Biology subjects

Fu, L.

Publications and source records attributed to Fu, L..

3 recordsLinked to original sources

bioRxiv2018

Ultra-high throughput multiplexing and sequencing of >500 bp amplicon regions on the Illumina HiSeq 2500 platform

Amplification, sequencing and analysis of the 16S rRNA gene affords characterization of microbial community composition. As this tool has become more popular and amplicon-sequencing applications have grown in the total number of samples, growth in sample multiplexing is becoming necessary while maintaining high sequence quality and sequencing depth. Here, modifications to the Illumina HiSeq 2500 platform are described which produce greater multiplexing capabilities and 300 bp paired-end reads of higher quality than produced by the current Illumina MiSeq platform. To improve the feasibility and flexibility of this method, a 2-Step PCR amplification protocol is also described that allows for targeting of different amplicon regions, thus improving amplification success from low bacterial bioburden samples.\n\nImportanceAmplicon sequencing has become a popular and widespread tool for surveying microbial communities. Lower overall costs associated with high throughput sequencing have made it a widely-adopted approach, especially for projects which necessitate sample multiplexing to eliminate batch effect and reduced time to acquire data. The method for amplicon sequencing on the Illumina HiSeq 2500 platform described here provides improved multiplexing capabilities while simultaneously producing greater quality sequence data and lower per sample cost relative to the Illumina MiSeq platform, without sacrificing amplicon length. To make this method more flexible to various amplicon targeted regions as well as improve amplification from low biomass samples, we also present and validate a 2-Step PCR library preparation method.

microbiology↗

bioRxiv2018

Temporal Evolution of Cortical Ensembles Promoting Remote Memory Retrieval

Studies of amnesic patients and animal models support a systems consolidation model, which posits that explicit memories formed in hippocampus are transferred to cortex over time1-6. Prelimbic cortex (PL), a subregion of the medial prefrontal cortex, is required for the expression of learned fear memories from hours after learning until weeks later7-12. While some studies suggested that prefrontal cortical neurons active during learning are required for memory retrieval13-15, others provided evidence for ongoing cortical circuit reorganization during memory consolidation10,16,17. It has been difficult to causally relate the activity of cortical neurons during learning or recent memory retrieval to their function in remote memory, in part due to a lack of tools18. Here we show that a new version of targeted recombination in active populations, TRAP2, has enhanced efficiency over the past version, providing brain-wide access to neurons activated by a particular experience. Using TRAP2, we accessed PL neurons activated during fear conditioning or 1-, 7-, or 14-day memory retrieval, and assessed their contributions to 28-day remote memory. We found that PL neurons TRAPed at later retrieval times were more likely to be reactivated during remote memory retrieval, and more effectively promoted remote memory retrieval. Furthermore, reducing PL activity during learning blunted the ability of TRAPed PL neurons to promote remote memory retrieval. Finally, a series of whole-brain analyses identified a set of cortical regions that were densely innervated by memory-TRAPed PL neurons and preferentially activated by PL neurons TRAPed during 14-day retrieval, and whose activity co-varied with PL and correlated with memory specificity. These findings support a model in which PL ensembles underlying remote memory undergo dynamic changes during the first two weeks after learning, which manifest as increased functional recruitment of cortical targets.

neuroscience↗

bioRxiv2017

Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of neurites

Reconstructing neuronal morphology across different regions or even the whole brain is important in many areas of neuroscience research. Large-scale tracing of neurites constitutes the core of this type of reconstruction and has many challenges. One key challenge is how to identify a weak signal from an inhomogeneous background. Here, we addressed this problem by constructing an identification model. In this model, empirical observations made from neuronal images are summarized into rules, which are used to design feature vectors that display the differences between the foreground and background, and a support vector machine is used to learn these feature vectors. We embedded this identification model into a tool that we previously developed, SparseTracer, and termed this integration SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace neurites with extremely weak signals (signal-to-background-noise ratio <1.1) against an inhomogeneous background. By testing 12 sub-blocks extracted from a whole imaging dataset, ST-LFV can achieve an average recall rate of 0.99 and precision rate of 0.97, which is superior to that of SparseTracer (which has an average recall rate of 0.93 and average precision rate of 0.86), indicating that this method is well suited to weak signal identification. We applied ST-LFV to trace neurites from large-scale images (approximately 105 GB). During the tracing process, obtaining results equivalent to the ground truth required only one round of manual editing for ST-LFV compared to 20 rounds of manual editing for SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the scale of an entire brain.

bioinformatics↗