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. 2021 Aug 16;16(8):e0256117. doi: 10.1371/journal.pone.0256117

Quantitative measurement of the histological features of alpha-1 antitrypsin deficiency-associated liver disease in biopsy specimens

George Marek 1,*, Amy Collinsworth 2, Chen Liu 3, Mark Brantly 1, Virginia Clark 4
Editor: Pavel Strnad5
PMCID: PMC8366994  PMID: 34398915

Abstract

Background

Pathological mutations in Alpha-1 Antitrypsin (AAT) protein cause retention of toxic polymers in the hepatocyte endoplasmic reticulum. The risk for cirrhosis in AAT deficiency is likely directly related to retention of these polymers within the liver. Polymers are classically identified on liver biopsy as inclusion bodies by periodic acid schiff staining after diastase treatment and immunohistochemistry. However, characterization of the polymer burden within a biopsy sample is limited to a semi-quantitative scale as described by a pathologist. Better methods to quantify polymer are needed to advance our understanding of pathogenesis of disease. Therefore, we developed a method to quantify polymer aggregation from standard histologic specimens. In addition, we sought to understand the relationship of polymer burden and other histologic findings to the presence of liver fibrosis.

Methods

Liver samples from a well-categorized AATD cohort were used to develop histo-morphometric tools to measure protein aggregation.

Results

Whole-slide morphometry reliably quantifies aggregates in AATD individuals. Despite very low levels of inclusions present (0–0.41%), accumulation of globules is not linear and is associated with higher fibrosis stages. Immunohistochemistry demonstrates that fibrosis is associated with polymer accumulation and not total AAT. A proportion of patients were found to be “heavy accumulators” with a polymer burden above the upper 25% of normal distribution. Males had significantly more liver inclusions and polymer than females. These measurements also highlight interrelated phenotypes of hepatocellular degeneration and autophagy in AATD liver disease.

Conclusion

Quantitative inclusion analysis measures AAT accumulation in liver biopsy specimens. Quantification of polymer may identify individuals at risk for progressive disease and candidates for therapeutic interventions. Furthermore, these methods may be useful for evaluating efficacy of drugs targeting accumulation of AAT.

Introduction

Alpha-1 antitrypsin deficiency (AATD) associated liver disease results from aggregates of polymerized alpha-1 antitrypsin. The most common disease variant is a single nucleotide missense mutation of glu-342-lys (PiZ) that causes improper folding and polymerization of AAT in the hepatocytes and unimpeded destruction of lung tissue during times of increased protease and inflammatory activity in the lung. The prevalence of homozygous individuals is approximately 1:3000 in the United States and over three million individuals are affected worldwide [1, 2]. Liver disease in AATD was originally described in 1969 as a phenomenon in infants who developed fulminant cirrhosis and abundant accumulation of intracellular inclusions of AAT glycoprotein that are Periodic Acid Schiff positive and diastase resistant (PAS-D) [3]. In the years since, it has become clear that adults also develop severe liver fibrosis caused by this “toxic gain of function” [4–6]. Retrospective analysis of liver transplant data has shown that decompensated liver disease requiring transplant is more frequent in adults than in children [7].

We recently showed that in a cross-section of non-cirrhotic adults with AATD, more than a third had clinically significant liver fibrosis, and fibrotic disease was strongly associated with PAS-D accumulation even when corrected for metabolic syndrome [8]. To establish this relationship, we developed a semi-quantitative grading score of PAS-D based on number of hepatocytes affected across a biopsy sample (none:0, rare: <5 cells, few: 5–20 cells, numerous: >20 cells). The goal of the scoring system was to establish a reproducible method for visual description of the polymer burden. In fact, the scoring system based on increased accumulation of AAT measured by PAS-D proved to be strongly associated with liver fibrosis. However, it likely underestimates the true polymer burden present within hepatocytes because not all polymerized AAT is captured by PAS-D staining. The contribution of the polymerized AAT that is outside of PAS-D aggregates to fibrosis is unknown. For these reasons and several other observations we made regarding PAS-D accumulation, more discriminative measurements of polymer burden are important. First, highly affected individuals appear to accumulate PAS-D globules in orders of magnitude greater than those with lower scores. Second, PAS-D aggregates were heterogenous in size and number, and the total area of the biopsy affected was variable. In a single biopsy sample, both small and large aggregates were present, and the distribution of globules was patchy, suggesting that hepatocytes accumulate AAT polymer differentially. Third, features of hepatocyte damage, inflammation, and steatosis were also associated with fibrosis, but their relationship with PAS-D accumulation requires further characterization.

To address these issues, we used baseline biopsy specimens from our previously described prospective clinical cohort of individuals with AATD to develop methods of quantitative analysis for PAS-D inclusions and to examine polymer specific immunochemistry of aggregated protein. Previous studies in the animal model of AATD have employed positive pixel counting and representative fields to quantify relative PAS-D accumulation in mice [9]. However, in humans inclusions are often confined to few cells that are dispersed throughout a specimen. Liver biopsy tissue from early disease is very limited in quantity, so quantification by molecular methods cannot be reliably achieved while also preserving enough tissue for histological analysis. We created a series of Macro based tools for measuring the area, size, and number of PAS-D inclusions in the entirety of a digitally scanned specimen. We also created methods for measuring immunohistochemistry of AAT protein. Using these tools, we show that AAT polymer accumulation in hepatocytes is associated with liver fibrosis and that quantitative inclusion analysis identifies individuals with cellular changes associated with accumulation of aggregated proteins. We postulate these individuals are at risk for progressive liver disease.

Materials and methods

Patient recruitment

Patients were recruited and enrolled in the study between November of 2013 and October 2016 by three mechanisms: AATD patient registries, AATD patient outreach events, and clinical practice at UF. Eligible and unique individuals in two different patient registries (Alpha-1 Foundation DNA and Tissue Bank at the University of Florida and the Alpha-1 Foundation Research Registry, at the Medical University of South Carolina), were sent a letter describing the study and invited to participate. These national research registries are a confidential databases established with the purpose to promote research initiatives in AATD. It is estimated that over 2,500 (PI*ZZ or PI*SZ) individuals participate in the national registry. In this respect the patients are expected to be representative of the known North American adult population with PiZZ AATD. Inclusion criteria included ZZ individuals ages 21–71, from the US or Canada, who were willing to consent to a liver biopsy. Decompensated liver disease (ascites, variceal bleeding, hepatic encephalopathy, hepatocellular carcinoma) or previous liver or lung transplant were exclusion criteria.

