Biology subjects

Luo, T.

Publications and source records attributed to Luo, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments

CRISPR epigenomic editing technologies enable functional interrogation of non-coding elements. However, current computational methods for guide RNA (gRNA) design do not effectively predict the power potential, molecular and cellular impact to optimize for efficient gRNAs, which are crucial for successful applications of these technologies. We present "launch-dCas9" (machine LeArning based UNified CompreHensive framework for CRISPR-dCas9) to predict gRNA impact from multiple perspectives, including cell fitness, wild-type abundance (gauging power potential), and gene expression in single cells. Our launch-dCas9, built and evaluated using experiments involving >1 million gRNAs targeted across the human genome, demonstrates relatively high prediction accuracy (AUC up to 0.81) and generalizes across cell lines. Method-prioritized top gRNA(s) are 4.6-fold more likely to exert effects, compared to other gRNAs in the same cis-regulatory region. Furthermore, launch-dCas9 identifies the most critical sequence-related features and functional annotations from >40 features considered. Our results establish launch-dCas9 as a promising approach to design gRNAs for CRISPR epigenomic experiments.

bioinformatics↗

bioRxiv2023

Limitation of switching sensory information flow in flexible perceptual decision making

Humans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks responsible for this remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of converting sensory inputs into decision evidence immediately after changing rules in perceptual tasks. Participants performed a flexible face categorization task in which they classified parametrically generated face stimuli based on one of two rules, switching every few trials. Although participants were always informed of a rule switch by a context cue, they showed a specific pattern of increase in reaction times, regardless of the time they were given to prepare for the switch. Psychophysical reverse correlation and computational modeling revealed a reduction in sensory weighting immediately after a rule switch, which recovered within a few hundred milliseconds after stimulus presentation. Furthermore, this cost depends on the sensory features being switched, suggesting a major bottleneck at the stage of adjusting the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on an abstract context cue alone, but rather require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

neuroscience↗

bioRxiv2023

Automated quantitative trait locus analysis (AutoQTL)

BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complex decisions related to analysis of complex traits and generate diverse solutions to describe relationships that exist in genetic data. ResultsUsing a dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model while also providing evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions from simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can be applied to genetic data and has the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection strategies.

genetics↗

bioRxiv2022

Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS

Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles, but are limited by the non-single cell level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to under-utilize histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful.

genetics↗

bioRxiv2022

Origin, evolution and diversification of plant mechanosensitive channel of small conductance-like (MSL) proteins

Mechanosensitive (MS) ion channels provide efficient molecular mechanism for transducing mechanical forces into intracellular ion fluxes in all kingdoms of life. The mechanosensitive channel of small conductance (MscS) was one of the best-studied MS channels and its homologs (MSL, MscS-like) were widely distributed in cell-walled organisms. However, the origin, evolution and expansion of MSL proteins in plants are still not clear. Here, we identified more than 2100 MSL proteins from 176 plants and conducted a broad-scale phylogenetic analysis. The phylogenetic tree showed that plant MSL proteins were divided into three groups (I, II and III) prior to the emergence of chlorophytae algae, which was consistent with their specific subcellular localization. MSL proteins were distributed unevenly into each of plant species, and four parallel expansion was identified in angiosperms. In Brassicaceae, most MSL duplicates were derived by whole-genome duplication (WGD)/segmental duplications. Finally, a hypothetical evolutionary model of MSL proteins in plants was proposed based on phylogeny. Our studies illustrate the evolutionary history of the MSL proteins and provide a guide for future functional diversity analyses of these proteins in plants.

