Sperm Cell Capture Based on ABH Antigen Differences to Separate Two Men in Mixed Seminal Stains

Personal identification of two individuals in mixed semen samples in forensic DNA testing in general usually involves analysis using autosomal and Y chromosome short tandem repeats (STRs). Results may exclude unrelated donors but cannot identify individuals. In this study, sperm cell capture based on ABH antigen differences was used to obtain the cells with the single ABO blood type. Immunohistochemical staining using labeled anti-A, anti-B, and anti-H antibodies and the laser microdissection system can be used to enrich sperm with different ABO types in mixed seminal stains from two individuals. Then, PCR amplification and capillary electrophoresis were performed to genotype the STR loci. To some extent, after sperm cell capture based on ABH antigen differences, autosomal STR typing using enriched single blood group cells can be utilized to partially identify different individuals in a mixed seminal stain sample from two individuals.

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

Background The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it is usually necessary to generate further specific datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare-cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. Results We demonstrate the effectiveness of our method for three independent use cases, each consisting of already published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8635). This use case was designed to take a larger imbalance ratio (~1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (~1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single-cell capture procedures and the impact of “less” rare-cell types. The third dataset refers to the murine data of the Allen Brain Atlas, including more than 1 million cells. For validation purposes only, all datasets have also been analyzed traditionally using common data analysis approaches, such as the Seurat workflow. Conclusions In comparison to baseline testing without oversampling, our approach identifies rare-cells with a robust precision-recall balance, including a high accuracy and low false positive detection rate. A practical benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis in R and Python is publicly available at FairdomHub, as well as GitHub, and can easily be transferred to identify other rare-cell types.

Water-powered self-propelled magnetic nanobot for rapid and highly efficient capture of circulating tumor cells

Nanosized robots with self-propelling and navigating capabilities have become an exciting field of research, attributable to their autonomous motion and specific biomolecular interaction ability for bio-analysis and diagnosis. Here, we report magnesium (Mg)-Fe3O4-based Magneto-Fluorescent Nanorobot (“MFN”) that can self-propel in blood without any other additives and can selectively and rapidly isolate cancer cells. The nanobots viz; Mg-Fe3O4-GSH-G4-Cy5-Tf and Mg-Fe3O4-GSH-G4-Cy5-Ab have been designed and synthesized by simple surface modifications and conjugation chemistry to assemble multiple components viz; (i) EpCAM antibody/transferrin, (ii) cyanine 5 NHS (Cy5) dye, (iii) fourth generation (G4) dendrimers for multiple conjugation and (iv) glutathione (GSH) by chemical conjugation onto one side of Mg nanoparticle. The nanobots propelled efficiently not only in simulated biological media, but also in blood samples. With continuous motion upon exposure to water and the presence of Fe3O4 shell on Mg nanoparticle for magnetic guidance, the nanobot offers major improvements in sensitivity, efficiency and speed by greatly enhancing capture of cancer cells. The nanobots showed excellent cancer cell capture efficiency of almost 100% both in serum and whole blood, especially with MCF7 breast cancer cells.

Multifunctional Gold Nano-Cytosensor With Quick Capture, Electrochemical Detection, and Non-Invasive Release of Circulating Tumor Cells for Early Cancer Treatment

Circulating tumor cells (CTCs) are metastatic tumor cells that shed into the blood from solid primary tumors, and their existence significantly increases the risk of metastasis and recurrence. The timely discovery and detection of CTCs are of considerable importance for the early diagnosis and treatment of metastasis. However, the low number of CTCs hinders their detection. In the present study, an ultrasensitive electrochemical cytosensor for specific capture, quantitative detection, and noninvasive release of EpCAM-positive tumor cells was developed. The biosensor was manufactured using gold nanoparticles (AuNPs) to modify the electrode. Three types of AuNPs with controllable sizes and conjugated with a targeting molecule of monoclonal anti-EpCAM antibody were used in this study. Electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) of the cytosensors were performed to evaluate the cell capture efficiency and performance. The captured 4T1 cells by the AuNPs hindered electron transport efficiency, resulting in increased EIS responses. The cell capture response recorded using EIS or DPV indicated that the optimal AuNPs size should be 17 nm. The cell capture response changed linearly with the concentration range from 8.0 × 10 to 1 × 107 cells/mL, and the limit of detection was 50 cells/mL. After these measurements, glycine-HCl (Gly-HCl) was used as an antibody eluent to destroy the binding between antigen and antibody to release the captured tumor cells without compromising their viability for further clinical research. This protocol realizes rapid detection of CTCs with good stability, acceptable assay precision, significant fabrication reproducibility with a relative standard deviation of 2.09%, and good recovery of cells. Our results indicate that the proposed biosensor is promising for the early monitoring of CTCs and may help customize personalized treatment options.

