For antinuclear antibody (ANA) screening, the gold standard method is an indirect immunofluorescence assay (IIFA) using HEp-2 cells, and a serial dilution test is needed to determine the endpoint titer. We aimed to evaluate the accuracy of the estimated endpoint titer (eEPT) by the NOVA View system, by comparing it with the EPT by the serial dilution method (dEPT). The endpoint titers of a total of 1518 ANA positive cases with five major patterns including speckled, homogeneous, centromere, nucleolar, and nuclear dots patterns were determined using both the estimation function and the serial dilution method by the NOVA View system. A significant correlation between the light intensity unit (LIU) values and dEPTs was identified in all five patterns with high ρ values, ranging from 0.666 to 0.832. However, the overall exact match rate between dEPT and eEPT was 22.1% (336/1518), with the ±one-titer match rate being highest in the centromere pattern (62.8%, 81/129), and lowest in the homogeneous pattern (37.6%, 200/532). This suggests that while LIU values correlate well with dEPT, there are discrepancies in numerical agreement. Most cases that did not show an exact match, showed one-to-three-titer overestimations by eEPT. Therefore, adjusting eEPT downward significantly improved the concordance rates with dEPTs. Further investigation for an appropriate cutoff of LIU values for determining eEPT should be performed for clinical application and contribution to the standardization of the ANA titer.

Energy storage and endocrine functions of the Drosophila fat body make it an excellent model for elucidating mechanisms that underlie physiological and pathophysiological organismal metabolism. Combined with Drosophila\'s robust genetic and immunofluorescence microscopy toolkits, studies of Drosophila fat body function are ripe for cell biological analysis. Unlike the larval fat body, which is easily removed as a single, cohesive sheet of tissue, isolating intact adult fat body proves to be more challenging, thus hindering consistent immunofluorescence labeling even within a single piece of adipose tissue. Here, we describe an improved approach to handling Drosophila abdomens that ensures full access of the adult fat body to solutions generally used in immunofluorescence labeling protocols. In addition, we assess the quality of fluorescence reporter expression and antibody immunoreactivity in response to variations in fixative type, fixation incubation time, and detergent used for cellular permeabilization. Overall, we provide several recommendations for steps in a whole-mount staining protocol that results in consistent and robust immunofluorescence labeling of the adult Drosophila fat body.

Sequestosome-1, encoded by the gene SQSTM1, functions as a bridge between ubiquitinated proteins and the proteasome or autophagosome, thereby regulating protein degradation pathways. Loss of Sequestosome-1 is hypothesized to enhance neurodegeneration progression in several diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal disorders (FTD). Sequestosome-1 reproducible research would be facilitated with the availability of well-characterized anti-Sequestosome-1 antibodies. In this study, we characterized seventeen Sequestosome-1 commercial antibodies for Western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. We identified many high-performing antibodies and encourage readers to use this report as a guide to select the most appropriate antibody for their specific needs.

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope\'s detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.

Breast cancer poses a global health challenge, yet the influence of ethnicity on the tumor microenvironment (TME) remains understudied. In this investigation, we examined immune cell infiltration in 230 breast cancer samples, emphasizing diverse ethnic populations. Leveraging tissue microarrays (TMAs) and core samples, we applied multiplex immunofluorescence (mIF) to dissect immune cell subtypes across TME regions. Our analysis revealed distinct immune cell distribution patterns, particularly enriched in aggressive molecular subtypes triple-negative and HER2-positive tumors. We observed significant correlations between immune cell abundance and key clinicopathological parameters, including tumor size, lymph node involvement, and patient overall survival. Notably, immune cell location within different TME regions showed varying correlations with clinicopathologic parameters. Additionally, ethnicities exhibited diverse distributions of cells, with certain ethnicities showing higher abundance compared to others. In TMA samples, patients of Chinese and Caribbean origin displayed significantly lower numbers of B cells, TAMs, and FOXP3-positive cells. These findings highlight the intricate interplay between immune cells and breast cancer progression, with implications for personalized treatment strategies. Moving forward, integrating advanced imaging techniques, and exploring immune cell heterogeneity in diverse ethnic cohorts can uncover novel immune signatures and guide tailored immunotherapeutic interventions, ultimately improving breast cancer management.