bioinformatics↗

bioRxiv2022

Smart squirrels use a mortise-tenon structure to fix nuts on understory twigs

Squirrels of temperate zones commonly store nuts or seeds under leaf litter, in hollow logs or even in holes in the ground; however, in humid rainforests, it is an evolutionary challenge for squirrels to hang elliptical or oblate nuts securely on trees to minimize germination or fungal infection. Here, we report a unique behaviour used by two species of small flying squirrels (Hylopetes phayrei electilis (G. M. Allen, 1925) and Hylopetes alboniger (Hodgson, 1870)) to cache nuts on Hainan Island. Squirrels intentionally carved grooves encircling ellipsoid nuts or distributed on the bottom of oblate nuts and used these grooves to fix nuts tightly between small twigs 0.1-0.6 cm in diameter and connected at angles of 25-40{degrees}. The resulting structures were similar to the mortise-tenon joint of ancient Chinese architecture. Cache sites were on small plants located 10-25 m away from the closest potentially nut-producing tree, a behaviour that likely deters discovery and consumption of the nuts by other animals. Their ability to shape individual nuts to store them more securely suggests it is a smart behavior, which is likely formed by the adaption to life in humid tropical rainforests to guarantee food supply and impacts the distribution of tree species.

ecology↗

bioRxiv2022

Field dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging

Single-molecule localization microscopy (SMLM) in a typical wide-field setup has been widely used for investigating sub-cellular structures with super resolution. However, field-dependent aberrations restrict the field of view (FOV) to only few tens of micrometers. Here, we present a deep learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit (GPU) based vectorial PSF fitter, we can fast and accurately model the spatially variant point spread function (PSF) of a high numerical aperture (NA) objective in the entire FOV. Combined with deformable mirror based optimal PSF engineering, we demonstrate high-accuracy 3D SMLM over a volume of ~180 x 180 x 5 m3, allowing us to image mitochondria and nuclear pore complex in the entire cells in a single imaging cycle without hardware scanning - a 100-fold increase in throughput compared to the state-of-the-art.

biophysics↗

bioRxiv2022

Droplet microfluidics forward for tracing target cells at single-cell transcriptome resolution

The rapid promotion of single-cell omics in various fields has begun to help solve many problems encountered in research including precision medicine, prenatal diagnosis, and embryo development. Meanwhile, single-cell techniques are also constantly updated with increasing demand. For some specific target cells, the workflow from droplet screening to single-cell sequencing is a preferred option, which also should reduce the impact of operation steps such as demulsification and cell recovery. We developed an all-in-droplet method integrating cell encapsulation, target sorting, droplet picoinjection, and single-cell transcriptome profiling on chips to achieve a labor-saving monitor of TCR-T cells. As a proof of concept, in this research, TCR-T cells were encapsulated, sorted, and performed single-cell transcriptome sequencing (scRNA-seq) by injecting reagents into droplet. It avoided the tedious operation of droplet breakage and re-encapsulation between droplet sorting and scRNA-seq. Moreover, convenient device operation will accelerate the progress of chip marketization. The strategy achieved an excellent recovery performance of single cell transcriptome with a median gene number over 4000 and a cross-contamination rate of 8.2 {+/-} 2%. Furthermore, this strategy allows us to develop a device with high integrability to monitor infused TCR-T cells, which will promote the development of adoptive T cell immunotherapy and their clinical application.

cell biology↗

bioRxiv2022

Linear Mixed-effects model to quantify the association between somatic cell count and milk production in Italian dairy herds

Milk production loss due to mastitis in dairy herds is economically important. Before estimating the economic impacts of mastitis, it is crucial to quantify the association between mastitis and milk production. The objective of this study was to estimate the association between somatic cell count (SCC, as an indicator of intramammary infection due to mastitis) and milk production for dairy cows in Lombardy (Italy). The test-day (TD) records data of 3,816 dairy herds located in 3 different geographical areas of Lombardy from January 2016 to December 2018 were used. After data editing, the final dataset comprised 10,445,464 TD records from 2,970 farms and 826,831 cows. The analysis was carried out by using a mixed-effects model with seven fixed effects (geographical area, breed, days in milk, parity, season and year) and nested random effects for each cow and herd. The results indicated that the SCC had a negative association with milk production. When the SCC increased every two-fold, the milk production lost 0.830 (95% CI: -0.832, -0.828) kg/cow/day in the whole Lombardy. These results can be used for economic calculations on the costs of mastitis

zoology↗

bioRxiv2022

A comprehensive comparison on cell type composition inference for spatial transcriptomics data

