Biology subjects

Cai, L.

Publications and source records attributed to Cai, L..

At least 127 records · Page 7Linked to original sources

bioRxiv2020

Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times

Dopamine (DA) neurons are known to encode reward prediction error (RPE), in addition to other signals, such as salience. While RPE is known to support learning, the role of salience in supporting learning remains less clear. To address this, we recorded and manipulated VTA DA neurons in mice during fear extinction, a behavior we observed to generate spatially segregated RPE and salience signals. We applied deep learning to classify mouse freezing behavior, eliminating the need for human scoring. Our fiber photometry recordings showed that DA neurons in medial and lateral VTA have distinct activity profiles during fear extinction: medial VTA activity more closely reflected RPE, while lateral VTA activity more closely reflected a salience-like signal. Optogenetic inhibition of DA neurons in either region slowed fear extinction, with the relevant time period for inhibition differing across regions. Our results indicate that salience-like signals can have similar downstream consequences to RPE-like signals, although with different temporal dependencies.

neuroscience↗

bioRxiv2019

Individualized ctDNA Fingerprints to Monitor Treatment Response and Recurrence in Multiple Cancer Types

Circulating tumor DNA (ctDNA) panels hold high promise of accurately predicting the therapeutic response of tumors while being minimally invasive and cost-efficient. However, their use has been limited to a small number of tumor types and patients. Here, we developed individualized ctDNA fingerprints suitable for most patients with multiple cancer types. The panels were designed based on individual whole-exome sequencing data in 521 Chinese patients and targeting high clonal population clusters of somatic mutations. Together, these patients represent 12 types of cancers and seven different treatments. The customized ctDNA panels have a median somatic mutation number of 19, most of which are patient-specific rather than cancer hotspot mutations; 66.8% of the patients were ctDNA-positive. We further evaluated the ctDNA content fraction (CCF) of the mutations, and analyzed the association between the change of ctDNA concentration and therapeutic response. We followed up 106 patients for clinical evaluation, demonstrating a significant correlation of changes in ctDNA with clinical outcomes, with a consistency rate of 93.4%. In particular, the median CCF increased by 204.6% in patients with progressive disease, decreased by 82.5% in patients with remission, and was relatively stable in patients with stable disease. Overall, 85% of the patients with a ctDNA-positive status experienced metastasis or relapse long before imaging detection, except for two patients who developed recurrence and metastasis almost simultaneously. The average lead time between the first ctDNA-positive finding and radiological diagnosis was 76 days in three patients that changed from a ctDNA-negative to -positive status. Our individualized ctDNA analysis can effectively monitor the treatment response, metastasis, and recurrence in multiple cancer types in patients with multiple treatment options, therefore offering great clinical applicability for improving personalized treatment in cancer.\n\nOne Sentence SummaryctDNA fingerprint panels were customized to predict the treatment response for multiple cancer types from individual whole-exome sequencing data.

cancer biology↗

bioRxiv2019

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

bioinformatics↗

bioRxiv2019

Computational staining of pathology images to study tumor microenvironment in lung cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally \"stain\" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.\n\nA Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.\n\nThis study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

bioinformatics↗

bioRxiv2019

Chromatin interaction analysis with updated ChIA-PET Tool (V3)

Understanding chromatin interactions is important since they create chromosome conformation and link the cis- and trans-regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. Previously we developed ChIA-PET Tool in 2010 for ChIA-PET data analysis. Here we present the updated version of ChIA-PET Tool (V3), is a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes the short-read data and long-read ChIA-PET data with multithreading and generates the statistics of results in a HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze a specific ChIA-PET data set with it. At present, other ChIA-PET data analysis tools have developed including ChiaSig, MICC, Mango and ChIA-PET2 and so on. We compared our tool with other tools using the same public data set in the same machine. Most of peaks detected by ChIA-PET Tool V3 overlap with those from other tools. There is higher enrichment for significant chromatin interactions of ChIA-PET Tool V3 in APA plot. ChIA-PET Tool V3 is open source and is available at GitHub (https://github.com/GuoliangLi-HZAU/ChIA-PET_Tool_V3/).

genomics↗

bioRxiv2019

METABOLIC DIVERSITY IN HUMAN NON-SMALL CELL LUNG CANCER CELLS

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

cancer biology↗

bioRxiv2019

DeepSV: Accurate calling of genomic deletions from high throughput sequencing data using deep convolutional neural network

BackgroundCalling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data? ResultsIn this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project. ConclutionsOur work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs. Availability and implementationDeepSVs source code and sample result as part of this project are readily available from GitHub at https://github.com/CSuperlei/DeepSV/.

bioinformatics↗

bioRxiv2019

Intra-familial phenotypic heterogeneity and telomere abnormality in von Hippel-Lindau disease

