Identification of neutrophils and eosinophils in upper airway mucosa with immunofluorescence multiplex image cytometry.

Characterization of inflammation in chronic rhinosinusitis with (CRSwNP) and without nasal polyps (CRSsNP) is an ongoing research process. To overcome limitations of current cytologic techniques, we investigated whether immunofluorescence multiplex image cytometry could quantify intact neutrophils, eosinophils, and other immune cells in solid upper airway mucosa. We used a four-channel immunofluorescence-microscopy technique for the simultaneous detection of the leukocyte marker CD45, the neutrophil marker myeloperoxidase, two eosinophil markers, i.e., major basic protein and eosinophil peroxidase, and DAPI (4',6-diamidin-2-phenylindole), in formalin-fixed paraffin-embedded upper airway tissue samples of patients with CRSwNP and CRSsNP, as well as of patients free of CRS with inferior turbinate hypertrophy (controls). Image acquisition and analysis were performed with TissueFAXS and StrataQuest (TissueGnostics, Vienna, Austria), respectively. Positive and negative immunostaining were differentiated with a specific fluorescence signal/background signal ratio. Isotype controls were used as negative controls. In six controls, nine patients with CRSsNP, and 11 patients with CRSwNP, the median area scanned and median cell count per patient were 14.2 mm2 and 34,356, respectively. In CRSwNP, the number of eosinophils was three times higher (23%) than that of neutrophils (7%). Three times more immune cells were encountered in CRSwNP (33%) compared to CRSsNP (11%). In controls, inflammation was balanced between the epithelial layer and lamina propria, in contrast to CRS (three times more pronounced inflammation in the lamina propria). The quantification of intact neutrophils, eosinophils, and other immune cells in solid tissue with undisrupted architecture seems feasible with immunofluorescence multiplex image cytometry.

DNA Image Cytometry for Screening the Carcinogenetic Risk of Oral Potential Malignant Disorders.

Background: Oral Submucosal Fibrosis (OSF) and Oral Leukoplakia (OLK) are well-known oral potentially malignant disorders, and cases of Oral Submucosal Fibrosis concomitant Oral Leukoplakia (OSF+OLK) are now being reported clinically. DNA image cytometry is an objective and non-invasive method for monitoring the risk of precancerous lesions in the oral cavity. Methods: A total of 111 patients with clinically characterized oral mucosal lesions underwent simultaneous and independent histopathological and DNA imaging cytometry assessments. Clinical data were also collected for each patient. Results: The frequency of DNA content abnormality was higher in the tongue than in other oral sites (P = 0.003) for OLK. The frequency of DNA content abnormality was higher in the tongue than in other oral sites (P = 0.035) for OSF+OLK. The differences of DNA content abnormality in age, sex, dietary habit, smoking, and alcohol intake were not observed in OLK and OSF+OLK. The study indicates an association between DNA content abnormality and pathological examination in OSF+OLK ( χ2 test, P = 0.007). OLK showed higher sensitivity and specificity than OSF, while the sensitivity and specificity of OSF+OLK are higher than OLK only and OSF only. Conclusion: DNA image cytometry can be utilized as an adjunctive device for the initial detection of oral potentially malignant disorders that require further clinical management.

A Multiplex Assay to Simultaneously Monitor Apoptosis and Necrosis Using the Cellaca® PLX Image Cytometer.

