Advertisement

Histopathologic Evaluation and Scoring of Viral Lung Infection

  • David K. MeyerholzEmail author
  • Amanda P. Beck
Protocol
  • 2.5k Downloads
Part of the Methods in Molecular Biology book series (MIMB, volume 2099)

Abstract

Emergent coronaviruses such as MERS-CoV and SARS-CoV can cause significant morbidity and mortality in infected individuals. Lung infection is a common clinical feature and contributes to disease severity as well as viral transmission. Animal models are often required to study viral infections and therapies, especially during an initial outbreak. Histopathology studies allow for identification of lesions and affected cell types to better understand viral pathogenesis and clarify effective therapies. Use of immunostaining allows detection of presumed viral receptors and viral tropism for cells can be evaluated to correlate with lesions. In the lung, lesions and immunostaining can be qualitatively described to define the cell types, microanatomic location, and type of changes seen. These features are important and necessary, but this approach can have limitations when comparing treatment groups. Semiquantitative and quantitative tissue scores are more rigorous as these provide the ability to statistically compare groups and increase the reproducibility and rigor of the study. This review describes principles, approaches, and resources that can be useful to evaluate coronavirus lung infection, focusing on MER-CoV infection as the principal example.

Key words

MERS-CoV infection Lung Scoring Pathology Immunostaining 

1 Introduction

Emergent coronaviruses such as severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS-CoV) have caused significant impacts on human health, especially during their initial outbreaks [1, 2]. People infected with these coronaviruses often have significant lung disease that contributes to clinical morbidity and mortality [3, 4, 5]. Histopathologic examination and immunostaining (e.g., immunohistochemistry) of lung tissues are essential to better understand disease pathogenesis and evaluate novel treatments of these current (and future) virus outbreaks [6, 7, 8, 9, 10]. Here, we will focus on MERS-CoV infection to present important principles for valid qualitative and quantitative evaluation of infected lung tissues.

1.1 Factors that Influence Evaluation

Preparation of quality lung tissue samples is important for histopathologic examination to optimize preservation of fine pulmonary architecture and, in the case of immunostaining, antigenicity of target epitopes [11, 12, 13]. A study by Engel and Moore identified more than 60 variables in this time frame, beginning with proper sample collection and handling and including multiple aspects of tissue collection, fixation, processing, embedding, slide drying, and storage [14]. Thus, attention to details and quality early will greatly aid the subsequent evaluation, interpretation, and impact of tissue examination.

To collect lungs for histology, samples should be harvested as soon as possible following death to minimize autolysis [11]. Autolysis (“self-digestion”) is a postmortem change characterized by degradation of cellular constituents (DNA, RNA, protein) and dissolution of the tissue [15]. Not only can this cause degradation of epitopes and increased nonspecific staining with immunohistochemistry, autolytic regions can be morphologically confused with foci of necrosis and edema [15, 16, 17]. If animals will be euthanized, it is preferable to select a method that does not target the lungs such as an intravenous agent. Even use of inhalational overdose of carbon dioxide, as is commonly used in rodents, can potentially cause minor edema/hemorrhage [11, 18, 19]. Evaluation of controls should be standard to evaluate for antemortem or euthanasia-related variables affecting lung evaluation. When examining rodents versus lungs from larger animals or humans, sampling becomes a relevant variable. For instance, mice have small lungs that can be sectioned onto one glass slide for widespread evaluation. Larger sized lungs cannot be sampled adequately using only one slide without introducing sampling bias. Therefore, several samples will need to be collected in larger lungs. The collection method will need to be defined in the methods of publications and should include collection site (standardized vs. lesions sites) and total number, the latter of which depends on the size of the lungs, distribution of lung lesions, and overarching goals of the study.

Proper and adequate fixation of the tissues is essential to retain optimal tissue morphology and cellular antigenicity for immunostaining techniques [11, 20]. However, it is important to remember that if lungs are to be assessed or scored for macroscopic (gross) indicators of disease (such as color, surface texture, and consistency), this must be done prior to fixation, which will affect all of these parameters. Macroscopic evaluation and scoring can be a nice tool to complement histopathology lesions [21, 22]. For tissues that will be paraffin embedded, sections are typically fixed in 10% neutral buffered formalin or 4% paraformaldehyde, though other fixatives may be employed based on the desired analysis endpoints. Collected lung samples can be placed in a minimum of 20:1 volume of fixative:tissue with a maximal thickness of tissue of no more than ~5 mm in at least one dimension to be consistently fixed [20]. For rodents, inflation of the lungs via intratracheal instillation of fixative is recommended to best preserve lung morphology and reduce artifactual atelectasis [15]. However, this approach is contraindicated for lung infection as this can alter the anatomic location of inflammation and cellular debris [22]. The lungs and heart of rodents can be removed en bloc for fixation. Freezing of tissue may be an alternative approach to preserve specific antigens, but this process typically results in suboptimal retention of cellular and tissue architectural detail [11].

After processing to dehydrate the fixed lungs, samples must be embedded and sectioned in a consistent manner. Due to the relatively small size of mice, all lung lobes can be embedded en bloc with the ventral lobar surfaces oriented down in the cassette, which results in sections showing longitudinal views of major conducting airways. An alternative approach for mice, or standard approach for larger species, lung lobes can be collected as multiple sections that fit into a cassette, with each sample embedded separately [11]. Slides are typically stained with hematoxylin and eosin (HE) for routine histologic evaluation. If immunostaining is desired, it is essential to optimize and validate each new antibody utilizing appropriate positive and negative controls to ensure accurate staining results [20, 23]. Similarly, if special histochemical stains will be employed, appropriate control slides and tissues should also be utilized for each batch.

