Biology subjects

Xie, K.

Publications and source records attributed to Xie, K..

61 records · Page 4Linked to original sources

bioRxiv2019

In vitro induced floral reversion in switchgrass (Panicum Virgatum L.)

Switchgrass (Panicum Virgatum L.) is a warm-season perennial grass native to North America, it was used as forage and vegetative filter strips in early days, and have developed into a bioenergy crop in recent years. In this study, we found that the switchgrass cultivar Alamo at elongation stage 4 have developed inflorescences about 1 cm in length, and in vitro incubation of the shoot apexes harboring inflorescences on Murashige and Skoogs basal medium supplemented with 3 mg/L 6-benzylaminopurine generated multiple shoot clumps. Anatomical study showed that some of the regenerated shoots originated from axillary buds on the explants, some of them originated from adventurous buds and some of them originated from young florets. Further study of shoots originated from young florets found that the floral organs degenerated or developed into leaf-like organs, and the flower terminal transformed into a vegetative shoot apical meristem, thats to say these shoots arise from flower reversion. In vitro induction of floral reversion provided a novel protocol to manipulate flower development in switchgrass, which might contribute a fundamental for flower development study in switchgrass and other plants.

developmental biology↗

bioRxiv2019

Ultrafast immunostaining of organ-scale tissues for scalable proteomic phenotyping

Studying the function and dysfunction of complex biological systems necessitates comprehensive understanding of individual cells. Advancements in three-dimensional (3D) tissue processing and imaging modalities have enabled rapid visualization and phenotyping of cells in their spatial context. However, system-wide interrogation of individual cells within large intact tissue remains challenging, low throughput, and error-prone owing to the lack of robust labeling technologies. Here we introduce a rapid, versatile, and scalable method, eFLASH, that enables complete and uniform labeling of organ-scale tissue within one day. eFLASH dynamically modulates chemical transport and reaction kinetics to establish system-wide uniform labeling conditions throughout the day-long labeling period. This unique approach enables the same protocol to be compatible with a wide range of tissue types and probes, enabling combinatorial molecular phenotyping across different organs and species. We applied eFLASH to generate quantitative maps of various cell types in mouse brains. We also demonstrated multidimensional cell profiling in a marmoset brain block. We envision that eFLASH will spur holistic phenotyping of emerging animal models and disease models to help assess their functions and dysfunctions.

bioengineering↗

bioRxiv2019

Scalable image processing techniques for quantitative analysis of volumetric biological images from light-sheet microscopy

Here we describe an image processing pipeline for quantitative analysis of terabyte-scale volumetric images of SHIELD-processed mouse brains imaged with light-sheet microscopy. The pipeline utilizes open-source packages for destriping, stitching, and atlas alignment that are optimized for parallel processing. The destriping step removes stripe artifacts, corrects uneven illumination, and offers over 100x speed improvements compared to previously reported algorithms. The stitching module builds upon Terastitcher to create a single volumetric image quickly from individual image stacks with parallel processing enabled by default. The atlas alignment module provides an interactive web-based interface that automatically calculates an initial alignment to a reference image which can be manually refined. The atlas alignment module also provides summary statistics of fluorescence for each brain region as well as region segmentations for visualization. The expected runtime of our pipeline on a whole mouse brain hemisphere is 1-2 d depending on the available computational resources and the dataset size.

bioengineering↗

bioRxiv2018

Dietary fatty acids promote lipid droplet diversity through seipin enrichment in an ER subdomain

Exogenous metabolites from microbial and dietary origins have profound effects on host metabolism. Here, we report that a sub-population of lipid droplets (LDs), which are conserved organelles for fat storage, is defined by metabolites-driven targeting of the C. elegans seipin ortholog, SEIP-1. Loss of SEIP-1 function reduced the size of a subset of LDs while over-expression of SEIP-1 had the opposite effect. Ultrastructural analysis revealed SEIP-1 enrichment in an endoplasmic reticulum (ER) subdomain, which co-purified with LDs. Analyses of C. elegans and bacterial genetic mutants indicated a requirement of polyunsaturated fatty acids (PUFAs) and microbial cyclopropane fatty acids (CFAs) for SEIP-1 enrichment, as confirmed by dietary supplementation experiments. In mammalian cells, heterologous expression of SEIP-1 promoted lipid droplet expansion from ER subdomains in a conserved manner. Our results suggest that microbial and polyunsaturated fatty acids serve unexpected roles in regulating cellular fat storage by enforcing LD diversity.

