Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression

Domain: neuroscience
Published: 2026-02-11
Editorial outcome: minor_revision

Abstract
Type 1 diabetes (T1D) is a progressive autoimmune condition that culminates in loss of insulin-producing beta cells. Pancreatic histopathology provides essential insights into disease initiation/progression yet an integrated perspective onto in situ pathogenic processes is lacking. Here, we combined multiplexed immunostaining, high-magnification whole-slide imaging, digital pathology, and semi-automated image analyses to interrogate pancreatic tail and head sections across T1D stages, including at-risk and at-onset cases. Deconvolution of architectural features, endocrine cell composition, immune cell burden, and spatial relations of ~25,000 islets effectively contextualizes previously established and additional pancreatic hallmarks in health and T1D. Our results reveal a spatially homogenous and islet size-contingent architectural organization of the endocrine pancreas, a notable coordination of organ-wide pathogenic processes, and multiple histopathological correlates that foreshadow distinctive T1D histopathology already at the preclinical stage. Altogether, we propose a revised natural history of T1D with implications for further histopathological investigations and considerations of pathogenetic modalities.

Introduction
Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by the destruction of insulin-producing beta cells in pancreatic islets1, 2. An interplay of genetic susceptibility3, environmental factors4, and immune dysregulation5 promotes multiple pathological alterations including pancreatic inflammation, islet-associated immune cell infiltration (insulitis), exocrine abnormalities, and systemic complications due to overt hyperglycemia6, 7, 8. Clinically, T1D progresses through three major stages: emergence of ≥2 T1D-associated islet autoantibodies but maintenance of normoglycemia (stage 1); additional development of dysglycemia (stage 2); and eventual clinical disease (stage 3)9.

Early histological interrogations of the pancreas established T1D as an autoimmune disease10 and defined pathological hallmarks11, yet extensive in situ studies have mostly been limited to the past ~15 years following the establishment of specialized tissue repositories such as the Network for Pancreatic Organ donors with Diabetes (nPOD) program12. Nevertheless, estimates suggest that <700 T1D donor pancreata are available for research globally13, a limited resource that is further compounded by substantial heterogeneity at the level of organ donors, pancreas anatomy (e.g., organ region, lobular insulitis), islet properties (e.g., architecture, endocrine composition, association with immune cells), and altered endocrine function beyond beta cells (e.g., alpha cells14, 15). Pancreata from donors at high risk (stage 1/2, elevated HLA class-II risk) and at-onset of symptomatic T1D, critical for pathogenesis studies, are exceptionally scarce.

In order to contend with these challenges, emerging investigative techniques such as whole-slide imaging11, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, high-dimensional tissue analysis26, 28, 29, 30, 31, 32, 33, 34, and three-dimensional (3D) visualization of immuno-stained thick pancreas slices35, 36, 37 have been deployed in conjunction with tailored digital analysis software. However, to date no standardized data analytics platform dedicated to pancreatic histopathology is readily accessible. Commercial platforms featuring “pancreas modules” and machine learning algorithms have aided the identification of pancreas and islet features19, 21, 26, 31, 38, 39, 40, but their proprietary nature restricts customization.

To our knowledge, a broad integration of the complementary strands of histopathological T1D pancreas investigation and digital pathology modalities12, 41 has not yet been performed. To this extent, we combined higher dimensional multiplex brightfield immunohistochemistry (IHC), whole-slide image acquisition at high magnification, and a semi-automated analysis pipeline implemented in QuPath, a versatile open-source digital pathology platform42, 43 that has been employed in recent T1D studies primarily focused on immune cell analyses22, 23, 24, 25, 44. Our results reveal a spatially homogenous and islet size-contingent architectural organization of the endocrine pancreas; they demonstrate a notable coordination of tissue-wide pathogenic processes characterized by alterations of islet properties independent of associated immune cell burden; and they document the presence of distinct histopathological disease correlates already at preclinical stage 1/2 T1D. Accordingly, we here propose a revised natural history of T1D as a chronic-progressive condition that readily contextualizes established hallmarks and additional observations of T1D pathology.

Results
Multiplexed IHC analysis of human pancreatic tissue sections

The detailed histopathological interrogation of healthy and diabetic human pancreatic sections remains a cornerstone of investigations into T1D pathogenesis12. To leverage recent advances in tissue imaging, we employed the Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS) platform45, 46, an iterative IHC assay that permits successive brightfield visualization of immunostaining patterns at high magnification (40x) across whole slides (Fig.1a). Previous MICSSS applications were largely restricted to characterization of CD45+ hematopoietic cells47, 48, 49, and our adaptation for the visualization of eight pancreatic endocrine hormones in addition to CD45, in part due to larger intracellular staining areas, required adjustments to signal amplification, destaining/blocking, and a deliberate determination of staining order since our targets of interest exhibited differential sensitivity to deterioration over repeated tissue processing steps (Figs.1b & S1a).

Development of a semi-automated image analysis pipeline

We used machine learning to analyze pancreatic whole-slide images and customized a series of specific tasks in the open-source digital pathology software QuPath42. We first captured total tissue specimen areas and excluded surrounding connective tissue and fat to demarcate parenchymal (exocrine and endocrine) areas (Fig.1c). Alignment of nine immuno-stained images per tissue section was performed using affine transformation and achieved cell-level accuracy. Pancreatic islets, defined here as endocrine cell clusters ≥1,000 μm2 (~36 μm diameter), were delineated by merging the areas of six selected hormone stains with residual non-endocrine areas to create contiguous objects (Fig.1d and Methods). Geometric and spatial properties of islet outlines were quantified to allow for a basic description of islet architecture and derivation of shape descriptors (Figs.1e & S1b) some of which reportedly change28, 50, 51 or not52 with T1D progression.

To delimit hormone staining areas within each islet, islet outlines were superimposed onto each image and a pixel classifier was used to segment positive staining areas. Given the complete lack of background signal and heterogeneous pattern of proinsulin (ProINS) expression as observed previously (high: Golgi/immature INS granules; low: cytoplasm/ER)53, 54, we quantified islet areas of both high and total ProINS content (Fig.1f). Representative images for other hormone area captures are shown in Fig.S1c. However, due to creeping introduction of some background signal over successive staining rounds, we refrained from distinguishing areas of low/high expression for all other hormones thus permitting proper demarcation of mutually exclusive staining areas (Fig.1g). The segmented regions from each hormone stain were further used to calculate total endocrine areas (i.e., union of eight hormone stains) and Jaccard indices, a statistic that quantifies the overlap of brightfield staining areas and corroborates the accuracy of our image alignment/segmentation strategy (Fig.S1d). Lastly, we incorporated the StarDist algorithm55 and an object classifier into our analysis pipeline to capture CD45+ immune cells within islets and the surrounding peri-islet areas (Figs.1g & S1c).

The importance of accounting for pancreatic polypeptide (PPY) expression, largely segregated to the uncinate process of the pancreatic head (PH)56, 57, 58, is illustrated by our observation that some PPY+ islets lack alpha and beta cells even in non-diabetic donors (Figs.1h & S1e); thus, any consideration of insulin-deficient islets (IDIs) in the PH needs to include PPY analyses. Altogether, automation of multiple image analysis tasks in combination with multiplexed whole-slide image acquisition offers unique opportunities to revisit pancreatic histology in health and T1D disease at scale.

Pancreas donors and cumulative endocrine contents across T1D stages

To interrogate the histopathological evolution of T1D, we performed MICSSS staining of pancreatic tail (PT) and PH sections from four donor groups: non-diabetic controls (Ctrl), autoantibody-positive stage 1/2 T1D (AAb), short duration stage 3 T1D (T1DS; <2 years including three at-onset donors), and longer duration T1D (T1DL; 8-11 years) (Tables 1 & Supplementary Data 1). Donor matching was performed on age and gender and, where possible, demographic (ethnicity) and clinical (body mass index/BMI) parameters; several outliers in our cohorts are described in Supplementary Data 1. HLA-II risk59, 60, 61, 62 was calculated to be low for Ctrl persons donors and similarly elevated for AAb, T1DS and T1DL donors (Supplementary Data 1 & Fig.S2a). Donor age, BMI, and total pancreas weight as well as other clinical parameters displayed significant positive correlations as expected63 (Fig.S2b/c).

Consistent with the T1D-associated loss of pancreas mass63, 64, relative pancreas weights (RPW) tend to decline in T1DS donors, with a more pronounced reduction in our T1DL cohort (Fig.S2d), a trend also reflected in a slight decrease of parenchymal tissue section areas; PT but not PH parenchymal areas further correlate positively with age (Fig.S2e/f). On average, we captured ~500 islets per tissue section, with absolute numbers broadly decreasing as T1D progresses (Fig.S2g). While islet densities show a similar 1.5-2.0-fold non-significant reduction across donor cohorts, cumulative islet areas decline significantly (Ctrl vs. T1DL PH: 1.8-fold; PT: 2.9-fold) corresponding to an overall 2.9-5.1-fold loss of islet mass (Figs.1i & S2h; cf. Supplementary Data 2). Quantifying cumulative islet hormone staining areas as a fraction of parenchymal tissue areas provides an initial orientation about disease-associated alterations of pancreatic endocrine content. As in earlier whole-slide imaging studies16, 17, insulin (INS) staining areas are significantly reduced in T1DS/T1DL donors while glucagon (GCG) areas remain largely preserved (Fig.S2i). Several additional observations are noteworthy: a ~2.7-fold loss of chromogranin A (CHGA) from Ctrl to T1DL stage accompanies the decline of relative islet areas; total ProINS areas are larger than INS areas whereas ProINShi areas are smaller and comparable to a previous report17; a decrease of ProINS and islet amyloid polypeptide (IAPP) areas in AAb donors (significant for PH but not PT) strikingly echoes recent findings about lower regional beta cell volume assessed by 3D morphometric analyses36; somatostatin (SST) staining areas remain unaltered in all donor groups; and PPY expression, minor in PT but prominent in PH, also appears unaffected by disease progression (Fig.S2i).

For additional context, we document a progressive decline of INS:GCG expression ratios with disease advancement (Fig.S2j) as well as an estimation of total endocrine cell type mass. Beta cell mass wanes precipitously (Ctrl vs. T1DL ProINS: ~780-fold; INS: ~1,250-fold), but both alpha- (ProGCG/GCG) and delta- (SST) cell mass also decrease in T1DL donors, albeit to a much lesser degree (~3-fold; PT>PH). In contrast, gamma cell (PPY) mass is minimal in the PT (~1mg) yet substantial and variable in the PH (~20-40mg), presumably due to different uncinate process proportions56, 57 in our tissue sections (Figs.1j & S2k). These findings readily recapitulate and refine the histopathological dynamics of T1D progression (including loss of ~70-80% beta cell mass in T1DS65); they emphasize a considerable alteration of the endocrine pancreas in the T1DL stage beyond loss of beta cells; and they provide a general framework for subsequent analyses focused on ~25,000 individual islets captured across the four donor cohorts.

Islet endocrine hormone contents throughout T1D progression

Extending our quantification of pancreatic endocrine contents to individual islets reveals commonalities and differences compared to cumulative staining area assessments. A profound reduction of ProINS, INS and IAPP contents in T1DS islets is similarly presaged by a trend towards decreased ProINS and IAPP but not INS expression in AAb donors; T1DS and T1DL islets present with a relative compensatory increase of ProGCG, GCG and SST expression areas due to loss of beta cells; and the variable PPY+ proportion of PH islets appears particularly large in T1DL donors (Figs.1k & S2l). At the same time, relative endocrine and CHGA areas remain constant throughout T1D progression thus corroborating, in conjunction with the ~2.7-fold reduction of total CHGA staining area (S2i), a net islet loss in T1DL. A direct comparison of PT and PH islets demonstrates that in all donor groups, despite comparable islet endocrine areas, PH islets express less CHGA, ProINS, INS, ProGCG, and GCG but equivalent IAPP and SST (Fig.S3a). While reduced alpha and beta cell mass in the PH of Ctrl donors has been noted earlier57, 66, quantifying average islet hormone contents cannot resolve the potential contribution of different islet subsets to what appears an overall distinctive PH endocrine composition.

Frequencies of endocrine content-stratified islet subsets

To evaluate how islet heterogeneity shapes net endocrine cell mass, we quantified islet frequencies based on their hormone content or lack thereof (<1% hormone staining area of islet area35). The significant increase of IDIs (ProINSneg or INSneg) with disease development (T1DS PT: ~64%, PH: 84%; T1DL PT/PH: >99%) constitutes the histopathological hallmark of T1D (Figs.2a & S3b). An unexpected feature of the non-diabetic pancreas revealed by recent 3D mapping is the abundance of small “GCG-deficient” islets35. We confirm this observation in our 2D interrogation by demonstrating that ~34% (PT) to ~56% (PH) of Ctrl islets are “GCG-deficient”, and that ProINS+GCGneg islets are significantly smaller than ProINS+GCG+ islets (Figs.2b/c & S3c/d). The absence of alpha cells, as recently shown with human pseudo-islets exclusively composed of beta cells, does not appear to impinge on their functionality67, corroborating the notion that “GCG-deficient” islets are an integral part of endocrine pancreas physiology. Importantly, the ProINS+GCGneg islet fraction declines with T1D progression, including a significant reduction in AAb vs. Ctrl donor PTs, indicating that this subset is particularly vulnerable in early T1D pathogenesis (Figs.2c & S3d).

Furthermore, a differential abundance of PPY+ islets in PT vs. PH aligns with cumulative PPY staining patterns (Fig.2d), and a subset stratification according to ProINS/PPY expression demonstrates that the Ctrl PH contains a ~10% fraction of small PPY+ islets lacking beta cells that increases to ~60% in T1DL (Fig.2e). Delta cells are found in ~65% of islets, and their relative abundance is somewhat elevated in clinical T1D as noted before68 (Figs.2f & S3e). Lastly, we traced hormone co-expression patterns in islets across disease course. For alpha cell hormones, a modest increase of Jaccard indices for CHGA, ProGCG and GCG with T1D progression is consistent with the relative rise of ProGCG/GCG content in islets afflicted by beta cell loss. For beta cell hormones, Jaccard indices slightly decline in AAb donors before markedly plunging with T1D onset; this decrease is more pronounced than that of individual hormone staining areas reflecting profound perturbations of INS synthesis69 (Fig.S3f).

Islet architecture and its histopathological alterations

The geometric properties of islets have mostly been studied in non-diabetic pancreata70, 71, 72, 73 and in accordance with these reports, Ctrl islet areas span across two orders of magnitude (103~105 μm2) following a frequency distribution that is notably skewed toward smaller islets: ~82% of PT and PH islets are found in the 103-104 μm2 range; due to their smaller size, however, they respectively contribute only ~41-50% to overall islet area (Figs.2g & S3g). Although these distribution patterns are roughly maintained throughout T1D progression, disease stage-specific deviations are discernible especially in the PT where islet size positively correlates with age (Fig.S3h). A relative loss of the smallest islets (<3,000 µm2) in AAb vs. Ctrl donors (Fig.2g) is notable since they account for ~50% of all islets in Ctrl persons donors and their corresponding density is decreased by ~1.6-fold in AAb donors. A subtle rightward extension and flattening of the T1DS islet area distribution curve hints at preferential preservation of larger islets, and the more pronounced left-shift of the corresponding T1DL curve indicates an overall islet size reduction (Fig.2g). Furthermore, the remaining small AAb islets are partially compromised due to some beta cell-depletion (i.e., reduction of the ProINS+ islet fraction) and a relative decrease of “GCG/SST/PPY-deficient” insulin-containing islets (ICIs) (Fig.2h) that are potentially marked for complete elimination following subsequent beta cell destruction at stage 3 T1D.

Consistent with an overall small islet loss in AAb donors, mixed model analyses demonstrate a significant increase of median islet size in stage 1/2 T1D (Fig.2i/j). A subsequent decline of median islet size in T1DS and especially T1DL donors (Fig.2j) is further accompanied by greater cellular densities in islets (Fig.2k), confirming an earlier report21 and conceivably reflecting local deprivation of trophic INS effects previously invoked as cause for exocrine pancreas shrinkage64. Therefore, a T1D-associated islet size decrease accompanies the reduction of overall islet densities (Fig.1i; see also ref.52) thus compounding the loss of islet mass. These patterns are not apparent in the PH (Fig.S3g-j; in part due to the preponderance of PPY+ IDIs, Fig.2e), yet higher cellular densities in T1DS/T1DL islets are comparable for both PT/PH compartments (Figs.2k & S3j).

Recent 3D mapping of INS- and GCG-staining volumes in non-diabetic donors demonstrated that average human islet size is smaller than previously appreciated35. For comparative purposes, and working with similar assumptions about circularity and sphericity of islet objects, we calculated median/mean PT/PH islet diameters and volumes from our islet area measurements yielding Ctrl group estimates that are in good alignment with islet size assessments in ref.35 (Fig.S3k). Similarly, while islets with >91 μm diameter constituted ~26% of all islets yet contributed ~75% to beta cell mass in that 3D study35, we find that islets above average size (>89 μm diameter) account for ~28% of islets (a proportion that is near identical across PT and PH as well as disease stages) and contribute ~70% to islet mass in all donor groups (Fig.S3l).

In contrast to the subtle alterations of islet areas and a trend towards increased islet perimeters despite declining islet sizes with advancing disease (Fig.S3m), the progression of two-dimensional shape descriptors is profound: islet aspect ratio, solidity, and circularity significantly decline from Ctrl and AAb to T1DS and T1DL stage, a pattern that again appears somewhat more accentuated for PT than PH islets (Figs.2l & S3n; aspect ratio, solidity and circularity are dimensionless measurements in the range of 0-1 that respectively describe object elongation, overall concavity, and similarity to a circular object; equations in Fig.1e). Together, these alterations signify a stark deterioration of islet architecture, likely a direct consequence of beta cell destruction. Our results indicate that inclusive whole-slide image analyses capture basic islet architectural and hormone expression features that readily align even with contemporary volumetric measurements35, 36, 37 and thus imbue the scope of observations reported here with greater confidence.

Basic exponential associations between islet size and other islet properties

The heterogeneity of islet composition has long been recognized65, 74 and in an attempt to account for disparate islet properties (including the conspicuous lack of alpha and/or beta cells in smaller Ctrl islets), we considered islet features as a function of their size75, 76 (Fig. S4a-h). Correlating islet size frequency distributions in Ctrl pancreata with specific islet properties, we find that simple exponential relations consistently capture these associations: increasing islet size correlates with greater islet cell numbers, solidity, endocrine/CHGA content and alpha cells but lower islet cell density, circularity as well as beta- and gamma cell contents (these patterns only diverge for the smallest islets with comparatively reduced beta cell fractions, and for SST content which is slightly elevated in both smaller and larger islets) (Fig.S4a/b). Consequently, islet INS:GCG expression ratios decline with increasing islet size (Fig.2m). Overall, this approach provides a simple organizing principle for islet heterogeneity that may be particularly useful for islet comparisons across disease stages.

