Biology subjects

Long, F.

Publications and source records attributed to Long, F..

26 records · Page 2Linked to original sources

bioRxiv2019

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.

neuroscience↗

bioRxiv2019

Single cell transcriptomics identifies a unique adipocyte population that regulates bone marrow environment

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells (MSCs), the true identity of MSCs and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing single cell transcriptome analysis, we identified MSCs and delineated their bi-lineage differentiation paths in young, adult and aging mice. Among several newly discovered mesenchymal subpopulations, one is a distinct population of adipose-lineage cells that we named marrow environment regulating adipose cells (MERAs). MERAs are non-proliferative, post-progenitor cells that express many mature adipocyte markers but are devoid of lipid droplets. They are abundant in the bone marrow of young mice, acting as pericytes and stromal cells that form numerous connections among themselves and with other cells inside bone, including endothelial cells. Genetic ablation of MERAs disrupts marrow vessel structure, promotes de novo bone formation. Taken together, MERAs represent a unique population of adipose lineage cells that exist only in the bone marrow with critical roles in regulating bone and vessel homeostasis.

cell biology↗

bioRxiv2019

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Acute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weighted F1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

pathology↗

bioRxiv2018

Biological variation in the sizes, shapes and locations of visual cortical areas in the mouse

Visual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serve different functions in the processing of visual information. In our previous work, we noted that retinotopic maps of cortical visual areas differed between mice, but did not quantify these differences or determine the relative contributions of biological variation and measurement noise. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse. We find that there is substantial biological variation in the sizes of visual areas, with some visual areas varying in size by two-fold across the population of mice.

neuroscience↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗

bioRxiv2018

DeepNeuron: An Open Deep Learning Toolbox for Neuron Tracing

Reconstructing three-dimensional (3D) morphology of neurons is essential to understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semi-automatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new open source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

bioinformatics↗

bioRxiv2017

CRISPR-typing PCR (ctPCR), a new Cas9-based DNA detection method

This study develops a new method for detecting and typing target DNA based on Cas9 nuclease, which was named as ctPCR, representing Cas9/sgRNA- or CRISPR-typing PCR. The technique can detect and discriminate target DNA easily, rapidly, specifically, and sensitively. This technique detects target DNA in three steps: (1) amplifying target DNA with PCR by using a pair of universal primers (PCR1); (2) treating PCR1 products with a process referred to as CAT, representing Cas9 cutting, A tailing and T adaptor ligation; (3) amplifying the CAT-treated DNA with PCR by using a pair of general-specific primers (gs-primers) (PCR2). The technique was verified by detecting HPV16 and HPV18 L1 gene in 13 different high-risk human papillomavirus (HPV) subtypes. The technique was also detected two high-risk HPVs (HPV16 and HPV18) in cervical carcinoma cells (HeLa and SiHa) by detecting the L1 and E6/E7 genes, respectively. In this method, PCR1 was performed to determine if the detected DNA sample contained the target DNA (such as virus infection), while PCR2 was performed to discriminate which genotypic target DNA was present in the detected DNA sample (such as virus subtypes). With these proof-of-concept experiments, this study provides a new CRISPR-based DNA detection and typing method.

biochemistry↗

bioRxiv2017

Single Molecule Sequencing of Cell-free DNA from Maternal Plasma for Noninvasive Trisomy Detection

The demand of non-invasive prenatal testing for autosomal aneuploidy using cell-free fetal DNA (cffDNA) in maternal plasma is a highly sought-after diagnostic, with a rapidly growing market. Current approaches developed by next generation sequencing (NGS) need PCR amplifcation during sample preparation, which results in amplification bias in GC-rich areas of the human genome. With these approaches, the minimum fetal fraction in maternal plasma is 4% for the small differences in circulating cfDNA between trisomic and disomic pregnancies to be detectable. In this paper, we performed single molecule sequencing of cell-free DNA from maternal plasma for noninvasive trisomy 13, 18 and 21 detections using the GenoCare platform. We found that single molecule sequencing is sensitive enough to detect these chromosome abnormalities when the fetal DNA fraction is as low as 2%. Compared to the Hiseq2500 platform, no significant GC bias was observed. The improved sensitivity and unbiased GC readout make GenoCare a promising platform for autosomal aneuploidy detections, even in the very early stage of pregnancy.

genomics↗