Age Dependence of Murine Spermatogenesis

1995 ◽  
Vol 50 (3-4) ◽  
pp. 303-310 ◽  
Author(s):  
Ursula Beate Hacker-Klom

The dependence of spermatogenesis function on murine age shall be investigated. Thus, testicular samples of at least 10 NMRI mice per group aged 0 to 26 months are analysed by flow cytometry after staining the DNA with DAPI. The aim of this study is to be able to account for the influence of age on mice. There are no changes in spermatogenesis in mice aged 11 weeks up to 16 months with respect to testis weight and to the frequency of different testicular cell types. From 16 months onwards, there is a tendency to a reduced spermatogenesis function; The frequency of round spermatids is decreased. In addition, there is an increased chromatin dispersion in elongated spermatids with age. Thus, older mice (>16 months) should not be used for experiments e.g. on radiation and drug effects any more. The frequency of abnormal diploid spermatozoa does not increase with age.

Author(s):  
Rosanna Serafini ◽  
Dickson D. Varner ◽  
Charles C. Love

2018 ◽  
Vol 66 ◽  
pp. 88
Author(s):  
R. Serafini ◽  
D.D. Varner ◽  
C. Hernández-Avilés ◽  
S.R. Teague ◽  
K.A. LaCaze ◽  
...  

2012 ◽  
Vol 12 (16) ◽  
pp. 1815-1833 ◽  
Author(s):  
Esvieta Tenorio-Borroto ◽  
Claudia G. Penuelas-Rivas ◽  
Juan C. Vasquez-Chagoyan ◽  
Francisco J. Prado-Pradoa ◽  
Xerardo Garcia-Mera ◽  
...  

1994 ◽  
Vol 32 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Steven K. Koester ◽  
Juhani U. Maenpaa ◽  
Valerie J. Wiebe ◽  
W. Jeffrey Baker ◽  
Gregory T. Wurz ◽  
...  

2017 ◽  
Vol 7 (1) ◽  
pp. 176
Author(s):  
Maryam Sadat Nezhadfazel ◽  
Kazem Parivar ◽  
Nasim Hayati Roodbari ◽  
Mitra Heydari Nasrabadi

Omentum mesenchymal stem cells (OMSCs) could be induced to differentiate into cell varieties under certain conditions. We studied differentiation of OMSCs induced by using placenta extract in NMRI mice. Mesenchymal stem cells (MSCs) were isolated from omentum and cultured with mice placenta extract. MSCs, were assessed after three passages by flow cytometry for CD90, CD44, CD73, CD105, CD34 markers and were recognized their ability to differentiate into bone and fat cell lines. Placenta extract dose was determined with IC50 test then OMSCs were cultured in DMEM and 20% placenta extract.The cell cycle was checked. OMSCs were assayed on 21 days after culture and differentiated cells were determined by flow cytometry and again processed for flow cytometry. CD90, CD44, CD73, CD105 markers were not expressed, only CD34 was their marker. OMSCs were morphologically observed. Differentiated cells are similar to the endothelial cells. Therefore, to identify differentiated cells, CD31 and FLK1 expression were measured. This was confirmed by its expression. G1 phase of the cell cycle shows that OMSCs compared to the control group, were in the differentiation phase. The reason for the differentiation of MSCs into endothelial cells was the sign of presence of VEGF factor in the medium too high value of as a VEGF secreting source.


2018 ◽  
Vol 20 (1) ◽  
pp. 19 ◽  
Author(s):  
Yadong Wei ◽  
Krishan Chhiba ◽  
Fengrui Zhang ◽  
Xujun Ye ◽  
Lihui Wang ◽  
...  

Sialic acid-binding Ig-like lectin 8 (Siglec-8) is expressed on the surface of human eosinophils, mast cells, and basophils—cells that participate in allergic and other diseases. Ligation of Siglec-8 by specific glycan ligands or antibodies triggers eosinophil death and inhibits mast cell degranulation; consequences that could be leveraged as treatment. However, Siglec-8 is not expressed in murine and most other species, thus limiting preclinical studies in vivo. Based on a ROSA26 knock-in vector, a construct was generated that contains the CAG promoter, a LoxP-floxed-Neo-STOP fragment, and full-length Siglec-8 cDNA. Through homologous recombination, this Siglec-8 construct was targeted into the mouse genome of C57BL/6 embryonic stem (ES) cells, and chimeric mice carrying the ROSA26-Siglec-8 gene were generated. After cross-breeding to mast cell-selective Cre-recombinase transgenic lines (CPA3-Cre, and Mcpt5-Cre), the expression of Siglec-8 in different cell types was determined by RT-PCR and flow cytometry. Peritoneal mast cells (dual FcεRI+ and c-Kit+) showed the strongest levels of surface Siglec-8 expression by multicolor flow cytometry compared to expression levels on tissue-derived mast cells. Siglec-8 was seen on a small percentage of peritoneal basophils, but not other leukocytes from CPA3-Siglec-8 mice. Siglec-8 mRNA and surface protein were also detected on bone marrow-derived mast cells. Transgenic expression of Siglec-8 in mice did not affect endogenous numbers of mast cells when quantified from multiple tissues. Thus, we generated two novel mouse strains, in which human Siglec-8 is selectively expressed on mast cells. These mice may enable the study of Siglec-8 biology in mast cells and its therapeutic targeting in vivo.


2001 ◽  
Vol 46 (No. 7–8) ◽  
pp. 190-198
Author(s):  
Z. Sládek ◽  
D. Ryšánek ◽  
M. Faldyna

Distribution of leukocyte types present in virgin bovine mammary glands was analysed in dot plots obtained by flow cytometry (FACS) of samples collected from 10 non-pregnant heifers after induction of leukocyte influx. Changes of percentage of leukocyte types during development and resolution of induced influx in comparison with blood leukocyte pattern allow identification of these cell types on FACS dot plot. The positions of mammary gland granulocyte and lymphocyte regions were identical with those of the corresponding peripheral blood cells. Two basic morphologically distinct types occupying separate regions in dot plots were observed in the population of mononuclear phagocytes (MoP): non-vacuolised monocyte-like macrophages (MoMAC) and vacuolised macrophages (MAC). Influx resolution was characterised by a marked shift of the MoMAC region towards that of MAC recognisable in dot plots by a separate region of intermediate MoP forms. The study provides a pattern of dynamics of percentages of mammary gland leukocyte types during influx development and resolution as imaged by FACS.


2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19013-e19013
Author(s):  
Marianne T. Santaguida ◽  
Ryosuke Kita ◽  
Steven A. Schaffert ◽  
Erica K. Anderson ◽  
Kamran A Ali ◽  
...  

e19013 Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.


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