Immunofluorescence with antibodies against phosphorylated forms of H2AX (γH2AX) is revolutionizing our understanding of repair and signaling of DNA double-strand breaks (DSBs). Unfortunately, the pattern of γH2AX foci depends upon a number of parameters (nature of stress, number of foci, radiation dose, repair time, cell cycle phase, gene mutations, etc…) whose one of the common points is chromatin condensation/decondensation. Here, we endeavored to demonstrate how chromatin conformation affects γH2AX foci pattern and influences immunofluorescence signal. DSBs induced in non-transformed human fibroblasts were analyzed by γH2AX immunofluorescence with sodium butyrate treatment of chromatin applied after the irradiation that decondenses chromatin but does not induce DNA breaks. Our data showed that the pattern of γH2AX foci may drastically change with the experimental protocols in terms of size and brightness. Notably, some γH2AX minifoci resulting from the dispersion of the main signal due to chromatin decondensation may bias the quantification of the number of DSBs. We proposed a model called \"Christmas light models\" to tentatively explain this diversity of γH2AX foci pattern that may also be considered for any DNA damage marker that relocalizes as nuclear foci.

Smooth muscle antibodies (SMA) with anti-microfilament actin (MF-SMA) specificity are regarded as highly specific markers of type 1 autoimmune hepatitis (AIH-1) but their recognition relying on immunofluorescence of vessel, glomeruli, and tubules (SMA-VGT pattern) in rodent kidney tissue, is restricted by operator-dependent interpretation. A gold standard method for their identification is not available. We assessed and compared the diagnostic accuracy for AIH-1 of an embryonal aorta vascular smooth muscle (VSM) cell line-based assay with those of the rodent tissue-based assay for the detection of MF-SMA pattern in AIH-1 patients and controls. Sera from 138 AIH-1 patients and 295 controls (105 primary biliary cholangitis, 40 primary sclerosing cholangitis, 50 chronic viral hepatitis, 20 alcohol-related liver disease, 40 steatotic liver disease, and 40 healthy controls) were assayed for MF-SMA and SMA-VGT using VSM-based and rodent tissue-based assays, respectively. MF-SMA and SMA-VGT were found in 96 (70%) and 87 (63%) AIH-1 patients, and 2 controls (P < 0.0001). Compared with SMA-VGT, MF-SMA showed similar specificity (99%), higher sensitivity (70% vs 63%, P = ns) and likelihood ratio for a positive test (70 vs 65). Nine (7%) AIH-1 patients were MF-SMA positive despite being SMA-VGT negative. Overall agreement between SMA-VGT and MF-SMA was 87% (kappa coefficient 0.870, [0.789-0.952]). MF-SMA were associated with higher serum γ-globulin [26 (12-55) vs 20 g/l (13-34), P < 0.005] and immunoglobulin G (IgG) levels [3155 (1296-7344) vs 2050 mg/dl (1377-3357), P < 0.002]. The easily recognizable IFL MF-SMA pattern on VSM cells strongly correlated with SMA-VGT and has an equally high specificity for AIH-1. Confirmation of these results in other laboratories would support the clinical application of the VSM cell-based assay for reliable detection of AIH-specific SMA.

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

The mandatory usage of extracellular matrix (ECM) gels in 3D cultures limits antibody penetration and increases background, while the removal of ECM gel causes disruption of morphology and sample loss. These factors pose challenges to effective immune labeling-based staining. Here, we present a protocol for whole-mount immunofluorescence staining of gel-embedded pancreatic organoids. We describe steps for sample fixation, blocking, and antibody incubation. We detail procedures for washing antibodies and mounting.

BACKGROUND: Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.
RESULTS: To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.
METHODS: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.