Biology subjects

Du, M.

Publications and source records attributed to Du, M..

4 recordsLinked to original sources

bioRxiv2018

Split Selectable Markers

Selectable markers are widely adopted in transgenesis and genome editing for selecting engineered cells with desired genotype but are limited in choices. We present here split selectable markers each allowing for selection of multiple "unlinked" transgenes in the context of lentivirus-mediated transgenesis as well as CRISPR/Cas-mediated biallelic knock-ins. Future development of split selectable markers may support enrichment or selection of "hyper-engineered" cells containing tens of transgenes or genetic modifications.

bioengineering↗

bioRxiv2018

Transient μm scale protein accumulation at the center of the T cell antigen presenting cell interface drives efficient IL-2 secretion

Supramolecular signaling assemblies are of interest for their unique signaling properties. A {micro}m scale signaling assembly, the central supramolecular signaling cluster (cSMAC), forms at the center of the interface of T cells activated by antigen presenting cells. We have determined that it is composed of multiple complexes of a supramolecular volume of up to 0.5{micro}m3 and associated with extensive membrane undulations. To determine cSMAC function, we have systematically manipulated the localization of three adaptor proteins, LAT, SLP-76, and Grb2. cSMAC localization varied between the adaptors and was diminished upon blockade of the costimulatory receptor CD28 and deficiency of the signal amplifying kinase Itk. Reconstitution of cSMAC localization restored IL-2 secretion which is a key T cell effector function as dependent on reconstitution dynamics. Our data suggest that the cSMAC enhances early signaling by facilitating signaling interactions and attenuates signaling thereafter through sequestration of a more limited set of signaling intermediates.

immunology↗

bioRxiv2018

Whole-exome sequencing identified rare variants associated with body length and girth in cattle

Body measurements can be used in determining body size to monitor the cattle growth and examine the response to selection. Despite efforts putting into the identification of common genetic variants, the mechanism understanding of the rare variation in complex traits about body size and growth remains limited. Here, we firstly performed GWAS study for body measurement traits in Simmental cattle, however there were no SNPs exceeding significant level associated with body measurements. To further investigate the mechanism of growth traits in beef cattle, we conducted whole exome analysis of 20 cattle with phenotypic differences on body girth and length, representing the first systematic exploration of rare variants on body measurements in cattle. By carrying out a three-phase process of the variant calling and filtering, a sum of 1158, 1151, 1267, and 1303 rare variants were identified in four phenotypic groups of two growth traits, higher/ lower body girth (BG_H and BG_L) and higher/lower body length (BL_H and BL_L) respectively. The subsequent functional enrichment analysis revealed that these rare variants distributed in 886 genes associated with collagen formation and organelle organization, indicating the importance of collagen formation and organelle organization for body size growth in cattle. The integrative network construction distinguished 62 and 66 genes with different co-expression patterns associated with higher and lower phenotypic groups of body measurements respectively, and the two sub-networks were distinct. Gene ontology and pathway annotation further showed that all shared genes in phenotypic differences participate in many biological processes related to the growth and development of the organism. Together, these findings provide a deep insight into rare genetic variants of growth traits in cattle and this will have a promising application in animal breeding.

genetics↗

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