Awareness of normal anatomy and morphology is necessary to recognize any type of change and when utilizing animal models of human disease, this includes knowing differences between the species [24, 25]. For example, there are a number of morphologic differences between the respiratory tract structures of mice and humans. Lobation is distinct, in that mice have four right lung lobes (cranial, middle, caudal, and accessory) and only one left lobe, while humans have three right lung lobes (upper, middle, and lower) and two left lobes (upper and lower) [15, 22]. Rats and mice lack intralobular septa, intrapulmonary bronchi, intrapulmonary submucosal glands, and respiratory bronchioles. Mice also have more club cells extending to the trachea, a thinner blood-gas barrier, and a smaller alveolar diameter than humans [11, 26]. These anatomic variations do not mean that rodents cannot be very valuable models of lung disease; rather they are highlighted here as an example of the type of knowledge necessary for correct interpretation of experimental models.

Inclusion of experienced board-certified pathologists, who are specially trained to examine and interpret tissues changes, as part of the multidisciplinary team can greatly enhance the quality of tissue evaluation [22, 27]. By histopathology, a skilled eye (ideally a pathologist familiar with the model) can not only define the types of inflammatory processes, but also corroborate these findings to clinical signs and/or data from other analyses [22, 27, 28, 29, 30]. In addition, pathologists have knowledge of correct lesion nomenclature, as well as potential effects of such variables as strain-related background lesions, husbandry, the microbiome, and diet on the interpretation of results [25]. If pathologists are not involved in designing translational experiments and interpreting lesions in animal models, bias may be introduced and the accuracy of the data and conclusions may be questionable. This approach, which lacks the expertise of a pathologist trained in tissue interpretation, has been labeled as “do-it-yourself pathology” and is linked to multiple publications containing erroneous interpretations [22, 25, 31, 32]. While observations made by biomedical personnel may be biologically accurate in some cases, it is important to note that tissue examination by non-pathologists (even those who are “scientific experts” for a particular disease) is prone to false-positive and false-negative errors and not recommended [33]. Ideally, tissues should be examined by a pathologist familiar with histopathology of the model (see Note 1). It is recognized that not all labs have access to pathologists for this role and in many situations a member of the investigational team is assigned to the role. In these situations, if possible, it helps to have a pathologist review the study findings prior to publication or have the examiner meet with a pathologist to screen the slides and data for accuracy.

Lungs have unique features compared to other organs that are important for consideration in designing experiments or when making interpretations. For study of infectious diseases, distribution and histologic appearance of lung lesions depends on a variety of factors including the viral inoculate concentration, route of exposure, regional deposition, cellular uptake, chronicity, and host immune response. For instance, inbred mouse strains can have variably sized airways that may affect viral droplet delivery or clinical disease manifestations such as airway obstruction [34]. Inbred mouse strains can also exhibit biased (e.g., Th1 vs. Th2 immune responses) or deficient immune signaling pathways that might influence infection susceptibility or severity [35, 36]. Sex can also be an influencing factor for infection and needs to be considered in the experimental design [37]. Even actions as simple as laying an animal in lateral recumbency to recover from anesthesia following viral inoculation may lead to more prominent lesions in certain lobe(s) [22]. For many of these features, inclusion of appropriate control animals (i.e., strain-, age-, and sex-matched, housed under identical husbandry conditions and free from confounding pathogens) is necessary and important to tease out any lesions unrelated to the treatments. Unlike the other organs in which the size is relatively static, the lung has dynamic size changes during normal respiration. Handing of the postmortem lung in a standardized manner is useful to prevent postmortem atelectasis or variable inter-animal insufflation. Right ventricular perfusion of fixative into the lungs prior to extraction can help with fixation as well as insufflate the airspaces without dislodging inflammation or mucocellular debris [22].

1.2 Histopathology

Histopathology is the microscopic examination of tissues for morphologic or structural changes that differ from normal and these changes are called lesions. Histopathology of coronavirus-infected lung in humans and animal models can be a useful tool to help define affected cells, illuminate the structural cause(s) of clinical signs, and clarify potential therapies. During disease outbreaks, clinical data including autopsy cases can be studied in parallel with animal model investigations to better define lung disease pathogenesis and therapies. For instance, in 2012 the novel human coronavirus known as MERS-CoV was first isolated from a patient dying in Saudi Arabia [2, 38]. In the region of the outbreak, local burial rituals along with the requirement for high biosecurity constrained autopsy studies from being performed until the first report in early 2016 [4]. Within a few years of the first reported MERS-CoV case in humans in 2012, several animal models were being studied and these models provided much of the initial critically important lung pathology data [39, 40, 41, 42, 43, 44].

Histopathologic examination of viral lung infection requires awareness of any anticipated lesions from clinical or published data, as it is available. Examples of MERS-CoV lesions are listed in Table 1. For instance, acute diffuse alveolar damage (DAD) is a common feature of MERS-CoV lung lesions and it is composed of lesions such as edema, inflammation, and alveolar septal injury [4, 48, 49, 50]. While awareness of reported lesions can help guide the pathologist in examination, it is also useful to have a consistent method for examination of experimental tissues to avoid unintentional bias that might cause a failure to detection of unexpected lesions [51]. Consistent examination of all tissues from control and treatment groups can reduce the chances of mistakenly diagnosing nonspecific model background phenotype as a MERS-CoV-specific lesion [22, 25, 29]. For instance, a lesion that is present in the controls and treatment groups can be defined as a background model/technique phenotype and should not be reported as a MERS-CoV specific lesion. Masking of the pathologist to the group assignments is useful to avoid observer bias and each type of masking method has certain advantages and limitations (Table 2, see Note 2) [22]. A common approach for histopathologic examination is to start at low magnification to screen for any obvious lesions and assess quality of the tissue section (see Note 3). This allows examination of microscopic structures such as airways, alveoli, alveolar septa, air spaces, vessels, and pleura. Examination at high magnification allows for screening of cellular and interstitial components of each structure for lesions (e.g., injury, inflammation, necrosis). Most slides will be examined using HE, but additional stains can be used on serial sections to further define any changes. For instance, mucus in goblet cells or secreted into air spaces can be highlighted by special stains like Periodic acid Schiff in glycogen-depleted tissues or Alcian blue [55, 56].
Table 1

