Fatty acid derivatization and cyclization of the immunomodulatory peptide RP-182 targeting CD206high macrophages improves anti-tumor activity.

As tumor-associated macrophages (TAMs) exercise a plethora of pro-tumor and immune evasive functions, novel strategies targeting TAMs to inhibit tumor progression have emerged within the current arena of cancer immunotherapy. Activation of the mannose receptor 1 (Mrc1; CD206) is a recent approach that recognizes immune suppressive CD206high M2-like TAMs as a drug target. Ligation of CD206 both induces reprogramming of CD206high TAMs towards a pro-inflammatory phenotype and selectively triggers apoptosis in these cells. CD206-activating therapeutics are currently limited to the linear, 10mer peptide RP-182, 1, which is not a drug candidate. Here we sought to identify a better suitable candidate for future clinical development by synthesizing and evaluating a series of RP-182 analogues. Surprisingly, fatty acid derivative 1a (RP-182-PEG3-K(palmitic acid)) not only showed improved stability but also increased affinity to the CD206 receptor through enhanced interaction with a hydrophobic binding motif of CD206. Peptide 1a showed superior in vitro activity in cell-based assays of macrophage activation which was restricted to CD206high M2-polarized macrophages. Improvement of responses was disproportionally skewed towards improved induction of phagocytosis including cancer cell phagocytosis. 1a reprogrammed the immune landscape in genetically engineered murine KPC pancreatic tumors towards increased innate immune surveillance and improved tumor control, and effectively suppressed tumor growth of murine B16 melanoma allografts.

RNA Sequencing Experimental Analysis Workflow Using Caenorhabditis elegans.

RNA sequencing is an approach to transcriptomic profiling that enables the detection of differentially expressed genes in response to genetic mutation or experimental treatment, among other uses. Here we describe a method for the use of a customizable, user-friendly bioinformatic pipeline to identify differentially expressed genes in RNA sequencing data obtained from C. elegans, with attention to the improvement in reproducibility and accuracy of results.

Reactive Oxygen Species-Sensitive Biodegradable Mesoporous Silica Nanoparticles Harboring TheraVac Elicit Tumor-Specific Immunity for Colon Tumor Treatment.

Immunotherapy has revolutionized the field of cancer treatment through invigorating robust antitumor immune response. Here, we report the development of a therapeutic vaccine [consisting of high mobility group nucleosome-binding protein 1 (HMGN1), resiquimod/R848, and anti-PD-L1 (αPD-L1)]-loaded reactive oxygen species (ROS)-responsive mesoporous silica nanoparticle (MSN@TheraVac) for curative therapy of colon cancer. In MSN@TheraVac, αPD-L1 conjugated onto the surface of MSNs via a diselenide bond, which can be rapidly released under the oxidative condition of the tumor microenvironment to avert immunosuppression and effector T cell exhaustion while coloaded HMGN1 and R848 would cooperatively trigger robust tumor-infiltrating dendritic cell (TiDC) maturation and elicitation of antitumor immune responses. Indeed, MSN@TheraVac induced the maturation and activation of dendritic cells (DCs) by promoting the surface expression of CD80, CD86, and CD103 as well as the production of pro-inflammatory cytokines, including TNFα, IL-12, and IL-1ß. Importantly, treatment with intravenous MSN@TheraVac led to a complete cure of 100% of BALB/c mice bearing large colon tumors and induced the generation of tumor-specific protective memory without apparent toxicity. Thus, MSN@TheraVac provides a timely release of TheraVac for the curative treatment of colon tumors and holds potential for translation into a clinical therapy for patients with immunologically "cold" colorectal cancers. This ROS-responsive MSN platform may also be tailored for the selective delivery of other cancer vaccines for effective immunotherapy.

Regression and Eradication of Triple-Negative Breast Carcinoma in 4T1 Mouse Model by Combination Immunotherapies.

