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Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor T (CAR-T) cell therapy targeting T cell tumors still faces many challenges, one of which is its fratricide due to the target gene expressed on CAR-T cells. Despite this, these CAR-T cells can be expanded in vitro by extending the culture time and effectively eliminating malignant T cells. However, the mechanisms underlying CAR-T cell survival in cell subpopulations, the molecules involved, and their regulation are still unknown. We performed single-cell transcriptome profiling to investigate the fratricidal CAR-T products (CD26 CAR-Ts and CD44v6 CAR-Ts) targeting T cells, taking CD19 CAR-Ts targeting B cells from the same donor as a control. Compared with CD19 CAR-Ts, fratricidal CAR-T cells exhibit no unique cell subpopulation, but have more exhausted T cells, fewer cytotoxic T cells, and more T cell receptor (TCR) clonal amplification. Furthermore, we observed that fratricidal CAR-T cell survival was accompanied by target gene expression. Gene expression results suggest that fratricidal CAR-T cells may downregulate their human leukocyte antigen (HLA) molecules to evade T cell recognition. Single-cell regulatory network analysis and suppression experiments revealed that exhaustion mediated by critical regulatory factors may contribute to fratricidal CAR-T cell survival. Together, these data provide valuable and first-time insights into the survival of fratricidal CAR-T cells.

Abstract Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .

Lectin microarray (LMA) is a high-throughput platform that enables the rapid and sensitive analysis of N- and O-glycans attached to glycoproteins in biological samples, including formalin-fixed paraffin-embedded (FFPE) tissue sections. Here, we evaluated the sensitivity of the advanced scanner based on the evanescent-field fluorescence principle, which is equipped with a 1× infinity correction optical system and a high-end complementary metal-oxide semiconductor (CMOS) image sensor in digital binning mode. Using various glycoprotein samples, we estimated that the mGSR1200-CMOS scanner has at least fourfold higher sensitivity for the lower limit of linearity range than that of a previous charge-coupled device scanner (mGSR1200). A subsequent sensitivity test using HEK293T cell lysates demonstrated that cell glycomic profiling could be performed with only three cells, which has the potential for the glycomic profiling of cell subpopulations. Thus, we examined its application in tissue glycome mapping, as indicated in the online LM-GlycomeAtlas database. To achieve fine glycome mapping, we refined the laser microdissection-assisted LMA procedure to analyze FFPE tissue sections. In this protocol, it was sufficient to collect 0.1 mm2 of each of the tissue fragments from 5-μm-thick sections, which differentiated the glycomic profile between the glomerulus and renal tubules of a normal mouse kidney. In conclusion, the improved LMA enables high-resolution spatial analysis, which expands the possibilities of its application classifying cell subpopulations in clinical FFPE tissue specimens. This will be used in the discovery phase for the development of novel glyco-biomarkers and therapeutic targets, and to expand the range of target diseases. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. Results: To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. Conclusion: Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp. [ABSTRACT FROM AUTHOR]

The panel was developed and optimized for monitoring changes in homing capacity and functional diversity of human CD4+ conventional and regulatory T cell subsets. The analysis was based on expression of only surface markers in freshly isolated peripheral blood mononuclear cells (PBMCs) to reduce at minimum any alteration due to permeabilization or freezing/thawing procedures. We included markers to assess the distribution of naïve and memory populations based on the expression of CD45RA, CCR7, CD25, CD28 and CD95 in both conventional and regulatory T cells. The identification of major functional subsets was performed using CCR4, CCR6, CCR10, CXCR3 and CXCR5. Homing capacity of these subsets to skin, airway tract, gut and inflammatory lesions could finally be assessed with the markers CLA, CCR3, CCR5 and integrin β7. The panel was tested on freshly isolated PBMCs from healthy donors and patients with allergic rhinitis or autoimmune disorders. [ABSTRACT FROM AUTHOR]

Infantile hemangioma (IH) is the most common benign tumor in infants and usually resolves on its own. However, a small portion of IH cases are accompanied by serious complications and other problems, impacting the physical and psychological health of the children affected. The pathogenesis of IH is highly controversial. Studies have shown that abnormal blood vessel formation is an important pathological basis for the development of IH. Compared with that in normal tissues, the equilibrium of blood vessel growth at the tumor site is disrupted, and interactions among other types of cells, such as immune cells, promote the rapid proliferation and migration of vascular tissue cells and the construction of vascular networks. Currently, propranolol is the most common systemic drug used to inhibit the growth of IHs and accelerate their regression. The purpose of this review is to provide the latest research on the mechanisms of angiogenesis in IH. We discuss the possible roles of three major factors, namely, estrogen, hypoxia, and inflammation, in the development of IH. Additionally, we summarize the key roles of tumor cell subpopulations, such as pericytes, in the proliferation and regression of IH considering evidence from the past few years, with an emphasis on the possible mechanisms of propranolol in the treatment of IH. Angiogenesis is an important event during the development of IH, and an in-depth understanding of the molecular mechanisms of angiogenesis will provide new insights into the biology and clinical treatment of IH. [ABSTRACT FROM AUTHOR]

Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

ABSTRACT Mechanisms of evolution and evolution of antibiotic resistance are both fundamental and world health problems. Stress-induced mutagenesis defines mechanisms of mutagenesis upregulated by stress responses, which drive adaptation when cells are maladapted to their environments—when stressed. Work in mutagenesis induced by antibiotics had produced tantalizing clues but not coherent mechanisms. We review recent advances in antibiotic-induced mutagenesis that integrate how reactive oxygen species (ROS), the SOS and general stress responses, and multichromosome cells orchestrate a stress response-induced switch from high-fidelity to mutagenic repair of DNA breaks. Moreover, while sibling cells stay stable, a mutable “gambler” cell subpopulation is induced by differentially generated ROS, which signal the general stress response. We discuss other evolvable subpopulations and consider diverse evolution-promoting molecules as potential targets for drugs to slow evolution of antibiotic resistance, cross-resistance, and immune evasion. An FDA-approved drug exemplifies “stealth” evolution-slowing drugs that avoid selecting resistance to themselves or antibiotics.

The advent of single-cell RNA sequencing (scRNA-seq) techniques opens up new opportunities for studying the cell-specific changes in the transcriptomic data. An important research problem related with scRNA-seq data analysis is to identify cell subpopulations with distinct functions. However, the expression profiles of individual cells are usually measured over tens of thousands of genes, and it remains a difficult problem to effectively cluster the cells based on the high-dimensional profiles. An additional challenge of performing the analysis is that, the scRNA-seq data are often noisy and sometimes extremely sparse due to technical limitations and sampling deficiencies. In this paper, we propose a biclustering-based framework called DivBiclust that effectively identifies the cell subpopulations based on the high-dimensional noisy scRNA-seq data. Compared with nine state-of-the-art methods, DivBiclust excels in identifying cell subpopulations with high accuracy as evidenced by our experiments on ten real scRNA-seq datasets with different size and diverse dropout rates. The supplemental materials of DivBiclust, including the source codes, data, and a supplementary document, are available at https://www.github.com/Qiong-Fang/DivBiclust. [ABSTRACT FROM AUTHOR]

Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Huang, Zheng, Liao, Ma and Wang.)

Chimeric antigen receptor T (CAR-T) cell therapy targeting T cell tumors still faces many challenges, one of which is its fratricide due to the target gene expressed on CAR-T cells. Despite this, these CAR-T cells can be expanded in vitro by extending the culture time and effectively eliminating malignant T cells. However, the mechanisms underlying CAR-T cell survival in cell subpopulations, the molecules involved, and their regulation are still unknown. We performed single-cell transcriptome profiling to investigate the fratricidal CAR-T products (CD26 CAR-Ts and CD44v6 CAR-Ts) targeting T cells, taking CD19 CAR-Ts targeting B cells from the same donor as a control. Compared with CD19 CAR-Ts, fratricidal CAR-T cells exhibit no unique cell subpopulation, but have more exhausted T cells, fewer cytotoxic T cells, and more T cell receptor (TCR) clonal amplification. Furthermore, we observed that fratricidal CAR-T cell survival was accompanied by target gene expression. Gene expression results suggest that fratricidal CAR-T cells may downregulate their human leukocyte antigen (HLA) molecules to evade T cell recognition. Single-cell regulatory network analysis and suppression experiments revealed that exhaustion mediated by critical regulatory factors may contribute to fratricidal CAR-T cell survival. Together, these data provide valuable and first-time insights into the survival of fratricidal CAR-T cells., Competing Interests: The authors declare no potential competing interests., (© 2024 The Authors.)

Over the last decade, the acceleration in the clinical use of mesenchymal stromal cells (MSCs) has been nothing short of spectacular. Perhaps most surprising is how little we know about the “MSC product.” Although MSCs are being delivered to patients at an alarming rate, the regulatory requirements for MSC therapies (for example in terms of quality assurance and quality control) are nowhere near the expectations of traditional pharmaceuticals. That said, the standards that define a chemical compound or purified recombinant protein cannot be applied with the same stringency to a cell-based therapy. Biological processes are dynamic, adaptive and variable. Heterogeneity will always exist or emerge within even the most rigorously sorted clonal cell populations. With MSCs, perhaps more so than any other therapeutic cell, heterogeneity pervades at multiple levels, from the sample source to the single cell. The research and clinical communities collectively need to recognize and take steps to address this troublesome truth, to ensure that the promise of MSC-based therapies is fulfilled.