Apoptosis is the programmed cell death pathway that is critical for maintaining homeostasis, in which cancer cells can evade to ensure survival. For pharmaceutical drug discovery, it is important to characterize and compare different cancer therapeutics (i.e., small molecules, antibody drugs, cell therapies) that can initiate the process of apoptosis, enabling the identification of potential therapeutic candidates. In this work, we developed and demonstrated a multiplex detection method for monitoring apoptosis and necrosis with Annexin V, Caspase-3, and Propidium Iodide (PI) using the Cellaca® PLX Image Cytometer (Revvity Health Sciences, Inc., Lawrence, MA). First, apoptosis was induced in Jurkat and K562 cell lines with staurosporine over the course of 24 h, where apoptosis and necrosis were assessed at 0, 1, 1.5, 2, 4, 20, and 24 h timepoints. Samples were stained with Hoechst 33342 (total dye), Annexin V-APC (early-stage apoptosis), Caspase-3 488 (late-stage apoptosis), and PI (necrosis) at each timepoint and evaluated using image cytometry. Results showed that apoptotic factors and cascades were successfully detected along the pathway from early- to late-stage apoptosis, and ultimately necrosis. A clear trend was observed analyzing apoptotic and necrotic populations during the first 1.5 h, showing differences of up to ~15% in single Annexin V+ and Caspase-3+ populations in treated Jurkat cells, however, a significant increase in double positive apoptotic/necrotic cells for Annexin V+PI+ and Capase-3+PI+ was not observed until 20 h. Upon further analysis between apoptotic populations only, Annexin V+ only populations were higher than Caspase-3+ only populations by up to ~20% between 0 and 1.5 h. Conversely, K562 cells did not exhibit a notable change in apoptotic and necrotic populations due to low sensitivity to staurosporine. The proposed image cytometric detection method may provide an effective and efficient tool for rapid and reliable simultaneous detection of early- late-stage apoptosis, and necrosis. Therefore, allowing researchers to better characterize and screen potential cancer therapeutic drug candidates for their treatment efficacy in a higher throughput manner.

Development of a high-throughput image cytometric screening method as a research tool for immunophenotypic characterization of patient samples from clinical studies.

Immunophenotyping has been the primary assay for characterization of immune cells from patients undergoing therapeutic treatments in clinical research, which is critical for understanding disease progression and treatment efficacy. Currently, flow cytometry has been the dominant methodology for characterizing surface marker expression for immunological research. Flow cytometry has been proven to be an effective and efficient method for immunophenotyping, however, it requires highly trained users and a large time commitment. Recently, a novel image cytometry system (Cellaca® PLX Image Cytometer, Revvity Health Sciences, Inc., Lawrence, MA) has been developed as a complementary method to flow cytometry for performing rapid and high-throughput immunophenotyping. In this work, we demonstrated an image cytometric screening method to characterize immune cell populations, streamlining the analysis of routine surface marker panels. The T cell, B cell, NK cell, and monocyte populations of 46 primary PBMC samples from subjects enrolled in autoimmune and oncological disease study cohorts were analyzed with two optimized immunophenotyping staining kits: Panel 1 (CD3, CD56, CD14) and Panel 2 (CD3, CD56, CD19). We validated the proposed image cytometry method by comparing the Cellaca® PLX and the AuroraTM flow cytometer (Cytek Biosciences, Fremont, CA). The image cytometry system was employed to generate bright field and fluorescent images, as well as scatter plots for multiple patient PBMC samples. In addition, the image cytometry method can directly determine cell concentrations for downstream assays. The results demonstrated comparable CD3, CD14, CD19, and CD56 cell populations from the primary PBMC samples, which showed an average of 5% differences between flow and image cytometry. The proposed image cytometry method provides a novel research tool to potentially streamline immunophenotyping workflow for characterizing patient samples in clinical studies.

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

High-Throughput SARS-CoV-2 Antiviral Testing Method Using the Celigo Image Cytometer.

