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.

Self-Assembling Lectin Nano-Block Oligomers Enhance Binding Avidity to Glycans

Lectins, carbohydrate-binding proteins, are attractive biomolecules for medical and biotechnological applications. Many lectins have multiple carbohydrate recognition domains (CRDs) and strongly bind to specific glycans through multivalent binding effect. In our previous study, protein nano-building blocks (PN-blocks) were developed to construct self-assembling supramolecular nanostructures by linking two oligomeric proteins. A PN-block, WA20-foldon, constructed by fusing a dimeric four-helix bundle de novo protein WA20 to a trimeric foldon domain of T4 phage fibritin, self-assembled into several types of polyhedral nanoarchitectures in multiples of 6-mer. Another PN-block, the extender PN-block (ePN-block), constructed by tandemly joining two copies of WA20, self-assembled into cyclized and extended chain-type nanostructures. This study developed novel functional protein nano-building blocks (lectin nano-blocks) by fusing WA20 to a dimeric lectin, Agrocybe cylindracea galectin (ACG). The lectin nano-blocks self-assembled into various oligomers in multiples of 2-mer (dimer, tetramer, hexamer, octamer, etc.). The mass fractions of each oligomer were changed by the length of the linkers between WA20 and ACG. The binding avidity of the lectin nano-block oligomers to glycans was significantly increased through multivalent effects compared with that of the original ACG dimer. Lectin nano-blocks with high avidity will be useful for various applications, such as specific cell labeling.

Continuous Flow Labeling and In-Line Magnetic Separation of Cells

There is an identified need for point-of-care diagnostic systems for detecting and counting specific rare types of circulating cells in blood. By adequately labeling such cells with immunomagnetic beads and quantum dots, they can be efficiently collected magnetically for quantification using fluorescence methods. Automation of this process requires adequate mixing of the labeling materials with blood samples. A static mixing device can be employed to improve cell labeling efficiency and eliminate error-prone laboratory operations. Computational fluid dynamics (CFD) were utilized to simulate the flow of a labeling-materials/blood mixture through a 20-stage in-line static mixer of the interfacial-surface-generator type. Optimal fluid mixing conditions were identified and tested in a magnetic bead/tumor cell model, and it was found that labeled cells could be produced at 1.0 mL/min flow rate and fed directly into an in-line magnetic trap. The trap design consists of a dual flow channel with three bends and a permanent magnet positioned at the outer curve of each bend. The capture of labeled cells in the device was simulated using CFD, finite-element analysis and magnetophoretic mobility distributions of labeled cells. Testing with cultured CRL14777 human melanoma cells labeled with anti-CD146 1.5 μm diameter beads indicated that 90 ± 10% are captured at the first stage, and these cells can be captured when present in whole blood. Both in-line devices were demonstrated to function separately and together as predicted.

Alveoli form directly by budding led by a single epithelial cell

Oxygen passes along the ramifying branches of the lung's bronchial tree and enters the blood through millions of tiny, thin-walled gas exchange sacs called alveoli. Classical histological studies have suggested that alveoli arise late in development by a septation process that subdivides large air sacs into smaller compartments. Although a critical role has been proposed for contractile myofibroblasts, the mechanism of alveolar patterning and morphogenesis is not well understood. Here we present the three-dimensional cellular structure of alveoli, and show using single-cell labeling and deep imaging that an alveolus in the mouse lung is composed of just 2 epithelial cells and a total of a dozen cells of 7 different types, each with a remarkable, distinctive structure. By mapping alveolar development at cellular resolution at a specific position in the branch lineage, we find that alveoli form surprisingly early by direct budding of epithelial cells out from the airway stalk between enwrapping smooth muscle cells that rearrange into a ring of 3-5 myofibroblasts at the alveolar base. These alveolar entrance myofibroblasts are anatomically and developmentally distinct from myofibroblasts that form the thin fiber partitions of alveolar complexes ('partitioning' myofibroblasts). The nascent alveolar bud is led by a single alveolar type 2 (AT2) cell following selection from epithelial progenitors; a lateral inhibitory signal transduced by Notch ensures selection of only one cell so its trailing neighbor acquires AT1 fate and flattens into the cup-shaped wall of the alveolus. Our analysis suggests an elegant new model of alveolar patterning and formation that provides the foundation for understanding the cellular and molecular basis of alveolar diseases and regeneration.