Throughout T1D progression, the exponential associations of islet size and architectural features are subject to a modest, largely size-independent upward (cellular density) or downward (solidity and circularity, somewhat sparing T1DS islets >10,000 µm2) shift of T1D stage-specific distribution curves indicative of pathogenetic processes encompassing islets of all sizes (Figs.2n/o & S4a/c/e/g). At the same time, corresponding relations for endocrine and CHGA contents remain strikingly resilient over T1D course (Figs.2p & S4b/d/f/h). While correlations of islet size with endocrine cell type-specific hormone abundance also remain broadly similar in Ctrl vs. AAb donors, a particular reduction of relative ProINS and IAPP but not INS proportions is discernible especially for AAb islets <6,000 µm2 (Figs.2q & S4d). The subsequent T1DS stage features three deviations from these patterns: an overall reduced yet relatively higher ProINS, INS and IAPP expression by larger islets, proportionally more ProGCG/GCG expression for all but largest islets, and a decline of SST expression as a function of islet size (Figs.2q & S4f). In T1DL persons donors, these patterns, beyond the profound beta cell loss, are exacerbated as reflected by the considerable variability of islet size-associated ProGCG, GCG and PPY contents (Fig.S4h). The dynamic regulation of these exponential relations reflects an early targeting of beta cells especially in smaller islets, a temporary preservation of residual beta cell mass in larger T1DS islets, and the markedly altered islet composition in T1DL donors (Fig.S4i). Collectively, our semi-automated whole-slide image analyses confirm central tenets of T1D pathology and readily identify multiple islet properties in normal pancreata and over the entire course of T1D.

Integrated histopathology throughout T1D progression

The true analytical potential of our approach to characterization of islet heterogeneity lies in elucidating complex combinatorial property patterns. We therefore adapted a dimensionality reduction tool commonly employed for transcriptomic single-cell analyses and performed UMAP clustering77 of all ~25,000 individual islets (rather than single-cells) captured in our four donor cohorts based on islet architectural and hormone expression properties as well as CD45+ cell associations and neighborhood relations (see Methods). This strategy generates five major clusters (I–V), four of which contain 3-4 subclusters (A-D), and a regional stratification illustrates that clusters III and IV are predominantly found in the PH, underscoring apparent differences in the broad organization of PH and PT endocrine compartments (Fig.3a).

Visualization of individual hormone expression levels across these clusters emphasizes both shared and distinct properties of cluster-constituent islets: endocrine areas are comparable for most clusters though slightly diminished for clusters III/IV, a pattern that is more pronounced for CHGA expression which is somewhat decreased in cluster III and strongly reduced in cluster IV (Fig.3b/c). Beta-cell-containing islets are found exclusively in clusters I/II/III; alpha-cells populate the majority but not all cluster I/II islets, are absent from cluster III and very infrequent in cluster IV, but a prominent presence in clusters V-A/BC; delta-cells are distributed across all major clusters; and PPY+ islets, outside a smattering found in clusters I/II, reside in cluster III and dominate cluster IV (Fig.3c). The islet feature histograms and heatmaps in Figs.3d & S4j provide a more granular perspective on distinct cluster properties by resolving architectural and hormone expression properties at the level of individual donors; they are complemented by various scatter plot arrangements to illustrate more subtle alterations and allow for clarification of significant differences between UMAP clusters and disease stages (Fig.S5a-j, which serves as a consolidated data resource).

Cluster stratification shows a hierarchy of average islet sizes (cluster II>>I>V-A>III~IV>V-BC) with near identical islet dimensions for respective PT and PH clusters (Figs.3d & S5a/c/i) indicating that the trend towards larger islets in the PT must be grounded in regionally different cluster abundances. Indeed, the PH of Ctrl donors contains a prominent contingent of small cluster IV islets (median diameter: 44 μm) but combined cluster I-III ICIs have the same median size as PT islets (64 μm diameter). A similar ranking of islet circularities (cluster I>II~III>IV/V-A/BC) suggests that cluster I islets, due to their higher circularity and general preponderance in the non-diabetic pancreas (see below), may well have contributed to the historic notion of a “standard islet”78, and few differences in circularity (Fig.S5i) and even endocrine properties (Fig.S5j) are observed for PT and PH islets within the same clusters. Also, note the differential ProINS and IAPP content for clustered ICIs (I>II>III); absence of alpha/beta cells and low CHGA but high PPY expression in cluster IV islets; and elevated ProGCG/GCG expression levels of cluster V islets in conjunction with slightly increased SST content (Figs.3d, S4j & S5b/d). In agreement with the idea that INS may exert paracrine trophic effects, islet cell densities in clusters I/II exhibit an inverse correlation with beta cell content across disease progression while IDIs in PH cluster IV and the T1D-associated clusters V-A/BC are ~35% denser than cluster I/II/III ICIs (Figs.3e, S4j & S6a). A succinct overview of these distinctive cluster properties is featured in Fig.3e.

While UMAP analyses, by design, foreground discriminating cluster features, a comparison of islet properties within individual clusters across T1D stages also is instructive: first, in transition from Ctrl to T1DS stage, ProINS and IAPP but less so INS content is progressively reduced in cluster I, II and III ICIs suggesting that a tissue-wide modulation of ProINS and IAPP expression reports T1D pathogenetic processes with particular sensitivity (Figs.S5f/h; also cf., Fig.2q). Second, the opposing trajectories for islet size increase vs. circularity decrease in cluster II ICIs and cluster V-A/BC IDIs can serve as a histopathological correlate for the dynamics of disease progression (Figs.S5e/g). Taking into account the reciprocal relation of islet size and circularity (Fig.2o) and the disease course-associated ICI to IDI transition (Fig.2a), our observations are in fact consistent with islet size-dependent T1D kinetics: an early disappearance of smaller/more circular cluster II ICIs (turning into smaller/more circular V-A/BC IDIs while leaving behind larger/less circular cluster II ICIs), and a later appearance of larger/less circular cluster V-A/BC IDIs that are derived from larger/less circular cluster II ICIs (Figs.S5e/g).

Islet cluster size redistribution as an early hallmark of T1D development

Arguably most relevant is the profound redistribution of relative cluster magnitudes with disease progression (Fig.4a). Heatmap (Fig.4b) and scatter plot (Fig.S6b) displays demonstrate that cluster I/II islets account for the majority of islets in Ctrl pancreata (PT: cluster I ~70%, II ~25%; PH: cluster I ~48%, II ~17%; also III ~25%; IV ~10%). In AAb donors, this distribution is markedly altered due to a 39-45% reduction of cluster I islets accompanied by a compensatory 45-87% increase of cluster II islets (Fig.4c); this conclusion also applies to the individual collapse of subclusters I-A/B that are primarily distinguished by the presence/absence of delta cells (Fig.S6c), and cluster III islets in the PH including both SST+ and SSTneg subsets (i.e., cluster III-A/C reduction and cluster III-B/D increase) (Fig.4d/e) therefore denoting the same fate for ICIs regardless of SST content.

In transition to the AAb stage, cluster V islets can be found in some donors, and a progressive deterioration of cluster I/II magnitude in T1DS donors is accompanied by a robust increase of cluster V islets. In the PH, this pattern coincides with a relative growth of cluster IV (which constitutively lacks alpha/beta cells), including all of its SST+ and SSTneg subclusters (Figs.4c & S6d). The reconfiguration of cluster sizes culminates in T1DL cases where IDIs in cluster V (PT) or clusters IV/V (PH) practically eclipse a minute fraction of residual cluster II/III ICIs (Fig.4a-e). Throughout T1D progression, distinctive differences between PT and PH cluster abundance (clusters I/II/V: PT>PH; clusters III/IV: PH>PT) remain broadly intact (Fig.S6e), indicating that despite regional differences, T1D pathogenesis proceeds in a somewhat synchronized fashion throughout the entire pancreas.

Translating the dynamic regulation of relative islet cluster magnitudes into the context of whole tissue sections illustrates the gradual progression of cluster-stratified islet densities with T1D stages, especially the significant reduction of AAb cluster I islet densities in the PT or of combined cluster I/III islet densities in the PH (Fig.4f). Furthermore, the conspicuous growth of PH cluster IV with disease progression (Fig.4c/f) is readily explained by cluster III ICIs that, after loss of beta cells, segregate together with cluster IV IDIs, thereby raising average islet size and lowering circularity (Fig.S5g). Lastly, a donor-specific assessment of cluster frequency distributions (Fig.4b) in conjunction with clinical donor parameters (Supplementary Data 1) permits the identification of several outliers (Ctrl 6454, AAb 6310, AAb 6450, T1DS 6405) and associated considerations, as detailed in the legend to Fig.S6e.

Insulitis and insulitic islet properties

To quantify immune cell abundance, distribution, and islet association, we first counted CD45+ immune cells localized within (intra-islet) or immediately surrounding (peri-islet) individual islets (cf., Figs.1g & S1c) and assessed instances of insulitis (≥15 CD45+ cells associated with ≥3 islets79). As shown in Fig.5a, automated insulitis diagnoses (0/7 Ctrl, 3/6 AAb, 6/8 T1DS, 1/4 T1DL donors) are in overall concordance with prior manual determination by nPOD pathologists (Supplementary Data 1). Insulitis is an expectedly rare occurrence in AAb donors (PT/PH: 0.8/0.6% of all islets), constitutes an infrequent event in T1DS (PT/PH: 3.2/1.3%), preferentially affects ICIs, and recedes to levels comparable to Ctrl persons donors in the T1DL stage12 (Fig.5b). According to our UMAP cluster stratification, insulitis distributions are preferentially restricted to cluster II where 14% (PT) to 18% (PH) of islets are insulitic in T1DS cases; such lesions are less pronounced in cluster V-A, very rare in clusters III/IV, and absent in clusters I/V-BC (Fig.5c/d). Despite lower insulitis frequencies in AAb donors, islet-associated CD45+ counts in cluster II are higher than for Ctrl individuals comparing pooled donor groups, and an average of 2-3 CD45+ cells per islet in cluster V-A of AAb, T1DS and T1DL donors constitutes a residual presence of immune cells no longer engaging beta cells (Fig.5d).

Since cluster II islets histologically appear to constitute the principal target of autoimmune attack, we next compared features of cluster II insulitic vs. non-insulitic islets in AAb and T1DS donors. Insulitic islets are notably larger with a modest reduction in cellular density, and they tend towards reduced circularity, solidity and beta cell content but increased alpha-cell proportions (Fig.5e). However, since these features track with islet size (cf., Fig.S4a-h), there appear to be no fundamental differences between most insulitic and non-insulitic islet properties. Altogether, our findings demonstrate the utility of automated image analysis strategies for expedient insulitis diagnosis and reveal a largely normal architectural and endocrine composition of insulitic islets.

Islet-associated immune cell burden: from physiological to pathological

In aggregate, i.e., across all disease stages and pancreas regions, ~52% of islet-associated immune cells are found in cluster II, ~8% in cluster III, ~13% in cluster IV, and ~26% in cluster V-A. Notably, ~40% of CD45+ cells therefore associate with cluster IV and V-A islets despite their lack of beta cells (frequencies of IDIs with elevated CD45+ cell counts or insulitis reportedly range from ~1-8%, depending on varying insulitis definitions, islet pools selected for analysis and T1D stage12, 28, 33; in contrast, our estimates emphasize overall immune cell-islet associations beyond IDI insulitis which in our comprehensive islet captures is consistently <1%; Fig.5b). Since the use of absolute islet-associated CD45+ cell numbers in the consensus insulitis definition does not clearly account for islet size79, some investigators have adopted strategies for islet size correction25, 28, 80. We employed a similar approach by quantifying islet-associated CD45+ frequencies, namely the numbers of CD45+ cells per nuclei in islets and peri-islet regions. Consistent with the overall cluster affiliation of islet-associated CD45+ cells, islets with increased CD45+ cell frequencies primarily localize to clusters II/V-A and, to a lesser extent, also clusters III/IV (Fig.6a/b). While these findings echo the distribution of insulitic islets, they further demonstrate a physiological component of immune cell infiltration since cluster II islets in Ctrl donors, accounting for ~17% (PH) to ~25% (PT) of all pancreatic islets, on average harbor ~1.7% CD45+ cells in the peri-islet region alone. This fraction increases by ~3-fold in T1DS donors (Figs.6c & S6f), even if T1DS 6209, a young child with notably high peri-islet infiltrates previously considered a more aggressive endotype 1 T1D case81, 82, is excluded from analyses. Intra-islet CD45+ frequencies tend to be lower but are also elevated 2.4-5.2-fold in the T1DS group, and similar considerations apply to PH cluster III islets (Figs.6c & S6f). In contrast, cluster IV/V-A IDIs follow a different pattern: higher peri-islet CD45+ cell frequencies in AAb donors (PT: 3.5%, PH: 4.2%) decline to ~2.1% in T1DL cases with less pronounced trends also observed for intra-islet CD45+ cells (Figs.6c & S6f). The fact that ratios of peri-islet to intra-islet CD45+ frequencies tend to rise in cluster II ICIs and fall in cluster V-A IDIs across T1D stages (Fig.S6g) may provide clues to better untangle the temporal progression of immune cell-mediated beta cell destruction.

Lastly, consideration of the overall islet-associated immune cell burden reveals a striking ~2.5-fold increase of CD45+ cell numbers and densities already at the AAb stage (Fig.6d). Though neither significant nor further increased with T1D onset, the proportional extent of this increase mirrors the relative enlargement of the islet-reactive CD8+T cell pool in AAb pancreata83. Taking into account corresponding beta cell-specific CD8+ T cell frequencies in peripheral blood83, early immune cell recruitment to pancreatic islets may thus be largely stochastic before enrichment of specific CD8+T cells becomes discernible at T1D onset. In the T1DL stage, islet infiltration has for the most part subsided (Fig.6d). Altogether, these observations combine into a specific histopathological correlate for the AAb stage: the frequency of islets associated with ≥1 CD45+ cell constitutes a straightforward metric that readily distinguishes Ctrl (25%) from both AAb and T1DS (45-55%) donors and therefore captures a significant immune cell recruitment to islets in stage 1/2 T1D (Fig.6e).

Compromised islet composition in the absence of immune cells

A donor group-specific correlation between CD45+ burden and islet properties in cluster II, the apparent histopathological locus of CD45+ cell infiltration, is confounded by the overall contingency of islet features on islet size (Figs.2m-q & S4a-h), including islet-associated CD45+ cell numbers and frequencies (Fig.S6h); instead, we compared size-binned cluster II islets across donor groups. Here, an increase of CD45+ numbers with disease progression appears to have little bearing on islet architecture or alpha and delta cell abundance (Figs.6f & S6i). In contrast, ProINS and IAPP, but not INS, content is markedly reduced in T1DS vs. Ctrl and AAb donors, especially in smaller islets (Fig.6f). Even more striking is the observation that similar though attenuated patterns are also recorded for cluster I islets, which do not harbor any CD45+ cells (Figs.6g & S6i). Cluster I islet impairment may arise following exhaustion of residual beta cells tasked with overwhelming metabolic demands52, 84 or as a consequence of beta cell-targeted autoimmune attack85 not captured here due to its dynamic nature and/or the restricted plane of histological analysis; in all likelihood, both scenarios apply to varying degrees to different cluster I and other ICIs6.

Collectively, our cluster I/II islet evaluation indicates the lack of a straightforward correlation between CD45+ cells and islet composition, suggesting that T1D pathogenesis is at once highly dynamic (i.e., CD45+ cells may “come and go” before complete beta cell destruction is achieved), initially targets islets below average size (compounding T1D symptomatology since smaller islets are capable of comparatively greater INS secretion86, 87), and appears broadly synchronized since it affects the majority of islets (i.e., ICIs present with reduced ProINS/IAPP expression even in the histological absence of immune cells). The utility of the insulitis concept and its proposed amendments notwithstanding88, considerations of the entire range of immune cell associations with pancreatic islets are therefore necessary to clarify progression of T1D autoimmune processes.

Spatial distribution of islets throughout T1D pathogenesis

We employed three complementary approaches to assess the spatial distribution of islets in pancreatic tissue sections: modified Ripley’s K function, Delaunay triangulation, and direct visualization of cluster-associated islet localities. Ripley’s K function89, 90 is a descriptive statistic for detecting deviations from spatial homogeneity, here the density enrichment of islets in contrast to their random distribution patterns (Fig.7a and Methods). Density enrichments, i.e., islets presenting as non-randomly distributed “aggregates”, are observed in the PT of all donor groups across radial distances from ~1,000-10,000 μm yet their extent is notably different (Fig.7b): in Ctrl donors, a modest ~1.2-fold density enrichment for islets in the ~600-1,500 μm range is accompanied by a convergence toward random distributions for greater radii (Fig.7b); thus, a relatively even distribution of islets throughout the PT readily aligns with recent 3D interrogations of non-diabetic pancreata35. While near identical patterns are recorded for AAb donors, more pronounced islet density enrichments occur in T1DS (~1.9-fold, peaking at 800 μm) and T1DL (~2.5-fold, peaking at 600 μm) (Figs.7b & S7a). Similar observations also pertain to the PH but the unique anatomy of the uncinate process (see below) is responsible for overall greater density enrichments in all donor groups (Figs.7c & S7a). Importantly, since islets do not change their anatomic locations, their density enrichments need to be considered in a relative fashion: because of islet loss at larger distances and a resulting “re-definition” for random distributions at lower islet densities, increasing density enrichments over disease course reflect a progressive reduction of tissue regions with unperturbed islet densities (Fig.7b/c). Altogether, these observations are consistent with the overall decrease of islet densities, cumulative areas, and mass (Figs.1i & S2h), and they are further confirmed by fractal dimension analyses91 as illustrated in Fig.S7b.

Progressive changes to islet neighborhoods

Delaunay triangulation organizes a set of points (i.e., islets) in a plane (i.e., tissue sections) into triangles whose circumcircles do not contain any of the points92; thus, mean Delaunay distances and triangle areas can serve as a more local metric for average adjacency between individual islets and their proximate neighbors (Fig.7d). Here, Delaunay distances for PT islets increase by ~30% from Ctrl to T1DL stage and display a similar trajectory in the PH (Figs.7e & S7c). At the same time, mean Delaunay distances are normally distributed in Ctrl pancreata with a slight extension of the right distribution tail representing islets that are more distant to each other; with T1D progression, the respective right distribution tails increase in particular in the PT (Fig.7f & S7c), and further alterations can be discerned by a cluster-level evaluation of Delaunay distances as detailed in Fig.S7d-g.

Collectively, our observations support the notion that regionalized patterns of T1D histopathology, rendered apparent by ICI distributions observed in common INS-stained tissue sections93, are underpinned by a tissue-wide islet depletion, the extent of which is partially obscured by its fundamentally dispersed nature.

Cluster-specific islet distributions and the dynamics of T1D progression

To visualize the above relations more directly, we projected cluster-stratified islets back onto their original tissue section locations (Figs.7g & S7h), revealing patterns unique to specific T1D stages: in the Ctrl PT, a relatively uniform distribution of cluster I islets is accompanied by a sparser presence of cluster II islets, a balance that in AAb donors shifts to visibly fewer cluster I islets. This pattern changes more decisively in T1DS cases where the appearance of predominantly cluster V-A islets crowds out remaining cluster II islet foci; this process is completed in T1DL with the prominent emergence of cluster V-BC islets that often appear to populate the tissue area periphery (Figs.7h & S8a). In the PH, a similar progression pertains to the redistribution of cluster I/II/V islets, yet the most distinctive aspect is a striking regionalization of cluster III/IV islets that represent the uncinate process (Figs.7h & S8a).

We employed islet cluster-specific Delaunay triangulations to quantify these impressions. Here, “isolated islets” were defined as islets for which, due to <2 islets from the same cluster within an 8mm distance, Delaunay triangulation was not possible. As cluster I magnitudes collapse from Ctrl to T1DS stage (Fig.4f), the fraction of “isolated islets” increases (Fig.7i). At the same time, “isolated islet” frequencies in cluster II decline somewhat, reflecting a relative condensation of residual cluster islands that are in the process of disappearing with T1D onset (Figs.4f & 7h/i). The sparse cluster III/IV islets in the PT are practically all isolated and remain so throughout all disease stages. In contrast, the more abundant cluster III ICIs in the PH become increasingly isolated as their transition to IDI status following beta cell loss leads to consolidation with cluster IV islets (cf., Fig.4d/f); in turn, the resultant growth of cluster IV is accompanied by a steep decline of “isolated islets” therein (Fig.7h/i). “Isolated islets” in clusters V-A/BC dominate at first appearance in the Ctrl and/or AAb stage, decline in T1DS with expansion of respective cluster magnitudes (Fig.4f), and for cluster V-BC are further reduced in T1DL (Fig.7h/i).

The combined dynamics of these changes indicate that cluster V-A represents a transitional and V-BC a terminal fate. Complementary quantifications of Delaunay areas for “non-isolated islets” reveal trends that partially mirror the “isolated islet dynamics” (e.g., increasing Delaunay areas for cluster I islets), yet differences across disease stages remain non-significant (Fig.7j). Altogether, these observations reinforce the notion that expanding areas of islet loss, increasingly populated by growing “diabetic” cluster V islands, encroach on progressively shrinking cluster I/II/III islands.

Insulitis and immune cell burden in spatial context

Finally, we leveraged the “back-projection” of islet subsets to visualize the neighborhood context of insulitic lesions. Using a color gradient to display the numbers of islet-associated immune cells, the resultant images demonstrate that rare instances of insulitis in AAb and T1DS donors often are the foci of larger neighborhoods with an overall increased CD45+ cell burden, a pattern that tends to be more pronounced for the PT than PH (Figs.8a & S8b). And lastly, our spatial data visualization also clarifies the distinctive pathology of previously discussed outlier cases and is detailed in Fig.S8b.

Methods
Organ procurement and experimental specimens detail

Donor organs and tissues were obtained by the Network for Pancreatic Organ donors with Diabetes (nPOD, RRID:SCR_014641) through its partnership with United States organ procurement organizations in accordance with federal guidelines for organ donation and as approved by the University of Florida (UF) Institutional Review Board, after consent for organ donation and research was obtained from donors’ legal representatives. Recovered tissues were processed by the nPOD Organ Processing and Pathology Core at UF, and all samples, associated data, and metadata were de-identified in full accordance with privacy and ethical guidelines. In the present study, which constitutes Not Human Subjects Research (according to 45 CFR 46), we used formalin-fixed, paraffin-embedded (FFPE) tissue sections (5 μm) from pancreatic tail and head regions of 25 organ donors allocated to four groups: 7 non-diabetic control donors (Ctrl), 6 autoantibody-positive (AAb) donors, 8 donors with short duration of clinical type 1 diabetes (T1DS, <2 years), and 4 donors with longer duration of clinical T1D (T1DL, 8-11 years); donor matching across the 4 groups was performed on age and gender with additional matching for demographic (ethnicity) and clinical (body mass index/BMI) parameters where possible; further details including demographic and clinical metadata are provided in Supplementary Data 1 (see also nPOD Data Portal https://portal.jdrfnpod.org/). To establish, validate and optimize staining protocols, additional pancreatic and splenic FFPE tissue sections were provided by the nPOD consortium and the ISMMS Biorepository and Pathology CoRE.

Immunohistochemistry (MICSSS)

FFPE tissue sections (5 µm) were sequentially stained for eight islet hormones and CD45+ immune cells adjusting the multiplexed immunohistochemical consecutive staining on single slide (MICSSS) method45, 46. Iterative staining order was empirically determined for pancreatic tissues to account for differential antigen sensitivity to deterioration during consecutive MICSSS cycles. Briefly, sections were baked overnight (o/n) at 37°C to ensure tissue adherence to slides in subsequent staining rounds. Following deparaffinization in histology-grade Xylene (Fisher Scientific #X3P-1GAL), sections were rehydrated by immersing them in a series of graded ethanol solutions (histology-grade, Fisher Scientific #A405F-1GAL) at decreasing concentrations (3x100%, 90%, 70%, and 50%) down to distilled water (5 min each) prior to heat-induced epitope retrieval (HIER) at pH6 (Citrate Buffer 20X concentrate; ThermoFisher Scientific #005000) in a 95°C water bath for 30 min. Tissue sections were cooled down to room temperature, endogenous hydrogen peroxidase activity was quenched by incubation with PeroxAbolish solution (Biocare Medical #PXA946) for 10 min and slides were subsequently washed in Tris Buffered Saline (2x5 min TBS, 10X concentrate, Cell Signaling #12498). Non-specific background due to Fc receptor binding was blocked by incubation with DAKO serum-free protein block (Agilent #X0909) for 15 min, endogenous biotin was blocked using the DAKO Biotin-Blocking System (Agilent #X0590) according to manufacturer instructions before addition of primary antibodies as specified in Table S1. Target antigens were revealed after incubation with biotinylated F(ab’) donkey-raised 2 secondary antibodies with minimal cross-reactivity (30 min RT; Biotin-SP AffiniPure F(ab’) Fragment, 2 Jackson ImmunoResearch: donkey anti-guinea pig IgG #706-066-148; donkey anti-mouse IgG #715-066-151; donkey anti-rabbit IgG #711-066-152; donkey anti-goat IgG #705-066-147), horseradish peroxidase (HRP)-conjugated streptavidin (30 min RT; DAKO Agilent #P0397), and ImmPACT AMEC Red substrate (Vector Laboratories #SK-4285) as per vendor’s instructions. Tissue sections were counterstained with Harris modified hematoxylin solution (Sigma #HHS32), cover-slipped with an aqueous mounting medium (DAKO Glycogel, Agilent #C0563), whole-slide images were acquired at 40x on a NanoZoomer S60 Digital Slide Scanner (Hamamatsu) and exported as .ndpi files (for some pilot experiments, images were acquired at 20x using a Pannoramic 250 Flash II Digital Scanner [3DHistech]; cf. Fig.S1a). Slides were stored protected from light at 4°C until further staining. For sequential staining of subsequent targets in MICSSS cycles #2-9, cover slips were carefully removed in hot water (∼50°C) and tissue sections were rinsed in distilled water, destained/dehydrated by immersing them in ethanol solutions at increasing concentration (50, 70, 100%; 3 min each) and xylene (3x2 min), and subsequently rehydrated through a graded ethanol series and distilled water as detailed before. Antigen retrieval at pH6 was performed for 10 min as specified above removing hematoxylin staining, endogenous and streptavidin-associated (from previous rounds of staining) hydrogen peroxidase activity, non-specific background, as well as endogenous and secondary antibody-mediated (from previous rounds of staining) biotin were blocked using PeroxAbolish, DAKO serum-free protein block, and DAKO Biotin-blocking system as above. To enable probing for target antigens using primary antibodies raised in the same species, tissue sections were incubated with 5% mouse or rabbit IgG (Jackson ImmunoResearch, normal mouse serum #015-000-001; normal rabbit serum #011-000-001) (depending on the primary antibody used in the immediate prior staining cycle) for 1h at RT followed by incubation with donkey-raised AffiniPure Fab Fragments (AffiniPure Fab Fragment, Jackson ImmunoResearch: donkey anti-mouse IgG #715-007-003; donkey anti-rabbit IgG #711-007-003) against the previously used primary antibody species (200 µg/mL) at 4°C o/n. Subsequent primary antibody staining, target revelation, and image acquisition were conducted as described above. For logistical reasons, each round of MICSSS staining was performed in three batches (16-17 slides/batch, containing samples from all donor groups) stained on three consecutive days.

QuPath analysis pipeline

Image alignment. Images were imported into QuPath version 0.2.342, with each set of 9 images of a single slide bundled into an individual project. Using the CD45 image as the base image, the other 8 project images were each aligned to it using a custom groovy script based on QuPath’s Interactive Image Alignment function, incorporating affine transforms at 5 µm resolution. A threshold pixel classifier was used to detect the entire tissue area on the CD45 image, which had been acquired last and therefore exhibited all of the accumulated tissue damage from the iterative MICSSS staining process. The pancreas tissue was then transferred to each image in the project, utilizing the pre-calculated affine transform matrix.

Islet detection. Further image processing was performed with QuPath version 0.4.3 or 0.5.0. Stain separation vectors were optimized for each hormone staining round and consistently applied to every image with that antibody to spectrally unmix the AMEC chromogen from hematoxylin. CHGA was expected to mark all islets yet staining was notably faint in some PPY+ and small GCGneg islets; we therefore delineated islets with a combination of six hormone stains (CHGA, ProINS, INS, GCG, SST and PPY; ProGCG and IAPP proved redundant for this task and were therefore not included to reduce computational complexity and time). On these six stains, we segmented objects with a low-resolution pixel classifier (0.88 μm), thresholding the AMEC channel to find all stained regions with an area of at least 50 μm2. All objects were transferred to their related CD45 base image and then merged. The large resulting annotation was split into individual objects. Holes were filled in to create contiguous objects, and then all objects less than 1,000 μm2 were removed (note that there is no official consensus about a minimal islet size that distinguishes it from smaller endocrine cell clusters; rather, our choice of 1,000 μm2 as a suitable threshold reflects a compromise based on historical context71, 74, 78). Additionally, objects that were within 10 μm of the tissue border were removed to prevent errors from incomplete islet capture and edge staining artifacts. The proto-islets were converted to detections and redistributed to all nine stains, where their overall AMEC staining intensity was measured (mean, standard deviation, min, max, and Haralick features for texture). The measurements from each object were gathered, along with shape features, and used to train a machine-learning object classifier to remove artifacts due to dust, non-specific staining, tissue damage, or loss of focus. The classifier was trained on multiple representative images from all four donor groups. We then measured and calculated shape descriptors for each final islet, and we performed Delaunay clustering on each slide with a maximum search radius of 4,000 μm (1 of 24,578 islets had no neighbors within 4 mm; this islet was assigned a Delaunay distance of 4 mm and a Delaunay area of p x 16 mm2).

Hormone staining areas & islet/endocrine cell type mass. Islet boundaries were transferred to each of the eight hormone-stained images and a high-resolution (0.22 μm2) pixel thresholder was applied to find regions of positive staining. ProINS was the first stain in the series and therefore allowed for reliable differentiation of darker staining, lighter staining, and background. As CHGA is expected to be in nearly all endocrine cells, we captured all staining areas including lighter stained sections. For all other hormones, we only captured darker staining areas to avoid inclusion of background signal. The detections representing the stained area per islet were all returned to the base image and the positive area in each islet was recorded as a percentage of total islet size. For every pair of hormone stains (56 pairs), the Java JTS topography suite was used to calculate the intersection and the union of the stained regions. These areas were subsequently used to calculate Jaccard indices and relative areas of double-positive regions per islet (note that MICSSS is not the technology of choice for identification of bi- or tri-hormonal endocrine cells, e.g., individual cells within pancreatic islets that co-express INS, GCG and/or SST; such analyses are better served by use of thinner tissue sections, concurrent immunofluorescent rather than successive brightfield staining, confocal microscopy, robust cell segmentation and additional visualization of endocrine cell lineage-specific transcription factors. Similar considerations also pertain to analyses of beta cell de-differentiation about which the current work has to remain ignorant). Additionally, we merged all hormone stains to find the total endocrine area per islet. Lastly, islet and endocrine cell type mass was calculated by multiplying relative fractions of islet areas or specific hormone staining areas with regional pancreas weights (PT or PH) (Supplementary Data 1).

Immune cells. Islet boundaries were expanded by 20 μm to mark the peri-islet region, using a watershed algorithm to ensure that the peri-islet boundaries of neighboring islets did not overlap. Within these regions, nuclei were detected via the QuPath implementation of Stardist55, with 1 μm expansion for the cytoplasm. An object classifier was trained to detect CD45+ immune cells. As the last stain in the MICSSS series, the CD45 image had the highest diffuse background staining within islets which furthermore varied between samples. To resolve this issue, the classifier used measurements of CD45 staining intensity in the nuclear, cytoplasmic, and membrane compartments, as well as the smoothed average of cells within a 100 μm neighborhood, and the difference between the cell’s intensity and that of a 10 μm circular tile, including extracellular regions. After classification, the number and frequency of CD45+ cells inside the islet and peri-islet region were calculated.

CytoMAP analysis. Islet measurements were exported to a .csv file and used for dimensionality reduction and clustering in the MATLAB implementation of CytoMAP99. The data input into UMAP77 for dimensionality reduction included: the area of eight hormone stains, the total (union) endocrine stain area, islet-associated CD45+ cell count, circularity, mean Delaunay area, and islet area. The total area, Delaunay area, and raw hormone areas (μm2) appeared log-normally distributed while the hormone staining areas as a percentage of islet areas were far from a normal distribution. Therefore, we used the log-transformed raw areas consistently. To balance the varying inputs, we calculated the z-scores for all of them. Pre-processing was performed in MATLAB. UMAP was run with the following parameters: n_neighbors = 50, min_dist = 0.1, n_epochs = 1000. Two rounds of DBScan clustering were performed on the UMAP output. First, to distinguish larger clusters, we used a high epsilon value (0.5) with a minimum number of 50 points. This yielded six clusters that largely corresponded to the visible clustering of the UMAP scatter plot (clusters 5 and 6 are in close proximity and were named V-A and V-BC). Second, to distinguish subclusters that were largely delineated by SST and GCG, we used a low epsilon (0.2) and a low minimum number of points (20). This yielded 16 clusters with some unclustered points. We used these results to divide the six major clusters (I, II, III, IV, V-A, V-BC) into subclusters for deeper analysis. The islet cluster assignment was exported from CytoMAP into a .csv file and reuploaded into QuPath as a detection measurement using custom scripts. The cluster ID was converted into a class and then class-specific Delaunay clustering with a maximum search radius of 8mm was performed.

Spatial analysis. For each slide, the automatically created tissue outline was manually edited to remove peripheral areas of adipose and connective tissue and fill in regions of missing tissue to determine the pancreas parenchymal area. This was exported from QuPath as a .geojson file and then imported into MATLAB along with the islet centroid locations. To calculate spatial distribution patterns in each slide, we used a modified Ripley’s K function100, 101 that incorporates the tissue boundary and the total pancreas density. Circles are placed at each islet with varying radii (400-10,000 μm); looping through each islet, the number of other islets within that circle is counted; and the fraction of the circle area that lies inside the tissue boundary is used as a weighting factor for the number of points found. The adjusted number of points found at each distance is averaged across all islets in a slide. For Ripley analysis, this count is normalized to the number of points expected if islets were randomly distributed in the pancreas area - the average density multiplied by the area of the circle. If, at a given radius, the islets had on average the expected number of neighboring islets, the modified K value would be 1; however, if there were twice as many islets within a radius as expected, the modified K value would be 2. This allows for a comparison of spatial islet density enrichments even if the pancreatic tissue sections have different sizes, islet densities, and complexities. These data were further used for fractal spatial analysis following the method of Jo et al.91. For each slide, search radii and boundary-adjusted islet counts were plotted on a log-log graph and the slope of the best-fit line was calculated, excluding radii ≤600 μm; the slopes, or fractal dimensions, of each slide were compared to demonstrate average changes in islet distribution with disease progression.

Data visualization. To ease data visualization and figure creation, the large matrix of islet measurements, including single and double hormone areas, locations, shape descriptors, spatial measurements and UMAP clusters were converted to .fcs files for visualization in FlowJo 10.10.0 (BDBiosciences) using the writeFCS function102. In some cases, contrast and brightness were adjusted for entire brightfield images using Adobe Photoshop (Adobe), and scale bars were added in Fiji/imageJ. Pseudofluorescence images were generated in QuPath using the spectrally separated AMEC channels per stain and the previously calculated affine transforms. Adobe Illustrator (Adobe) was used to prepare figures.

HLA haplotype T1D risk classification

HLA class I and class II haplotype data were classified for T1D risk according to published data and are summarized in Supplementary Data 1. HLA class II DR-DQ genotypes were binned based on T1D risk, high, moderate, neutral or protective, determined from individuals of European ancestry59. Bins were assigned numerical values for the purposes of this study, high risk (+2), moderate risk (+1), neutral (0), or protective (-1). Alternatively, HLA class II risk was classified using published odds ratios (OR) calculated for individuals of European (DR-DQ haplotype combinations60), African (individual DR-DQ haplotypes61, 62), or admixed Hispanic/Latino descent (individual DR-DQ haplotypes62). HLA class I risk was classified for HLA-A and -B genotypes using odds ratios (OR) calculated from individuals of European descent103.

Quantification and statistical analysis

Data analysis and graphical representation were performed in GraphPad Prism 9 and 10 (GraphPad Software). Normal distribution was determined by D'Agostino-Pearson test. Statistical significance was assessed by unpaired or paired Student’s t-tests or non-parametric Mann-Whitney U test as applicable for comparison between two groups; by one-way ANOVA with Tukey’s multiple comparisons post hoc testing for analysis of more than two groups with normally distributed values; or by one sample t test using a hypothetical mean as indicated. Analyses of islet sizes were conducted using mixed effect models with subjects as random effects specifying a compound-symmetry correlation matrix in the model, and orthogonal contrasts were used for pair-wise comparison of the four donor groups. Summary data are displayed as scatter plots with mean, violin plots with median and quartiles, bar or line diagrams (meanSE), frequency distributions (mean95% CI error band), or correlation plots adopting the following convention: *p<0.05, **p<0.01, ***p <0.001, ****p<0.0001; ns or no symbol, non-significant.