The COVID-19 pandemic has created a worldwide public health crisis that has since resulted in 6.8 million reported deaths. The pandemic prompted the immediate response of researchers around the world to engage in rapid vaccine development, surveillance programs, and antiviral testing, which resulted in the delivery of multiple vaccines and repurposed antiviral drug candidates. However, the emergence of new highly transmissible SARS-CoV-2 variants has renewed the desire for discovering new antiviral drug candidates with high efficacy against the emerging variants of concern. Traditional antiviral testing methods employ the plaque-reduction neutralization tests (PRNTs), plaque assays, or RT-PCR analysis, but each assay can be tedious and time-consuming, requiring 2-3 days to complete the initial antiviral assay in biologically relevant cells, and then 3-4 days to visualize and count plaques in Vero cells, or to complete cell extractions and PCR analysis. In recent years, plate-based image cytometers have demonstrated high-throughput vaccine screening methods, which can be adopted for screening potential antiviral drug candidates. In this work, we developed a high-throughput antiviral testing method employing the Celigo Image Cytometer to investigate the efficacy of antiviral drug candidates on SARS-CoV-2 infectivity using a fluorescent reporter virus and their safety by measuring the cytotoxicity effects on the healthy host cell line using fluorescent viability stains. Compared to traditional methods, the assays defined here eliminated on average 3-4 days from our standard processing time for antiviral testing. Moreover, we were able to utilize human cell lines directly that are not typically amenable to PRNT or plaque assays. The Celigo Image Cytometer can provide an efficient and robust method to rapidly identify potential antiviral drugs to effectively combat the rapidly spreading SARS-CoV-2 virus and its variants during the pandemic.

DNA image cytometry of bronchial washing as a diagnostic adjunct to radial endobronchial ultrasound-guided sampling of peripheral lung lesions: A single center prospective study.

OBJECTIVE: The objective of this study is to study the adjunct role of combining DNA aneuploidy analysis with radial endobronchial ultrasound (R-EBUS)-guided sampling for diagnosis of peripheral lung lesions (PPLs). METHOD: A single-center prospective study was conducted in patients undergoing R-EBUS-guided sampling for PPLs. DNA image cytometry (DNA-ICM) was used to analyze DNA aneuploidy in bronchial washing from the bronchial segment of the PPL. Clinical information, R-EBUS data, pathology, DNA-ICM results, and follow-up data were analyzed. Sensitivity, specificity, and predictive values for R-EBUS-guided sampling, DNA-ICM, and the two methods combined were measured. Binary logistic regression was performed to determine influencing factors on diagnostic positivity rate. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff point for DNA-ICM. RESULTS: A total of 101 patients were enrolled. Sixty-four (63.4%) patients had confirmed malignant tumor, of whom 33 were confirmed by R-EBUS-guided sampling (biopsy and/or bronchial brush and wash cytology), and 31 by surgery or percutaneous lung biopsy. Thirty-seven patients were finally considered to have benign lesions, based on clinical information and 1-year follow-up. The sensitivity for malignant disease was 51.6% by R-EBUS, and specificity was 100%. DNA-ICM had a sensitivity of 67.2% and a specificity of 86.5%. When combining the two methods, sensitivity increased to 78.1% and specificity was 86.5%. Lesion size and whether the R-EBUS probe was located in the lesion were significantly associated with positivity rate of the combined methods. The optimal cutoff point for DNA-ICM was 5c for max DNA content, and 1 for aneuploid cell count (sensitivity 67.2%, specificity 86.5%, accuracy 63.4%). CONCLUSION: In malignant PPLs, DNA-ICM combined with R-EBUS-guided sampling can improve diagnostic positivity compared with either method alone.

Cellaca® PLX image cytometer as an alternative for immunophenotyping, GFP/RFP transfection efficiencies, and apoptosis analysis.