Human subjects

In this study, we examined percutaneous liver biopsy samples from individuals with PiZZ AATD who are enrolled in a single center prospective cohort study [8]. Liver pathologists examined histopathology at the University of Florida. PAS-D accumulation was scored as P0 (no cells) with inclusions P1(<5 cells/biopsy) P2 (5–20 Cells/biopsy) or P3 (>20 cells). The University of Florida IRB approved all human subjects research in this study. The protocol was listed at clinicaltrials.gov (identifier NCT01810458) before recruitment. Additionally, PAS-D slides were examined from five explanted cirrhotic liver specimens from the Alpha-1 DNA and Tissue Bank. These specimens were fully de-identified and only specimen number and tissue diagnosis were known. Patients who participated in the DNA and tissue bank gave written informed consent for the use of explanted tissues. Study data were collected and managed using REDCap electronic data capture tools hosted at the University of Florida [10].

Tissue preparation and staining

We obtained biopsy samples using a 16 gauge BioPince full-core biopsy needle, the samples were immediately fixed in 10% formalin and transferred to UF Health Clinical Pathology Laboratories for paraffin embedding, H&E, PAS-D, PAS, and AAT immunohistochemistry (IHC) staining. The UF Molecular Pathology Core performed all other IHC. Supporting information contains additional details.

Whole slide imaging

Whole slide images were obtained using an Aperio CS2 slide scanner 20x/0.75 NA Plan Apo objective. 200x fields were scanned and processed into SVS formatted whole slide pyramidal tiff images. SVS formatted images were read and analyzed directly using ImageJ (FIJI build) [11–13].

Quantification of PAS-D inclusions

We quantified PAS-D stained 4 um liver sections using whole-slide images using tools in Image J (FIJI) distribution with its built-in image processing plugins [14–16]. Specimens smaller than 12mm or with fewer than 10 portal tracts were not included in the analysis. Results represent data from n = 89 individuals. The first steps of the analysis select and measure the biopsy elements from background so that our measurements are normalized to the actual area of the tissue present on a slide. Then the specimens were background subtracted and subjected to thresholding, color deconvolution, binary pixel separation, and particle analysis. To compare computer imaging measurement to human observers, we used a random number generator to select 10 biopsies for manual measurements by two observers. Each observer measured individual globules by circular region of interest selection in the whole slide image at 200x. Proportionate area measurements were then compared with computerized algorithms with optimized cut-offs for size and circularity. See Supporting information for the full macro and more detailed methodology.

Quantification of polymer and total AAT area

We used whole slide images of biopsies from the same cohort, stained with either total or polymer specific IHC against AAT. We measured Polymer specific AAT with a novel polymer specific monoclonal antibody which was previously created and described by Miranda et al. [17]. Similarly to the PAS-D images measurements, we normalized to the area of each biopsy. The Hematoxylin and Diaminobenzidine chromogen were separated using color deconvolution, and then positive pixel counts of Diaminobenzidine were measured as a proportion of total biopsy area. Importantly, these are estimates of histological area encumbered by polymer and not absolute quantity because chromogenic substrates and density of stain are not strictly stoichiometric with respect to protein quantity [18]. Further details including antibodies and dilutions are in the Supporting information.

Analysis of microtubule associated protein light chain 3 beta (LC3B) immunohistochemistry and Annexin V Foci

20 subjects (10M, 10F) were selected for IHC analysis of LC3B and Annexin V. 10 subjects had evidence of hepatocellular degeneration as scored by a liver pathologist and 10 did not. Annexin V foci were counted in whole slide images and normalized to the area of the biopsy in mm2.

Statistical analysis

Statistical analyses were performed using Graphpad Prism version 6.1. Gaussian data is presented as mean and SD and bivariate comparisons were performed using 2-sample t-tests. Non-Gaussian data as determined by D’Agostino- Pearson test is presented as median and interquartile range. Logarithmically distributed data for PAS-D globules were y = log10(y) transformed and analyzed with one-way ANOVA with Tukey’s Multiple comparison’s test. Mann-Whitney U testing was used to make two group comparisons. Lins Concordance Correlation Coefficient was computed to determine the accuracy of computer algorithms compared to average hand measured globules by human observers.

Results

Clinical cohort

The cohort consisted of 94 subjects homozygous for the PiZ allele, 33 (35%) were male, and the median age of the subjects was 57. Histopathological characteristics and clinical correlates related to fibrosis for this cohort have been previously reported (8). 33 (35%) subjects had a METAVIR stage of F2 or greater. 89 (94.6%) individuals had PAS-D inclusions by pathological scoring and 93 (98.9%) had positive IHC for total AAT staining. Selected baseline characteristics are reported in Table 1. PAS-D slides from explanted cirrhotic PiZZ specimens were also examined for the purpose of measuring globules in the upper extent of disease, but no further clinical data were available from these specimens for comparison.

Table 1. Selected baseline characteristics of the study population.

Baseline n = 94
Age—median(IQR) 57(52–63)
Sex M(%), F(%) 33(35), 61(65)
METAVIR Score (0–3) 26 / 35 / 27 / 6
Fibroscan Median (kPa) 6.4±2.8
ALT (U/L) 26±19
AST (U/L) 27±13
GGT (U/L) 40±78
PT (s) 14±0.78
INR 1±0.084
PTT (s) 31±2.7
Platelets (1000/mm3) 224±59
FVC% predicted 88±19
FEV1% predicted 63±28
FEV1/FVC 70±21
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ALT: Alanine aminotransferase, AST: Aspartate aminotransferase, GGT: Gamma glutamyl transferase, PT: Prothrombin time, INR: International Normalized Ratio, PTT: Partial thromboplastin time, FVC%: forced vital capacity % of predicted, FEV1%: forced expiratory volume % of predicted.

PAS-D area is associated with liver damage

Quantitative inclusion analysis of biopsy specimens distinguishes globules from connective tissue and other PAS staining in biopsy slides (Fig 1A, S1 Fig). We tuned algorithms to determine which most accurately measures the % biopsy area occupied by inclusions. We found that size and number of globules per square millimeter was also correlated with liver disease. The median globule size for individuals was 10.31 um2 (IQR 8.6–12.3) and the average size ranged from 6.2 to 41.2 um2 though some individual globules were >100 um2. Median globule number was 8.8 per mm2 (IQR 2.2–21.1) with a range from zero globules to as many as 201 per mm2. The % of total tissue occupied by globules provided the clearest way to account for differences in biopsy size and was most directly related to liver damage in the specimens.

Fig 1. Whole slide inclusion analysis defines the exponential nature of AAT accumulation as globules and its relationship with fibrosis.

Fig 1

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(A). Stepwise representation of quantitative analysis of PAS-D inclusions 200 um scale bar (B) Selection of algorithm, points represent % area of a single biopsy occupied by inclusions measured by computer (C) or human observers (1, 2) bars represent median and IQR points represent % area measured from whole slide images. (C) Comparison between pathologist-based qualitative staging and quantitative analysis as median PAS-D % area and IQR (D) PAS-D % area compared to fibrosis stages bars represent median and IQR statistical testing via Kruskal-Wallis test with Dunn’s multiple comparisons (* p<0.05, **P<0.01, ***p<0.001, ****p<0.0001) points represent % area measurements from individual subjects via whole slide images. F4 individuals are explanted cirrhotic liver from the Alpha 1 DNA and tissue bank Y- scale is Log10 (E) Separation of population based on quartiles and outliers (outliers defined as inclusion % area greater than or equal to Q3+1.5*IQR).

The computer method was accurate in estimating globule area. We tested this by hand, measuring the biopsy and individual globules across 10 entire biopsy specimens using ROI measurement tools. We then tuned the object size and circularity parameters of our macro to develop a method which approximated human operators. We arrived at 5 algorithms that approximated the median area of accumulation. We tested these with Lins correlation coefficient and found that the coefficient between algorithm 4 and manual measurement was 0.93 (95% CI 0.75–0.99, p<0.0001). We then used this to examine all of the specimens (Fig 1B).

Inclusions represent a small percentage of the biopsy area but have significant associations with liver pathology. PAS-D measurements ranged from 0% (n = 1) to 0.41% of the biopsy area, the median % accumulation was 0.012% but varied widely with an IQR = 0.002–0.032%. Pathologist scoring of globules organizes accumulation into P0 (None), P1(<5 cells/biopsy), P2 (5–20 cells), or P3 (>20 cells). Our quantitative analysis agrees with increases in globules across pathological scores, but shows that the relationship between score and globule area is not strictly linear (Fig 1C). As predicted, accumulation increased with METAVIR stage, the median and interquartile range of PAS-D % area measurements were F0: 0.004% (0.0012–0.069), F1: 0.011% (0.003–0.2), F2: 0.019% (0.001–0.033), and F3: 0.056% (0.015–0.11). In explanted cirrhotic tissue F4, the median PAS-D % area measurement was 0.28% (0.21–0.73). There was a 2.75-fold change between no fibrosis and F1, a 1.72-fold change between F1 and F2, and a 2.95-fold change between F2 and F3 as shown in (Fig 1D). The data is presented on a log scale to emphasize the variability in accumulation between individuals at each fibrosis stage. The data was tested for normality using D’Agostino-Pearson testing, and it was not normally distributed. Kruskal-Wallis Testing showed significant difference between median number of globules across fibrosis stages Kruskal-Wallis statistic was 27.64 and the p-value was <0.0001 post-Hoc analysis with Dunn’s multiple comparisons test showed significant increases in PAS-D % area between F0vF2, F0vF3, and F1vF3. Comparisons between the biopsy tissue and explanted cirrhotic tissue were significant for F0vF4, F1vF4, and F2vF4, but not F3vF4. When evaluating PAS-D % area without regard to fibrosis score, a group of PiZZ individuals were identified as “high accumulators” and are in the upper quartile or outliers with accumulation that is >1.5x the interquartile range of the population (Fig 1E). We hypothesize that these individuals with the greatest accumulation are at risk for progressive liver fibrosis, which can only be shown over time.

Polymer accumulation and liver fibrosis in AATD

Although PAS-D inclusions are classic histological findings in AATD, AAT also accumulates in the liver as polymers that are not part of PAS-D inclusion bodies. The polymeric AAT can be detected using monoclonal antibodies raised against AAT polymer as described previously (17). The other stainable fraction of AAT is the total fraction which includes pas-d globules polymers and non-polymeric AAT, we refer to this as total AAT. The relationship between polymer and globules has not been previously examined in non-cirrhotic individuals with AATD. We hypothesized that quantitative histological analysis of these polymers could also identify individuals with increased risk of liver fibrosis. We evaluated IHC slides from the same biopsies using a positive pixel counting algorithm to calculate the % area positive for total and polymer specific immunohistochemistry (Fig 2A and 2B). We examined polymer and total AAT in the same subjects as PAS-D. Polymer IHC could not be performed on 4 subjects in the original cohort and was not possible in explanted cirrhotic tissue due to technical issues with specimens collected and preserved with different methods and years apart. The median area occupied by total AAT staining is 19% but varied between individuals (0.41–56%). Reliable staining of total AAT was difficult because specimens had large amounts of AAT total staining and positive edge artifacts were frequent. Seven specimens were not included in the analysis as they lacked size or had significant staining artifacts in recuts. Polymer % Area showed a median staining of 1.8% that was variable (IQR 0.067%-12%). Polymer area was increased with higher fibrosis stages. The groups were not normally distributed as determined by D’Agostino-Pearson testing. Kruskal-Wallis testing with Dunn’s multiple comparisons showed significant differences in polymer accumulation between fibrosis stages p<0.0002 and Kruskal Wallis statistic was 19.29, post-hoc analysis was then performed F0 had significant differences with F2 and F3, F1 was different than F3. However, Total IHC measurements had no statistically significant relationship with fibrosis (Fig 2C and 2D). Polymer area also identified 5 of the 8 individuals who were outliers in PAS-D analysis. Although three individuals did not have discernable PAS-D globules, all subjects had Polymer detected by immunohistochemistry.

Fig 2. Polymer IHC % area is a complementary measure of AAT accumulation.

Fig 2

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(A) Semi-serial sections of PAS-D, Total and Polymer AATD with IgG control (B) Representation of IHC threshold-based quantification of AATD liver (C) (D) Quantification of total and polymer specific immunohistochemistry across METAVIR fibrosis stages Kruskal-Wallis testing with Dunn’s multiple comparisons *p<0.05, **p<0.01. ***p<0.0001 bars represent median and IQR, points represent measurement from individual whole slide images.

AAT accumulation is associated with male sex

In animal models of AATD, male sex is associated with increased AAT aggregation (17). Sex differences in AAT aggregation in humans has not been previously evaluated, so we sought to address this question. 32 males and 57 females had biopsies sufficient for analysis. AAT accumulation as measured by both median PAS-D% area and Polymer % area was higher in males. Median PAS-D % area in males was 0.0195% vs 0.0100% in females (p = 0.0167, Fig 3A). Polymer area was also higher in males than females (2.399% vs 1.96%, p = 0.0456) Fig 3B. Furthermore, the outlier group of “high accumulators” was evaluated by sex, and nearly all of them were male (87.5% n = 7) Fig 3C. This observation is interesting because male sex is reported as a clinical risk factor for cirrhosis (18). It can be hypothesized that higher accumulation of AAT in males provides some biological plausibility for the increased fibrosis risk.

Fig 3. Males are more affected by AAT accumulation.

Fig 3

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(A) Quantitative inclusion analysis in male and female subjects p-value calculated via Mann-Whitney U-test (B) Polymer % area in male and female subjects p-value calculated via Mann-Whitney U-test (C) Distribution of PAS-D inclusions between males and female distribution calculated from PAS-D % area and outliers are individuals with PAS-D % area greater than or equal to Quartile 3 + 1.5*IQR.

Hepatocellular degeneration in aggregate related liver disease

The presence of inflammation and hepatocytes with mild hydropic changes, abundant glycogen, and scattered macrophage activation termed “hepatocellular degeneration” are histological findings intertwined with fibrosis and AAT accumulation. (Fig 4A) shows how these characteristics affect groups of hepatocytes. When hepatocellular degeneration was present in the biopsy sample, the median fibrosis scores were increased and PAS-D accumulation was 7.5 fold higher (PAS-D% Area = 0.030 vs. 0.0040, p<0.0001) as shown in (Fig 4B and 4C). Polymer % area was 2 times greater (2.52 vs. 1.13, p = 0.0001) when hepatocellular degeneration was present (Fig 4D). Polymer was present in hepatocytes with early cytoplasmic clearing and signs of degeneration. However, some groups of hepatocytes had cleared cytoplasm and negative polymer staining (Fig 4E).

Fig 4. Hepatocyte degeneration is a predominant feature of AATD related polymer accumulation.

Fig 4

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(A) Hepatocyte degeneration is a predominant feature of AATD liver disease. H&E slides (Left panel) show partial cytoplasmic clearing with or without the presence of inclusion bodies, these cells are glycogenated as evidenced by PAS stain (middle panel) and some but not all degenerate hepatocytes have PAS-D AAT inclusions (right panel) (B) (C) (D) Degeneration is associated with higher median fibrosis scores Mann-Whitney U Test, p<0.05 considered significant. bars represent median and IQR n = 94 fibrosis, n = 89 PAS-D, Polymer % Area (E) Polymer staining was present in partially cleared hepatocytes but absent from cells with more severe degeneration. Scale bars = 200um.

We hypothesize that the observed hepatocyte clearing is connected to autophagy of cellular constituents. AATD has been previously associated with autophagic responses in humans, animal models, and cell culture systems [19]. In the PiZ animal model of AATD, boosting autophagy clears AAT inclusions [20, 21]. We used LC3B immunohistochemistry to highlight macro-autophagy in biopsy specimens from individuals with high (n = 10) and low (n = 10) AAT aggregation [22]. LC3 IHC was positive in the cytoplasm, nuclei, and in discrete puncta. AAT inclusion bodies displayed circumferential LC3B staining. Individuals with low accumulation of AAT had faint LC3B staining and a paucity of LC3B puncta. Hepatocytes with cleared cytoplasm and moderate to heavy polymer accumulation displayed abundant LC3 puncta (Fig 5A). Furthermore, we hypothesized that presence of degeneration could be associated with early apoptotic cell death. Annexin V staining was used as a surrogate marker for early apoptosis. Positive foci were more abundant in individuals with hepatocellular degeneration (0.90±0.32 vs 0.58±0.28 foci/mm2, p = 0.0366). However, these foci were not localized directly to hepatocytes with cytoplasmic clearing (Fig 5B).

Fig 5. Autophagy connects toxic gain of function to cellular degeneration.

Fig 5

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(A) Representative images from n = 20 PiZZ subjects comparing polymer accumulation, LC3B staining and hepatocyte degeneration. The top row is an inset of LC3B staining from the middle row (marked with a black box). AAT polymer staining in the same region is presented in the bottom row. individuals with moderate and high accumulation of AAT. Black Arrows represent puncta of LC3B in degenerate hepatocytes. Red Arrows represent staining of LC3B around AAT inclusions. (B) Representation and quantification of Annexin V Foci (Black Arrows) were more prominent in individuals with degeneration, Bars represent mean and standard deviation p-value is calculated from student’s t-test.

Discussion and conclusions

AATD liver disease is an occult phenomenon with the potential for progression to liver cirrhosis. The driving force for liver injury is the retention and accumulation of AAT within hepatocytes, which can be semi-quantitatively measured by PAS-D staining of liver tissue. More precise quantification of polymerized AAT is possible with molecular methods, but these require tissue destruction and are not standardized because obtaining adequate liver tissue in a rare disease population such as AATD is difficult. In this work, we describe a describe a method for quantification of AATD globules in liver biopsy specimens using an imaging technique that can be applied easily applied to a clinically obtained specimen. The technique described here allows for precise characterization of polymer accumulation and the relationship with fibrosis. Specifically we showed there are statistically significant differences in PAS-D globules and Polymer IHC between people with lower fibrosis score and people with higher fibrosis score. There were not differences in total AAT Immunohistochemical staining through different fibrosis stages.

Our characterization of early liver disease in AATD showed that there is significant heterogeneity in accumulation of AATD globules and polymer despite a homogenous mutational background. The quantitative analysis highlights that globules occupy only a very small portion (<0.5%) of a given liver sample despite all individuals having PiZZ AATD. As expected, we showed that PAS-D aggregates are related to liver fibrosis. Importantly, the more precise method for quantification with PAS-D% area demonstrated that the relationship between fibrosis stages and PAS-D globules is logarithmic rather than strictly linear. Furthermore, there are significant differences between accumulation of polymer measured by PAS-D % area between the lowest and highest stages of fibrosis. However, high accumulation of polymer was also seen at low fibrosis stages, so it correlates poorly in early fibrosis. We hypothesize that these individuals with high accumulation and low fibrosis are early in the natural history of disease. An alternate explanation is other unknown factors prevent progression of fibrosis despite accumulation of AAT. This is a limitation of this cross-sectional sample and can only be addressed with long term follow up.

The accumulation of total-AAT staining in excess of polymer and PAS-D helps to show that AAT, in multiple forms, is retained in the liver. As anticipated, the % area of biopsy with polymer when measured by IHC (median 1.8%) was greater than when measured by PAS-D (median 0.012%) because the antibody stains AAT polymer within inclusions as well as the polymer outside of inclusions. Regardless, polymer and inclusions are both associated with fibrosis. If changes in polymer % area are sensitive to detect decreases in AAT production after therapeutic intervention remains to be determined. It is important to note that these data are based on histological data. Ideally enough tissue would be available for quantitative analysis though direct and destructive methods such as mass spectrometry. However, additional specimens for this purpose were not obtained due to safety and technical considerations in our cohort. Instead, our data are based on paraffin imbedded specimen commonly available with clinical biopsies. Thus, quantitative analysis could reasonably be performed on both recent or archived specimens from patients who have had liver biopsies to determine the extent of AAT aggregation.

