map, which is realized by subtracting the best-fitting plane. It allows to obtain background stability, which will contribute to better cell segmentation; removal of systematic errors in the recovered phase resulting from the aberration of DHM optical system including imperfections of a Petri dish. This step is simple Comparison of fixed and living biological cells parameters investigated with a digital holographic microscope P. Nienałtowski, M. Baczewska, M. Kujawińska Faculty of Mechatronics, Warsaw University of Technology, Sw. A. Boboli 8, 02-525 Warszawa, Received December 30, 2020; accepted March 28, 2020; published March 31, 2020 doi: 10.4302/plp.v12i1. 971 PHOTONICS LETTERS OF POLAND, VOL. 12 (1), 13-15 (2020) http://www.photonics. pl/PLP © 2020 Photonics Society of Poland 14 if a reference phase map can be captured (for each measurement) and subtracted from the actual phase measurement. However, in this case, it is impossible and therefore the method of averaging measurements from one series has been applied [5]. The conditions for using this method are: (i) the presence of a specimen with a large dimension in comparison with the unitary field of view and (ii) stochastic distribution of the component objects (cells). They ensure that information about objects will not be removed in the averaging process. Then average subtraction is applied to each of the processed holograms separately. The integrated phase map in this processing path is determined as: ( , ) ( , ) ( , ) , z x y x y x y      where z – the measured phase,  – the average phase. The postprocessing algorithm enables to achieve background stability, emphasize the measured objects and eliminate errors that may affect the calculated parameters and the results of further analysis. This is a key step to make reliable measurements. Fig. 2. Block diagram of the image segmentation process. Based on efficiency, simplicity, and usability for biological data processing, two segmentation algorithms were selected for testing the watershed method [6] and the clustering method [7]. As an input image, a corrected phase map from a digital holographic microscope was used. The results of the segmentation by means of both algorithms are shown in Fig. 3. Fig. 3. Segmentation results and calculated cell surfaces using the method of: a) watershed segmentation and b) clustering segmentation with the k-means algorithm; the area marked in red has been classified as a cell, the black area is the original biological cell. The final selection of the segmentation algorithm was based on a visual comparison of the effects of segmentation with both methods, the main criterion was the degree of coverage of the segmented cell with a reference. For further processing of a phase, the k-means method of clustering was selected. It is a non-hierarchical open-source algorithm implemented in Matlab [7]. The schemes of phase maps processing are shown in Fig. 4. After division into clusters, the cluster containing background is removed from the image. The detection of a cluster containing background is based on a number of pixels. Due to the nature of the measurements, the background cluster contains the majority of pixels. After choosing clusters containing cells, the process of creating a binary mask follows. Areas in the image containing cells have a value of 1 and the background has a value of 0. The next step is to perform a few morphological operations such as dilatation with a spherical structural element [8]. The prepared binary image is labeled to analyze each cell separately. In this algorithm, an 8-pixel connection condition was used. The last stage of image segmentation is based on surface classification of objects to eliminate connected objects that are not cells (Fig. 4b). Fig. 4. The schemes of phase processing: a) clustering of the image; cluster 1 contains the background, cluster 2 contains the nucleus of the cell, cluster 3 contains the plasmatic structure, b) separation of individual cells from phase maps. Determined cell parameters were based on phase values from which single cells were segmented. The parameters on which the statistical analysis is based are: (i) cell surface S, which is calculated using dimensions of the pixel a in sample plane and number N of pixels classified as a cell: