Biology subjects

Casella, G.

Publications and source records attributed to Casella, G..

5 recordsLinked to original sources

bioRxiv2026

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, 33 RAR, and 8 kidney control (KC) biopsies. We developed a computational pipeline to segment and classify tubules, capillaries, and glomeruli in whole-slide images using three trained neural networks (Renal Damage diagnosis, RDDx). RDDx achieved high accuracy and generalizability, reliably identifying small capillaries and differentiating tubular and vascular inflammation in kidney tissues. Both LuN and RAR showed reduced tubular and capillary areas with expanded interstitial space. LuN displayed patchy clusters of stressed and inflamed tubules, whereas RAR exhibited diffuse injury. Within RAR, T cell-mediated rejection (TCMR) showed intense tubulitis while antibody-mediated rejection (ABMR) featured proliferating and inflamed capillaries near atrophic tubules. RDDx quantitative metric outputs correlated with histopathological scores, highlighting their reproducibility and clinical relevance. Stressed tubules in mildly inflamed LuN biopsies suggested they were a sensitive injury marker, while proliferating capillaries revealed microvascular remodeling in ABMR. These findings indicated RDDx can identify and quantify damage mechanisms specific to each renal disease thus facilitating future mechanistic studies and therapeutic target discovery.

immunology↗

bioRxiv2025

Resolution of in situ inflammation in human lupus nephritis into principal immune cell trajectories

In human lupus nephritis (LuN), tubulointerstitial inflammation (TII) is prognostically more important than glomerular inflammation. However, a comprehensive understanding of both TII complexity and heterogeneity is lacking. Herein, we used high-dimensional confocal microscopy and specialized computer vision techniques to quantify immune cell populations and localize these within normal and diseased renal cortex structures. With these tools, we compared LuN to renal allograft rejection (RAR) and normal kidney. In both LuN and RAR, the 33 characterized immune cell populations formed discrete subgroups whose constituents co-varied in prevalence across biopsies. In both diseases, these co-variant immune cell subgroups organized into the same unique niches. Therefore, inflammation could be resolved into trajectories representing the relative prevalence and density of cardinal immune cell members of each co-variant subgroup. Indeed, in any one biopsy, the inflammatory state could be characterized by quantifying constituent immune cell trajectories. Remarkably, LuN heterogeneity could be captured by quantifying a few myeloid immune cell trajectories while RAR was more complex with additional T cell trajectories. Our studies identify rules governing renal inflammation and thus provide an approach for resolving LuN into discrete mechanistic categories. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/638655v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@109dd33org.highwire.dtl.DTLVardef@995037org.highwire.dtl.DTLVardef@161525dorg.highwire.dtl.DTLVardef@6692d8_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

immunology↗

bioRxiv2024

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue, which is providing exciting insights into the spatial features of human disease [1]. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging [2]. To address this need, we have adapted spectral angle mapping - an algorithm used widely in hyperspectral image analysis - to compress the channel dimension of high-plex immunofluorescence images. As many high-plex immunofluorescence imaging experiments probe unique sets of protein markers, existing cell and pixel classification models do not typically generalize well. Pseudospectral angle mapping (pSAM) uses reference pseudospectra - or pixel vectors - to assign each pixel in an image a similarity score to several cell class reference vectors, which are defined by each unique staining panel. Here, we demonstrate that the class maps provided by pSAM can directly provide insight into the prevalence of each class defined by reference pseudospectra. In a dataset of high-plex images of colon biopsies from patients with gut autoimmune conditions, sixteen pSAM class representation maps were combined with instance segmentation of cells to provide cell class predictions. Finally, pSAM detected a diverse set of structure and immune cells when applied to a novel dataset of kidney biopsies imaged with a 43-marker panel. In summary, pSAM provides a powerful and readily generalizable method for evaluating high-plex immunofluorescence image data. Significance StatementUnderstanding the cellular constituents captured by highly multiplexed tissue imaging is a major limitation affecting the usability of these novel imaging methods. Many imaging experiments have uniquely designed staining panels, reducing the generalizability of cell classification models to new datasets. We present pseudospectral angle mapping (pSAM), which can compress high-dimensional image data into class representations. We demonstrate that the class representations generated by pSAM can be used to interpret high-plex image data and guide cell classification. Importantly, we also demonstrate that pSAM can generalize to new image datasets--collected with a different staining panel in samples from different tissues--without manual image annotation, subjective intensity gating, or re-training an algorithm.

