Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.
Current methods for assessing cell proliferation in 3D scaffolds rely on changes in metabolic activity or total DNA, however, direct quantification of cell number in 3D scaffolds remains a challenge. To address this issue, we developed an unbiased stereology approach that uses systematic-random sampling and thin focal-plane optical sectioning of the scaffolds followed by estimation of total cell number (StereoCount). This approach was validated against an indirect method for measuring the total DNA (DNA content); and the Bürker counting chamber, the current reference method for quantifying cell number. We assessed the total cell number for cell seeding density (cells per unit volume) across four values and compared the methods in terms of accuracy, ease-of-use and time demands. The accuracy of StereoCount markedly outperformed the DNA content for cases with ~ 10,000 and ~ 125,000 cells/scaffold. For cases with ~ 250,000 and ~ 375,000 cells/scaffold both StereoCount and DNA content showed lower accuracy than the Bürker but did not differ from each other. In terms of ease-of-use, there was a strong advantage for the StereoCount due to output in terms of absolute cell numbers along with the possibility for an overview of cell distribution and future use of automation for high throughput analysis. Taking together, the StereoCount method is an efficient approach for direct cell quantification in 3D collagen scaffolds. Its major benefit is that automated StereoCount could accelerate research using 3D scaffolds focused on drug discovery for a wide variety of human diseases.
Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep learning (DL) offers the possibility of improving throughput by automating the collection of stereology data. We have recently shown that DL can effectively achieve comparable accuracy to manual stereology but with higher repeatability, improved throughput, and less variation due to human factors by quantifying the total number of immunostained cells at their maximal profile of focus in extended depth of field (EDF) images. In the first of two novel contributions in this work, we propose a semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to automatically generate ground truth on EDF images for training our deep learning (DL) models to automatically count cells using unbiased stereology methods. This update increases the amount of training data, thereby improving the accuracy and efficiency of automatic cell counting methods, without a requirement for extra expert time. The second contribution of this work is a Multi-channel Input and Multi-channel Output (MIMO) method using a U-Net deep learning architecture for automatic cell counting in a stack of z-axis images (also known as disector stacks). This DL-based digital automation of the ordinary optical fractionator ensures accurate counts through spatial separation of stained cells in the z-plane, thereby avoiding false negatives from overlapping cells in EDF images without the shortcomings of 3D and recurrent DL models. The contribution overcomes the issue of under-counting errors with EDF images due to overlapping cells in the z-plane (masking). We demonstrate the practical applications of these advances with automatic disector-based estimates of the total number of NeuN-immunostained neurons in a mouse neocortex. In summary, this work provides the first demonstration of automatic estimation of a total cell number in tissue sections using a combination of deep learning and the disector-based optical fractionator method.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Mice MeSH
- Neocortex * MeSH
- Neurons MeSH
- Cell Count methods MeSH
- Artificial Intelligence * MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
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.
The aim of this study was to determine the effect of autologous serum (AS) eye drops on the density of human leucocyte antigen (HLA)-DR-positive epithelial cells and Langerhans cells on the ocular surface of patients with bilateral severe dry eye disease (DED) due to graft-versus-host disease (GvHD) or Sjögren's syndrome (SS). The study was conducted on 24 patients (48 eyes). AS was applied 6-10 times daily for 3 months together with regular artificial tear therapy. HLA-DR-positive cells were detected by direct immunocytochemistry on upper bulbar conjunctiva imprints obtained before and after treatment. The application of AS drops led to a statistically significant increase in the mean density of aberrant HLA-DR-positive conjunctival epithelial cells (p < 0.05) and HLA-DR-positive Langerhans cells (p < 0.05) in the GvHD group. Aberrant HLA-DR-positive epithelial cells in the SS group were decreased non-significantly. All patients reported a significant decrease in the Ocular Surface Disease Index (p < 0.01), which indicates improvement of the patient's subjective feelings after therapy. There was an expected but non-significant decrease of aberrant HLA-DR-positive conjunctival epithelial cells in the SS group only. However, the increased density of HLA-DR-positive cells, indicating slight subclinical inflammation, does not outweigh the positive effect of AS in patients with DED from GvHD.