In addition to aggregated protein, the phenotype of hepatocellular degeneration is closely related with fibrosis. Individuals with degeneration have more inclusions, polymer, and higher fibrosis stages. However, these damaged cells appear to have less polymer. Similar age-related phenotypes in the PiZ mouse model suggest that degenerate cells may be pre-malignant [23]. There is evidence of autophagic responses in humans, mice and in-vitro models of AATD. These studies show lysosomal degradation pathways are active, but endogenous autophagic activity is insufficient to prevent polymer aggregation [19, 24]. Indeed, the PiZ animal model has an autophagic response associated with fibrosis, but enhancing autophagy decreases accumulation of AAT and may prevent fibrosis [9, 20, 21, 25]. However, the functional consequences of the autophagic response in cells after they clear polymer have not been extensively studied. In this population, we observed signs of an active autophagic response that was also associated with hepatocyte damage. These data raise the question of whether or not long-term autophagic responses, or exogenously stimulated autophagy as a therapy will halt or drive liver disease related hepatocyte degeneration in afflicted individuals.

In this cohort, male sex was not an independent predictor of fibrosis after adjusting for presence of metabolic disease [8], although other studies have suggested males have increased risk for cirrhosis. In prior studies transgenic male animals have increased AAT accumulation that can be partially replicated in female mice supplemented with androgens [26]. Our data show that men and women both have significant accumulation of both polymer and inclusions, but overall men had more AAT aggregation as measured by PAS-D % area. Although there is a predilection for men to have more aggregates, AAT accumulation is variable in both sexes. Many women with AATD have both fibrosis and heavy accumulation. The biological causes of these differences have not been elucidated and need further investigation especially with regards to differences in response to targeted therapeutics (S1 Graphical abstract).

Advances in imaging and computer technology in the last decade allow us to glean quantitative insights from tissue specimens using pixel data encoded by the in-situ dynamics of an individual’s tissue [27]. In-vitro work shows that luminal motility in the endoplasmic reticulum is compromised in AATD [28]. There are certainly technical issues which may exist in the application of these methods to other sample groups. Specifically, the samples in our study represent a relatively controlled group of tissues in that they were all collected and processed in the same fashion. Artifacts and sample processing variances may complicate the direct application of these methods or require retuning of parameters to accommodate differences in technique.

Current therapeutic trials focus on inhibiting accumulation of AAT in liver by inducing autophagy or silencing expression of AAT before a patient develops decompensated cirrhosis. The methods we provide in this work are intended to advance the quantitative value of histological samples for research. These tools may also be useful for evaluating the efficacy of therapeutics aimed at reducing accumulation of AAT. We used these algorithms and a systematic analysis of histological characteristics to confirm previous suppositions about the dynamics of AAT and provide quantitative insight into how toxic gain of function may predict disease progression. However, it remains to be seen if high accumulators have longitudinal progression of disease. We may have additional insights into this important question as the study subject’s complete longitudinal follow-up.

Supporting information

S1 Text. Supporting methods.

(DOCX)

Click here for additional data file. (32.6KB, docx)
S1 Fig. Representative steps and processing of inclusion analysis.

(A) Positive pixel counting in PAS-D slides over-represents accumulation in humans (B) Representation of whole slide processing low resolution (C) Representation of steps involved in inclusion analysis.

(TIF)

Click here for additional data file. (10.2MB, tif)
S1 Data. Path data.

(XLSX)

Click here for additional data file. (19.7KB, xlsx)
S1 Graphical abstract

Top panel, whole slide imaging of PAS-D and polymer specific IHC, Middle panel, median PAS-D, Polymer and Total IHC shows the different fractions and quantitative analysis identifies specific individuals who has high and very high AAT accumulation. Bottom panel, High accumulators identified by inclusion analysis may be at higher risk of progressive disease and theoretically may benefit from future interventions.

(TIF)

Click here for additional data file. (1.2MB, tif)

Acknowledgments

The UF Molecular Pathology Core provided histological services and consultation on methods development and slide scanning.

We are grateful to William Dunn, PhD his help on autophagy and histological analysis of hepatocellular degeneration.

Data Availability

Data analyzed in the publication are available in included spreadsheet. Specific algorithms used to analyze whole slide images are included in the Supporting Information files.

Funding Statement

Alpha 1 Foundation Professoship (MB), NIH NCATS University of Florida CTSA award L1TR001427 (VC, MB). The funders had no role in study design, data collection and analysis, decision to publish, or reparation of the manuscript. Dr. Collinsworth is currently affiliated with Advanced Pathology Solutions however she was faculty at University of Florida during the work included in this project. Advanced Pathology Solutions was not involved in the funding, design or execution of the work presented and did not provide relevant salary or other support to Dr. Collinsworth or any members of the research team during her work on this project.

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Decision Letter 0

Pavel Strnad

18 Feb 2021

PONE-D-21-03416

Quantitative measurement of the histological features of Alpha-1 Antitrypsin Deficiency-associated liver disease in biopsy specimens

PLOS ONE

Dear Dr. Marek,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

While both reviewers agreed that this is a carefully performed study, several issues were raised that should be addressed prior to a publication.

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Pavel Strnad

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PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: In their study Marek et al. developed a method to quantify polymer aggregation in alpha-1 antitrypsin deficiency from standard histological specimens to improve our understanding of disease pathogenesis. In addition, they sought to understand the relationship between polymer burden and the presence of liver fibrosis, and evaluated gender differences.

The authors used 94 PiZZ individuals of their well-characterized AATD cohort to develop histo-morphometric tools for the quantitative measurement of PAS-D inclusions and examined polymer specific immunhistochemistry of aggregated protein. These tools demonstrated that the relationship between fibrosis stages and PAS-D globules appears to be logarithmic rather than linear and the area of biopsy occupied by PAS-D globules increases with fibrosis stage. IHC staining showed that liver fibrosis is associated with polymer accumulation and not total AAT.

Since there are only semi-quantitative grading scores and promising new therapeutic options are currently uprising, the results of this study are of great interest. Nevertheless, there are a few weaknesses, that diminish the value of the presented data:

1. As artifacts and processing deviations may confound and complicate this technique, the transferability to samples collected in a slightly different fashion seems to be complicated.

Moreover, the biological role and significance of histological techniques should not be overestimated. Nevertheless, the sample size is of course impressive given that AATD belongs to the rare diseases.

2. Are there already any longitudinal data (any decompensated liver disease etc.) available on the examined individuals to estimate the prognostic importance?

3. Next to male sex, there are other risk factors whose presence is already known to contribute to the development of liver disease, such as obesity, higher age, or the presence of diabetes mellitus. Are there also differences in AAT accumulation when comparing subgroups of the factors mentioned?

Minor comments:

- Page 19, line 374 ff. “The finding of PAS-D aggregates in an individual with AATD who has early liver fibrosis indicates a of toxic gain of function that is meaningful.” – remove “of”

- Page 21, line 436 “figure 6, graphical summary”- somehow I cannot find figure 6…..

