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  1. Screening and subcloning of high producer transfectomas using semisolid media and automated colony picker
    Dharshanan S, Hung CS
    Methods Mol Biol, 2014;1131:105-12.
    PMID: 24515462 DOI: 10.1007/978-1-62703-992-5_7
    Generation of high-producing clones is a perquisite for achieving recombinant protein yields suitable for biopharmaceutical production. However, in many industrially important cell lines used to produce recombinant proteins such as Chinese hamster ovary, mouse myeloma line (NS0), and hybridomas, only a minority of clones show significantly above-average productivity. Thus, in order to have a reasonable probability of finding rare high-producing clones, a large number of clones need to be screened. Limiting dilution cloning is the most commonly used method, owing to its relative simplicity and low cost. However the use of liquid media in this method makes the selection of monoclonal hybridoma and transfectoma colonies to be labor intensive and time consuming, thus significantly limiting the number of clones that can be feasibly screened. Hence, we describe the use of semisolid media to immobilize clones and a high-throughput, automated colony picker (ClonePix FL) to efficiently isolate monoclonal high-producing clones secreting monoclonal antibodies.
  2. Fulltext Stable expression of H1C2 monoclonal antibody in NS0 and CHO cells using pFUSE and UCOE expression system
    Dharshanan S, Chong H, Cheah SH, Zamrod Z
    Cytotechnology, 2014 Aug;66(4):625-33.
    PMID: 23881539 DOI: 10.1007/s10616-013-9615-x
    From our recent publications, it was found that the deimmunization method (Dharshanan et al. (2012) Sci Res Essays 7:2288-2299) should be applied for the development of humanized anti-C2 monoclonal antibody (H1C2 mAb). However, the overlapping-PCR mutagenesis procedure used to insert the variable regions into cloning vectors was laborious and time-consuming. Additionally, the expression of H1C2 mAb in NS0 cells was low in static culture vessels. Therefore H1C2 mAb was redeveloped by deimmunization method with the following modifications in order to optimize the production of H1C2 mAb. First, instead of the overlapping-PCR mutagenesis procedure, synthetic DNA coding the variable regions were used to express the mAb. Second, two expression vectors, pFUSE and UCOE, were used to express H1C2 mAb in NS0 cells and CHO cells in order to investigate the combination that gave the highest number of high producing stable clones. This will provide the highest chance of finding clones with the requisite high productivity and stability required for manufacturing. We found that transfection of UCOE in CHO cells generated the highest number of high producing stable clones. To our knowledge, this is the first time that H1C2 mAb has been expressed in CHO cells.
  3. Fulltext Identification and validation of a novel autoantibody biomarker panel for differential diagnosis of pancreatic ductal adenocarcinoma
    Mowoe MO, Allam H, Nqada J, Bernon M, Gandhi K, Burmeister S, et al.
    Front Immunol, 2025;16:1494446.
    PMID: 39949781 DOI: 10.3389/fimmu.2025.1494446
    INTRODUCTION: New biomarkers are urgently needed to detect pancreatic ductal adenocarcinoma (PDAC) at an earlier stage for individualized treatment strategies and to improve outcomes. Autoantibodies (AAbs) in principle make attractive biomarkers as they arise early in disease, report on disease-associated perturbations in cellular proteomes, and are static in response to other common stimuli, yet are measurable in the periphery, potentially well in advance of the onset of clinical symptoms.

    METHODS: Here, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).

    RESULTS: Combinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.

    CONCLUSION: This AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.

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