Examples of lesions seen in MERS-CoV lung infections

Lesions

Necrosis/cell death [45]

Edema [8, 21, 45]

Hyaline membranes/fibrin [21]

Inflammation [8]

Thrombi [8, 46]

Congestion [8]

Hemorrhage [45, 46]

Pneumonia [46, 47]

Type II hyperplasia [47]

Syncytia [47]

Table 2

Methods of masking to prevent observer bias [22, 52, 53, 54]

Method

Approach

Usage

Comprehensive

Samples are labeled without group identification (1, 2, 3, 4 …), minimal background information provided

Allows for experienced observers to score well-defined models, otherwise susceptible to errors

Grouped

Samples are labeled according to de-identified groups (A1, A2, A3, B1, B2, B3…)

Allows for masked evaluation of groups while observer is informed about experimental context

Post-examination

Samples are examined in a transparent manner to determine the type and scope of tissue changes, samples are then masked for scoring

Allows for full examination and disclosure of experimental context; groups with small N may let observer recall sample group assignment

After the slides and stains have been examined for all groups, the results will need to be prepared for publication. Qualitative characterization of the findings is very important to understand features of the disease including cellular tropism, anatomic predisposition, and nature of lesions leading to clinical signs (see Note 4) [10, 21]. Qualitative descriptions of lesions include type (e.g., epithelial sloughing/necrosis), location (e.g., alveoli), distribution (e.g., locally extensive), inflammation (e.g., neutrophilic), and cell types involved (e.g., type I pneumocytes). Qualitative features can be sufficiently described in the text and exemplified in representative figures. Use of arrows and other forms of annotation are valuable in figures to clarify and guide readers through the images. High-quality descriptions will help the reader (including reviewers) better understand what was seen and allow for others to reproduce the study.

1.3 Immunostaining

Immunostaining (immunohistochemistry) is a valuable tool in viral lung disease investigations as it can be used to study cellular localization of receptors and viral targets. For instance, detection of the MERS-CoV receptor dipeptidyl peptidase 4 (DPP4) virus receptor can give insights to cell tropism to help explain disease pathogenesis [4, 6, 21, 42, 57, 58, 59].

There are several tissue handling (preanalytical) factors that can significantly affect the quality and specificity of immunostaining and its analysis. These have been discussed earlier sections of the paper and in several reviews [20, 52, 60, 61, 62, 63]. Similarly, there are many factors during the staining procedure that itself can also influence the results. Deparaffinization, lack of control tissues, optimization/validation techniques, species, batch effects, and chromogens can all influence the final quality and assessment of immunostaining methods. Standard operating procedures for each of the technical steps, if used by all biomedical staff, can significantly mitigate many of these issues. Use of positive and negative control tissues for each batch of immunostained tissues can help in validating appropriate staining and also making clear any potential nonspecific immunostaining. After the stained slides have been examined for all groups, qualitative statements about the immunostaining can be made and prepared for publication text and images. Descriptive text of immunostaining (receptor or virus) could include cell types (e.g., type I pneumocytes), cell integrity (necrotic vs. intact cells), and subcellular location (e.g., diffuse cytoplasmic). Demonstration of immunostaining using annotated images can strengthen the qualitative data.

1.4 Scoring

As shown above, qualitative descriptions of tissue changes are useful and necessary, but they are less applicable in terms of group comparisons. More robust and reproducible methods are desirable and these criteria can be sought in tissue scoring systems (semiquantitative and quantitative) that produce data that allow for statistical analyses for evaluation of group differences (see Note 5) [52, 53]. Importantly, these scoring principles can be applied to tissue lesions (gross and/or histopathologic) as well as immunostained sections.

1.4.1 Nominal Approaches

Nominal approaches do not score or make quantitative measurements on tissue samples, but rather each sample is assigned to well-defined categories [52, 54]. The numbers of samples assigned to each category are recorded and evaluated with appropriate statistical tests. As a simple mock example, consider examining the lungs of wild-type (WT) or mutated mice for the presence or absence of edema, a common feature of DAD. Each mouse would be assigned to either “no edema” (Fig. 1a) or “edema” (Fig. 1b, c) categories. If 10 mice per group were evaluated, the WT group might have nine with edema and one without, while the mutated group has three with edema and seven without. Evaluating these data using a Fisher exact test results in a significant difference (P = 0.02) between WT and mutated mice. The presence of any lesion or immunostaining can be similarly assessed in this manner, but it is important to have clear guidelines or thresholds to distinguish the categories.
Fig. 1

Mock example of mouse lung lesions during MERS-CoV infection. (a) Normal bronchiole and alveolar structures. (b, c) Pulmonary edema (pink color filling alveoli). (d, e) Hyaline membranes (red crescents lining alveolar walls)

1.4.2 Semiquantitative Approaches

Semiquantitative approaches are used to transform qualitative tissue changes into numerical scores using specific morphologic criteria [52, 53]. Semiquantitative methods have several advantages in that they can be done with minimal technical resources, quickly at the microscope for small to medium studies, provide guidance for future quantitative studies, and provide complementary data for publication [52, 53, 54]. The most commonly used semiquantitative methods produce ordinal scores. Ordinal implies there is an order or progression of severity in the assigned grades that define each score, with typically four to five grades being optimal (e.g., 0, 1, 2, 3, 4). Each grade should be well defined so there is minimal ambiguity in assigning samples. Use of simple descriptive modifiers such as normal, rare, mild, moderate, and severe is discouraged as these have different meanings for each observer and thus limit reproducibility of the scoring. As a mock example of ordinal scoring, WT and mutated mice might be evaluated for the extent of hyaline membranes lining alveolar walls. The scoring grades might look like: “0”—none, “1”— <25% (see Fig. 1d), “2”—26–50% (see Fig. 1e), “3”—51–75%, and “4” >75% of alveolar walls in the lung section. If the ordinal scoring for seven mice per group produced the following results for WT (3, 3, 2, 3, 4, 3, 4) and mutated mice (1, 2, 1, 1, 1, 1, 2), then the data can be statistically analyzed. Importantly, ordinal scores do not meet the assumptions required for parametric tests; thus nonparametric tests should be used [33]. For the mock example, the difference between groups using a Mann-Whitney U-test was significant (P = 0.002).

