Machine-based counting of mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections

Machine-based counting of mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine-based counting of mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections Shingo Sakashita, Mai Sakashita, Noriko Motoi, Gaku Yamamoto, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3901509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Visualizing mitochondria in cancer cells from human pathological specimens may improve our understanding of cancer biology. However, using immunohistochemistry to evaluate mitochondria remains difficult because almost all cells contain mitochondria and the number of mitochondria per cell may have important effects on mitochondrial function. Herein, we established an objective system (Mito-score) for evaluating mitochondria using machine-based processing of hue, saturation, and value color spaces. Methods The Mito-score was defined as the number of COX4 (mitochondrial inner membrane) immunohistochemistry-positive pixels divided by the number of nuclei per cell. The system was validated using four lung cancer cell lines, normal tissues, and lung cancer tissues (199 cases). Results The Mito-score correlated with MitoTracker, a fluorescent dye used to selectively label and visualize mitochondria within cells under a microscope (R 2 = 0.68) and with the number of mitochondria counted using electron microscopy (R 2 = 0.79). Histologically, the Mito-score of small cell carcinoma (57.25) was significantly lower than that of adenocarcinoma (147.5, p < 0.0001), squamous cell carcinoma (120.6, p = 0.0004), and large cell neuroendocrine carcinoma (111.8, p = 0.002). Conclusion The Mito-score method enables the analysis of the mitochondrial status of human formalin-fixed paraffin-embedded specimens and may provide insights into the metabolic status of cancer. mitochondria cancer COX4 MitoTracker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Mitochondria perform several important functions in cells, including the production of energy; generation of reactive oxygen species, redox molecules, and metabolites; regulation of cell signaling and cell death; and biosynthesis of metabolites (Zong et al. 2016 ; Nunnari and Suomalainen 2012 ). Normal cells primarily rely on mitochondrial oxidative phosphorylation for energy generation. Conversely, most cancer cells produce excess lactate in the presence of sufficient oxygen (Warburg 1956 ), a phenomenon known as the “Warburg effect.” Although Warburg originally considered this effect to reflect mitochondrial dysfunction in cancer cells, many cancer cells exhibiting the Warburg effect may possess intact mitochondrial function but altered metabolism (Vander Heiden et al. 2009 ). Mutations in mitochondrial DNA contribute to tumor progression by enhancing the metastatic potential of cancer cells (Ishikawa et al. 2008 ). In small cell lung cancer, pyruvate kinase M1 converts phosphoenolpyruvate to pyruvate during glycolysis and promotes tumor growth (Morita et al. 2018 ). In lung adenocarcinoma, the ovarian cancer immunoreactive antigen domain-containing 2, which may cause malignant transformation in lung adenocarcinoma at an early phase, may be a key factor in maintaining mitochondrial function or structure (Sakashita et al. 2018 ). The ability to observe mitochondria in cancer cells from human pathological specimens may improve our understanding of the relationship between mitochondrial status and cancer. Although many studies have explored mitochondria in various contexts, relatively few have examined mitochondria in human cancer histological specimens using immunohistochemistry (IHC) primarily because this method has several limitations. For instance, almost all cells contain mitochondria; therefore, determining a cutoff value for positive IHC staining is impossible. The enumeration of mitochondria is necessary to determine their functions; however, counting the number of mitochondria using IHC remains challenging. The quantity of mitochondria may also be correlated with cellular functions, such as metabolism; however, this correlation in each cell is difficult to establish using light microscopy. Recently, digital pathology has been demonstrated to have a relatively high accuracy for the automated detection of IHC samples, thereby eliminating subjectivity (Harmon et al. 2021 ; Wetstein et al. 2021 ). Herein, we developed an objective evaluation system based on the hue, saturation, and value (HSV) color space to determine the immunohistochemical status of mitochondria using formalin-fixed and paraffin-embedded (FFPE) sections. This system enabled separation of the color of the IHC (DAB) from that of the stained nucleus (hematoxylin); thus, we simultaneously measured the IHC score and number of nuclei. This system was validated using four lung cancer cell lines and FFPE sections obtained from normal tissues. This system was also used to evaluate the status of mitochondria in resected human lung cancer specimens. Materials and Methods MitoTracker analysis using lung cancer cell lines A549, NCI_H358, NCI_H1373, and NCI_H1975 cells were seeded at 1.5 × 10 5 cells/well in 6-well plates the day before the experiment. MitoTracker™ RED FM (M22425) was added at a concentration of 100 nM and incubated for 30 min at 37℃ in a humidified incubator with 5% CO 2 . Cells were then harvested and analyzed via fluorescence cytometry using a BD FACSCanto IITM (Becton Dickinson), and the mean fluorescence intensity was calculated using FlowJoTM software (ver 10.7.1). Electron microscope analysis using lung cancer cell lines For electron microscopic analysis, cells were fixed with 2.5% glutaraldehyde (pH 7.2, 0.1 M Phosphate buffer) and then cell blocks were prepared. After creating the cell blocks, Epon embedding was performed, and after ultrathin sectioning, analysis was performed using an electron microscope (JEM-1400Flash, JEOL Tokyo, Japan). Then, 10 cells with the largest split surfaces were selected, and the number of mitochondria was visually measured and subsequently averaged. Tissues samples Lung carcinoma specimens were used that had been surgically resected from patients originally diagnosed at the National Cancer Center Hospital (Tokyo, Japan) between 2017 and 2019. Tumor slides and blocks of samples from 199 patients were used for histological evaluation and tissue microarray (TMA) construction. The clinicopathological characteristics of the patients are summarized in Table 1 . To evaluate the validity of the system, we also used specimens containing only normal tissues from patients with surgically resected stomach and kidney carcinomas at the National Cancer Center Hospital (Chiba, Japan) in 2021. The clinical data of all patients were collected from their medical records. Informed consent was obtained from all patients for the use of their materials. The Institutional Review Board approved the use of the specimens (2016 − 124, National Cancer Center, Japan, 2020 − 242, National Cancer Center East, Japan). The study was conducted in accordance with the principles of the Declaration of Helsinki. Table 1 Patient clinicopathological information. Total AD SQ Small LCNEC Case Number 199 96 57 27 19 Gender Female 67 45 9 8 5 Male 132 51 48 19 14 Age 68 66 71 69 69 pT factor T1 52 31 8 11 2 T2 74 29 22 14 9 T3 52 30 15 2 5 T4 21 6 12 0 3 pN factor N0 126 57 38 18 13 N1 30 11 10 6 3 N2 42 27 9 3 3 N3 1 1 pM factor M0 192 90 56 27 19 M1 7 6 1 0 0 pStage Ⅰ 79 40 14 17 8 Ⅱ 54 18 23 7 6 Ⅲ 59 32 19 3 5 Ⅳ 7 6 1 Subtype Lepidic 3 Acinar 11 Papillary 37 Solid 23 Micropapillary 15 IMA 4 Other (enteric, colloid and fetal) 4 W/D 7 M/D 38 P/D 12 AD: Adenocarcinoma; SQ: squamous cell carcinoma; Small: small cell carcinoma; LCNEC: large cell neuroendocrine carcinoma IHC using TMAs and cell blocks of the lung cancer cell lines TMAs, comprising 2-mm cores with two cores from each tumor, were constructed from 10% FFPE blocks. The two cores were separately embedded in two blocks. Four representative tumor areas were marked on hematoxylin and eosin-stained slides. In total, 796 specimens had adequate cores for immunohistochemical analysis. For cell blocks of the lung carcinoma cell lines, cells were sedimented by centrifugation, fixed in 10% neutral buffered formalin, embedded in paraffin blocks, and thinly sliced to 4 µm. IHC was performed on 3-µm–thick sections prepared from TMAs. Sections were deparaffinized and rehydrated, and antigen retrieval was performed using Cell Conditioning solution, CC1 (EDTA, pH 8.5; Roche, Basel, Switzerland) at 95°C for 64 min. Immunohistochemical staining was performed using rabbit polyclonal anti-cytochrome c oxidase subunit IV isoform 1 antibody diluted at 1:500 (HPA002485, Sigma-Aldrich, St. Louis, MO, USA) for 1 h at room temperature using a Roche Ventana BenchMark Ultra autostainer and Ventana Ultra View Universal DAB Detection kit (Roche), and using rabbit polyclonal antivoltage-dependent anion channel 1 (VDAC1) antibody diluted at 1:50 (HPA030780, Sigma-Aldrich, St. Louis, MO, USA) for 1 h at room temperature using a Roche Ventana BenchMark Ultra autostainer and Ventana Ultra View Universal DAB. An antibody specific for cytochrome c oxidase subunit IV isoform 1, a marker of the mitochondrial inner membrane, was selected because the granular staining pattern in the cytoplasm clearly indicates the presence of mitochondria. IHC evaluation using whole slide images Stained TMA were scanned at 40× magnification using a whole slide scanner NanoZoomer S360 (Hamamatsu Photonics, Hamamatsu, Japan). A pathologist annotated two circled areas of the carcinoma and stroma (0.08 m 2 each) using Labelme software (Wada 2016 ). We preferentially selected areas where the carcinoma and stroma were physically separated because accurately annotating overlapping areas of carcinoma and stroma was difficult as these areas include inflammatory cells, macrophages, and vascular endothelium. A pixel-based approach for the spatial annotation of mitochondrial expression was used. The image processing steps