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome with poor survival. The current recommendations have proposed uniform surveillance strategies for all patients, neglecting the obvious phenotypic varieties. In this study, we aim to confirm the phenotypic heterogeneity in VHL disease and the underlying mechanism. A total of 151 parent-child pairs were enrolled for genetic anticipation analysis, and 77 sibling pairs for birth order effect analysis. Four statistical methods were used to compare the onset age of patients among different generations and different birth orders. The results showed that the average onset age was 18.9 years earlier in children than in their parents, which was statistically significant in all of the four statistical methods. Furthermore, the first-born siblings were affected 8.3 years later than the other ones among the maternal patients. Telomere shortening was confirmed to be associated with genetic anticipation in VHL families, while it failed to explain the birth order effect. Moreover, no significant difference was observed for overall survival between parents and children (p=0.834) and between first-born patients and the other siblings (p=0.390). This study provides definitive evidence and possible mechanisms of intra-familial phenotypic heterogeneity in VHL families, which is helpful to the update of surveillance guidelines.

genetics↗

bioRxiv2019

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

genetics↗

bioRxiv2018

ChIA-PIPE: A fully automated pipeline for ChIA-PET data analysis and visualization

ChIA-PET enables the genome-wide discovery of chromatin interactions involving specific protein factors, with base-pair resolution. Interpreting ChIA-PET data depends on having a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, analysis, and visualization. ChIA-PIPE performs linker filtering, read mapping, peak calling, loop calling, chromatin-contact-domain calling, and can resolve allele-specific peaks and loops. ChIA-PIPE also automates quality-control assessment for each dataset. Furthermore, ChIA-PIPE generates input files for visualizing 2D contact maps with Juicebox and HiGlass, and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. With minimal adjusting, ChIA-PIPE can also be suited for the analysis of other related chromatin-mapping data.

bioinformatics↗

bioRxiv2018

Lin28B is involved in curcumin-reversed paclitaxel chemoresistance and associated with poor prognosis in hepatocellular carcinoma

Chemoresistance remains a big challenge in hepatocellular carcinoma (HCC) treatment. Several studies indicated that RNA-binding protein Lin28B serves an oncogenic role in HCC, but its activity in HCC chemotherapy has never been assessed. In this study, we found that overexpression of Lin28B significantly increased the paclitaxel chemoresistance in two different HCC cells lines while silencing Lin28B reduced the chemoresistance in paclitaxel-resistance HCC cells. Curcumin, a natural anti-cancer agent, increased the sensitivity of HCC cells to paclitaxel through inhibiting NF-{kappa}B stimulated Lin28B expression both in vitro and in vivo. Furthermore, by analyzing TCGA (The Cancer Genome Atlas) LIHC (liver hepatocellular carcinoma) and GSE14520 databases, we found that Lin28B was highly up-regulated in HCC tissue compared with that in normal tissue and associated with -fetoprotein levels, and that patients with Lin28B higher expression had a significant shorter overall survival time than those with Lin28B lower expression. Our data reveal that Lin28B may serve as a predictive biomarker and a treatment target to reverse HCC chemotherapy resistance in future clinical practice.\n\nSummary statementupregulation of Lin28B not only confers poor prognosis in HCC patients but also increases chemoresistance in HCC cells. Thus, Lin28B may serve as a predictive biomarker for use to reverse chemoresistance in clinical practice.

cell biology↗

bioRxiv2018

A familial case of Syndactyly type IV due to a novel duplication of ~222.23 kb covering exons 2-17 of the LMBR1 gene: a case report

Syndactyly is one of the most frequent hereditary limb malformations with clinical and genetical complexity. Autosomal dominant Syndactyly type IV (SD4) is a very rare form of syndactyly, occurring as a result of heterozygous mutation in an SHH regulatory element (ZRS) that resides in intron 5 of the LMBR1 gene on chromosome 7q36.3. The SD4 is characterized by complete cutaneous syndactyly of all fingers, cup-shaped hands due to flexion of the fingers and accompanied by polydactyly. Here, we firstly reported a big Chinese family, manifesting cup-shaped hands consistent with SD4 and intrafamilial heterogeneity in clinical phenotype of tibial and fibulal shortening, triphalangeal thumb-polysyndactyly syndrome (TPTPS). Genetically, we identified a novel duplication of [~]222.23 kb covering exons 2-17 of the LMBR1 gene in this family by next generation sequencing. This case expands our new clinical understanding of SD4 phenotype.

genetics↗

bioRxiv2018

Genome-wide scan and fine-mapping of rare nonsynonymous associations implicates intracellular lipolysis genes in fat distribution and cardio-metabolic risk