1.4.3 Quantitative Approaches

Quantitative methods are tissue techniques that measure specific tissue components (length, area, volume, number, percentage, etc.) [52]. Quantitative methods tend to have greater precision and sensitivity than semiquantitative methods. These methods often require high-quality images and specialized software to properly analyze the tissues, which can make the methods costlier for some labs than semiquantitative techniques. The growing interest in automation and artificial intelligence may increase future efficiency and cost-effectiveness of quantification of tissue parameters, especially for large projects [64, 65, 66, 67].

Quantification of viral lesions and immunostaining in tissues is an option; however, quantification is not commonly performed in tissue sections due to potential confounding factors such as random distribution of viral inoculum and difficulty in objectively quantifying lesions. If choosing to perform quantitative scoring, evaluation of clinically relevant anatomic compartments (airways or alveoli) can help standardize the assessment. As a mock example, viral immunostaining could be evaluated as a percent of cell number in mouse bronchioles (Fig. 2a–c; 0%, 12.5%, and 43.8%, respectively) or as an alternative one could also assess the area of immunostaining as a percent of the bronchiolar epithelium area. In contrast, the alveolar compartment can be more difficult to assess than airways because of their thin walls, which makes evidence of necrosis/sloughing or immunostaining a challenge. To normalize analysis, one could assess the percent of alveoli with immunostaining (Fig. 2d–e). However, this would likely require extensive time/labor or specialized software. If quantitation is not feasible but is an important variable, one could revert to semiquantitative scoring to assess immunostaining as a percentage of affected alveolar walls. Using the distribution scoring system defined for Fig. 1, one could score the samples in Fig. 2d–e, as ordinal scores of 1 and 4, respectively. While the mock example is simple, reality often paints a more complex portrait of lesion or immunostaining distribution (Fig. 2f).
Fig. 2

Mock example of viral immunostaining during MERS-CoV infection. (a) No immunostaining in control lung. (b, c) Immunostaining (black color) in airways. (d, e) Immunostaining in alveoli. (f) Immunostaining in airway and alveoli

When it comes to tissue scoring, each project is unique. Investigators will have to evaluate the lung samples to determine the best scoring approaches in relation to the breadth of lesions and goals of the project. Most importantly, any scoring that is performed should be corroborated, when possible, with other data to validate the findings [22, 52, 53]. For instance, if group A has more immunostaining than group B, this could be validated by ELISA or Western blots of whole lung homogenates. Alternatively, lesion severity could be corroborated to measurements of clinical data (see Note 6). Validation can help give more confidence in the data rigor and reproducibility.

1.4.4 Statistical Analyses

Inappropriate use of paired t-tests and shopping for significance are two issues that have slipped into the published literature and potentially compromise the interpretation and reproducibility of studies [33]. For the various scoring methods, statistical analyses of the data should involve the collaborative expertise of a statistician to be able to identify the most relevant tests to confidently evaluate for group differences [22, 33, 52, 53].

2 Summary

Examination of infected lung tissues for histopathology and immunostaining are common and needed approaches to study viral lung infection, especially in emergent coronaviruses like MERS-CoV. Following the principles and concepts above will help guide and lead studies to more valid and reproducible data.

3 Notes

  1. 1.

    Ideally a pathologist familiar with the model is available for the lab to evaluate experimental tissues. If not, then a pathologist collaborator should be sought to perform or review of the results of examination prior to submission for publication. This prevents publication of data that is flawed or needs subsequent retraction.

     
  2. 2.

    Masking is important to prevent potential bias by the observer pathologist (Table 2). For new projects, the post-examination is preferred as this helps the pathologist understand the goals/experimental design of the project as well as see quality and scope of lesions/stains. For most other research projects where the pathologist is familiar with the model, these can be masked in grouped fashion to maximize the interpretative power of the pathologist to screen for biologically relevant changes in a group-specific manner. Comprehensive masking is often discouraged as it effectively constrains the ability of the pathologist in defining relevant versus unconnected data and therefore limits the sensitivity and specificity of the pathology data.

     
  3. 3.

    Evaluation of slides from all treatment and control groups prior to detailed examination is useful to give the pathologist an overview and primer of the type, scope and severity of lesions/stains.

     
  4. 4.

    Detailed examination of the tissues allows for extrapolation of qualitative descriptive data. If there are questions regarding the cells/tissues that can be addressed by specific stains—these could be done at this time to corroborate/clarify descriptive findings.

     
  5. 5.

    When biologically relevant lesions are defined in the project, group-specific changes may be evaluated for by semiquantitative or quantitative scores. Semiquantitative approaches are often done initially and the results can be used as screening tools to set up primary scoring approaches or be used as primary/supplemental data for reporting group differences in lesions or stains. Quantitative approaches may be performed by at the microscope (e.g., cell counts) or automated on digital images by specialized software.

    Regardless of the masking method (see Note 2), it is often useful to score the slides in a random masked fashion and in one sitting to prevent diagnostic drift. After scoring, it is sometimes beneficial to take scoring data to see if these same differences are morphologically detectable in the respective groups. If the pathologist can see these differences, it gives further confidence to the scoring approach and final interpretations. If not, it can raise questions as to the scoring methods.

     
  6. 6.

