NanoCellect Biomedical (United States)

Monozygotic twins are derived from the split of a single zygote early in embryogenesis. Although it was hypothesized that the timing of twining is overall associated with fetal membrane configuration of twins, i.e., chorionicity and amnionicity, our understanding of early embryonic clonal dynamics underlying human twinning is limited. Here we explored the segregations of early embryonic lineages in 7 dichorionic diamniotic (DCDA), 7 monochorionic diamniotic (MCDA), 8 monochorionic monoamniotic (MCMA) monozygotic twins, and 1 dichorionic triamniotic (DCTA) monozygotic triplets, using post-zygotic early embryonic mutations (EEMs) as endogenous lineage barcodes. Patterns of the early lineage distributions among monozygotic twins revealed three apparent clonal categories, referred to as para-identical, sub-identical, and full-identical twins, which largely correlated with the amnionicity of the twins. Rather, despite conventional wisdom, chorionicity was not substantially associated with early clonal compositions, but with blood exchanges in utero. In sub-identical twins, where one co-twin was clonally a part of the other, our data suggested that the foundation of the latter co-twin was established after acquisition of a median of 6 additional post-zygotic mutations (range: 2–13), corresponding to ~5 early cell divisions. Additional in-depth analysis on the matched placenta from an MCDA twin suggested that separation of two co-twins can precede the separation of the placenta and embryonic proper, and a single chorion can be formed even with multiclonal origin. Our findings provide insights into the clonal dynamics, twinning processes, and cell fate decisions in early human embryogenesis.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

We have demonstrated a microfluidic device that can not only achieve three-dimensional flow focusing but also confine particles to the center stream along the channel. The device has a sample channel of smaller height and two sheath flow channels of greater height, merged into the downstream main channel where 3D focusing effects occur. We have demonstrated that both beads and cells in our device display significantly lower CVs in velocity and position distributions as well as reduced probability of coincidental events than they do in conventional 2D-confined microfluidic channels. The improved particle confinement in the microfluidic channel is highly desirable for microfluidic flow cytometers and in fluorescence-activated cell sorting (FACS). We have also reported a novel method to measure the velocity of each individual particle in the microfluidic channel. The method is compatible with the flow cytometer setup and requires no sophisticated visualization equipment. The principles and methods of device design and characterization can be applicable to many types of microfluidic systems.

Anticancer drug delivery has been hindered due to cell membrane permeability and the lack of a selective marker for tumor cells. Cell permeability is related to the bioavailability of drugs and has therefore been considered to be an essential step for achieving therapeutic efficacy. While different types of transporters currently exist, cell penetrating peptides (CPPs) have become one of the most popular and effective tools for intracellular drug delivery. Most of the original CPPs are short peptides with basic residues. The mechanism of CPP cell entry remains to be established; however, the CPPs can deliver any type of molecular cargo including solid nanoparticles. Herein, this paper will discuss the classification of CPPs, the mechanism of cell entry, the application of CPPs in tumor therapy, and recent advances in targeted cell penetration that involve CPPs.