- Figure 2A: “Polymer(2C1)” – reduce the rear bracket

Reviewer #2: The manuscript by Marek et.al. entitled “Quantitative measurement of the histological features of Alpha-1 Antitrypsin Deficiency-associated liver disease in biopsy specimens” is a descriptive paper of alpha-1 antitrypsin polymers designed to enhance our understanding of this previously sidelined aspect of AATD associated liver disease. The paper can be improved by addressing the following issues:

Major

1. I was impressed with the very low (0-0.41%) “levels” of inclusions. It seems like the more specific measurement of the denominator in mm2 or other specific measurement metric would make this a more universally accepted method.

2. The introduction on line 96-98 discusses the heterogeneity of PAS-D globules that suggested that hepatocytes accumulate AAT polymer differentially. This is not known and you should prove this in the current paper.

3. In the introduction lines 100-101 you note that “PAS-D aggregates represent only a fraction of the total AAT polymer present in hepatocytes”. I believe the more accurate statement is that PAS-D aggregates are made up of both non-polymeric and polymeric forms of retained AAT. Therefore, this statement needs correction.

4. Line 152. It is not at all clear what is meant by “measure the specimens”. Does this mean that analyzed areas were normalized for the slide area visualized? If so, were the scoring systems for similarly normalized when counting PAS-D inclusions?

5. I would assume that more than one slide per patient were used to minimize random effects? Was this done? How many slides, how many fields?

6. Why would you normalize Annexin V to mm2, but the other analyses are left to counts on a 200x field? (Line 180)

7. I am getting lost in the analysis numbers. Were all 94 samples subjected to globule analysis but only 20 were chosen for IHC of polymer?

8. “The variability of accumulation… was exponential” suggests that analysis should be done using a log transformation of the data for all the analyses. I do not see that this was done.

9. Concordance is usually measured using a Kappa statistic for categorical values or Lins concordance correlation (or others) for continuous data. Which was used? Put it in the methods. Confidence intervals would then denote whether there was difference between computer and human observers.

10. Just when I think I have a handle on your data, you throw in data from explanted cirrhotic F4 tissue in line 235. Where did this come from? Do you want to go back and put this in the methods and do some comparisons?

11. When I look at the graphs for PAS-D% between FO and F2, it looks like the data is normally distributed. I will need a test for normality to fail before I would allow a Kruskal-Wallis with post hoc Dunn’s to be used. The alternative explanation is that PAS-D% is poorly correlated with early stage measures of fibrosis until F3 and F4 are encountered. This is clearly an important point since in the discussion (line 387), this is one of your major conclusions. I disagree with this conclusion after looking at your data. The alternative is that a small subset of individuals have a very high PAS-D burden associated with fibrosis.

12. Line 387 suggests you have moved from association analyses to cause and effect thinking. You can speculate, but not conclude such.

13. Figure 1 graph E adds no value in my opinion. Please justify what this adds.

14. As I read your numbers, the attempt to quantify polymer was in 20 patients and in 7 patients this was not possible for technical reasons. Then the following is quoted “Polymer area was increased with higher fibrosis stages. However, Total IHC measurements had no statistically significant relationship with fibrosis”. Just say that you could not find a relationship between polymer numbers and fibrosis if you cannot prove it statistically.

Minor

1. Abstract line 54 remove “a”

2. Introduction: line 65 ”Alpha-1 Antitrypsin Deficiency” should not be capitalized. This is a disease state similar to diabetes, coronary artery disease, etc.

3. Introduction Line 66: Alpha-1 antitrypsin is a protein and should not be capitalized similar to elastin, collagen, interleukin-6, etc.

4. Line 78 …requiring transplant “is” more frequent in adults.

5. Line 85 and again on line 128. This scoring system requires that you state per high powered field and the magnification at which the slide was stained.

6. Sentence 103 is awkward and should be reworded.

7. Sentence 110 should be broken into 2-3 sentences.

8. 141: You should write out immunohistochemistry (IHC) before using the abbreviation

9. Line 177: You should write out LC3B the first time of use.

10. Line 195: Do you mean a positive IHC for AAT polymer?

11. Table 1. FEV1/FVC does not have %. The primary ratio should be used.

12. Sentence 397 is awkward and should be rewritten.

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Reviewer #2: No

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PLoS One. 2021 Aug 16;16(8):e0256117. doi: 10.1371/journal.pone.0256117.r002

Author response to Decision Letter 0

30 Apr 2021

Dear Dr. Strnad and Review Committee,

Thank you for reviewing our work and offering us the opportunity to incorporate the comments and revisions requested by the review committee. Our paper entitled Quantitative measurement of the histological features of alpha-1 antitrypsin deficiency-associated liver disease in biopsy specimens uses whole slide imaging to study baseline histologic data from a large longitudinal cohort of people who have PiZZ alpha-1 antitrypsin deficiency. We demonstrate and provide new tools which can be used to estimate severity of alpha 1 antitrypsin accumulation in standard pathology specimens. We have addressed each reviewers major and minor revisions as below. We have also made editorial revisions as noted.

Editorial Revisions

1) We have addressed the style requirements and file naming for PLOS ONE

2) We have clarified this in human subjects section, see line 141

3) We have included an additional participant recruitment section which details this information, Table 1 provides basic demographic information for the population and more extensive details have been previously published in our original descriptive work [8]

4) Figure 6 (graphical summary) has been uploaded

5) We have updated the supplementary material as requested

6) With respect to Dr. Collinsworth’s affiliation with a commercial histopathology group:

Financial Interests Statement:

Dr. Collinsworth is currently affiliated with Advanced Pathology Solutions however she was faculty at University of Florida during the work included in this project. Advanced Pathology Solutions was not involved in the funding, design or execution of the work presented and did not provide relevant salary or other support to Dr. Collinsworth or any members of the research team during her work on this project.

Competing Interests Statement:

Dr. Amy Collinsworth is currently employed at Advanced Pathology Solutions. However, her work on this project was completed while faculty at the University of Florida. Advanced Pathology Solutions was not a part of the design or funding of this project and she has not been involved in work on this project since her transition to her new commercial affiliation. Dr. Collinsworth’s current affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials and Advanced Pathology Solutions has had no research interest in our work.

Reviewer #1

Reviewer #1 had specific major concerns related to the generalizability of our methods to wider use and was interested in longitudinal and comparisons of PAS-D accumulation with other known metabolic diseases, sex and aging.

1. We certainly agree that histological analysis should not be overestimated. One advantage of this method is that it was designed around a standard stain (PAS-D) which is used across many institutions and is standard practice for evaluating liver biopsies of most people who have had a liver biopsy should have a PAS-D slide archived. Even the path specimens in our cohort were not strictly without differences in hue and stain intensity which is why background subtraction tools, color deconvolution and binary operations are used.