Spatial transcriptomic (ST) technologies allow researchers to examine high-quality RNA-sequencing data along with maintained two-dimensional positional information as well as a co-registered histology image. A popular use of ST omics data is to provide insights about tissue structure and spatially unique features. However, due to the technical nature unique to most ST data, the resolution varies from a diameter of 2-10m to 50-100m instead of single-cell resolution, which brings uncertainty into cell number and cell mixture within each ST spot. Motivated by the important role for spatial arrangement of cell types within a tissue in physiology and disease pathogenesis, several ST deconvolution methods have been developed and are being used to explore gene expression variation and identification of spatial domains. The aim of this work is to review state-of-the-art methods for ST deconvolution, while comparing their strengths and weaknesses. Specifically, we use four real datasets to examine the performance of eight methods across different tissues and technological platforms. Key PointsO_LICell mixture inference is a critical step in the analysis of spatial transcriptomics (ST) data to prevent downstream analysis suffering from confounding factors at the spot level. C_LIO_LIExisting ST deconvolution methods can be classified into three groups: probabilistic-based, non-negative matrix factorization and non-negative least squares based, and other deep learning framework-based methods. C_LIO_LIWe compared eight ST deconvolution methods by using two single cell level resolution datasets and two spot level resolution ST datasets. We provided practical guidelines for the choice of method under different scenarios as well as the optimal subsets of genes to use for each method. C_LI

genetics↗

bioRxiv2021

Ratiometric 4Pi single molecule localization with optimal resolution and color assignment

4Pi single molecule localization microscopy (4Pi-SMLM) with two opposing objectives achieves sub-10 nm isotropic 3D resolution with as few as 250 photons collected by each objective. Here, we developed a new ratiometric multi-color imaging strategy for 4Pi-SMLM which employed the intrinsic multi-phase interference intensity without increasing the complexity of the system and achieved both optimal 3D resolution and color separation. By partially linking the photon parameters between channels with interference difference of {pi} during global fitting of the multi-channel 4Pi single molecule data, we showed on simulated data that the loss of the localization precision is minimal compared with the theoretical minimum uncertainty, the Cramer-Rao lower bound (CRLB).

biophysics↗

bioRxiv2021

Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome greatly influence some traits and complex diseases by their own regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in the human non-coding sequences. However, it is difficult for users with insufficient knowledge about the performances of computational methods to select appropriate computational methods from dozens of methods. In order to solve this problem, we assessed 12 performance measures of 24 methods on four independent non-coding variant benchmark datasets: (I) rare germline variant from ClinVar, (II) rare somatic variant from COSMIC, (III) common regulatory variant dataset, and (IV) disease associated common variant dataset. All 24 tested methods performed differently under various conditions, indicating that these methods have varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable in the rare germline variant from ClinVar with area under curves (AUCs) of 0.4481 - 0.8033 and poor in the rare somatic variant from COSMIC (AUCs: 0.4984 - 0.7131), common regulatory variant dataset (AUCs: 0.4837 - 0.6472), and disease associated common variant dataset (AUCs: 0.4766 -0.5188). We also compared the prediction performance among 24 methods for non-coding de novo mutations in autism spectrum disorder and found that the CADD and CDTS methods showed better performance. Summarily, we assessed the performances of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and new method development in interpreting non-coding variants.

bioinformatics↗

bioRxiv2021

Ferroptosis assassinates tumor (FAST)