Immediate Sample Fixation Increases Circulating Tumour Cell (CTC) Capture and Preserves Phenotype in Head and Neck Squamous Cell Carcinoma: Towards a Standardised Approach to Microfluidic CTC Biomarker Discovery

Introduction: Research demonstrates strong evidence that circulating tumour cells (CTCs) can provide diagnostic and/or prognostic biomarkers in head and neck squamous cell carcinoma (HNSCC) and a potential tool for therapeutic stratification. However, the question still remains as to the optimum method of CTC enrichment and how this can be translated into clinical practice. We aimed to evaluate the Parsortix microfluidic device for CTC enrichment and characterisation in HNSCC, seeking to optimise a sample collection and processing protocol that preserves CTC integrity and phenotype. Method: Spiking experiments of the FaDu and SCC040 HNSCC cell lines were used to determine the Parsortix capture rate of rare “CTC-like” cells. Capture rates of cancer cells spiked into EDTA blood collections tubes (BCTs) were compared to the Transfix fixative BCT and Cytodelics whole blood freezing protocol. The Lexogen Quantseq library preparation was used to profile gene expression of unfixed cells before and after microfluidic enrichment and enriched cell line spiked Transfix blood samples. An antibody panel was optimised to enable immunofluorescence microscopy CTC detection in HNSCC patient Transfix blood samples, using epithelial (EpCAM) and mesenchymal (N-cadherin) CTC markers. Results: Across a spiked cell concentration range of 9–129 cells/mL, Parsortix demonstrated a mean cell capture rate of 53.5% for unfixed cells, with no significant relationship between spiked cell concentration and capture rate. Samples preserved in Transfix BCTs demonstrated significantly increased capture rates at 0 h (time to processing) compared to EDTA BCTs (65.3% vs. 51.0%). Capture rates in Transfix BCTs were maintained at 24 h and 72 h timepoints, but dropped significantly in EDTA BCTs. Gene expression profiling revealed that microfluidic enrichment of unfixed cell lines caused downregulation of RNA processing/binding gene pathways and upregulation of genes involved in cell injury, apoptosis and oxidative stress. RNA was successfully extracted and sequenced from Transfix preserved cells enriched using Parsortix, demonstrating epithelial specific transcripts from spiked cells. In a proof-of-concept cohort of four patients with advanced HNSCC, CTCs were successfully identified and visualised with epithelial and epithelial-mesenchymal phenotypes. Conclusion: We have optimised a protocol for detection of CTCs in HNSCC with the Parsortix microfluidic device, using Transfix BCTs. We report a significant benefit, both in terms of cell capture rates and preserving cell phenotype, for using a fixative BCT- particularly if samples are stored before processing. In the design of large cohort multi-site clinical trials, such data are of paramount importance.

Eukaryotic Cell Capture by Amplified Magnetic in situ Hybridization Using Yeast as a Model

A non-destructive approach based on magnetic in situ hybridization (MISH) and hybridization chain reaction (HCR) for the specific capture of eukaryotic cells has been developed. As a prerequisite, a HCR-MISH procedure initially used for tracking bacterial cells was here adapted for the first time to target eukaryotic cells using a universal eukaryotic probe, Euk-516R. Following labeling with superparamagnetic nanoparticles, cells from the model eukaryotic microorganism Saccharomyces cerevisiae were hybridized and isolated on a micro-magnet array. In addition, the eukaryotic cells were successfully targeted in an artificial mixture comprising bacterial cells, thus providing evidence that HCR-MISH is a promising technology to use for specific microeukaryote capture in complex microbial communities allowing their further morphological characterization. This new study opens great opportunities in ecological sciences, thus allowing the detection of specific cells in more complex cellular mixtures in the near future.

820 Machine learning significantly improves neoantigen-HLA predictions utilizing > 26,000 data points from the PACTImmuneTM Database

BackgroundPACT Pharma has developed a state-of-the-art approach to validate predicted neoepitopes (neoEs) and their cognate T cell receptors (neoTCRs) by capturing neoepitope-specific T cells from peripheral blood. This neoTCR discovery and validation process is being applied in clinical trial (NCT03970382) evaluating personalized neoTCR-T cell therapy to treat patients across eight solid tumor types. Extensive pre-, on- and post-treatment data related to this trial has been accumulated in the PACTImmune Database (PIDB) which represents a growing data asset for patient-specific tumor immunogenicity in solid tumors. Here we present a specific use case of applying machine learning (ML) to significantly improve neoE-HLA predictions and further model anticipated improvements of TCR capture as a direct consequence.MethodsPACT has developed capabilities for high-throughput manufacturing of single polypeptide (comPACT protein) which consists of the predicted neoE peptide together with Beta-2-Microglobulin and the HLA heavy chain. comPACT molecules are considered successfully produced when protein yields reach concentrations >1uM. Data used for this study consisted of >26000 neoE-HLA predictions for 62 different HLA alleles. We applied ML to learn patterns that are predictive of neoE-HLAs that can be successfully produced as comPACTs, using scikit-learn and XGBoost. Data was first split into training and testing data. Models were trained on training data and model hyperparameters were tuned using 5-fold cross validation (5xCV). The performance of the models during 5xCV and on test data was measured using the area under the receiver operating characteristic curve (AUC). We additionally performed experimental prospective validation of the models. To do this, 603 neoE-HLAs (from 7 previously unseen cancer samples) were selected for comPACT production using netMHCpan4.1 and the newly trained models.ResultsThe mean AUC for the 5xCV of the selected models ranged from 0.75 to 0.86 depending upon the HLA allele (SD <0.05 for every model). The AUC on the test data ranged from 0.75 to 0.92 (median = 0.85). Prospective validation resulted on average in a 22% higher success rate (range 11%–39%) using the new models as compared to the netMHCpan4.1 predictions. This is expected to result in increased capture of neoepitope-specific CD8+ T cells as the PIDB indicates that 3.2% of the successful comPACTs result in validated neoTCRs.ConclusionsPIDB based ML predictions of neoE-HLAs led to a significant increase in TCR-capturing comPACT success rates. Because of this work, it is predicted both neoE-specific CD8+ T cell capture and actionable neoTCR options will increase per patient.