Cell and gene therapy is a fast-growing field for cancer therapeutics requiring reliable instrumentation and technologies. Key parameters essential for satisfying Chemistry Manufacturing and Controls criteria standards are routinely performed using flow cytometry. Recently, image cytometry was developed for cell characterization and cell-based assays but had not yet demonstrated sufficient sensitivity for surface marker detection. We developed the Cellaca® PLX image cytometry system and the respective methodologies required for immunophenotyping, GFP and RFP transfection/transduction efficiencies, and cell health analyses for routine cell characterization. All samples tested were compared directly to results from the CytoFLEX flow cytometer. PBMCs were stained with T-cell surface markers for immunophenotyping, and results show highly comparable CD3, CD4, and CD8 populations (within 5 %). GFP- or RFP-expressing cell lines were analyzed for transfection/transduction efficiencies, and the percentage positive cells and respective viabilities were equivalent on both systems. Staurosporine-treated Jurkat cells were stained for apoptotic markers, where annexin V and caspase-3 positive cells were within 5 % comparing both instruments. The proposed system may provide a complementary tool for performing routine cell-based experiments with improved efficiency and sensitivity compared to prior image cytometers, which may be significantly valuable to the cell and gene therapy field.

Analysis of cells of epithelial, connective tissue and immune differentiation in HPV-positive-, HPV-negative oropharyngeal carcinoma and normal oropharyngeal tissue by immunofluorescence multiplex image cytometry: a preliminary report.

BACKGROUND: Epithelial, connective tissue and immune cells contribute in various ways to the pathophysiology of HPV positive (HPV+) and HPV negative (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). We aimed to investigate the abundance of these cell lineages and their coexpression patterns in patients with HPV + and HPV- OPSCC. METHODS: We used a 4-channel immunofluorescence-microscopy technique for the simultaneous detection of three direct-conjugated antibodies (pancytokeratin, vimentin and CD45/CD18) and DAPI (4',6-Diamidin-2-phenylindole) in formalin fixed paraffin-embedded tissue samples (FFPE) of patients with HPV + and HPV- OPSCC, and of control patients. Image acquisition and analysis were performed with TissueFAXS and StrataQuest (TissueGnostics, Vienna, Austria), respectively, in tumor cell clusters/stroma in OPSCC specimens and epithelial layer/lamina propria in control specimens. Cell populations were created based on antibodies' coexpression patterns. Isotype and positive controls were examined for plausibility. RESULTS: The proportion of cells of epithelial differentiation in tumor cell clusters was higher in HPV + OPSCC (55%) than in HPV- OPSCC samples (44%). The proportion of connective tissue cells in tumor cell cluster was lower in HPV + OPSCC patients (18%) than in HPV- OPSCC patients (26%). The proportion of immune cells in tumor cell clusters was higher in HPV + OPSCC patients (25%) than in HPV- OPSCC patients (18%). The percentage of anaplastic, potentially de-differentiated cells, was 2% in control patients, and it was higher in HPV- OPSCC (21%) than in HPV + OPSCC samples (6%). CONCLUSIONS: This study provided the first quantitative data for the abundance of cells of epithelial, connective tissue and immune differentiation, in patients with OPSCC and control patients. The abundance of these different crucial cell populations was consistently originating from the same tissue sample. De-differentiation of tumor cells was higher in HPV- OPSCC than in HPV + OPSCC. In tumor cells clusters, the antitumoral host immune response was higher in HPV + OPSCC than in HPV- OPSCC, whereas the fibroblast response was higher in HPV- OPSCC than in HPV + OPSCC. This study contributed to the understanding of histopathologic differences between HPV + OPSCC and HPV- OPSCC patients.

Multiple noninvasive auxiliary tests-assisted photodynamic therapy in actinic cheilitis: A case report.

Actinic cheilitis (AC) is recognized as the most common precursor lesion of squamous cell carcinoma (SCC) of the lip, with a higher risk of invasiveness and metastasis. Early accurate diagnosis and appropriate therapy are essential to prevent carcinogenesis and progression of AC. Topical 5-aminolevulinic acid-mediated photodynamic therapy (ALA-PDT), a non-surgical and minimally invasive modality, has been proposed as an effective treatment for oral potentially malignant diseases (OPMDs) and oral cancers. Herein, we report a 64-year-old female patient with AC on the lower lip who received 3 sessions of ALA-PDT with an interval of 1 week. Multiple noninvasive auxiliary tests including autofluorescence imaging, toluidine blue staining, and aneuploidy with DNA image cytometry (DNA-ICM) using brushing from screening through diagnosis, treatment, and follow-up. The patient successfully showed a complete response with no adverse effects and no evidence of recurrence at the 20-month follow-up. Noninvasive auxiliary tests assisted PDT is attractive and well-tolerated and may have synergistic effects against AC.