Aciniccell carcinoma of the salivary glands: clinical and histopathological study of 12 cases

Objective:Acinic cell carcinoma (CCA) is the third malignant epithelial tumor of the salivary glands in adults; low-grade tumor of malignancy, composed of neoplastic cells with serous acinar differentiation. The objective of this work was to analyze 12 cases of CCA according to their location, clinical characteristics, histological and immunohistochemical pattern and cell types, following the latest classification of the World Health Organization. Methods: The study included 12 cases of CCA from the files of salivary tumor biopsies of our work team, corresponding to the period 1997-2020. A numerical code was used to identify the samples, preserving the identity of the patients. Histological sections of the paraffin-embedded biopsies were evaluated with H/E, PAS and Toluidine blue and immunostained with the monoclonal antibodies pancytokeratin AE1 / AE3, Ki67, MUC-1 and mammaglobin. Results: The most frequent histologic pattern was the solid type as a single pattern or integrated with other patterns of lesser development, with almost exclusive location in the parotid gland and more frequent in women. Cells like normal acinar serocytes predominated in the solid growth pattern. The most frequent cell type in the microcystic patternwas the nonspecific glandular cell together with a lower proportion of acinar and intercalated duct-like cells. The papillary-cystic pattern was lined by nonspecific glandular cells. No clear cells found. With Ki67 a low cell proliferation was demonstrated in all the cases studied. Cell labeling for MUC-1 was grade 1 positive (less than 10% immunoreactive cells) and negative for mammaglobin.Conclusions: Patient follow-up is a priority because CCA tends to recur and metastasize and its behavior can become aggressive. We must deepen the study of its proliferative capacity as a treatment and prognosis tool, especially with immunohistochemistry and standardized molecular biology methods.

Magnitude and Breadth of Antibody Cross-reactivity Induced by Recombinant Influenza Hemagglutinin Trimer Vaccine Is Enhanced by Combination Adjuvants

The effects of adjuvants for increasing the immunogenicity of influenza vaccines are well known. However, the effect of adjuvants on increasing the breadth of cross-reactivity is less well understood. In this study we have performed a systematic screen of different toll-like receptor (TLR) agonists, with and without a squalene-in-water emulsion on the immunogenicity of a recombinant trimerized hemagglutinin (HA) vaccine in mice after single-dose administration. Antibody (Ab) cross-reactivity for other variants within and outside the immunizing subtype (homosubtypic and heterosubtypic cross-reactivity, respectively) was assessed using a protein microarray approach. Of all the formulations tested, a combination of CpG, MPLA and AddaVAX (termed “IVAX-1”) yielded the greatest breadth and magnitude of Ab responses, particularly against the HA1 region (which includes the variable head domain) of HA. Antigen-specific plasma cell labeling experiments show the components of IVAX-1 are synergistic. This adjuvant preferentially stimulates CD4 T cells to produce Th1>Th2 type (IgG2c>IgG1) antibodies and cytokine responses. Moreover, IVAX-1 induces identical homo- and heterosubtypic IgG and IgA cross-reactivity profiles when administered intranasally. Consistent with these observations, a single-cell transcriptomics analysis demonstrated significant increases in expression of IgG1, IgG2b and IgG2c genes of B cells in H5/IVAX-1 immunized mice relative to naïve mice, as well as significant increases in expression of the IFNg gene of both CD4 and CD8 T cells. These data support the use of adjuvants for enhancing the breath and durability of antibody responses of influenza virus vaccines.

Structural and functional organization of visual responses in the inferior olive of larval zebrafish

The olivo-cerebellar system plays an important role in vertebrate sensorimotor control. According to a classical theory of cerebellar cortex, the inferior olive (IO) provides Purkinje cells with error information which drives motor learning in the cerebellum. Here we investigate the sensory representations in the IO of larval zebrafish and their spatial organization. Using single-cell labeling of genetically identified IO neurons we find that they can be divided into at least two distinct groups based on their spatial location, dendritic morphology, and axonal projection patterns. In the same genetically targeted population, we recorded calcium activity in response to a set of visual stimuli using 2-photon imaging. We found that most IO neurons showed direction selective and binocular responses to visual stimuli and that functional properties were spatially organized within the IO. Light-sheet functional imaging that allowed for simultaneous activity recordings at the soma and axonal level revealed tight coupling between soma location, axonal projections and functional properties of IO neurons. Taken together, our results suggest that anatomically-defined classes of inferior olive neurons correspond to distinct functional types, and that topographic connections between IO and cerebellum contribute to organization of the cerebellum into distinct functional zones.