    Effective reporting of pathology data requires transparency of methods, numbers of animals, statistical analyses, etc. Producing graphs of scoring data with matching images that are annotated can be very powerful tools in conveying the results to readers.

     

Notes

Acknowledgements

We would like to thank The Lamb Art Studio (www.faithlamb.com) for excellent assistance with graphic artwork.

References

  1. 1.
    Booth CM, Matukas LM, Tomlinson GA, Rachlis AR, Rose DB, Dwosh HA, Walmsley SL, Mazzulli T, Avendano M, Derkach P, Ephtimios IE, Kitai I, Mederski BD, Shadowitz SB, Gold WL, Hawryluck LA, Rea E, Chenkin JS, Cescon DW, Poutanen SM, Detsky AS (2003) Clinical features and short-term outcomes of 144 patients with SARS in the greater Toronto area. JAMA 289(21):2801–2809.  https://doi.org/10.1001/jama.289.21.JOC30885CrossRefPubMedGoogle Scholar
  2. 2.
    Zumla A, Hui DS, Perlman S (2015) Middle East respiratory syndrome. Lancet 386(9997):995–1007.  https://doi.org/10.1016/S0140-6736(15)60454-8CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Khalid I, Alraddadi BM, Dairi Y, Khalid TJ, Kadri M, Alshukairi AN, Qushmaq IA (2016) Acute management and long-term survival among subjects with severe Middle East respiratory syndrome coronavirus pneumonia and ARDS. Respir Care 61(3):340–348.  https://doi.org/10.4187/respcare.04325CrossRefPubMedGoogle Scholar
  4. 4.
    Ng DL, Al Hosani F, Keating MK, Gerber SI, Jones TL, Metcalfe MG, Tong S, Tao Y, Alami NN, Haynes LM, Mutei MA, Abdel-Wareth L, Uyeki TM, Swerdlow DL, Barakat M, Zaki SR (2016) Clinicopathologic, immunohistochemical, and ultrastructural findings of a fatal case of Middle East respiratory syndrome coronavirus infection in the United Arab Emirates, april 2014. Am J Pathol 186(3):652–658.  https://doi.org/10.1016/j.ajpath.2015.10.024CrossRefGoogle Scholar
  5. 5.
    Peiris JS, Chu CM, Cheng VC, Chan KS, Hung IF, Poon LL, Law KI, Tang BS, Hon TY, Chan CS, Chan KH, Ng JS, Zheng BJ, Ng WL, Lai RW, Guan Y, Yuen KY, Group HUSS (2003) Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet 361(9371):1767–1772CrossRefGoogle Scholar
  6. 6.
    Cockrell AS, Johnson JC, Moore IN, Liu DX, Bock KW, Douglas MG, Graham RL, Solomon J, Torzewski L, Bartos C, Hart R, Baric RS, Johnson RF (2018) A spike-modified Middle East respiratory syndrome coronavirus (MERS-CoV) infectious clone elicits mild respiratory disease in infected rhesus macaques. Sci Rep 8(1):10727.  https://doi.org/10.1038/s41598-018-28900-1CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Hua X, Vijay R, Channappanavar R, Athmer J, Meyerholz DK, Pagedar N, Tilley S, Perlman S (2018) Nasal priming by a murine coronavirus provides protective immunity against lethal heterologous virus pneumonia. JCI Insight 3(11):99025.  https://doi.org/10.1172/jci.insight.99025CrossRefPubMedGoogle Scholar
  8. 8.
    Li K, Wohlford-Lenane C, Perlman S, Zhao J, Jewell AK, Reznikov LR, Gibson-Corley KN, Meyerholz DK, McCray PB Jr (2016) Middle East respiratory syndrome coronavirus causes multiple organ damage and lethal disease in mice transgenic for human dipeptidyl peptidase 4. J Infect Dis 213(5):712–722.  https://doi.org/10.1093/infdis/jiv499CrossRefGoogle Scholar
  9. 9.
    Menachery VD, Yount BL Jr, Debbink K, Agnihothram S, Gralinski LE, Plante JA, Graham RL, Scobey T, Ge XY, Donaldson EF, Randell SH, Lanzavecchia A, Marasco WA, Shi ZL, Baric RS (2015) A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat Med 21(12):1508–1513.  https://doi.org/10.1038/nm.3985CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Meyerholz DK, Lambertz AM, McCray PB Jr (2016) Dipeptidyl peptidase 4 distribution in the human respiratory tract: implications for the Middle East respiratory syndrome. Am J Pathol 186(1):78–86.  https://doi.org/10.1016/j.ajpath.2015.09.014CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Aeffner F, Bolon B, Davis IC (2015) Mouse models of acute respiratory distress syndrome: a review of analytical approaches, pathologic features, and common measurements. Toxicol Pathol 43(8):1074–1092.  https://doi.org/10.1177/0192623315598399CrossRefPubMedGoogle Scholar
  12. 12.
    Dunstan RW, Wharton KA Jr, Quigley C, Lowe A (2011) The use of immunohistochemistry for biomarker assessment-can it compete with other technologies? Toxicol Pathol 39(6):988–1002.  https://doi.org/10.1177/0192623311419163CrossRefPubMedGoogle Scholar
  13. 13.
    Eaton KA, Danon SJ, Krakowka S, Weisbrode SE (2007) A reproducible scoring system for quantification of histologic lesions of inflammatory disease in mouse gastric epithelium. Comp Med 57(1):57–65PubMedGoogle Scholar
  14. 14.
    Engel KB, Moore HM (2011) Effects of preanalytical variables on the detection of proteins by immunohistochemistry in formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med 135(5):537–543.  https://doi.org/10.1043/2010-0702-RAIR.1CrossRefPubMedGoogle Scholar
  15. 15.
    Renne R, Brix A, Harkema J, Herbert R, Kittel B, Lewis D, March T, Nagano K, Pino M, Rittinghausen S, Rosenbruch M, Tellier P, Wohrmann T (2009) Proliferative and nonproliferative lesions of the rat and mouse respiratory tract. Toxicol Pathol 37(7 Suppl):5S–73S.  https://doi.org/10.1177/0192623309353423CrossRefPubMedGoogle Scholar
