Lysosome-Like Bodies in the Hepatopancreas of Oniscus Ascellus

1971 ◽  
Vol 29 ◽  
pp. 416-417
Author(s):  
John W. Roberts ◽  
E. R. Witkus
Keyword(s):  
B Cells ◽  
Digestive Tract ◽  
Mitotic Activity ◽  
Cell Types ◽  
Serial Sections ◽  
Straight Line ◽  
Blind Ending ◽  
Regenerative Cells

The hepatopancreas of the Isopod, Oniscus ascellus is comprised of two pairs of blind-ending diverticula which empty into the straight-line digestive tract through the hepatopancreatic duct. The cells at the blind distal tip are unspecialized regenerative cells. Mitotic activity of the cells in this region pushes older cells proximally in the lobe. As this occurs, the cells differentiate into either secretory B-cells or absorptive S-cells. By making serial sections, it is possible to trace the differentiation of the regenerative cells into both cell types.Lysosome-like bodies were observed within the secretory B-cells. These bodies were found to measure approximately 0.5 to 2.5 microns in diameter. These bodies also were observed to contain many membrane-figures (fig. 1).

Hepatopancreatic Secretions in the Isopod, Oniscus ascellus

1972 ◽  
Vol 30 ◽  
pp. 168-169
Author(s):  
John W. Roberts ◽  
E. R. Witkus
Keyword(s):  
B Cells ◽  
Digestive System ◽  
Functional Differentiation ◽  
Transitional Region ◽  
Thin Sections ◽  
Straight Line ◽  
Blind Ending ◽  
Glandular Cells ◽  
Regenerative Cells ◽  
And Storage

The hepatopancreas of the isopod, Oniscus ascellus, is comprised of two pairs of blind-ending diverticula which empty into the straight-line digestive system posterior to the foregut. The functional differentiation of the regenerative cells in the tip of each lobe was studied with the light and electron microscope by making alternate thick and thin sections from the blind-ending tip throughout the length of each lobe. Two types of cells specialized from the regenerative cells: B-cells, specialized for secretion and S-cells which function in absorption and storage of nutrients.In the B-cells, the secretory vesicles first appear in association with rough endoplasmic reticulum and Golgi apparatus in the transitional region of the lobe, between the regenerative and mature glandular cells (Fig. l).

Control of Secretory Production in the Isopod Hepatopancreas

1972 ◽  
Vol 30 ◽  
pp. 166-167
Author(s):  
John W. Roberts ◽  
E. R. Witkus
Keyword(s):  
B Cells ◽  
Morphological Data ◽  
Secretory Product ◽  
Secretory Function ◽  
Secretory Material ◽  
Blind Ending ◽  
Secretory Production ◽  
Regenerative Cells ◽  
Immature Cells ◽  
And Storage

The isopod hepatopancreas, as exemplified by Oniscus ascellus. is comprised of four blind-ending diverticula. The regenerative cells at the tip of each diverticula differentiate into either club-shaped B-cells, which serve a secretory function, or into conoid S-cells, which serve in the absorption and storage of nutrients.The glandular B-cells begin producing secretory material with the development of rough endoplasmic reticulum during their process of maturation from the undifferentiated regenerative cells. Cytochemical and morphological data indicate that the hepatopancreas sequentially produces two types of secretory material within the large club-shaped cells. The production of the carbohydrate-like secretory product in immature cells seems to be phased out as the production of the osmiophilic secretion was phased in as the cell matured.

scholarly journals Expression of neural cell adhesion molecule (N-CAM) in rat islets and its role in islet cell type segregation

1994 ◽  
Vol 107 (6) ◽  
pp. 1429-1436 ◽  
Author(s):  
V. Cirulli ◽  
D. Baetens ◽  
U. Rutishauser ◽  
P.A. Halban ◽  
L. Orci ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
B Cells ◽  
Pancreatic Polypeptide ◽  
Islet Cells ◽  
Cell Types ◽  
Serial Sections ◽  
Cell Type ◽  
Endocrine Tissue ◽  
Pancreatic Islets Of Langerhans