are illustrated in Fig. 1 a. All image patches were derived with reference to 2000 × 2000-pixel regions under a 40× objective within the annotated regions of the carcinoma. Extraction of IHC staining and nucleus by converting an acquired red, green, and blue image to HSV color space We transformed the image from the red, green, and blue color spaces into the HSV color space using the OpenCV (3.4.15) library. Cells were then separated from the background by changing the value (V) component and applying V to eliminate the background. A V value of 10–220 was adopted for nuclear separation and 0–160 was used for IHC separation. We extracted the color of the IHC (brownish color of DAB) and nuclei (bluish color of hematoxylin) using the hue (H) component. H values of 0°–60° and 320°–360° were determined as the color of IHC staining, and H values of 200°–320° were determined as the color of the nucleus by certified pathologists (Fig. 2 ). Using this process, we classified the pixels according to whether they were annotated as cells (carcinoma or stroma) or background. Nonspecific staining was eliminated using the saturation (S) component. An S value of 20–255 was used to separate the nuclei while a value of 60–255 was used to separate the IHC staining. Evaluation of mitochondria IHC To evaluate mitochondria, the Mito-score was defined as the number of COX4 IHC-positive pixels divided by the number of nuclei, as we hypothesized that the number of mitochondria per cell was related to their function. COX4 IHC-positive pixels were counted after separation by IHC staining. The nuclear counting system was as follows (Fig. 1 b). First, the image threshold of the nucleus was obtained using Otsu’s thresholding technique or discriminant analysis (Otsu 1979 ). Rough cell edges and holes in the nucleus can cause an inaccurate nuclear count. Therefore, we performed the following steps to reduce the image noise (denoising). Dilation was conducted to smooth the nucleus edges by gradually enlarging the boundaries of foreground pixel regions. Closing was performed to remove the holes in the nucleus. Next, a distance map was created by calculating the distance between the background and the nucleus. Erosion was used to remove the boundaries of the foreground pixel regions. Overlapping cells can be counted as single cells, causing inaccurate cell counting. The apex of the nucleus was extracted and magnified to reduce any inaccuracy. The position of the center of gravity was calculated using the number, position, and contour of the nucleus. Nuclei were enumerated using digital image processing and the apex was circled using a computer. Certified pathologists confirmed the circled area as the nucleus. However, this counting system may not be effective for crowded cells. In such cases, the denoising treatment was changed and the calculations were repeated. Finally, the Mito-score was calculated as the number of COX4 IHC-positive pixels divided by the number of nuclei. Statistical analysis Statistical analyses were performed using GraphPad Prism software (version 9; GraphPad Inc., La Jolla, CA, USA). Significance was defined as p < 0.05. The association between Mito-score and various subtypes of lung carcinoma was analyzed using an unpaired t -test. The correlation between the Mito-scores of the tumor and stroma was assessed using linear regression analysis. Results Method validation using lung cancer cell lines Four lung cancer cell lines (A549, NCI_H358, NCI_H1373, and NCI_H1975) were used to identify correlations between general mitochondrial abundance measurements and the mitochondrial abundance measurements obtained using Mito-score. Specifically, we measured the correlation between measurements by MitoTracker and Mito-score (Fig. 3 a–c), and between actual measurements by electron microscopy and Mito-score (Fig. 3 a, b, d). In the MitoTracker measurement, the correlation analysis had an R 2 = 0.6736, suggesting a correlation between the MitoTracker and Mito-score (Fig. 3 c). Correlation analysis also showed a correlation of R 2 = 0.7907 with the actual values measured by electron microscopy (Fig. 3 D). These results indicate that the proposed method correlates with existing methods for measuring mitochondrial abundance and that this can be measure the number of mitochondria can be achieved using IHC. Validation of evaluation method using single cells The evaluation method was validated using a single-cell model. We selected macrophages for validation because macrophage boundaries do not make contact with each other, and each single cell can be easily annotated. Macrophages showed variable mitochondrial staining patterns (Fig. 4 a), and their Mito-scores were calculated. Images with the top and bottom 10 Mito-scores are shown in Fig. 4 b. Validation of evaluation method using normal tissue The evaluation method was validated using normal tissues obtained from surgically resected kidney, stomach, and lung carcinomas without cancer cells. The Mito-scores are shown in Fig. 4 c, and COX4 IHC images of normal tissues are shown in Fig. 4 d. In the kidneys, the average Mito-scores for the proximal tubule, distal tubule, and glomerulus were 1446, 365, and 5.7, respectively. In the stomach, the average Mito-scores for the fundic gland and foveolar epithelium were 880 and 38, respectively. In the lungs, the average Mito-scores for the bronchus and alveolar epithelium were 58.5 and 0.00031, respectively. Next, we used digital imaging to extract each COX4-stained granule. Some granules were in the range of 1–2 µm in size, which is the reported size range for mitochondria (Fig. 5 a) (Egner et al. 2002 ); moreover, in this image, one pixel was 0.23 µm; thus, the resolution was sufficient to reflect the size of mitochondria. In the kidneys, the proximal and distal tubules contain many mitochondria because active transport is required for ion absorption. The proximal tubules contain more mitochondria than any other structure in the kidneys because higher levels of active transport are required in this region (Bhargava and Schnellmann 2017 ). Conversely, glomerular filtration is a passive process, and the glomeruli do not require large amounts of energy. In the stomach, parietal cells contain many mitochondria (Lawn 1960 ). The proximal tubule and fundic gland showed relatively high Mito-scores, which is consistent with the results of previous studies validating the proposed system (Bhargava and Schnellmann 2017 ). Evaluation of mitochondria in lung carcinoma and stroma Our system was used to evaluate the surgically resected lung carcinomas. Using whole sections of lung adenocarcinoma sections (Fig. 5 b), the Mito-score was obtained from the COX4 IHC images. The objective numerical value was determined from the Mito-score, and the mitochondrial distribution was observed in a heatmap. The Mito-score of the lung carcinoma and stroma varied from high to low (Fig. 6 a (a, b)). The Mito-score of the stroma varied because of the presence of macrophages, lymphocytes, plasmacytes, fibroblasts, and the vascular endothelium. The Mito-score of the tumor showed a weak positive correlation with that of the stroma (R 2 = 0.1632, p < 0.0001) (Fig. 6 a (c)). To evaluate the mitochondrial score of the carcinoma, the stroma was excluded from the mitochondrial evaluation using annotation software, and representative images of the top, middle, and bottom Mito-scores for lung carcinoma are shown in Fig. 6 b. Calculation of Mito-score in various histological subtypes of lung carcinoma The Mito-scores of various subtypes of lung carcinoma, including adenocarcinoma, squamous cell, small cell, and large cell neuroendocrine carcinoma, were examined (Fig. 7 a). The COX4 IHC-stained images of these carcinomas are shown in Fig. 7 b. The Mito-score of small cell carcinoma (57.25) was significantly lower than that of adenocarcinoma (147.5, p < 0.0001), squamous cell carcinoma (120.6, p = 0.0004), and large cell neuroendocrine carcinoma (111.8, p = 0.002). The Mito-score of adenocarcinoma did not significantly differ from that of squamous cell carcinoma (p = 0.16). Adenocarcinomas and squamous cell carcinomas were further analyzed according to their subtypes. In adenocarcinomas, no significant differences were observed between the lepidic, acinar, papillary, solid, and micropapillary subtypes (Fig. 7 C). In squamous cell carcinoma, no significant differences were observed among various degrees of differentiation (well, moderately, and poorly differentiated) (Fig. 7 D). Discussion Herein, we established an objective mitochondrial evaluation system using machine-based processing based on the HSV color space obtained using human FFPE samples. Except for studies that have used cultured cells for automated mitochondrial analysis (live cell imaging) (Zahedi et al. 2018 ), few reports have described the immunohistochemical evaluation of mitochondria in human specimens. We selected an antibody specific for COX4, a marker of the mitochondrial inner membrane, for IHC analysis to evaluate the mitochondria. We also examined the expression of voltage-dependent anion-selective channel protein 1 (VDAC1), a mitochondrial outer membrane marker. The granular staining pattern of the cytoplasm was better distinguished using COX4 as a marker than VDAC1 (S1 Figure), and we therefore selected COX4 to evaluate the mitochondria using digital image processing. The granular staining pattern of mitochondria is difficult to evaluate using conventional methods, such as the H-score (Budwit-Novotny et al. 1986 ). The H-score was evaluated from the area of carcinoma, which was defined as the summed percentage of positively stained cells multiplied by the weighted intensity of staining (Budwit-Novotny et al. 1986 ). Therefore, we could not count the number of positive cells per nucleus using the H-score because this can be biased by the density of cancer cells and the types of coexisting cells. Pathologists typically recognize the color of IHC-prepared samples as dark, light, or slightly light brown, and this perception can be expressed