Basic Principles and Recent Advances in Magnetic Cell Separation

Magnetic cell separation has become a key methodology for the isolation of target cell populations from biological suspensions, covering a wide spectrum of applications from diagnosis and therapy in biomedicine to environmental applications or fundamental research in biology. There now exists a great variety of commercially available separation instruments and reagents, which has permitted rapid dissemination of the technology. However, there is still an increasing demand for new tools and protocols which provide improved selectivity, yield and sensitivity of the separation process while reducing cost and providing a faster response. This review aims to introduce basic principles of magnetic cell separation for the neophyte, while giving an overview of recent research in the field, from the development of new cell labeling strategies to the design of integrated microfluidic cell sorters and of point-of-care platforms combining cell selection, capture, and downstream detection. Finally, we focus on clinical, industrial and environmental applications where magnetic cell separation strategies are amongst the most promising techniques to address the challenges of isolating rare cells.

Rarity of fetal cells in exocervical samples for noninvasive prenatal diagnosis

Objectives The possibility to isolate fetal cells from pregnant women cervical samples has been discussed for five decades but is not currently applied in clinical practice. This study aimed at offering prenatal genetic diagnosis from fetal cells obtained through noninvasive exocervical sampling and immuno-sorted based on expression of HLA-G. Methods We first developed and validated robust protocols for cell detection and isolation on control cell lines expressing (JEG-3) or not (JAR) the HLA-G antigen, a specific marker for extravillous trophoblasts. We then applied these protocols to noninvasive exocervical samples collected from pregnant women between 6 and 14 weeks of gestational age. Sampling was performed through insertion and rotation of a brush at the ectocervix close to the external os of the endocervical canal. Finally, we attempted to detect and quantify trophoblasts in exocervical samples from pregnant women by ddPCR targeting the male SRY locus. Results For immunohistochemistry, a strong specific signal for HLA-G was observed in the positive control cell line and for rare cells in exocervical samples, but only in non-fixative conditions. HLA-G positive cells diluted in HLA-G negative cells were isolated by flow cytometry or magnetic cell sorting. However, no HLA-G positive cells could be recovered from exocervical samples. SRY gene was detected by ddPCR in exocervical samples from male (50%) but also female (27%) pregnancies. Conclusions Our data suggest that trophoblasts are too rarely and inconstantly present in noninvasive exocervical samples to be reliably retrieved by standard immunoisolation techniques and therefore cannot replace the current practice for prenatal screening and diagnosis.

The Role of Dielectrophoresis for Cancer Diagnosis and Prognosis

Liquid biopsy is emerging as a potential diagnostic tool for prostate cancer (PC) prognosis and diagnosis. Unfortunately, most circulating tumor cells (CTC) technologies, such as AdnaTest or Cellsearch®, critically rely on the epithelial cell adhesion molecule (EpCAM) marker, limiting the possibility of detecting cancer stem-like cells (CSCs) and mesenchymal-like cells (EMT-CTCs) that are present during PC progression. In this context, dielectrophoresis (DEP) is an epCAM independent, label-free enrichment system that separates rare cells simply on the basis of their specific electrical properties. As compared to other technologies, DEP may represent a superior technique in terms of running costs, cell yield and specificity. However, because of its higher complexity, it still requires further technical as well as clinical development. DEP can be improved by the use of microfluid, nanostructured materials and fluoro-imaging to increase its potential applications. In the context of cancer, the usefulness of DEP lies in its capacity to detect CTCs in the bloodstream in their epithelial, mesenchymal, or epithelial–mesenchymal phenotype forms, which should be taken into account when choosing CTC enrichment and analysis methods for PC prognosis and diagnosis.

Challenging the Current Paradigm of Liquid Biopsy Through Dielectrophoresis (DEP) In Prostate Cancer

Liquid biopsy via isolation of circulating tumour cells (CTCs) represents a promising diagnostic tool capable of supplementing state-of-the-art for prostate cancer (PC) prognosis. Unfortunately, most of CTC technologies, such as AdnaTest or Cellsearch, critically rely on the Epithelial-Cell-Adhesion-Molecule (EpCAM) marker, limiting the possibility of detecting stem-like cells (CSCs) and mesenchymal-like cells (EMT-CTCs) that are present during PC progression. In this tontext, dielectrophoresis (DEP) is an epCAM independent, label-free, enrichment system, separating rare cells simply on the basis of their specific electrical properties. As compared to other technollgies, DEP represents a superior technique in terms of running costs, cells yield and specificity, but due to its higher complexity, requires still further technical as well as clinical development. Interestingly, DEP can be improved by the use of microfluid, nanostructured materials and fluoroimaging in order to increase its potential applications. In the context of PC, the utility of DEP can be translated in its capacity to detect CTC in the bloodstream in their epithelial, mesenchymal, or epithelial-mesenchymal phenotypes, which should be taken into account when choosing CTC enrichment and analysis methods for PC prognosis and early diagnosis.

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

AbstractscRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.

A dielectrophoresis-based microfluidic chip for trapping circulating tumor cells using a porous membrane

Circulating tumor cells (CTCs) have been widely considered as novel biomarkers for clinical diagnosis of cancer. CTCs are the cells detached from the parent tumors and shed into the blood stream to initiate tumor metastasis. Although CTCs are rare, their detection in one’s blood sample is essential for cancer early diagnosis and for starting the treatment procedure. Here, we introduce a novel method for trapping CTCs using dielectrophoresis (DEP), which effectively employs pores of a replaceable porous membrane as CTC traps. The applied dielectrophoretic force efficiently traps and holds CTCs in a stable position and further enables us to perform various on chip analysis on them. First, using finite element method, the performance of the system was simulated for different physical conditions. Then, the chip was fabricated and its trapping performance was experimentally validated. Cells were entered into the microchannel and trapped in the pores of a polydimethylsiloxane (PDMS) membrane. The proposed microfluidic chip is capable of detecting rare cells in a large cell population.

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.

Sub-Cluster Identification through Semi-Supervised Optimization of Rare-cell Silhouettes (SCISSORS) in Single-Cell Sequencing

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choice can greatly alter clustering solutions and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which is not favorable for identifying cells of extremely low abundance due to their subtle contributions towards overall patterns of gene expression. Here we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within major cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by implementing a multi-step, semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of rare cells, which may be used for further study. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes for two example datasets, is freely available at https://github.com/jrleary/SCISSORS.