Over the last decade, the acceleration in the clinical use of mesenchymal stromal cells (MSCs) has been nothing short of spectacular. Perhaps most surprising is how little we know about the "MSC product." Although MSCs are being delivered to patients at an alarming rate, the regulatory requirements for MSC therapies (for example in terms of quality assurance and quality control) are nowhere near the expectations of traditional pharmaceuticals. That said, the standards that define a chemical compound or purified recombinant protein cannot be applied with the same stringency to a cell-based therapy. Biological processes are dynamic, adaptive and variable. Heterogeneity will always exist or emerge within even the most rigorously sorted clonal cell populations. With MSCs, perhaps more so than any other therapeutic cell, heterogeneity pervades at multiple levels, from the sample source to the single cell. The research and clinical communities collectively need to recognize and take steps to address this troublesome truth, to ensure that the promise of MSC-based therapies is fulfilled. [ABSTRACT FROM AUTHOR]

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics. [ABSTRACT FROM AUTHOR]

Cancer cells are usually heterogeneous, whose subpopulations contain distinct biological information that related to their unique phenotype. However, preparative separating heterogeneous cells into subpopulations is very challenging, especially based on their surface charge status. Here, free-flow zone electrophoresis (FFZE) was employed to separate and fractionate lung cells. The experimental parameters for FFZE, including voltage, running speed, and buffer pH, were optimized and the optimized method was then implemented to separate MRC-5 and H1299 cells. Normal lung cells MRC-5 could be separated into one major peak and few minor peaks. For lung cancer cell line H1299, two major peaks and a few minor peaks were fractionated. Those prepared major peaks were subjected to quantitative proteomics analysis for heterogeneous subpopulation. For H1299 cells, 2076 proteins were identified in the first major peak, and 1398 proteins were revealed in the second major peak, which increased by 13% on average (n = 3) when compared to that of before FFZE fractionation. Furthermore, we found that H1299 cells in the first major peak have higher mobility than the cells in the second one. Many cell mobility and metastasis related proteins, such as RHBDF2, TRIM59, and ADAM17, were solely expressed in the first major peak of H1299 after FFZE, indicating our method can distinguish cancer cell subpopulations with higher metastatic phenotype. Our results showed that FFZE can effectively fractionate heterogeneous cell subpopulations, which might be used for the comprehensive analysis of unique phenotypes of different cancers. [Display omitted] • A robust free-flow zone electrophoresis strategy was established to separate the subpopulations of heterogeneous cells. • Lung cancer cell subpopulations were successfully fractionated for proteomics analysis. • Cancer metastasis associated proteins were revealed in fractionated cell subpopulation. [ABSTRACT FROM AUTHOR]

The use of flow cytometry and fluorescence-activated cell sorting to roughly separate subpopulations of cells in Schmidtea mediterranea is long established. In this chapter, we describe a method for the immunostaining-either single or double-of live planarian cells, using mouse monoclonal antibodies reactive against S. mediterranea plasma membrane antigens. This protocol allows to sort live cells according to their membrane signature, offering the possibility to further characterize the cell populations in S. mediterranea in a variety of downstream applications, like transcriptomics and cell transplantation, also at the single-cell level., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Mechanisms of evolution and evolution of antibiotic resistance are both fundamental and world health problems. Stress-induced mutagenesis defines mechanisms of mutagenesis upregulated by stress responses, which drive adaptation when cells are maladapted to their environments-when stressed. Work in mutagenesis induced by antibiotics had produced tantalizing clues but not coherent mechanisms. We review recent advances in antibiotic-induced mutagenesis that integrate how reactive oxygen species (ROS), the SOS and general stress responses, and multichromosome cells orchestrate a stress response-induced switch from high-fidelity to mutagenic repair of DNA breaks. Moreover, while sibling cells stay stable, a mutable "gambler" cell subpopulation is induced by differentially generated ROS, which signal the general stress response. We discuss other evolvable subpopulations and consider diverse evolution-promoting molecules as potential targets for drugs to slow evolution of antibiotic resistance, cross-resistance, and immune evasion. An FDA-approved drug exemplifies "stealth" evolution-slowing drugs that avoid selecting resistance to themselves or antibiotics.