bioinformatics↗

bioRxiv2021

In lupus nephritis, specific in situ inflammatory states are associated with refractory disease and progression to renal failure

In human lupus nephritis (LN), tubulointerstitial inflammation (TII) on biopsy predicts progression to end stage renal disease (ESRD). However, while approximately half of patients with moderate or severe TII develop ESRD, half do not. Therefore, we hypothesized that TII is heterogeneous, with distinct inflammatory states associated with different renal outcomes. We interrogated renal biopsies from LN longitudinal and cross-sectional cohorts using both conventional and highly multiplexed confocal microscopy. To accurately segment cells across whole biopsies, and to understand their spatial relationships, we developed unique computational pipelines by training and implementing several deep learning models and other computer vision techniques. Surprisingly, across biopsies, high B cell densities were strongly associated with protection from ESRD. In contrast, elevated CD4-T cell population densities, which included CD8, {gamma}{delta} and double negative (CD4-CD8-{delta}-, DN) T cells, were associated with both acute refractory renal failure and gradual progression to ESRD. Interestingly, lymphocytes and dendritic cells were organized into discrete clusters or neighborhoods that could be characterized by the enrichment for specific cell populations. B cells were often organized into large neighborhoods with CD4+ T cells including T follicular helper-like cells. In contrast, the CD4-T cell populations formed small cellular neighborhoods whose frequency predicted subsequent progression to ESRD. These data reveal that in LN, specific in situ inflammatory states are associated with refractory disease and progression to ESRD. One sentence summaryUsing deep machine learning to analyze confocal microscopy data, we demonstrate that in lupus nephritis, CD4-T cell populations, including CD8+ and {gamma}{delta} T cells, organize into specific spatial neighborhoods that predict progression to renal failure.

immunology↗

bioRxiv2020

Targeting CSF-1 ameliorates experimental autoimmune encephalomyelitis by depleting inflammatory monocytes and microglia in the central nervous system without affecting quiescent microglia.

The receptor for colony stimulating factor 1 (CSF-1R) is important for the survival and function of myeloid cells that mediate pathology during experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis (MS). CSF-1 and IL-34, the ligands of CSF-1R, have similar bioactivities but distinct tissue and context-dependent expression patterns, suggesting that they have different roles. This could be the case in EAE, given that CSF-1 expression is upregulated in the CNS, while IL-34 remains constitutively expressed. We found that targeting CSF-1 with neutralizing mAb halted ongoing EAE, with efficacy superior to CSF-1R inhibitor BLZ945, whereas IL-34 neutralization had no effect, suggesting that pathogenic myeloid cells were maintained by CSF-1, not IL-34. Both anti-CSF-1- and BLZ945-treatment greatly reduced numbers of monocyte-derived cells and microglia in the CNS. However, anti-CSF-1 selectively depleted inflammatory microglia and monocytes in inflamed CNS areas, whereas BLZ945 depleted virtually all myeloid cells, including quiescent microglia, throughout the CNS. Anti-CSF-1 treatments reduced the size of demyelinated lesions, and microglial activation in the grey matter. Lastly, we found that bone marrow-derived immune cells were the major mediators of CSF-1R-dependent pathology, while microglia played a lesser role. Our findings suggest that targeting CSF-1 could be effective in ameliorating MS pathology, while preserving the homeostatic functions of myeloid cells, thereby minimizing risks associated with total ablation of CSF-1R-dependent cells. Significance StatementMultiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), are autoimmune diseases characterized by accumulation of myeloid immune cells into the central nervous system (CNS). Both harmful and beneficial myeloid cells are present in EAE/MS, and a goal of MS therapy is to preferentially remove harmful myeloid cells. The receptor for CSF-1 (CSF-1R) is found on myeloid cells and it is important for their survival. CSF-1R can bind two ligands, CSF-1 and IL-34, but is unknown whether their functions in EAE/MS differ. We found that blocking CSF-1 depleted only harmful myeloid cells in the CNS and suppressed EAE, whereas blocking IL-34 had no effect. Thus, we propose that blocking CSF-1 could be a novel therapy for MS.

immunology↗