Triple-negative breast carcinoma (TNBC) is one of the most aggressive types of solid-organ cancers. While immune checkpoint blockade (ICB) therapy has significantly improved outcomes in certain types of solid-organ cancers, patients with immunologically cold TNBC are afforded only a modest gain in survival by the addition of ICB to systemic chemotherapy. Thus, it is urgently needed to develop novel effective therapeutic approaches for TNBC. Utilizing the 4T1 murine model of TNBC, we developed a novel combination immunotherapeutic regimen consisting of intratumoral delivery of high-mobility group nucleosome binding protein 1 (HMGN1), TLR2/6 ligand fibroblast-stimulating lipopeptide (FSL-1), TLR7/8 agonist (R848/resiquimod), and CTLA-4 blockade. We also investigated the effect of adding SX682, a small-molecule inhibitor of CXCR1/2 known to reduce MDSC trafficking to tumor microenvironment, to our therapeutic approach. 4T1-bearing mice responded with significant tumor regression and tumor elimination to our therapeutic combination regimen. Mice with complete tumor regressions did not recur and became long-term survivors. Treatment with HMGN1, FSL-1, R848, and anti-CTLA4 antibody increased the number of infiltrating CD4+ and CD8+ effector/memory T cells in both tumors and draining lymph nodes and triggered the generation of 4T1-specific cytotoxic T lymphocytes (CTLs) in the draining lymph nodes. Thus, we developed a potentially curative immunotherapeutic regimen consisting of HMGN1, FSL-1, R848, plus a checkpoint inhibitor for TNBC, which does not rely on the administration of chemotherapy, radiation, or exogenous tumor-associated antigen(s).

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification.

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network.

Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.

Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification.

Coronavirus disease has rapidly spread globally since early January of 2020. With millions of deaths, it is essential for an automated system to be utilized to aid in the clinical diagnosis and reduce time consumption for image analysis. This article presents a generative adversarial network (GAN)-based deep learning application for precisely regaining high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents for COVID-19 identification. Respectively, using the building blocks of GAN, we introduce a modified enhanced super-resolution generative adversarial network plus (MESRGAN+) to implement a connected nonlinear mapping collected from noise-contaminated low-resolution input images to produce deblurred and denoised HR images. As opposed to the latest trends of network complexity and computational costs, we incorporate an enhanced VGG19 fine-tuned twin network with the wavelet pooling strategy in order to extract distinct features for COVID-19 identification. We demonstrate our proposed model on a publicly available dataset of 11,920 samples of chest X-ray images, with 2980 cases of COVID-19 CXR, healthy, viral and bacterial cases. Our proposed model performs efficiently both on the binary and four-class classification. The proposed method achieves accuracy of 98.8%, precision of 98.6%, sensitivity of 97.5%, specificity of 98.9%, an F1 score of 97.8% and ROC AUC of 98.8% for the multi-class task, while, for the binary class, the model achieves accuracy of 99.7%, precision of 98.9%, sensitivity of 98.7%, specificity of 99.3%, an F1 score of 98.2% and ROC AUC of 99.7%. Our method obtains state-of-the-art (SOTA) performance, according to the experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential role in addressing the issues facing COVID-19 examination and other diseases.

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network.

Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.

WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis.

Timely discovery of COVID-19 could aid in formulating a suitable treatment plan for disease mitigation and containment decisions. The widely used COVID-19 test necessitates a regular method and has a low sensitivity value. Computed tomography and chest X-ray are also other methods utilized by numerous studies for detecting COVID-19. In this article, we propose a CNN called depthwise separable convolution network with wavelet multiresolution analysis module (WMR-DepthwiseNet) that is robust to automatically learn details from both spatialwise and channelwise for COVID-19 identification with a limited radiograph dataset, which is critical due to the rapid growth of COVID-19. This model utilizes an effective strategy to prevent loss of spatial details, which is a prevalent issue in traditional convolutional neural network, and second, the depthwise separable connectivity framework ensures reusability of feature maps by directly connecting previous layer to all subsequent layers for extracting feature representations from few datasets. We evaluate the proposed model by utilizing a public domain dataset of COVID-19 confirmed case and other pneumonia illness. The proposed method achieves 98.63% accuracy, 98.46% sensitivity, 97.99% specificity, and 98.69% precision on chest X-ray dataset, whereas using the computed tomography dataset, the model achieves 96.83% accuracy, 97.78% sensitivity, 96.22% specificity, and 97.02% precision. According to the results of our experiments, our model achieves up-to-date accuracy with only a few training cases available, which is useful for COVID-19 screening. This latest paradigm is expected to contribute significantly in the battle against COVID-19 and other life-threatening diseases.

Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans.

It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Early diagnosis can minimise the risk of proliferated diabetic retinopathy, which is the advanced level of this disease, and having higher risk of severe impairment. Therefore, it becomes important to classify DR stages. To this effect, this paper presents a weighted fusion deep learning network (WFDLN) to automatically extract features and classify DR stages from fundus scans. The proposed framework aims to treat the issue of low quality and identify retinopathy symptoms in fundus images. Two channels of fundus images, namely, the contrast-limited adaptive histogram equalization (CLAHE) fundus images and the contrast-enhanced canny edge detection (CECED) fundus images are processed by WFDLN. Fundus-related features of CLAHE images are extracted by fine-tuned Inception V3, whereas the features of CECED fundus images are extracted using fine-tuned VGG-16. Both channels' outputs are merged in a weighted approach, and softmax classification is used to determine the final recognition result. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method tested on the Messidor dataset reports an accuracy level of 98.5%, sensitivity of 98.9%, and specificity of 98.0%, whereas on the Kaggle dataset, the proposed model reports an accuracy level of 98.0%, sensitivity of 98.7%, and specificity of 97.8%. Compared with other models, our proposed network achieves comparable performance.

COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network.

Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.

Derivatives of Dictyostelium differentiation-inducing factors suppress the growth of Plasmodium parasites in vitro and in vivo.

Malaria, which is caused by protozoa of the genus Plasmodium, remains a major endemic public health problem worldwide. Since artemisinin combination therapies are used as a first-line treatment in all endemic regions, the emergence of parasites resistant to these regimens has become a serious problem. Differentiation-inducing factor 1 (DIF-1) is a chlorinated alkylphenone originally found in the cellular slime mold Dictyostelium discoideum. DIF-1 and its derivatives exhibit a range of biological activities. In the present study, we investigated the effects of 41 DIF derivatives on the growth of Plasmodium falciparum in vitro using four laboratory strains and 12 field isolates. Micromolar concentrations of several DIF derivatives strongly suppressed the growth of the four laboratory strains, including strains that exhibited resistance to chloroquine and artemisinin, as well as strains that were susceptible to these drugs. In addition, DIF-1(+2), the most potent derivative, strongly suppressed the growth of 12 field isolates. We also examined the effects of DIF-1(+2) on the activity of the rodent malarial parasite Plasmodium berghei in mice. Intraperitoneal administration of DIF-1(+2) over 4 days (50 or 70 mg/kg/day) significantly suppressed the growth of the parasite in the blood with no apparent adverse effects, and a dose of 70 mg/kg/day significantly prolonged animal survival. These results suggest that DIF derivatives, such as DIF-1(+2), could serve as new lead compounds for the development of antimalarial agents.

Identification of Proteins Differentially Expressed by Adipose-derived Mesenchymal Stem Cells Isolated from Immunodeficient Mice.

Although cell therapy using adipose-derived mesenchymal stem cells (AdMSCs) regulates immunity, the degree to which cell quality and function are affected by differences in immunodeficiency of donors is unknown. We used liquid chromatography tandem-mass spectrometry (LC MS/MS) to identify the proteins expressed by mouse AdMSCs (mAsMSCs) isolated from normal (C57BL/6) mice and mice with severe combined immunodeficiency (SCID). The protein expression profiles of each strain were 98%-100% identical, indicating that the expression levels of major proteins potentially associated with the therapeutic effects of mAdMSCs were highly similar. Further, comparable levels of cell surface markers (CD44, CD90.2) were detected using flow cytometry or LC MS/MS. MYH9, ACTN1, CANX, GPI, TPM1, EPRS, ITGB1, ANXA3, CNN2, MAPK1, PSME2, CTPS1, OTUB1, PSMB6, HMGB1, RPS19, SEC61A1, CTNNB1, GLO1, RPL22, PSMA2, SYNCRIP, PRDX3, SAMHD1, TCAF2, MAPK3, RPS24, and MYO1E, which are associated with immunity, were expressed at higher levels by the SCID mAdMSCs compared with the C57BL/6 mAdMSCs. In contrast, ANXA9, PCBP2, LGALS3, PPP1R14B, and PSMA6, which are also associated with immunity, were more highly expressed by C57BL/6 mAdMSCs than SCID mAdMSCs. These findings implicate these two sets of proteins in the pathogenesis and maintenance of immunodeficiency.