Missing and excluded data: for the purpose of the present study, pancreatic islets are defined as endocrine objects ≥1,000 μm (~36 μm diameter); accordingly, we excluded smaller endocrine structures and single cells from all of our analyses. Information about HLA haplotypes and HbA1c values was available for most but not all donors (Supplementary Data 1 & Fig.S2a/c). Subcluster IV-E (Fig.3a) is sample-biased and was not further considered. In some analyses of islet size frequency distributions and UMAP cluster-stratified islets, not all donors have islets present in all islet size bins or clusters; for islet property analyses, we additionally excluded values if <3 islets or <2 donors were represented in a size bin (Figs.2m-q & S4a-h) or UMAP cluster; the resultant numbers of donors represented in each cluster or disease stage is indicated by color-coded values on top of respective x-axes in Figs.7i/j, S5 & S6a/f/g (black: all donors; gray: reduced number of donors; red: instances with 2 donors only warranting interpretative caution, or absent/excluded data). CHGA and INS staining of Ctrl 6162 PT tissue sections and INS staining of AAb 6450 PT sections was notably weak; in the absence of CHGA and INS staining irregularities in the corresponding PH sections as well as normal ProINS and IAPP staining in both PT and PH sections, we attribute this observation to a technical staining issue and therefore excluded the respective PT CHGA and INS data from Figs.1k, 2p/q, 3d, S1d, S2i-k, S3a/b/f, S4b/d & S5b/f/j. For calculation of endocrine cell type mass, the two 5-year-old donors Ctrl 6382 and T1DS 6209 were excluded in Figs.1j & S2k, and the PH data from T1DS 6380 was not included in modified Ripley’s K analyses due to very scarce islets (Figs.7c & S8a).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "in this manuscript the Authors performed a detailed, integrated histopathological assessment of the pancreas in 7 control subjects, 6 people without T1D but with autoantibody positivity, 8 with short duration T1D (< 2yrs) and 4 with longer duration T1D (8-11 yrs). They used sophisticated tools for data generation and analysis (including multiplexed brightfield immunehistochemistry, high magnification imaging, digital pathology, a semi-automated analysis pipeline of open-source pathology platform QuPath, integration thereof). They confirmed findings previously reported in T1D histopathology and described several novel findings, leading to the proposal of a revised natural history of T1D.\n\nThe study is of relevance and well written. Several points deserve clarifications:\n1. The use of pancreatic head samples is a confounding factor, since apparently they contain, in non-precised but certainly variable proportions, parts derived from the ventral bud (uncinate process and posterior portion of the head) and parts derived from the dorsal bud (anterior portion of the head). This makes very difficult to clearly interpret the data generated and compare with the tail situation. My suggestion, if the \"ventral\" and \"dorsal\" head results cannot be distinguished, is to clear the head set of data, to have a more straight manuscript.\n2. The lack of information on smaller islets (< 1,000 microm2) is indeed a major limitation, of which the Authors are well aware. This is particularly true since cluster II islets (small and mostly containing beta cells only) are key in the architecture of the study. The Authors are encouraged to provide at least some information on this, and hopefully not to let us wait for ongoing investigations\n3. The population studied comprises different ethnicities, which the Authors mentioned without elaborating much; would it be possible to have more information on this?\n4. The reviewer may be wrong, but does not see if there were cells containg both glucagon and insulin, and others with even triple positivities (as reported previously by other Authors); could this be clarified?\n5. Previous work has shown that beta cell de-differentiation might occur before and after the onset of T1D (doi: 10.1016/j.cmet.2020.03.002; doi: 10.3389/fendo.2024.1427723); did the Authors check for this?", "reviewer_2": "The authors assessed the characteristics of pancreatic tissue (head and tail regions) in non-diabetic, autoantibody positive (AAb), and type 1 diabetic (T1D) donors, these latter with short duration (T1DS) or long duration (T1DL) of the disease. Sections were sequentially stained for endocrine cell markers and for the immune cell marker CD45 and were analyzed using a semi-automated digital analysis pipeline. In addition, combinatorial patterns, characterizing the islet heterogeneity in the conditions studied, was assessed using a dimensionality reduction tool. This accurate approach together with the uniqueness of the samples obtained from the nPOD consortium, represent key strengths of the study in which the authors present a redefined model of the natural history of T1D histopathology. In particular, the progression from the non-diabetic to the T1D condition is characterized by changes in islet cell composition, immune cell infiltration and presence of insulitis. Data support the concept that the modulation of ProINS and IAPP expression relates to pathogenetic processes in T1D and the kinetic of the disease is islet size dependent. In addition, the study and comparison of different regions of the pancreas consolidates the concept of a multifocal and synchronized origin and progression of T1D. These novel aspects are of significance in the field since they may prompt towards investigations seeking to elucidate the pathogenesis of T1D. Furthermore, some data are confirmatory of previous studies that are acknowledged. The study is primarily technically oriented; however, it provides a detailed description of the workflow, from sample preparation to histological analysis and spatial analysis. Data interpretation and conclusions are well sustained by the overall analysis.\n\nThe study is of interest, though some parts need refinement for clarity and smoothness.\n1) The amount of data generated—both main and supplementary—is substantial and is discussed thoroughly, which in some cases makes it difficult to understand the overall message. We would suggest refining certain sections where possible, relocating potentially redundant information to the supplementary materials. For example, in Figure 1 panels B, G, and I could be simplified by including only one representative image, while more detailed illustrations of the computational analysis could be moved to the supplementary materials. Panel C may be removed, and the staining order could be described in the figure legend. If possible, please revise all the Figures accordingly.\n2) In Results, the first two paragraphs (page 4, lines 72-112) are descriptive of the methodology, which the authors propose as a novel analytical approach. However, they might be moved to the Methods section, as they are not descriptive of the results. In addition, the statement “Pancreatic islets, defined here as endocrine cell clusters ≥1,000 μm2 (~10 cells/~36μm diameter)” (line 90) can be omitted since included in Methods (page 34, line 1108).\n3) In Discussion, the statement “Based on the proposition that islet size distribution can serve as a simple organizing principle for the substantial heterogeneity of islet composition, our observation that alterations of islet architectural features in early T1D are independent of islet size provides initial support for this notion” (page 16, line 497) is not clear, could the authors explain better?\n4) In Methods (page 31, line 1004), it is stated “note that there is no official consensus about a minimal islet size that distinguishes it from smaller endocrine cell clusters; rather, our choice of 1,000μm2 as a suitable threshold reflects a compromise based on historical conventions”. Could the authors add supporting reference/s?\n5) The number of islets studied is impressive (about 500 islets per tissue section, about 25,000 in total). However, the number is not consistent among the cohorts (Fig.7, Fig.S8). These reviewers suggest providing a simple table in which is reported the number of islets studied in each section (H and T) for each donor, together with the area of the respective pancreas/acinar section.", "reviewer_3": "Summary\nHeide et al perform extensive histopathological and computational analysis of the pancreata of non-diabetic, autoantibody positive (AAb) and T1D organ donors. Their analyses focused on islet hormonal composition, the geometric architecture of islets, patterns of CD45+ cell infiltration, and the spatial distribution of islets throughout the pancreas. Major findings include 1) changes to islet size and geometry during T1D progression that are evident even in islets that are not visibly infiltrated by immune cells: 2) changes in islets as early as stage I/II that develop further during T1D. 3) clustering of islets (perhaps of the same “type”) in pancreatic neighborhoods accompanied by tissue-wide islet depletion. Together, these data further characterize the progression of T1D pathology, and the computational methods may have practical value for the analysis of large pancreas imaging datasets.\n\nMajor Strengths\n1. The focus on geometric shape descriptors is interesting. Several observations regarding their changes over the course of T1D are compelling.\n2. Generation of a comprehensive dataset that will be a valuable resource for the T1D community.\n3. An emphasis on the analysis of AAb cases which are very important but understudied.\n\nMajor weaknesses\n4. Several key claims could be better supported (see numbered sections after 12. for more details):\n5. Key findings are limited. Specifically, Figure 1 merely describes the method but contains no findings. The first half of Figure 2 is primarily validating the technique by recapitulating known changes. Figure 3 introduces sub-clusters of insulin-containing islets (ICIs) and insulin-deficient islets (IDIs) but does not report any meaningful differences between them. In Figure 4, the same data is plotted in A-D just organized differently. In Figure 5, it’s not clear that breaking down the analysis by sub-cluster is of value; it is not truly a finding that immune cells are enriched in specific islet clusters because immune cells were used to define the islet clusters. Figures 6-7 are interesting, but they should be interpreted conservatively.\n6. In the text, I found many of the conclusions very challenging to understand due to the writing.\nMinor weaknesses\n7. Some of the findings have been previously published but are not cited or if cited, the specific findings are not engaged with:\na. Geometric measurements:\ni. Seiron et al, measure islet diameter, circularity and islets/mm.\nii. Damond et al, 10.1016/j.cmet.2018.11.014 examine islet solidity and extent over different stages of disease. They also show reductions in both in T1D.\nb. ProINS/IAPP reduction in AAb\ni. Teresa Rodriguez-Calvo et al, https://doi.org/10.2337/db16-1343 show that proinsulin is increased in AAb+ vs Ctl cases, in contrast to the your data. Potential explanations for this discrepancy should be discussed.\nc. Immune infiltration in AAb cases\ni. Insulitis is present in a small fraction of early onset T1D cases and an even smaller fraction of autoantibody-positive cases. In’t Veld et al, https://doi.org/10.2337/db07-0416.\nd. Reduction in islet areas with T1D\ni. Wilcox et al, 2010, 10.1007/s00125-010-1817-6\ne. IDIs with immune cells\ni. Barlow et al, ELife, https://doi.org/10.7554/eLife.100535.3\nf. Clustering of islets regionally\ni. Barlow et al, ELife\n8. Lack of explanation or even speculation of the biological processes underlying changes in geometric shape descriptors. It would be valuable to show example images of islets and have a pathologist to review them.\n9. I am skeptical that this method will be useful in practice given the significant effort required to perform the staining and image registration. There are now many multiplexed immunofluorescence platforms that seem more feasible.\n10. It would be very valuable to demonstrate that the geometric changes are not artifacts of the image processing pipeline. This could be done using published highly multiplexed datasets from Damond (Cell Metabolism) et al and Barlow et al (ELife).\n11. The Discussion section should cite relevant figures with each key claim to make it easier for the reader to evaluate the support for the statement.\nSupport for key claims continued\nMajor claims (broken down point by point from Abstract/Discussion):\n12. Line 64-68 are very general and conceptual but not clear. They could be more specific and centered on key findings.\n13. “new histopathological correlates for the stage 1/2 pancreas suggest AAb subjects are “on cusp” of developing T1D hallmarks”:\na. “higher frequencies of islets with ≥1 associated immune cell”. Immune infiltration in AAb cases has been published but it is even rarer than in early-onset T1D so this is hardly a feature of AAb subjects or islets.\nb. “early targeting of small, including “GCG-deficient” islets”. The claim that GCG-deficient islets are targeted early is circumstantial and incomplete (see section 19).\nc. “collapse of islet cluster I magnitude”. The magnitude of Cluster 1 vs 2 is likely driven by CD45 count so this is the same point as a) above.\n14. histopathology of T1D:\na. “tissue-wide distributed rather than strictly localized disease process”. It is unclear what supports this. Is this a reference to the similarities across PT and PH?\nb. “alterations of islet architectural features in early T1D are independent of islet size” This argues against the claim that that islet size distribution is important. It is not clear what a ‘simple organizing principle’ means practically.\nc. “absence of a direct correlation between islet composition and associated immune cell abundance”. The fact that small islets without CD45+ cells also decrease ProINS expression could be easily explained since only 2D sections are analyzed and immune infiltrate is typically sparse and concentrated at one point. It is reasonable to expect a “dropout effect” for small islets.\nd. “consider the entire spectrum of immune cell associations with islets beyond those affected by insulitis”. Unclear\ne. Lines 504-508. Unclear\nMajor claims (broken down from Results):\nUnclear:\n15. Line 178: “absence of alpha cells…suggesting that “GCG-deficient” islets constitute an integral part endocrine pancreas physiology”. If the cited work shows no effect of alpha cells on beta cells, how can they conclude GCG-deficient islets are integral.\n16. The observation in Figure 6 that the distribution of islets can change from being uniform to aggregated is quite interesting but in the exact text in line 428-431“Since islet locations are immutable… cumulative areas, and mass”, I don’t understand what the specific claim is because the definitions of “tissue regions with unperturbed islet densities” or “islets at shorter distances” is unclear. So, I can’t confidently evaluate the merit of the claim.\nOverinterpreted/unsubstantiated\n17. line 150 “they emphasize a profound alteration of the endocrine pancreas in the T1DL stage beyond the loss of beta cells”. It’s not clear what is profound about the findings.\n18. Line 181: Authors report a trend in ProINShi islets from Ctrl to AAb (Figure 2D). They would need to show side by side the %ProINS/GCG-negative and %ProINS/GCG-positive islets to say the GCG-deficient are more vulnerable than GCG-positive islets.\n19. Line 252: Are Ins- and Ins+ islets included in 2Q (ProINS through SST columns)? Without knowing, it’s hard to separate the fraction of beta cells in each islet vs the fraction of islets that are completely insulin deficient. \na. ”reflects an early loss of beta cells especially in smaller islets”. By “early loss”, do you mean in Ctl vs AAb or Ctl vs T1DS?\nb. “preservation of residual beta cell mass in larger T1DS islets”. The idea that larger islets preserve beta cell mass is interesting, but the order of events can’t be discerned from these data. It is possible that islets shrink after losing beta cells or they all shrink in parallel as disease progresses, not necessarily that smaller islets are targeted first. It would be good to invoke the insulitis data to make the case that islet size effects targeting.\n20. Line 304: “early targeting of smaller ICIs and later appearance of larger IDIs” First, it’s unclear what is meant by “appearance”. Second “larger” is confusing because IDIs are smaller than ICIs. If I understand correctly, the claim is that the gradual increase in islet size (in ICIs and IDIs) over the course of T1D suggests that smaller islets are losing insulin and becoming IDIs before larger islets do. Again, I think this is interesting but should not be overstated. If this were the only cause, one would expect IDIs at late stage T1D to be similar size to ICIs (once all islets lose insulin). To be fair, the authors do not claim that size-dependent disease kinetics is the only cause. Conclusions should be qualified and elaborated upon in the discussion.\n21. Line 340-342: 5A looks like there are 3 AAb cases with insulitis and these are the three that also have CD45+ cells. Text should be clarified to explain that CD45+ cells can be found in specific AAb cases, not that CD45+ cells are elevated at the stage generally.\n22. Line 388: Is there evidence in this dataset that CD45+ cells are recruited to islets directly? For example, are there increases in CD45+ cells in acinar tissue outside islets? If not, it could help substantiate that immune cells are recruited actively at the AAb stage.\n23. Line 443. “Collectively, our observations support the notion that the typically regionalized patterns of T1D represent residuals of a tissue-wide islet depletion, the extent of which is partially obscured by its fundamentally dispersed nature.” Can volumetric data be used to validate this? Is there any evidence in literature or biological mechanism for islets “disappearing” vs growth of exocrine cells?\nMethodology\n1. The islet and cell mass calculations are unreliable. IHC is not quantitative enough to evaluate the extremely large dynamic range of hormone secretion. It is better to simply show the pancreatic mass and islet/endocrine cell area separately.\n2. It is unclear the extent to which the differences in percentages between PH and PT throughout the manuscript are due to the PPY islets in the uncinate process. Would it not be clearer to characterize the uncinate process and then exclude this area from all following analyses?\n3. Panels S3F using Jaccard index (Line 189) are not meaningfully mathematically different from the fraction of those hormones as reported in the main figure.\n4. Figure 2K left vs right. How can AAb have a different islet distribution than the other 3 groups on the left but T1DL has the different distribution than the other 3 in the right?\n5. To ensure that differences in sphericity and circularity that accompany T1D are not an artifact of the image processing, (e.g perhaps the reduced hormone expression and imprecise thresholding leads to cell drop out and hence inaccurate measurements of the perimeter and other features). It would be helpful to show multiple images of islets that represent different sphericities, aspect ratios, and solidity. A quantitative validation would also be helpful. For example, since chromogranin is unchanged, you could calculate the geometric shape descriptors only using chromogranin.\n6. 2Q should contain error bars, ideally via bootstrapping.\n7. In Figure 2D, is the z-score computed column wise or row wise, and separately or pooled between PT and PH islets.\n8. Is figure 4A intended to show the frequency of islets in each cluster or the ProINS expression over time. It doesn’t serve either purpose well in its current form.\n9. Are only hormones used in islet UMAP or geometric features as well?\n10. In Figures 3-4, It’s essential to show the features that were used for the umap and their means in each cluster. The umap uses the islet area and CD45+ cell count which clearly will separate clusters 1 and clusters 2 but this was not clear at all from the text or figures.\n11. Figure 5E needs statistics\n12. In Figure 7, the use of Delaunay triangulation and ‘isolated islets’ does not effectively describe the regionalization of islet clusters because it is highly influenced by the abundance of the clusters. A manual approach could include repeatedly sampling neighboring islets vs random islets from the same tissue and compare the enrichment of a given cluster in the sample. Alternatively, a spatial correlation method such as a cross-K function would be more direct and appropriate.\nOther comments:\nIs any prescreening performed on the tissues for insulitis before selecting blocks for the study? If so, this minimizes the ability to use CD45+cells as a diagnostic as proposed in line 387.\nDo islet cluster foci in Figure 7 comport to lobules boundaries?\nTo validate the utility of geometric shape descriptors, can they be measured in HandE images and applied to large datasets?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n\n(Remarks on code availability)\nI am not able to assess the code, as it falls outside my technical expertise and background. Therefore, I cannot evaluate the reproducibility of the results or the usability of the code for the broader community."}, "author_response": "Response to Referees\nWe wish to thank the Reviewers for their time and effort to assess our manuscript, and we appreciate the opportunity to respond to their comments. Below, please find our point-by-point reply in which all callouts to manuscript lines, figures and tables refer to the revised manuscript, unless noted otherwise (Reviewer comments are in black font, our replies are in blue font). In the revised manuscript itself, all changes are highlighted by gray background shading to readily identify the edits made throughout the entire text, and changes to accompanying figures are summarized in our reply below.