2. We have some data available, including yearly non-invasive measurements which suggest association between worsening transaminases and liver stiffness measurements and AST to platelet ratio index which we plan to analyze together with other clinical markers together as part of forthcoming publications.

3. This is a very appropriate question, the answer is yes, but we have already published these relationships in our original paper discussing this same cohort and with our qualitative scores.

Minor comments:

- We made these changes as requested.

Reviewer #2

Reviewer #2 had several major revisions and comments. One important feature of these comments which needs to be clarified is the nature of our measurements. There are several questions discussing the data in terms of standard methods using high-powered fields. It is very important to note that we are providing a method to estimate globule area in whole slide images. This is extensively discussed in our methods and supplementary material and is essential to interpretation of our data. We have addressed this and additional concerns including requests for additional statistical analysis and details as below.

1. The data is presented as % area because we analyzed whole slides and globules have both size and number that contribute to how much of a biopsy is occupied.

2. We have added additional data about the size and number of globules including their confidence intervals which helps to quantify these differences. Additionally, our histologic polymer analysis shows very clearly that only a portion of any given individual’s liver is occupied by polymer and differences in hepatocellular accumulation of AAT are discussed with relation to hepatocyte degeneration and autophagy in results and discussion of this paper. Additionally, our original publication on the clinical characteristics also describes the zonal distribution of globules and has already been published.

3. With respect to the relationship between globules and polymer, the original statement is correct and is supported by our data showing polymer staining occupies a larger proportion of the biopsies than PAS-D aggregates alone. PAS-D aggregates are large aggregates of polymer, but not all polymer is within a PAS-D aggregate. Total AAT antibodies stain both polymeric and non-polymeric AAT, and total AAT staining did not have a relationship with other aspects of liver pathology. See also Fig 6 which we added in our revised manuscript.

4. We added additional statements in the methods and supplement to help clarify this point. The method is based on a whole slide analysis, which means the whole slide is scanned initially, which includes background white space. Our analysis measures the area of the biopsy based on total pixels that are positive after background subtraction. The measurements of globules within this area are then normalized the total area of each biopsy so that the size of the biopsy ultimately does not impact the quantification by this method.

5. This is whole slide imaging, so every field on a biopsy is counted. This is a seminal feature of our work in that the quantification of PAS-D is not biased by the number or location where fields are chosen.

6. The counts are whole slide analysis so this data represents all the annexin foci in the entire biopsy and divided by the size of tissue in mm2 so it is not a representation of a number of fields of view.

7. Polymer IHC was examined on the same population as PAS-D. The individual point values are shown in figure 2. Four samples couldn’t be analyzed because of tissue constraints, and total staining has high background and artifacts, so 7 samples couldn’t be used for this analysis.

8. We did do these analyses as well. Log transformed data reduced the skew and created a normal distribution for running ANOVA but this did not change the overall conclusions of the data.

9. We developed the algorithms using lins statistic comparing the observers to five putative algorithms and chose the best from here. I have added this information to the methods and results.

10. The explanted specimens used are archived slides some of which come from early days of the DNA and tissue bank. Very few of these are available from explanted cirrhosis tissue and they patients are not well characterized. For our purposes we examined them to measure PAS-D to provide the upper extremes of disease because our cohort of well characterized biopsies excluded decompensated cirrhosis. We do not have further data to make comparisons with these specimens.

11. The data looks normally distributed because it is placed on a log scale to emphasize the nature of variability between individuals. The groups failed D’Aogstino-Pearson testing prior to Kruskal-Wallis testing. We agree with the alternative explanation that you suggest and have added additional emphasis on this. PAS-D does correlate poorly in early fibrosis because there are people with high and low PAS-D who have early fibrosis. However, the high accumulators are of interest to identify with our methods because we suspect that over time they have risk of worsening fibrosis. Our forthcoming longitudinal studies will help clarify this.

12. This is an appropriate criticism and we have edited our discussion to reflect these comments.

13. We analyzed slides to find people at the upper aspects of AAT accumulation regardless of fibrosis stage. Figure 1E is how we identified this group. We then use these groupings throughout the rest of the paper to make further comparisons with people who have high or low accumulation.

14. We used our tools to estimate polymer area in the whole population minus patients with limited tissues or tissue that wouldn’t be enough for a pathologist to make an appropriate reading. Figure 3 shows this point. Total IHC does not have a relationship with fibrosis but polymer does.

Minor Comments:

We have made the minor changes as requested.

In this manuscipt we present open source tools developed out of a large cohort of primary biopsy specimens from a rare disease population. We believe that they offer insight into the heterogeneity of disease and cellular damage in early liver fibrosis. We are grateful for the opportunity to share our work with the scientific community through PLOS ONE. We appreciate our reviewers thoughful questions and suggested revisions and have addressed them as above. Please do not hesitate to contact us with any further questions.

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Submitted filename: Response to Reviewers.pdf

Click here for additional data file. (234.1KB, pdf)

Decision Letter 1

Pavel Strnad

14 Jun 2021

PONE-D-21-03416R1

Quantitative measurement of the histological features of Alpha-1 Antitrypsin Deficiency-associated liver disease in biopsy specimens

PLOS ONE

Dear Dr. Marek,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

As you can see, one of the reviewers still raised substantial concerns about the interpretation of your data (i.e. a clear statement which parameters show a statistically significant association with fibrosis stage a which ones do not).

Please submit your revised manuscript by Jul 29 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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We look forward to receiving your revised manuscript.

Kind regards,

Pavel Strnad

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In their work the authors performed quantitative measurement of the histological features of alpha-1 antitrypsin deficiency in biopsy specimens and stated that the quantification of polymer may identify individuals at risk for progressive disease and candidates for therapeutic interventions.

As mentioned before, the presented data are of relevance, but nevertheless presented some weaknesses in their first manuscript draft. In the revised version of their work, the authors responded to the multiple comments and edited their discussion. They emphasized again the advantage of using a standard stain for their method that is widely available across many institutions. And as such it provides valuable results.

Reviewer #2: The manuscript by Marek et al. is improved. There are still some pretty important things that need correction before this is published.

Major

1) The comparisons between fibrosis stage and IHC measurements are not statistically significant. Therefore, how can you say that both polymer and inclusions are both associated with fibrosis?

These sentences should be removed from the abstract and from the discussion (Lines 104, 443).

2) Subgroup Kruskal-Wallis testing with Dunn’s multiple comparisons should not be done (Fig 2) unless the primary analysis is significant. Therefore, the “trend” toward association between polymer and fibrosis can be mentioned. More importantly, you can discuss the reason why you were not able to prove this association (unmeasurable biopsies that limited sample number, etc).