  16. 16.
    Rollins KE, Meyerholz DK, Johnson GD, Capparella AP, Loew SS (2012) A forensic investigation into the etiology of bat mortality at a wind farm: barotrauma or traumatic injury? Vet Pathol 49(2):362–371.  https://doi.org/10.1177/0300985812436745CrossRefPubMedGoogle Scholar
  17. 17.
    Williams JM, Duckworth CA, Burkitt MD, Watson AJ, Campbell BJ, Pritchard DM (2015) Epithelial cell shedding and barrier function: a matter of life and death at the small intestinal villus tip. Vet Pathol 52(3):445–455.  https://doi.org/10.1177/0300985814559404CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Boivin GP, Bottomley MA, Schiml PA, Goss L, Grobe N (2017) Physiologic, behavioral, and histologic responses to various euthanasia methods in C57BL/6NTac male mice. J Am Assoc Lab Anim Sci 56(1):69–78PubMedPubMedCentralGoogle Scholar
  19. 19.
    Marquardt N, Feja M, Hunigen H, Plendl J, Menken L, Fink H, Bert B (2018) Euthanasia of laboratory mice: are isoflurane and sevoflurane real alternatives to carbon dioxide? PLoS One 13(9):e0203793.  https://doi.org/10.1371/journal.pone.0203793CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Olivier AK, Naumann P, Goeken A, Hochstedler C, Sturm M, Rodgers JR, Gibson-Corley KN, Meyerholz DK (2012) Genetically modified species in research: opportunities and challenges for the histology core laboratory. J Histotechnol 35(2):63–67CrossRefGoogle Scholar
  21. 21.
    Li K, Wohlford-Lenane CL, Channappanavar R, Park JE, Earnest JT, Bair TB, Bates AM, Brogden KA, Flaherty HA, Gallagher T, Meyerholz DK, Perlman S, McCray PB Jr (2017) Mouse-adapted MERS coronavirus causes lethal lung disease in human DPP4 knockin mice. Proc Natl Acad Sci U S A 114(15):E3119–E3128.  https://doi.org/10.1073/pnas.1619109114CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Meyerholz DK, Sieren JC, Beck AP, Flaherty HA (2018) Approaches to evaluate lung inflammation in translational research. Vet Pathol 55(1):42–52.  https://doi.org/10.1177/0300985817726117CrossRefPubMedGoogle Scholar
  23. 23.
    Gibson-Corley KN, Hochstedler C, Sturm M, Rogers J, Olivier AK, Meyerholz DK (2012) Successful integration of the histology core laboratory in translational research. J Histotechnol 35(1):17–21.  https://doi.org/10.1179/2046023612Y.0000000001CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Klopfleisch R (2013) Multiparametric and semiquantitative scoring systems for the evaluation of mouse model histopathology-a systematic review. BMC Vet Res 9:123.  https://doi.org/10.1186/1746-6148-9-123CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ward JM, Schofield PN, Sundberg JP (2017) Reproducibility of histopathological findings in experimental pathology of the mouse: a sorry tail. Lab Anim (NY) 46(4):146–151.  https://doi.org/10.1038/laban.1214CrossRefGoogle Scholar
  26. 26.
    Meyerholz DK, Suarez CJ, Dintis SM, Frevert CW (2018) Respiratory system. In: Treuting PM, Dintis SM, Montine KS (eds) Comparative anatomy and histology: a mouse, rat and human atlas, 2nd edn. Elsevier, London, pp 147–162CrossRefGoogle Scholar
  27. 27.
    Adissu HA, Estabel J, Sunter D, Tuck E, Hooks Y, Carragher DM, Clarke K, Karp NA, Sanger Mouse Genetics P, Newbigging S, Jones N, Morikawa L, White JK, McKerlie C (2014) Histopathology reveals correlative and unique phenotypes in a high-throughput mouse phenotyping screen. Dis Model Mech 7(5):515–524.  https://doi.org/10.1242/dmm.015263CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ladiges W, Ikeno Y, Liggitt D, Treuting PM (2013) Pathology is a critical aspect of preclinical aging studies. Pathobiol Aging Age Relat Dis:3.  https://doi.org/10.3402/pba.v3i0.22451CrossRefGoogle Scholar
  29. 29.
    Treuting PM, Snyder JM, Ikeno Y, Schofield PN, Ward JM, Sundberg JP (2016) The vital role of pathology in improving reproducibility and translational relevance of aging studies in Rodents. Vet Pathol 53(2):244–249.  https://doi.org/10.1177/0300985815620629CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Ward JM, Youssef SA, Treuting PM (2016) Why animals die: an introduction to the pathology of aging. Vet Pathol 53(2):229–232.  https://doi.org/10.1177/0300985815612151CrossRefPubMedGoogle Scholar
  31. 31.
    Cardiff RD, Ward JM, Barthold SW (2008) ‘One medicine-one pathology’: are veterinary and human pathology prepared? Lab Investig 88(1):18–26.  https://doi.org/10.1038/labinvest.3700695CrossRefPubMedGoogle Scholar
  32. 32.
    Wolf JC, Wheeler JR (2018) A critical review of histopathological findings associated with endocrine and non-endocrine hepatic toxicity in fish models. Aquat Toxicol 197:60–78.  https://doi.org/10.1016/j.aquatox.2018.01.013CrossRefPubMedGoogle Scholar
  33. 33.
    Meyerholz DK, Tintle NL, Beck AP (2019) Common pitfalls in analysis of tissue scores. Vet Pathol 56(1):39–42.  https://doi.org/10.1177/0300985818794250CrossRefPubMedGoogle Scholar
  34. 34.