\nReviewer #1 (Remarks to the Author)\nin this manuscript the Authors performed a detailed, integrated histopathological assessment of the pancreas in\n7 control subjects, 6 people without T1D but with autoantibody positivity, 8 with short duration T1D (< 2yrs) and\n4 with longer duration T1D (8-11 yrs). They used sophisticated tools for data generation and analysis (including\nmultiplexed brightfield immunehistochemistry, high magnification imaging, digital pathology, a semi-automated\nanalysis pipeline of open-source pathology platform QuPath, integration thereof). They confirmed findings\npreviously reported in T1D histopathology and described several novel findings, leading to the proposal of a\nrevised natural history of T1D.\nThe study is of relevance and well written. We thank the Reviewer for this assessment.\nSeveral points deserve clarifications:\nThe use of pancreatic head samples is a confounding factor, since apparently they contain, in non-precised but\ncertainly variable proportions, parts derived from the ventral bud (uncinate process and posterior portion of the\nhead) and parts derived from the dorsal bud (anterior portion of the head). This makes very difficult to clearly\ninterpret the data generated and compare with the tail situation. My suggestion, if the \"ventral\" and \"dorsal\" head\nresults cannot be distinguished, is to clear the head set of data, to have a more straight manuscript. The Reviewer\nis correct to assert that the pancreatic head (PH) sections used in our study were not selected according to a\ndefined preponderance of uncinate process tissue therein; accordingly, some of our experimental readouts\nexhibit greater variability (e.g., proportion of PPY-containing islets). Although the “PPY-rich” PH lobe has been\nrecognized for almost half a century (and some of the older histological analyses in fact explicitly distinguish\nbetween posterior and anterior PH sections), more recent reports in the context of T1D histopathology tend to\nemphasize differences or similarities between PH and pancreas tail (PT) (and/or pancreatic body) in the absence\nof PPY stains and thus typically without any reference to the uncinate process. We have structured the data\npresentation in our manuscript starting with more traditional outcome metrics (e.g., cumulative staining areas of\nindividual hormones) and then proceeding to more contemporary data display as afforded by our multiplex\nstaining (e.g., “single-islet” UMAPs). Accordingly, we first confirm and refine known PH vs. PT differences using\nthe former analysis modalities, and subsequently demonstrate that use of the latter modalities (i.e., UMAP cluster\nstratification and their “back-projection” onto pancreatic tissue sections; cluster III/IV islet “back-projection”\nspecifically delineates uncinate process proportions in all 25 PH sections as shown in Figs.7h & Sh8) suggests\n“an essentially identical endocrine organization across pancreas regions that locates residual differences\nspecifically to the uncinate process in the PH” (lines 502-504; also note that our main figures foreground PT\nanalyses with a good portion of the PH data relegated to the Supplement). Importantly, and in response to the\nReviewer’s concern, we have now included a new supplementary Fig.S5i/j that provides a comprehensive\nsummary of the main data in support of our contention. We believe this conclusion to be of considerable relevance\nfor an inclusive assessment of pancreatic histopathology throughout the course of T1D development, and we\ntherefore prefer to maintain the respective data and their discussion in the manuscript. Perhaps our report will\nalso incentivize future efforts to experimentally distinguish anterior and posterior PH portions, much as has been\ndone in the older literature using more limited analytical tools.\n2. The lack of information on smaller islets (< 1,000 microm2) is indeed a major limitation, of which the Authors\nare well aware. This is particularly true since cluster II islets (small and mostly containing beta cells only) are key\nin the architecture of the study. The Authors are encouraged to provide at least some information on this, and\nhopefully not to let us wait for ongoing investigations. Questions about our choice of a 1,000µm2 islet size\nthreshold have also been raised by the other Reviewers, and we contend that this matter is in fact more\ncomplicated than at first may appear. We will therefore provide a more detailed account covering this topic.\nthat distinguishes it from smaller endocrine cell clusters; rather, our choice of 1,000μm2 as a suitable threshold\nreflects a compromise based on historical context”. Islet size ranges have historically been determined using a\nvariety of methods including traditional two-dimensional (2D) histology, three-dimensional (3D) tissue analyses\nand examination of isolated islets providing a range of estimates for islet diameters, areas and volumes, as well\nas diameter calculations based on the assumption of circularity or sphericity = 1 (i.e., diameters are calculated\nfor circle- or sphere-equivalent islet areas or volumes). For the purpose of providing the following brief synopsis,\nwe feature all values as area measurements (i.e., in μm2).\nFor some 40 years now, we have known that “in spite of the distinction previously made between islets of\nLangerhans and so-called extra-islet cells, they are in fact both part of a continuous distribution” (PMID:\n3526638). Nevertheless, the nominal distinction of “small islets”, “extra-islet cells” or “endocrine cell clusters” has\npersisted, thresholds appear to vary according to investigator preference, and some authors seemingly suggest\nthat even single endocrine cells can be considered “islets” (“Islet sizes are in a wide range from a single endocrine\ncell to a large islet consisting of several thousand cells”, PMID: 22653677). Proposals for an applied threshold\nfor islet capture range from 2,000μm2 (PMID: 20606719, PMID: 39720415) to 1,275μm2 (PMID: 20185817,\nPMID: 30936150; the latter publication explicitly stating that “while singly scattered endocrine cells and small\nclusters are always included, in this study, only islets >40μm in diameter [i.e., >1,257μm2] were examined for\nmeaningful sizes of islets”) to 1,000μm2 (PMID: 17708340, PMID: 21641386), 962μm2 (PMID: 40705030),\nand further down to 707μm2 (PMID: 40502779), 660μm2 (PMID: 38632302), and 490μm2 (PMID: 31493350).\nA 2018 review of both 2D and 3D islet size quantifications by Huang et al. indicates that islets are found in the\nrange of 707μm2 to >125,660μm2 (PMID: 29954219), and Ionescu-Tirgoviste et al. (PMID: 26417671) provide an\nin our view sensible islet size allocation into four major bins (<1,000μm2, 1,000-10,000μm2, 10,000-100,000μm2,\nand >100,000μm2). While the field would certainly benefit from a consensus and more consistent use of\nterminology, the above outline suggests that in the absence thereof, our choice of a 1,000μm2 for the present\nstudy represents a reasonable compromise (as requested by Reviewer #2, we added a selection of the above\ncited references in line 1,058 to provide historical context). Please see our replies to individual Reviewer\ncomments below for additional detail.\nIn regard to Reviewer #2’s comments about “smaller islets”, we note that “small endocrine objects”, long\nrecognized and equally long neglected, have only this year become a topic of serious interest in the context of\nT1D (PMID: 38922355, PMID: 40705030 and https://www.biorxiv.org/content/10.1101/2025.04.11.648319v1).\nIn fact, the latter two manuscripts examined a large number of archival images of pancreatic tissue sections\nstained for insulin (INS) and glucagon (GCG) and taken together, came to the same conclusion: “small endocrine\nobjects”, especially those expressing INS in the absence of GCG, constitute important targets in the early stages\nof T1D pathogenesis. This observation aligns beautifully with our own findings that the smallest islets (i.e., those\njust above the size threshold of 1,000µm2) are consigned to the same fate, i.e., they are the targets whose beta\ncell content is preferentially reduced and even depleted already in the autoantibody+ T1D stage 1/2 and beyond.\nThese findings also support our contention that the novel histopathological correlates we describe at early\ndisease stages are consistent with a chronic-progressive disease course in which the autoimmune destruction\nof beta cells proceeds at a relatively constant rate throughout all stages of T1D (as per a very recent hypothesis\nadvanced in PMID: 39106185).\nSo, while we are in the process of conducting similar and additional “small endocrine object” analyses with our\nown data sets, the work is far from concluded (not least because we need to consider multiple additional metrics\nbeyond INS and GCG stains) and furthermore would considerably expand on an already long and data-rich\nmanuscript. And while we certainly agree with the Reviewer that additional “small endocrine object” analyses can\ncontribute to a refined understanding of the T1D natural history, we prefer to conclude this work without haste\nand present new data in a follow-up manuscript with the adequate scope and depth that the topic deserves. We\nalso emphasize that despite our exclusion of endocrine objects <1,000µm2, we capture, count and characterize\nvirtually all islets in all tissue sections from all donors above this threshold yielding a respectable total number of\nislets (~25,000) that, we believe, contributes to the robustness of our study design, its execution and the\nrespective conclusions.\nLastly, please note that cluster II islet size medians are actually the largest in our UMAP cluster stratification.\nHowever, the Reviewer is correct in that we propose a particular vulnerability of small islets to pathogenic\nprocesses, cf., Figs.2c/g/h/q & 6f/g; also, see our replies to respective comments by Reviewer #3 below. We\n2\nrealize that at first blush, there appears to be contradiction in stating that small islets are a preferred early\nautoimmune target while the overall larger cluster II islets constitute “the apparent histopathological locus of\nCD45+ cell infiltration” (lines 402-403). However, our analyses resolve this seeming contradiction by expanding\nour histopathological focus beyond instances of insulitis and considering a progressive reduction of beta cell\nhormone areas rather than only complete beta cell loss.\n3. The population studied comprises different ethnicities, which the Authors mentioned without elaborating much;\nwould it be possible to have more information on this? All available information about donor ethnicities is provided\nin Table S1 and is taken into account for our HLA risk score calculations (see Tables S1 & Fig.S2a);\nunfortunately, our overall group sizes are too small to allow for a meaningful donor subset analysis stratified\naccording to race/ethnicity.\n4. The reviewer may be wrong, but does not see if there were cells containg both glucagon and insulin, and\nothers with even triple positivities (as reported previously by other Authors); could this be clarified? Bi- and tri-\nhormonal pancreatic endocrine cells constitute an intriguing field of study to which the present study, however,\ncannot convincingly contribute. Although our image overlay of the same MICSSS-stained tissue sections\nachieves cell-level accuracy, we decided to forgo an enumeration of islet endocrine cell types due to their tight\nspatial organization in the islets and the considerable challenges associated with robust cell segmentation; we\ntherefore decided to report specific hormone staining areas instead. A convincing characterization of bi- and tri-\nhormonal endocrine cells requires, in our opinion, the use of targeted fluorescence-based multiplex staining\npanels (e.g., INS, GCG, SST, ideally in combination with lineage-specific transcription factors and robust cell\nsegmentation) that do not rely on image overlays.\n5. Previous work has shown that beta cell de-differentiation might occur before and after the onset of T1D (doi:\n10.1016/j.cmet.2020.03.002; doi: 10.3389/fendo.2024.1427723); did the Authors check for this? This is an\ninteresting question. However, we have not included stains for endocrine cell lineage-specific transcription factors\nor similar targets of interest and we therefore cannot make any informed speculations regarding potential beta\ncell de-differentiation in the course of T1D development and progression.\n(Remarks on code availability) I gave a look at the code, it provides the README file and an appropriate\nindication of the files; several of them are complex, and I could not make my mind fully. We have updated the\ngithub-deposited README file to better explain the logical flow for each script\n(https://github.com/saramcardle/Image-Analysis-Scripts/tree/master/MICSSS_Pancreas).\nReviewer #2 (Remarks to the Author)\nThe authors assessed the characteristics of pancreatic tissue (head and tail regions) in non-diabetic,\nautoantibody positive (AAb), and type 1 diabetic (T1D) donors, these latter with short duration (T1DS) or long\nduration (T1DL) of the disease. Sections were sequentially stained for endocrine cell markers and for the immune\ncell marker CD45 and were analyzed using a semi-automated digital analysis pipeline. In addition, combinatorial\npatterns, characterizing the islet heterogeneity in the conditions studied, was assessed using a dimensionality\nreduction tool. This accurate approach together with the uniqueness of the samples obtained from the nPOD\nconsortium, represent key strengths of the study in which the authors present a redefined model of the natural\nhistory of T1D histopathology. In particular, the progression from the non-diabetic to the T1D condition is\ncharacterized by changes in islet cell composition, immune cell infiltration and presence of insulitis. Data support\nthe concept that the modulation of ProINS and IAPP expression relates to pathogenetic processes in T1D and\nthe kinetic of the disease is islet size dependent. In addition, the study and comparison of different regions of the\npancreas consolidates the concept of a multifocal and synchronized origin and progression of T1D. These novel\naspects are of significance in the field since they may prompt towards investigations seeking to elucidate the\npathogenesis of T1D. Furthermore, some data are confirmatory of previous studies that are acknowledged. The\nstudy is primarily technically oriented; however, it provides a detailed description of the workflow, from sample\npreparation to histological analysis and spatial analysis. Data interpretation and conclusions are well sustained\nby the overall analysis. We thank the Reviewer for this assessment.\nThe study is of interest, though some parts need refinement for clarity and smoothness.\n1) The amount of data generated—both main and supplementary—is substantial and is discussed thoroughly,\nwhich in some cases makes it difficult to understand the overall message. We would suggest refining certain\nsections where possible, relocating potentially redundant information to the supplementary materials. For\nexample, in Figure 1 panels B, G, and I could be simplified by including only one representative image, while\nmore detailed illustrations of the computational analysis could be moved to the supplementary materials. Panel\nC may be removed, and the staining order could be described in the figure legend. If possible, please revise all\nthe Figures accordingly. We appreciate these thoughts and in response to them, we have modified the figures\nas follows:\nFig.1: former panel B has been moved to the supplement (now Fig.S1a), and former panel I has been reduced\nfrom 8 to 4 islet images (now Fig.1h). While we considered including the information in former panel C (MICSSS\nstaining order) in the figure legend, overall figure layout constraints provided us the opportunity to maintain it as\nFig.1b. Importantly, and also in response to a concern by Reviewer #3, we have moved experimental data from\nthe previous Fig.2 to what is now Fig.1i-k.\nFig.2: the figure has been reduced by removal of previous panels A-D, with additional changes made in response\nto Reviewer #3 discussed below.\nFig.4: we removed former panel C, now presented in the supplement as Fig.S6b (also, cf., comment by Reviewer\n#3 below).\nFigs.5-6: to improve the data presentation of former Fig.5, we have created two new figures, i.e., Figs.5 & 6.\nFig.5 now features data on insulitis and insulitic islet properties and includes the new panels b/right (insulitis\nfrequencies of ICI and IDI subsets) addressing concerns raised by Reviewer #3. Fig.6 now displays aspects of\nUMAP cluster-stratified and overall islet-associated immune cell burden, with former Figs.6 & 7 relabeled\naccordingly.\nOverall, our revised design of the main figures seeks to capture and summarize specific topics (i.e., Fig.1:\nmethodology and basic analyses; Fig.2: more complex analyses based largely on traditional analysis modalities\nand/or simple islet subset stratification; Fig.3: introduction of “single-islet” UMAP clustering and description of\npertinent cluster features; Fig.4: UMAP cluster magnitude “dynamics” throughout T1D progression; Fig.5:\ninsulitis and insulitic islet properties; Fig.6: UMAP cluster-stratified and overall islet associated immune cell\nburden; Fig.7: analyses of spatial islet distributions; and Fig.8: additional spatial analyses emphasizing immune\ncell:islet associations and a revised T1D natural history model). We hope this organization is sensible and have\nrefrained from taking full advantage of all 10 display items permitted by Nature Communications policies.\n2) In Results, the first two paragraphs (page 4, lines 72-112) are descriptive of the methodology, which the\nauthors propose as a novel analytical approach. However, they might be moved to the Methods section, as they\nare not descriptive of the results. In addition, the statement “Pancreatic islets, defined here as endocrine cell\nclusters ≥1,000μm2 (~10 cells/~36μm diameter)” (line 90) can be omitted since included in Methods (page 34,\nline 1108). The Reviewer is correct that lines 72-112 primarily discuss our experimental approach and analytical\nstrategies. While a considerably more detailed description is provided in the Methods section, we believe that the\nsuccinct outline in the Results section is warranted to introduce the reader to the scope and focus of our\nsubsequent data presentation. Emphasizing the nature, quality, extent, and limitations of our principal analytical\nreadouts is in our view essential as it supports the robustness of each of our conclusions. Lastly, as detailed in\nitem 1) above, we have condensed information regarding methodology and analysis strategies in Fig.1 and\nadded experimental results in respective panels i-k.\nRegarding the proposed threshold of 1,000μm2 for islet capture, please see our response to Reviewer #2 above\nthe above. In light of the attention that this topic has generated in the review process, we prefer to briefly mention\nour choice of threshold for islet capture in the Results section for contextual clarity.\n3) In Discussion, the statement “Based on the proposition that islet size distribution can serve as a simple\norganizing principle for the substantial heterogeneity of islet composition, our observation that alterations of islet\narchitectural features in early T1D are independent of islet size provides initial support for this notion” (page 16,\nline 497) is not clear, could the authors explain better? The statement has been removed. Its content has been\nrephrased and is now featured in lines 501-502 and 513-515 (see also comment by Reviewer #3, section 14.b.).\n4) In Methods (page 31, line 1004), it is stated “note that there is no official consensus about a minimal islet size\nthat distinguishes it from smaller endocrine cell clusters; rather, our choice of 1,000μm2 as a suitable threshold\nreflects a compromise based on historical conventions”. Could the authors add supporting reference/s?\nthe use of varying size thresholds used for definition of islets is provided in our reply to Reviewer #2 above.\n5) The number of islets studied is impressive (about 500 islets per tissue section, about 25,000 in total). However,\nthe number is not consistent among the cohorts (Fig.7, Fig.S8). These reviewers suggest providing a simple table\nin which is reported the number of islets studied in each section (H and T) for each donor, together with the area\nof the respective pancreas/acinar section. The Reviewer is correct to note that there is islet count variability within\ndonor groups as well as an overall reduction of islet numbers with T1D progression, the latter observation also\nexpressed as “islet densities” and consistent with the cited literature. We have graphically displayed the\nparameters discussed by the Reviewer in Fig.S2g (absolute number of islets captured in each donor PT/PH\ntissue section), Fig.S2e (absolute size of parenchymal tissue section in mm2, and fraction of parenchymal tissue\narea of total tissue section area in percent for each donor PT/PH sample), and Figs.1i & S2h (islet densities\nexpressed as number of islets per mm2 parenchymal pancreas area for each PT/PH tissue section). As suggested\nby the Reviewer, we have now added a new tab to Table S2 featuring these data in tabular format.\nIn the case of our insulitis discussion, individual donor-stratified islet numbers captured by our analyses are also\nfeatured in the Fig.5a panels themselves. Importantly, please note that we did not select for or exclude any islets;\nrather, we captured and analyzed all islets in all PT/PH tissue sections from all donors (in regard to the number\nof tissue section displays featuring islets color-coded according to UMAP cluster affiliation, we show all 50\nsections in Figs.7h & S8a; the same applies to all 50 sections displaying the islet-associated CD45 cell burden\nin Figs.8a & S8b).\nReviewer #3 (Remarks to the Author)\nWith all due respect, the Reviewer provides a very extensive itemized list of comments, critiques and concerns\nthat we at times found somewhat difficult to address, in part due to an occasional lack of clarity and assertions\nregarding statements that we did not make. While we have sought to address each item individually, issues of\nrepetition resulted in a need to use abundant cross-referencing of our arguments throughout this reply. We hope\nthat our responses nevertheless help to clarify the issues raised by the Reviewer.