Biology subjects

Cong, L.

Publications and source records attributed to Cong, L..

3 recordsLinked to original sources

bioRxiv2017

Gossypol biosynthesis in cotton revealed through organ culture, plant grafting and gene expression profiling

Gossypol plays an important role in defense mechanism of Gossypium species and the presence of gossypol also limits the utilization of cottonseeds. However, little is known about the metabolism of gossypol in cotton plant. Here, Detection on the dynamic tendency of gossypol content illustrated that at the germination stage, the main source of gossypol was cotyledon, and at the later stages, gossypol mainly came from root system. Plant grafting between cottons and sunflower proved that gossypol was mainly synthesized in the root systems of cotton plants and both of the glanded and glandless cottons had the ability of gossypol biosynthesis. Besides, the pigment glands expression was uncoupled with gossypol biosynthesis. Root tip and rootless seedling organ culture in vitro further revealed other parts of the seedlings also got the ability to synthesize gossypol except root system. Moreover, root system produced the racemic gossypol and plant synthesized the optically active gossypol. The expression profiling of key genes in the gossypol biosynthetic pathway suggested that downstream key genes had relatively high expression levels in root systems which confirmed that gossypol was mainly synthesized in the root systems. Taken together, our results helped to clarify the complex mechanism of gossypol metabolism.

synthetic biology↗

bioRxiv2017

Rapid Whole Brain Imaging Of Neural Activities In Freely Behaving Larval Zebrafish

The internal brain dynamics that link sensation and action are arguably better studied during natural animal behaviors. Here we report on a novel volume imaging and 3D tracking technique that monitors whole brain neural activity in freely swimming larval zebrafish (Danio rerio). We demonstrated the capability of our system through functional imaging of neural activity during visually evoked and prey capture behaviors in larval zebrafish.

neuroscience↗

bioRxiv2017

Composite measurements and molecular compressed sensing for highly efficient transcriptomics

RNA profiling is an excellent phenotype of cellular responses and tissue states, but can be costly to generate at the massive scale required for studies of regulatory circuits, genetic states or perturbation screens. Here, we draw on a series of advances over the last decade in the field of mathematics to establish a rigorous link between biological structure, data compressibility, and efficient data acquisition. We propose that very few random composite measurements - in which gene abundances are combined in a random linear combination - are needed to approximate the high-dimensional similarity between any pair of gene abundance profiles. We then show how finding latent, sparse representations of gene expression data would enable us to \"decompress\" a small number of random composite measurements and recover high-dimensional gene expression levels that were not measured (unobserved). We present a new algorithm for finding sparse, modular structure, which improves the ability to interpret samples in terms of small numbers of active modules, and show that the modular structure we find is sufficient to recover gene expression profiles from composite measurements (with ~100-fold fewer composite measurements than genes). Moreover, the knowledge that sparse, modular structures exist allows us to recover expression profiles from composite measurements, even without access to any training data. Finally, we present a proof-of-concept experiment for making composite measurements in the laboratory, involving the measurement of linear combinations of RNA abundances. Altogether, our results suggest new compressive modalities in experimental biology that can form a foundation for massive scaling in high-throughput measurements, while also offering new insights into the interpretation of high-dimensional data.

systems biology↗