3) There is an opportunity, even with small patient numbers to create a table of results that might make your trend lines more believable. For instance, if your message is that there is a subset of PAD-D heavy accumulators that have more fibrosis, then define a threshold at which you make your division and show a table of age, race, sex, BMI, PAS-D scores, IHC scores, and fibrosis scoring with statistics comparing the 2 columns of data. If there are other things beside PAS-D stains and fibrosis that are different, then a multivariable analysis is indicated.

Minor

1) Table 1 FEV1/FVC is misspelled

2) Discussion- line 415. Please use English not parentheses to describe the alternative of PAS-D standardization and lack of validation.

3) Fig 4D has a Y axis that is flipped

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Aug 16;16(8):e0256117. doi: 10.1371/journal.pone.0256117.r004

Author response to Decision Letter 1

29 Jul 2021

Dear Dr. Strnad and Review Committee,

Thank you for reviewing our work and offering us the opportunity to incorporate the comments and revisions requested by the review committee. Our paper entitled Quantitative measurement of the histological features of alpha-1 antitrypsin deficiency-associated liver disease in biopsy specimens uses whole slide imaging to study baseline histologic data from a large longitudinal cohort of people who have PiZZ alpha-1 antitrypsin deficiency. We demonstrate and provide new tools which can be used to estimate severity of alpha 1 antitrypsin accumulation in standard pathology specimens. We have addressed each reviewers major and minor revisions as below. We have also made editorial revisions as noted.

Editorial Revisions

Reviewer #1

Reviewer #1 did not have further requests for revision.

Reviewer #2

Reviewer #2 had several major revisions and comments.

Reviewer #2:

1) The comparisons between fibrosis stage and IHC measurements are not statistically significant. Therefore, how can you say that both polymer and inclusions are both associated with fibrosis?

These sentences should be removed from the abstract and from the discussion (Lines 104, 443).

2) Subgroup Kruskal-Wallis testing with Dunn’s multiple comparisons should not be done (Fig 2) unless the primary analysis is significant. Therefore, the “trend” toward association between polymer and fibrosis can be mentioned. More importantly, you can discuss the reason why you were not able to prove this association (unmeasurable biopsies that limited sample number, etc).

We performed Dunn’s multiple comparisons test after the primary ANOVA showed differences in the median PAS-D accumulation (94) samples including cirrhosis samples and Polymer IHC measurements (84 samples). Both of these analysis showed significant differences and Post-Hoc analysis was performed at this point. I have attached a charts for these below, and the results are described in the manuscript, I added additional clarification of this in our results section. An important point that we described is that when you stain a biopsy with total AAT immunohistochemistry such that polymeric and non-polymeric AAT is stained and then measured, this is not statistically significant.

PAS-D Globule Measurements (Figure 2)

Table Analyzed PAS-D Area

Kruskal-Wallis test

P value <0.0001

Exact or approximate P value? Approximate

P value summary ****

Do the medians vary signif. (P < 0.05)? Yes

Number of groups 5

Kruskal-Wallis statistic 27.64

Data summary

Number of treatments (columns) 5

Number of values (total) 94

Polymeric AAT Immunohistochemistry (Figure 3)

Table Analyzed 2C1 Area

Kruskal-Wallis test

P value 0.0002

Exact or approximate P value? Approximate

P value summary ***

Do the medians vary signif. (P < 0.05)? Yes

Number of groups 4

Kruskal-Wallis statistic 19.29

Data summary

Number of treatments (columns) 4

Number of values (total) 84

3) There is an opportunity, even with small patient numbers to create a table of results that might make your trend lines more believable. For instance, if your message is that there is a subset of PAD-D heavy accumulators that have more fibrosis, then define a threshold at which you make your division and show a table of age, race, sex, BMI, PAS-D scores, IHC scores, and fibrosis scoring with statistics comparing the 2 columns of data. If there are other things beside PAS-D stains and fibrosis that are different, then a multivariable analysis is indicated.

This is a really good point and has been a big part of our research with this cohort already. We answered some of these questions when we performed multivariate analysis of AAT Accumulation in Clark et al. 2019 where we defined the clinical changes which are associated with higher accumulators on a qualitative scoring basis by our pathologists. This work was born out of that understanding. In figure 1C we compare these qualitative scores with our quantiative data. The “threshold” you speak of is something that is yet to be defined. The closest we are able to define in this group are outliers based on the statistical distribution of the data but it is unknown whether or not this will have lonigtudinal relevance. Our plan is to define this threshhold based on receiver-operator analysis on progression data as the cohort finishes follow-up such that we know who has developed worstening fibrosis and use these baseline data to determine a clinically relevant amount of accumulation that has sensitivity and specificity to predicting progression.

Minor

1) Fixed

2) Fixed

3) Fixed

Attachment

Submitted filename: Response to Reviewers.doc

Click here for additional data file. (94.5KB, doc)

Decision Letter 2

Pavel Strnad

2 Aug 2021

Quantitative measurement of the histological features of Alpha-1 Antitrypsin Deficiency-associated liver disease in biopsy specimens

PONE-D-21-03416R2

Dear Dr. Marek,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Pavel Strnad

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Acceptance letter

Pavel Strnad

6 Aug 2021

PONE-D-21-03416R2

Quantitative measurement of the histological features of alpha-1 antitrypsin deficiency-associated liver disease in biopsy specimens

Dear Dr. Marek:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Pavel Strnad

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Text. Supporting methods.

    (DOCX)

    Click here for additional data file. (32.6KB, docx)
    S1 Fig. Representative steps and processing of inclusion analysis.

    (A) Positive pixel counting in PAS-D slides over-represents accumulation in humans (B) Representation of whole slide processing low resolution (C) Representation of steps involved in inclusion analysis.

    (TIF)

    Click here for additional data file. (10.2MB, tif)
    S1 Data. Path data.

    (XLSX)

    Click here for additional data file. (19.7KB, xlsx)
    S1 Graphical abstract

    Top panel, whole slide imaging of PAS-D and polymer specific IHC, Middle panel, median PAS-D, Polymer and Total IHC shows the different fractions and quantitative analysis identifies specific individuals who has high and very high AAT accumulation. Bottom panel, High accumulators identified by inclusion analysis may be at higher risk of progressive disease and theoretically may benefit from future interventions.

    (TIF)

    Click here for additional data file. (1.2MB, tif)
    Attachment

    Submitted filename: Response to Reviewers.pdf

    Click here for additional data file. (234.1KB, pdf)
    Attachment

    Submitted filename: Response to Reviewers.doc

    Click here for additional data file. (94.5KB, doc)

    Data Availability Statement

    Data analyzed in the publication are available in included spreadsheet. Specific algorithms used to analyze whole slide images are included in the Supporting Information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

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