Practical Characterization Strategies for Comparison, Qualification, and Selection of Cell Viability Detection Methods for Cellular Therapeutic Product Development and Manufacturing.

Cellular therapy development and manufacturing has focused on providing novel therapeutic cell-based products for various diseases. The International Organization for Standardization (ISO) has provided guidance on critical quality attributes (CQAs) that shall be considered when testing and releasing cellular therapeutic products. Cell count and viability measurements are two of the CQAs that are determined during development, manufacturing, testing, and product release. The ISO Cell Counting Standard Part 1 and 2 addressed the needs for improving the quality of cell counting results. However, there is currently no guidance on the qualification and selection of a fit-for-purpose cell viability detection method. In this work, we present strategies for the characterization and comparison of AO/PI and AO/DAPI staining methods using the heat-killed (HK) and low temperature/nutrient-deprived (LT/ND) cell death models to evaluate the comparability of cell viability measurements and identify potential causes of differences. We compared the AO/PI and AO/DAPI staining methods using HK and LT/ND-generated dead cells, investigated the staining time effects on cell viability measurements, and determined their viability linearity with different mixtures of live and dead cells. Furthermore, we validated AO/PI and AO/DAPI cell viability measurement with a long-term cell proliferation assay. Finally, we demonstrate a practical example of cell viability measurement comparison using AO/PI and AO/DAPI on antibiotic-selected transduced Jurkat and THP-1 cells to select a fit-for-purpose method for functional genomics screening. The proposed strategies may potentially enable scientists to properly characterize, compare, and select cell viability detection methods that are critical for cellular therapeutic product development and manufacturing.

Assessment of Ploidy Status in Oral Potentially Malignant Disorders - A Systematic Review.

Malignant and potentially malignant epithelial lesions are often associated with various abnormalities such as epithelial dysplasia, abnormal DNA content, loss of heterozygosity, and chromosomal number aberrations. Screening and early detection of such abnormalities facilitates proper care and also helps to prevent further progression of potentially malignant lesions to malignancy. In such way, the presence of DNA aneuploidy in oral potentially malignant disorders (OPMDs) may serve as an indicator for the malignant transforming potential. Various assessment methods have been proposed to find the DNA ploidy status of cells. This current systematic review is mainly designed to assess the importance of ploidy status in OPMD while measuring the feasibility of using this biomarker for evaluating the hazard of malignant transformation. As an upshot of this systematic review, we can conclude that use of DNA ploidy status can serve as an independent bio-marker for predicting the malignant transformation of lesions. Furthermore, as a future scope the use of DNA ploidy analysis in normal mucosa of smokers will help to assess the malignancy risk and this technique might also help to predict the genetic predisposition of patients with malignancy.

A rapid and high-throughput T cell immunophenotyping assay for cellular therapy bioprocess using the Cellaca® PLX image cytometer.