Endocrine cell types are non-randomly distributed within pancreatic islets of Langerhans. In the rat, insulin-secreting B-cells occupy the core of the islets and are surrounded by A-, D- and PP-cells, secreting glucagon, somatostatin and pancreatic polypeptide, respectively. Furthermore, dissociated islet cells have the ability in vitro to form aggregates with the same cell-type organization as native islets (pseudoislets). These observations suggest that a differential expression of cell adhesion molecules (CAMs) might characterize B- and non-B-cells (A-, D- and PP-cells), and be in part responsible for the establishment and maintenance of islet architecture. Indirect immunofluorescence using antibodies against CAMs and islet hormones was performed on serial sections of the splenic and duodenal parts of the rat pancreas. Staining for the Ca(2+)-dependent CAM E-cadherin was detected on both exocrine and endocrine tissue and was uniform over the entire islet section, in both pancreatic regions. By contrast, staining for the Ca(2+)-independent neural CAM (N-CAM) was restricted to endocrine tissue and nerve endings. Furthermore, N-CAM staining of endocrine cells was stronger in the islet periphery, a region composed mostly of non-B-cells. Serial sections demonstrate that cells staining strongly for N-CAM in the splenic part correspond to glucagon cells and in the duodenal part to pancreatic polypeptide cells. Within pseudoislets in vitro a stronger staining for N-CAM was also observed on peripheral cells, corresponding to non-B-cells.

Copper Storage Bodies in the Hepatopancreas of the Isopod, Oniscus Asellus

1975 ◽  
Vol 33 ◽  
pp. 698-699 ◽  
Author(s):  
John W. Roberts ◽  
E.R. Witkus
Keyword(s):  
B Cells ◽  
Secretory Cells ◽  
Common Stock ◽  
Blind Ending ◽  
Regenerative Cells ◽  
Storage Cells ◽  
Transitional Cells ◽  
Copper Storage ◽  
And Storage ◽  
Cell Storage

The hepatopancreas of the Isopod, Oniscus asellus consists of two pairs of blind-ending diverticula. Two types of mature cells are found: secretory cells (B-cells) and storage cells (S-cells). Both differentiate from a common stock of regenerative cells located at the blind end of each lobe.The appearance of what Weiser (1968) calls copper-storage bodies in the S-cells first occurs in the area of each lobe where the transitional cells differentiate into either mature secretory (B) cells or storage (S) cells. Figure 1 shows a basophilic S-cell sandwiched between two acidophilic B-cells. Figure 2 shows an electron micrograph of S-cell storage bodies with different affinities for electron-dense stain.By employing the method of Scheuer-et al (1967) copper localization was demonstrated only in S-cells. No copper was adsorbed into the B-cells but was actively absorbed into adjacent S-cells. This might indicate a mechanism for selective copper absorption in S-cells and no such mechanism in B-cells.

Ca++-ATPase activity in the hepatopancreas cells of Oniscus asellus

1992 ◽  
Vol 50 (1) ◽  
pp. 820-821
Author(s):  
G.M. Vernon ◽  
A. Surace ◽  
R. Witkus
Keyword(s):  
B Cells ◽  
Epithelial Cell ◽  
Atpase Activity ◽  
Calcium Transport ◽  
Carcinus Maenas ◽  
Cell Types ◽  
Molt Cycle ◽  
Copper And Zinc

The hepatopancreas consists of a pair of bilobed tubules comprised of two epithelial cell types. S cells are absorptive and accumulate metals such as copper and zinc. Ca++ concentrations vary between the S and B cells and during the molt cycle. Roer and Dillaman implicated Ca++-ATPase in calcium transport during molting in Carcinus maenas. This study was undertaken to compare the localization of Ca++-ATPase activity in the S and B cells during intermolt.

scholarly journals 521. Similarities and Differences in Transcriptomic Host Response between SARS-CoV-2 and Other Viral Infections

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S326-S327
Author(s):  
Simone A Thair ◽  
Yudong He ◽  
Yehudit Hasin-Brumshtein ◽  
Suraj Sakaram ◽  
Rushika R Pandya ◽  
...  
Keyword(s):  
Immune Response ◽  
B Cells ◽  
Nk Cells ◽  
Pathway Analysis ◽  
Host Response ◽  
Viral Infections ◽  
Cell Types ◽  
Gene Signature ◽  
Gene Signatures ◽  
Similarities And Differences

