Supporting data for "3D Printed Functional and Biological Materials on Moving Freeform Surfaces"

Full description in the file "ZhuReadme.txt".The data set includes the experimental data supporting the results reported in Zhu, Zhijie, Shuang‐Zhuang Guo, Tessa Hirdler, Cindy Eide, Xiaoxiao Fan, Jakub Tolar, and Michael C. McAlpine. "3D printed functional and biological materials on moving freeform surfaces." Advanced Materials, 30(23), 1707495. Conventional 3D printing technologies typically rely on open‐loop, calibrate‐then‐print operation procedures. An alternative approach is adaptive 3D printing, which is a closed‐loop method that combines real‐time feedback control and direct ink writing of functional materials in order to fabricate devices on moving freeform surfaces. Here, it is demonstrated that the changes of states in the 3D printing workspace in terms of the geometries and motions of target surfaces can be perceived by an integrated robotic system aided by computer vision. A hybrid fabrication procedure combining 3D printing of electrical connects with automatic pick‐and‐placing of surface‐mounted electronic components yields functional electronic devices on a free‐moving human hand. Using this same approach, cell‐laden hydrogels are also printed on live mice, creating a model for future studies of wound‐healing diseases. This adaptive 3D printing method may lead to new forms of smart manufacturing technologies for directly printed wearable devices on the body and for advanced medical treatments.National Institutes of Health, Grant 1DP2EB020537Regenerative Medicine Minnesota, Grant RMM102516006National Institutes of Health, Grant R01AR063070The graduate school of the University of Minnesota, 2017-18 Interdisciplinary Doctoral Fellowshi

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa

<div><p>Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at <a href="https://github.com/kuanglab/scVDMC" target="_blank">https://github.com/kuanglab/scVDMC</a>.</p></div

Clustering performance on mESC and Lung datasets.

<p>(A) & (C) show the clustering results of the scVDMC algorithm compared with the baseline methods. Pooled <i>k</i>-means, separated <i>k</i>-means and scVDMC are tested with varying numbers of selected marker genes. Seurat, cellTree, SNN-Cliq and SC3 are tested using all the genes as input to the software/program. (B) & (D) show the PCA of scVDMC, pooled <i>k</i>-means, and separated <i>k</i>-means results on the selected top marker genes. PCA is applied on each individual domain for separated <i>k</i>-means and the combined data for pooled <i>k</i>-means and scVDMC. For each dot, the layer (outer) color indicates the true cell type, while the inner color indicates the predicted cell type. The error is measured on the best one-to-one matching between the detected clusters and the true clusters. The hyper-parameters for scVDMC are λ = 20, <i>w</i> = 0.1, <i>α</i> = 0.5 on the mESC dataset and λ = 50, <i>w</i> = 0.1, <i>α</i> = 1 on the Lung dataset.</p

For each RDEB or WT individual, the number of single-cells used for downstream analysis is indicated as well as the average number of reads for the single-cells from each individual.

<p>For each RDEB or WT individual, the number of single-cells used for downstream analysis is indicated as well as the average number of reads for the single-cells from each individual.</p

Patient-Specific Naturally Gene-Reverted Induced Pluripotent Stem Cells in Recessive Dystrophic Epidermolysis Bullosa

Spontaneous reversion of disease-causing mutations has been observed in some genetic disorders. In our clinical observations of severe generalized recessive dystrophic epidermolysis bullosa (RDEB), a currently incurable blistering genodermatosis caused by loss-of-function mutations in COL7A1 that results in a deficit of type VII collagen (C7), we have observed patches of healthy-appearing skin on some individuals. When biopsied, this skin revealed somatic mosaicism resulting in the self-correction of C7 deficiency. We believe this source of cells could represent an opportunity for translational ‘natural’ gene therapy. We show that revertant RDEB keratinocytes expressing functional C7 can be reprogrammed into induced pluripotent stem cells (iPSCs) and that self-corrected RDEB iPSCs can be induced to differentiate into either epidermal or hematopoietic cell populations. Our results give proof-of-principle that an inexhaustible supply of functional patient-specific revertant cells can be obtained—potentially relevant to local wound therapy and systemic hematopoietic cell transplantation. This technology may also avoid some of the major limitations of other cell therapy strategies, e.g., immune rejection and insertional mutagenesis, which are associated with viral- and nonviral-mediated gene therapy. We believe this approach should be the starting point for autologous cellular therapies using ‘natural’ gene therapy in RDEB and other diseases

Distinct single-cell populations from six RDEB patients and their matched siblings.

<p>In (A), (B) and (C) PCA is applied to the combined single cell profiles of the learned marker genes by scVDMC from the six cell populations. parameters <i>α</i> = 0, 10 and 20 are tested. (D) PCA is applied to the combined single cell profiles of all the genes from the six cell populations and the clusters are found by SC3 are shown. Each plot shows the projection by the first two principle components. The cluster centers are indicated by the diamonds.</p

Four datasets used in the experiments.

<p>Four datasets used in the experiments.</p