In cellular therapies chimeric antigen receptor (CAR) T or NK cells undergo phenotypic analysis at multiple stages during discovery and development of novel therapies. Patient samples are routinely analyzed via flow cytometry for population identification and distribution of CD3, CD4, and CD8 positive T cells. As an alternative or orthogonal method, image cytometry systems have been used to perform simple cell-based assays in lieu of flow cytometry. Recently, a new image cytometry system, the Cellaca® PLX (Revvity Health Sciences, Inc., Lawrence, MA), was developed for high-throughput cell counting and viability, immunophenotyping, transfection/transduction efficiency, and cell health assays. This novel instrument allows investigators to quickly assess several critical quality attributes (CQAs) such as cell identity, viability, and other relevant biological functions recommended by the International Organization for Standardization using the ISO Cell Characterization documents focused on cellular therapeutic products. In this work, we demonstrate a rapid and high-throughput image cytometry detection method for cellular immunophenotyping and viability using the Cellaca PLX system for samples throughout the cellular therapy workflow. Freshly isolated peripheral blood mononuclear cells (PBMCs) underwent red blood cell (RBC) lysis and CD3 enrichment. Samples were then subsequently stained with Hoechst/CD3/CD4/CD8 or Hoechst/CD3/CD8/RubyDead Dye surface marker kits and measured on the Cellaca PLX and three different flow cytometers for side-by-side comparison and assay validation. Acquisition and analysis of cell viability and cell populations was shown to be faster and more efficient process compared to flow while achieving highly comparable results between the two technology platforms. This data shows that the Cellaca PLX Image Cytometer may provide a rapid alternative or orthogonal method for PBMC immunophenotyping experiments, as well as potentially streamline the workflow to quickly move precious patient samples downstream within the development processes.

Deep Learning-based Image Cytometry Using a Bit-pattern Kernel-filtering Algorithm to Avoid Multi-counted Cell Determination.

BACKGROUND/AIM: In pathology, the digitization of tissue slide images and the development of image analysis by deep learning have dramatically increased the amount of information obtainable from tissue slides. This advancement is anticipated to not only aid in pathological diagnosis, but also to enhance patient management. Deep learning-based image cytometry (DL-IC) is a technique that plays a pivotal role in this process, enabling cell identification and counting with precision. Accurate cell determination is essential when using this technique. Herein, we aimed to evaluate the performance of our DL-IC in cell identification. MATERIALS AND METHODS: Cu-Cyto, a DL-IC with a bit-pattern kernel-filtering algorithm designed to help avoid multi-counted cell determination, was developed and evaluated for performance using tumor tissue slide images with immunohistochemical staining (IHC). RESULTS: The performances of three versions of Cu-Cyto were evaluated according to their learning stages. In the early stage of learning, the F1 score for immunostained CD8+ T cells (0.343) was higher than the scores for non-immunostained cells [adenocarcinoma cells (0.040) and lymphocytes (0.002)]. As training and validation progressed, the F1 scores for all cells improved. In the latest stage of learning, the F1 scores for adenocarcinoma cells, lymphocytes, and CD8+ T cells were 0.589, 0.889, and 0.911, respectively. CONCLUSION: Cu-Cyto demonstrated good performance in cell determination. IHC can boost learning efficiencies in the early stages of learning. Its performance is expected to improve even further with continuous learning, and the DL-IC can contribute to the implementation of precision oncology.

In situ cell division and mortality rates of SAR11, SAR86, Bacteroidetes, and Aurantivirga during phytoplankton blooms reveal differences in population controls.