    Thiesse J, Namati E, Sieren JC, Smith AR, Reinhardt JM, Hoffman EA, McLennan G (2010) Lung structure phenotype variation in inbred mouse strains revealed through in vivo micro-CT imaging. J Appl Physiol (1985) 109(6):1960–1968.  https://doi.org/10.1152/japplphysiol.01322.2009CrossRefGoogle Scholar
  35. 35.
    Feng CG, Scanga CA, Collazo-Custodio CM, Cheever AW, Hieny S, Caspar P, Sher A (2003) Mice lacking myeloid differentiation factor 88 display profound defects in host resistance and immune responses to Mycobacterium avium infection not exhibited by Toll-like receptor 2 (TLR2)- and TLR4-deficient animals. J Immunol 171(9):4758–4764.  https://doi.org/10.4049/jimmunol.171.9.4758CrossRefPubMedGoogle Scholar
  36. 36.
    Schulte S, Sukhova GK, Libby P (2008) Genetically programmed biases in Th1 and Th2 immune responses modulate atherogenesis. Am J Pathol 172(6):1500–1508.  https://doi.org/10.2353/ajpath.2008.070776CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Channappanavar R, Fett C, Mack M, Ten Eyck PP, Meyerholz DK, Perlman S (2017) Sex-based differences in susceptibility to severe acute respiratory syndrome coronavirus infection. J Immunol 198(10):4046–4053.  https://doi.org/10.4049/jimmunol.1601896CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA (2012) Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 367(19):1814–1820.  https://doi.org/10.1056/NEJMoa1211721CrossRefGoogle Scholar
  39. 39.
    Coleman CM, Matthews KL, Goicochea L, Frieman MB (2014) Wild-type and innate immune-deficient mice are not susceptible to the Middle East respiratory syndrome coronavirus. J Gen Virol 95(Pt 2):408–412.  https://doi.org/10.1099/vir.0.060640-0CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    de Wit E, Prescott J, Baseler L, Bushmaker T, Thomas T, Lackemeyer MG, Martellaro C, Milne-Price S, Haddock E, Haagmans BL, Feldmann H, Munster VJ (2013) The Middle East respiratory syndrome coronavirus (MERS-CoV) does not replicate in Syrian hamsters. PLoS One 8(7):e69127.  https://doi.org/10.1371/journal.pone.0069127CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    de Wit E, Rasmussen AL, Falzarano D, Bushmaker T, Feldmann F, Brining DL, Fischer ER, Martellaro C, Okumura A, Chang J, Scott D, Benecke AG, Katze MG, Feldmann H, Munster VJ (2013) Middle East respiratory syndrome coronavirus (MERS-CoV) causes transient lower respiratory tract infection in rhesus macaques. Proc Natl Acad Sci U S A 110(41):16598–16603.  https://doi.org/10.1073/pnas.1310744110CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Meyerholz DK (2016) Modeling emergent diseases: lessons from Middle East respiratory syndrome. Vet Pathol 53(3):517–518.  https://doi.org/10.1177/0300985816634811CrossRefPubMedGoogle Scholar
  43. 43.
    Yao Y, Bao L, Deng W, Xu L, Li F, Lv Q, Yu P, Chen T, Xu Y, Zhu H, Yuan J, Gu S, Wei Q, Chen H, Yuen KY, Qin C (2014) An animal model of MERS produced by infection of rhesus macaques with MERS coronavirus. J Infect Dis 209(2):236–242.  https://doi.org/10.1093/infdis/jit590CrossRefGoogle Scholar
  44. 44.
    Zhao J, Li K, Wohlford-Lenane C, Agnihothram SS, Fett C, Zhao J, Gale MJ Jr, Baric RS, Enjuanes L, Gallagher T, McCray PB Jr, Perlman S (2014) Rapid generation of a mouse model for Middle East respiratory syndrome. Proc Natl Acad Sci U S A 111(13):4970–4975.  https://doi.org/10.1073/pnas.1323279111CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Yu P, Xu Y, Deng W, Bao L, Huang L, Xu Y, Yao Y, Qin C (2017) Comparative pathology of rhesus macaque and common marmoset animal models with Middle East respiratory syndrome coronavirus. PLoS One 12(2):e0172093.  https://doi.org/10.1371/journal.pone.0172093CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Dietert K, Gutbier B, Wienhold SM, Reppe K, Jiang X, Yao L, Chaput C, Naujoks J, Brack M, Kupke A, Peteranderl C, Becker S, von Lachner C, Baal N, Slevogt H, Hocke AC, Witzenrath M, Opitz B, Herold S, Hackstein H, Sander LE, Suttorp N, Gruber AD (2017) Spectrum of pathogen- and model-specific histopathologies in mouse models of acute pneumonia. PLoS One 12(11):e0188251.  https://doi.org/10.1371/journal.pone.0188251CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Falzarano D, de Wit E, Feldmann F, Rasmussen AL, Okumura A, Peng X, Thomas MJ, van Doremalen N, Haddock E, Nagy L, LaCasse R, Liu T, Zhu J, McLellan JS, Scott DP, Katze MG, Feldmann H, Munster VJ (2014) Infection with MERS-CoV causes lethal pneumonia in the common marmoset. PLoS Pathog 10(8):e1004250.  https://doi.org/10.1371/journal.ppat.1004250CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Alsaad KO, Hajeer AH, Al Balwi M, Al Moaiqel M, Al Oudah N, Al Ajlan A, AlJohani S, Alsolamy S, Gmati GE, Balkhy H, Al-Jahdali HH, Baharoon SA, Arabi YM (2018) Histopathology of Middle East respiratory syndrome coronovirus (MERS-CoV) infection—clinicopathological and ultrastructural study. Histopathology 72(3):516–524.  https://doi.org/10.1111/his.13379CrossRefGoogle Scholar
  49. 49.
    Fehr AR, Channappanavar R, Perlman S (2017) Middle East respiratory syndrome: emergence of a pathogenic human coronavirus. Annu Rev Med 68:387–399.  https://doi.org/10.1146/annurev-med-051215-031152CrossRefGoogle Scholar