In 2020, nearly 20 million peoples got cancer and nearly 10 million peoples died of cancer, indicating the current therapies do not meet the cancer treatment and cancer remains a great threat to human health and life. New therapies are still in urgent demand. In a recent study, we developed a new effective cancer therapy, gene-interfered ferroptosis therapy (GIFT), by combining cancer cell-specific knockdown of two iron efflux genes (FPN and LCN2) with iron nanoparticles (FeNPs). GIFT shows wide antitumor activity, high cancer specificity, certain cancer eradication potential, and biosafety. To further improve the therapy, we here develop an updated GIFT named as Ferroptosis ASsassinates Tumor (FAST) by knocking down five additional ferroptosis-resistance genes (FSP1, FTH1, GPX4, SLC7A11, NRF2). As a result, we found that FAST showed more significant antitumor activity than GIFT. Especially, FAST eradicated three different types of tumors (leukemia, colon cancer and lung metastatic melanoma) from over 50 percent of cancer mice, making the mice to survive up to 250 days without tumor relapse. FAST also significantly inhibited and prevented growth of spontaneous breast cancer and improved survival in mice. Additionally, FAST showed high pan-antitumor efficacy, high cancer specificity, and in vivo safety.

cancer biology↗

bioRxiv2021

Recurrent micropollutant exposure leads to divergent degradation abilities and community succession in a freshwater microbiome

The increasing-volume release of micropollutants into natural surface waters has raised great concern due to their environmental accumulation. Persisting micropollutants can impact multiple generations of organisms, but their microbially-mediated degradation and their influence on community assembly remain understudied. Here, freshwater microbes were treated with several common micropollutants, alone or in combination, and then transferred every 5 days to fresh medium containing the same micropollutants to mimic the repeated exposure of microbes. Metabarcoding of 16S rRNA gene makers was chosen to study the succession of bacterial assemblages following micropollutant exposure. The removal rates of micropollutants were then measured to assess degradation capacity of the associated communities. The degradation of micropollutants did not accelerate over time but altered the microbial community composition. Community assembly was dominated by stochastic processes during early exposure, via random community changes and emergence of seedbanks, and deterministic processes later in the exposure, via advanced community succession. Early exposure stages were characterized by the presence of sensitive microorganisms such as Actinobacteria and Planctomycetes, which were then replaced by more tolerant bacteria such as Bacteroidetes and Gammaproteobacteria. Our findings have important implication for ecological feedback between microbe-micropollutants under anthropogenic climate change scenarios.

ecology↗

bioRxiv2021

Resolving the graft ischemia-reperfusion injury during liver transplantation at the single cell resolution

Ischemia-reperfusion injury (IRI) remains the major reason for impaired donor graft function and increased mortality post-liver transplantation. The mechanism of IRI involves multiple pathophysiological processes and numerous types of cells. However, a systematic and comprehensive single-cell transcriptional profile of intrahepatic cells during liver transplantation is still unclear. We performed a single-cell transcriptome analysis of 14,313 cells from liver tissues collected from pre-procurement, at the end of preservation and 2 h post-reperfusion. We made detailed annotations of mononuclear phagocyte, endothelial cell, NK/T, B and plasma cell clusters, and we described the dynamic changes of the transcriptome of these clusters during IRI and the interaction between mononuclear phagocyte clusters and other cell clusters. In addition, we found that TNFAIP3 interacting protein 3 (TNIP3), specifically and highly expressed in Kupffer cell clusters post-reperfusion, may have a protective effect on IRI. In summary, our study provides the first dynamic transcriptome map of intrahepatic cell clusters during liver transplantation at single-cell resolution.

