Label-free ghost cytometry for manufacturing of cell therapy products.

Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs (ROC-AUC: area under receiver operating characteristic curve = 0.975), T cells and non-T cells in white blood cells (ROC-AUC = 0.969), activated T cells and quiescent T cells in PBMCs (ROC-AUC = 0.990), and particulate impurities in PBMCs (ROC-AUC ≧ 0.998). The results support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.

Label-free cell detection of acute leukemia using ghost cytometry.

Early diagnosis and prompt initiation of appropriate treatment are critical for improving the prognosis of acute leukemia. Acute leukemia is diagnosed by microscopic morphological examination of bone marrow smears and flow cytometric immunophenotyping of bone marrow cells stained with fluorophore-conjugated antibodies. However, these diagnostic processes require trained professionals and are time and resource-intensive. Here, we present a novel diagnostic approach using ghost cytometry, a recently developed high-content flow cytometric approach, which enables machine vision-based, stain-free, high-speed analysis of cells, leveraging their detailed morphological information. We demonstrate that ghost cytometry can detect leukemic cells from the bone marrow cells of patients diagnosed with acute lymphoblastic leukemia and acute myeloid leukemia without relying on biological staining. The approach presented here holds promise as a precise, simple, swift, and cost-effective diagnostic method for acute leukemia in clinical practice.

In silico-labeled ghost cytometry.

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics.

Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals of cells, thereby relieving a computational bottleneck for real-time data analysis in high-throughput imaging cytometry. In our previous work, we demonstrated that this image-free approach could distinguish cells from two cell lines prepared with the same fluorescence staining method. However, the demonstration using different cell lines could not exclude the possibility that classification was based on non-morphological factors such as the speed of cells in flow, which could be encoded in the compressed signals. In this study, we show that GC can classify cells from the same cell line but with different fluorescence distributions in space, supporting the strength of our image-free approach for accurate morphological cell analysis. © 2020 International Society for Advancement of Cytometry.

Enhanced selectivity of CO2 from a ternary gas mixture in an interdigitated porous framework.

A flexible porous coordination polymer with interdigitated structure (CID-3) has been synthesized whose pore size and structural flexibility are suitable for CO(2) capture, providing us with highly selective adsorption properties of CO(2) from a ternary O(2), N(2) and CO(2) mixture.

Rapid preparation of flexible porous coordination polymer nanocrystals with accelerated guest adsorption kinetics.

Porous coordination polymers, in particular flexible porous coordination polymer networks that change their network structure on guest adsorption, have enormous potential in applications involving selective storage, separation and sensing. Despite the expected significant differences in their adsorption properties, porous coordination polymer nanocrystals remain largely unexplored, and there have been no reports about studies on flexible porous coordination polymer nanocrystals, mainly due to a lack of preparation methods. Here, we present a new technique for the rapid preparation of porous coordination polymer nanocrystals that combines non-aqueous inverse microemulsion with ultrasonication. Uniform nanocrystals of {[Zn(ip)(bpy)]}(n) (ip = isophthalate, bpy = 4,4'-bipyridyl; CID-1), a flexible porous coordination polymer, have been prepared by this method and analysed using field-emission scanning electron microscopy, energy-dispersive X-ray analysis, infrared spectroscopy, Raman spectroscopy and X-ray powder diffraction. A model for particle formation and growth is presented and discussed. Adsorption experiments with methanol show that the overall adsorption capacities of nanoparticles and bulk are almost identical, but the shapes of the sorption isotherms differ significantly and the adsorption kinetics increase dramatically.

A block PCP crystal: anisotropic hybridization of porous coordination polymers by face-selective epitaxial growth.

Two porous coordination polymers (PCPs) with different pore surface functionality were integrated into one single crystal by face-selective epitaxial growth, leading to BAB-type block PCP crystals with the core crystal A between the second crystals B.

Heterogeneously hybridized porous coordination polymer crystals: fabrication of heterometallic core-shell single crystals with an in-plane rotational epitaxial relationship.

MOF on MOF: Core-shell porous coordination polymer (PCP) crystals are fabricated at the single-crystal level by epitaxial growth in solution. Synchrotron X-ray diffraction measurements unveiled the structural relationship between the shell crystal and the core crystal, where in-plane rotational epitaxial growth compensates the difference in lattice constant.

Selective guest sorption in an interdigitated porous framework with hydrophobic pore surfaces.

An interdigitated porous coordination polymer with hydrophobic pore surface shows size and affinity dependent selective gas sorption properties accompanying the reversible structure transformation.