in the HSV color space. In our system, the hue component corresponds to all colors and describes the most dominant wavelength. The bluish color of the nucleus (hematoxylin) and dark brownish color of the mitochondria (DAB) were objectively expressed using the hue color component. The value (V) component of HSV, which corresponds to brightness, was used to remove the background. Saturation is a basic measure of the purity or gray level of a pixel. Thus, the granular staining pattern of mitochondria can be distinguished from that of other cells based on their hue properties (Wang et al. 2021 ), with the HSV color model providing additional information on color, shade, and brightness (Shete and Kharate 2015 ; Yabusaki et al. 2014 ). Therefore, we used the HSV color space to evaluate IHC staining and observed that the granular staining pattern of the mitochondria was well distinguished from nuclear staining. We hypothesized that mitochondrial function could be predicted by determining the number of mitochondria per cell. Therefore, to evaluate the mitochondria, we defined the Mito-score as the number of COX 4 IHC-positive pixels divided by the number of nuclei. We validated the method by comparing this with the conventional method, MitoTracker, and electron microscopy using lung cancer cell lines, and a correlation with the Mito-Score method was obtained. This method was validated using a single cell to examine whether the score calculated by this system correlated with the conventional visual examination and obtained consistent results. The system was validated using normal tissues such as proximal tubules from the kidneys and fundic glands of the stomach (Bhargava and Schnellmann 2017 ; Lawn 1960 ). Thus, this system can be used to evaluate various carcinomas, including lung carcinomas. Digitation offers several advantages. The number of mitochondria per cell can be quantified, and the distribution of the Mito-score can be visualized for an entire section by converting the score into a heatmap. In addition, the mitochondria of the tumors and stroma can be analyzed separately by adding annotations. Finally, several histological subtypes were analyzed. Mitochondrial quantification revealed heterogeneity in mitochondrial distribution, which was more apparent across the entire section (Fig. 4 b). Interestingly, the Mito-score of small cell carcinoma was significantly lower than that of the other subtypes of lung carcinoma. Small cell carcinoma, which has a high mitotic count, has a very poor prognosis (2-year survival rate of approximately 8%) (Nicholson et al. 2016 ). Both glucose and nitrogen metabolism are altered during the malignant progression of carcinoma (Kodama et al. 2020 ; Zhang et al. 2017 ), and elevated levels of c-Myc induce the expression of enzymes, such as phosphoribosyl pyrophosphate aminotransferase (PPAT), which is involved in nucleotide biosynthetic pathways (Cunningham et al. 2014 ; Liu et al. 2008 ). The fate of glutamine nitrogen is steered from the anaplerotic pathway to the tricarboxylic acid cycle in the mitochondria to enhance nucleotide biosynthesis (Kodama et al. 2020 ). This shift is controlled by PPAT, and the induced expression of this enzyme enhances nucleotide biosynthesis. PPAT expression is particularly strong in small cell lung carcinoma (Kodama et al. 2020 ); thus, the tricarboxylic acid cycle is inhibited in the mitochondria, which may explain the low Mito-score. However, further analysis is required to confirm this hypothesis. The Mito-scores of adenocarcinoma and squamous cell carcinoma varied, and no significant difference was observed among the subtypes of these carcinomas (Fig. 6 ). Therefore, the number of mitochondria did not correlate with subtype of these cancers. Although our technique is expected to have wider application in clinical settings, this study has several limitations. Mitochondria are dynamic organelles and their morphology and other factors change in response to external stimuli and metabolic cues (Wai and Langer 2016 ). We hypothesized that the number of mitochondria reflects their function; therefore, we analyzed the number of mitochondria. However, whether mitochondrial number reflects functional status requires further confirmation. Second, we did not analyze the mutation status of mitochondrial DNA in carcinoma or the correlation between the number of mitochondria and the metabolic state. Among the subtypes, the Mito-scores of adenocarcinoma and squamous cell carcinoma varied, and several carcinomas showed markedly high or low Mito-scores that could not be explained by the histological type. Further studies are needed to overcome these limitations. In conclusion, we established an objective machine-based evaluation system for enumerating mitochondria based on the IHC analysis of FFPE sections. This system enables the measurement of a value reflecting the number of mitochondria per cell (Mito-score) and the visualization of the distribution of mitochondria. The Mito-score of small cell lung carcinoma was significantly lower than that of other subtypes of lung carcinoma. This objective evaluation system can be used for the detailed examination of mitochondria in human FFPE specimens. Declarations Acknowledgements We would like to thank Editage (www.editage.jp) for English language editing. Funding statement This study was supported by JSPS KAKENHI Grant Numbers JP 21K06899, 18K07036, 21K06900, and AMED 20ck0106532. Competing Interests All authors have no relevant financial or non-financial interests to disclose. Author contributions Mai Sakashita, Noriko Motoi, Atsushi Ochiai, and Shingo Sakashita. conceptualized and designed the study, developed the methodology, and were involved in the writing, reviewing, and revision of the paper; Yukihiro Yoshida, Shun-ichi Watanabe, and Kazunori Aoki provided material support. All authors read and approved the final version of the manuscript. Data availability statement The data that support the findings of this study are available from the corresponding author [Shingo Sakashita] upon reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. The Institutional Review Board approved the use of the specimens (2016-124, National Cancer Center, Japan, 2020-242, National Cancer Center East, Japan). Informed consent was obtained from all patients for the use of their materials. References Bhargava P, Schnellmann RG (2017) Mitochondrial energetics in the kidney. Nat Rev Nephrol 13:629–646. https://doi.org/10.1038/nrneph.2017.107 Budwit-Novotny DA, McCarty KS, Cox EB, Soper JT, Mutch DG, Creasman WT, et al (1986) Immunohistochemical analyses of estrogen receptor in endometrial adenocarcinoma using a monoclonal antibody. Cancer Res 46:5419–5425 Cunningham JT, Moreno MV, Lodi A, Ronen SM, Ruggero D (2014) Protein and nucleotide biosynthesis are coupled by a single rate-limiting enzyme, PRPS2, to drive cancer. Cell 157:1088–1103. https://doi.org/10.1016/j.cell.2014.03.052 Egner A, Jakobs S, Hell SW (2002) Fast 100-nm resolution three-dimensional microscope reveals structural plasticity of mitochondria in live yeast. Proc Natl Acad Sci U S A 99:3370–3375. https://doi.org/10.1073/pnas.052545099 Harmon SA, Patel PG, Sanford TH, Caven I, Iseman R, Vidotto T, et al (2021) High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts. Mod Pathol 34:478–489. https://doi.org/10.1038/s41379-020-00674-w Ishikawa K, Takenaga K, Akimoto M, Koshikawa N, Yamaguchi A, Imanishi H, et al. (2008) ROS-generating mitochondrial DNA mutations can regulate tumor cell metastasis. Science 320:661–664. https://doi.org/10.1126/science.1156906 Kodama M, Oshikawa K, Shimizu H, Yoshioka S, Takahashi M, Izumi Y, et al (2020) A shift in glutamine nitrogen metabolism contributes to the malignant progression of cancer. Nat Commun 11:1320. https://doi.org/10.1038/s41467-020-15136-9 Lawn AM (1960) Observations on the fine structure of the gastric parietal cell of the rat. J Biophys Biochem Cytol 7:161–166. https://doi.org/10.1083/jcb.7.1.161 Liu YC, Li F, Handler J, Huang CR, Xiang Y, Neretti N, et al (2008) Global regulation of nucleotide biosynthetic genes by c-Myc. PLOS ONE 3:e2722. https://doi.org/10.1371/journal.pone.0002722 Morita M, Sato T, Nomura M, Sakamoto Y, Inoue Y, Tanaka R, et al (2018) PKM1 confers metabolic advantages and promotes cell-autonomous tumor cell growth. Cancer Cell 33:355–367.e7. https://doi.org/10.1016/j.ccell.2018.02.004 Nicholson AG, Chansky K, Crowley J, Beyruti R, Kubota K, Turrisi A, et al (2016) The international association for the study of lung cancer lung cancer staging project: proposals for the revision of the clinical and pathologic staging of small cell lung cancer in the forthcoming eighth edition of the TNM classification for lung cancer. J Thorac Oncol 11:300–311. https://doi.org/10.1016/j.jtho.2015.10.008 Nunnari J, Suomalainen A (2012) Mitochondria: in sickness and in health. Cell 148:1145–1159. https://doi.org/10.1016/j.cell.2012.02.035 Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/TSMC.1979.4310076 Sakashita M, Sakashita S, Murata Y, Shiba-Ishii A, Kim Y, Matsuoka R, et al (2018) High expression of ovarian cancer immunoreactive antigen domain containing 2 (OCIAD2) is associated with poor prognosis in lung adenocarcinoma. Pathol Int 68:596–604. https://doi.org/10.1111/pin.12724 Shete PG, Kharate GK (2015) Evaluation of immunohistochemistry (IHC) biomarkers in breast cancer using digital image processing. J Life Sci Technol 3:36–42. https://doi.org/10.18178/jolst.3.2.36-42 Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029–1033. https://doi.org/10.1126/science.1160809 Wada K (2016) Label me: image Polygonal Annotation with Python. https://githubcom/wkentaro/labelme Wai T, Langer T (2016) Mitochondrial dynamics and metabolic regulation. Trends Endocrinol Metab 27:105–117. https://doi.org/10.1016/j.tem.2015.12.001 Wang X, Wang L, Bu H, Zhang N, Yue M, Jia Z, et al (2021) How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies. NPJ Breast Cancer 7:61. https://doi.org/10.1038/s41523-021-00268-y Warburg O (1956) On respiratory impairment in cancer cells. Science 124:269–270. https://doi.org/10.1126/science.124.3215.269 Wetstein SC, Stathonikos N, Pluim JPW, Heng YJ, Ter Hoeve ND, Vreuls CPH, et al (2021) Deep learning-based grading of ductal carcinoma in situ in breast histopathology images. Lab Invest 101:525–533. https://doi.org/10.1038/s41374-021-00540-6 Yabusaki K, Faits T, McMullen E, Figueiredo JL, Aikawa M, Aikawa E (2014) A novel quantitative approach for eliminating sample-to-sample variation using a hue saturation value analysis program. PLOS ONE 9:e89627. https://doi.org/10.1371/journal.pone.0089627 Zahedi A, On V, Phandthong R, Chaili A, Remark G, Bhanu B, Talbot P (2018) Deep analysis of mitochondria and cell health using machine learning. Sci Rep 8:16354. https://doi.org/10.1038/s41598-018-34455-y Zhang J, Pavlova NN, Thompson CB (2017) Cancer cell metabolism: the essential role of the nonessential amino acid, glutamine. EMBO J 36:1302–1315. https://doi.org/10.15252/embj.201696151 Zong WX, Rabinowitz JD, White E (2016) Mitochondria and cancer. Mol Cell 61:667–676. https://doi.org/10.1016/j.molcel.2016.02.011 Additional Declarations No competing interests reported. Supplementary Files S1.jpg S1 Figure. Comparison between COX4 and VDAC1. Granular staining pattern of the cytoplasm was better distinguished with COX4 as a marker than with VDAC1. Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted Editorial decision: Revision requested 09 Feb, 2024 Reviews received at journal 07 Feb, 2024 Reviewers agreed at journal 27 Jan, 2024 Reviewers invited by journal 27 Jan, 2024 Editor assigned by journal 27 Jan, 2024 Submission checks completed at journal 27 Jan, 2024 First submitted to journal 26 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3901509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269928662,"identity":"bf834569-eaca-41ba-94d7-3561a67435df","order_by":0,"name":"Shingo Sakashita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACZiA+wMOPKshDhBbJBhBFihYGgwMoWvAA+f7Djw1+/LkjY3y8+fHnDwx35Pkb2C8+YJC5g1OLwY0048Tetmc8ZmeOmUkcYHhmOOMAT7EBA88z3FokGIwP8DYc5jG7kWAGdNjhBKDyNAkGnsN4HHb888E/fw7zGM9//vkDUVoYDuQYJ/OwHeYxkAAiiBb2Y3i1GNzIKTaWbTvMI3Emp0zijMFhwxmHeZgNEvD4BeiwzZJv/hy2528/vvlDRcVhef729ocPPvbgDjF0S4GYmceAIbHnALFawID9AQPDD9K0jIJRMApGwbAGAOa0WEaGaiZxAAAAAElFTkSuQmCC","orcid":"","institution":"National Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Shingo","middleName":"","lastName":"Sakashita","suffix":""},{"id":269928663,"identity":"8aa3eaa4-ac4b-4919-a95a-7219a1f1038d","order_by":1,"name":"Mai Sakashita","email":"","orcid":"","institution":"Saitama Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"","lastName":"Sakashita","suffix":""},{"id":269928664,"identity":"936c5afc-0858-48f1-9f8f-c75af70e4fba","order_by":2,"name":"Noriko Motoi","email":"","orcid":"","institution":"Saitama Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Motoi","suffix":""},{"id":269928665,"identity":"6c51e701-459d-4e21-84a9-1f1ca6ef2d07","order_by":3,"name":"Gaku Yamamoto","email":"","orcid":"","institution":"National Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Gaku","middleName":"","lastName":"Yamamoto","suffix":""},{"id":269928666,"identity":"85b20a08-28ff-4ede-94b2-eb3622136517","order_by":4,"name":"Emi Gambe","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Emi","middleName":"","lastName":"Gambe","suffix":""},{"id":269928667,"identity":"8c4b1053-c9a9-40d8-ab8f-4c1790ba5687","order_by":5,"name":"Masanori Suzuki","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Suzuki","suffix":""},{"id":269928668,"identity":"10ca0a2f-79de-4bb8-af3c-c360c2c88c90","order_by":6,"name":"Yukihiro Yoshida","email":"","orcid":"","institution":"National Cancer Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yukihiro","middleName":"","lastName":"Yoshida","suffix":""},{"id":269928669,"identity":"479c34c7-58fc-46b6-9890-fa9bf464c182","order_by":7,"name":"Shun-ichi Watanabe","email":"","orcid":"","institution":"National Cancer Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shun-ichi","middleName":"","lastName":"Watanabe","suffix":""},{"id":269928670,"identity":"a560c14f-bbf2-45e0-bcbd-1176eaac2f58","order_by":8,"name":"Yutaka Takazawa","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yutaka","middleName":"","lastName":"Takazawa","suffix":""},{"id":269928671,"identity":"e7e21f74-d71c-4c79-9b58-6c8b98d59bc8","order_by":9,"name":"Kazunori Aoki","email":"","orcid":"","institution":"National Cancer Center Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kazunori","middleName":"","lastName":"Aoki","suffix":""},{"id":269928672,"identity":"24d02889-0649-4792-8450-fcec3e0fe1d8","order_by":10,"name":"Atsushi Ochiai","email":"","orcid":"","institution":"Tokyo University of Science","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Ochiai","suffix":""}],"badges":[],"createdAt":"2024-01-27 01:44:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3901509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3901509/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00432-024-05653-1","type":"published","date":"2024-04-03T15:01:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50379906,"identity":"696cec90-0a53-4b0c-a66f-233e004d4f12","added_by":"auto","created_at":"2024-01-30 16:35:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1523026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIHC scoring and nucleus counting using a machine-based system\u003c/strong\u003e. a. Overview of manual separation of carcinoma and stroma from tissue microarray. TMA: tissue microarray, IHC: immunohistochemistry/immunohistochemical. b. Nuclear counting system. Image thresholding of the nucleus was performed using Otsu’s thresholding technique (Otsu 1979). After denoising, a distance map was created by calculating the distances between the background and nucleus. Finally, apexes of the distance map were counted to determine the number of nuclei using digital image processing. The apexes were circled by a computer for validation as nuclei by certified pathologists\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/46cd3f64aca93ab80f26b8c6.jpg"},{"id":50379907,"identity":"571d9ad4-6f00-4924-987d-1934e058c95c","added_by":"auto","created_at":"2024-01-30 16:35:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1376411,"visible":true,"origin":"","legend":"\u003cp\u003eExtraction of nucleus and immunohistochemistry color results using the hue component. Hues of 0°–60° and 320°–360° were determined as the color of the immunohistochemistry results, and hues of 200°–320° were determined as the color of the nuclei. HSV: hue, saturation, value, IHC: immunohistochemistry\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/fc5a45aaf344f2be4d9c5484.jpg"},{"id":50380252,"identity":"3d20a13b-23e5-4f11-b7d9-dc08ac2e7a47","added_by":"auto","created_at":"2024-01-30 16:43:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1237254,"visible":true,"origin":"","legend":"\u003cp\u003eMethod validation using four lung cancer cell lines. a. COX IV (IHC), MitoTracker, and electron microscopy images for each lung cancer cell line. b. Distribution of MitoTracker fluorescence intensity and number of mitochondria for each cell lines. c. Correlation analysis between MitoTracker and Mito-score. d. Correlation analysis between electronic microscope and Mito-score\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/0aeda096efa20ab70dceb5d3.jpg"},{"id":50379912,"identity":"7b998102-f774-4ad5-bd0f-e5a2cefc21ec","added_by":"auto","created_at":"2024-01-30 16:35:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1598613,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the method using normal tissue. a. Validation of the evaluation method using macrophages as single cells.\u003cstrong\u003e \u003c/strong\u003eMacrophages showed variable Mito-scores. b.\u003cstrong\u003e \u003c/strong\u003eMito-score of macrophages. Images of the top ten and bottom 10 Mito-scores are shown. c. Mito-score of normal kidney, stomach, and lung tissues. D. COX4 IHC image of normal tissue. (a) Distal tubule, (b) proximal tubule, (c) glomerulus, (d) foveolar epithelium, (e) fundic gland, (f) bronchus, and (g) alveolar epithelium\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/4804c5b0680632804860a50b.jpg"},{"id":50380253,"identity":"b43aa47a-83a6-4b1a-88cc-9dcf7cdfa624","added_by":"auto","created_at":"2024-01-30 16:43:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1530202,"visible":true,"origin":"","legend":"\u003cp\u003eIHC and digital image.\u003cstrong\u003e \u003c/strong\u003eA. COX4 IHC image and extracted COX4 IHC image using HSV color space. (a) COX4 IHC images are shown. (b, c) Extracted COX4 IHC images using the HSV color space. (c) Magnified image of a(b).\u003cstrong\u003e \u003c/strong\u003eb. Mito-score of lung adenocarcinoma using whole section.\u003cstrong\u003e \u003c/strong\u003eThe Mito-score of adenocarcinoma was obtained from the COX4 IHC image using our system. The objective numerical number was obtained using the Mito-score, and the mitochondria distribution in lung adenocarcinoma is shown in a heatmap.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/c10c3b3101ed100c3177a028.jpg"},{"id":50379909,"identity":"5534f38c-188d-4a4e-8ed0-76b6aa2ccdf0","added_by":"auto","created_at":"2024-01-30 16:35:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1304576,"visible":true,"origin":"","legend":"\u003cp\u003eMito-score of lung carcinoma. A. Mito-score of tumor and stroma.