Net growth of microbial populations, that is, changes in abundances over time, can be studied using 16S rRNA fluorescence in situ hybridization (FISH). However, this approach does not differentiate between mortality and cell division rates. We used FISH-based image cytometry in combination with dilution culture experiments to study net growth, cell division, and mortality rates of four bacterial taxa over two distinct phytoplankton blooms: the oligotrophs SAR11 and SAR86, and the copiotrophic phylum Bacteroidetes, and its genus Aurantivirga. Cell volumes, ribosome content, and frequency of dividing cells (FDC) co-varied over time. Among the three, FDC was the most suitable predictor to calculate cell division rates for the selected taxa. The FDC-derived cell division rates for SAR86 of up to 0.8/day and Aurantivirga of up to 1.9/day differed, as expected for oligotrophs and copiotrophs. Surprisingly, SAR11 also reached high cell division rates of up to 1.9/day, even before the onset of phytoplankton blooms. For all four taxonomic groups, the abundance-derived net growth (-0.6 to 0.5/day) was about an order of magnitude lower than the cell division rates. Consequently, mortality rates were comparably high to cell division rates, indicating that about 90% of bacterial production is recycled without apparent time lag within 1 day. Our study shows that determining taxon-specific cell division rates complements omics-based tools and provides unprecedented clues on individual bacterial growth strategies including bottom-up and top-down controls. IMPORTANCE The growth of a microbial population is often calculated from their numerical abundance over time. However, this does not take cell division and mortality rates into account, which are important for deriving ecological processes like bottom-up and top-down control. In this study, we determined growth by numerical abundance and calibrated microscopy-based methods to determine the frequency of dividing cells and subsequently calculate taxon-specific cell division rates in situ. The cell division and mortality rates of two oligotrophic (SAR11 and SAR86) and two copiotrophic (Bacteroidetes and Aurantivirga) taxa during two spring phytoplankton blooms showed a tight coupling for all four taxa throughout the blooms without any temporal offset. Unexpectedly, SAR11 showed high cell division rates days before the bloom while cell abundances remained constant, which is indicative of strong top-down control. Microscopy remains the method of choice to understand ecological processes like top-down and bottom-up control on a cellular level.

Single-cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates.

Bacteria often grow into matrix-encased three-dimensional (3D) biofilm communities, which can be imaged at cellular resolution using confocal microscopy. From these 3D images, measurements of single-cell properties with high spatiotemporal resolution are required to investigate cellular heterogeneity and dynamical processes inside biofilms. However, the required measurements rely on the automated segmentation of bacterial cells in 3D images, which is a technical challenge. To improve the accuracy of single-cell segmentation in 3D biofilms, we first evaluated recent classical and deep learning segmentation algorithms. We then extended StarDist, a state-of-the-art deep learning algorithm, by optimizing the post-processing for bacteria, which resulted in the most accurate segmentation results for biofilms among all investigated algorithms. To generate the large 3D training dataset required for deep learning, we developed an iterative process of automated segmentation followed by semi-manual correction, resulting in >18,000 annotated Vibrio cholerae cells in 3D images. We demonstrate that this large training dataset and the neural network with optimized post-processing yield accurate segmentation results for biofilms of different species and on biofilm images from different microscopes. Finally, we used the accurate single-cell segmentation results to track cell lineages in biofilms and to perform spatiotemporal measurements of single-cell growth rates during biofilm development.

A novel image-based method for simultaneous counting of Lactobacillus and Saccharomyces in mixed culture fermentation.

Mixed microorganism cultures are prevalent in the food industry. A variety of microbiological mixtures have been used in these unique fermenting processes to create distinctive flavor profiles and potential health benefits. Mixed cultures are typically not well characterized, which may be due to the lack of simple measurement tools. Image-based cytometry systems have been employed to automatically count bacteria or yeast cells. In this work, we aim to develop a novel image cytometry method to distinguish and enumerate mixed cultures of yeast and bacteria in beer products. Cellometer X2 from Nexcelom was used to count of Lactobacillus plantarum and Saccharomyces cerevisiae in mixed cultures using fluorescent dyes and size exclusion image analysis algorithm. Three experiments were performed for validation. (1) Yeast and bacteria monoculture titration, (2) mixed culture with various ratios, and (3) monitoring a Berliner Weisse mixed culture fermentation. All experiments were validated by comparing to manual counting of yeast and bacteria colony formation. They were highly comparable with ANOVA analysis showing p-value > 0.05. Overall, the novel image cytometry method was able to distinguish and count mixed cultures consistently and accurately, which may provide better characterization of mixed culture brewing applications and produce higher quality products.

PD-1+CD8+ T Cells Proximal to PD-L1+CD68+ Macrophages Are Associated with Poor Prognosis in Pancreatic Ductal Adenocarcinoma Patients.

