Improved Canker Processing and Viability Droplet Digital PCR Allow Detection of Erwinia amylovora Viable Nonculturable Cells in Apple Bark.

The bacterium Erwinia amylovora causes fire blight and continues to threaten global commercial apple and pear production. Conventional microbiology techniques cannot accurately determine the presence of live pathogen cells in fire blight cankers. Several factors may prevent E. amylovora from growing on solid culture media, including competing microbiota and the release of bacterial-growth-inhibitory compounds by plant material during sample processing. We previously developed a canker processing methodology and a chip-based viability digital PCR (v-dPCR) assay using propidium monoazide (PMA) to bypass these obstacles. However, sample analysis was still time-consuming and physically demanding. In this work, we improved the previous protocol using an automatic tissue homogenizer and transferred the chip-based v-dPCR to the BioRad QX200 droplet dPCR (ddPCR) platform. The improved sample processing method allowed the simultaneous, fast, and effortless processing of up to six samples. Moreover, the transferred v-ddPCR protocol was compatible with the same PMA treatment and showed a similar dynamic range, from 7.2 × 102 to 7.6 × 107 cells mL-1, as the previous v-dPCR. Finally, the improved protocol allowed, for the first time, the detection of E. amylovora viable but nonculturable (VBNC) cells in cankers and bark tissues surrounding cankers. Our v-ddPCR assay will enable new ways to evaluate resistant pome fruit tree germplasm, further dissect the E. amylovora life cycle, and elucidate E. amylovora physiology, epidemiology, and new options for canker management.

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method.

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products.

Ultrastructural characterization and quantification of hemocytes in engorged female Amblyomma sculptum ticks.

Amblyomma sculptum (formerly Amblyomma cajennense) ticks have been implicated in the transmission of pathogens that cause diseases in animals and humans. Their wide geographic distribution and high impact on animal health and zoonotic disease transmission highlight the importance of studying and implementing effective control measures to mitigate the risks associated with this tick species. The aim of this study was to quantify and characterize the morphology and the ultrastructure of different types of hemocytes in the hemolymph in engorged A. sculptum females fed on rabbits. The hemolymph samples were collected by perforation of the cuticle in the dorsal region. Hemocyte types, sizes, and differential counts were determined using light microscopy, while ultrastructural analysis of hemocytes was performed using transmission electron microscopy. The average number of total hemocytes in the hemolymph was 1024 ± 597.6 cells µL-1. Five morphologically distinct cell types were identified in A. sculptum females: prohemocytes (6 % ± 8.8), plasmatocytes (10 % ± 7.7), granulocytes (78 % ± 12.2), spherulocytes (5 % ± 4.48), and oenocytoids (1 % ± 1.6). In general, prohemocytes were the smallest hemocytes. The ultrastructural morphology of A. sculptum hemocytes described in the present study agrees with the findings for other hard ticks. This is the first study to investigate ultrastructural characteristics of hemocytes of female A. sculptum ticks.

Analysis of cellularity in H&E-stained rat bone marrow tissue via deep learning.

Our objective was to develop an automated deep-learning-based method to evaluate cellularity in rat bone marrow hematoxylin and eosin whole slide images for preclinical safety assessment. We trained a shallow CNN for segmenting marrow, 2 Mask R-CNN models for segmenting megakaryocytes (MKCs), and small hematopoietic cells (SHCs), and a SegNet model for segmenting red blood cells. We incorporated the models into a pipeline that identifies and counts MKCs and SHCs in rat bone marrow. We compared cell segmentation and counts that our method generated to those that pathologists generated on 10 slides with a range of cell depletion levels from 10 studies. For SHCs, we compared cell counts that our method generated to counts generated by Cellpose and Stardist. The median Dice and object Dice scores for MKCs using our method vs pathologist consensus and the inter- and intra-pathologist variation were comparable, with overlapping first-third quartile ranges. For SHCs, the median scores were close, with first-third quartile ranges partially overlapping intra-pathologist variation. For SHCs, in comparison to Cellpose and Stardist, counts from our method were closer to pathologist counts, with a smaller 95% limits of agreement range. The performance of the bone marrow analysis pipeline supports its incorporation into routine use as an aid for hematotoxicity assessment by pathologists. The pipeline could help expedite hematotoxicity assessment in preclinical studies and consequently could expedite drug development. The method may enable meta-analysis of rat bone marrow characteristics from future and historical whole slide images and may generate new biological insights from cross-study comparisons.