  50. 50.
    Sweeney RM, McAuley DF (2016) Acute respiratory distress syndrome. Lancet 388(10058):2416–2430.  https://doi.org/10.1016/S0140-6736(16)00578-XCrossRefPubMedGoogle Scholar
  51. 51.
    Drew T, Vo ML, Wolfe JM (2013) The invisible gorilla strikes again: sustained inattentional blindness in expert observers. Psychol Sci 24(9):1848–1853.  https://doi.org/10.1177/0956797613479386CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Meyerholz DK, Beck AP (2018) Principles and approaches for reproducible scoring of tissue stains in research. Lab Investig 98(7):844–855.  https://doi.org/10.1038/s41374-018-0057-0CrossRefPubMedGoogle Scholar
  53. 53.
    Meyerholz DK, Beck AP (2019) Fundamental concepts for semiquantitative tissue scoring in translational research. ILAR J 59:13–17.  https://doi.org/10.1093/ilar/ily025CrossRefGoogle Scholar
  54. 54.
    Gibson-Corley KN, Olivier AK, Meyerholz DK (2013) Principles for valid histopathologic scoring in research. Vet Pathol 50(6):1007–1015.  https://doi.org/10.1177/0300985813485099CrossRefPubMedGoogle Scholar
  55. 55.
    Meyerholz DK, Beck AP, Goeken JA, Leidinger MR, Ofori-Amanfo GK, Brown HC, Businga TR, Stoltz DA, Reznikov LR, Flaherty HA (2018) Glycogen depletion can increase the specificity of mucin detection in airway tissues. BMC Res Notes 11(1):763.  https://doi.org/10.1186/s13104-018-3855-yCrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Vergara-Alert J, Vidal E, Bensaid A, Segales J (2017) Searching for animal models and potential target species for emerging pathogens: experience gained from Middle East respiratory syndrome (MERS) coronavirus. One Health 3:34–40.  https://doi.org/10.1016/j.onehlt.2017.03.001CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Haagmans BL, van den Brand JM, Provacia LB, Raj VS, Stittelaar KJ, Getu S, de Waal L, Bestebroer TM, van Amerongen G, Verjans GM, Fouchier RA, Smits SL, Kuiken T, Osterhaus AD (2015) Asymptomatic Middle East respiratory syndrome coronavirus infection in rabbits. J Virol 89(11):6131–6135.  https://doi.org/10.1128/JVI.00661-15CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Vergara-Alert J, van den Brand JM, Widagdo W, Munoz MT, Raj S, Schipper D, Solanes D, Cordon I, Bensaid A, Haagmans BL, Segales J (2017) Livestock susceptibility to infection with Middle East respiratory syndrome coronavirus. Emerg Infect Dis 23(2):232–240.  https://doi.org/10.3201/eid2302.161239CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Widagdo W, Raj VS, Schipper D, Kolijn K, van Leenders G, Bosch BJ, Bensaid A, Segales J, Baumgartner W, Osterhaus A, Koopmans MP, van den Brand JMA, Haagmans BL (2016) Differential expression of the Middle East respiratory syndrome coronavirus receptor in the upper respiratory tracts of humans and dromedary camels. J Virol 90(9):4838–4842.  https://doi.org/10.1128/JVI.02994-15CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Janardhan KS, Jensen H, Clayton NP, Herbert RA (2018) Immunohistochemistry in investigative and toxicologic pathology. Toxicol Pathol 46(5):488–510.  https://doi.org/10.1177/0192623318776907CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Kim SW, Roh J, Park CS (2016) Immunohistochemistry for pathologists: protocols, pitfalls, and tips. J Pathol Transl Med 50(6):411–418.  https://doi.org/10.4132/jptm.2016.08.08CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Ramos-Vara JA, Miller MA (2014) When tissue antigens and antibodies get along: revisiting the technical aspects of immunohistochemistry-the red, brown, and blue technique. Vet Pathol 51(1):42–87.  https://doi.org/10.1177/0300985813505879CrossRefPubMedGoogle Scholar
  63. 63.
    Ward JM, Rehg JE (2014) Rodent immunohistochemistry: pitfalls and troubleshooting. Vet Pathol 51(1):88–101.  https://doi.org/10.1177/0300985813503571CrossRefPubMedGoogle Scholar
  64. 64.
    Aeffner F, Adissu HA, Boyle MC, Cardiff RD, Hagendorn E, Hoenerhoff MJ, Klopfleisch R, Newbigging S, Schaudien D, Turner O, Wilson K (2018) Digital microscopy, image analysis, and virtual slide repository. ILAR J 59:66–79.  https://doi.org/10.1093/ilar/ily007CrossRefPubMedGoogle Scholar
  65. 65.
    Barisoni L, Gimpel C, Kain R, Laurinavicius A, Bueno G, Zeng C, Liu Z, Schaefer F, Kretzler M, Holzman LB, Hewitt SM (2017) Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology. Clin Kidney J 10(2):176–187.  https://doi.org/10.1093/ckj/sfw129CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Colling R, Pitman H, Oien K, Rajpoot N, Macklin P, Snead D, Sackville T, Verrill C, Group CM-PAiHW (2019) Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol.  https://doi.org/10.1002/path.5310CrossRefGoogle Scholar
  67. 67.
    Niazi MKK, Parwani AV, Gurcan MN (2019) Digital pathology and artificial intelligence. Lancet Oncol 20(5):e253–e261.  https://doi.org/10.1016/S1470-2045(19)30154-8CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of PathologyUniversity of Iowa Carver College of MedicineIowa CityUSA
  2. 2.Department of PathologyAlbert Einstein College of MedicineBronxUSA

Personalised recommendations