Immune complexity status in the TME has been linked to clinical outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. TME assessments with current cell marker and cell density-based analyses do not identify the original phenotypes of single cells with multilineage selectivity, the functional status of the cells, or cellular spatial information in the tissues. Here, we describe a method that circumvents these problems. The combined strategy of multiplexed IHC with computational image cytometry and multiparameter cytometric quantification allows us to assess multiple lineage-selective and functional phenotypic biomarkers in the TME. Our study revealed that the percentage of CD8+ T lymphoid cells expressing the T cell exhaustion marker PD-1 and the high expression of the checkpoint PD-L1 in CD68+ cells are associated with a poor prognosis. The prognostic value of this combined approach is more significant than that of lymphoid and myeloid cell density analyses. In addition, a spatial analysis revealed a correlation between the abundance of PD-L1+CD68+ tumor-associated macrophages and PD-1+CD8+T cell infiltration, indicating pro-tumor immunity associated with a poor prognosis. These data highlight the implications of practical monitoring for understanding the complexity of immune cells in situ. Digital imaging and multiparameter cytometric processing of cell phenotypes in the TME and tissue architecture can reveal biomarkers and assessment parameters for patient stratification.

High-throughput method to analyze the cytotoxicity of CAR-T Cells in a 3D tumor spheroid model using image cytometry.

Solid tumors account for approximately 90% of all adult human cancers. As such, the development of novel cellular therapies has become of increasing importance to target solid tumor malignancies, such as prostate, lung, breast, bladder, colon, and liver cancers. One such cellular therapy relies on the use of chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are engineered to target specific antigens on tumor cells. To date, there are six FDA-approved CAR-T cell therapies that have been utilized for hematologic B cell malignancies. Immune cell trafficking and immunosuppressive factors within the tumor microenvironment increase the relative difficulty in developing a robust CAR-T cell therapy against solid tumors. Therefore, it is critical to develop novel methodologies for high-throughput phenotypic and functional assays using 3D tumor spheroid models to assess CAR-T cell products against solid tumors. In this manuscript, we discuss the use of CAR-T cells targeted towards PSMA, an antigen that is found on prostate cancer tumor cells, the second most common cause of cancer deaths among men worldwide. We demonstrate the use of high-throughput, plate-based image cytometry to characterize CAR-T cell-mediated cytotoxic potency against 3D prostate tumor spheroids. We were able to kinetically evaluate the efficacy and therapeutic value of PSMA CAR-T cells by analyzing the cytotoxicity against prostate tumor spheroids. In addition, the CAR-T cells were fluorescently labeled to visually identify the location of the T cells as cytotoxicity occurs, which may provide more meaningful information for assessing the functionality of the CAR-T cells. The proposed image cytometry method can overcome limitations placed on traditional methodologies to effectively assess cell-mediated 3D tumor spheroid cytotoxicity and efficiently generate time- and dose-dependent results.

Correlative Multi-Modal Microscopy: A Novel Pipeline for Optimizing Fluorescence Microscopy Resolutions in Biological Applications.

The modern fluorescence microscope is the convergence point of technologies with different performances in terms of statistical sampling, number of simultaneously analyzed signals, and spatial resolution. However, the best results are usually obtained by maximizing only one of these parameters and finding a compromise for the others, a limitation that can become particularly significant when applied to cell biology and that can reduce the spreading of novel optical microscopy tools among research laboratories. Super resolution microscopy and, in particular, molecular localization-based approaches provide a spatial resolution and a molecular localization precision able to explore the scale of macromolecular complexes in situ. However, its use is limited to restricted regions, and consequently few cells, and frequently no more than one or two parameters. Correlative microscopy, obtained by the fusion of different optical technologies, can consequently surpass this barrier by merging results from different spatial scales. We discuss here the use of an acquisition and analysis correlative microscopy pipeline to obtain high statistical sampling, high content, and maximum spatial resolution by combining widefield, confocal, and molecular localization microscopy.