\u003cstrong\u003e \u003c/strong\u003e(a)\u003cstrong\u003e \u003c/strong\u003eMito-score of tumor. (b) Mito-score of stroma. (c) Correlation of Mito-score of tumor with Mito-score of stroma. B. Representative image of COX4 IHC staining of lung carcinoma. The IHC score, nucleus count, and Mito-score are shown\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/5533a5ad16e1fd15ef761ec9.jpg"},{"id":50379911,"identity":"21d95aa6-70c0-4581-b854-3801e8e98268","added_by":"auto","created_at":"2024-01-30 16:35:04","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1649824,"visible":true,"origin":"","legend":"\u003cp\u003eMito-score of various subtypes of lung carcinoma. A. Mito-scores of various subtypes of lung carcinoma, including adenocarcinoma (AD), squamous cell carcinoma (SQ), small cell carcinoma (Small), and large cell neuroendocrine carcinoma (LCNEC) were examined. The Mito-score of small cell carcinoma (57.25) was significantly lower than that of AD (147.5, p \u0026lt; 0.0001), SQ (120.6, p = 0.0004), and LCNEC (111.8, p = 0.002). b. Representative COX4 IHC images of several subtypes of lung carcinoma. c. Mito-score of AD. In AD, no significant differences were observed among lepidic, acinar, papillary, solid, or micropapillary subtypes. d. Mito-score of SQ. In SQ, no significant difference was observed among differentiation status: well differentiated (W/D), moderately differentiated (M/D), and poorly differentiated (P/D)\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/594909ac0587270872b3ebf2.jpg"},{"id":54303766,"identity":"8d637fe3-8296-425a-8d7e-da4b5d540efd","added_by":"auto","created_at":"2024-04-08 15:11:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/36dff32b-337f-4ef1-aeb3-5cd25ec283e7.pdf"},{"id":50380251,"identity":"dc2e9aba-63bb-4f94-8159-931ba74a21b8","added_by":"auto","created_at":"2024-01-30 16:43:04","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1703142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS1 Figure. Comparison between COX4 and VDAC1. \u003c/strong\u003eGranular staining pattern of the cytoplasm was better distinguished with COX4 as a marker than with VDAC1.\u003c/p\u003e","description":"","filename":"S1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3901509/v1/4b4ccdc10ff0a31d73034001.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine-based counting of mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMitochondria perform several important functions in cells, including the production of energy; generation of reactive oxygen species, redox molecules, and metabolites; regulation of cell signaling and cell death; and biosynthesis of metabolites (Zong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nunnari and Suomalainen \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Normal cells primarily rely on mitochondrial oxidative phosphorylation for energy generation. Conversely, most cancer cells produce excess lactate in the presence of sufficient oxygen (Warburg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), a phenomenon known as the \u0026ldquo;Warburg effect.\u0026rdquo; Although Warburg originally considered this effect to reflect mitochondrial dysfunction in cancer cells, many cancer cells exhibiting the Warburg effect may possess intact mitochondrial function but altered metabolism (Vander Heiden et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Mutations in mitochondrial DNA contribute to tumor progression by enhancing the metastatic potential of cancer cells (Ishikawa et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In small cell lung cancer, pyruvate kinase M1 converts phosphoenolpyruvate to pyruvate during glycolysis and promotes tumor growth (Morita et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In lung adenocarcinoma, the ovarian cancer immunoreactive antigen domain-containing 2, which may cause malignant transformation in lung adenocarcinoma at an early phase, may be a key factor in maintaining mitochondrial function or structure (Sakashita et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The ability to observe mitochondria in cancer cells from human pathological specimens may improve our understanding of the relationship between mitochondrial status and cancer. Although many studies have explored mitochondria in various contexts, relatively few have examined mitochondria in human cancer histological specimens using immunohistochemistry (IHC) primarily because this method has several limitations. For instance, almost all cells contain mitochondria; therefore, determining a cutoff value for positive IHC staining is impossible. The enumeration of mitochondria is necessary to determine their functions; however, counting the number of mitochondria using IHC remains challenging. The quantity of mitochondria may also be correlated with cellular functions, such as metabolism; however, this correlation in each cell is difficult to establish using light microscopy. Recently, digital pathology has been demonstrated to have a relatively high accuracy for the automated detection of IHC samples, thereby eliminating subjectivity (Harmon et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wetstein et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Herein, we developed an objective evaluation system based on the hue, saturation, and value (HSV) color space to determine the immunohistochemical status of mitochondria using formalin-fixed and paraffin-embedded (FFPE) sections. This system enabled separation of the color of the IHC (DAB) from that of the stained nucleus (hematoxylin); thus, we simultaneously measured the IHC score and number of nuclei. This system was validated using four lung cancer cell lines and FFPE sections obtained from normal tissues. This system was also used to evaluate the status of mitochondria in resected human lung cancer specimens.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eMitoTracker analysis using lung cancer cell lines\u003c/h2\u003e\n\u003cp\u003eA549, NCI_H358, NCI_H1373, and NCI_H1975 cells were seeded at 1.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/well in 6-well plates the day before the experiment. MitoTracker\u0026trade; RED FM (M22425) was added at a concentration of 100 nM and incubated for 30 min at 37℃ in a humidified incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e. Cells were then harvested and analyzed via fluorescence cytometry using a BD FACSCanto IITM (Becton Dickinson), and the mean fluorescence intensity was calculated using FlowJoTM software (ver 10.7.1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eElectron microscope analysis using lung cancer cell lines\u003c/h2\u003e\n\u003cp\u003eFor electron microscopic analysis, cells were fixed with 2.5% glutaraldehyde (pH 7.2, 0.1 M Phosphate buffer) and then cell blocks were prepared. After creating the cell blocks, Epon embedding was performed, and after ultrathin sectioning, analysis was performed using an electron microscope (JEM-1400Flash, JEOL Tokyo, Japan). Then, 10 cells with the largest split surfaces were selected, and the number of mitochondria was visually measured and subsequently averaged.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eTissues samples\u003c/h2\u003e\n\u003cp\u003eLung carcinoma specimens were used that had been surgically resected from patients originally diagnosed at the National Cancer Center Hospital (Tokyo, Japan) between 2017 and 2019. Tumor slides and blocks of samples from 199 patients were used for histological evaluation and tissue microarray (TMA) construction. The clinicopathological characteristics of the patients are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. To evaluate the validity of the system, we also used specimens containing only normal tissues from patients with surgically resected stomach and kidney carcinomas at the National Cancer Center Hospital (Chiba, Japan) in 2021. The clinical data of all patients were collected from their medical records. Informed consent was obtained from all patients for the use of their materials. The Institutional Review Board approved the use of the specimens (2016\u0026thinsp;\u0026minus;\u0026thinsp;124, National Cancer Center, Japan, 2020\u0026thinsp;\u0026minus;\u0026thinsp;242, National Cancer Center East, Japan). The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatient clinicopathological information.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSQ\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSmall\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLCNEC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCase Number\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epT factor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epN factor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epM factor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eM0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eM1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epStage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eⅠ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eⅡ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eⅢ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eⅣ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSubtype\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLepidic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAcinar\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePapillary\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMicropapillary\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIMA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOther (enteric, colloid and fetal)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eW/D\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eM/D\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP/D\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eAD: Adenocarcinoma; SQ: squamous cell carcinoma; Small: small cell carcinoma; LCNEC: large cell neuroendocrine carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eIHC using TMAs and cell blocks of the lung cancer cell lines\u003c/h2\u003e\n\u003cp\u003eTMAs, comprising 2-mm cores with two cores from each tumor, were constructed from 10% FFPE blocks. The two cores were separately embedded in two blocks. Four representative tumor areas were marked on hematoxylin and eosin-stained slides. In total, 796 specimens had adequate cores for immunohistochemical analysis.\u003c/p\u003e\n\u003cp\u003eFor cell blocks of the lung carcinoma cell lines, cells were sedimented by centrifugation, fixed in 10% neutral buffered formalin, embedded in paraffin blocks, and thinly sliced to 4 \u0026micro;m.