62 Applying machine vision to empower preclinical development of cell engager and adoptive cell therapeutics in patient-derived organoid models of solid tumors

BackgroundCell engager and adoptive cell therapeutics have emerged as efficacious and durable treatments in patients with B-cell malignancies. Though many analogous strategies are under development in solid tumors, none have received approval. Preclinical development of these therapies requires cell labeling of immortalized cell lines and/or primary expanded T cells to distinguish target and effector cells. However, cell engager and adoptive cell therapies have had limited evidence of reproducibility in primary patient-derived models such as tumor organoid cultures thus far. Here, we build upon our tumor organoid platform1 to measure organoid specific responses to these therapies. Utilizing machine vision coupled with time-lapse-microscopy, we obtain multiparameter kinetic readouts of patient-derived tumor organoid cell killing and allogeneic MHC-matched primary peripheral blood mononuclear cells (PBMCs).MethodsThe patient-derived tumor organoids were co-cultured with PBMCs in the presence of engagers/activators and vital dyes and incubated for 96 hrs. Cell death was measured by quantifying the caspase 3/7 vital dye pixel intensities at different time points using high throughput imaging. As a first step, a fully convolutional neural network was trained to segment out organoids from brightfield images comprised of organoids, immune cells and potential background artifacts. This segmentation mask was then transferred over to registered caspase 3/7 images to quantify tumor cell specific phenotypes in a rapid and automated manner.ResultsThe time-lapse imaging assay allowed for both the tracking of the organoid growth over time as well as the quantification of the kinetics of engagers/activators in comparison to controls resulting in accurate and precise technical reproducibility. Further, this assay allowed for the co-localization of the organoids and the immune cells over time, thus, enabling a spatiotemporal summary of dose dependent efficacy of candidate therapeutics.ConclusionsWe demonstrate the scalability and throughput of a machine vision tumor organoid immune co-culture platform across multiple unique patient-derived tumor organoid lines bearing a target of interest, enabling future discovery of biomarkers of therapeutic response and resistance.ReferenceLarsen B, Kannan M, Langer LF, Khan AA, Salahudeen AA, A pan-cancer organoid platform for precision medicine. Cell Reports 2021; 36:109429

Poly(N,N-dimethylacrylamide)-coated upconverting NaYF4:Yb,Er@NaYF4:Nd core–shell nanoparticles for fluorescent labeling of carcinoma cells

Upconverting luminescent lanthanide-doped nanoparticles (UCNP) belong to promising new materials that absorb infrared light able to penetrate in the deep tissue level, while emitting photons in the visible or ultraviolet region, which makes them favorable for bioimaging and cell labeling. Here, we have prepared upconverting NaYF4:Yb,Er@NaYF4:Nd core–shell nanoparticles, which were coated with copolymers of N,N-dimethylacrylamide (DMA) and 2-(acryloylamino)-2-methylpropane-1-sulfonic acid (AMPS) or tert-butyl [2-(acryloylamino)ethyl]carbamate (AEC-Boc) with negative or positive charges, respectively. The copolymers were synthesized by a reversible addition-fragmentation chain transfer (RAFT) polymerization, reaching Mn ~ 11 kDa and containing ~ 5 mol% of reactive groups. All copolymers contained bisphosphonate end-groups to be firmly anchored on the surface of NaYF4:Yb,Er@NaYF4:Nd core–shell nanoparticles. To compare properties of polymer coatings, poly(ethylene glycol)-coated and neat UCNP were used as a control. UCNP with various charges were then studied as labels of carcinoma cells, including human hepatocellular carcinoma HepG2, human cervical cancer HeLa, and rat insulinoma INS-1E cells. All the particles proved to be biocompatible (nontoxic); depending on their ξ-potential, the ability to penetrate the cells differed. This ability together with the upconversion luminescence are basic prerequisites for application of particles in photodynamic therapy (PDT) of various tumors, where emission of nanoparticles in visible light range at ~ 650 nm excites photosensitizer.

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&E-stained tissue.