cell biology↗

bioRxiv2017

PPARD regulation in gastric progenitor cells drives gastric tumorigenesis in mice

Little is known about the cell origin of gastric cancer. Peroxisome proliferator-activated receptor-delta (PPARD) is a druggable ligand-activated nuclear receptor that impacts protumorigenic cellular events. However, PPARDs role in tumorigenesis, especially gastric tumorigenesis, remains to be defined. We found that targeting PPARD overexpression in murine gastric progenitor cells (GPC), via a villin promoter, spontaneously induced gastric tumorigenesis that progressed to invasive adenocarcinoma. PPARD overexpression in GPC upregulated tumorigenic proinflammatory cytokine and CD44 expression, expanded GPC population in vivo, enhanced GPC self-renewal and proliferation in organoid cultures, and endowed these cells with tumorigenic properties. Our findings identify PPARD as a driver of gastric tumorigenesis via GPC transformation.

cancer biology↗

bioRxiv2017

Neural code uses self-information principle to organize the brain’s universal cell-assembly coding

The brain generates cognition and behavior through firing changes of its neurons, yet, with enormous firing variability, the organizing principle underlying real-time neural code remains unclear. Here, we test the Neural Self-Information Theory that neural code is constructed via the self-information principle under which each inter-spike-interval (ISI) is inherently self-tagged with discrete information based on its relation to ISI variability-probability distribution - higher-probability ISIs, which reflect the balanced excitation-inhibition ground state, convey minimal information, whereas lower-probability ISIs, which signify statistical surprisals, carry more information. Moreover, temporally coordinated ISI surprisals across neural cliques intrinsically give rise to real-time cell-assembly neural code. As a result, this self-information-based neural coding is uniquely intrinsic to the neurons themselves, with no need for outside observers to set any reference point to manually mark external or internal inputs. Applying this neural self-information concept, we devised an unbiased general decoding strategy and successfully uncovered 15 distinct cell-assembly patterns from multiple cortical and hippocampal circuits associated with different sleep cycles, earthquake, elevator-drop, foot-shock experiences, navigation or various actions in five-choice visual-discrimination operant-conditioning tasks. Detailed analyses of all 15 cell assemblies revealed that ~20% of the skewed ISI distribution tails were responsible for the emergence of robust cell-assembly codes, conforming to the Pareto Principle. These findings support the notion that neural coding is organized via the self-information principle to generate real-time information across brain regions, cognitive modalities, and behaviors.

neuroscience↗

bioRxiv2017

Spike-Timing Patterns Conform to Gamma Distribution with Regional and Cell Type-Specific Characteristics

Spike-timing patterns - crucial for synaptic plasticity and neural computation - are often modeled as Poisson-like random processes, log-normal distribution or gamma-distribution patterns, each with different underlying assumptions that may or may not be biologically true. However, it is not entirely clear whether (and how well) these different models would or would not capture spike-timing statistical patterns across different neurons, regions, animal species and cognitive states. Here, we examine statistical patterns of spike-timing irregularity in 13 different cortical and subcortical regions from mouse, hamster, cat and monkey brains. In contrast to the widely-assumed Poisson or log-normal distribution patterns, we show that spike-timing patterns of various projection neurons-including cortical excitatory principal cells, hippocampal pyramidal cells, inhibitory striatal medium spiny neurons and dopaminergic neurons, as well as fast-spiking interneurons - all invariantly conform to the gamma-distribution model. While higher regularity in spike-timing patterns are observed in a few cases, such as mouse DA neurons and monkey motor cortical neurons, there is no clear tendency in increased firing regularity from the sensory and subcortical neurons to prefrontal or motor cortices, as previously entertained. Moreover, gamma shapes of spike-timing patterns remain robust over various natural cognitive states, such as sleep, awake periods, or during fearful episodic experiences. Interestingly, ketamine-induced general anesthesia or unconsciousness is associated with the breakdown of forebrain spike patterns from a singular gamma distribution into two distinct subtypes of gamma distributions, suggesting the importance of this spike-timing pattern in supporting natural cognitive states. These results suggest that gamma-distribution patterns of spike timing reflect not only a fundamental property conserved across different neurons, regions and animal species, but also an operation crucial for supporting natural cognitive states. Such gamma-distribution-based spike-timing patterns can also have important implications for real-time neural coding and realistic neuromorphic computing.

neuroscience↗