- MeSH
- Adult MeSH
- Epithelium metabolism MeSH
- Epithelial Cells drug effects metabolism MeSH
- HLA-DR Antigens metabolism MeSH
- Immunohistochemistry methods MeSH
- Conjunctiva drug effects metabolism MeSH
- Middle Aged MeSH
- Humans MeSH
- Graft vs Host Disease drug therapy metabolism MeSH
- Ophthalmic Solutions therapeutic use MeSH
- Cell Count methods MeSH
- Aged MeSH
- Serum metabolism MeSH
- Sjogren's Syndrome drug therapy metabolism MeSH
- Dry Eye Syndromes drug therapy metabolism MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Cell quantification is widely used in basic or applied research. The current sensitive methods of cell quantification are exclusively based on the analysis of non-fixed cells and do not allow the simultaneous detection of various cellular components. A fast, sensitive and cheap method of the quantification of fixed adherent cells is described here. It is based on the incubation of DAPI- or Hoechst 33342-stained cells in a solution containing SDS. The presence of SDS results in the quick de-staining of DNA and simultaneously, in an up-to-1,000-fold increase of the fluorescence intensity of the used dyes. This increase can be attributed to the micelle formation of SDS. The method is sufficiently sensitive to reveal around 50-70 human diploid cells. It is compatible with immunocytochemical detections, the detection of DNA replication and cell cycle analysis by image cytometry. The procedure was successfully tested for the analysis of cytotoxicity. The method is suitable for the quantification of cells exhibiting low metabolic activity including senescent cells. The developed procedure provides high linearity and the signal is high for at least 20 days at room temperature. Only around 90 to 120 minutes is required for the procedure's completion.
- MeSH
- Staining and Labeling methods MeSH
- Cell Adhesion MeSH
- Cell Line MeSH
- Cell Cycle MeSH
- Cytophotometry methods MeSH
- Diploidy * MeSH
- DNA analysis chemistry MeSH
- Sodium Dodecyl Sulfate chemistry MeSH
- Fluorescent Dyes chemistry MeSH
- HeLa Cells MeSH
- Humans MeSH
- Cell Line, Tumor MeSH
- Cell Count instrumentation methods MeSH
- DNA Replication * MeSH
- Reproducibility of Results MeSH
- Cell Survival MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Relation of diabetes mellitus (DM) to the various stages of corneal nerve fiber damage is well accepted. A possible association between changes in the cornea of diabetic patients and diabetic retinopathy (DR), DM duration, and age at the time of DM diagnosis were evaluated. The study included 60 patients with DM type 1 (DM1) and 20 healthy control subjects. The density of basal epithelial cells, keratocytes and endothelial cells, and the status of the subbasal nerve fibers were evaluated using in vivo corneal confocal microscopy. Basal epithelial cell density increased with age (p=0.026), while stromal and endothelial cell density decreased with age (p=0.003, p=0.0005, p<0.0001). After the DM1 diagnosis was established, this association with age weaken. We showed nerve fiber damage in DM1 patients (p<0.0001). The damage correlated with the degree of DR. DM1 patients with higher age at DM1 diagnosis had a higher nerve fiber density (p=0.0021). These results indicated that age at DM1 diagnosis potentially has an important effect on final nerve fiber and corneal cell density.
- MeSH
- Diabetes Mellitus, Type 1 epidemiology pathology MeSH
- Diabetic Retinopathy epidemiology pathology MeSH
- Adult MeSH
- Epithelial Cells pathology MeSH
- Microscopy, Confocal methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Nerve Fibers pathology MeSH
- Cell Count methods MeSH
- Prospective Studies MeSH
- Cornea pathology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
The porcine liver is frequently used as a large animal model for verification of surgical techniques, as well as experimental therapies. Often, a histological evaluation is required that include measurements of the size, nuclearity or density of hepatocytes. Our aims were to assess the mean number-weighted volume of hepatocytes, the numerical density of hepatocytes, and the fraction of binuclear hepatocytes (BnHEP) in the porcine liver, and compare the distribution of these parameters among hepatic lobes and macroscopic regions of interest (ROIs) with different positions related to the liver vasculature. Using disector and nucleator as design-based stereological methods, the morphometry of hepatocytes was quantified in seven healthy piglets. The samples were obtained from all six hepatic lobes and three ROIs (peripheral, paracaval and paraportal) within each lobe. Histological sections (thickness 16 μm) of formalin-fixed paraffin-embedded material were stained with the periodic acid-Schiff reaction to indicate the cell outlines and were assessed in a series of 3-μm-thick optical sections. The mean number-weighted volume of mononuclear hepatocytes (MnHEP) in all samples was 3670 ± 805 μm(3) (mean ± SD). The mean number-weighted volume of BnHEP was 7050 ± 2550 μm(3) . The fraction of BnHEP was 4 ± 2%. The numerical density of all hepatocytes was 146 997 ± 15 738 cells mm(-3) of liver parenchyma. The porcine hepatic lobes contained hepatocytes of a comparable size, nuclearity and density. No significant differences were identified between the lobes. The peripheral ROIs of the hepatic lobes contained the largest MnHEP with the smallest numerical density. The distribution of a larger MnHEP was correlated with a larger volume of BnHEP and a smaller numerical density of all hepatocytes. Practical recommendations for designing studies that involve stereological evaluations of the size, nuclearity and density of hepatocytes in porcine liver are provided.