Clinical significance of cytomegalovirus and other herpes virus infections in ulcerative colitis.

Herpes virus infection leads to severe and fatal disease in individuals with suppressed immunity. In patients with inflammatory bowel disease (IBD), particularly those with ulcerative colitis (UC), those undergoing immunosuppressive therapy, or those unresponsive to medical therapy, cytomegalovirus (CMV) has been found to be associated with significant clinical morbidity. In addition, other herpes viruses, particularly human herpes virus 6 (HHV­6) and Epstein-Barr virus (EBV), have been identified recently in the colonic mucosa of individuals with IBD, although the relationship between herpes virus infection other than CMV and exacerbation of IBD remains unknown. In this review, we discuss herpes virus infection in patients with UC, focusing on the prevalence and diagnosis of CMV infection as well as the prevalence of single or mixed infection with herpes virus (HHV­6 and EBV) in addition to CMV. Moreover, significance of genotyping of CMV in UC is discussed.

A Liquid Chromatography with Tandem Mass Spectrometry-Based Proteomic Analysis of Primary Cultured Cells and Subcultured Cells Using Mouse Adipose-Derived Mesenchymal Stem Cells.

Adipose-derived mesenchymal stem cells (MSC-ATs) are representative cell sources for cell therapy. However, how cell stress resulting from passage influences the MSC-AT protein expression has been unclear. In this study, a protein expression analysis was performed by liquid chromatography with tandem mass spectrometry (LC-MS/MS) using mouse primary cultured cells (P0) and cells passaged three times (P3) as samples. A total of 256 proteins were classified as cellular process-related proteins, while 179 were classified as metabolic process-related proteins in P0. These were considered to be adaptive responses of the cells to an in vitro environment. However, seven proteins of growth were identified (Csf1, App, Adam15, Alcam, Tbl1xr1, Ninj1, and Sbds) in P0. In addition, four proteins of antioxidant activity were also identified (Srxn1, Txndc17, Fam213b, and Apoe) in P0. We identified 1139 proteins expressed in both P0 and P3 cells that had their expression decreased to 69.4% in P3 cells compared with P0 cells, but 1139 proteins are very likely proteins that are derived from MSC-AT. The function of MSC-ATs was maintained after three passages. However, the LC-MS/MS analysis data showed that the protein expression was degraded after three passages. MSC-ATs retained about 70% of their protein expression ability in P3 cells.

A Comparison of Proteins Expressed between Human and Mouse Adipose-Derived Mesenchymal Stem Cells by a Proteome Analysis through Liquid Chromatography with Tandem Mass Spectrometry.

Adipose-derived mesenchymal stem cells (ADSCs) have become a common cell source for cell transplantation therapy. Clinical studies have used ADSCs to develop treatments for tissue fibrosis, such as liver cirrhosis and pulmonary fibroma. The need to examine and compare basic research data using clinical research data derived from mice and humans is expected to increase in the future. Here, to better characterize the cells, the protein components expressed by human ADSCs used for treatment, and mouse ADSCs used for research, were comprehensively analyzed by liquid chromatography with tandem mass spectrometry. We found that 92% (401 type proteins) of the proteins expressed by ADSCs in humans and mice were consistent. When classified by the protein functions in a gene ontology analysis, the items that differed by >5% between human and mouse ADSCs were "biological adhesion, locomotion" in biological processes, "plasma membrane" in cellular components, and "antioxidant activity, molecular transducer activity" in molecular functions. Most of the listed proteins were sensitive to cell isolation processes. These results show that the proteins expressed by human and murine ADSCs showed a high degree of correlation.