\u003c/p\u003e\n\u003cp\u003eIHC was performed on 3-\u0026micro;m\u0026ndash;thick sections prepared from TMAs. Sections were deparaffinized and rehydrated, and antigen retrieval was performed using Cell Conditioning solution, CC1 (EDTA, pH 8.5; Roche, Basel, Switzerland) at 95\u0026deg;C for 64 min. Immunohistochemical staining was performed using rabbit polyclonal anti-cytochrome c oxidase subunit IV isoform 1 antibody diluted at 1:500 (HPA002485, Sigma-Aldrich, St. Louis, MO, USA) for 1 h at room temperature using a Roche Ventana BenchMark Ultra autostainer and Ventana Ultra View Universal DAB Detection kit (Roche), and using rabbit polyclonal antivoltage-dependent anion channel 1 (VDAC1) antibody diluted at 1:50 (HPA030780, Sigma-Aldrich, St. Louis, MO, USA) for 1 h at room temperature using a Roche Ventana BenchMark Ultra autostainer and Ventana Ultra View Universal DAB.\u003c/p\u003e\n\u003cp\u003eAn antibody specific for cytochrome c oxidase subunit IV isoform 1, a marker of the mitochondrial inner membrane, was selected because the granular staining pattern in the cytoplasm clearly indicates the presence of mitochondria.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eIHC evaluation using whole slide images\u003c/h2\u003e\n\u003cp\u003eStained TMA were scanned at 40\u0026times; magnification using a whole slide scanner NanoZoomer S360 (Hamamatsu Photonics, Hamamatsu, Japan). A pathologist annotated two circled areas of the carcinoma and stroma (0.08 m\u003csup\u003e2\u003c/sup\u003e each) using Labelme software (Wada \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). We preferentially selected areas where the carcinoma and stroma were physically separated because accurately annotating overlapping areas of carcinoma and stroma was difficult as these areas include inflammatory cells, macrophages, and vascular endothelium.\u003c/p\u003e\n\u003cp\u003eA pixel-based approach for the spatial annotation of mitochondrial expression was used. The image processing steps are illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea. All image patches were derived with reference to 2000 \u0026times; 2000-pixel regions under a 40\u0026times; objective within the annotated regions of the carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtraction of IHC staining and nucleus by converting an acquired red, green, and blue image to HSV color space\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe transformed the image from the red, green, and blue color spaces into the HSV color space using the OpenCV (3.4.15) library. Cells were then separated from the background by changing the value (V) component and applying V to eliminate the background. A V value of 10\u0026ndash;220 was adopted for nuclear separation and 0\u0026ndash;160 was used for IHC separation.\u003c/p\u003e\n\u003cp\u003eWe extracted the color of the IHC (brownish color of DAB) and nuclei (bluish color of hematoxylin) using the hue (H) component. H values of 0\u0026deg;\u0026ndash;60\u0026deg; and 320\u0026deg;\u0026ndash;360\u0026deg; were determined as the color of IHC staining, and H values of 200\u0026deg;\u0026ndash;320\u0026deg; were determined as the color of the nucleus by certified pathologists (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eUsing this process, we classified the pixels according to whether they were annotated as cells (carcinoma or stroma) or background. Nonspecific staining was eliminated using the saturation (S) component. An S value of 20\u0026ndash;255 was used to separate the nuclei while a value of 60\u0026ndash;255 was used to separate the IHC staining.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eEvaluation of mitochondria IHC\u003c/h2\u003e\n\u003cp\u003eTo evaluate mitochondria, the Mito-score was defined as the number of COX4 IHC-positive pixels divided by the number of nuclei, as we hypothesized that the number of mitochondria per cell was related to their function. COX4 IHC-positive pixels were counted after separation by IHC staining. The nuclear counting system was as follows (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). First, the image threshold of the nucleus was obtained using Otsu\u0026rsquo;s thresholding technique or discriminant analysis (Otsu \u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e). Rough cell edges and holes in the nucleus can cause an inaccurate nuclear count. Therefore, we performed the following steps to reduce the image noise (denoising). Dilation was conducted to smooth the nucleus edges by gradually enlarging the boundaries of foreground pixel regions. Closing was performed to remove the holes in the nucleus. Next, a distance map was created by calculating the distance between the background and the nucleus. Erosion was used to remove the boundaries of the foreground pixel regions. Overlapping cells can be counted as single cells, causing inaccurate cell counting. The apex of the nucleus was extracted and magnified to reduce any inaccuracy. The position of the center of gravity was calculated using the number, position, and contour of the nucleus. Nuclei were enumerated using digital image processing and the apex was circled using a computer. Certified pathologists confirmed the circled area as the nucleus. However, this counting system may not be effective for crowded cells. In such cases, the denoising treatment was changed and the calculations were repeated. Finally, the Mito-score was calculated as the number of COX4 IHC-positive pixels divided by the number of nuclei.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analyses were performed using GraphPad Prism software (version 9; GraphPad Inc., La Jolla, CA, USA). Significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The association between Mito-score and various subtypes of lung carcinoma was analyzed using an unpaired \u003cem\u003et\u003c/em\u003e-test. The correlation between the Mito-scores of the tumor and stroma was assessed using linear regression analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eMethod validation using lung cancer cell lines\u003c/h2\u003e\n\u003cp\u003eFour lung cancer cell lines (A549, NCI_H358, NCI_H1373, and NCI_H1975) were used to identify correlations between general mitochondrial abundance measurements and the mitochondrial abundance measurements obtained using Mito-score. Specifically, we measured the correlation between measurements by MitoTracker and Mito-score (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;c), and between actual measurements by electron microscopy and Mito-score (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, d).\u003c/p\u003e\n\u003cp\u003eIn the MitoTracker measurement, the correlation analysis had an R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.6736, suggesting a correlation between the MitoTracker and Mito-score (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Correlation analysis also showed a correlation of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.7907 with the actual values measured by electron microscopy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). These results indicate that the proposed method correlates with existing methods for measuring mitochondrial abundance and that this can be measure the number of mitochondria can be achieved using IHC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of evaluation method using single cells\u003c/h2\u003e\n\u003cp\u003eThe evaluation method was validated using a single-cell model. We selected macrophages for validation because macrophage boundaries do not make contact with each other, and each single cell can be easily annotated. Macrophages showed variable mitochondrial staining patterns (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea), and their Mito-scores were calculated. Images with the top and bottom 10 Mito-scores are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of evaluation method using normal tissue\u003c/h2\u003e\n\u003cp\u003eThe evaluation method was validated using normal tissues obtained from surgically resected kidney, stomach, and lung carcinomas without cancer cells. The Mito-scores are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec, and COX4 IHC images of normal tissues are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed. In the kidneys, the average Mito-scores for the proximal tubule, distal tubule, and glomerulus were 1446, 365, and 5.7, respectively. In the stomach, the average Mito-scores for the fundic gland and foveolar epithelium were 880 and 38, respectively. In the lungs, the average Mito-scores for the bronchus and alveolar epithelium were 58.5 and 0.00031, respectively. Next, we used digital imaging to extract each COX4-stained granule. Some granules were in the range of 1\u0026ndash;2 \u0026micro;m in size, which is the reported size range for mitochondria (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea) (Egner et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e); moreover, in this image, one pixel was 0.23 \u0026micro;m; thus, the resolution was sufficient to reflect the size of mitochondria.\u003c/p\u003e\n\u003cp\u003eIn the kidneys, the proximal and distal tubules contain many mitochondria because active transport is required for ion absorption. The proximal tubules contain more mitochondria than any other structure in the kidneys because higher levels of active transport are required in this region (Bhargava and Schnellmann \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, glomerular filtration is a passive process, and the glomeruli do not require large amounts of energy. In the stomach, parietal cells contain many mitochondria (Lawn \u003cspan class=\"CitationRef\"\u003e1960\u003c/span\u003e). The proximal tubule and fundic gland showed relatively high Mito-scores, which is consistent with the results of previous studies validating the proposed system (Bhargava and Schnellmann \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eEvaluation of mitochondria in lung carcinoma and stroma\u003c/h2\u003e\n\u003cp\u003eOur system was used to evaluate the surgically resected lung carcinomas. Using whole sections of lung adenocarcinoma sections (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), the Mito-score was obtained from the COX4 IHC images. The objective numerical value was determined from the Mito-score, and the mitochondrial distribution was observed in a heatmap.