Abstract Background COVID-19 is a pandemic caused by the SARS-CoV-2 virus that shares and differs in clinical characteristics of known viral infections. Methods We obtained RNAseq profiles of 62 prospectively enrolled COVID-19 patients and 24 healthy controls (HC). We collected 23 independent studies profiling 1,855 blood samples from patients covering six viruses (influenza, RSV, HRV, Ebola, Dengue and SARS-CoV-1). We studied host whole-blood transcriptomic responses in COVID-19 compared to non-COVID-19 viral infections to understand similarities and differences in host response. Gene signature threshold was absolute effect size ≥1, FDR ≤ 0.05%. Results Differential gene expression of COVID-19 vs HC are highly correlated with non-COVID-19 vs HC (r=0.74, p< 0.001). We discovered two gene signatures: COVID-19 vs HC (2002 genes) (COVIDsig) and non-COVID-19 vs HC (635 genes) (nonCOVIDsig). Pathway analysis of over-expressed signature genes in COVIDsig or nonCOVIDsig identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection and cytokine production. Conversely, for under-expressed genes, pathways indicated repression of lymphocyte differentiation and activation (Fig1). Intersecting the two gene signatures found two genes significantly oppositely regulated (ACO1, ATL3). We derived a third gene signature using COCONUT to compare COVID-19 to non-COVID-19 viral infections (416 genes) (Fig2). Pathway analysis did not result in significant enrichment, suggesting identification of novel biology (Fig1). Statistical deconvolution of bulk transcriptomic data found M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells and total B cells changed in the same direction across COVID-19 and non-COVID-19 infections. Cell types that increased in COVID-19 relative to non-COVID-19 were CD56bright NK cells, M2 macrophages and total NK cells. Those that decreased in non-COVID-19 relative to COVID-19 were CD56dim NK cells & memory B cells and eosinophils (Fig3). Figure 1 Figure 2 Figure 3 Conclusion The concordant and discordant responses mapped here provide a window to explore the pathophysiology of COVID-19 vs other viral infections and show clear differences in signaling pathways and cellularity as part of the host response to SARS-CoV-2. Disclosures Simone A. Thair, PhD, Inflammatix, Inc. (Employee, Shareholder) Yudong He, PhD, Inflammatix Inc. (Employee) Yehudit Hasin-Brumshtein, PhD, Inflammatix (Employee, Shareholder) Suraj Sakaram, MS in Biochemistry and Molecular Biology, Inflammatix (Employee, Other Financial or Material Support, stock options) Rushika R. Pandya, MS, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Timothy Sweeney, MD, PHD, Inflammatix, Inc. (Employee, Shareholder)

Fine structure of the kinetochores in six species of the Coleoptera

Genome ◽  
1997 ◽  
Vol 40 (3) ◽  
pp. 379-385
Author(s):  
Klaus Werner Wolf
Keyword(s):  
Direct Contact ◽  
Karyotype Evolution ◽  
Coccinella Septempunctata ◽  
Cell Types ◽  
Male Meiosis ◽  
Serial Sections ◽  
Cell Type ◽  
Present Survey ◽  
Transmission Electron ◽  
Size Variability

Kinetochore structure was examined in a total of 6 species from 5 different families of the Coleoptera using transmission electron microscopy of ultrathin serial sections. Metaphase spermatogonia and primary and secondary spermatocytes were studied in Tenebrio molitor (Tenebrionidae) to determine whether kinetochore structure varies depending on the cell type. In all three cell types, the kinetochore microtubules (MTs) were in direct contact with the chromosomal surface, and kinetochore plates were not detectable. In the other species, only metaphase I spermatocytes were examined. As in T. molitor, distinct kinetochore plates were also absent in Adelocera murina (Elateridae), Agapanthia villosoviridescens (Cerambycidae), and Coccinella septempunctata (Coccinellidae). However, bivalents in male meiosis of two representatives of the Chrysomelidae, Agelastica alni and Chrysolina graminis, showed roughly spherical kinetochores at their poleward surfaces. Microtubules were in contact with this material. Thus, although the present survey covers only a small number of species, it is clear that at least two kinetochore types occur in the Coleoptera. The cytological findings are discussed in the context of chromosome number and genome size variability in the Coleopteran families studied. It is suggested that properties of the kinetochores could play a role in karyotype evolution in the Coleoptera.Key words: bivalent, microtubule, meiosis, metaphase, spermatocyte.

scholarly journals Phenotype of recovering lymphoid cell populations after marrow transplantation.

1985 ◽  
Vol 161 (6) ◽  
pp. 1483-1502 ◽  
Author(s):  
K A Ault ◽  
J H Antin ◽  
D Ginsburg ◽  
S H Orkin ◽  
J M Rappeport ◽  
...  
Keyword(s):  
T Cell ◽  
B Cells ◽  
B Cell ◽  
Nk Cells ◽  
Cell Types ◽  
Lymphoid Cells ◽  
The Other ◽  
Hla Dr ◽  
T Cell Antigens ◽  
Leu 7

