Biology subjects

Baharvand, H.

Publications and source records attributed to Baharvand, H..

5 recordsLinked to original sources

bioRxiv2026

Implementation and Evaluation of Marker Rescue and qPCR Assays for Detection of Replication-Competent Retrovirus in Gene Therapy Products

Quality control of retroviral gene therapy vectors requires reliable detection of replication-competent retrovirus (RCR) particles. Cell-based assays remain the standard for detecting RCR, while PCR-based assays serve as rapid, complementary methods. Despite their widespread use, few studies provide comprehensive protocols and performance data to guide assay setup. Here we provide a detailed description and performance assessment of a cell-based firefly luciferase marker rescue assay for RCR testing. Additionally, we describe the setup and assessment of a qPCR-based RCR assay targeting retroviral pol gene sequence. Different viral copy numbers of MuLV were used to determine the final sensitivity of the marker rescue assay with and without initial amplification. Different q-PCR strategies were investigated for RCR testing using different types of primers, including env and pol genes. The marker rescue assay detected 73.2 RCR genome particles without amplification, while the amplification phase enabled detection of functional RCR particles and increased sensitivity, with the amplification step increasing viral particles by up to 5.23x103 fold. The PCR-based RCR assay using retroviral pol gene could detect approximately 7.32x103 viral particles. This study offers a resource for establishing RCR testing procedures for clinical-grade retroviral vector products.

cancer biology↗

bioRxiv2026

Assessment of Q-PCR based vector copy numbers and viral titration in CAR T cell products, Probe vs SYBR Green.

Assessment of vector copy numbers (VCNs) in comprised of the chimeric antigen receptor (CAR (T cells as a safety parameter and viral titration in the vector bank (VB) as an efficacy parameter are essential in the CAR T cell production procedure. Here, we developed a quantitative-PCR (Q-PCR) based VCN and viral titration analysis using both Probe and SYBR Green methods. Cellular viral titration was also applied for comparison of viral titration results. The VCN in CAR T cells with SYBR Green was 1.72, 2.31, and 2.46 respectively, while in the Probe based analysis, it was 1.33, 1.83, and 2.20. The molecular viral titration in SYBR Green resulted in 2.90*109, 5.24*1010, and 9.76*1010, while in the Probe it was 1.54*109, 1.13*109, and 7.73*108. The cellular titration resulted in 1.73*106, 2.16*106, and 2.49*106. In summary, both SYBR Green and Probe revealing that Probe strategy had a lower dependency on sample contamination and could be a better strategy for the Q-PCR method. The SYBR Green based viral titration method is significantly different from the Probe based method, with the Probe based method yielding better results compared to the cellular titration method. However, no significant difference was reported in both methods for VCNs.

immunology↗

bioRxiv2024

Engineering CD3 Subunits with Endoplasmic Reticulum Retention Signal Facilitates Allogeneic CAR T Cell Production

The success of autologous CAR T cell therapies has driven interest in developing off-the-shelf allogeneic CAR T cells as a scalable and readily available option for broader patient access. Most of the current approaches involve the knockout of T cell receptor (TCR) subunits via genome editing for preventing graft-versus-host disease (GvHD). However, clinical translation of these methods faces challenges due to manufacturing complexities and emerging safety concerns like unintended long deletions and chromosomal loss. In this study, we explored an alternative approach by engineering synthetic CD3 subunits containing an endoplasmic reticulum retention (ERR) signal to suppress TCR surface expression by disrupting its trafficking to the plasma membrane. We screened multiple CD3-ERR candidate designs to identify the construct with the highest efficacy in TCR downregulation. The selected candidate, CD3{zeta}-ERR, was further characterized, demonstrating its ability to minimize TCR-mediated activation and alloreactivity without affecting T cell phenotype, cell cycle and cytokine-induced expansion. Subsequent assays revealed that CD3{zeta}-ERR CD19 CAR T cells retained their CAR-mediated cytotoxic function against CD19+ malignant cells. This study presents an alternative approach for TCR downregulation that circumvents genome editing. By using a transgene compatible with conventional viral vector delivery, this approach holds promise for scalable clinical-grade manufacturing of allogeneic CAR T cell therapies. Translational Impact StatementOur study introduces a scalable method to engineer allogeneic CAR T cells by reducing TCR expression without genome editing, thereby minimizing the risk of immune rejection (GvHD) while maintaining antitumor efficacy. This approach offers a practical and clinically translatable solution for producing "off-the-shelf" CAR T cell therapies, potentially broadening access to these life-saving treatments and streamlining their integration into existing clinical manufacturing processes.

immunology↗

bioRxiv2023

CRISPR/Cas9-mediated Generation of COL7A1-deficient Keratinocyte Model of Recessive Dystrophic Epidermolysis Bullosa

ObjectiveRecessive dystrophic epidermolysis bullosa (RDEB) is a genetic skin fragility and ultimately lethal blistering disease caused by mutations in the COL7A1 gene which is responsible for coding type VII collagen. Investigating the pathological mechanisms and novel candidate therapies for RDEB could be fostered by new cellular models. Here, we developed multiple immortalized COL7A1-deficient keratinocyte cell lines using CRISPR/Cas9 technology as RDEB cellular model. Materials and MethodsIn this experimental study, we used transient transfection to express COL7A1-targeting gRNA and Cas9 in HEK001 immortalized keratinocyte cell line followed by enrichment with fluorescent-activated cell sorting (FACS) via GFP expressing cells (GFP+ HEK001). Homogenous single-cell clones were then isolated, genotyped, and evaluated for type VII collagen expression. We performed a scratch assay to confirm the functional effect of COL7A1 knockout. ResultsWe achieved 46.1% (p < 0.001) efficiency of indel induction in the enriched transfected cell population. Except for 4% of single nucleotide insertions, the remaining indels were deletions of different sizes. Out of nine single clones expanded, two homozygous and two heterozygous COL7A1-deficient cell lines were obtained with defined mutation sequences. No off-target effect was detected in the knockout cell lines. Immunostaining and western blot analysis showed the lack of type VII collagen (COL7A1) protein expression in these cell lines. We also showed that COL7A1-deficient cells had higher motility compared with their wild-type counterparts. ConclusionWe reported the first isogenic immortalized COL7A1-deficient keratinocyte lines that provide a useful cell culture model to investigate aspects of RDEB biology and potential therapeutic options.

cell biology↗

bioRxiv2018

Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

AimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.\n\nMethods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.\n\nConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of \"irregularity or abnormal waveforms\" in an in vitro model of cardiotoxicity experiments.

bioinformatics↗