Automated Image Analysis for Characterization of Circulating Tumor Cells and Clusters Sorted by Magnetic Levitation.

Magnetic levitation-based sorting technologies have revolutionized the detection and isolation of rare cells, including circulating tumor cells (CTCs) and circulating tumor cell clusters (CTCCs). Manual counting and quantification of these cells are prone to time-consuming processes, human error, and inter-observer variability, particularly challenging when heterogeneous cell types in 3D clusters are present. To overcome these challenges, we developed "Fastcount," an in-house MATLAB-based algorithm for precise, automated quantification and phenotypic characterization of CTCs and CTCCs, in both 2D and 3D. Fastcount is 120 times faster than manual counting and produces reliable results with a ±7.3% deviation compared to a trained laboratory technician. By analyzing 400 GB of fluorescence imaging data, we showed that Fastcount outperforms manual counting and commercial software when cells are aggregated in 3D or staining artifacts are present, delivering more accurate results. We further employed Fastcount for automated analysis of 3D image stacks obtained from CTCCs isolated from colorectal adenocarcinoma and renal cell carcinoma blood samples. Interestingly, we observed a highly heterogeneous spatial cellular composition within CTCCs, even among clusters from the same patient. Overall, Fastcount can be employed for various applications with lab-chip devices, such as CTC detection, CTCC analysis in 3D and cell detection in biosensors.

Characterization of virus-mediated autoimmunity and the consequences for pathological process in patients with systemic lupus erythematosus.

INTRODUCTION/OBJECTIVES: This study aimed to identify differentially expressed genes (DEGs) of systemic lupus erythematosus (SLE) using gene expression-based computational methodologies to analyze disease-immune interactions, which affect the development and progression of SLE. METHOD: Twenty-six patients with SLE and 46 healthy controls were selected from the Gene Expression Omnibus (GEO) database. The significantly enriched immune and virus-related gene lists were computed and visualized by using the DEGs from the gene set enrichment analysis (GSEA). Quantification of 38 immune cells was performed in determining the impact of immune cells on the virus mediated immunity in SLE by using ImmQuant algorithm. RESULTS: Thirty-nine upregulated and 57 downregulated were identified in SLE patient compared to the healthy controls. Upregulated genes were significantly implicated in Gene Ontology gene sets as cytokine mediated signaling, secretion, and exocytosis in immune response pathways in 26 female SLE patients. In addition, these genes were enriched in hepatitis C, influenza A, measles, Epstein-Barr virus, and herpes simplex virus 1 infection in Kyoto Encyclopedia of Genes and Genomes pathways. Especially, FCGR1A, IRF7, OAS2, CAMP, MX1, OAS3, OAS1, DEFA3, ISG15, and RSAD2 were involved in virus mediated SLE mechanism, and the expression for OAS1, OAS2, and IRF7 was closely associated with the quantities of colony forming unit-monocyte and colony forming unit-granulocyte. CONCLUSIONS: Identifying virus-mediated SLE genes and quantifies of immune cells were used to understand the pathological process and perform early diagnosis of female SLE, and will lead to clinical tools for treating SLE in patients. Key Points • Using gene expression-based computational methodologies, the 57 immune and viral genes were significantly upregulated in 26 SLE patients. • The identified three key  viral genes such as OAS1, OAS2, and IF7 were closely associated with colony-forming unit-monocytes and colony-forming unit-granulocytes, which affect the virus mediated immunity in SLE. • The viral genes and quantifies of immune cells are useful in understanding pathogenesis of SLE, and this will provide clinical strategies of potential treatment choices in SLE patients.