A Liquid Chromatography with Tandem Mass Spectrometry-Based Proteomic Analysis of the Proteins Secreted by Human Adipose-Derived Mesenchymal Stem Cells.

Liquid chromatography using a tandem mass spectrometer (LC-MS/MS) is a method of proteomic analysis. A shotgun analysis by LC-MS/MS comprehensively identifies proteins from tissues and cells with high resolution. The hepatic function of mice with acute hepatitis following the intraperitoneal administration of CCL4 was improved by the tail vein administration of the culture conditional medium (CM) of human mesenchymal stem cells from adipose tissue (hMSC-AT). In this study, a secreted protein expression analysis of hMSC-AT was performed using LC-MS/MS; 128 proteins were identified. LC-MS/MS showed that 106 new functional proteins and 22 proteins (FINC, PAI1, POSTN, PGS2, TIMP1, AMPN, CFAH, VIME, PEDF, SPRC, LEG1, ITGBL, ENOA, CSPG2, CLUS, IBP4, IBP7, PGS1, IBP2, STC2, CTHR1, CD9) were previously reported in hMSC-AT-CMs. In addition, various proteins associated with growth (SAP, SEM7A, PTK7); immune system processes (CO1A2, CO1A1, CATB, TSP1, GAS6, PTX3, C1 S, SEM7A, G3P, PXDN, SRCRL, CD248, SPON2, ENPP2, CD109, CFAB, CATL1, MFAP5, MIF, CXCL5, ADAM9, CATK); and reproduction (MMP2, CATB, FBLN1, SAP, MFGM, GDN, CYTC) were identified in hMSC-AT-CMs. These results indicate that a comprehensive expression analysis of proteins by LC-MS/MS is useful for investigating new factors associated with cellular components, biological processes, and molecular functions.

A Comparison of the Preservation of Mouse Adipose Tissue-Derived Mesenchymal Stem Cells Using the University of Wisconsin Solution and Hank's Balanced Salt Solution.

Preservation of adipose tissue before the isolation of cells is one of the most important steps in maintaining the cell viability of adipose tissue-derived mesenchymal stem cells (ADSCs) for clinical use. Hank's balanced salt solution (HBSS) is one of the main ADSC preservation solutions used clinically. However, this step is known to lead to decreased cell viability. The University of Wisconsin (UW) solution is recognized by transplant physicians as an excellent organ preservation solution. We aimed to investigate the effectiveness of UW solution in preservation of the viability of ADSCs. We collected adipose tissue from the inguinal fat pad of mice and compared preservation in UW solution and HBSS overnight by measuring cell viability after isolation. We found that the number of viable cells harvested per gram of adipose tissue mass was higher in UW solution- than HBSS-preserved tissue.

A Liquid Chromatography with Tandem Mass Spectrometry-Based Proteomic Analysis of Cells Cultured in DMEM 10% FBS and Chemically Defined Medium Using Human Adipose-Derived Mesenchymal Stem Cells.

Human adipose-derived mesenchymal stem cells (hADSCs) are representative cell sources for cell therapy. Classically, Dulbecco's Modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS) has been used as culture medium for hADSCs. A chemically defined medium (CDM) containing no heterologous animal components has recently been used to produce therapeutic hADSCs. However, how the culture environment using a medium without FBS affects the protein expression of hADSC is unclear. We subjected hADSCs cultured in CDM and DMEM (10% FBS) to a protein expression analysis by tandem mass spectrometry liquid chromatography and noted 98.2% agreement in the proteins expressed by the CDM and DMEM groups. We classified 761 proteins expressed in both groups by their function in a gene ontology analysis. Thirty-one groups of proteins were classified as growth-related proteins in the CDM and DMEM groups, 16 were classified as antioxidant activity-related, 147 were classified as immune system process-related, 557 were involved in biological regulation, 493 were classified as metabolic process-related, and 407 were classified as related to stimulus responses. These results show that the trend in the expression of major proteins related to the therapeutic effect of hADSCs correlated strongly in both groups.