Difficulties in identifying causal variants and genes underlying genetic associations have limited the translational potential of genetic studies of body fat distribution, an important, partly-heritable risk factor for cardio-metabolic disease. Rare variant associations facilitate fine-mapping of causal alleles, but their contribution to fat distribution is understudied. We performed a genome-wide scan of rare nonsynonymous variants for body mass index-adjusted waist-to-hip-ratio (BMI-adjusted WHR; a widely-used measure of fat distribution) in 450,562 European ancestry individuals, followed by systematic Bayesian fine-mapping at six genome-wide (p<5x10-08; main-analysis) and two subthreshold signals (significant at a Bonferroni-corrected p<1.3x10-06). We found strong statistical evidence of causal association for nonsynonymous alleles in CALCRL (p.L87P, pconditional=5.9x10-12; posterior-probability of association [PPA]=52%), PLIN1 (p.L90P, pconditional=5.5x10-13; PPA>99%), PDE3B (p.R783X, pconditional=6.2x10-15; PPA>99%), ACVR1C (p.I195T; pconditional=5.4x10-12; PPA>99%), and FGF1 (p.G21E, pconditional=1.6x10-07; PPA=98%). Alleles at the four likely-causal main-analysis genes affected fat distribution primarily via larger hip-rather than smaller waist-circumference and six of nine conditionally-independent WHR-lowering index-variants were associated with protection from cardiovascular or metabolic disease. All four genes are expressed in adipose tissue and have been linked with the regulation of intracellular lipolysis, which controls fat retention in mature cells. Targeted follow-up analyses of key intracellular-lipolysis genes revealed associations for a variant in the initiator of intracellular lipolysis PNPLA2 (p.N252K) with higher BMI-adjusted-WHR and higher cardio-metabolic risk. This study provides human genetic evidence of a link between intracellular lipolysis, fat-distribution and its cardio-metabolic complications in the general population.

genetics↗

bioRxiv2018

Dynamics and spatial genomics of the nascent transcriptome in single mESCs by intron seqFISH

Recent single cell experiments have revealed significant heterogeneities at the levels of transcription, DNA methylation and chromosome organization in individual cells. However, existing method of profiling mRNAs effectively averages transcriptional dynamics over many hours due to hours-long life time of most mRNAs. To capture the instantaneous activity of the transcriptome that reflects the rapid regulatory changes in cells, we imaged up to 10,421 nascent transcription active sites (TAS) in single mouse embryonic stem cells using seqFISH followed by multiple rounds of single molecule FISH and immunofluorescence. We observed that nascent transcription active sites appear to be distributed on the surface of individual chromosome territories and are dispersed throughout the nucleus. In addition, there are significant variability in the number of active transcription sites in single cells, representing globally more active to quiescent states. These states interconverted on the time scale of 2 hours as determined by a single cell pulse-chase experiment. Thus, transcriptome level seqFISH experiments provide an unprecedented spatial and dynamic view of chromosome organization and global nascent transcription activity in single cells.

systems biology↗

bioRxiv2018

Seasonal changes of metabolites in phloem sap from Broussonetia papyrifera

Gas chromatography-Mass spectrometry (GC-MS) were employed to analyze the whole metabolites in phloem sap of Broussonetia papyrifera and the seasonal changes of content of these metabolites were also investigated. Thirty-eight metabolites were detected in BP phloem exudates. The highest content (44.59mg g-1) of total metabolites was presented in March. High contents of organic acids and sugars were detected in BP phloem exudates from all growing months. Smaller amounts of fatty acids and alcohols were also detected in BP phloem exudates. Interestingly, some metabolites, such as PI3 kinase inhibitor, Chlorogenic acid, Chelerythrine and palmitic acid, which have properties of bioactivity to anticancer and anti-inflammation, were also detected. Quininic acid was the most abundant organic acid, representing up to 86.3% (average value) of all organic acids. D-fructose, D-glucose, and sucrose were the major soluble sugars in phloem saps and the maximum of sugars content was 19.76mg g-1 (average value) in November. Seasonal changes of contents of metabolites were different among individuals. The metabolites analysis double confirmed that the BP phloem sap can be serviced as an important resource for synthesis of pharmaceutical and human health products.

plant biology↗

bioRxiv2018

Inward tubulation of the plasma membrane expedites membrane exchange and receptor presentation

The plasma membrane is a crucial barrier between the cell and its external environment, and it also enables efficient passage of materials and information. Membrane vesicle trafficking allows precise delivery of materials but is rather inefficient. The mechanism for efficient membrane exchange remains elusive. Here we describe inward tubulation of the plasma membrane (PM tubes) that extends deep into the cytoplasm. These widespread PM tubes elongate along microtubules and are stabilized by actin filaments and cholesterol. PM tubes are preferred sites for connection between the endoplasmic reticulum and the plasma membrane. PM tubes facilitate receptor presentation at the surface of cells, possibly also shortening the distance for transported cargo to reach the external environment.\n\nIn BriefA new type of tubular membrane structures was discovered in cells, revealing a shortcut that cells employ to expedite material exchange with their external environment.\n\nHighlightsO_LIInward tubulation of the plasma membrane (PM tubes), transiently interacts with the Golgi apparatus\nC_LIO_LIMicrotubule side-binding proteins pull PM tubes, while actin filaments and cholesterol stabilize PM tubes\nC_LIO_LIPM tubes are preferred sites where ER-PM contacts form in response to increased cytoplasmic calcium concentration\nC_LIO_LIPM tubes are preferred sites for the surface presentation of GLUT1 upon glucose deprivation\nC_LI

cell biology↗

bioRxiv2018

Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome

Pathology slide images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology slides is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology slides. From the identified regions, we extracted 22 well-defined tumor shape features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor shape-based prognostic model was developed and validated in an independent patient cohort (n=389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.

cancer biology↗

bioRxiv2018

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing-and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their spatial environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states.

bioinformatics↗