Four patients who received bone marrow transplants were studied sequentially during the posttransplant period to define the pattern of recovering lymphoid cell types. Three patients received T cell-depleted, HLA-matched marrow, and one received untreated marrow from an identical twin. Blood lymphoid cells were labeled with 25 different pairs of monoclonal antibodies. In each sample, one antibody was conjugated to fluorescein and one to phycoerythrin, thus allowing simultaneous assessment of the expression of the two markers using the fluorescence activated cell sorter. A total of 14 antibodies were used, routinely including HLE, Leu-M3, Leu-4, Leu-1, Leu-5, Leu-9, Leu-6, Leu-2, Leu-3, HLA-DR, Leu-7, Leu-11, Leu-15, and Leu-12. Other antibodies were used to further define some populations. This study has allowed us to define six distinct cell types that have appeared in all four patients by day 90 posttransplantation, and which account for 90-100% of all circulating lymphoid cells. These cell types are (a) T helper cells expressing Leu-1, Leu-4, Leu-9, Leu-5, Leu-3, and variable amounts of HLA-DR; (b) T suppressor cells expressing Leu-1, Leu-4, Leu-9, Leu-5, Leu-2, and variable amounts of HLA-DR; (c) B cells expressing Leu-12, B1, HLA-DR, IgD, and IgM, but none of the T cell antigens; (d) an unusual B cell phenotype (Leu-1 B) expressing all of the B cell markers, and also having low amounts of Leu-1, but none of the other T cell antigens; (e) natural killer (NK) cells expressing Leu-11, Leu-15, Leu-5 but none of the other T cell or B cell markers; (f) NK cells expressing Leu-11, Leu-15, Leu-5, and low levels of Leu-2. Both NK types also express Leu-7 on some, but not all cells. The relative frequencies of these cell types varied among the patients and with time, but the striking findings were the presence of relatively few mature T cells, large numbers of NK cells, and the preponderance of the unusual Leu-1 B cell over conventional B cells in all three patients who developed B cells. Sorting experiments confirmed the NK activity of the major NK cell phenotypes, and DNA analysis confirmed that all of the cells studied were of donor origin. In addition, analysis of Ig genes in one patient showed that the Leu-1 B cells were not clonally rearranged.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Th2 Cytokine Modulates Herpesvirus Reactivation in a Cell Type Specific Manner

2021 ◽  
Author(s):  
Guoxun Wang ◽  
Christina Zarek ◽  
Tyron Chang ◽  
Lili Tao ◽  
Alexandria Lowe ◽  
...  
Keyword(s):  
B Cells ◽  
Cell Types ◽  
The Body ◽  
Receptor Expression ◽  
Conditional Knockout ◽  
Interleukin 4 ◽  
Virus Reactivation ◽  
Cell Type ◽  
Murine Gammaherpesvirus ◽  
Cell Type Specific

Gammaherpesviruses, such as Epstein-Barr virus (EBV), Kaposi’s sarcoma associated virus (KSHV), and murine γ-herpesvirus 68 (MHV68), establish latent infection in B cells, macrophages, and non-lymphoid cells, and can induce both lymphoid and non-lymphoid cancers. Research on these viruses has relied heavily on immortalized B cell and endothelial cell lines. Therefore, we know very little about the cell type specific regulation of virus infection. We have previously shown that treatment of MHV68-infected macrophages with the cytokine interleukin-4 (IL-4) or challenge of MHV68-infected mice with an IL-4-inducing parasite leads to virus reactivation. However, we do not know if all latent reservoirs of the virus, including B cells, reactivate the virus in response to IL-4. Here we used an in vivo approach to address the question of whether all latently infected cell types reactivate MHV68 in response to a particular stimulus. We found that IL-4 receptor expression on macrophages was required for IL-4 to induce virus reactivation, but that it was dispensable on B cells. We further demonstrated that the transcription factor, STAT6, which is downstream of the IL-4 receptor and binds virus gene 50 N4/N5 promoter in macrophages, did not bind to the virus gene 50 N4/N5 promoter in B cells. These data suggest that stimuli that promote herpesvirus reactivation may only affect latent virus in particular cell types, but not in others. Importance Herpesviruses establish life-long quiescent infections in specific cells in the body, and only reactivate to produce infectious virus when precise signals induce them to do so. The signals that induce herpesvirus reactivation are often studied only in one particular cell type infected with the virus. However, herpesviruses establish latency in multiple cell types in their hosts. Using murine gammaherpesvirus-68 (MHV68) and conditional knockout mice, we examined the cell type specificity of a particular reactivation signal, interleukin-4 (IL-4). We found that IL-4 only induced herpesvirus reactivation from macrophages, but not from B cells. This work indicates that regulation of virus latency and reactivation is cell type specific. This has important implications for therapies aimed at either promoting or inhibiting reactivation for the control or elimination of chronic viral infections.

scholarly journals High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 17-18
Author(s):  
Jose C Villasboas ◽  
Patrizia Mondello ◽  
Angelo Fama ◽  
Melissa C. Larson ◽  
Andrew L. Feldman ◽  
...  
Keyword(s):  
T Cells ◽  
Spatial Distribution ◽  
T Cell ◽  
B Cells ◽  
Research Funding ◽  
Memory T Cells ◽  
Cell Types ◽  
T Cell Subsets ◽  
Cell Contact ◽  
Private Company

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

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