cell biology↗

bioRxiv2020

Role and Dynamics of Vacuolar pH during Cell-in-Cell mediated Death

The non-autonomous cell death by entosis was mediated by the so-called cell-in-cell structures, which were believed to kill the internalized cells by a mechanism dependent on acidified lysosomes. However, the precise values and roles of pH critical for the death of the internalized cells remained undetermined yet. We creatively employed keima, a fluorescent protein that displays different excitation spectra in responding to pH changes, to monitor the pH dynamics of the entotic vacuoles during cell-in-cell mediated death. We found that different cells varied in their basal intracellular pH, and the pH was relatively stable for entotic vacuoles containing live cells, but sharply dropped to a narrow range along with the inner cell death. In contrast, the lipidation of entotic vacuoles by LC3 displayed previously underappreciated complex patterns associated with entotic and apoptotic death, respectively. The pH decline seemed to play distinct roles in the two types of inner cell deaths, where apoptosis is preceded with moderate pH decline while a profound pH decline is likely to be determinate for entotic death. Whereas the cancer cells seemed to be lesser tolerant to acidified environments than non-cancerous cells, manipulating vacuolar pH could effectively control inner cell fates and switch the ways whereby inner cell die. Together, this study demonstrated for the first time the pH dynamics of entotic vacuoles that dictate the fates of internalized cells, providing a rationale for tuning cellular pH as a potential way to treat cell-in-cell associated diseases such as cancer.

cell biology↗

bioRxiv2020

SARS-CoV-2 and Malayan pangolin coronavirus infect human endoderm, ectoderm and induced lung progenitor cells

Since the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in several somatic cells, little is known about the infection of SASRS-CoV-2 and its related pangolin coronavirus (GX_P2V). Here we present for the first time that SARS-CoV-2 pseudovirus and GX_P2V could infect lung progenitor and even anterior foregut endoderm cells causing these cells death, which differentiated from human embryonic stem cells (hESCs). The infection and replication of SARS-CoV-2 and GX_P2V were inhibited when treated with whey protein of breastmilk and Remdesivir, confirming that these two viruses could infect lung progenitor and even anterior foregut endoderm. Moreover, we found that SARS-CoV-2 pseudovirus could infect endoderm and ectoderm. We found that whey protein blocked SARS-CoV-2 infecting these cells. In line with the SARS-CoV-2 results, GX_P2V could also infected endoderm and ectoderm, and also was inhibited by Remdesivir treatment. Although expressing coronavirus related receptor such as ACE2 and TMPRSS2, mesoderm cells are not permissive for SARS-CoV-2 and GX_P2V infection, which needed further to study the mechanisms. Interestingly, we also found that hESCs, which also express ACE2 and TMPRSS2 markers, are permissive for GX_P2V but not SARS-CoV-2 pseudovirus infection and replication, indicating the widespread cell types for GX_P2V infection. Heparin treatment blocked efficiently viral infection. These results provided insight that these stem cells maybe provided a stable repository of coronavirus function or genome. The potential consequence of SARS-CoV-2 and animal coronavirus such as GX_P2V infection in hESCs, germ layer and induced progenitors should be closely monitored.

microbiology↗

bioRxiv2020

Common variants contribute to intrinsic functional architecture of human brain

The human brain remains active in the absence of explicit tasks and forms networks of correlated activity. Resting-state functional magnetic resonance imaging (rsfMRI) measures brain activity at rest, which has been linked with both cognitive and clinical outcomes. The genetic variants influencing human brain function are largely unknown. Here we utilized rsfMRI from 44,190 individuals of multiple ancestries (37,339 in the UK Biobank) to discover and validate the common genetic variants influencing intrinsic brain activity. We identified hundreds of novel genetic loci associated with intrinsic functional signatures (P < 2.8 x 10-11), including associations to the central executive, default mode, and salience networks involved in the triple network model of psychopathology. A number of intrinsic brain activity associated loci colocalized with brain disorder GWAS (e.g., Alzheimers disease, Parkinsons disease, schizophrenia) and cognition, such as 19q13.32, 17q21.31, and 2p16.1. Particularly, we detected a colocalization between one (rs429358) of the two variants in the APOE {varepsilon}4 locus and function of the default mode, central executive, attention, and visual networks. Genetic correlation analysis demonstrated shared genetic influences between brain function and brain structure in the same regions. We also detected significant genetic correlations with 26 other complex traits, such as ADHD, major depressive disorder, schizophrenia, intelligence, education, sleep, subjective well-being, and neuroticism. Common variants associated with intrinsic brain activity were enriched within regulatory element in brain tissues.

genetics↗