Spermatogonial Transplantation.

Spermatogonial transplantation is the unequivocal method to detect spermatogonial stem cells (SSCs) based strictly on the functional definition of stem cells - the cells' regenerative capacity. This method further allows for SSC quantification. A weakness of spermatogonial transplantation is its time-consuming nature; it takes 2 months to confirm the production of terminally differentiated cells in spermatogenesis, spermatozoa, in mice, which gives the assay endpoint. Using the mouse as the model system, we here describe the basic techniques of spermatogonial transplantation and provide practical guidance to successfully carry out this technique and to interpret data generated.

Novel Cell Quantification Method Using a Single Surrogate Calibration Curve Across Various Biological Samples.

Quantitative polymerase chain reaction (qPCR) is generally used to quantify transplanted cell therapy products in biological samples. As the matrix effects on PCR amplification and variability in DNA recovery from biological samples are well-known limitations that hinder the assay's performance, a calibration curve is conventionally established for each matrix. Droplet digital PCR (ddPCR) is based on the endpoint assay and advantageous in avoiding matrix effects. Moreover, the use of an external control gene may correct assay fluctuations to minimize the effects caused by inconsistent DNA recovery. In this study, we aimed to establish a novel and robust ddPCR method capable of quantifying human cells across various mouse biological samples using a single surrogate calibration curve in combination with an external control gene and DNA recovery normalization. Acceptable accuracy and precision were observed for quality control samples from different tissues, indicating the excellent quantitative and versatile potential of the developed method. Furthermore, the established method enabled the evaluation of human CD8+ T cell biodistribution in immunodeficient mice. Our findings provide new insights into the use of ddPCR-based quantification methods in biodistribution studies of cell therapy products.

Deetect: A Deep Learning-Based Image Analysis Tool for Quantification of Adherent Cell Populations on Oxygenator Membranes after Extracorporeal Membrane Oxygenation Therapy.

The strong interaction of blood with the foreign surface of membrane oxygenators during ECMO therapy leads to adhesion of immune cells on the oxygenator membranes, which can be visualized in the form of image sequences using confocal laser scanning microscopy. The segmentation and quantification of these image sequences is a demanding task, but it is essential to understanding the significance of adhering cells during extracorporeal circulation. The aim of this work was to develop and test a deep learning-supported image processing tool (Deetect), suitable for the analysis of confocal image sequences of cell deposits on oxygenator membranes at certain predilection sites. Deetect was tested using confocal image sequences of stained (DAPI) blood cells that adhered to specific predilection sites (junctional warps and hollow fibers) of a phosphorylcholine-coated polymethylpentene membrane oxygenator after patient support (>24 h). Deetect comprises various functions to overcome difficulties that occur during quantification (segmentation, elimination of artifacts). To evaluate Deetects performance, images were counted and segmented manually as a reference and compared with the analysis by a traditional segmentation approach in Fiji and the newly developed tool. Deetect outperformed conventional segmentation in clustered areas. In sections where cell boundaries were difficult to distinguish visually, previously defined post-processing steps of Deetect were applied, resulting in a more objective approach for the resolution of these areas.

A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed.

BACKGROUND: Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. RESULTS: We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. CONCLUSIONS: The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.

In Situ Quantification and Isolation of Müller Glial Cells by Fluorescence-Activated Cell Sorting from the Regenerating Larval Zebrafish Retina.

Müller glia (MG) are a relatively quiescent radial glial cell population capable of dedifferentiating to regenerate cells in the zebrafish retina that are lost due to damage. Here, we provide a protocol to both quantify MG cell dedifferentiation behavior during a regenerative response and isolate MG cells by fluorescence activated cell sorting (FACS). First, the retina is exposed to high-intensity light to induce retinal damage and either processed for immunohistochemistry or live MG cells are isolated by FACS that can be used for subsequent genomic or transcriptomic analyses. This method allows us to correlate MG cell behavior observed in situ with their transcriptomic profile at different stages during the regenerative response.