- MeSH
- Hepatocytes * MeSH
- Liver anatomy & histology cytology MeSH
- Cell Count methods MeSH
- Swine MeSH
- Cell Size * MeSH
- Animals MeSH
- Check Tag
- Male MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
We studied the expression and distribution of the microtubule-severing enzyme spastin in 3 human glioblastoma cell lines (U87MG, U138MG, and T98G) and in clinical tissue samples representative of all grades of diffuse astrocytic gliomas (n = 45). In adult human brains, spastin was distributed predominantly in neuronsand neuropil puncta and, to a lesser extent, in glia. Compared with normal mature brain tissues, spastin expression and cellular distribution were increased in neoplastic glial phenotypes, especiallyin glioblastoma (p < 0.05 vs low-grade diffuse astrocytomas). Overlapping punctate and diffuse patterns of localization wereidentified in tumor cells in tissues and in interphase and mitotic cells ofglioblastoma cell lines. There was enrichment of spastin in the leading edges of cells in T98G glioblastoma cell cultures and in neoplastic cell populations in tumor specimens. Real-time polymerase chain reaction and immunoblotting experiments revealed greater levels of spastin messenger RNA and protein expression in theglioblastoma cell lines versus normal human astrocytes. Functional experiments indicated that spastin depletion resulted in reduced cell motility and higher cell proliferation of T98G cells. Toour knowledge, this is the first report of spastin involvement incellmotility. Collectively, our results indicate that spastinexpression in glioblastomas might be linked to tumor cell motility, migration, and invasion.
- MeSH
- Adenosine Triphosphatases genetics metabolism MeSH
- Child MeSH
- Glioblastoma enzymology pathology MeSH
- Infant MeSH
- Middle Aged MeSH
- Humans MeSH
- RNA, Messenger metabolism MeSH
- Microtubules MeSH
- Young Adult MeSH
- Brain enzymology pathology MeSH
- Cell Line, Tumor MeSH
- Brain Neoplasms enzymology pathology MeSH
- Cell Count methods MeSH
- Cell Movement physiology MeSH
- Cell Proliferation MeSH
- Gene Expression Regulation, Neoplastic physiology MeSH
- Age Factors MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
In the present study, we compared three single platform methods for CD34+ hematopoietic stem cell (HSC) enumeration by flow cytometry. For this purpose, we analyzed the performance characteristics and results obtained from different HSC sources. Interlaboratory coefficients of variation (CV) for precision/reproducibility analysis varied from 4.0% to 6.7% / 6.7% to 9.2% for the low and 3.2% to 4.1% / 4.3% to 6.7%, respectively, for the high stem cell control. Correlation between methods ranged from 0.92% to 0.99%; Wilcoxon test showed no significant differences (p > 0.05); Bland-Altman analysis confirmed good agreement between assays (mean bias ranging from -0.48 to 6.91). Our results demonstrate very good intralaboratory correlation and agreement between methods, confirm the major impact of single platform strategy for accurate and reproducible HSC enumeration and suggest that high interlaboratory variability could be influenced by incorrect performance of validated methods.
- MeSH
- Antigens, CD34 analysis immunology MeSH
- Bone Marrow Cells MeSH
- Dactinomycin analogs & derivatives MeSH
- Granulocyte Colony-Stimulating Factor pharmacology MeSH
- Fluorescein-5-isothiocyanate MeSH
- Fluorescent Dyes MeSH
- Phycoerythrin MeSH
- Hematopoietic Stem Cells MeSH
- Blood Cells MeSH
- Blood Cell Count MeSH
- Laboratories MeSH
- Leukapheresis MeSH
- Humans MeSH
- Hematopoietic Stem Cell Mobilization MeSH
- Antibodies, Monoclonal immunology MeSH
- Cell Count methods MeSH
- Antineoplastic Agents pharmacology MeSH
- Flow Cytometry methods MeSH
- Reproducibility of Results MeSH
- Sensitivity and Specificity MeSH
- Antibody Specificity MeSH
- Bone Marrow Examination MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Comparative Study MeSH