The current methods of isolation of adipose tissue-derived stem cells result in a heterogeneous population that might interfere with their differentiation potential and makes it difficult to compare the results between different groups. Partition in aqueous two-phase systems is one of the few techniques that separate cells on the basis of surface properties, gentle enough to isolate fragile cell types in isotonic conditions without altering their structure, and can be easily scaled. In this study, stem cells isolated from human adipose tissue seeded and expanded in vitro were fractionated by using centrifugal countercurrent distribution in an aqueous two-phase system. The separated subpopulations revealed the high heterogeneity of adipose tissue-derived stem cell samples. Comparative partition analyses showed that aging induces a loss of heterogeneity, which is not due to a loss of cell viability associated to age. The phosphatidylserine externalization, an apoptotic feature, is the main factor in cell partition that results in a decreased hydrophobicity of the cell surface. This procedure may be suitable for separating adipose tissue-derived stem cell populations enriched in some functional and/or structural surface characteristics. The possibility of a very effective separation of different subpopulations in opposite phases would be an interesting development of the method. [ABSTRACT FROM AUTHOR]

Abstract: Sperm head morphometry is a parameter in the evaluation of semen that has been associated with fertility in two ways: comparing morphometric measures between predefined groups of fertility; or analyzing morphometric data by multivariate techniques to identify cell populations. We analyzed the morphometry of ram sperm head by three procedures and checked its relationship with male fertility. A Computer-Aided Sperm Morphometric Assessment procedure (CASMA), an image analysis software (NIS-Elements) in combination with an optical microscope (MO-NIS) and this image analysis software in combination with a scanning electron microscope (SEM-NIS) were used. Eight morphometric parameters were assessed: length, width, area, perimeter, ellipticity, form factor, elongation and regularity. We observed significant differences between the morphometric data of sperm head obtained with three study procedures. The CASMA procedure shows the highest values for all parameters and the SEM-NIS procedure the lowest. The analysis of a semen sample, when only the mean of morphometric parameters is used to describe the cell population, is too limited to interpret their fertilizing capacity. It is essential to analyze the complex structure of the samples by defining subpopulations by multivariate methods. With few exceptions, the means of each morphometric parameter differ between the three subpopulations analyzed in each procedure. Only the subpopulations obtained with the MO-NIS procedure showed a significant correlation with male fertility. In short, it is necessary to establish an instrumental standard for the analysis of sperm morphometry to obtain reliable results and we believe that the MO-NIS system presents these basic requirements. [Copyright &y& Elsevier]

Summary In vivo determination of protein synthesis in immune cells reflects metabolic activity and immunological activation. An intravenous injection of endotoxin to healthy volunteers was used as a human sepsis model, and in vivo protein synthesis of T lymphocytes and leucocytes was measured. The results were related to plasma concentrations of selected cytokines, peripheral cell counts and subpopulations of immune cells. The subjects (n = 8 + 8) were randomized to an endotoxin (4 ng/kg) or a saline group. In vivo protein synthesis was determined twice: before and 1–2·5 h after the endotoxin/saline injection. Protein synthesis decreased in isolated T lymphocytes, but increased in leucocytes. Plasma levels of TNF-α , IL-8, IL-6, IL-1 ra and IL-10 were elevated, whereas IL-2 and IFN-γ , produced predominantly by T lymphocytes, did not change in response to endotoxin. Neutrophils increased, whereas lymphocytes and monocytes decreased 2·5 h after the endotoxin injection. Flow cytometry revealed a drop in total CD3+ T lymphocytes and CD56+ natural killer cells, accompanied by an increase in CD15+ granulocytes. In summary, in vivo protein synthesis decreased in T lymphocytes, while the total leucocyte population showed a concomitant increase immediately after the endotoxin challenge. The changes in protein synthesis were accompanied by alterations in immune cell subpopulations and in plasma cytokine levels. [ABSTRACT FROM AUTHOR]