\nSummary\nHeide et al perform extensive histopathological and computational analysis of the pancreata of non-diabetic,\nautoantibody positive (AAb) and T1D organ donors. Their analyses focused on islet hormonal composition, the\ngeometric architecture of islets, patterns of CD45+ cell infiltration, and the spatial distribution of islets throughout\nthe pancreas. Major findings include 1) changes to islet size and geometry during T1D progression that are\nevident even in islets that are not visibly infiltrated by immune cells: 2) changes in islets as early as stage I/II that\ndevelop further during T1D. 3) clustering of islets (perhaps of the same “type”) in pancreatic neighborhoods\naccompanied by tissue-wide islet depletion. Together, these data further characterize the progression of T1D\npathology, and the computational methods may have practical value for the analysis of large pancreas imaging\ndatasets.\nMajor Strengths\n1. The focus on geometric shape descriptors is interesting. Several observations regarding their changes over\nthe course of T1D are compelling.\n2. Generation of a comprehensive dataset that will be a valuable resource for the T1D community.\n3. An emphasis on the analysis of AAb cases which are very important but understudied.\nWe thank the Reviewer for this assessment.\nMajor weaknesses\n4. Several key claims could be better supported (see numbered sections after 12. for more details): Please see\nour responses below as well as item 12. following.\n5\n5. Key findings are limited. Specifically, Figure 1 merely describes the method but contains no findings. The\nReviewer is correct to note that Fig.1 describes our experimental methodology and analytical strategies. Accurate\ndemarcation of tissue section areas, capture of islets, derivation of shape descriptors, delineation of hormone\nstaining areas and identification of CD45+ cells are essential for the generation of downstream readouts, and we\nargue that this information needs to be displayed prominently rather than relegated to the supplement. For further\ndetails, please see our response to Reviewer #2, item 1) where we detail our reduction of original Fig.1 display\nitems and the addition of experimental data in the new respective panels i-k.\nThe first half of Figure 2 is primarily validating the technique by recapitulating known changes. Again, the\nReviewer is correct in noting that the first half of former Fig.2 contains data confirming expected histopathological\nchanges in T1D development and progression. However, a prominent display of the data we selected for what\nare now Figs.1i-k & 2a is warranted for three related reasons:\n1. The principal goal of our study is to derive more detailed insights into the natural history of T1D by conducting\ncomprehensive histopathological analyses. For this purpose we have selected a multiplexed IHC approach,\nacquisition of high-resolution whole-slide images and, importantly, we developed and applied a semi-\nautomated image analysis pipeline since comprehensive manual evaluation of the large data sets is not\npossible. Therefore, we need to convincingly demonstrate that our strategy yields results that readily align\nwith the major established hallmarks of T1D histopathology; only then can we be confident that the novel\nobservations reported here are grounded in a robust automation of image analysis tasks (and we state as\nmuch in lines 231-234).\n2. Despite shared histopathological hallmarks across various donor cohorts in different studies, unique aspects\npertaining to the donor groups under investigation need to be considered to better appreciate both\ncommonalities and differences in comparison to the published literature; thus, key data to this extent needs\nto be readily accessible and discernable.\n3. The reporting of “standard outcomes” (i.e., “recapitulating known changes”) also provides important context\nto better appreciate less frequently reported outcomes (e.g., Fig.1k displays the progression of islet\nendocrine content not only for INS and GCG but for all major hormones across T1D stages).\nFigure 3 introduces sub-clusters of insulin-containing islets (ICIs) and insulin-deficient islets (IDIs) but does not\nreport any meaningful differences between them. We respectfully disagree. The majority of Fig.3 (and the related\nFig.S4j) is in fact devoted to detail UMAP cluster differences (at the level of donor groups, individual donors,\nand pancreas regions), and Fig.3e provides a succinct summary of distinctive UMAP cluster features. These data\nare essential to outline the basic outputs of our UMAP clustering strategy and therefore act as a foundation of\nsubsequent analyses in Figs.4, 5c-e, 6a-c/f/g, 7g-j, S5, S6, S7d/e/h & S8a as well as our model cartoon in\nFig.8b. Lastly, while UMAP subclusters are identified and shown in Fig.3a-c, they do not constitute a topic of\ndiscussion at this stage in the narrative; rather, relevant subcluster properties are shown and discussed in\nFigs.4b/d/e & S6c/d.\nIn Figure 4, the same data is plotted in A-D just organized differently. We propose in line 315 following that the\nprofound redistribution of relative UMAP cluster magnitudes with disease progression is arguable one of the most\nrelevant observations in our analyses. Accordingly, we devote the entirety of Fig.4 to this topic. Specifically,\nFig.4a resolves UMAP cluster display according to pancreas region and T1D stage and thus provides a visual\nconnection to Fig.3a-c; ProINS expression is added as a color gradient to facilitate identification of beta cell-\ncontaining islets. The heatmap visualization of UMAP cluster magnitudes in Fig.4b offers a more granular display\nat the level of donor groups, individual donors, UMAP sub/clusters and pancreas regions. In short, it is intended\nto allow for a straightforward appreciation of donor similarities and differences across these parameters. The\nprevious Fig.4C/D did indeed “plot the same data just organized differently” for the simple purpose to emphasize\nstatistically significant differences and associated trends. Following the Reviewer’s suggestion, we have removed\nformer panel C from the main figures (now featured in the supplement as Fig.S6b), and Fig.4c now displays\nUMAP cluster magnitudes across disease stages.\nTo better appreciate cluster III subcluster differences, Fig.4d provides a succinct overview (that also contains\nsimilar information on cluster IV subclusters which are examined in Fig.S6d). Fig.4e, then, utilizes the same\ndisplay style as in Fig.4c to specifically demonstrate that subcluster dynamics in UMAP cluster III are essentially\nsimilar to those in clusters I and II thus emphasizing the shared fates of all ICIs. Lastly, Fig.4f translates the\nrelative UMAP cluster dynamics of the preceding panels into their spatial context by calculating respective islet\ndensities. Altogether, our data display seeks to convey the complex UMAP cluster dynamics in a simple fashion,\n6\nand the importance of these findings, at least in our view, is emphasized by their contribution to our overall model\ndisplayed in Fig.8b.\nIn Figure 5, it’s not clear that breaking down the analysis by sub-cluster is of value; it is not truly a finding that\nimmune cells are enriched in specific islet clusters because immune cells were used to define the islet clusters.\nWhile subject to investigator opinion, we respectfully disagree with this contention. The Reviewer is correct in\nthat CD45+ cell association with islets contributes to cluster segregation; however, it is certainly not the only\nparameter (details for UMAP clustering are provided in Methods lines 1,092-1,110). The Reviewer’s concerns\nregarding our data on CD45+ cell association with islets are addressed in detail in our responses below to items\n13.a./c., 14.c./d., 21., 22., Methodology/9. and 10. as well as Other comments (also note that the data in former\nFig.5 is now distributed across the new Figs.5 & 6 to reduce the overall amount of data panels per figure). Lastly,\nwe do not claim that CD45+ cell allocation to specific sub/clusters constitutes a relevant finding in itself; rather,\nthese allocations allow us to focus our downstream analyses in a meaningful fashion as detailed below (cf.,\nFigs.5d/e, 6c/f/g & S6f-i).\nFigures 6-7 are interesting, but they should be interpreted conservatively. Throughout the manuscript, we aim to\nadvance our data interpretation with due caution. A specific concern pertaining to former Fig.6 (now Fig.7) is\narticulated by the Reviewer in item 16 where we also provide our reply; however, no specific concern is raised\nabout former Fig.7 (now Fig.8) data interpretation.\n6. In the text, I found many of the conclusions very challenging to understand due to the writing. The Reviewer\nmentions specific instances in the itemized list below, and we provide clarifications and proposed modifications\nof the phrasing of our conclusions in an effort to improve clarity.\nMinor weaknesses\n7. Some of the findings have been previously published but are not cited or if cited, the specific findings are not\nengaged with:\na. Geometric measurements:\ni. Seiron et al, measure islet diameter, circularity and islets/mm.\nii. Damond et al, 10.1016/j.cmet.2018.11.014 examine islet solidity and extent over different stages of disease.\nThey also show reductions in both in T1D. Seiron et al. (PMID: 31493350) report unchanged circularity of T1D\nislets while Damond et al. (PMID: 30713109) find decreased solidity of pseudostage 3 islets. We now provide\ncitations of these reports in line 93 as well as those by Wright et al. (PMID: 32388592) and Tegehall et al. (PMID:\n40473678) for other analyses of some shape descriptors in the context of T1D (see item 10. below for further\ndetails). The reduction of islet density (“islets/mm2”) in T1D reported by Seiron et al. was already discussed in\nline 212.\nb. ProINS/IAPP reduction in AAb\ni. Teresa Rodriguez-Calvo et al, https://doi.org/10.2337/db16-1343 show that proinsulin is increased in AAb+ vs\nCtl cases, in contrast to the your data. Potential explanations for this discrepancy should be discussed. We cite\nRodriguez-Calvo et al. (PMID: 28137793) in lines 132 and 136 in regard to our distinction of total ProINS and\nProINShi staining areas. Apparent discrepancies pertaining to cumulative ProINS staining areas in the cited\nreference vs. our work are discussed in lines 769-775, as is a suggested explanation for this matter.\nc. Immune infiltration in AAb cases\ni. Insulitis is present in a small fraction of early onset T1D cases and an even smaller fraction of autoantibody-\npositive cases. In’t Veld et al, https://doi.org/10.2337/db07-0416. These observations are well established across\nmultiple studies and we cite the review PMID: 34755679 line 349 instead (see also item 7.e. below).\nd. Reduction in islet areas with T1D\ni. Wilcox et al, 2010, 10.1007/s00125-010-1817-6. Willcox et al. 2010 report mean ICI size in Ctrl donors\n(~12,000µm2) vs. mean ICI size (~18,000µm2) and IDI size (~9,000µm2) in T1DS donors. However, the\n7\ninformation remains limited since no further breakdown of the data or a discussion thereof are provided. For\nfurther details about islet size matters, please see our discussion in items 10., 14.b., 19.a.-c. and 20. below.\ne. IDIs with immune cells\ni. Barlow et al, ELife, https://doi.org/10.7554/eLife.100535.3. An association of immune cells with IDIs has been\nreported in multiple publications over the past 40 years but quantifications are at times difficult to compare due\nto varying definitions of insulitis, selection of islet pools for analysis, and T1D stages interrogated (e.g., Foulis &\nStewart 1984 [PMID: 6381192]: ~1% of IDIs are insulitic but insulitis is not clearly defined; Willcox et al. 2009\n[PMID: 19128359]: ~5% insulitic IDIs with insulitis defined as ≥5 islet-associated CD45+ cells; Damond et al.\n2019: ~8% of pseudostage 2 IDIs at T1D onset are insulitic using the 2013 consensus insulitis definition; Barlow\net al. 2025 [PMID: 36993739]: ~3.8% of “late-stage” IDIs have >2 associated CD8 T cells and >7 macrophages/\ndendritic cells).\nFor clarification and comparative purposes, we have now added Fig.5b/right panels which display insulitis\nfrequencies according to ICI and IDI status using the standard insulitis definition, and we reference review PMID:\n34755679 (line 349) in regard to earlier ICI and IDI insulitis frequency reports. In lines 368-371 we now discuss\nelevated islet-associated immune cell counts and insulitis of IDIs with citations of review PMID: 34755679,\nDamond et al. and Barlow et al.\nf. Clustering of islets regionally\ni. Barlow et al, ELife. Unfortunately, we are unsure what is meant with this comment. Barlow et al. employ both\nhierarchical clustering and the dimensionality reduction algorithm UMAP to deconvolute highly-multiplexed\nimmunostaining images, similar to Damond et al., who use both hierarchical clustering and tSNE (Wang et al.\n[PMID: 30713110] also utilize hierarchical clustering for the same purpose). In all publications, analysis modalities\npertain to single cells. In contrast, our UMAP analyses are conducted with “single islets”. If the Reviewer is instead\nreferring to spatial “islet aggregations” (the term we previously used to avoid confusion with UMAP “clustering”),\nthis is not something that Barlow et al. have addressed (we have now decided to drop the term “aggregation”\nfrom the manuscript and to use “density enrichment” instead; however, we use the plural term “aggregates” in\nlines 430 and 689/Fig.7b legend to better explain the concept of “density enrichments”; also cf., “Major\nclaims/item 16.” below).\n8. Lack of explanation or even speculation of the biological processes underlying changes in geometric shape\ndescriptors. It would be valuable to show example images of islets and have a pathologist to review them. We\nnow added the speculation in lines 230-231 that alterations of islet architecture are “likely a direct consequence\nof beta cell destruction”, and we added sample images in Fig.S1b featuring four selected islets and their\nassociated shape descriptor values (also, see comment “Methodology/5.” below).\n9. I am skeptical that this method will be useful in practice given the significant effort required to perform the\nstaining and image registration. There are now many multiplexed immunofluorescence platforms that seem more\nfeasible. If by “this method” the Reviewer means MICSSS technology, we agree that contemporary\nimmunostaining platforms affording higher analytical dimensionality are likely to become a preferred choice\n(though residual advantages of MICSSS - such as the use of standard IHC equipment, reagents and protocols;\nno need for antibody conjugation; generation of pathology-grade bright-field images and/or whole-slide imaging-\ncompatibility - may retain their utility for targeted projects). Importantly, however, we believe that digital pathology\nand semi-automated analyses of whole-slide pancreas images as well as the deployment of dimensionality\nreduction algorithms will become ever more important in T1D pathology, and our foray into this field may provide\na foundation for future adaptations and extensions. In fact, in lines 538-541/Fig.S8c we provide a simple “key”\nthat permits an approximation of islet UMAP clustering as performed here for analysis of archival images of\npancreas sections stained only for INS, GCG and CD45 (and PPY for more refined PH analyses); since such\nanalyses can then be conducted with considerably larger donor cohorts, important donor stratifications according\nto age, age of T1D onset, gender, ethnicity and various clinical variable will be possible to better account for\ndifferences in disease course and presentation.\n10. It would be very valuable to demonstrate that the geometric changes are not artifacts of the image processing\npipeline. This could be done using published highly multiplexed datasets from Damond (Cell Metabolism) et al.\nand Barlow et al (ELife). The generation of islet objects is detailed in Methods lines 1,046-1,068. It employs six\nhormone stains, yields contiguous objects incorporating non-endocrine areas, and is systematically applied to all\nislets for the derivation of shape descriptors. It is not clear how the highly multiplexed data sets by Damond et al.\nand/or Barlow et al. can be leveraged for the purpose of ruling out artifacts in our image analysis pipeline. For\n8\none, Barlow et al. do not report on geometric properties of islets (although they must have determined islet sizes\nsince they state that islet-associated immune cell counts were adjusted for islet size). Damond et al. quantified\nislet solidity across pseudostages and demonstrate a significant decline of this parameter in pseudostage 3\ncompared to pseudostages 1 and 2. Pseudostages, however, do not strictly correlate with clinical stages and\nwhile an approximation (based on additional hierarchical clustering and tSNE analyses of Ctrl and T1D donors)\nsuggests that pseudostage 1 ~ Ctrl, pseudostage 2 ~ T1DS, and pseudostage 3 ~ T1DL (no AAb donors\ninterrogated), the reported solidity values for pseudostage 1 (~0.85), pseudostage 2 (~0.83) and pseudostage 3\n(~0.7) (their Fig.3F) are nearly identical to our solidity data for Ctrl and T1DL groups; however, we also find a\nsignificant solidity reduction in our T1DS group with a value of ~0.78 (Figs.2l & S3n). Lastly, Damond et al. also\ndetermined islet extent (islet area divided by islet bounding box), a related metric that we have not quantified;\nwe\nbelieve that the concurrent decline of islet circularity, solidity and aspect ratio with disease progression as shown\nin Figs.2l & S3n sufficiently describes the progressive degradation of islet shapes. See also above “Minor\nweaknesses/7.a.” and “Methodology/5.” below.\n11. The Discussion section should cite relevant figures with each key claim to make it easier for the reader to\nevaluate the support for the statement. Figure callouts have been added to the Discussion as requested (though\nit is not immediately clear if this conforms with Nature Communications style policies).\nSupport for key claims continued\nMajor claims (broken down point by point from Abstract/Discussion):\n12. Line 64-68 are very general and conceptual but not clear. They could be more specific and centered on key\nfindings. We agree that the summary of our findings in lines 64-70 of the Introduction lacks specifics; however,\nfor the purpose of concluding the Introduction section, we have deliberately chosen a more general summary\naccount of our observations since the multiplicity of specific findings reported in our manuscript cannot be\nconvincingly featured without more extensive contextual references. Please note that we have rephrased the\nrespective passage in lines 64-70 to improve clarity.\n13. “new histopathological correlates for the stage 1/2 pancreas suggest AAb subjects are “on cusp” of\ndeveloping T1D hallmarks”: Please see our replies to item 13. subsections below.\na. “higher frequencies of islets with ≥1 associated immune cell”. Immune infiltration in AAb cases has been\npublished but it is even rarer than in early-onset T1D so this is hardly a feature of AAb subjects or islets. We\ncertainly agree that islet immune cell infiltration at the AAb stage has been reported previously (and provide\ncitations accordingly), and that it is typically less pronounced than in early-onset T1D. However, we do not claim\nthat immune cell infiltration is a distinctive “feature of AAb subjects or islets”. Rather, we report on a novel metric\nthat robustly captures the increased islet-associated immune cell burden present in the AAb stage, namely the\nfrequency of islets associated with ≥1 CD45+ cell(s) (Fig.6e). A notable advantage of this metric is its simplicity,\nand it thus can be readily applied to analyses of suitable pancreas section images that cover a sufficient number\nof islets.\nb. “early targeting of small, including “GCG-deficient” islets”. The claim that GCG-deficient islets are targeted\nearly is circumstantial and incomplete (see section 19). Unclear what is meant by “circumstantial and incomplete”.\nFig.2b demonstrates a relative reduction of all GCG-deficient islets in the PT of AAb, T1DS and T1DL donors\nthat, however, is significant only for T1DS donors (the data display is akin to the typical presentation of IDI\nfrequencies such as shown in the adjacent panel Fig.2a). Subsequently, the Fig.2c heatmap displays relative\nmagnitudes of four PT islet subsets differentiated according to ProINS/GCG expression across disease course.\nHere, we find a significant relative magnitude reduction specifically for the ProINS+GCGneg subset already in AAb\ndonors, hence our conclusion about the “early targeting of small, including GCG-deficient islets” (while similar\npatterns are also observed in the PH, significant differences for ProINS/GCG-stratified islet subsets are mostly\nfound in T1DS subjects; Fig.S3c/d). Moreover, we have now added a new panel Fig.2h that shows a modest\nreduction of small ICIs lacking alpha, gamma and delta cells at the AAb stage.\nc. “collapse of islet cluster I magnitude”. The magnitude of Cluster 1 vs 2 is likely driven by CD45 count so this is\nthe same point as a) above. The Reviewer is correct that one among other features distinguishing UMAP clusters\nI and II is the presence (cluster II) vs. absence (cluster I) of CD45+ cells (cf., Fig.6a), and that the readouts in\nFig.6e (see item a. above) and Fig.4f (“collapse of cluster I magnitude”) are related on account of considering\nislet-associated CD45+ cells. The difference, however, lies in the simple enumeration of islets with ≥1 associated\nof any UMAP clustering) vs. islet subset stratification according to UMAP clusters (Fig.4f; demonstrating a\nprogressive cluster I magnitude reduction across the entire disease course; also note that the UMAP stratification,\nin addition to cluster I, reveals other clusters without associated CD45+ cells [III-A/C, IV-A/C and V-BC]; cf.,\nFig.6a). The latter findings therefore contribute to our revised T1D history model since they suggest an islet\n“reallocation” from cluster I to cluster II beginning already in the AAb stage (Fig.8b).