\u003c/p\u003e\n\u003cp\u003eThe Mito-score of the lung carcinoma and stroma varied from high to low (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea (a, b)). The Mito-score of the stroma varied because of the presence of macrophages, lymphocytes, plasmacytes, fibroblasts, and the vascular endothelium. The Mito-score of the tumor showed a weak positive correlation with that of the stroma (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.1632, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea (c)). To evaluate the mitochondrial score of the carcinoma, the stroma was excluded from the mitochondrial evaluation using annotation software, and representative images of the top, middle, and bottom Mito-scores for lung carcinoma are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eCalculation of Mito-score in various histological subtypes of lung carcinoma\u003c/h2\u003e\n\u003cp\u003eThe Mito-scores of various subtypes of lung carcinoma, including adenocarcinoma, squamous cell, small cell, and large cell neuroendocrine carcinoma, were examined (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). The COX4 IHC-stained images of these carcinomas are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb. The Mito-score of small cell carcinoma (57.25) was significantly lower than that of adenocarcinoma (147.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), squamous cell carcinoma (120.6, p\u0026thinsp;=\u0026thinsp;0.0004), and large cell neuroendocrine carcinoma (111.8, p\u0026thinsp;=\u0026thinsp;0.002). The Mito-score of adenocarcinoma did not significantly differ from that of squamous cell carcinoma (p\u0026thinsp;=\u0026thinsp;0.16). Adenocarcinomas and squamous cell carcinomas were further analyzed according to their subtypes. In adenocarcinomas, no significant differences were observed between the lepidic, acinar, papillary, solid, and micropapillary subtypes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). In squamous cell carcinoma, no significant differences were observed among various degrees of differentiation (well, moderately, and poorly differentiated) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHerein, we established an objective mitochondrial evaluation system using machine-based processing based on the HSV color space obtained using human FFPE samples. Except for studies that have used cultured cells for automated mitochondrial analysis (live cell imaging) (Zahedi et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), few reports have described the immunohistochemical evaluation of mitochondria in human specimens. We selected an antibody specific for COX4, a marker of the mitochondrial inner membrane, for IHC analysis to evaluate the mitochondria. We also examined the expression of voltage-dependent anion-selective channel protein 1 (VDAC1), a mitochondrial outer membrane marker. The granular staining pattern of the cytoplasm was better distinguished using COX4 as a marker than VDAC1 (S1 Figure), and we therefore selected COX4 to evaluate the mitochondria using digital image processing.\u003c/p\u003e \u003cp\u003eThe granular staining pattern of mitochondria is difficult to evaluate using conventional methods, such as the H-score (Budwit-Novotny et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The H-score was evaluated from the area of carcinoma, which was defined as the summed percentage of positively stained cells multiplied by the weighted intensity of staining (Budwit-Novotny et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Therefore, we could not count the number of positive cells per nucleus using the H-score because this can be biased by the density of cancer cells and the types of coexisting cells.\u003c/p\u003e \u003cp\u003ePathologists typically recognize the color of IHC-prepared samples as dark, light, or slightly light brown, and this perception can be expressed in the HSV color space. In our system, the hue component corresponds to all colors and describes the most dominant wavelength. The bluish color of the nucleus (hematoxylin) and dark brownish color of the mitochondria (DAB) were objectively expressed using the hue color component. The value (V) component of HSV, which corresponds to brightness, was used to remove the background. Saturation is a basic measure of the purity or gray level of a pixel. Thus, the granular staining pattern of mitochondria can be distinguished from that of other cells based on their hue properties (Wang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the HSV color model providing additional information on color, shade, and brightness (Shete and Kharate \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yabusaki et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, we used the HSV color space to evaluate IHC staining and observed that the granular staining pattern of the mitochondria was well distinguished from nuclear staining.\u003c/p\u003e \u003cp\u003eWe hypothesized that mitochondrial function could be predicted by determining the number of mitochondria per cell. Therefore, to evaluate the mitochondria, we defined the Mito-score as the number of COX 4 IHC-positive pixels divided by the number of nuclei. We validated the method by comparing this with the conventional method, MitoTracker, and electron microscopy using lung cancer cell lines, and a correlation with the Mito-Score method was obtained. This method was validated using a single cell to examine whether the score calculated by this system correlated with the conventional visual examination and obtained consistent results. The system was validated using normal tissues such as proximal tubules from the kidneys and fundic glands of the stomach (Bhargava and Schnellmann \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lawn \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). Thus, this system can be used to evaluate various carcinomas, including lung carcinomas. Digitation offers several advantages. The number of mitochondria per cell can be quantified, and the distribution of the Mito-score can be visualized for an entire section by converting the score into a heatmap. In addition, the mitochondria of the tumors and stroma can be analyzed separately by adding annotations. Finally, several histological subtypes were analyzed. Mitochondrial quantification revealed heterogeneity in mitochondrial distribution, which was more apparent across the entire section (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eInterestingly, the Mito-score of small cell carcinoma was significantly lower than that of the other subtypes of lung carcinoma. Small cell carcinoma, which has a high mitotic count, has a very poor prognosis (2-year survival rate of approximately 8%) (Nicholson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Both glucose and nitrogen metabolism are altered during the malignant progression of carcinoma (Kodama et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and elevated levels of c-Myc induce the expression of enzymes, such as phosphoribosyl pyrophosphate aminotransferase (PPAT), which is involved in nucleotide biosynthetic pathways (Cunningham et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The fate of glutamine nitrogen is steered from the anaplerotic pathway to the tricarboxylic acid cycle in the mitochondria to enhance nucleotide biosynthesis (Kodama et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This shift is controlled by PPAT, and the induced expression of this enzyme enhances nucleotide biosynthesis. PPAT expression is particularly strong in small cell lung carcinoma (Kodama et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); thus, the tricarboxylic acid cycle is inhibited in the mitochondria, which may explain the low Mito-score. However, further analysis is required to confirm this hypothesis.\u003c/p\u003e \u003cp\u003eThe Mito-scores of adenocarcinoma and squamous cell carcinoma varied, and no significant difference was observed among the subtypes of these carcinomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, the number of mitochondria did not correlate with subtype of these cancers.\u003c/p\u003e \u003cp\u003eAlthough our technique is expected to have wider application in clinical settings, this study has several limitations. Mitochondria are dynamic organelles and their morphology and other factors change in response to external stimuli and metabolic cues (Wai and Langer \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We hypothesized that the number of mitochondria reflects their function; therefore, we analyzed the number of mitochondria. However, whether mitochondrial number reflects functional status requires further confirmation. Second, we did not analyze the mutation status of mitochondrial DNA in carcinoma or the correlation between the number of mitochondria and the metabolic state. Among the subtypes, the Mito-scores of adenocarcinoma and squamous cell carcinoma varied, and several carcinomas showed markedly high or low Mito-scores that could not be explained by the histological type. Further studies are needed to overcome these limitations.\u003c/p\u003e \u003cp\u003eIn conclusion, we established an objective machine-based evaluation system for enumerating mitochondria based on the IHC analysis of FFPE sections. This system enables the measurement of a value reflecting the number of mitochondria per cell (Mito-score) and the visualization of the distribution of mitochondria. The Mito-score of small cell lung carcinoma was significantly lower than that of other subtypes of lung carcinoma. This objective evaluation system can be used for the detailed examination of mitochondria in human FFPE specimens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by JSPS KAKENHI Grant Numbers JP 21K06899, 18K07036, 21K06900, and AMED 20ck0106532.