Generic Multiplex Digital PCR for Accurate Quantification of T Cells in Copy Number Stable and Unstable DNA Samples.

An accurate T cell quantification is prognostically and therapeutically relevant in various clinical applications, including oncology care and research. In this chapter, we describe how T cell quantifications can be obtained from bulk DNA samples with a multiplex digital PCR experiment. The experimental setup includes the concurrent quantification of three different DNA targets within one reaction: a unique T cell DNA marker, a regional corrector, and a reference DNA marker. The T cell marker is biallelically absent in T cells due to VDJ rearrangements, while the reference is diploid in all cells. The so-called regional corrector allows to correct for possible copy number alterations at the T cell marker locus in cancer cells. By mathematically integrating the measurements of all three markers, T cells can be accurately quantified in both copy number stable and unstable DNA samples.

Quantification of Microbial Fluorescent Sensors During Live Intracellular Infections.

The interaction of pathogens with their eukaryotic hosts during intracellular growth is critical to many diseases. However, the relative scarcity of pathogen biomolecules versus the abundant host biomolecule concentration can make quantitative evaluation of pathogen intracellular responses difficult. Recent years have seen an explosion in utilization of fluorescent proteins to serve as transcriptional reporters and biosensors for quantification of pathogen responses. Here, we describe a method to establish a fluorescent assay quantifying pathogen behavior during intracellular infection and to quantify these results at a single cell level. The sensitivity of these fluorescent assays permits the live observation of changing pathogen responses, while the ability to measure at a single cell level uncovers subpopulations of pathogens whose existence may be missed during the population-level assays often required to accumulate sufficient pathogen biomolecules for analysis.

Quantification of protein abundance and interaction defines a mechanism for operation of the circadian clock.

The mammalian circadian clock exerts control of daily gene expression through cycles of DNA binding. Here, we develop a quantitative model of how a finite pool of BMAL1 protein can regulate thousands of target sites over daily time scales. We used quantitative imaging to track dynamic changes in endogenous labelled proteins across peripheral tissues and the SCN. We determine the contribution of multiple rhythmic processes coordinating BMAL1 DNA binding, including cycling molecular abundance, binding affinities, and repression. We find nuclear BMAL1 concentration determines corresponding CLOCK through heterodimerisation and define a DNA residence time of this complex. Repression of CLOCK:BMAL1 is achieved through rhythmic changes to BMAL1:CRY1 association and high-affinity interactions between PER2:CRY1 which mediates CLOCK:BMAL1 displacement from DNA. Finally, stochastic modelling reveals a dual role for PER:CRY complexes in which increasing concentrations of PER2:CRY1 promotes removal of BMAL1:CLOCK from genes consequently enhancing ability to move to new target sites.

miR-9a regulates levels of both rhomboid mRNA and protein in the early Drosophila melanogaster embryo.

MicroRNAs can have subtle and combinatorial effects on the levels of the targets and pathways they act on. Studying the consequences of a single microRNA knockout often proves difficult as many such knockouts exhibit phenotypes only under stress conditions. This has often led to the hypothesis that microRNAs buffer the effects of intrinsic and environmental stochasticity on gene expression. Observing and understanding this buffering effect entails quantitative analysis of microRNA and target expression in single cells. To this end, we have employed single-molecule fluorescence in situ hybridization, immunofluorescence, and high-resolution confocal microscopy to investigate the effects of miR-9a loss on the expression of the serine-protease Rhomboid in Drosophila melanogaster early embryos. Our single-cell quantitative approach shows that spatially, the rhomboid mRNA pattern is identical in WT and miR-9a knockout embryos. However, we find that the number of mRNA molecules per cell is higher when miR-9a is absent, and the level and temporal accumulation of rhomboid protein shows a more dramatic increase in the miR-9a knockout. Specifically, we see accumulation of rhomboid protein in miR-9a mutants by stage 5, much earlier than in WT. The data, therefore, show that miR-9a functions in the regulation of rhomboid mRNA and protein levels. While further work is required to establish whether rhomboid is a direct target of miR-9 in Drosophila, our results further establish the miR-9 family microRNAs as conserved regulators of timing in neurogenic processes. This study shows the power of single-cell quantification as an experimental tool to study phenotypic consequences of microRNA mis-regulation.

Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions.

Microscopy has revolutionised our view on biology and has been vital for many discoveries since its invention around 200 years ago. Recent developments in cell biology have led to a strong interest in generating spheroids and organoids that better represent tissue. However, the current challenge faced by many researchers is the culture and analysis of these three-dimensional (3D) cell cultures. With the technological improvements in reconstructing volumetric datasets by optical sections, it is possible to quantify cells, their spatial arrangement, and the protein distribution without destroying the physical organization. We assessed three different microwell culture plates and four analysis tools for 3D imaging data for their applicability for the analysis of 3D cultures. A key advantage of microwell plates is their potential to perform high-throughput experiments in which cell cultures are generated and analysed in one single system. However, it was shown that this potential could be impacted by the material composition and microwell structure. For example, antibody staining was not possible in a hydrogel microwell, and truncated pyramid-structured microwells had increased background fluorescence due to their structure. Regarding analysis tools, four different software, namely CellProfiler, Fiji/ImageJ, Nikon GA3 and Imaris, were compared for their accuracy and applicability in analysing datasets from 3D cultures. The results showed that the open-access software, CellProfiler and Fiji, could quantify nuclei and cells, yet with varying results compared to manual counting, and may require post-processing optimisation. On the other hand, the GA3 and Imaris software packages showed excellent versatility in usage and accuracy in the quantification of nuclei and cells, and could classify cell localisation. Together these results provide critical considerations for microscopic imaging and analysis of 3D cell cultures.

RapID Cell Counter: Semi-Automated and Mid-Throughput Estimation of Cell Density within Diverse Cortical Layers.

Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience research, enabling a greater understanding of mechanisms underlying nervous system morphogenesis. Widely used experimental methods to quantify cells labeled with fluorescent markers, such as immunohistochemistry (IHC), in situ hybridization, and expression of transgenes via stable lines or transient in utero electroporations (IUEs), depend on accurate and consistent quantification of images. Current methods to quantify fluorescently-labeled cells rely on labor-intensive manual counting approaches, such as the Fiji plugin Cell Counter, which requires custom macros to enable higher-throughput analyses. Here, we present RapID Cell Counter, a semi-automated cell-counting tool with an easy-to-implement graphical user interface (GUI), which facilitates quick and consistent quantifications of cell density within user-defined boundaries that can be divided into equally-partitioned segments. Compared with the standard manual counting approach, we show that RapID matched accuracy and consistency and only required ∼10% of user time relative to manual counting methods, when quantifying the distribution of fluorescently-labeled neurons in mouse IUE experiments. Using RapID, we recapitulated previously published work focusing on two genes, SRGAP2 and CUL5, important for projection neuron (PN) migration in the neocortex and used it to quantify PN displacement in a mouse knock-out model of RBX2 Moreover, RapID is capable of quantifying other cell types in the brain with complex cell morphologies, including astrocytes and dopaminergic neurons. We propose RapID as an efficient method for neuroscience researchers to process fluorescently-labeled brain images in a consistent, accurate, and mid-throughput manner.

DNA Dyes-Highly Sensitive Reporters of Cell Quantification: Comparison with Other Cell Quantification Methods.

Cell quantification is widely used both in basic and applied research. A typical example of its use is drug discovery research. Presently, plenty of methods for cell quantification are available. In this review, the basic techniques used for cell quantification, with a special emphasis on techniques based on fluorescent DNA dyes, are described. The main aim of this review is to guide readers through the possibilities of cell quantification with various methods and to show the strengths and weaknesses of these methods, especially with respect to their sensitivity, accuracy, and length. As these methods are frequently accompanied by an analysis of cell proliferation and cell viability, some of these approaches are also described.

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy.

Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.

A Cell Segmentation/Tracking Tool Based on Machine Learning.

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.