\n14. histopathology of T1D:\na. “tissue-wide distributed rather than strictly localized disease process”. It is unclear what supports this. Is this\na reference to the similarities across PT and PH? The quoted statement above pertains to the entire pancreas,\ni.e., PT and PH regions alike. The conclusion draws on multiple strains of evidence shown in the modified\nFig.2n/o/q as well as Figs.6f/g, 7, 8a, S4a/c/e/g, S5f/h, S7 & S8; for details, please see items b.-d. below (also,\nnote that we have added a new Fig.S5I/J that summarizes the similarities of UMAP cluster-stratified PT and PH\nislets, see “Methodology/2.”). Parts of the Discussion have now been rephrased to clarify which data support our\ncontention of a tissue-wide pathogenetic process (lines 513-515 and 521-532).\nb. “alterations of islet architectural features in early T1D are independent of islet size” This argues against the\nclaim that that islet size distribution is important. It is not clear what a ‘simple organizing principle’ means\npractically. The heterogeneity of islets at the level of size, shape, and endocrine composition etc. has long been\nrecognized (PMID: 30936150). By considering islet properties as a function of their size, our data in the modified\nFigs.2m-q & S4a-h demonstrate that there is for the most part a simple exponential relation (one-phase\nexponential association or decay) between islet size and other islet properties (cf., line 236 following; also note\nthat we added a new panel Fig.2m showing islet size-dependent INS:GCG expression ratios). We have\nrephrased the paragraph lines 236-246 in the Results section to improve clarity and restricted usage of the term\n“organizing principle” to the concluding sentence of that paragraph (lines 245-246). Applying this approach to\npathological states permits more granular analyses since a consideration of islet properties in the context of\nspecific islet size bins can reveal disease-associated differences that cannot be discerned or appreciated by a\n“bulk comparison” of islets. Of the alterations shown in Fig.2n-q, the changes in architectural features (islet cell\ndensity, solidity and circularity, Fig.2n/o), as per the above quoted sentence, are in fact somewhat independent\nof islet size (i.e., observed for many islet sizes to a similar extent); this finding also contributes to our claim that\nthe disease process is more wide-spread since it affects all islets. In contrast, more pronounced changes can be\nobserved in T1DS for islet hormone composition, and in particular for ProINS, INS and IAPP expression which,\nin fact, are reduced more obviously in an islet size-dependent fashion (Fig.2q). Note that in response to the\nspecific request in “Methodology/6.” below, we made changes to some data analysis and display in Figs.2n-q &\nS4a-h and as a result of these changes, the narrative in lines 236-264/Results and the Discussion has been\nedited to improve clarity.\nc. “absence of a direct correlation between islet composition and associated immune cell abundance”. The fact\nthat small islets without CD45+ cells also decrease ProINS expression could be easily explained since only 2D\nsections are analyzed and immune infiltrate is typically sparse and concentrated at one point. It is reasonable to\nexpect a “dropout effect” for small islets. The Reviewer is correct in raising this interpretive possibility and we\nstate as much in lines 545-547. The fact remains, however, that beta cell content of these islets is reduced\n(Fig.6g), an observation that to our knowledge has not previously been reported, and that constitutes part of our\nargument that a) it is important to analyze islet properties even in the “2D-absence” of associated CD45+ cells as\nthe islets may present with T1D-associated alterations, and that b) the disease process is more wide-spread\nthroughout the pancreas since a relative reduction rather than complete absence of beta cell content in these\nislets likely has gone unnoticed in prior work and thus contributed to the more focal impression of pathogenic\nprocesses.\nd. “consider the entire spectrum of immune cell associations with islets beyond those affected by insulitis”.\nUnclear. Traditionally, the T1D-specific histopathological hallmark involving immune cells has been insulitis,\nthe\npresence of three of more islets associated with ≥15 CD45+ cells. Despite its inarguable diagnostic utility, there\nare obvious limitations to this definition as it features an absolute number of cells in the absence of a specific\nreference islet size (cf., lines 371-373) and does not consider islets associated with a CD45+ cell burden of <15\nimmune cells. We argue that considering the entire spectrum of immune cell associations ranging from 1 CD45+\ncell (e.g., Fig.6e) to many CD45+ cells (e.g., Figs.6d & 8a) provides a more inclusive perspective on\nhistopathological alterations that, in present work, provide evidence for our claims about an early diagnostic\n10\nmetric for AAb donors as well as a more tissue-wide progression of pathogenic disease processes than previously\nappreciated.\ne. Lines 504-508. Unclear. The Reviewer is referring to the following statement: “And lastly, our analyses of\nspatial islet distributions foreground the “negative spaces” of T1D histopathology, namely an apparent loss of\nwhole islets from expanding pancreas regions that is accompanied by a contraction of residual areas with\n“normal” islet density, thus consolidating the concept of a multifocal and more synchronized origin and\nprogression of T1D pathology”. This sentence from the Discussion pertains to our findings as summarized in the\nResults section which now has been rephrased for greater clarity in lines 438-441 (further details in item 16.\nbelow). If the Reviewer specifically refers to our usage of the term “negative space” in line 525 (in graphic design,\nthe term denotes the “empty space” around an object image; specifically, “negative space” surrounding an object\nguides visual attention by better revealing the object shape contours), we believe it is an appropriate choice since\nit readily captures a specific aspect of T1D histopathology: areas in which islets appear to “drop out” (i.e., their\nlocal densities are reduced) constitute the “negative space” that grows with disease progression; at the same\ntime, areas with “normal” (i.e., unperturbed) islet densities shrink progressively as they give way to the expanding\n“negative space”.\nMajor claims (broken down from Results):\nUnclear: please see our clarifications below.\n15. Line 178: “absence of alpha cells…suggesting that “GCG-deficient” islets constitute an integral part endocrine\npancreas physiology”. If the cited work shows no effect of alpha cells on beta cells, how can they conclude GCG-\ndeficient islets are integral. The cited work PMID: 39169271 demonstrates that human beta cell pseudoislets\n(i.e., containing no alpha cells) retain the normal glucose-regulated mitochondrial respiration, INS secretion and\nexendin-4 responses of entire islets. Together with recent 3D mapping of normal pancreata by Lehrstrand et al.\ndocumenting an abundance of small “GCG-deficient” islets in Ctrl pancreata, the data suggest that “GCG-\ndeficient” islets are not a “histological artifact” but rather, “constitute an integral part endocrine pancreas\nphysiology”.\n16. The observation in Figure 6 that the distribution of islets can change from being uniform to aggregated is\nquite interesting but in the exact text in line 428-431“Since islet locations are immutable… cumulative areas, and\nmass”, I don’t understand what the specific claim is because the definitions of “tissue regions with unperturbed\nislet densities” or “islets at shorter distances” is unclear. So, I can’t confidently evaluate the merit of the claim. In\nthe Ctrl pancreas, islets are mostly randomly distributed (i.e., the respective Ripley K curve shows only modest\ndeviations from random distributions corresponding to a value of “1”). With disease progression, some islets “drop\nout” (i.e., their local density decreases) while adjacent areas with “unperturbed” (i.e., unchanged) islet densities\nnow appear as “aggregates” (although their local islet density has not changed, it is now higher in comparison to\nadjacent areas with lower densities due to islet drop-out; mathematically, this is reflected in a “density enrichment”\nin the Ripley’s K function curves shown in Fig.7b/c and further confirmed by fractal dimension analysis in\nFig.S7b). And to be sure, islets of course stay fixed in place, so “density enrichments” emerge by “subtraction”\n(i.e., loss of islets in neighboring areas) and not by “addition” (i.e., islets moving closer to each other; actually,\none might speculate that loss of exocrine tissue in T1D could bring islets into closer proximity with each other;\nthat, however, appears not to be the case since overall Delaunay distances [Fig.7e] and Delaunay areas for ICI\nislet clusters I, II and III [Fig.7j] tend to increase and not decrease). In any case, the revised sentence below\naims to describe these “density enrichment” dynamics and we further inserted another figure callout to clarify\nwhat parts of the sentence refer to which figures (lines 438-444): “Importantly, since islets do not change their\nanatomic locations, their density enrichments need to be considered in a relative fashion: because of islet loss\nat larger distances and a resulting “re-definition” for random distribution at lower islet densities, increasing density\nenrichments over disease course reflect a progressive reduction of tissue regions with unperturbed islet densities\n(Fig.7b/c). Altogether, these observations are consistent with the overall decrease of islet densities,\ncumulative areas, and mass (Figs.1i & S2h) ...” (also, see item 14.e. above).\nOverinterpreted/unsubstantiated: as detailed in our responses below, we believe that our conclusions are\nreadily supported by our data and thus they are neither overinterpreted nor unsubstantiated.\n17. line 150 “they emphasize a profound alteration of the endocrine pancreas in the T1DL stage beyond the loss\nof beta cells”. It’s not clear what is profound about the findings. This conclusion pertains to the data presented in\nFigs.1i/j & S2g-k; we believe that in aggregate, and beyond the loss of beta cells, the reduction of islet densities,\n11\ncumulative islet and CHGA areas, islet mass as well as alpha and delta cell mass constitutes a notable alteration\nof the endocrine pancreas in T1D. Whether this is can be regarded as “profound” or not may remain a matter of\ndebate, so we hope that by using the word “considerable” instead, our assessment will appear less controversial.\n18. Line 181: Authors report a trend in ProINShi islets from Ctrl to AAb (Figure 2D). They would need to show\nside by side the %ProINS/GCG-negative and %ProINS/GCG-positive islets to say the GCG-deficient are more\nvulnerable than GCG-positive islets. Former Fig.2D, now Fig.1k (PT), and corresponding Fig.S2l (PH), display\na trend toward reduction of the ProINShi staining area in islets of AAb vs. Ctrl subjects; they do not differentiate\nbetween GCG+ and GCGneg islet subsets. Line 170 following refers to Figs.2b/c & S3c/d which stratify islet\nsubsets according to ProINS/GCG expression; please see our response to item 13.b. above for details about\npreferential targeting of small, including GCG-deficient, islets.\n19. Line 252: Are Ins- and Ins+ islets included in 2Q (ProINS through SST columns)?\nWithout knowing, it’s hard to separate the fraction of beta cells in each islet vs the\nfraction of islets that are completely insulin deficient. The data in previous Fig.2P/Q,\nnow Fig.2n-q, features islet size-stratifications of combined PT and PH islets within\n60\neach donor group, i.e., both ICIs and IDIs are included. Together with our earlier\ndemonstration of an apparent loss of small islets (<3,000µm2) in the AAb stage 40\n(Fig.2g; updated as per item “Methodology/4.” below) and the modest reduction of the\nProINS+ (i.e., ICI) fraction in that contingent (new Fig.2h), the data in Fig.2q indicate 20\nthat overall reduced ProINS/IAPP (but not INS) expression likely results from a\n0\ncombination of ProINS/IAPP depletion in particular in the smallest islets and reduced\nexpression of these hormones, especially in slightly bigger islets (3,000-6,000µm2; 103 104 105\nalso, see adjacent figure where we display relative ProINS content only in PT ICIs). Islet size bins (µm2)\nFurther analyses of UMAP cluster I ICIs (Figs.3d, 4a & S5b/d) presented later on\ndemonstrate a ProINS/IAPP expression reduction preferentially in small islets even in\nthe histopathological absence of CD45+ cells (Fig.6g).\na. ”reflects an early loss of beta cells especially in smaller islets”. By “early loss”, do you mean in Ctl vs AAb or\nCtl vs T1DS? This conclusion follows the callouts to previous Figs.2P/Q & S4C-H where pronounced differences\nare observed for T1DS vs. Ctrl donors. However, these findings are narratively preceded by our discussion of a\nsmall islet loss (islets in the 1,000-3,000µm2 size range) already at the AAb stage as detailed in Fig.2g (see\nupdate as per item “Methodology/4.” below) and the new panel Fig.2h (also, see above), and we therefore opted\nin the sentence above for the expression “early loss” since it covers both AAb stage and at-onset T1DS cases.\nb. “preservation of residual beta cell mass in larger T1DS islets”. The idea that larger islets preserve beta cell\nmass is interesting, but the order of events can’t be discerned from these data. It is possible that islets shrink\nafter losing beta cells or they all shrink in parallel as disease progresses, not necessarily that smaller islets are\ntargeted first. It would be good to invoke the insulitis data to make the case that islet size effects targeting. We\nagree that overall, there is a reduction of median islet size in T1DL vs. Ctrl donors that is significant in the PT but\nnot in the PH (Figs.2j & S3h). However, our temporal reconstruction of pathological events is based on the\nconsideration of islet subsets differentiated according to size and, later on, UMAP cluster allocation (also, see\nour response to comments 13.b. and 19.b. above where we emphasize the apparent physical loss of islets that\nare <3,000µm2 already in the AAb stage). Please see item 20. below for more details about how the results of\nour analyses support the above conclusions.\nLastly, it is not clear how invocation of insulitis data will be helpful at this stage of the narrative. We only address\nthe concept of insulitis later on (line 342 following) and one of the issues discussed therein is the whiff of circular\nlogic that burdens the insulitis definition: by setting a threshold in absolute numbers (i.e., ≥15 islet-associated\nimmune cells) in the absence of a fixed denominator (i.e., islet size is not specified), the definition privileges larger\nislets which, due to their size, have a greater probability to be associated with immune cells. Thus, the conclusion\nthat insulitis is preferentially observed for larger islets, while strictly speaking correct, does not imply that larger\nislets are, in fact, the preferred autoimmune targets since the insulitis definition itself is biased towards larger\nislets (also, cf., Fig.S6h and corresponding legend; regarding insulitis, see also items 14.d. above and 21. below).\n20. Line 304: “early targeting of smaller ICIs and later appearance of larger IDIs” First, it’s unclear what is meant\nby “appearance”. Second “larger” is confusing because IDIs are smaller than ICIs. If I understand correctly, the\nclaim is that the gradual increase in islet size (in ICIs and IDIs) over the course of T1D suggests that smaller\n12\nevitaleR\n%\naera\ntelsi\n+SNIorP\nICI (ProINS+)\nfrequency distributions\n**\n**\nStatistics refer to bin-\nspecific unpaired t-tests\nislets are losing insulin and becoming IDIs before larger islets do. Again, I think this is interesting but should not\nbe overstated. If this were the only cause, one would expect IDIs at late stage T1D to be similar size to ICIs (once\nall islets lose insulin). To be fair, the authors do not claim that size-dependent disease kinetics is the only cause.\nConclusions should be qualified and elaborated upon in the discussion. The Reviewer is correct in stating that,\ndespite considerable variability, IDIs tend to be smaller than ICIs; this is implied in the data presentation for PT\nislets in Fig.2j where most islets in Ctrl donors are ICIs and most islets in T1DL donors are IDIs (cf., Fig.2a; see\nalso item 19.b. above). However, our islet subset analyses by UMAP clustering complicate this conclusion since,\nfor example, cluster I ICIs have about the same size as cluster V-A IDIs (Fig.S5a, left column). At the same time,\nconsidering UMAP cluster-specific islet sizes provides analytical opportunities to better understand the\n“histopathological dynamics” of disease progression.\nThe sentence quoted above represents our conclusion specifically pertaining to islet property differences within\nthe same UMAP clusters as assessed across disease stages (as opposed to our earlier comparisons of islet\nproperties across different clusters). Specifically, we stated that “... opposing trajectories for islet size increase\nvs. circularity decrease in cluster II ICIs and especially cluster IV (PH) and V-A/V-BC IDIs can serve as a\nhistopathological correlate for the dynamics of disease progression (Figs.S5e/g)”. Focusing, for example, on islet\nsizes in cluster V-BC (Figs.S5e/g, lower left plots; cluster V-BC islets are by definition all IDIs), the data shows\nthat Ctrl islets have a small size whereas AAb, T1DS and T1DL islets demonstrate progressively larger sizes\n(note that this data display does not account for the numbers of islets in cluster V-BC which are of course very\nlow for Ctrl and very high for T1DL subjects). Now, consider the same data for cluster V-A islets (Figs.S5e/g,\nleft, second from bottom; cluster V-A islets are also all IDIs): Ctrl donors do not have any cluster V-A islets;\nhowever, they “appear” in the AAb stage and as disease advances, their average sizes become larger. Lastly,\nnote that these “dynamics” are accompanied by a decrease of islet circularity (Figs.S5e/g, second column,\nbottom and second from bottom panels). Based on the implied disease kinetics as illustrated in our model\n(Fig.8b), and assuming that beta cell destruction will never lead to islet size increase (though it may very well\nmay lead to islet size decrease), we interpret the “opposing trajectories for islet size increase vs. circularity\ndecrease” with disease progression as an early loss of small ICIs (that become small IDIs after destruction of\ntheir beta cells), and a later loss of larger ICIs (that correspondingly become larger IDIs and thereby raise the\naverage size of all IDIs).\nWe have now rephrased that passage in lines 308-313 to improve clarity. Lastly, please note that this\ninterpretation is not contradicted by our observation in lines 206-207/Fig.2j that demonstrate an apparent\nincrease of PT islet sizes in the AAb stage; we explain this “paradoxical” outcome by the physical loss – and not\ntransition to an IDI state - of very small ICIs (Fig.2g) such that the median size of remaining islets in fact increases.\n21. Line 340-342: 5A looks like there are 3 AAb cases with insulitis and these are the three that also have CD45+\ncells. Text should be clarified to explain that CD45+ cells can be found in specific AAb cases, not that CD45+\ncells are elevated at the stage generally. Unfortunately, it is unclear to us what the Reviewer means. We state in\nlines 344-346 that 3 out of 6 AAb cases present with insulitis (i.e., having ≥3 islets with ≥15 associated immune\ncells) with a callout to Fig.5a. We do not claim that “these are the three [AAb donors] that also have CD45+ cells”\nsince some immune cell association with islets, albeit to different degrees, is found in all donors. Also, note that\nwe have reallocated data panels from the former Fig.5 to the new Figs.5 & 6; Fig.5 now features data on insulitis\n(including new panels Fig.5b/right showing ICI and IDI insulitis frequencies) and insulitic islet properties, and\nFig.6 displays the UMAP cluster-stratified and overall islet-associated immune cell burden. Regarding the latter\npoint, we consider the increase of islet-associated CD45+ cell numbers and densities already in the AAb stage\nnoteworthy despite the lack of statistical significance (Fig.6d), we discuss the matter in line 390 following, and\nwe describe what we believe to be a rather useful metric in Fig.6e that reflects the enhanced islet-associated\nimmune cell burden in stage 1/2 T1D (also see other comments about CD45+ cell analyses above).\n22. Line 388: Is there evidence in this dataset that CD45+ cells are recruited to islets directly? For example, are\nthere increases in CD45+ cells in acinar tissue outside islets? If not, it could help substantiate that immune cells\nare recruited actively at the AAb stage. In the present study, we have only quantified the islet-associated immune\ncell burden and we document an overall increase, though not significant, of islet-associated CD45+ cell numbers\nand densities at the AAb stage (Fig.6d). However, preceding analyses focused on UMAP cluster II (Fig.5d) as\nwell as the development of a novel metric as discussed above (Fig.6e) reveal a significant difference already at\nthe AAb stage. An increase of immune cells in exocrine tissue of AAb donors has been previously reported and\nwas found to be significant for CD11c-expressing antigen-presenting cells (Rodriguez-Calvo et al. PMID:\n24947367); more recent studies quantifying diverse immune cell populations in the exocrine pancreas either did\n13\nnot include AAb donors (Damond et al.) or were limited to only two AAb donors, one of which was a GADA+\n“stage 0” donor (Barlow et al.).\n23. Line 443. “Collectively, our observations support the notion that the typically regionalized patterns of T1D\nrepresent residuals of a tissue-wide islet depletion, the extent of which is partially obscured by its fundamentally\ndispersed nature.” Can volumetric data be used to validate this? Is there any evidence in literature or biological\nmechanism for islets “disappearing” vs growth of exocrine cells? We agree that this is a very interesting question.