Head-down tilt bed rest (HDT) is used as a model for studying the physiological changes occurring in weightlessness during spaceflight. In the present study, eight volunteers were subjected to a strict HDT of –6° for 42 days. Blood samples were obtained 37 and 13 days before, at days 13, 34, and 41 during, and 12, 33, and 47 days after HDT. FACScan analysis was used to determine cell subpopulations. Plasma was used to quantify various circulating hormone levels. Whole blood and reconstituted blood were stimulated with various activators such as phytohaemagglutinin-P (PHA), PHA combined with phorbol-12-myristate 13-acetate (PMA), anti-CD2, anti-CD3, and lipopolysaccharide. Supernatants were collected and analysed for the interleukins IL-1β, IL-2, IL-6, and IL-10, interferon-γ (IFN-γ) and tumour necrosis factor-α (TNF-α). The total number of T lymphocytes and monocytes did not change significantly, whereas the number of polymorphonuclear cells increased during HDT. The percentage of CD2+ and CD3+ cells was increased at day 35 of HDT. The percentage and total number of natural killer cells (CD2+/CD3–/CD56+) was increased 12 days before and 14 days after HDT. TNF-α secretion did not change significantly during HDT. IL-2, IL-10 and IFN-γ were increased at day 34 of HDT. IL-1β levels were increased before and during HDT compared to post-HDT measurements. No significant changes were observed in plasma immunoglobulin, complement factors and other factors of the inflammatory system. Prolactin levels increased slightly but significantly at day 35 of HDT, thyreotropin and growth hormone levels remained virtually unchanged. Cortisol decreased slightly but significantly over the entire duration of the study. The changes observed during HDT do not indicate that the immune system is blunted, and these changes do not seem to correlate with the duration of HDT. Taken together these results show that a HDT does not reproduce the changes in immune responses observed after spaceflight. [ABSTRACT FROM AUTHOR]

The eggshell of the Hawaiian species Drosophila grimshawi was analyzed in terms of its structure and biochemical composition. It is generally similar to D. melanogaster in radial structure except that the inner endochorion is relatively compact and the outer endochorion is thick (1.9 μm) and traversed by numerous 50-nm-wide aeropyles (not present in D. melanogaster). Regionally the eggshell consists of the various specialized regions, which are: the holey posterior pole; the dorsal ridge; the operculum with very tall imprint borders; the collar with discontinuous outer endochorion; the micropyle with an external eccentric hole and an internal eccentric paracrystalline region; and the four very long respiratory appendages which exhibit tightly covered surface features for underwater plastron respiration. On the basis of the above structural data we suggest that the entire D. grimshawi eggshell (various layers and specialized regions) is the product of at least eight follicle cell subpopulations. In addition, in vitro culturing withH-proline indicates that the eggshell is made according to a strict developmental program (analogous to D. melanogaster) and consists of several proteins ranging in molecular weight from 14,000 to 37,000 daltons. [ABSTRACT FROM AUTHOR]

Objective: The aim was to test the hypothesis that in single guinea pig ventricular myocytes a large stretch induced increase in resting calcium was sensitive to the mechanosensitive channel blocker streptomycin. Methods: Carbon fibres were used to stretch cells loaded with the fluorescent calcium indicator indo-1. Force, sarcomere length, and internal calcium activity ([Ca2+]i) were measured. Results: In approximately 60% of the cells studied, a stretch which increased sarcomere length by approximately 6% caused a large increase in [Ca2+]i (up to 60% of the size of a [Ca2+]i transient at 0.25 Hz). When a mixture of antibiotics (streptomycin-penicillin) was used in solutions to isolate and store cells, this phenomenon was never observed (n = 19 cells). Direct application of physiological saline solution (PSS) could not reverse the increase in [Ca2+]i within 60 s of application (n = 7 cells). Direct application of penicillin [1000 IU per 50 ml (40 μM)] reversed the increase in [Ca2+]i within 60 s of application in only 3/7 cells. In contrast direct application of the aminoglycoside antibiotic streptomycin (40 μM) rapidly reversed the large increase in [Ca2+]i induced by stretch in each of 13 cells [within 18(SD 10) s of application]. Acute application of 40 μM streptomycin did not modify L-type Ca2+ currents measured under whole cell patch clamp conditions. Measurement of the resting tension – sarcomere length curves in cells stored in solution containing streptomycin and penicillin revealed two populations of cells on the basis of their stiffness. Conclusions: This stretch induced increase in [Ca2+]i may be associated with stretch activated arrhythmias in the heart. The effects of streptomycin are consistent with its reported inhibitory action on stretch activated channels.Cardiovascular Research 1994;28:1193-1198 [ABSTRACT FROM PUBLISHER]

A kinetic study was performed on leukemic blasts from patients with acute myeloid leukemia, separated into 2 subpopulations by a specific density gradient. The growth curve and the [H]-thymidine uptake were simultaneously analyzed. While cumulative nucleotide uptake fitted with the growth kinetics in the low-density fraction, such a concordance was not found in the high-density subpopulation. That indicated the occurrence of simultaneous growth and loss in the high density fraction, which could not be evaluated by a simple numerical determination. [ABSTRACT FROM AUTHOR]