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMai Sakashita, Noriko Motoi, Atsushi Ochiai, and Shingo Sakashita. conceptualized and designed the study, developed the methodology, and were involved in the writing, reviewing, and revision of the paper; Yukihiro Yoshida, Shun-ichi Watanabe, and Kazunori Aoki provided material support.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author [Shingo Sakashita] upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. The Institutional Review Board approved the use of the specimens (2016-124, National Cancer Center, Japan, 2020-242, National Cancer Center East, Japan). Informed consent was obtained from all patients for the use of their materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhargava P, Schnellmann RG (2017) Mitochondrial energetics in the kidney. Nat Rev Nephrol 13:629\u0026ndash;646. https://doi.org/10.1038/nrneph.2017.107\u003c/li\u003e\n\u003cli\u003eBudwit-Novotny DA, McCarty KS, Cox EB, Soper JT, Mutch DG, Creasman WT, et al (1986) Immunohistochemical analyses of estrogen receptor in endometrial adenocarcinoma using a monoclonal antibody. Cancer Res 46:5419\u0026ndash;5425\u003c/li\u003e\n\u003cli\u003eCunningham JT, Moreno MV, Lodi A, Ronen SM, Ruggero D (2014) Protein and nucleotide biosynthesis are coupled by a single rate-limiting enzyme, PRPS2, to drive cancer. Cell 157:1088\u0026ndash;1103. https://doi.org/10.1016/j.cell.2014.03.052\u003c/li\u003e\n\u003cli\u003eEgner A, Jakobs S, Hell SW (2002) Fast 100-nm resolution three-dimensional microscope reveals structural plasticity of mitochondria in live yeast. Proc Natl Acad Sci U S A 99:3370\u0026ndash;3375. https://doi.org/10.1073/pnas.052545099\u003c/li\u003e\n\u003cli\u003eHarmon SA, Patel PG, Sanford TH, Caven I, Iseman R, Vidotto T, et al (2021) High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts. Mod Pathol 34:478\u0026ndash;489. https://doi.org/10.1038/s41379-020-00674-w\u003c/li\u003e\n\u003cli\u003eIshikawa K, Takenaga K, Akimoto M, Koshikawa N, Yamaguchi A, Imanishi H, et al. (2008) ROS-generating mitochondrial DNA mutations can regulate tumor cell metastasis. Science 320:661\u0026ndash;664. https://doi.org/10.1126/science.1156906\u003c/li\u003e\n\u003cli\u003eKodama M, Oshikawa K, Shimizu H, Yoshioka S, Takahashi M, Izumi Y, et al (2020) A shift in glutamine nitrogen metabolism contributes to the malignant progression of cancer. Nat Commun 11:1320. https://doi.org/10.1038/s41467-020-15136-9\u003c/li\u003e\n\u003cli\u003eLawn AM (1960) Observations on the fine structure of the gastric parietal cell of the rat. J Biophys Biochem Cytol 7:161\u0026ndash;166. https://doi.org/10.1083/jcb.7.1.161\u003c/li\u003e\n\u003cli\u003eLiu YC, Li F, Handler J, Huang CR, Xiang Y, Neretti N, et al (2008) Global regulation of nucleotide biosynthetic genes by c-Myc. PLOS ONE 3:e2722. https://doi.org/10.1371/journal.pone.0002722\u003c/li\u003e\n\u003cli\u003eMorita M, Sato T, Nomura M, Sakamoto Y, Inoue Y, Tanaka R, et al (2018) PKM1 confers metabolic advantages and promotes cell-autonomous tumor cell growth. Cancer Cell 33:355\u0026ndash;367.e7. https://doi.org/10.1016/j.ccell.2018.02.004\u003c/li\u003e\n\u003cli\u003eNicholson AG, Chansky K, Crowley J, Beyruti R, Kubota K, Turrisi A, et al (2016) The international association for the study of lung cancer lung cancer staging project: proposals for the revision of the clinical and pathologic staging of small cell lung cancer in the forthcoming eighth edition of the TNM classification for lung cancer. J Thorac Oncol 11:300\u0026ndash;311. https://doi.org/10.1016/j.jtho.2015.10.008\u003c/li\u003e\n\u003cli\u003eNunnari J, Suomalainen A (2012) Mitochondria: in sickness and in health. Cell 148:1145\u0026ndash;1159. https://doi.org/10.1016/j.cell.2012.02.035\u003c/li\u003e\n\u003cli\u003eOtsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62\u0026ndash;66. https://doi.org/10.1109/TSMC.1979.4310076\u003c/li\u003e\n\u003cli\u003eSakashita M, Sakashita S, Murata Y, Shiba-Ishii A, Kim Y, Matsuoka R, et al (2018) High expression of ovarian cancer immunoreactive antigen domain containing 2 (OCIAD2) is associated with poor prognosis in lung adenocarcinoma. Pathol Int 68:596\u0026ndash;604. https://doi.org/10.1111/pin.12724\u003c/li\u003e\n\u003cli\u003eShete PG, Kharate GK (2015) Evaluation of immunohistochemistry (IHC) biomarkers in breast cancer using digital image processing. J Life Sci Technol 3:36\u0026ndash;42. https://doi.org/10.18178/jolst.3.2.36-42\u003c/li\u003e\n\u003cli\u003eVander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029\u0026ndash;1033. https://doi.org/10.1126/science.1160809\u003c/li\u003e\n\u003cli\u003eWada K (2016) Label me: image Polygonal Annotation with Python. https://githubcom/wkentaro/labelme\u003c/li\u003e\n\u003cli\u003eWai T, Langer T (2016) Mitochondrial dynamics and metabolic regulation. Trends Endocrinol Metab 27:105\u0026ndash;117. https://doi.org/10.1016/j.tem.2015.12.001\u003c/li\u003e\n\u003cli\u003eWang X, Wang L, Bu H, Zhang N, Yue M, Jia Z, et al (2021) How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies. NPJ Breast Cancer 7:61. https://doi.org/10.1038/s41523-021-00268-y\u003c/li\u003e\n\u003cli\u003eWarburg O (1956) On respiratory impairment in cancer cells. Science 124:269\u0026ndash;270. https://doi.org/10.1126/science.124.3215.269\u003c/li\u003e\n\u003cli\u003eWetstein SC, Stathonikos N, Pluim JPW, Heng YJ, Ter Hoeve ND, Vreuls CPH, et al (2021) Deep learning-based grading of ductal carcinoma in situ in breast histopathology images. Lab Invest 101:525\u0026ndash;533. https://doi.org/10.1038/s41374-021-00540-6\u003c/li\u003e\n\u003cli\u003eYabusaki K, Faits T, McMullen E, Figueiredo JL, Aikawa M, Aikawa E (2014) A novel quantitative approach for eliminating sample-to-sample variation using a hue saturation value analysis program. PLOS ONE 9:e89627. https://doi.org/10.1371/journal.pone.0089627\u003c/li\u003e\n\u003cli\u003eZahedi A, On V, Phandthong R, Chaili A, Remark G, Bhanu B, Talbot P (2018) Deep analysis of mitochondria and cell health using machine learning. Sci Rep 8:16354. https://doi.org/10.1038/s41598-018-34455-y\u003c/li\u003e\n\u003cli\u003eZhang J, Pavlova NN, Thompson CB (2017) Cancer cell metabolism: the essential role of the nonessential amino acid, glutamine. EMBO J 36:1302\u0026ndash;1315. https://doi.org/10.15252/embj.201696151\u003c/li\u003e\n\u003cli\u003eZong WX, Rabinowitz JD, White E (2016) Mitochondria and cancer. Mol Cell 61:667\u0026ndash;676. https://doi.org/10.1016/j.molcel.2016.02.011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"mitochondria, cancer, COX4, MitoTracker","lastPublishedDoi":"10.21203/rs.3.rs-3901509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3901509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eVisualizing mitochondria in cancer cells from human pathological specimens may improve our understanding of cancer biology. However, using immunohistochemistry to evaluate mitochondria remains difficult because almost all cells contain mitochondria and the number of mitochondria per cell may have important effects on mitochondrial function. Herein, we established an objective system (Mito-score) for evaluating mitochondria using machine-based processing of hue, saturation, and value color spaces.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Mito-score was defined as the number of COX4 (mitochondrial inner membrane) immunohistochemistry-positive pixels divided by the number of nuclei per cell. The system was validated using four lung cancer cell lines, normal tissues, and lung cancer tissues (199 cases).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Mito-score correlated with MitoTracker, a fluorescent dye used to selectively label and visualize mitochondria within cells under a microscope (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.68) and with the number of mitochondria counted using electron microscopy (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.79). Histologically, the Mito-score of small cell carcinoma (57.25) was significantly lower than that of adenocarcinoma (147.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), squamous cell carcinoma (120.6, p\u0026thinsp;=\u0026thinsp;0.0004), and large cell neuroendocrine carcinoma (111.8, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Mito-score method enables the analysis of the mitochondrial status of human formalin-fixed paraffin-embedded specimens and may provide insights into the metabolic status of cancer.\u003c/p\u003e","manuscriptTitle":"Machine-based counting of mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 16:34:59","doi":"10.21203/rs.3.rs-3901509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-09T06:04:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-07T10:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f1978c97-6805-42cf-92c7-e47fa2759a61","date":"2024-01-27T19:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-27T16:12:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-27T05:55:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-27T05:55:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2024-01-27T01:34:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2344afc7-35c8-4362-b656-4b5dafc95115","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T15:04:03+00:00","versionOfRecord":{"articleIdentity":"rs-3901509","link":"https://doi.org/10.1007/s00432-024-05653-1","journal":{"identity":"journal-of-cancer-research-and-clinical-oncology","isVorOnly":false,"title":"Journal of Cancer Research and Clinical Oncology"},"publishedOn":"2024-04-03 15:01:07","publishedOnDateReadable":"April 3rd, 2024"},"versionCreatedAt":"2024-01-30 16:34:59","video":"","vorDoi":"10.1007/s00432-024-05653-1","vorDoiUrl":"https://doi.org/10.1007/s00432-024-05653-1","workflowStages":[]},"version":"v1","identity":"rs-3901509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3901509","identity":"rs-3901509","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}