\nFor example, Seiron et al. (PMID: 31493350) report that islet density (i.e., islet numbers per mm2) is significantly\nreduced in T1DS and T1DL cohorts compared to Ctrl donors. This observation is particularly noteworthy since\nother studies have observed comparatively reduced pancreas weights/volumes with T1D onset (e.g., PMID:\n33471743), and since the vast majority of weight/volume is contributed by exocrine tissue, the “denominator” for\nislet density calculations is actually smaller in T1D. Seiron et al. privilege a data interpretation by which “failure\nto establish a sufficient islet number to reach beta cell mass needed to cope with increased insulin demand\ncontributes to T1D susceptibility”. Along similar lines, Murrall et al. suggest that reduced “endocrine object”\ndensities observed especially in “endotype 1” T1D subjects (more aggressive, early age T1D onset) reflect a\npotential combination of inherently smaller beta cell mass together with the selective targeting of “small”\nendocrine objects early in the disease process which results in a subsequent failure to generate “larger”\nendocrine objects (https://www.biorxiv.org/content/10.1101/2025.04.11.648319v1). These studies, just as our\nown, are limited by their cross-sectional nature, and our own conclusions emphasizing the seeming loss of islets\nin T1D does not and cannot challenge the possibility that inherently smaller pancreata and associated lower beta\ncell mass constitute a relevant risk factor for T1D development, and we state as much in lines 545-547.\nTo our knowledge, volumetric analyses of post-mortem pancreas tissue remain limited to date, but we cite the\nstudies by Lehrstrand et al. (only Ctrl pancreata) and Drotar et al. (PMID: 38850534) that reports a non-significant\ntrend towards reduced islet densities in pancreatic slices from AAb and T1D donors; a recent preprint by Rippa\net al. also confirms reduced islet densities and loss of small GCG-deficient islets in 3D analyses of T1DS cases\n(https://www.biorxiv.org/content/10.1101/2025.05.14.654045v1). Definitive resolution of these questions,\nhowever, will likely have to await future longitudinal in vivo high-resolution imaging studies. Lastly, we have\nrephrased the above quoted sentence in lines 455-458 to improve clarity.\nMethodology:\n1. The islet and cell mass calculations are unreliable. IHC is not quantitative enough to evaluate the extremely\nlarge dynamic range of hormone secretion. It is better to simply show the pancreatic mass and islet/endocrine\ncell area separately. Calculation of beta cell mass as done here (multiplication of fractional beta cell area in whole\nslide images with corresponding pancreas weight) is an admittedly imperfect but nevertheless widely employed\npractice (e.g., PMID: 26581594 or 22875233; similar calculations are also performed for other endocrine cells\nand whole islets). We therefore report these data for comparative purposes, and no claims about the dynamic\nrange of hormone secretion are implied. Absolute and relative pancreas weights are featured in Table S1 and\nFig.S2d, respectively, and fractional hormone staining areas are shown in Fig.S2i.\n2. It is unclear the extent to which the differences in percentages between PH and PT throughout the manuscript\nare due to the PPY islets in the uncinate process. Would it not be clearer to characterize the uncinate process\nand then exclude this area from all following analyses? We have structured our parallel analyses and discussion\nof PT and PH regions by first featuring traditional analysis modalities (e.g., quantification of fractional hormone\nstaining areas in tissue sections or islets; islet architecture etc.) to demonstrate, where applicable, the accuracy\nof our semi-automated image analysis pipeline in relation to the relevant literature (note, however, that quite a\nfew publications interrogating PH sections leave the unique anatomy of the uncinate process unmentioned). Only\nthen do we conduct UMAP and spatial analyses that together reveal that seeming differences between average\nPT and PH islet properties obtained with traditional experimental readouts are in fact due to admixture of islets\nwith unique properties found in the PH uncinate process. We believe that the overall trajectory of our\nexperimentation and argumentation is sensible, and we have now added a new supplementary Fig.S5i/j that\ncombines a direct comparison of UMAP cluster-stratified PT/PH islet architectural features (taken from the\nprevious Fig.S6A/B) with UMAP cluster-stratified PT/PH islet endocrine properties to provide ready support for\nour contention of “... an essentially identical endocrine organization across pancreas regions that locates residual\ndifferences specifically to the uncinate process in the PH” (Discussion lines 502-504).\n3. Panels S3F using Jaccard index (Line 189) are not meaningfully mathematically different from the fraction of\nthose hormones as reported in the main figure. Please note that we undertook the analyses for beta cell\n14\nhormones with particular consideration of Leete et al. PMID: 32172310 who reported a relative increase of\nProINS/INS co-localization (quantified as “Manders overlap coefficient”) in T1DS cases, and we therefore\ndetermined the overlap areas of dual combinations of hormones co-expressed by alpha cells (CHGA, ProGCG,\nGCG) or beta cells (CHGA, ProINS, ProINShi, INS, IAPP) by calculation of Jaccard indices. In the legend to\nFig.S3f, we note that our observation about reduction of ProINS-INS Jaccard indices with T1D progression\nappears to contravene the results by Leete et al., but importantly, we provide a succinct discussion invoking both\ntechnical considerations as well as study cohort differences to suggest a resolution for this issue.\nIn regard to the Reviewer’s specific concern about a lack of differences between Jaccard indices and individual\nhormone staining areas, we agree that for alpha cell hormones (GCG, ProGCG, and CHGA), changes in Jaccard\nindices (Fig.S3f) reflect an increase in relative hormone staining area (cf., Fig.1k). However, for beta cells the\ndecrease of the “intersection over union” (IOU) with disease progression is actually greater than the decrease of\nindividual INS, ProINS and IAPP areas. To demonstrate this, we calculated the probable or “expected” IOU for\nvarious hormone pairs and then compared it to the measured IOU. The table below shows the ratio of these two\nvalues, where low numbers mean the hormones are more segregated than expected by multiplication of the\nareas (if the Jaccard index were purely a function of the prevalence of each hormone, then the intersection should\nbe the product of the two areas, each normalized to the union area).\nGCG-CHGA GCG-ProGCG ProGCG-CHGA INS-ProINS INS-IAPP IAPP-ProINS INS-CHGA\nCtrl PH 1.00 0.80 0.92 0.94 0.89 0.98 0.93\nCtrl PT 1.00 0.81 0.89 0.95 0.84 0.98 0.95\nAAb PH 1.01 0.85 0.95 0.95 0.77 0.99 0.93\nAAb PT 1.00 0.82 0.95 0.94 0.78 0.98 0.96\nT1DS PH 0.90 0.76 0.90 0.65 0.35 0.55 0.90\nT1DS PT 0.98 0.84 0.93 0.74 0.52 0.74 0.94\nT1DL PH 1.09 0.78 0.98\nT1DL PT 0.98 0.78 0.93\nFor alpha cell hormones, the relationship between measured IOU and the calculated “probable” IOU is the same\nin all T1D states (although GCG-ProGCG was sometimes lower than expected, there is no change with disease\nprogression). In contrast, for beta cell hormones, the measured IOU notably decreases compared to the\nmathematically “likely” IOU in T1DS cases (and in the AAb case, for the INS-IAPP pair). Notably, there is no\ndecrease in the measured vs. expected ratio for the overlap between INS-CHGA. The text has been updated\naccordingly in lines 184-189 and 794-801.\n4. Figure 2K left vs right. How can AAb have a different islet distribution than the other 3 groups on the left but\nT1DL has the different distribution than the other 3 in the right? Fig.2g/left displays islet frequency distributions,\ni.e., relative islet numbers (%), whereas Fig.2g/right displays islet area distributions, i.e., the product of relative\nislet numbers (%) and average islet area in respective bins, normalized to 100% for each donor group. While we\nemphasize a broad similarity of islet frequency and area distribution curves across donor groups, we also note\nand discuss more subtle disease stage-specific differences. To clarify the latter aspect, we have now adjusted\nthe data display (Fig.2g presents data as mean+/-95% confidence interval [CI] error bands, similar to the display\nin the modified Fig.2m-q) and conducted islet size bin-specific ANOVA tests to highlight statistically significant\ndifferences. We have modified the narrative in lines 199-205 accordingly to provide a better and more detailed\naccount of our data interpretation.\n5. To ensure that differences in sphericity and circularity that accompany T1D are not an artifact of the image\nprocessing, (e.g perhaps the reduced hormone expression and imprecise thresholding leads to cell drop out and\nhence inaccurate measurements of the perimeter and other features). It would be helpful to show multiple images\nof islets that represent different sphericities, aspect ratios, and solidity. A quantitative validation would also be\nhelpful. For example, since chromogranin is unchanged, you could calculate the geometric shape descriptors\nonly using chromogranin. The shape descriptors as employed here pertain to entire islet structures, not individual\nhormone staining areas (accordingly, CHGA staining areas cannot be used as islet shape descriptors). The\ngeneration of islet objects is detailed in Methods lines 1,046-1,068, employs six hormone stains, yields\ncontiguous objects incorporating non-endocrine areas, and is systematically applied to all islets. As mentioned\nin “Minor weaknesses/8.”, we have now included the requested sample islet images in Fig.S1b.\nWe regret that it is unclear to us what exactly the Reviewer considers a “quantitative validation”; however, we\ndiscuss solidity values as determined in the present study in comparison to those derived by Damond et al. in\n15\nsection “Minor weaknesses/item 10”. Also, note that we do not quantify sphericity; rather, as mentioned in lines\n216-219 (now rephrased), we calculate the “spherical equivalent volume” (cf., Fig.1e) for all islets captured in the\npresent study, and we feature the summary results in Fig.S3k for comparative purposes (and with specific regard\nto Lehstrand et al. PMID: 38632302).\n6. 2Q should contain error bars, ideally via bootstrapping. The previous Fig.2P/Q has been reformatted in\nresponse to the Reviewer’s request and the data is now presented in Fig.2n-q; similar changes have also been\nmade to the corresponding Fig.S4a-h (that features the same data as Fig.2n-q as well as additional related data\nin simplified panels to better detail error distributions and exponential curve fits). In brief, the previous panels\nplotted donor group means of islet properties from combined PT/PH islets against islet size bins. We have now\ncalculated donor-specific islet property means allowing for display of errors (95% CI shown as error bands)\nthroughout Figs.2m-q & S4a-h, and we performed islet size bin-specific ANOVA tests as detailed in the modified\nfigure legend to Fig.2n-q (note that we have removed the T1DL GCG and SST data from respective Fig.2p\npanels to avoid crowding; those data can be found in Fig.S4h). As a result of these changes, the narrative in\nlines 235-264 has been edited accordingly.\n7. In Figure 2D, is the z-score computed column wise or row wise, and separately or pooled between PT and PH\nislets. It appears that the Reviewer is referring to the heatmap in Fig.2c rather than the scatter plots in the\nprevious Fig.2D. The heat map does not display z-scores but rather the relative magnitude (mean percentages)\nof indicated ProINS/GCG islet subsets in respective donor groups (i.e., it is organized “row-wise”); the data\npertains to PT islets only (we state in the figure legend that all summary plots pertain to the PT, unless mentioned\notherwise); and corresponding PH data are found in Fig.S3d (similar considerations apply to the heatmap in\nFig.2e).\n8. Is figure 4A intended to show the frequency of islets in each cluster or the ProINS expression over time. It\ndoesn’t serve either purpose well in its current form. We respectfully disagree with this assessment. Fig.4a, in\ncontrast to Fig.3b/c (which features all islets combined from all donors and pancreas regions), stratifies the\nUMAP display of islets across pancreas regions (PT and PH) as well as disease stage (Ctrl, AAb, T1DS, T1DL);\nthe additional color-coding of ProINS expression serves to highlight the presence of ICIs. As such the figure is\nintended to demonstrate the implied dynamics of islet UMAP cluster allocation with disease progression, and we\nbelieve that the chosen visual display achieves this purpose (see also “Major weaknesses/item 5.” above).\n9. Are only hormones used in islet UMAP or geometric features as well? UMAP analyses, including a summary\nof all input features utilized in the present analyses, are detailed in the Methods section under the header\n“CytoMAP analysis” lines 1,092-1,110.\n10. In Figures 3-4, It’s essential to show the features that were used for the umap and their means in each cluster.\nThe umap uses the islet area and CD45+ cell count which clearly will separate clusters 1 and clusters 2 but this\nwas not clear at all from the text or figures. As mentioned in item 9. above, UMAP analysis details are provided\nin the Methods section lines 1,092-1,110. The heatmap-adjacent histograms in Figs.3d & S4j display islet feature\ndistributions combined from all donors islets stratified according to cluster allocation (i.e., corresponding to the\nUMAPs in Fig.3c). We believe that presenting feature distributions here, rather than means, is more informative.\nThe heatmaps, in turn, display donor-stratified means of indicated islet properties for each major cluster I – V-\nBC. Fig.3e provides a succinct summary of distinctive architectural and endocrine UMAP cluster features,\nincluding the different sizes, circularities and ProGCG/GCG content between cluster I and II islets. Lastly, the\nReviewer is correct that another feature distinguishing clusters I and II is the absence (cluster I) or presence\n(cluster II) of CD45+ cells; these data are shown in Fig.6a and they are discussed in depth in line 373 following\nand line 402 following (see also rest of Figs.5 & 6).\n11. Figure 5E needs statistics. Statistical differences between properties of insulitic vs. non-insulitic islets were\ncalculated with paired Student’s t-test using data from individual AAb and T1DS PT and PH tissue sections as\ndetailed in the modified figure legend, with asterisks added where applicable to the histogram panels in Fig.5e.\nAlso, we have rephrased the narrative in lines 356-361 to clarify that insulitic islets, other than being larger and\nexhibiting lesser cellular density, are not fundamentally different from non-insulitic islets.\n12. In Figure 7, the use of Delaunay triangulation and ‘isolated islets’ does not effectively describe the\nregionalization of islet clusters because it is highly influenced by the abundance of the clusters. A manual\napproach could include repeatedly sampling neighboring islets vs random islets from the same tissue and\ncompare the enrichment of a given cluster in the sample. Alternatively, a spatial correlation method such as a\n16\ncross-K function would be more direct and appropriate. We agree that Delaunay triangulation alone is not\nsufficient to fully describe spatial islet organization (please note that Delaunay triangulation is discussed in former\nFig.6 rather than former Fig.7, and which are now represented as Figs.7 & 8). We therefore conducted density\nenrichment calculations based on Ripley’s K function as shown in Figs.7b/c & S7a. Here, the distances between\nislets are normalized to the total islet density in each section individually; accordingly, the enrichment shows how\nmuch higher the density is around the average islet compared to the whole tissue density. We strongly believe\nthat automation of this task is preferable to manual analyses since it compares every islet within the tissue section\nwith every other islet therein, something that cannot be recapitulated manually.\nOther comments:\nIs any prescreening performed on the tissues for insulitis before selecting blocks for the study? If so, this\nminimizes the ability to use CD45+cells as a diagnostic as proposed in line 387. As mentioned in lines 49-51,\npancreata from stage 1/2 donors and those at onset of stage 3 are exceptionally rare. Given their importance for\nnatural history T1D studies, we therefore worked with the nPOD organ procurement core to provide us with as\nmany of those at-risk and at-onset donor samples as possible; we also included donors with very short duration\nof clinical disease (both at onset and short-duration T1D cases constitute our T1DS group), and Ctrl as well as\nT1DL donors were matched accordingly as summarized in lines 992-995. Hence,\nPH\nwhile we did not engage in pre-screening, AAb donor 6310, despite having only\n100 100\na single autoantibody and therefore classified as “stage 0”, was specifically added ✱\n80 80 ✱\ndue to the presence of “low-grade” insulitis as determined by nPOD pathologists. 60 60\nHowever, our semi-automated image analyses of PT and PH tissue sections 40 40\nmade available to us did not confirm this diagnosis. All of this information is 20 20\nprovided in Tables 1 & S1. Lastly, deliberate exclusion of donor AAb 6310 from 0 0\nA\nor\nN\nT\nO\nD\nA\nS\nt\ng\ne\nr\ns\no\nts\nu p\nin\ns\nF\na\ni\ns\ng\n.\ns\n6\nh\ne\no\nd\nw\no\nn\ne\ni\ns\nn\nn\nth\no\ne\nt a\na\nb\nd\no\nja\nlis\nc\nh\ne n\ns\nt\ni g\nfi\nn\ng\ni\nu\nfi\nr\nc\ne\na\n.\nn t differences between Ctrl, AAb Ctrl AAb\nT1\nDS\nT1\nDL Ctrl AAb\nT1\nDS\nT1\nDL\nDo islet cluster foci in Figure 7 comport to lobules boundaries? This is an interesting question (the Reviewer is\nlikely asking specifically about “insulitic islet” foci in Fig.8a), and visual inspection appears to suggest that these\nfoci do not necessarily comport to lobule boundaries. However, we have not yet been able to incorporate a robust\ndemarcation of lobule boundaries into our analysis pipeline which is necessary to assess this hypothesis in an\nunbiased and comprehensive fashion.\nTo validate the utility of geometric shape descriptors, can they be measured in HandE images and applied to\nlarge datasets? Yes, this could be done and we advocate for future application of our image analysis pipeline,\nsome modifications notwithstanding, to analyses of archival images in lines 538-541/Fig.8c. We would like to\nemphasize that the segmentation workflow developed here for MICSSS images does not directly apply to H&E\nimages, and developing a new segmentation algorithm of H&E-stained pancreas images is beyond the scope of\nthis work. As mentioned above, however, future studies can certainly leverage our object analysis pipeline to\nmeasure shape descriptors in other datasets.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early\nCareer Researchers who co-review manuscripts. We thank the Reviewer for their efforts.\nReviewer #4 (Remarks on code availability):\nI am not able to assess the code, as it falls outside my technical expertise and background. Therefore, I cannot\nevaluate the reproducibility of the results or the usability of the code for the broader community.\n17\nhtiw\nstelsi\n%\nsllec\n+54DC\n0>\nPT\n✱\n✱✱\nResponse to Referees\nOnce again, we wish to thank the Reviewers for their time and effort to assess our manuscript, and we in particular\nappreciate the additional task taken on by Reviewers #1 and #2 to evaluate the extensive discussion of Reviewer\n#3 comments. Below, please find our point-by-point reply in which the callout to manuscript lines refers to the\nnewly revised manuscript (Reviewer comments are in black font, our replies are in blue font). In the revised\nmanuscript itself, changes are highlighted by gray background shading to readily identify the edits made\nthroughout the entire text.\nReviewer #1 (Remarks to the Author):\nThe Authors have satisfactorily addressed all the points raised by this reviewer. They are nevertheless invited to\nsuccinctly mention the issues discussed in their responses to point 4 (co-stainings) and point 5 (beta cell\ndedifferentiation) in the manuscript, for the sake of completeness. We thank the Reviewer for this assessment,\nand we now emphasize the limitations of our experimental approach in lines 662-668 by detailing why MICSSS\ntechnology is not the method of choice for robust identification of bi- or tri-hormonal endocrine cells in islets,\nand why we cannot make any statements about potential beta cell dedifferentiation.\nReviewer #1 (Remarks on code availability):\nThe code can be now better appreciated. We thank the Reviewer for this assessment.\nReviewer #2 (Remarks to the Author):\nThe manuscript has been revised. Figures have been modified in line with reviewers’ recommendations, and a\nsupplementary table has been included to present the quantitative data underlying Fig. S2e, Fig. S2g, and Figs.\n1i and S2h (including total tissue area, parenchymal tissue area, and the number of islets analyzed per section).\nThe issues raised in comments 3 and 4 have been comprehensively addressed, and the response to comment\n2 is convincing. We thank the Reviewer for this assessment.\nReviewer #3 comments mediated by reviewer #1 and #2\nI have gone through the comments by Reviewer #3 and the responses by the the Authors. The comments are\nvery detailed and (generally speaking), pertinent. In some cases they are not fully clear, and in a few instances\nlook somewhat repetitive. Having said that, the Authors have seriously taken into consideration the many points\nraised, and to my opinion they have provided reasonable and satisfactory answers.\nAll the comments raised by Reviewer #3 have been addressed, either through amendments to the manuscript or\nby providing clarifications in the rebuttal. We thank the Reviewers for these assessments.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early\nCareer Researchers who co-review manuscripts. We thank the Reviewer for their effort."}]

Decision letter